A method and system for managing an intelligent industrial chain based on data graphs

Abstract

The present invention relates to the technical field of data graph data processing, and specifically discloses an intelligent industrial chain management method and system based on a data graph. The method analyzes network environment data to which industrial chain data is updated, and evaluates the network reliability factor of industrial chain data update preparation, thereby selecting the best network environment to update the industrial chain data. At the same time, the industrial chain data update process parameters are analyzed to obtain the monitoring anomaly index of the industrial chain data update process. If the data update process has a high degree of anomaly, the update deviation index of the associated chart to which the industrial chain data belongs is determined, and the intelligent update management of the industrial chain data of the data graph is fed back according to the update deviation level, thereby ensuring the accuracy of the industrial chain data update in the data graph. At the same time, the method can automatically optimize the industrial chain data update process, reduce manual intervention, and improve the management efficiency of the industrial chain data in the data graph.

Description

Technical Field

[0001] The present invention relates to the field of data graph data processing technology, and specifically to a smart industrial chain management method and system based on data graphs. Background Art

[0002] Currently, when updating industrial chain data, many systems generally adopt the method of directly receiving and processing data from external or internal data sources. This "update as you receive" approach simplifies the data processing process to a certain extent, but ignores the multiple challenges that may be encountered in the process of updating industrial chain data, resulting in difficulty in ensuring data consistency and accuracy, low update efficiency, and the intelligent industrial chain system faces security and stability risks, making it difficult to meet the dynamic development needs of the industrial chain.

[0003] For example, the invention patent with publication number CN113568928A discloses a data management system applied to the energy industry chain early warning system, which relates to the field of power system technology. The data management system includes a data acquisition module, a data service module and a data mining module; the data acquisition module is used to obtain the initial data of the energy industry chain early warning system; the data service module is used to classify and store the initial data, obtain result information, and visualize the result information; the data mining module is used to match the preset data mining components according to the result information.

[0004] For example, the invention patent with the announcement number CN112347752B announces a method and device for autonomous drawing and management of an industrial chain, which includes the following steps: autonomous drawing of the industrial chain; composition map drawing, structured drawing of the constituent link data of the industrial chain; manufacturing process map drawing, structured drawing of the manufacturing process map of the products in each link of the industrial chain; enterprise supply relationship map drawing, structured drawing and management of the enterprises related to the products in each link of the industrial chain and the supply relationships formed between enterprises through products; industrial chain dimensional development data management, structured and unified storage of dimensional development data of each link in the industrial chain.

[0005] However, in the process of implementing the embodiments of the present application, it was found that the above-mentioned technology has at least the following technical problems: when updating the industrial chain data, the existing technology directly updates the received data, which makes it difficult to ensure the security and integrity of the industrial chain data update, thereby causing the industrial chain data update to be distorted and damaged. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a smart industrial chain management method and system based on data graphs, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides an intelligent industrial chain management method based on a data map, including: S1. Based on the industrial chain data update preparation process of the data map, collect network environment data to which the industrial chain data update belongs, evaluate the network reliability factor of the industrial chain data update preparation, and compare it with the network reliability threshold. If the network reliability factor of the industrial chain data update preparation is less than the network reliability threshold, then feedback is given to the network environment to which the industrial chain data update preparation belongs. If the network reliability factor of the industrial chain data update preparation is greater than or equal to the network reliability threshold, thereby obtaining the reliable network environment to which the industrial chain data update process belongs, then execute S2; S2. Based on the reliable network environment to which the industrial chain data update process belongs, obtain the industrial chain data update process parameters, analyze the monitoring anomaly index of the industrial chain data update process, and compare it with the monitoring anomaly threshold. If the monitoring anomaly index of the industrial chain data update process is less than the monitoring anomaly threshold, then feedback is given to the industrial chain data update. If the monitoring anomaly index of the industrial chain data update process is greater than or equal to the monitoring anomaly threshold, execute S3; S3. Collect the industrial chain data update results of the data map, thereby determine the update deviation index of the associated chart to which the industrial chain data belongs, and provide feedback to the intelligent update management of the industrial chain data of the data map.

[0008] As a further method, the industrial chain data update preparation process based on the data map collects the network environment data to which the industrial chain data update belongs. Specifically, it is a process for preparing the industrial chain data of the data map before updating, wherein the above preparation process is a network preparation process, thereby obtaining the industrial chain data update network preparation process of the data map, and collecting corresponding data, which is recorded as the network environment data to which the industrial chain data update belongs.

[0009] As a further method, feedback is provided on the network environment to which the industrial chain data update preparation belongs. The specific feedback process is: if the network reliability factor of the industrial chain data update preparation is less than the network reliability threshold, a network replacement instruction is generated. Based on the processing result of the network replacement instruction, the network reliability factor of the industrial chain data update preparation is evaluated again, thereby providing feedback on the network environment to which the industrial chain data update preparation belongs.

[0010] As a further method, the feedback on the industrial chain data update is specifically conducted as follows: based on the monitoring anomaly index of the industrial chain data update process being less than the monitoring anomaly threshold, a successful industrial chain data update log is generated, and feedback on the industrial chain data update is conducted based on the successful industrial chain data update log.

[0011] The second aspect of the present invention provides an application such as a system of an intelligent industrial chain management method based on a data map, including: a network environment evaluation module, which is used for the industrial chain data update preparation process based on the data map, collects network environment data to which the industrial chain data update belongs, evaluates the network reliability factor of the industrial chain data update preparation, and compares it with the network reliability threshold. If the network reliability factor of the industrial chain data update preparation is less than the network reliability threshold, feedback is given to the network environment to which the industrial chain data update preparation belongs. If the network reliability factor of the industrial chain data update preparation is greater than or equal to the network reliability threshold, thereby obtaining a reliable network environment to which the industrial chain data update process belongs, an update process analysis module is executed; The update process analysis module is used to obtain the parameters of the industrial chain data update process based on the reliable network environment to which the industrial chain data update process belongs, analyze the monitoring anomaly index of the industrial chain data update process, and compare it with the monitoring anomaly threshold. If the monitoring anomaly index of the industrial chain data update process is less than the monitoring anomaly threshold, feedback is given to the industrial chain data update; if the monitoring anomaly index of the industrial chain data update process is greater than or equal to the monitoring anomaly threshold, the update result judgment module is executed; the update result judgment module is used to collect the update results of the industrial chain data of the data map, thereby determining the update deviation index of the associated chart to which the industrial chain data belongs, and providing feedback to the intelligent update management of the industrial chain data of the data map.

