A digital business data processing method for the power industry

Abstract

The present invention discloses a digital business data processing method for the electric power industry, and relates to the technical field of electric power business data processing. When analyzing the business data processing effect, by collecting the business data generated by the designated electric power facilities, and obtaining the corresponding effect influencing parameters when processing the business data, it is beneficial to evaluate the actual efficiency and effect of the business data processing, so as to ensure that in actual operation, the business processing efficiency can be quantified based on specific data, and then the data processing process can be optimized and improved, and the operating efficiency and cost-effectiveness of the electric power facilities can be improved. When analyzing the integrity of the business data, the business data is transmitted and stored through the blockchain technology, and the integrity influencing parameters corresponding to the business data in the transmission and storage process are obtained, which is beneficial to ensure the integrity and reliability of the data in the transmission and storage process, and effectively prevent data tampering or loss.

Description

Technical Field

[0001] The present invention relates to the technical field of power business data processing, and in particular to a digital business data processing method for the power industry. Background Art

[0002] Energy Internet is promoting the transformation of the power industry towards digitalization, networking and intelligence in the context of Energy Internet. Energy Internet connects and optimizes the configuration of various energy resources through information networks, and requires efficient data processing methods to support its operation and management.

[0003] Prior art, such as the invention patent application with announcement number CN114723257A, discloses a method, device and equipment for processing digital business data in the electric power industry. The method classifies the business scenarios of the acquired basic data of the electric power business to be evaluated, and matches the digital capability system and the solution structure according to the obtained business scenario types, thereby automatically generating digital solutions for the electric power business. By classifying the business scenarios through neural networks, the recognition efficiency of business scenario types can be improved, the generation of digital solutions can be accelerated, and the generation efficiency of electric power business solution documents can be improved.

[0004] With respect to the above scheme, the applicant of the present invention has found that the above technology has at least the following technical problems: 1. The above scheme mentions the use of neural networks to classify business scenarios to improve recognition efficiency, but does not specify the specific parameters or algorithm details used for classification, and does not mention the specific analysis method for business data processing efficiency or the process of obtaining parameters that affect the effect. For example, collecting information such as the time interval and processing time of business data processing on edge devices is crucial to evaluating business processing efficiency, but this information is not described in detail.

[0005] 2. It does not clearly explain how to ensure the integrity of business data during transmission and storage, nor does it mention whether security assessment parameters similar to blockchain technology are used. This is critical to ensuring data security and integrity, but the lack of these detailed descriptions may lead to security risks in actual applications. The article does not describe how to conduct a comprehensive assessment to determine whether the comprehensive performance of business data processing meets the requirements, which may lead to unauthorized access, tampering or loss of data, thus seriously affecting the operation and trust of the power business. Summary of the invention

[0006] The purpose of the present invention is to provide a digital business data processing method for the electric power industry, which solves the problems existing in the background technology.

[0007] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a digital business data processing method for the power industry, including: Step 1, processing effect analysis: collecting business data generated by designated power facilities, and obtaining corresponding effect influencing parameters when processing business data, and then analyzing the corresponding data processing efficiency evaluation coefficient during the business data processing process.

[0008] Step 2: Business data integrity analysis: After the business data is collected and processed, it is transmitted and stored through blockchain technology to obtain the integrity impact parameters corresponding to the business data during the transmission and storage process, and then analyze the data integrity assessment coefficient corresponding to the business data during the transmission and storage process.

[0009] Step 3: Business data security analysis: Obtain the security assessment parameters corresponding to the business data during transmission and storage, and then analyze the data security assessment coefficients corresponding to the business data during transmission and storage.

[0010] Step 4: Comprehensive process analysis: Analyze the comprehensive evaluation coefficient corresponding to the business data of the specified power facility during the processing process, so as to evaluate whether the comprehensive performance corresponding to the business data during the processing process meets the set requirements.

[0011] Step 5: Early warning: When the comprehensive performance of business data during processing meets the set requirements, an early warning will be issued.

