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Research Article
The Safe Financial Processing Method for Realizing Supply Chain
Integrated Business Intelligence Using Blockchain
Application Scenarios
Xianqiu Tan
1
and Kaza Mojtahe
2
1
Nanjing Institute of Railway Technology, Nanjing 210031, China
2
Department of Computer Engineering, Kyrgyz-Turkish Manas University, Bishkek, Kyrgyzstan
Correspondence should be addressed to Kaza Mojtahe; kaza.mojtahe@mail.cu.edu.kg
Received 16 June 2022; Revised 12 July 2022; Accepted 16 July 2022; Published 24 August 2022
Academic Editor: Chi Lin
Copyright ©2022 Xianqiu Tan and Kaza Mojtahe. is is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
In order to improve the financial processing effect of supply chain integrated business intelligence, this study applies blockchain
technology to the financial processing of supply chain integrated business intelligence and conducts an evolutionary game analysis
on the factors that affect the selection of information-sharing strategies for logistic service supply chain nodes. By establishing the
evolutionary game model of logistic service integrators and logistic service providers under the strategy of information sharing
and information nonsharing, the value of information sharing in the logistic service supply chain is discussed from a qualitative
and quantitative perspective. In addition, this study constructs the expected profit mathematical model of a two-level logistic
service supply chain composed of logistic service integrators and logistic service providers. e experimental research results show
that the safe financial processing method for realizing supply chain integrated business intelligence using blockchain application
scenarios has good results.
1. Introduction
Nowadays, with the development of science and technology
and the continuous improvement of people’s demand for a
high-quality life, the entire social needs, production
methods, and social division of labor are constantly
changing. e production of commodities has changed from
a model of complete production by a single company to a
model of cooperative production by multiple companies.
Moreover, market competition has evolved from simple
customer competition to competition between the entire
supply chain involving multiple links in commodity pro-
duction [1]. Banks and other funding parties radiate fi-
nancial products and services to the upstream and
downstream and even the entire industry chain around
relevant core companies to solve the financial problems of
corporate procurement, sales, and inventory in the supply
chain. is financial operation mode is called supply chain
finance. Supply chain finance mainly includes financing
services such as goods mortgage, advance payment, and
accounts receivable. e supply chain finance business is
mainly participated by banks and other funders, large-scale
core enterprises, small and medium-sized suppliers, and
other enterprises. Due to the special requirements of the
supply chain finance industry for information privacy and
security, the current supply chain finance system has many
problems [2].
e blockchain technology is an organic collection of
many existing technologies, which mainly include cryp-
tography, consensus algorithms, and distributed systems.
e blockchain is simply a distributed ledger, which allows
every distributed node in the blockchain network to par-
ticipate in the blockchain, and each participant shares the
same permanent and transparent transaction record ledger.
Blockchain technology uses cryptography-related technol-
ogies to realize that every transaction in the blockchain can
Hindawi
Mobile Information Systems
Volume 2022, Article ID 1454855, 15 pages
https://doi.org/10.1155/2022/1454855
be traced back, and it ensures that no one can change the
transactions recorded in the blockchain shared ledger. At the
same time, the distributed consensus algorithm protocol is
used to allow each participant of the blockchain to reach a
consensus on the transaction result and record it in the
shared ledger. Blockchain smart contract technology can
replace manual operations, set the trigger conditions of the
smart contract in advance, and automatically execute the
smart contract to complete the transfer of assets after the
transaction is executed to a certain stage [3].
e blockchain system can be regarded as a distributed
multinode shared database ledger. Each distributed node in
the blockchain system communicates through a peer-to-peer
protocol, that is, P2P technology; and each transaction in the
system can use a one-way hash function, asymmetric en-
cryption, and other related cryptography technologies to
achieve high security. Since the blockchain is equivalent to a
distributed system in which all participants share the ledger,
any company participating in the same blockchain has a
copy of the entire ledger as the upstream supplier of the
financial participants of the blockchain supply chain.
Downstream buyers, core companies, and financial insti-
tutions can share benefits from related financing businesses,
while minimizing policy and operating risks without the
need to build a financial system.
is study combines the blockchain technology to an-
alyze the security financial management of the supply chain
integrated business intelligence, builds an intelligent system,
and verifies the performance of the system to improve the
security financial management effect of the supply chain
integrated business intelligence.
2. Related Work
e “ghost protocol” proposed in document [4] ensures the
integrity of the blockchain, records the entire process of
value transfer (transaction) immutably, and uses decen-
tralization and trustless methods to collectively maintain the
reliability of a databook sexual solution; this databook is
completely public and not under the control of any orga-
nization. Literature [5] proposes that blockchain is a tech-
nology with breakthrough significance across the ages, and
the core ideas behind this technology, such as decentral-
ization, trustlessness, and national accounting, are its
greatness. erefore, blockchain technology will be the core
technology that has the most potential to trigger the fourth
industrial revolution after steam engine, electricity, infor-
mation, and internet technology.
e core feature of blockchain technology is that the
transaction information on the chain cannot be tampered
with, and it is difficult to interfere, modify, or delete it
under the existing technical conditions. Literature [6]
proposes that immutability is also the most important
difficulty in realizing landing applications. e immuta-
bility of blockchain may turn its advantages into disad-
vantages in the activities of reversing financial services.