[0012] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0013] (1) The present invention provides an intelligent industrial chain management method and system based on a data map. By analyzing the network environment data to which the industrial chain data is updated and evaluating the network reliability factor of the industrial chain data update preparation, the optimal network environment is selected to update the industrial chain data. At the same time, the parameters of the industrial chain data update process are analyzed to obtain the monitoring abnormality index of the industrial chain data update process. If the abnormality level of the data update process is high, the update deviation index of the associated chart to which the industrial chain data belongs is determined, and the intelligent update management of the industrial chain data of the data map is fed back according to the update deviation level, thereby ensuring the accuracy of the industrial chain data update in the data map. At the same time, it can automatically optimize the industrial chain data update process, reduce manual intervention, and improve the management efficiency of the industrial chain data in the data map.

[0014] (2) The present invention analyzes the key parameters involved in the industrial chain data update process to obtain the monitoring anomaly index of the industrial chain data update process, so as to timely discover the potential risk points in the industrial chain data update process and issue early warnings, and then take countermeasures in advance to reduce the possibility of risk occurrence and mitigate the losses caused by the risk.

[0015] (3) The present invention obtains the update deviation index of the associated graph to which the industrial chain data belongs by analyzing the update results of the data graph, and further obtains the update error level of the associated graph, so as to conduct targeted feedback management on the industrial chain data update operation in the data graph, which not only ensures the accuracy of the industrial chain data update in the data graph, but also promotes the dynamic adjustment and optimization of the industrial chain data update strategy, thereby comprehensively ensuring the reliability of the industrial chain data update. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0017] Figure 1 The figure is a schematic flow chart of the method steps of the present invention.

[0018] Figure 2 It is a schematic diagram of system module connection of the present invention.

[0019] Figure 3 This is a schematic diagram of the cycle duration involved in the present invention.

[0020] Figure numerals: 1. Update result detection cycle duration; 2. Data operation cycle duration. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] Reference Figure 1 As shown, the first aspect of the present invention provides an intelligent industrial chain management method based on a data map, including: S1. An industrial chain data update preparation process based on a data map, collecting network environment data to which the industrial chain data update belongs, evaluating the network reliability factor of the industrial chain data update preparation, and comparing it with the network reliability threshold. If the network reliability factor of the industrial chain data update preparation is less than the network reliability threshold, then feedback is given to the network environment to which the industrial chain data update preparation belongs. If the network reliability factor of the industrial chain data update preparation is greater than or equal to the network reliability threshold, thereby obtaining a reliable network environment to which the industrial chain data update process belongs, then S2 is executed.

[0023] It should be explained that the network environment data to which the above-mentioned industrial chain data updates belong includes the number of vulnerabilities, key length, and firewall throughput. In addition to being applicable to the above-mentioned parameters, this example can also be used for other parameters.

[0024] Specifically, the data graph-based industrial chain data update preparation process collects network environment data to which the industrial chain data update belongs, specifically, the process of preparing the industrial chain data of the data graph before updating, wherein the above-mentioned preparation process is a network preparation process, thereby obtaining the network preparation process of the industrial chain data update of the data graph, and collecting corresponding data, which is recorded as the network environment data to which the industrial chain data update belongs. It should be explained that the industrial chain of the data graph in this embodiment involves enterprises, banks, etc., and the industrial chain data includes ticket usage data (such as invoice amount and receipt amount, etc.), industry and commerce (such as registered capital and operating status, etc.) and bidding (such as procurement needs and bidding status, etc.), etc. The update of industrial chain data is an important link to ensure the continuity of industrial chain business. If the network environment is not safe when the industrial chain data is updated, it may cause the data update process to be interrupted or failed, thereby affecting the normal operation and decision-making of each enterprise in the industrial chain. At the same time, an unsafe network environment may also trigger risk events such as industrial chain data leakage, further exacerbating the risk of industrial chain business interruption. Therefore, one of the key points described in this example is to analyze the network environment before the industrial chain data in the data graph is updated, so as to ultimately ensure that the industrial chain data is updated in a safe and reliable network environment.

[0025] Specifically, the feedback on the network environment to which the industrial chain data update preparation belongs is as follows: if the network reliability factor of the industrial chain data update preparation is less than the network reliability threshold, a network replacement instruction is generated; based on the processing result of the network replacement instruction, the network reliability factor of the industrial chain data update preparation is evaluated again, thereby providing feedback on the network environment to which the industrial chain data update preparation belongs; the above-mentioned network reliability threshold represents the minimum value of a reasonable range of the network reliability factor of the industrial chain data update preparation, which is extracted from the management database and is comprehensively evaluated by network analysis specialists based on network architecture, equipment performance, bandwidth resources, and security policies, and is formulated after understanding the actual status of network operation. If the network reliability factor of the industrial chain data update preparation is less than the network reliability threshold, it indicates that the current network environment of the data map administrator is insufficient in stability and security, and cannot provide sufficient support for the update process of the industrial chain data. Therefore, a network replacement instruction is automatically generated and displayed to the data map administrator through a visual pop-up box, with a striking title clearly pointing out the current situation, such as "the network environment is not sufficient to support the industrial chain data update preparation". The data map administrator can choose whether to change the network environment, such as converting the public WI-FI network environment to a 5G network environment. If the data map administrator chooses to change the network environment, the processing result of the network replacement instruction is to change the network environment. Therefore, the network reliability factor of the industrial chain data update preparation is evaluated again. The network environment analyzed this time is the network environment after the replacement. If the network reliability factor of the industrial chain data update preparation evaluated this time is less than the network reliability threshold, the network replacement instruction continues to be generated until the network reliability factor of the industrial chain data update preparation is greater than or equal to the network reliability threshold or the data map administrator chooses not to change the network environment, that is, the processing result of the network replacement instruction is not to change the network environment;If there is a network reliability factor greater than or equal to the network reliability threshold for the industrial chain data update, when the network environment with the network reliability factor greater than or equal to the network reliability threshold appears for the first time, it is directly marked as a reliable network environment for the industrial chain data update process, and the network environment analysis is stopped, and a visual pop-up box is displayed to express "industry chain data operations can be performed" to reduce the time waste on network environment selection. If there is only a network change instruction, the processing result is not to change the network environment and there is no network reliability factor greater than or equal to the network reliability threshold for the industrial chain data update, then a "continue to update data instruction" is automatically generated. The instruction clearly includes the judgment on whether to continue to update data in the current network environment. If the judgment is yes, the network reliability factors of the industrial chain data update that have been analyzed are sorted in order from large to small, and the network environment corresponding to the network reliability factor ranked first is extracted, and it is marked as a reliable network environment for the industrial chain data update process and automatically selected as the network environment for industrial chain data update, and a visual pop-up box is displayed to express "industry chain data operations can be performed". If the judgment result is no, the data map administrator stops operating the industrial chain data, thereby completing the feedback process. ;

[0026] It needs to be explained that if the above-mentioned processing result of only the network change instruction is not to change the network environment and there is no network reliability factor greater than or equal to the network reliability threshold for the industrial chain data update preparation, it means that the network reliability factors of the currently replaced network environment are all less than the network reliability threshold. Therefore, when the judgment result of the received continue to update data instruction is yes, it indicates that the data map administrator wants to continue to operate the industrial chain data. Therefore, the system automatically selects the network environment corresponding to the maximum value of the network reliability factor of the industrial chain data update preparation that has been analyzed as the network environment for the industrial chain data operation, thereby maximizing the security of the industrial chain data update process.