[0012] Preferably, the effect influencing parameter corresponding to the processing of the business data is obtained, and the specific acquisition process is as follows: the edge device deployed at the edge of the designated power facility is equipped with a built-in clock and timer, and the time interval for business data collection is obtained, that is, the business data collection frequency corresponding to the business data of the designated power facility when the edge is collected;

[0013] Mark each item in the business data, record the timestamp when the processing of each item of data starts after it arrives at the edge device, which is the start time point, and the timestamp corresponding to the end of the processing of each item of data, which is the end time point. Calculate the absolute value of the difference between the start time point and the end time point corresponding to each item of data, so as to obtain the processing time difference corresponding to each item of data. The result obtained by the mean calculation is the processing time corresponding to the business data of the specified power facility when it is processed;

[0014] The traffic monitoring tool is used to monitor the traffic in the business data collection channel, thereby obtaining the business data traffic corresponding to the business data of the specified power facility when collecting and processing business data at the edge.

[0015] Preferably, the data processing efficiency evaluation coefficient corresponding to the analysis of the business data processing process is specifically analyzed as follows: obtaining standard effect impact parameters corresponding to the business data collection and processing process from a database, the standard effect impact parameters including standard business data collection frequency, standard processing time and standard business data flow;

[0016] By calculating the formula The corresponding data processing efficiency evaluation coefficient α is obtained in the process of business data processing, where c, T, and L represent the data collection frequency, processing time, and business data flow of the business data of the specified power facility when collecting and processing business data at the edge, c′, T′, and L′ represent the standard business data collection frequency, standard processing time, and standard business data flow, respectively, and η 1 , η 2 , η 3 They are the weight factor corresponding to the set data collection frequency, the weight factor corresponding to the processing time, and the weight factor corresponding to the business data flow.

[0017] Preferably, the integrity impact parameter corresponding to the service data in the transmission and storage process is obtained, and the specific acquisition process is as follows: by traversing the entire data chain, and then obtaining the integrity status of each data block by statistics, thereby obtaining the number of complete data blocks;

[0018] By simulating the triggering of node failures, when each node fails, business data is recovered from the remaining nodes, and the number of successful business data recovery from the remaining nodes and the total number of attempts to recover the business data are recorded. The number of successful business data recovery from the remaining nodes is divided by the total number of attempts to recover the business data. The result is the data recovery success rate.

[0019] Preferably, the data integrity assessment coefficient corresponding to the transmission and storage process of the business data is analyzed, and the specific analysis process is as follows: obtaining the standard integrity impact parameters corresponding to the business data collection and processing process from the database, the standard integrity impact parameters including the number of standard complete data blocks and the standard data recovery success rate;

[0020] By calculating the formula The data integrity evaluation coefficient β corresponding to the business data in the transmission and storage process is obtained, where A and B represent the number of complete data blocks and the data recovery success rate, respectively, A′ and B′ represent the number of standard complete data blocks and the standard data recovery success rate, respectively, and μ 1 , μ 2 They are respectively the weight factor corresponding to the set number of complete data blocks and the weight factor corresponding to the data recovery success rate.

[0021] Preferably, the integrity impact parameters corresponding to the transmission and storage processes of the business data are obtained, and the specific acquisition process is as follows: by consulting the security configuration and related documents of the central processing system, the encryption key length used by the central processing system is obtained, and the query is performed through a specified command line to obtain the number of nodes currently participating in the verification;

[0022] According to the network scope that needs to measure the network connection density, collect the information of all nodes, including the number of nodes, the identifier of each node and the connection relationship between nodes, and use the collected node information to build a network topology diagram. In the network topology diagram, each node represents each network entity, and the edge represents the connection between entities. Count all the edges in the network topology diagram, that is, the actual number of connections between nodes, divide the actual number of edges by the total number of edges, and the result is the network connection density.