Literature [7] pointed out that for some time, blockchain
technology wanted to replace legal currency in the form of
blockchain tokens, and even wanted to replace government
agencies with decentralized functions, but it was difficult to
achieve in actual operation. Yes, blockchain technology is
just a technology, and it needs to find an application
scenario suitable for this technology in order to play its own
role. Literature [8] believes that the blockchain has an
“impossible triangle” in the three aspects of “decentral-
ization,” “high efficiency and low energy,” and “security.” It
is difficult to achieve three goals at the same time. To
achieve two of them, it is necessary to abandon another
goal, which means that if two of the attributes are
strengthened, the third attribute will be automatically
weakened. e premise of the use of blockchain technology
is that users should keep the private key they generated.
Once the private key is lost, they cannot perform any
operations on the assets in their account. Individual or-
dinary users or users without much technical experience
may feel private. If the key is lost, it is enough to apply for
a replacement like an ID card, but in fact, it is impossible.
Literature [9] believes that with the development of so-
ciety, a large number of mathematical algorithms have
been developed, resulting in the blockchain algorithm that
may be cracked by new algorithms, so it may be a
blockchain formed by mathematical principles someday
in the future. e algorithmic security of the technology
will be threatened. Such risks may not exist in the short
term but may exist in the long term. erefore, the ap-
plication of blockchain technology to the business process
of the securitization of accounts receivable assets may
bring risks.
e blockchain technology is an emerging field of re-
search. Domestic and foreign scholars in various industries
use the theory of blockchain technology to solve problems
that arise. Most scholars analyze the application of this
technology theoretically. Literature [10] believes that the
development of blockchain technology can not only make
the asset securitization of accounts receivable become a
reality but can also become a boost to social development;
taking the application of blockchain technology in various
industries as the research direction, the new intermediary
will be based on blockchain technology, which provides the
possibility to build a trust mechanism that does not rely on
third-party platforms. One argument for the popularity of
blockchain in society is that blockchain technology can
rebuild business models, so it will change the production
relations of traditional society. In essence, it is a human
cognitive revolution, and capital is profit-seeking. Yes, the
financial industry takes the lead after the emergence of a
new technology. e literature [11] surveyed more than
1,300 financial institutions around the world. It can be seen
that the number of financial institutions that use the
combination of blockchain technology and traditional
business processes will account for 55%; in 2020, it will
reach 77%. Literature [12] starts with the business process
of asset securitization of accounts receivable. e problems
encountered in the business process of the asset securiti-
zation of accounts receivable are reflected in the following
aspects [13]: the authenticity of the relevant assets in the
pool cannot be effectively determined; the lack of repay-
ment rules after the financial institution itself has problems;
2Mobile Information Systems
in accounts receivable, the characteristics of asset securi-
tization products are that there are many trading entities,
the transaction structure of securitization products is
relatively complicated, and the data transmission chain is
long. ese difficulties also indirectly lead to deeper
problems, that is, it is difficult to obtain low capital costs for
issuing securitized products, and the cost limits its further
development [14]. Literature [15] believes that the tradi-
tional problem of the securitization of accounts receivable
assets is that the transaction structure is complicated, the
transaction chain of the participants is very long, and there
may be external uncertain factors, which further trigger the
problem of information asymmetry. Literature [16] pro-
poses to establish a negative list system. Starting from the
top level, the regulatory authorities play an intermediate
role to solve the relationship between blockchain service
organizations and specific landing application scenarios,
and better regulate the development of blockchain
technology.
3. Blockchain-Based Supply Chain Business
Intelligence Security Financial
Processing Model
e final profit of the game subject is determined by the
strategy chosen by itself and the strategy chosen by the other
party in the game. It represents the expected return when the
two parties in the game choose to share or not share
information.
Game party 1 adopts the information-sharing strategy,
and the expected benefits are [17] as follows:
U11 �y R1+gK1Q2+αQ1−C1−θQ1
+(1−y)R1−C1−θQ1
.(1)
Game party 1 adopts the information nonsharing
strategy, and the expected benefits are as follows:
U12 �y R1+K1Q2
+(1−y)R1.(2)
e average expected return of the game party 1 is as
follows:
U1�xU11 +(1−x)U12.(3)
Game party 1 chooses the copy dynamic equation of the
information-sharing strategy as [18] follows:
F(x) � dx
dt �x U11 −U1
.(4)
Substituting formulas (1)–(3) into (4), we get the
following:
dx
dt �x(1−x)U11 −U12
�x(1−x)y K1Q2(g−1) + αQ1
−C1−θQ1
.
(5)
According to formula (5), when dx/dt �0, three pos-
sible equilibrium solutions can be obtained as follows:
x∗
1�0, x∗
2�1, and y∗�C1+θQ1/K1Q2(g−1) + αQ1.