[0027] Furthermore, the network reliability factor for evaluating the industrial chain data update preparation is specifically evaluated as follows: extracting the number of vulnerabilities of the network to which the industrial chain data update preparation belongs within the environment detection cycle from the network environment data to which the industrial chain data update belongs, performing ratio processing with the duration corresponding to the environment detection cycle, and obtaining the vulnerability density of the network to which the industrial chain data update preparation belongs within the environment detection cycle; the above-mentioned environment detection cycle refers to the time period for detecting the performance of the network to which the industrial chain data update belongs, and its specific duration is determined by the network analysis specialist, and the duration of the environment detection cycle is between three seconds and eight seconds; the number of vulnerabilities of the network to which the industrial chain data update preparation belongs within the environment detection cycle is obtained by deploying a vulnerability scanner (such as Nessus security scanner) and identifying the type of network to which the industrial chain data update preparation belongs. The vulnerability scanner automatically adjusts the corresponding scanning depth and the type of vulnerability scanned, thereby obtaining the initial number of vulnerabilities of the network to which the industrial chain data update preparation belongs within the environment detection cycle, and removing duplicate vulnerability records by comparing the unique identifier (such as CVE number) and description information of the vulnerability, thereby obtaining the number of vulnerabilities of the network to which the industrial chain data update preparation belongs within the environment detection cycle by deduplication.

[0028] The key length of the network to which the industrial chain data update is prepared and the firewall throughput of the network to which the industrial chain data update is prepared during the environmental detection cycle are extracted from the network environment data to which the industrial chain data update is prepared; the key length of the network to which the above-mentioned industrial chain data update is prepared is obtained by querying the network protocol standard. For example, if the network to which the industrial chain data update is prepared is a 5G network, the key length of the network obtained by query is 192 bits; the firewall throughput of the network to which the above-mentioned industrial chain data update is prepared during the environmental detection cycle is tested by network testing instruments (such as Spirent), and the network management specialist sets reasonable configuration parameters in the network testing instruments, including network topology, data packet type, traffic size and other parameter tests to obtain the firewall throughput of the network to which the industrial chain data update is prepared during the environmental detection cycle.

[0029] Thus, the network reliability factor of the industrial chain data update preparation is comprehensively evaluated. In this embodiment, the network reliability factor of the industrial chain data update preparation is obtained by comprehensively analyzing the vulnerability density of the network to which the industrial chain data update preparation belongs during the environmental detection period, the key length of the network to which the industrial chain data update preparation belongs, and the firewall throughput of the network to which the industrial chain data update preparation belongs during the environmental detection period. It is a numerical value used to determine the network reliability of the industrial chain data update preparation. The specific expression is:

[0030] +1;

[0031] The vulnerability density of the network prepared for industrial chain data update during the environmental detection cycle reflects the density of security vulnerabilities in the network and is an important indicator for measuring the security status of the network.

[0032] The key length of the network prepared for the industrial chain data update refers to the number of bits contained in the network key, that is, the number of bits. A network with a longer key length can better protect sensitive information in the industrial chain data from being illegally obtained or tampered with. In this embodiment, the key length ranges from 1024 bits to 3072 bits. Within this range, the longer the key length, the better.

[0033] The firewall throughput of the network prepared for the industrial chain data update during the environmental detection cycle refers to the number of data packets that the network firewall can process per unit time without packet loss. When the firewall throughput is large, it can handle a large amount of network traffic and potential attack traffic in a timely manner, ensuring the safe and smooth operation of the network.

[0034] e is a natural constant, It is the impact factor corresponding to the preset unit value of vulnerability density in the management database, which represents the numerical value of the impact degree of the unit value of vulnerability density on the network reliability factor of the industrial chain data update process. When used, the impact factor corresponding to the unit value of vulnerability density can be directly obtained from the management database, and the corresponding relationship can be a preset mapping relationship. For example, the vulnerability density and the impact factor corresponding to the unit value of vulnerability density preset in the management database form a mapping set, and the real-time vulnerability density is input into the mapping set to obtain the impact factor corresponding to the unit value of vulnerability density. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0035] It is the impact factor corresponding to the key length unit value preset in the management database, which represents the degree of influence of the key length unit value on the network reliability factor of the industrial chain data update process. When used, the impact factor corresponding to the key length unit value can be directly obtained from the management database, and the corresponding relationship can be a preset mapping relationship. For example, the key length and the impact factor corresponding to the key length unit value preset in the management database form a mapping set, and the real-time key length is input into the mapping set to obtain the impact factor corresponding to the key length unit value. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0036] It is the impact factor corresponding to the unit value of firewall throughput preset in the management database, which represents the degree of influence of the unit value of firewall throughput on the network reliability factor of the industrial chain data update process. When used, the impact factor corresponding to the unit value of firewall throughput can be directly obtained from the management database. The corresponding relationship can be a preset mapping relationship. For example, the firewall throughput and the impact factor corresponding to the unit value of firewall throughput preset in the management database form a mapping set. The real-time firewall throughput is input into the mapping set to obtain the impact factor corresponding to the unit value of firewall throughput. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0037] in, The network reliability factor prepared for the industrial chain data update, when the key length is short, indicates that the network security is low, which makes the network easy to be destroyed and thus increases the network vulnerability density. At the same time, if the firewall throughput is small, it may not be able to timely and effectively handle a large amount of network traffic and potential attack traffic, resulting in the firewall being unable to effectively prevent attacks when facing network vulnerabilities, thereby indirectly increasing the risk of successful exploitation of vulnerabilities, that is, increasing the risk of vulnerability density. High vulnerability density means that there are more security risks and weaknesses in the network. In the network prepared for the industrial chain data update, using a sufficiently long key length can protect sensitive data from being illegally obtained or tampered with, thereby improving the reliability of the network. If the firewall throughput is insufficient, it may not be able to timely and effectively handle a large amount of network traffic and attack traffic, resulting in an increased risk of network attacks, which jointly affect the network reliability of the industrial chain data update preparation. Therefore, the three parameters are integrated and analyzed and data analysis is used to expand the influence of the three parameters on the network reliability factor of the industrial chain data update preparation, which can quickly identify the high risks faced by network security, thereby ensuring that the highly sensitive industrial chain data information in the data map is updated reliably and stably, and reducing decision-making errors caused by data leakage or tampering.