[0023] Preferably, the data security assessment coefficient corresponding to the transmission and storage process of the business data is analyzed, and the specific analysis process is as follows: obtaining standard security assessment parameters corresponding to the business data collection and processing process from the database, the standard security assessment parameters including standard encryption key length, standard number of nodes participating in verification and standard network connection density;

[0024] By calculating the formula The data security assessment coefficient χ corresponding to the business data transmission and storage process is obtained, where l, q, and ρ represent the encryption key length, the number of nodes participating in verification, and the network connection density corresponding to the business data transmission and storage process, respectively; l′, q′, and ρ′ represent the quasi-encryption key length, the standard number of nodes participating in verification, and the standard network connection density, respectively; κ 1 , κ 2 , κ 3 They are the weight factor corresponding to the set encryption key length, the weight factor corresponding to the number of nodes participating in the verification, and the weight factor corresponding to the network connection density.

[0025] Preferably, the analysis specifies the comprehensive evaluation coefficient corresponding to the business data of the power facility during the processing, and the specific analysis process is as follows: by calculating the formula Obtain the comprehensive evaluation coefficient corresponding to the business data of the specified power facility during the processing process where λ 1 , 2 , 3 The weight factor corresponding to the set data processing efficiency evaluation coefficient, the weight factor corresponding to the data integrity evaluation coefficient, and the weight factor corresponding to the data security evaluation coefficient.

[0026] Preferably, whether the comprehensive performance corresponding to the business data during the processing is evaluated meets the set requirements, and the specific evaluation process is as follows: comparing the comprehensive evaluation coefficient corresponding to the business data of the specified power facility during the processing with the set comprehensive evaluation coefficient threshold; if the comprehensive evaluation coefficient corresponding to the business data of the specified power facility during the processing is greater than or equal to the set comprehensive evaluation coefficient threshold, it means that the comprehensive performance corresponding to the evaluated business data during the processing meets the set requirements; if the comprehensive evaluation coefficient corresponding to the business data of the specified power facility during the processing is less than the set comprehensive evaluation coefficient threshold, it means that the comprehensive performance corresponding to the evaluated business data during the processing does not meet the set requirements.

[0027] The beneficial effects of the present invention are as follows: 1. A digital business data processing method for the electric power industry provided by the present invention, when analyzing the business data processing effect, collects the business data generated by the specified power facilities, and obtains the corresponding effect influencing parameters when processing the business data, which is conducive to evaluating the actual efficiency and effect of the business data processing, thereby ensuring that in actual operation, the business processing efficiency can be quantified based on specific data, thereby optimizing and improving the data processing process, and improving the operating efficiency and cost-effectiveness of the power facilities. When analyzing the integrity of the business data, the business data is transmitted and stored through blockchain technology, and the integrity influencing parameters corresponding to the business data in the transmission and storage process are obtained, which is conducive to ensuring the integrity and reliability of the data in the transmission and storage process, and effectively preventing data tampering or loss. For the electric power industry field with extremely high requirements for data accuracy, it can enhance the credibility and security of the data and ensure the stability of the operation of the power facilities.

[0028] 2. When analyzing the security of business data, the embodiments of the present invention help protect data from unauthorized access and attacks by obtaining security assessment parameters corresponding to the business data during the transmission and storage processes. When conducting comprehensive process analysis, the embodiments of the present invention help comprehensively evaluate the business data processing performance of power facilities by calculating the comprehensive assessment coefficients corresponding to the business data during the processing process. This comprehensive assessment helps decision makers understand the comprehensive balance between processing efficiency, data integrity and security, thereby optimizing business processes and decision making, and ensuring the efficient operation and sustainable development of power facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1It is a schematic flow chart of the implementation steps of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] See also Figure 1 As shown, the present invention provides a digital business data processing method for the power industry, the method comprising: step one, processing effect analysis: collecting business data generated by designated power facilities, and obtaining corresponding effect influencing parameters when processing the business data, and then analyzing the corresponding data processing efficiency evaluation coefficient during the business data processing process.

[0033] In a specific embodiment, the effect influencing parameters include business data collection frequency, processing time and business data traffic, the integrity influencing parameters include the number of complete data blocks, data recovery success rate, and the security assessment parameters include encryption key length, the number of nodes involved in verification and network connection density.