However, these three solutions are not necessarily all evo-
lutionary stable strategies. According to the stability theo-
rem of differential equations, when dx/dt �0 and F′(x)<0
are satisfied, it is an evolutionary stable strategy in a stable
state. erefore, when we find the first derivative of formula
(5), we get the following:
F′(x) � (1−2x)y K1Q2(g−1) + αQ1
−C1−θQ1
.(6)
rough formulas (5) and (6), it can be analyzed that the
evolutionary stable strategies of game party 1 are [19] as
follows:
(1) If y�y∗�C1+θQ1/K1Q2(g−1) + αQ1, then
dx/dt ≡0, which shows that the three solutions are
all evolutionary stable strategies.
(2) If y≠y∗�C1+θQ1/K1Q2(g−1) + αQ1, x∗
1�0, x∗
2�1
are all evolutionary stable equilibrium points of x.
erefore, the different situations of C1+θQ1/
K1Q2(g−1) + αQ1are discussed and analyzed.
(i) When C1+θQ1>K1Q2(g1−1) + αQ1, for any
y(0≤y≤1), F′(x∗�0)<0, and x∗
1�0 is the evo-
lutionary stable strategy of game party 1.
(ii) When C1+θQ1<K1Q(g−1) + αQ1, we discuss in
two situations.
When y<y∗�C1+θQ1/K1Q2(g−1) + αQ1, F′(x∗
1
�0)<0, F′(x∗
2�1)>0, so x∗
1�0 is a stable equilibrium
point. When y>y∗�C1+θQ1/K1Q2(g−1) + αQ1,
F′(x∗
1−0)>0, F′(x∗
2−1)<0, so x∗
2�1 is a stable equilib-
rium point. It can be seen that in the case of
C1+θQ1<K1Q2(g−1) + αQ1, the steady state is affected by
the value of y∗. e larger the y∗is, the smaller the
probability that ywill fall within the interval [y∗,1], and the
probability that game party 1 chooses the information-
sharing strategy will decrease accordingly.
Game party 2 chooses the expected benefits of the in-
formation-sharing strategy as [20]follows:
U21 �x R2+gK2Q1+αQ2−C2−θQ2
+(1−x)R2−C2−θQ2
.(7)
e expected return of the game party 2 who chooses the
information nonsharing strategy is as follows:
U22 �x R2+K2Q1
+(1−x)R2.(8)
e average expected return of the game party 2 is as
follows:
U2�yU21 +(1−y)U22 .(9)
e copy dynamic equation for the game party 2 to
choose the information-sharing strategy is as follows:
F(y) � dy
dt �y U21 −U2
.(10)
Substituting formulas (7)–(9) into (10), we get the
following:
Mobile Information Systems 3
dy
dt �y(1−y)U21 −U22
�y(1−y)x K2Q1(g−1) + αQ2
−C2−θQ2
.
(11)
According to formula (11), when dy/dt �0, three
possible stable solutions can be obtained as follows:
y∗
1�0, y∗
2�1, x∗�C2+θQ2/K2Q1(g−1) + αQ2. How-
ever, these three solutions do not necessarily belong to
evolutionary stable strategies. From the stability theorem of
differential equations, we know that when dy/dt �0 and
F′(y)<0, it is an evolutionary stable strategy in a stable
state. erefore, by taking the first-order derivative of
equation (11), we obtain the following:
F′(y) � (1−2y)x K2Q1(g−1) + αQ2
−C2−θQ2
.(12)
rough formulas (11) and (12), it can be analyzed that
the evolutionary stable strategies of game party 2 are as
follows:
(1) If x�x∗�C2+θQ2/K2Q1(g−1) + αQ2, then
dy/dt ≡0, which means that the three solutions are
all evolutionary stable strategies, and the results of
the two strategies of information sharing and non-
sharing for game party 2 are the same.
(2) If x≠x∗�C2+θQ2/K2Q1(g−1) + αQ2, y∗
1�0, y∗
2�1
are all evolutionary stable equilibrium points of y.
erefore, the different situations of C2+ (θ−α)Q2/
K2Q1(g−1)are discussed and analyzed.
(i) When C2+θQ2>K2Q1(g−1) + αQ2, for any
x(0≤x≤1), F′(y∗�0)<0, y∗
1�0 is the evolu-
tionary stable strategy of game party 2.
(ii) When C2+θQ2<K2Q1(g−1) + αQ2, we discuss in
two situations [21].
If x<x∗�C2+θQ2/K2Q1(g−1) + αQ2, F′(y∗
1�0)
<0, F′(y∗
2�1)>0, so y∗
1�0 is a stable equilibrium point. If
x>x∗�C2+θQ2/K2Q1(g−1) + αQ2,F′(y∗
1�0)>0,
F′(y∗
2�1)<0, so y∗
2�1 is a stable equilibrium point. It can
be seen from the above that in the case of
C2+θQ2<K2Q1(g−1) + αQ2, the steady state is affected by
the value of x∗. e larger the x∗, the smaller the probability
that xfalls within the [x∗,1]interval, and the possibility of
the player 2 choosing the information-sharing strategy will
decrease.
e two parties of the game are independent of each
other. However, it requires the cooperation of both parties to
form a stable evolution strategy. is is not only to analyze
the evolution process of information-sharing behavior
among supply chain members but also to consider the
situation of both parties. From formulas 5 and 11, it can be
seen that the possible stable strategies are
(0,0),(0,1),(1,0),(1,1),(x∗,y∗), and the existing evolu-
tionary situations are as follows:
(1) When C1+θQ1>K1Q2(g−1) + αQ1, C2+θQ2
>K2Q1(g−1) + αQ2,the additional benefits ob-
tained by both parties of the game by taking
information-sharing behaviors are small, and they
are not enough to make up for the cost and existing
risks of information-sharing inputs. As shown in
Figure 1(a), the evolution process converges to
x∗�0, y∗�0, and the stable point is (0,0). e
evolutionary stable strategy of integrators and
providers is (not sharing and not sharing).