[0038] In this exemplary embodiment, the network reliability factor and its corresponding parameter change table prepared for the above industrial chain data update are shown in Table 1:

[0039] Table 1. Changes in network reliability factors and their corresponding parameters for industrial chain data update preparation

[0040]

[0041] In this example embodiment, the value of the impact factor corresponding to the unit value of the vulnerability density is set to 0.4, the value of the impact factor corresponding to the unit value of the key length is set to 0.25, and the value of the impact factor corresponding to the unit value of the firewall throughput is set to 0.35. It can be seen from Table 1 that when the value of the network reliability factor is 541% of the maximum value in the table, the vulnerability density corresponding to the network reliability factor at this time is the minimum value of the vulnerability density in Table 1, and the key length and firewall throughput corresponding to the network reliability factor at this time are the maximum values ​​of the key length and firewall throughput in Table 1, respectively. It can be seen that the smaller the vulnerability density, the larger the key length and the firewall throughput, the larger the network reliability factor, indicating that the network reliability is greater.

[0042] S2. Based on the reliable network environment to which the industrial chain data update process belongs, obtain the parameters of the industrial chain data update process, analyze the monitoring anomaly index of the industrial chain data update process, and compare it with the monitoring anomaly threshold. If the monitoring anomaly index of the industrial chain data update process is less than the monitoring anomaly threshold, feedback is given to the industrial chain data update. If the monitoring anomaly index of the industrial chain data update process is greater than or equal to the monitoring anomaly threshold, execute S3.

[0043] In a specific embodiment, the present invention analyzes the key parameters involved in the industrial chain data update process to obtain a monitoring anomaly index of the industrial chain data update process, thereby being able to timely discover potential risk points in the industrial chain data update process and issue early warnings, and then being able to take countermeasures in advance to reduce the possibility of risk occurrence and mitigate the losses caused by the risk.

[0044] The above-mentioned monitoring anomaly threshold represents the maximum value of the reasonable range of the monitoring anomaly index in the process of updating industrial chain data. It is extracted from the management database and is formulated by the data map administrator based on the monitoring data in the process of updating historical industrial chain data. The monitoring data includes key indicators such as update frequency, data fluctuation range and anomaly occurrence. If the monitoring anomaly index in the process of updating industrial chain data is less than the monitoring anomaly threshold, it indicates that the anomaly in the process of updating industrial chain data is within the reasonable range of the monitoring anomaly index, and the update of industrial chain data can be directly fed back. If the monitoring anomaly index in the process of updating industrial chain data is greater than or equal to the monitoring anomaly threshold, it indicates that the anomaly in the process of updating industrial chain data is outside the reasonable range of the monitoring anomaly index, and the update results of industrial chain data need to be further analyzed.

[0045] It should be explained that the above-mentioned industrial chain data update process parameters include the number of operations, network usage traffic and server response time. In addition to being applicable to the above-mentioned parameters, this example can also be used for other parameters.

[0046] Specifically, the monitoring abnormality index of the data update process of the analysis industry chain is analyzed in the following specific analysis process:

[0047] The number of operations of the industrial chain data within the data operation cycle is extracted from the industrial chain data update process parameters; the above-mentioned data operation cycle refers to the time period for processing the industrial chain data. The specific duration is determined according to the actual situation. The end time point of the environmental detection cycle is the start time point of the data operation cycle. When the monitoring anomaly index of the industrial chain data update process analyzed in real time is greater than or equal to the monitoring anomaly threshold, the system visualizes a pop-up box saying "The process detection is relatively abnormal and the operation needs to be suspended", and when the update button at this time receives a click task, no update instruction is sent. The time point at this time is the end time point of the data operation cycle. When the data operation cycle duration is equal to the maximum value of the data operation cycle duration set by the data map administrator, and the industrial chain data analyzed in real time during the process The monitoring anomaly index of the update process is less than the monitoring anomaly threshold, indicating that the overall data processing process anomalies are relatively small, and the update results of the industrial chain data are relatively complete. The end time point of the data operation cycle in this case is the end time point of the environmental detection cycle plus the time point corresponding to the maximum duration of the data operation cycle; the above-mentioned number of operations refers to the number of instructions received for industrial chain data processing. In one embodiment, if the business license data of a certain enterprise is identified and stored when the industrial chain data is collected, the data map administrator modifies the code in the identified business license and clicks the update button to send the update instruction, the data map administrator fills in the validity period in the identified business license and clicks the update button to send the update instruction, then the number of operations is 2 times.

[0048] The network usage traffic of each operation belonging to the industrial chain data within the data operation cycle is extracted from the industrial chain data update process parameters, and the average network usage traffic of the operation belonging to the industrial chain data within the data operation cycle is obtained by mean processing; the network usage traffic of each operation belonging to the above-mentioned industrial chain data within the data operation cycle, wherein the network usage traffic of a certain operation within the data operation cycle refers to the sum of the network usage traffic between the time point of receiving the instruction of the operation to process the industrial chain data to the time point of receiving the instruction of the next operation to process the industrial chain data, that is, the network usage traffic of the operation within the data operation cycle. In an example embodiment, the network usage traffic between the time point of receiving the instruction to modify the industrial and commercial information of a certain enterprise to the time point of receiving the instruction to modify the electronic invoice information of a certain enterprise is 500 bits, then the network usage traffic of this operation, that is, the operation of modifying the industrial and commercial information of a certain enterprise is 500 bits. This process can be monitored and obtained through a network traffic analysis tool.

[0049] The server response time of each operation belonging to the industrial chain data within the data operation cycle is extracted from the industrial chain data update process parameters, and the average server response time of the operations belonging to the industrial chain data within the data operation cycle is obtained by mean processing, and the monitoring anomaly index of the industrial chain data update process is obtained by comprehensive analysis; the server response time of each operation belonging to the above-mentioned industrial chain data within the data operation cycle refers to the time between the time point of receiving the instruction for processing the industrial chain data and the time point of processing the instruction for processing the industrial chain data, which is obtained by monitoring the time point of receiving the instruction for processing the industrial chain data and the time point of processing the instruction for processing the industrial chain data through the Xinruisi tool in the application performance management tool. In an example embodiment, the time between the time point of receiving the instruction to modify the amount data in the electronic invoice of an enterprise and the time point of processing the instruction to modify the amount data in the electronic invoice of an enterprise is 4 milliseconds, and the server response time of this operation within the data operation cycle is 4 milliseconds.

[0050] It needs to be explained that the reason for the above operations is that in the process of collecting and processing industrial chain data, the collection of key information such as business licenses often relies on image recognition technology. Although this technology can process large amounts of data efficiently and automatically, given the complexity and diversity of the data, insufficient recognition accuracy, misjudgment or omissions are inevitable in the image recognition process. To ensure the accuracy and completeness of the data, the data map administrator needs to intervene to make necessary corrections, filling or verification operations. By in-depth analysis of the process of each operation, the risks of the industrial chain data update process in the data map can be identified to reduce the security risks introduced by the human operation process, thereby improving the efficiency and accuracy of industrial chain data processing.