[0034] In a specific embodiment, the effect impact parameter corresponding to the processing of the business data is obtained, and the specific acquisition process is as follows: the edge device deployed at the edge of the designated power facility is equipped with a built-in clock and timer, and the time interval for business data collection is obtained, that is, the business data collection frequency corresponding to the business data of the designated power facility when the edge is collected;

[0035] Mark each item in the business data, record the timestamp when the processing of each item of data starts after it arrives at the edge device, which is the start time point, and the timestamp corresponding to the end of the processing of each item of data, which is the end time point. Calculate the absolute value of the difference between the start time point and the end time point corresponding to each item of data, so as to obtain the processing time difference corresponding to each item of data. The result obtained by the mean calculation is the processing time corresponding to the business data of the specified power facility when it is processed;

[0036] The traffic monitoring tool is used to monitor the traffic in the business data collection channel, thereby obtaining the business data traffic corresponding to the business data of the specified power facility when collecting and processing business data at the edge.

[0037] It should be noted that processing time refers to the time interval between data collection from IoT devices or sensors and initial processing by local edge computing nodes or gateways.

[0038] In a specific embodiment, the data processing efficiency evaluation coefficient corresponding to the business data processing process is analyzed, and the specific analysis process is as follows: obtaining the standard effect impact parameters corresponding to the business data collection and processing process from the database, and the standard effect impact parameters include the standard business data collection frequency, the standard processing time and the standard business data flow;

[0039] By calculating the formula The corresponding data processing efficiency evaluation coefficient α is obtained in the process of business data processing, where c, T, and L represent the data collection frequency, processing time, and business data flow of the business data of the specified power facility when collecting and processing business data at the edge, c′, T′, and L′ represent the standard business data collection frequency, standard processing time, and standard business data flow, respectively, and η 1 , η 2 , η 3 They are the weight factor corresponding to the set data collection frequency, the weight factor corresponding to the processing time, and the weight factor corresponding to the business data flow.

[0040] It should be noted that η 1 , η 2 , η 3 The value of is greater than 0 and less than 1.

[0041] It should also be noted that the relative importance of data collection frequency, processing time, and business data traffic is determined based on business needs and the criticality of the data processing process, including the requirements for business data collection frequency, the impact of business data processing time on overall performance, and the occupancy of network resources by business data traffic. The weight factors corresponding to the set data collection frequency, processing time, and business data traffic are determined through expert evaluation and historical data analysis.

[0042] Step 2: Business data integrity analysis: Obtain the integrity impact parameters corresponding to the business data during transmission and storage, and then analyze the data integrity assessment coefficients corresponding to the business data during transmission and storage.

[0043] It should be noted that after the business data is collected and processed, the transmission and storage process of the business data is managed through blockchain technology.

[0044] In a specific embodiment, the integrity impact parameter corresponding to the service data in the transmission and storage process is obtained, and the specific acquisition process is as follows: by traversing the entire data chain, and then obtaining the integrity status of each data block by statistics, thereby obtaining the number of complete data blocks;

[0045] By simulating the triggering of node failures, when each node fails, business data is recovered from the remaining nodes, and the number of successful business data recovery from the remaining nodes and the total number of attempts to recover the business data are recorded. The number of successful business data recovery from the remaining nodes is divided by the total number of attempts to recover the business data. The result is the data recovery success rate.

[0046] It should be noted that IoT devices and sensors are deployed at the edge of designated power facilities. The sensors include but are not limited to temperature sensors, humidity sensors and current sensors, which are used to monitor the business data of designated power facilities in real time. The business data include but are not limited to power consumption, equipment operation status, temperature and humidity. The edge devices include IoT devices and sensors. Business data is collected through edge devices. While business data is generated, the data preprocessing algorithm on the edge device preprocesses the collected business data. The preprocessing process includes preliminary cleaning of data, data formatting, data compression, and data aggregation.