(2) When C1+θQ1>K1Q2(g−1) + αQ1, C2+θQ2
<K2Q1(g−1) + αQ2, the cost and risk borne by the
integrator for information sharing are higher than
the benefits that can be obtained by information
sharing. However, the additional benefits obtained
by the provider’s choice of information-sharing
strategy are greater than the costs and risks involved
in information sharing. As shown in Figure 1(b), the
evolution process converges to x∗�0,y∗�1, and
the stable point is (0,1). e evolutionary and stable
strategy of integrators and providers is (not sharing
and sharing).
(a) Dynamic evolution phase diagram of case 1.
(b) Dynamic evolution phase diagram of case 2.
(3) When C1+θQ1<K1Q2(g−1) + αQ1, C2+θQ2>K2
Q1(g−1) + αQ2, when the integrator’s information
cost and risk are less than the shared benefits, in-
formation sharing is the best strategy for the inte-
grator. However, the cost and risk of provider
information sharing exceed the benefits obtained,
and the best strategy is to not share information.
erefore, as shown in Figure 2(a), the evolution
process ends at x∗�1,y∗�0, the stable point is
(1,0), and the evolutionary stability strategy is
(shared and not shared).
In the two situations shown in formulas (2) and (3),
the game player unilaterally adopts an information-
sharing strategy, with one party choosing infor-
mation sharing and the other party choosing not to
share. Entities that adopt information-sharing
strategies can obtain greater additional benefits
when they begin to share. However, as the game
process is repeated, the information input cost and
information-sharing risk of the information-shar-
ing party will gradually rise. In the end, it will also
evolve into unwillingness to share information.
erefore, in both cases, the final result of the in-
formation-sharing game between integrators and
providers is that information is not shared, and the
evolutionary stability point is (0, 0), as shown in
Figure 2(b).
(4) When C1+ (θ−α)Q1<K1Q2(g−1), C2+θQ2
<K2Q1(g−1) + αQ2, the benefits obtained by the
integrator and the provider by adopting the infor-
mation-sharing strategy are greater than the cost and
risk of the input. However, there are two evolu-
tionary stable points (0,0) and (1,1) for both parties,
which are (shared and shared) and (not shared and
not shared), as shown in Figure 3.
4Mobile Information Systems
e broken line ADC is the dividing line of the two
evolutionary results (0,0) and (1,1). In the OADC region, the
evolution process converges to x∗�0,y∗�0, and the stable
point is (0,0). In the end, both parties of the game will choose
the information nonsharing strategy. In the ABCD region,
the evolution process gradually converges to x∗�1, y∗�1,
the final stable state is (1,1), and both players in the game
choose to share information. e critical point D(x∗, y∗)
determines the final result of the evolutionary game, and the
strategic choice of the game player will change with the
change in the critical point. When the critical point
D(x∗, y∗)is close to the origin, the area OADC decreases
and the area ABCD increases, and the game player is more
likely to choose an information-sharing strategy. Con-
versely, if the value of the critical point D(x∗, y∗)becomes
larger, the area of OABC becomes larger, the area of ABCD
decreases, and the two sides of the game are more likely to
tend to nonsharing of information. e change in the critical
point D(x∗, y∗)is closely related to the parameters of both
parties. Among them, the area of ABCD is as follows:
SABCD �1−1
2
C1+θQ1
K1Q2(g−1) + αQ1
+C2+θQ2
K2Q1(g−1) + αQ2
.
(13)
From the above evolutionary game analysis process, it
can be seen that whether a logistic service supply chain node
enterprise will choose to share information depends on the
enterprise’s own capabilities, the payment matrix of the
evolutionary game, and the initial values of various
O (0,0) A (1,0)
B (1,1)
C (0,1)
(a)
O (0,0) A (1,0)
B (1,1)
C (0,1)
(b)
Figure 2: Dynamic evolution phase diagram 2. (a) Dynamic evolution phase diagram of case 3. (b) e dynamic evolution phase diagram of
case 2 and case 3.
O (0,0) A (1,0)
B (1,1)C (0,1)
D (x', y')
Figure 3: Dynamic evolution phase diagram of case 4.
O (0,0) A (1,0)
B (1,1)
C (0,1)
(a)
O (0,0) A (1,0)
B (1,1)
C (0,1)
(b)
Figure 1: Dynamic evolution phase diagram 1. (a) Dynamic evolution phase diagram of case 1. (b) Dynamic evolution phase diagram of
case 2.