[0051] Specifically, the feedback on the update of industrial chain data is as follows:

[0052] According to the monitoring anomaly index of the industrial chain data update process being less than the monitoring anomaly threshold, an industrial chain data update success log is generated, and the industrial chain data update is fed back based on the industrial chain data update success log; when the data operation cycle duration is equal to the maximum data operation cycle duration set by the data map administrator, and the monitoring anomaly index of the industrial chain data update process analyzed in real time during the process is less than the monitoring anomaly threshold, it indicates that the overall data processing process is less abnormal and the industrial chain data update result is relatively complete. Therefore, at the end time of the data operation cycle, the industrial chain data update success log is generated through the log record in the internal logic of the application, and a visual pop-up window of "All updates have been completed" is displayed, and the industrial chain data update success log is visually fed back in the form of a message. The chain data is updated data, so you can view the latest list of upstream and downstream enterprises of core enterprise customers, view the various links, corresponding enterprises, geographical distribution, etc. of the upstream, midstream and downstream of the key industrial chain, accurately grasp the overall layout of the industrial chain, and combine a variety of scoring and labeling systems to determine the leading backbone enterprises, key supporting enterprises, and single advantage enterprises, and judge whether they are core suppliers and customers, evaluate the market recognition and comprehensive strength of the subject, achieve accurate docking of the list system, and improve service efficiency; if the end time point of the data operation cycle is the end time point of the environmental detection cycle plus the time point corresponding to the maximum length of the data operation cycle, and the monitoring anomaly index of the industrial chain data update process at this time point is greater than or equal to the monitoring anomaly threshold, it is necessary to further analyze the monitoring anomaly index of the industrial chain data update process.

[0053] Furthermore, the monitoring anomaly index of the industrial chain data update process, in this embodiment, is obtained by comprehensive analysis of the number of operations of the industrial chain data in the data operation cycle, the average network usage flow of the operation to which the industrial chain data belongs in the data operation cycle, and the average response time of the server, and is used to evaluate the monitoring anomaly degree of the industrial chain data update process. The specific expression is:

[0054] ;

[0055] It refers to the number of operations on industrial chain data during the data operation cycle. These operations may include data reading, writing, updating, and deleting. The number of operations is an important indicator for measuring data activity and system load.

[0056] It is the average network usage traffic of the operations to which the industrial chain data belongs during the data operation cycle, reflecting the scale and frequency of network transmission during the update process of the industrial chain data. It is a key indicator to measure the network bandwidth usage and potential network congestion risks during the update process of the industrial chain data.

[0057] It is the average server response time of the operations belonging to the industrial chain data within the data operation cycle. A longer response time may mean that the server load is too high or there is a performance bottleneck.

[0058] The reference number of operations represents the reference value of the number of operations for analyzing industrial chain data within the data operation cycle. The formulation process is based on the historical data analysis results, combined with the actual operation status and business needs of the data graph. The graph data administrator sets an operation reference number as a reference, which can not only reflect the needs of normal business, but also avoid a large number of unnecessary operations in a short period of time, thereby ensuring the stable operation of the system.

[0059] It is the reference network usage traffic, which represents the reference value of the average network usage traffic of the operation to which the industrial chain data belongs during the data operation cycle, and is consistent with the formulation process of the reference number of operations.

[0060] It is the reference response time of the server, which indicates the reference value of the average response time of the server for the operation to which the industrial chain data belongs within the data operation cycle, and is consistent with the formulation process of the reference number of operations.

[0061] e is a natural constant, It is the correction factor corresponding to the preset number of operations in the management database, which represents the numerical value of the degree of correction for the change in the number of operations. When used, the correction factor corresponding to the number of operations can be directly obtained from the management database, and the corresponding relationship can be a preset mapping relationship. For example, the number of operations and the correction factors corresponding to the preset number of operations in the management database form a mapping set, and the real-time number of operations is input into the mapping set to obtain the correction factor corresponding to the number of operations. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0062] It is the correction factor corresponding to the average network usage traffic preset in the management database, which represents the numerical value of the degree of correction for the change in the average network usage traffic. When used, the correction factor corresponding to the average network usage traffic can be directly obtained from the management database, and the corresponding relationship can be a preset mapping relationship. For example, the average network usage traffic and the correction factor corresponding to the average network usage traffic preset in the management database form a mapping set, and the real-time average network usage traffic is input into the mapping set to obtain the correction factor corresponding to the average network usage traffic. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0063] It is the correction factor corresponding to the average response time of the server preset in the management database, which represents the numerical value of the degree of correction for the change of the average response time of the server. When used, the correction factor corresponding to the average response time of the server can be directly obtained from the management database. The corresponding relationship can be a preset mapping relationship. For example, the average response time of the server and the correction factor corresponding to the average response time of the server preset in the management database form a mapping set. The real-time average response time of the server is input into the mapping set to obtain the correction factor corresponding to the average response time of the server. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0064] in, is the monitoring anomaly index of the industrial chain data update process. The number of operations of the industrial chain data in the data operation cycle deviates far from the historical average level, that is, the deviation from the reference number is too large, which causes the average network usage traffic to increase accordingly. The increase in the average network usage traffic may cause network congestion, especially when the network bandwidth is limited. Network congestion will directly affect the speed and efficiency of the industrial chain data update transmission, which may cause the server to wait for data when processing the industrial chain data update request. Increased time, thereby extending the average server response time. If the number of operations is much higher than the historical average, and is accompanied by extended server response time or a surge in network traffic, it may indicate that there is an abnormality in the industrial chain data update process. The sudden surge in network traffic may mean that there is a risk of data leakage. If the average server response time increases significantly and lasts for a long time, it may indicate that the system has performance bottlenecks or insufficient resources, which may affect the efficiency and quality of data updates, and even lead to serious consequences such as data loss or damage. Therefore, in the process of industrial chain data update, there is a close correlation and mutual influence between the above three parameters. It is necessary to simultaneously analyze the impact of the three parameters on the monitoring anomaly of the industrial chain data update process, reduce the one-sidedness brought by a single parameter, and improve the accuracy of anomaly detection, so as to ensure the safety, efficiency and reliability of the industrial chain data update process.

[0065] S3. Collect the update results of the industrial chain data of the data map, thereby determining the update deviation index of the associated chart to which the industrial chain data belongs, and provide feedback on the intelligent update management of the industrial chain data of the data map.

[0066] It should be explained that the update results of the industrial chain data of the above data map include the total number of updated related charts, the time point when the update is completed, and the network traffic used for the update. In addition to being applied to the above parameters, this example can also be used for other parameters.