[0047] The number of complete data blocks refers to the ratio of the number of intact data blocks in the data chain to the total number of data blocks. The distributed storage characteristics of blockchain technology mean that data has redundant backups on multiple nodes. The data recovery success rate evaluates the ability of the central processing system to successfully recover data from the remaining nodes in the event of node failure or data loss. Each data block in the blockchain network contains a specific number of transactions and data records. The number of data blocks reflects the amount of data stored on the blockchain within a specific period of time and the total number of data blocks, which is used to evaluate data storage needs and system capacity planning.

[0048] In a specific embodiment, the data integrity assessment coefficient corresponding to the transmission and storage process of the business data is analyzed, and the specific analysis process is as follows: obtaining the standard integrity impact parameters corresponding to the business data collection and processing process from the database, the standard integrity impact parameters including the number of standard complete data blocks and the standard data recovery success rate;

[0049] By calculating the formula The data integrity evaluation coefficient β corresponding to the business data in the transmission and storage process is obtained, where A and B represent the number of complete data blocks and the data recovery success rate, respectively, A′ and B′ represent the number of standard complete data blocks and the standard data recovery success rate, respectively, and μ 1 , μ 2 They are respectively the weight factor corresponding to the set number of complete data blocks and the weight factor corresponding to the data recovery success rate.

[0050] It should be noted that μ 1 , μ 2 The value of is greater than 0 and less than 1.

[0051] It should also be noted that the weight factors corresponding to the number of historical complete data blocks and the weight factors corresponding to each data recovery success rate corresponding to each historical data integrity assessment coefficient are obtained from the database, and the medians of the weight factors corresponding to the number of historical complete data blocks and the weight factors corresponding to the data recovery success rate are selected respectively to obtain the weight factors corresponding to the set number of complete data blocks and the weight factors corresponding to the data recovery success rate.

[0052] Step 3: Business data security analysis: Obtain the security assessment parameters corresponding to the business data during transmission and storage, and then analyze the data security assessment coefficients corresponding to the business data during transmission and storage.

[0053] In a specific embodiment, the integrity impact parameter corresponding to the transmission and storage process of the business data is obtained, and the specific acquisition process is as follows: by consulting the security configuration and related documents of the central processing system, the encryption key length used by the central processing system is obtained, and the query is performed through a specified command line to obtain the number of nodes currently participating in the verification;

[0054] According to the network scope that needs to measure the network connection density, collect the information of all nodes, including the number of nodes, the identifier of each node and the connection relationship between nodes, and use the collected node information to build a network topology diagram. In the network topology diagram, each node represents each network entity, and the edge represents the connection between entities. Count all the edges in the network topology diagram, that is, the actual number of connections between nodes, divide the actual number of edges by the total number of edges, and the result is the network connection density.

[0055] It should be noted that the network topology diagram is used to intuitively display the connection relationship between nodes. For an undirected graph network containing N nodes, the total number of edges is N*(N-1) / 2. Then, the actual number of edges can be divided by N*(N-1) / 2 to obtain the network connection density.

[0056] In a specific embodiment, the data security assessment coefficient corresponding to the transmission and storage process of the business data is analyzed, and the specific analysis process is as follows: obtaining standard security assessment parameters corresponding to the business data collection and processing process from the database, the standard security assessment parameters including standard encryption key length, standard number of nodes participating in verification and standard network connection density;

[0057] By calculating the formula The data security assessment coefficient χ corresponding to the business data transmission and storage process is obtained, where l, q, and ρ represent the encryption key length, the number of nodes participating in verification, and the network connection density corresponding to the business data transmission and storage process, respectively; l′, q′, and ρ′ represent the quasi-encryption key length, the standard number of nodes participating in verification, and the standard network connection density, respectively; κ 1 , κ 2 , κ 3 They are the weight factor corresponding to the set encryption key length, the weight factor corresponding to the number of nodes participating in the verification, and the weight factor corresponding to the network connection density.

[0058] It should be noted that κ 1 , κ 2 , κ 3 The value of is greater than 0 and less than 1.

