Haiyan Wu’s research while affiliated with Guangdong University of Foreign Studies and other places

The price of the product has a great impact on the market competitiveness of the product. The manager can adjust the operation strategy according to the cost information of the product, cut costs, adjust the market decision and make the product more competitive in the market. Therefore, the research of product costing method has a very important significance. Considering that the physical resources in the processing of the virtual network mapping (VNM) fragment embedded in the request are rejected, and the problem of physical resource usage is weakened, a VNM algorithm based on the optimal subnet edge matching algorithm is proposed. After the VNM request is coarsened to the subnet, the breadth-first search algorithm is used to create the physical subnet candidate set, and the constraints of the virtual node and the coarsening network topology are complied with. The simulation results show that the proposed algorithm reduces the number of link mapping hops and can improve the acceptance rate and cost-benefit ratio of virtual networks. First of all, this paper analyzes the research background of manufacturing cost calculation and the research situation at home and abroad, discusses the advantages and disadvantages of standard cost calculation and studies and analyzes the principles, advantages and disadvantages of standard cost calculation in daily business activities. Second, design a new mathematical accounting model based on the mathematical model of standard cost calculation in daily business activities. This combines the advantages of standard costing and time-based activity costing, and provides theoretical support for manufacturing companies to use this accounting method. Finally, the research results of this paper are summarized, and the shortcomings of this paper are analyzed. Cost accounting method needs to be further studied.

With the continuous development of the Internet of Things, the data security and privacy protection issues of the Internet of Things are becoming more and more noticeable. This article mainly introduces the application of the image rain removal algorithm in the big data platform architecture of the Internet of Things. In order to design multiple small files generated by multiple sensors, we used the IndoorSTG indoor RFID data generation tool to simulate the data generated by 1,000 sensors. In order to increase the calculation speed and save the calculation cost, this paper randomly selects 9100 image pairs regardless of whether it is raining or not, and then randomly retrieves the images. As a network training group, 30,000 image patch pairs will be generated regardless of whether it is raining or not. In the network training process, the Xavier weight initialization method is used, and the Adam optimization algorithm is used. The batch size is 32, the learning rate is set to 0.0005, and a total of 175,000 repeats. DMFS has an average 60% higher write throughput when facing small files than Redis. The results show that the image rain elimination algorithm can improve the stability of the IoT big data platform architecture.KeywordsImage rain removal algorithmInternet of ThingsBig dataImage decomposition

The power IoT terminal security system is a key system to understand the operation and control of low-voltage and medium-voltage distribution networks. The distribution network has the function of monitoring and controlling information. The technical characteristics of the Internet of Things are embodied in both error handling and implementation of the structure of the application. The development of power Internet of Things terminal security system technology is an inevitable trend of power system modernization, and power Internet of Things terminal security protection is the main means to ensure reliability. However, with our country’s network security situation becoming tense year after year, and the second round of overhaul of the national power system, the network security of distributed network automation systems has attracted more and more attention. This article starts with the Internet of Things network security protection system, focusing on the Internet of Things power terminal to discuss the analysis and research of the security protection system. This article mainly introduces the decision tree method and the reinforcement learning method. This paper uses decision tree method and reinforcement learning method to analyze and research the terminal security protection system of power Internet of Things, and establish a potential mathematical model. The model is solved by the reinforcement learning method, and the analysis status is evaluated, and the model is revised using historical data to improve the accuracy of analysis and detection. The experimental results of this paper show that the reinforcement learning method improves the analysis efficiency of the power IoT terminal security protection system by 13%. Finally, by comparing the security protection system and the efficiency before and after protection, the influence of machine learning on the analysis of the terminal security protection system of the power Internet of Things is analyzed.

The Internet of Things can be simply understood as the mode of connecting things in the Internet environment. It is mainly the organic combination of computer, Internet technology and other information technology means. At the same time, the organic combination of the Internet of Things, big data, cloud computing and other advanced information thinking and computing methods is the direction of the future smart city construction. In the experiment, due to the characteristics of real-time and massive data of the Internet of Things, the fusion processing of big data cannot be carried out well. Based on the Internet of Things technology, cloud computing technology and fusion algorithm are selected for research. Experimental data show that the number of four types of nodes is set as 30, and the system has 100 nodes in total, and the length of one rotation of the system is 50s. The ordinary Leacha algorithm is used first, and then the improved Leach algorithm is used in the same environment. The experimental results show that there are four different types of nodes in the monitoring environment obtained by the test, and the initial data transmission rates of each node are 60, 90, 110 and 80 respectively. Therefore, the adjusted and improved LEACH algorithm can allocate the required data transmission time according to the different failure levels of the data. If the aging level is higher, the data transmission time of node data will be relatively longer. The improved strategy is in line with the characteristics of the Internet of Things and has the feasibility to complete the experiment.

In the new era of rapid development of Internet information technology and data processing technology, although the emergence of Internet technology has brought changes that cannot be underestimated in various fields of society, the computer performance evaluation system based on the Internet in the era of big data will definitely follow Technology trends in the new era. The balanced scorecard is one of the most commonly used financial performance appraisal tools in academia. The research purpose of this article is to study the innovation of enterprise computer performance appraisal management system in the current Internet era with the continuous development of big data technology. This paper first uses a neural network algorithm to build a model for it. Through this algorithm combined with a questionnaire survey method to screen the evaluation indicators of the computer performance evaluation system of Internet companies in the eastern coastal area, and finally build a BP model through the enterprise computer performance evaluation system. Model calculation samples, after calculation, the computer performance analysis of the target company can be obtained, and on the basis of summarizing the computer performance evaluation system and the balanced score card of the enterprise in the Internet era, the in-depth understanding and conclusion can be drawn. After studying the setting of performance appraisal system indicators, the setting of personal growth indicators is insufficient, the lack of effective cost control and the setting of computer performance indicators have not yet formed a complete system problem. This paper combines the actual situation of computer performance appraisal of Internet companies in the eastern coastal areas, applies the balanced scorecard to set up a management index system for the company’s computer performance appraisal, and uses the analytic hierarchy process to analyze the weights of the various indicators of the corporate computer performance appraisal balanced scorecard. Compare the evaluation results with the designated enterprise computer performance management evaluation standards. Experimental research results show that the balanced scorecard can effectively improve the efficiency of the computer performance appraisal system, and the computer performance appraisal can be clearer, and based on the Internet background and big data technology, the performance appraisal system can be made clearer.