[0067] In a specific embodiment, the present invention obtains the update deviation index of the associated chart to which the industrial chain data belongs by analyzing the industrial chain data update results of the data map, and further obtains the update error level of the associated chart, so as to conduct targeted feedback management on the industrial chain data update operation in the data map, which not only ensures the accuracy of the industrial chain data update in the data map, but also promotes the dynamic adjustment and optimization of the industrial chain data update strategy, thereby comprehensively ensuring the reliability of the industrial chain data update.

[0068] Specifically, the intelligent update management of the industrial chain data of the data map is fed back, and the specific feedback process is:

[0069] The update deviation index of the associated graph to which the industrial chain data belongs is matched with the update error level corresponding to each update deviation index interval stored in the management database, thereby obtaining the update error level of the associated graph to which the industrial chain data belongs, and based on the update error level of the associated graph to which the industrial chain data belongs, feedback is given to the intelligent update management of the industrial chain data of the data graph; the update error level of the associated graph to which the above industrial chain data belongs, the specific matching rule is: each update deviation index interval corresponds to the update error level, and the update deviation index interval to which the update deviation index of the associated graph to which the industrial chain data belongs is queried, then the update error level corresponding to the update deviation index interval is the update error level of the associated graph to which the industrial chain data belongs. The specific matching rules and values ​​of the new error level are formulated by the graph data administrator; the above-mentioned feedback on the intelligent update management of the industrial chain data of the data graph refers to querying the update adjustment schemes corresponding to different update error levels from the management database according to different update error levels, and automatically adjusting the parameters in the update adjustment schemes, thereby providing feedback on the intelligent update management of the industrial chain data of the data graph; in an example embodiment, there are four levels of update error levels. If the update error level is level one, the update adjustment scheme obtained by the update error level query includes adjusting the data packet size, re-mechanism and index structure in the network transmission protocol, so the data packet size is automatically adjusted according to the parameters in the update adjustment scheme , re-mechanism and index structure, and a pop-up window is displayed to visualize "the industrial chain data can continue to be updated", and when the update button at this time receives a click task, an update instruction is sent; if the update error level is level two, the update adjustment plan obtained by the update error level query includes adjusting the data packet size in the network transmission protocol and introducing a CRC data verification mechanism, so the data packet size is automatically adjusted and data verification is performed according to the parameters in the update adjustment plan, and a pop-up window is displayed to visualize "the industrial chain data can continue to be updated", and when the update button at this time receives a click task, an update instruction is sent; if the update error level is level three, the update adjustment plan obtained by the update error level query includes executing data back Rollback and use Hamming code to correct data, so according to the parameters in the update adjustment plan, data rollback and Hamming code are automatically performed for data correction, and a pop-up window is displayed to visualize "the industrial chain data can continue to be updated", and when the update button at this time receives a click task, an update instruction is sent; if the update error level is level four, the update adjustment plan obtained by the update error level query includes adjusting the storage partition, index structure and cache strategy, and revoking the updated data, thereby re-updating the industrial chain data based on the updated data. After the updated data is re-updated, a pop-up window is displayed to visualize "the industrial chain data can continue to be updated", and when the update button at this time receives a click task, an update instruction is sent;Therefore, after updating the management feedback, the industrial chain data can be further updated. At this time, the update process can better ensure the security and integrity of the industrial chain data update. At the same time, after the industrial chain data is updated, the latest list of upstream and downstream enterprises of core enterprise customers can be viewed, so that the industrial chain can be managed based on the latest data. ;

[0070] Furthermore, the update deviation index of the association graph to which the industrial chain data belongs is determined, and the specific determination process is as follows:

[0071] The total number of updated associated graphs of the associated graphs belonging to the industrial chain data within the update result detection period is extracted from the update result of the industrial chain data of the data map, the total number of associated graphs that need to be updated of the associated graphs belonging to the industrial chain data within the update result detection period is obtained, and ratio processing is performed to obtain the update efficiency of the associated graphs belonging to the industrial chain data; the total number of updated associated graphs of the associated graphs belonging to the above industrial chain data within the update result detection period is obtained by querying the data change log; the total number of associated graphs that need to be updated of the associated graphs belonging to the above industrial chain data within the update result detection period refers to obtaining each update field corresponding to each operation of the industrial chain data within the data operation period, locating and counting the graphs corresponding to each update field, so as to obtain the total number of associated graphs that need to be updated of the associated graphs belonging to the industrial chain data within the update result detection period; the above update efficiency refers to the total number of updated associated graphs divided by the total number of associated graphs that need to be updated.

[0072] It should be explained that the update result detection cycle is the time period for detecting the update results of the industrial chain data, such as Figure 3 As shown in the cycle duration diagram, the update result detection cycle duration 1 coincides with the data operation cycle duration 2, and the start and end time points of the two cycles are the same, which is used to distinguish and analyze the two aspects of the industrial chain data update.

[0073] From the industrial chain data update results of the data map, the update completion time points of each updated associated chart of the associated chart belonging to the industrial chain data within the update result detection cycle are extracted, and the difference processing is performed with the start time point of the update result detection cycle to obtain the update process duration of each updated associated chart of the associated chart belonging to the industrial chain data within the update result detection cycle; the update completion time points of each updated associated chart of the associated chart belonging to the above industrial chain data within the update result detection cycle are obtained by querying the data update log; the above update process duration refers to the update completion time point minus the start time point of the update result detection cycle. It should be explained that the update instructions of the updated associated charts are not all sent at the start time point of the update result detection cycle, but in order to reduce the complexity of data calculation, the duration obtained by subtracting the update completion time point from the start time point of the update result detection cycle is marked as the update process duration.

[0074] The network traffic used for updating each updated associated graph of the associated graph to which the industrial chain data belongs within the update result detection cycle is extracted from the update results of the industrial chain data of the data graph. The network traffic used for updating each updated associated graph of the associated graph to which the industrial chain data belongs within the update result detection cycle is obtained by querying the update logs of each updated associated graph.

[0075] The update deviation index of the association graph to which the industrial chain data belongs is obtained by comprehensive determination. In this embodiment, the update deviation index of the association graph to which the industrial chain data belongs is obtained by comprehensive analysis of the update efficiency of the association graph to which the industrial chain data belongs, the update process duration of the sth updated association graph of the association graph to which the industrial chain data belongs within the update result detection cycle, and the network traffic used for the update. It is a numerical value used to evaluate the update deviation degree of the association graph to which the industrial chain data belongs. The specific expression is:

[0076] ;

[0077] It is the update efficiency of the associated graph of the industrial chain data, which comprehensively reflects the ability to process update requests, the efficiency of data transmission, the speed of data processing and the accuracy of update results. High update efficiency means that more update operations can be completed in a shorter time.

[0078] The update process duration of the sth updated associated graph of the associated graph to which the industrial chain data belongs within the update result detection cycle is an important indicator for measuring update efficiency because it directly reflects the speed of processing a single update request.