[0059] It should also be noted that the historical encryption key lengths, the historical numbers of nodes participating in verification, and the historical network connection densities corresponding to each historical data security assessment coefficient are obtained from the database, and the median is selected to obtain the weight factor corresponding to the set encryption key length, the weight factor corresponding to the number of nodes participating in verification, and the weight factor corresponding to the network connection density.

[0060] Step 4: Comprehensive process analysis: Analyze the comprehensive evaluation coefficient corresponding to the business data of the specified power facility during the processing process, so as to evaluate whether the comprehensive performance corresponding to the business data during the processing process meets the set requirements.

[0061] In a specific embodiment, the analysis specifies the comprehensive evaluation coefficient corresponding to the business data of the power facility during the processing. The specific analysis process is as follows: By calculating the formula Obtain the comprehensive evaluation coefficient corresponding to the business data of the specified power facility during the processing process where λ 1 , 2 , 3 The weight factor corresponding to the set data processing efficiency evaluation coefficient, the weight factor corresponding to the data integrity evaluation coefficient, and the weight factor corresponding to the data security evaluation coefficient.

[0062] It should be noted that λ 1 , 2 , 3 The value of is greater than 0 and less than 1.

[0063] It should also be noted that when the corresponding comprehensive performance of the business data obtained from the database during the processing process does not meet the set requirements, the number of times the data processing efficiency evaluation coefficient does not meet the set requirements, the number of times the data integrity evaluation coefficient does not meet the set requirements, and the number of times the data security evaluation coefficient does not meet the set requirements are accumulated to obtain the total number of times, and the number of times the data processing efficiency evaluation coefficient does not meet the set requirements, the number of times the data integrity evaluation coefficient does not meet the set requirements, and the number of times the data security evaluation coefficient does not meet the set requirements are divided by the total number of times respectively, and the result obtained is the weight factor corresponding to the set data processing efficiency evaluation coefficient, the weight factor corresponding to the data integrity evaluation coefficient, and the weight factor corresponding to the data security evaluation coefficient.

[0064] In a specific embodiment, whether the comprehensive performance corresponding to the business data during the processing process meets the set requirements, the specific evaluation process is as follows: the comprehensive evaluation coefficient corresponding to the business data of the specified power facility during the processing process is compared with the set comprehensive evaluation coefficient threshold. If the comprehensive evaluation coefficient corresponding to the business data of the specified power facility during the processing process is greater than or equal to the set comprehensive evaluation coefficient threshold, it means that the comprehensive performance corresponding to the evaluated business data during the processing process meets the set requirements; if the comprehensive evaluation coefficient corresponding to the business data of the specified power facility during the processing process is less than the set comprehensive evaluation coefficient threshold, it means that the comprehensive performance corresponding to the evaluated business data during the processing process does not meet the set requirements.

[0065] It should be noted that when the corresponding comprehensive performance of business data during the processing process does not meet the set requirements, a rapid response troubleshooting team should be established to conduct timely analysis and repairs, collect historical data and analysis results from the database, identify potential performance bottlenecks and safety hazards, and then take measures to improve them, including regular training and skills improvement plans.

[0066] When analyzing the security of business data, the embodiments of the present invention help protect data from unauthorized access and attacks by obtaining security assessment parameters corresponding to the business data during the transmission and storage processes. When performing comprehensive process analysis, the embodiments of the present invention help comprehensively evaluate the business data processing performance of power facilities by calculating the comprehensive assessment coefficients corresponding to the business data during the processing process. This comprehensive assessment helps decision makers understand the comprehensive balance between processing efficiency, data integrity and security, thereby optimizing business processes and decision making, and ensuring the efficient operation and sustainable development of power facilities.

[0067] Step 5: Early warning: When the comprehensive performance of business data during processing meets the set requirements, an early warning will be issued.