Mobile Information Systems 5
parameters. With the passage of time and the repeated
progress of the game process, the changes in the values of
various parameters may cause the players of the game to
choose different strategies. is study uses critical points
D(x∗, y∗)to analyze various parameters that may affect the
outcome of the evolutionary game.
From x∗� (C2+θQ2)/[K2Q1(g−1) +αQ2], y∗� (C1
+θQ1)/[K1Q2(g−1) + αQ1], it can be seen that the infor-
mation-sharing cost, risk coefficient, incentive coefficient,
synergy coefficient, information absorption and utilization
capacity, and information-sharing degree of each node of the
supply chain are important factors that affect the size of the
critical point.
Under uncertain market conditions, customer demand is
generally price-sensitive. erefore, the market logistic
service demand Qfaced by integrators is uncertain. Here, the
random variable Qhas the characteristics of the probability
distribution F (Q), and the customer enterprise logistic
service demand can be expressed by the following formula:
Volume �initial demand volume
+volume decrease rate ×(price)
+volume variation range
×sin price
variation cycle
.
(14)
Among them, initial demand volume is the initial de-
mand for logistic services, volume decrease rate is the slope
of change in service demand, volume variation range is the
range of change in service demand, and variation cycle is the
price cycle of service demand changes.
e change in customer demand described by a sine
curve is shown in Figure 4.
From Figure 4 above, we can see the impact of price
changes on the anticipated demand for logistic services by
client companies. Demirkan (2008) uses a normal distri-
bution function to describe the effect of random demand on
price. In order to facilitate model analysis and calculation,
this study uses a uniform distribution function to express the
demand price sensitivity in the logistic service supply chain
model, that is, we assume that the demand for logistic
services of price-sensitive client companies in a period is
uniformly distributed in the interval [Q(p) − θ, Q(p) + θ](θ
is the demand fluctuation coefficient, θ>0), as shown in
Figure 5 below as follows:
Q(p) � a−bp. (15)
Among them, a is a known constant, such as the actual
demand in the previous cycle. b is a constant, which is the
price sensitivity coefficient of market demand, a>, and b>0.
In order to simplify the model, this study does not
consider the marginal cost of both the supply chain and the
demand side, and directly uses revenue instead of profit for
calculation. According to the uniform distribution proba-
bility density function F (Q) in the above figure, the expected
profit function of the integrator is as follows:
collection �s
Q(p)− θ
[(P−V)Q(p)]F(Q)dQ +Q(p)+θ
S
[(P−V)S]F(Q)dQ
�− (P−V)S2+2(P−V)[Q(p) + θ]S− (P−V)[Q(P) − θ]2
4θ
� − P−V
4θS− [Q(P) + θ]
{ }2+(P−V)Q(p)
� − P−V
4θ[S− (a−bP +θ)]2+(P−V)(a−bP).
(16)
e first term in formula (15) represents the income
when the actual logistic service demand is less than the
logistic service capacity S purchased by the integrator. e
second term represents the income when the actual logistic
service demand is greater than the logistic service capacity S
purchased by the integrator.
e income of the logistic service provider is equal to the
product of the logistic service capability S purchased by the
Initial demand
Degree of variation
Variation slope
Period
Price
Customer service demand
0
Figure 4: Price-sensitive customer service demand curve.
6Mobile Information Systems
integrator and the unit logistic service price V. e cost of
the provider is mainly composed of two parts. One is the unit
logistic service capacity cost m, such as fixed costs such as
logistic elements, which reflect the normal economic scope
of capacity. e other is the operation and management cost
n, which is related to the management of the enterprise and
mainly includes the cost of improving the management
ability and the increase in the complexity of the business
model. at is, the profit function of the logistic service
provider is as follows:
Supply
�VS −mS −nS2.(17)
Due to the randomness of customer demand, uncertain
market demand will affect the logistic service costs of in-
tegrators and providers. When the logistic service capabil-
ities cannot meet customer needs, integrators and provider
groups will incur lost opportunity costs. When the logistic
service capacity is higher than the customer’s demand, this
will produce waste.
e expected cost when the logistic service capacity
purchased by the integrator is lower than the actual logistic
demand is as follows:
L(Q) � Q(p)+o
S
α[Q(p) − S]F(Q)dQ
�α
2θ
S2
2−S[Q(p) + θ] + [Q(p) + θ]2
2
�α
4θS− [Q(p) + θ]
2�α
4θ[S− (a−bP +θ)]2.
(18)
e parameter αrepresents the opportunity cost of unit
loss caused when the customer’s needs cannot be met.
erefore, the expected cost when the logistic service ca-
pability purchased by the integrator is higher than the lo-
gistic demand is as follows:
M(Q) � S
Q(p)− θ
V[S−Q(p)]F(Q)dQ
�V
2θS2−S[Q(p) − θ] − S2
2+[Q(p) − θ]2
2
�V
4θS− [Q(p) − θ]
2�V
4θ[S− (a−bP −θ)]2.