[0079] The network traffic used for updating the sth updated associated graph of the associated graph to which the industrial chain data belongs during the update result detection cycle reflects the degree of demand for network resources for the update operation. If the network traffic used for the update deviates greatly from the reference value, it indicates that the industrial chain data is exposed during the update transmission process, increasing the risk of data tampering.

[0080] It is a reference for updating network traffic, indicating the reference value of the updated network traffic of the sth updated associated graph of the associated graph to which the analyzed industrial chain data belongs within the update result detection cycle, and is formulated by the data graph administrator based on historical operation data.

[0081] s is the number of each updated associated chart, , r is the total number of updated association graphs, e is a natural constant, It is the impact factor corresponding to the preset update efficiency unit value in the management database, which represents the influence degree of the update efficiency unit value on the update deviation index of the associated chart to which the industrial chain data belongs. When used, the impact factor corresponding to the update efficiency unit value can be directly obtained from the management database, and the corresponding relationship can be a preset mapping relationship. For example, the update efficiency and the impact factor corresponding to the preset update efficiency unit value in the management database form a mapping set, and the real-time update efficiency is input into the mapping set to obtain the impact factor corresponding to the update efficiency unit value. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0082] It is the impact factor corresponding to the unit value of the update process duration preset in the management database, which represents the numerical value of the influence of the unit value of the update process duration on the update deviation index of the associated chart to which the industrial chain data belongs. When used, the impact factor corresponding to the unit value of the update process duration can be directly obtained from the management database, and the corresponding relationship can be a preset mapping relationship. For example, the update response time and the impact factor corresponding to the unit value of the update process duration preset in the management database form a mapping set, and the real-time update response time is input into the mapping set to obtain the impact factor corresponding to the unit value of the update process duration. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0083] It is the correction factor corresponding to the update network traffic preset in the management database, which represents the numerical value of the degree of correction for the change of the update network traffic. When used, the correction factor corresponding to the update network traffic can be directly obtained from the management database, and the corresponding relationship can be a preset mapping relationship. For example, the update network traffic and the correction factor corresponding to the update network traffic preset in the management database form a mapping set, and the real-time update network traffic is input into the mapping set to obtain the correction factor corresponding to the update network traffic. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0084] in, is the update deviation index of the associated graph to which the industrial chain data belongs. A longer update process may mean that there are delays in the transmission, processing or verification of industrial chain data. These delays are often accompanied by an abnormal increase in the network traffic used for the update, that is, the deviation from the reference update uses a large amount of network traffic. At the same time, a longer update process will take up more system resources, limit the concurrency of update operations, and may delay the update of other associated graphs, which will lead to a reduction in the number of graphs that can be updated within the update result detection cycle, thereby reducing the overall update efficiency. The three parameters jointly affect the update deviation degree of the associated graph to which the industrial chain data belongs. Therefore, a comprehensive analysis of the above parameters can understand the resource consumption of the associated graph during the update process, thereby formulating differentiated adjustment strategies, which will help reduce the resource consumption of unified specification adjustments, and at the same time improve the update efficiency and accuracy of industrial chain data.

[0085] In a specific embodiment, the present invention provides an intelligent industrial chain management method and system based on a data map. By analyzing the network environment data to which the industrial chain data is updated and evaluating the network reliability factor of the industrial chain data update preparation, the optimal network environment is selected to update the industrial chain data. At the same time, the industrial chain data update process parameters are analyzed to obtain the monitoring anomaly index of the industrial chain data update process. If the degree of abnormality in the data update process is high, the update deviation index of the associated chart to which the industrial chain data belongs is determined, and the intelligent update management of the industrial chain data of the data map is fed back according to the update deviation level, thereby ensuring the accuracy of the industrial chain data update in the data map. At the same time, it can automatically optimize the industrial chain data update process, reduce manual intervention, and improve the management efficiency of the industrial chain data in the data map.

[0086] Reference Figure 2 As shown, the second aspect of the present invention provides an application such as a system of an intelligent industrial chain management method based on a data map, including: a network environment assessment module, an update process analysis module and an update result determination module.

[0087] The second aspect of the present invention provides an application such as a system of a smart industrial chain management method based on a data map, which also includes a management database, wherein the management database is used to store the impact factor corresponding to the unit value of vulnerability density, the impact factor corresponding to the unit value of key length, the impact factor corresponding to the unit value of firewall throughput, the correction factor corresponding to the number of operations, the correction factor corresponding to the average network usage traffic, the correction factor corresponding to the average server response time, the impact factor corresponding to the unit value of update efficiency, the impact factor corresponding to the unit value of update process duration, the correction factor corresponding to the update usage network traffic, the update error level corresponding to each update deviation index interval, the monitoring abnormality threshold and the network reliability threshold.

[0088] The network environment evaluation module is connected to the update process analysis module and the management database, the update process analysis module is connected to the update result determination module and the management database, and the update result determination module is connected to the management database.

[0089] The network environment assessment module is used for the industrial chain data update preparation process based on the data map, collects the network environment data belonging to the industrial chain data update, evaluates the network reliability factor of the industrial chain data update preparation, and compares it with the network reliability threshold. If the network reliability factor of the industrial chain data update preparation is less than the network reliability threshold, feedback is given to the network environment belonging to the industrial chain data update preparation. If the network reliability factor of the industrial chain data update preparation is greater than or equal to the network reliability threshold, thereby obtaining the reliable network environment belonging to the industrial chain data update process, the update process analysis module is executed.

[0090] The update process analysis module is used to obtain the parameters of the industrial chain data update process based on the reliable network environment to which the industrial chain data update process belongs, analyze the monitoring anomaly index of the industrial chain data update process, and compare it with the monitoring anomaly threshold. If the monitoring anomaly index of the industrial chain data update process is less than the monitoring anomaly threshold, feedback is given to the industrial chain data update; if the monitoring anomaly index of the industrial chain data update process is greater than or equal to the monitoring anomaly threshold, the update result judgment module is executed.

[0091] The update result determination module is used to collect the update results of the industrial chain data of the data map, thereby determining the update deviation index of the associated chart to which the industrial chain data belongs, and providing feedback on the intelligent update management of the industrial chain data of the data map.