[0068] The present invention provides a digital business data processing method for the electric power industry. When analyzing the business data processing effect, the business data generated by the specified electric power facilities is collected, and the corresponding effect influencing parameters when processing the business data are obtained, which is conducive to evaluating the actual efficiency and effect of the business data processing, thereby ensuring that in actual operation, the business processing efficiency can be quantified based on specific data, thereby optimizing and improving the data processing process, and improving the operating efficiency and cost-effectiveness of the electric power facilities. When analyzing the integrity of the business data, the business data is transmitted and stored through the blockchain technology, and the integrity influencing parameters corresponding to the business data in the transmission and storage process are obtained, which is conducive to ensuring the integrity and reliability of the data in the transmission and storage process, and effectively preventing data tampering or loss. For the electric power industry field with extremely high requirements for data accuracy, it can enhance the credibility and security of the data and ensure the stability of the operation of the electric power facilities.

[0069] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art 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 concept of the invention or exceed the scope defined in this specification, they shall all fall within the protection scope of the present invention.

Claims

1. A digital business data processing method for the power industry, characterized in that: include: Step 1: Processing effect analysis: collect the business data generated by the designated power facilities, and obtain the corresponding effect impact parameters when processing the business data, and then analyze the corresponding data processing efficiency evaluation coefficient during the business data processing process; The data processing efficiency evaluation coefficient corresponding to the analysis business data processing process is as follows: Obtaining standard effect impact parameters corresponding to the process of business data collection and processing from the database, the standard effect impact parameters including standard business data collection frequency, standard processing time and standard business data flow; By calculating the formula The corresponding data processing efficiency evaluation coefficient α in the process of business data processing is obtained, where c, T, and L respectively represent the data collection frequency, processing time, and business data flow corresponding to the business data of the specified power facility when collecting and processing business data at the edge, c′, T′, and L′ respectively represent the standard business data collection frequency, standard processing time, and standard business data flow, and η1, η2, and η3 respectively represent the weight factors corresponding to the set data collection frequency, the weight factors corresponding to the processing time, and the weight factors corresponding to the business data flow; Step 2: Business data integrity analysis: Obtain integrity impact parameters corresponding to the business data during transmission and storage, and then analyze the data integrity assessment coefficients corresponding to the business data during transmission and storage; The data integrity assessment coefficient corresponding to the transmission and storage process of the analysis business data is as follows: Obtaining standard integrity impact parameters corresponding to the process of business data collection and processing from the database, the standard integrity impact parameters including the number of standard complete data blocks and the standard data recovery success rate; By calculating the formula The data integrity assessment coefficient β corresponding to the business data in the transmission and storage process is obtained, where A and B represent the number of complete data blocks and the data recovery success rate, respectively; A′ and B′ represent the standard number of complete data blocks and the standard data recovery success rate, respectively; μ1 and μ2 represent the weight factor corresponding to the set number of complete data blocks and the weight factor corresponding to the data recovery success rate, respectively; Step 3: Business data security analysis: Obtain the security assessment parameters corresponding to the business data during transmission and storage, and then analyze the data security assessment coefficients corresponding to the business data during transmission and storage; The data security assessment coefficient corresponding to the transmission and storage process of the analysis business data is as follows: Obtain standard security assessment parameters corresponding to the process of business data collection and processing from the database, the standard security assessment parameters including standard encryption key length, standard number of nodes participating in verification and standard network connection density; By calculating the formula The data security assessment coefficient χ corresponding to the business data in the transmission and storage process is obtained, where l, q, and ρ represent the encryption key length, the number of nodes participating in the verification, and the network connection density corresponding to the business data in the transmission and storage process, respectively; l′, q′, and ρ′ represent the quasi-encryption key length, the standard number of nodes participating in the verification, and the standard network connection density, respectively; κ1, κ2, and κ3 are the weight factors corresponding to the set encryption key length, the weight factors corresponding to the number of nodes participating in the verification, and the weight factors corresponding to the network connection density, respectively; Step 4: Comprehensive process analysis: Analyze the comprehensive evaluation coefficient corresponding to the business data of the specified power facility during the processing process, so as to evaluate whether the comprehensive performance corresponding to the business data during the processing process meets the set requirements; The analysis specifies the comprehensive evaluation coefficient corresponding to the business data of the power facility during the processing, and the specific analysis process is as follows: By calculating the formula Obtain the comprehensive evaluation coefficient corresponding to the business data of the specified power facility during the processing process Among them, λ1, λ2, and λ3 are the weight factors corresponding to the set data processing efficiency evaluation coefficient, the weight factor corresponding to the data integrity evaluation coefficient, and the weight factor corresponding to the data security evaluation coefficient; Step 5: Early warning: When the comprehensive performance of business data during processing meets the set requirements, an early warning will be issued.