(19)
Unlike the product supply chain, logistic services are
intangible and perishable. erefore, excess service capacity
neither generates inventory nor residual value.
e single-loop information flow mode refers to the
unidirectional flow of information on the chain. In this
model, logistic service supply chain integrators and pro-
viders independently predict the logistic demand of the
market, and there is no demand information sharing be-
tween the two. e integrator makes the logistic service
integration planning decision based on the customer’s de-
mand information, and the provider makes the logistic
service supply planning decision based on the integrator’s
order information, as shown in Figure 6.
Under the single-loop information-sharing model, in-
tegrators and providers reach a consensus on services
through negotiation. In this mode, because the integrator
directly contacts with the customer and grasps important
customer information and market demand status, it belongs
to the dominant party, and the provider belongs to the
follower. erefore, the logistic service integrator first
maximizes its expected profit and determines the optimal
purchase amount S. However, integrators will generate
uncertain capacity risks and need to bear the uncertain cost
of logistic service volume. erefore, the expected profit
function of the integrator is as follows:
Ψcollection �Πcollection −M(Q) − L(Q)
� − P+α
4θ[S− (a−bP +θ)]2+P(a−bP) − VS.
(20)
However, the provider’s profit function is
ΨSupply �ΠSupply, and the provider determines the optimal
price V through the integrator’s service purchase volume
S. We assume that dΨSupply/dS �V−m−2nS �0, and the
relationship between the provider’s optimal pricing and
purchase volume can be obtained as follows:
V�m+2nS. (21)
F (Q)
Q (P1)-θQ (P1)+θQ (P1)Q (P2)-θQ (P2)+θQ (P2)
Figure 5: Price-sensitive customer service demand.
Mobile Information Systems 7
Incorporating formula (20) into the integrator’s profit
function equation (19), when the following two conditions
are met, the integrator’s own expected profit is the largest as
follows:
zΨcollection
zS� − P+α
2θ+4n
S+(P+α)(a−bP +θ)
2θ−m�0
zΨcollection
zP� − S2
4θ+(a−bP +θ)
2θS−(a−bP +θ)2
4θ+b(P+α)(a−bP +θ)
2θ−b(P+α)
2θ+a−2bP �0
.
⎧
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎩
(22)
According to equation (22), the optimal purchase
quantity of the integrator can be obtained as follows:
S1�(P+α)(a−bP +θ) − 2mθ
P+α+8nθ.(23)
It is the optimal logistic service volume of the logistic
service supply chain, and the provider determines the op-
timal unit logistic service capacity price V through the
optimal purchase volume S to maximize its own revenue.
en, the total expected profit of the logistic service
supply chain is as follows:
Ψ1�Ψcollection +ΨSupply �Πcollection −M(Q) − L(Q) + ΠSupply
� − P+α
4θ+n
S2+(P+α)(a−bP +θ)
2θ−m
S−(P+α)
4θ(a−bP +θ)2+P(a−bP).
(24)
We bring the optimal solution S1into formulas (16), (19),
and (23) to obtain the expected profits of logistic service
providers, integrators, and the entire supply chain,
respectively.
e collaborative control information flow model is an
information-sharing model led by logistic service integra-
tors. e integrator takes the initiative to share the cus-
tomer’s logistic service demand information with the
provider for collaboration. In this mode, the provider makes
Functional
logistics service
provider
Logistics
Services
Integrator
Client
enterprises
Logistics service
supply plan
Logistics service
integration
program
Logistics service
demand plan
Provide
predictive
information
Integrator
demand
information
Integrator
forecast
information
Customer
demand
information
Demand forecast Demand forecast Service demand
forecast
Logistics service
outsourcing
Service demand Service demand
Logistics service
query Market
Layer of
logical
relations layer
Information
flow layer
Customer
forecast
information
Market information
Figure 6: Single-loop information flow mode.
8Mobile Information Systems
logistic service supply planning decisions based on the in-
tegrator’s order information and the customer’s service
demand information, as shown in Figure 7.
Under this information flow model, information
asymmetry may occur between the integrator and the
provider. at is, the order volume predicted by the inte-
grator differs from the customer demand predicted by the
provider. At this time, the provider coordinates the supply
chain by adjusting the number S of logistic service capa-
bilities. erefore, the cost of uncertain capacity is borne by
the provider. However, the integrator only needs to bear the
purchase cost of the logistic service capability, that is, the
expected profit function of the integrator is as follows:
Ψcollection �Πcollection
� − P−V
4θ[S− (a−bP +θ)]2+(P−V)(a−bP).
(25)
When formula (15) satisfies the following two condi-
tions, the integrator expects the profit to be maximized as
follows:
zΨcollection
zS�(P−V)(a−bP +θ−S)
2θ�0
zΨcollection
zP� − S2
4θ+(a−bP +θ) − b(P−V)
2θS−(a−bP +θ)2
4θ+b(P−V)(a−bP +θ)
2θ+a−2bP +bV �0
.
⎧⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎩
(26)
From equation (26), the optimal purchase amount of the
integrator can be obtained as follows:
Scollection �a−bP +θ.(27)
is shows that when integrators do not have to bear the
cost of uncertain capabilities; in order to meet customer
needs, integrators always tend to purchase the maximum
amount of logistic services.