[0092] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A method for managing an intelligent industrial chain based on a data graph, characterized in that: include: S1. Based on the data graph, the data of the industrial chain data update preparation process is collected, the network environment data of the industrial chain data update is evaluated, and the network reliability factor of the industrial chain data update preparation is compared with the network reliability threshold. If the network reliability factor of the industrial chain data update preparation is less than the network reliability threshold, the network environment of the industrial chain data update preparation is fed back. If the network reliability factor of the industrial chain data update preparation is greater than or equal to the network reliability threshold, the reliable network environment of the industrial chain data update process is obtained, and S2 is executed; S2. Based on the reliable network environment to which the industrial chain data update process belongs, obtain the parameters of the industrial chain data update process, analyze the monitoring anomaly index of the industrial chain data update process, and compare it with the monitoring anomaly threshold. If the monitoring anomaly index of the industrial chain data update process is less than the monitoring anomaly threshold, feedback is given to the industrial chain data update. If the monitoring anomaly index of the industrial chain data update process is greater than or equal to the monitoring anomaly threshold, execute S3. S3. Collect the update results of the industrial chain data of the data map, thereby determining the update deviation index of the associated chart to which the industrial chain data belongs, and provide feedback on the intelligent update management of the industrial chain data of the data map; The monitoring abnormality index of the industrial chain data update process is analyzed, and the specific analysis process is as follows: Extract the operation times of the industrial chain data in the data operation cycle from the industrial chain data update process parameters; Extract the network usage flow of each operation of the industrial chain data within the data operation cycle from the industrial chain data update process parameters, and perform mean processing to obtain the average network usage flow of the operation of the industrial chain data within the data operation cycle; The server response time of each operation of the industrial chain data within the data operation cycle is extracted from the parameters of the industrial chain data update process, and the average server response time of the operation of the industrial chain data within the data operation cycle is obtained by mean processing, and the monitoring anomaly index of the industrial chain data update process is obtained by comprehensive analysis; The monitoring anomaly index of the industrial chain data update process is specifically expressed as: ； in, It is the monitoring abnormality index of the industrial chain data update process. is the number of operations of the industrial chain data within the data operation cycle, It is the average network usage flow of the operation of the industrial chain data during the data operation cycle. It is the average server response time of the operation of the industrial chain data within the data operation cycle. is the operation reference number, For reference network usage traffic, is the reference response time of the server, e is a natural constant, It is the correction factor corresponding to the preset number of operations in the management database. It is the correction factor corresponding to the average network usage flow preset in the management database. It is the correction factor corresponding to the average server response time preset in the management database.

2. According to claim 1, a method for managing an intelligent industrial chain based on a data map is characterized in that: The industrial chain data update preparation process based on the data map collects the network environment data to which the industrial chain data update belongs. Specifically, it is the process of preparing the industrial chain data of the data map before updating, wherein the above-mentioned preparation process is the network preparation process, thereby obtaining the industrial chain data update network preparation process of the data map, and collecting corresponding data, which is recorded as the network environment data to which the industrial chain data update belongs.

3. According to claim 1, a method for managing an intelligent industrial chain based on a data map is characterized in that: The network reliability factor for evaluating the industrial chain data update preparation, the specific evaluation process is: Extract the number of vulnerabilities of the network to which the industrial chain data update belongs within the environmental detection cycle from the network environment data to which the industrial chain data update belongs, perform ratio processing with the duration corresponding to the environmental detection cycle, and obtain the vulnerability density of the network to which the industrial chain data update belongs within the environmental detection cycle; The key length of the network to which the industrial chain data update preparation belongs and the firewall throughput of the network to which the industrial chain data update preparation belongs during the environment detection cycle are extracted from the network environment data to which the industrial chain data update preparation belongs, thereby comprehensively evaluating the network reliability factor of the industrial chain data update preparation.

4. The method for managing an intelligent industrial chain based on a data graph according to claim 1, characterized in that: The feedback of the network environment to which the industrial chain data update preparation belongs is specifically provided as follows: If the network reliability factor of the industrial chain data update preparation is less than the network reliability threshold, a network replacement instruction is generated. Based on the processing result of the network replacement instruction, the network reliability factor of the industrial chain data update preparation is evaluated again, thereby providing feedback on the network environment to which the industrial chain data update preparation belongs.

5. The method for managing an intelligent industrial chain based on a data graph according to claim 1, characterized in that: The feedback on the update of industrial chain data is specifically conducted as follows: According to the monitoring anomaly index of the industrial chain data update process being less than the monitoring anomaly threshold, an industrial chain data update success log is generated, and feedback on the industrial chain data update is given based on the industrial chain data update success log.

6. The method for managing an intelligent industrial chain based on a data graph according to claim 1, characterized in that: The update deviation index of the association graph to which the industrial chain data belongs is determined in the following specific determination process: Extract the total number of updated associated graphs of the associated graphs of the industrial chain data within the update result detection period from the industrial chain data update result of the data map, obtain the total number of associated graphs that need to be updated within the update result detection period of the associated graphs of the industrial chain data, and perform ratio processing to obtain the update efficiency of the associated graphs of the industrial chain data; Extract the update completion time point of each updated associated graph of the associated graph to which the industrial chain data belongs within the update result detection cycle from the update result of the industrial chain data in the data map, and perform difference processing with the start time point of the update result detection cycle to obtain the update process duration of each updated associated graph of the associated graph to which the industrial chain data belongs within the update result detection cycle; The network traffic used for updating each updated associated graph of the associated graph to which the industrial chain data belongs within the update result detection cycle is extracted from the update result of the industrial chain data of the data map, and the update deviation index of the associated graph to which the industrial chain data belongs is obtained by comprehensive determination.

7. The method for managing an intelligent industrial chain based on data graph according to claim 1, characterized in that: The specific feedback process of the intelligent update management of the industrial chain data of the data map is as follows: The update deviation index of the associated graph to which the industrial chain data belongs is matched with the update error level corresponding to each update deviation index interval stored in the management database, thereby obtaining the update error level of the associated graph to which the industrial chain data belongs, and based on the update error level of the associated graph to which the industrial chain data belongs, feedback is provided to the intelligent update management of the industrial chain data of the data map.

8. A system using a data graph-based intelligent industrial chain management method as described in any one of claims 1 to 7, characterized in that: include: The network environment assessment module is used for the data update preparation process of the industrial chain based on the data map, collects the network environment data of the industrial chain data update, evaluates the network reliability factor of the industrial chain data update preparation, and compares it with the network reliability threshold. If the network reliability factor of the industrial chain data update preparation is less than the network reliability threshold, the network environment to which the industrial chain data update preparation belongs is fed back. If the network reliability factor of the industrial chain data update preparation is greater than or equal to the network reliability threshold, thus obtaining the reliable network environment to which the industrial chain data update process belongs, the update process analysis module is executed; The update process analysis module is used to obtain the parameters of the industrial chain data update process based on the reliable network environment to which the industrial chain data update process belongs, analyze the monitoring anomaly index of the industrial chain data update process, and compare it with the monitoring anomaly threshold. If the monitoring anomaly index of the industrial chain data update process is less than the monitoring anomaly threshold, the industrial chain data update is fed back. If the monitoring anomaly index of the industrial chain data update process is greater than or equal to the monitoring anomaly threshold, the update result judgment module is executed; The update result determination module is used to collect the update results of the industrial chain data of the data map, thereby determining the update deviation index of the associated chart to which the industrial chain data belongs, and providing feedback on the intelligent update management of the industrial chain data of the data map.

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