2. A digital business data processing method for the electric power industry according to claim 1, characterized in that: The effect influencing parameters include business data collection frequency, processing time and business data traffic; the integrity influencing parameters include the number of complete data blocks and data recovery success rate; the security assessment parameters include encryption key length, the number of nodes involved in verification and network connection density.

3. A digital business data processing method for the electric power industry according to claim 2, characterized in that: The specific acquisition process of the effect impact parameter corresponding to the business data processing is as follows: The edge device deployed at the edge of the designated power facility is equipped with a built-in clock and timer to obtain the time interval for business data collection, that is, the business data collection frequency corresponding to the business data of the designated power facility when the business data is collected at the edge; Mark each item in the business data, record the timestamp when the processing of each item of data starts after it arrives at the edge device, which is the start time point, and the timestamp corresponding to the end of the processing of each item of data, which is the end time point. Calculate the absolute value of the difference between the start time point and the end time point corresponding to each item of data, so as to obtain the processing time difference corresponding to each item of data. The result obtained by the mean calculation is the processing time corresponding to the business data of the specified power facility when it is processed; The traffic monitoring tool is used to monitor the traffic in the business data collection channel, thereby obtaining the business data traffic corresponding to the business data of the specified power facility when collecting and processing business data at the edge.

4. A digital business data processing method for the electric power industry according to claim 3, characterized in that: The process of obtaining the integrity impact parameters corresponding to the business data during transmission and storage is as follows: By traversing the entire data chain, the integrity status of each data block is statistically obtained, thereby obtaining the number of complete data blocks; By simulating the triggering of node failures, when each node fails, business data is recovered from the remaining nodes, and the number of successful business data recovery from the remaining nodes and the total number of attempts to recover the business data are recorded. The number of successful business data recovery from the remaining nodes is divided by the total number of attempts to recover the business data. The result is the data recovery success rate.

5. A digital business data processing method for the electric power industry according to claim 4, characterized in that: The process of obtaining the integrity impact parameters corresponding to the business data during transmission and storage is as follows: By consulting the security configuration and related documents of the central processing system, the encryption key length used by the central processing system can be obtained, and the number of nodes currently participating in the verification can be obtained by querying through the specified command line; According to the network scope that needs to measure the network connection density, collect the information of all nodes, including the number of nodes, the identifier of each node and the connection relationship between nodes, and use the collected node information to build a network topology diagram. In the network topology diagram, each node represents each network entity, and the edge represents the connection between entities. Count all the edges in the network topology diagram, that is, the actual number of connections between nodes, divide the actual number of edges by the total number of edges, and the result is the network connection density.

6. A digital business data processing method for the electric power industry according to claim 5, characterized in that: The evaluation process of whether the comprehensive performance of the business data during the processing meets the set requirements is as follows: The comprehensive evaluation coefficient corresponding to the business data of the designated power facility during the processing is compared with the set comprehensive evaluation coefficient threshold. If the comprehensive evaluation coefficient corresponding to the business data of the designated power facility during the processing is greater than or equal to the set comprehensive evaluation coefficient threshold, it means that the comprehensive performance corresponding to the evaluated business data during the processing meets the set requirements. If the comprehensive evaluation coefficient corresponding to the business data of the designated power facility during the processing is less than the set comprehensive evaluation coefficient threshold, it means that the comprehensive performance corresponding to the evaluated business data during the processing does not meet the set requirements.

Images