Functional
logistics service
provider
Logistics
Services
Integrator
Client
enterprises
Logistics service
supply plan
Logistics service
integration
program
Logistics service
demand plan
Provide
predictive
information
Integrator
demand
information
Integrator
forecast
information
Customer
demand
information
Demand forecast Demand forecast Service demand
forecast
Logistics service
outsourcing
Service demand Service demand
Logistics service
query Market
Layer of
logical
relations layer
Information
flow layer
Customer
forecast
information
Market information
Figure 7: Cooperative control information flow mode.
Mobile Information Systems 9
Substituting formula (26) into the expected profit
function of the provider, we get the following:
ΨSupply �ΠSupply −M(Q) − L(Q) � − V+α
4θ+n
S2+α
2θ(a−bP +θ) + V
2θ(a−bP −θ) + V−m
S−α
4θ(a−bP +θ)2
−V
4θ(a−bP −θ)2� − nS2+ (V−m)S−mθ.
(28)
We assume that dΨSupply/dS � − 2nS +V−m�0, and
the optimal service volume of the available provider is
SSupply �V−m/2n, that is, V�m+2nS. It shows that no
matter how the cost of the provider changes, the optimal
pricing strategy is always only related to the volume of
logistic services. In order to balance the capabilities of the
entire supply chain, the integrator’s purchase volume should
be equal to the provider’s supply volume. We assume
dScollection/dP � − b<0, and Scollection is a monotonically
decreasing function of P. We assume dSSupply /dV �1/2n>0,
and SSupply is a monotonically increasing function of V. It
shows that there must be a unique equilibrium solution for
Scollection �SSupply to maximize the profits of both, that is,
P�2n(a+θ) − V+m/2nb. e provider decides the price
V that maximizes its own interests based on the integrator’s
maximum purchase volume, and the integrator decides the
price P for the customer on the basis of the provider’s price
V. e total expected profit of the logistic service supply
chain is as follows:
Ψ2�Ψ1/4−+Ψ1©�Π1/4−+Π1©−M(Q) − L(Q).(29)
We bring the optimal solution of S2�Scollection �SSupply
into formulas (15), (23), and (26) to obtain the expected
profits of logistic service integrators, providers, and the
entire supply chain.
e centralized control information flow model is a way to
use information technology to share information in the supply
chain network. In this model, a decentralized supply chain can
achieve optimal organizational performance. Information
sharing among supply chain members can produce
Information exchange
Customer demand
information
Functional
logistics service
provider
Logistics
Services
Integrator
Client
enterprises
Logistics service
supply plan
Logistics service
integration
program
Logistics service
demand plan
Provide
predictive
information
Integrator
forecast
information
Customer
demand
information
Demand forecast Demand forecast Service demand
forecast
Logistics service
outsourcing
Service demand Service demand
Logistics service
query Market
Layer of
logical
relations layer
Information
flow layer
Customer
forecast
information
Market information
Figure 8: Centralized control information flow mode.
10 Mobile Information Systems
deterministic changes, such as reducing or eliminating the
“bullwhip effect.” e use of information transmission and
interaction technology to increase vertical information sharing
can improve the service performance of providers in the logistic
service supply chain and the entire supply chain. Providers use
information-sharing technology to shorten the distance with
customers and can synchronize with integrators to obtain
customer logistic demand information in the market, and
providers can directly make service supply chain decisions
based on customer demand information. In this way, the
provider can take the initiative to adopt corresponding strat-
egies to support the integrator’s decision-making, in order to
achieve the balance of capabilities of the entire logistic service
supply chain, as shown in Figure 8.
Under this information-sharing model, in the logistic
service supply chain of integrators, providers, and customer
enterprises, each member shares market demand informa-
tion and makes unified decisions, with the goal of maxi-
mizing the overall benefits of the logistic service supply
chain. erefore, the expected profit of the entire supply
chain is the sum of the expected profit of the integrator and
the provider. Subsequently, the uncertain capacity cost
is eliminated, and the cost is ultimately borne by the
integrator and the provider. To simplify the model and
Supervision
department
Bank e third party
logistics
Electronic
vouchers
Electronic warehouse
receipt
Secondary
supplier First dealer First supplier Core
enterprise
Data storage:
Electronic commerce four flow
data, Financing data
Electronic voucher: Split,
Circulation, Financing
Data operation record:
Reading, Writing,
Authorization
Smart contract: Online contract
execution
Blockchain + supply chain financial system ....
Participate in the main
body on the chain
Storage on the
data chain
Operation on the
business chain
......
Figure 9: e operating mechanism of blockchain + supply chain finance.
Mobile Information Systems 11
Suppliers at all levels
Asset input
Capital output
Core enterprise
Credit input
Value output
Banking institution
Asset input
Profit and financial
asset outputs
Supply chain platform
Real name
authentication
Certificate
management Financing
management
Supervision and
control of funds
Finance
KYC
System of
account
Tru s t e d
hardware
Electronic
signature
Blockchain
technology
Judicial deposit
certificate
Other ….
Figure 10: Schematic diagram of using blockchain application scenarios to realize the core functions of supply chain integrated business
intelligence financial processing.
Business
closure
Tra n s actio n
information
Income self-
compensation
Structured
risk
Reputation
capitalization
Assetallocation
factors
Financing
factors
Polic y
factors
1
0.5
0
-0.5
-1
Figure 11: e processing effect of the safe financial processing method for realizing supply chain integrated business intelligence using
blockchain application scenarios.
12 Mobile Information Systems
calculations, we assume that the commitment ratio is 1 :1,
that is, Ψcollection �Πcollection −1/2[M(Q) + L(Q)],ΨSupply �
ΠSupply −1/2[M(Q) +L(Q)]. erefore, the total revenue of
the supply chain is simply calculated as follows:
Ψ3�Ψcollection +ΨSupply
� − P+α
4θ+n
S2+(P+α)(a−bP +θ)
2θ−m
S−(P+α)
4θ(a−bP +θ)2+P(a−bP)
(30)
In order to maximize the total revenue of the supply
chain, the following two conditions must be met as follows:
zΨ
zS� − P+α
2θ+2n
S+(P+α)(a−bP +θ)
2θ−m�0
zΨ
zP� − S2
4θ+(a−bP +θ)
2θS−(a−bP +θ)2
4θ+b(P+α)(a−bP +θ)
2θ−b(P+α)
2θ+a−2bP �0
⎧⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎩
(31)
According to formula (29), the optimal logistic service
volume of the supply chain is as follows:
S3�(P+α)(a−bP +θ) − 2mθ
P+α+4nθ.(32)
We assume that dΨcollection/dS � − 2P−V+α/4θS
+2P−V+α/4θ(a−bP +θ) − V/2 �0, and the available
service volume of the integrator is
Scollection �a−bP +θ−2Vθ/2P−V+α. We assume that
dΨSupply/dS � − (V+α/4θ+2n)S+V+α/4θ(a−bP +θ) +
V/2m�0,and the optimal service volume of the available
provider is SSupply � (V+α)(a−bP +θ) + 2Vθ
−4mθ/V+α+8nθ.
4. The Safe Financial Processing Method for
Realizing Supply Chain Integrated Business
Intelligence Using Blockchain
Application Scenarios
On the basis of the above algorithm analysis, the blockchain
application scenario is used to realize the secure financial
processing of supply chain integrated business intelligence,
and the model in this study is constructed and the
performance is analyzed. e behaviors and activities of
multiple participating entities involved in the development
of supply chain finance business, banks, core enterprises,
upstream and downstream enterprises, and related service
entities must comply with commercial rules and contract
requirements. However, the complex structure and so-
phistication of business operations increase the difficulty of
risk control for banks. e characteristics of blockchain
technology can effectively meet the control of the causes of
supply chain financial risks. e application of blockchain to
the supply chain financial business can effectively reduce the
difficulty of business risk management, thereby reducing the
probability of risk occurrence. Figure 9 shows the operating
mechanism of blockchain technology embedded in supply
chain financial services.
As shown in Figure 10, the core functions of the supply
chain platform mainly include four parts: real-name au-
thentication, credential management, financing manage-
ment, and capital management and control.
Based on the above model, the safe financial processing
method for realizing supply chain integrated business intel-
ligence using blockchain application scenarios was validated,
and the results shown in Figure 11 below were obtained.
60
65
70
75
80
85
90
95
100
0 200 400 600 800 1000
Number
Clustering effect
Figure 12: e clustering effect of the system.
Mobile Information Systems 13
From the results shown in Figure 11 above, the safe
financial processing method for realizing supply chain in-
tegrated business intelligence using blockchain application
scenarios has good results. On this basis, this study clusters
the effects of the model and obtains the results shown in
Figure 12.
From the above analysis, it can be seen that the safe
financial processing method for realizing supply chain in-
tegrated business intelligence using blockchain application
scenarios has good results and can play an important role in
the safe financial processing of supply chain integrated
business intelligence.
5. Conclusions
e blockchain-based supply chain financial system mainly
uses core companies and suppliers to record the information
involved in transactions. When the core enterprise partic-
ipates in the transaction in the supply chain financial system,
it only needs to confirm the transaction order with the
corresponding private key, which reduces the risk of banks
and other financial institutions in the supply chain to a
certain extent. Furthermore, the reduction in financing risks
has further reduced the financing costs of participating
companies in the supply chain and promoted the efficient
operation of the supply chain financial system. In addition,
the blockchain-based supply chain financial system uses
smart contracts to replace manual operations. is study
combines the blockchain technology to analyze the security
financial management of the supply chain integrated busi-
ness intelligence, builds an intelligent system, and verifies
the performance of the system to improve the security fi-
nancial management effect of the supply chain integrated
business intelligence. e experimental research results
show that the safe financial processing method for realizing
supply chain integrated business intelligence using block-
chain application scenarios has good results.
Data Availability
e data used to support the findings of this study are in-
cluded within the article.
Conflicts of Interest
e authors declare that there are no conflicts of interest
regarding this work.
Acknowledgments
is research was supported by the Research Fund of
China’s Jiangsu Province Higher Education Teaching Re-
form Project (2021JSJG439).
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Mobile Information Systems 15
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