Panoramic assessment method, device and equipment for station end data section and medium

Abstract

The invention belongs to the technical field of electric power big data, and particularly relates to a station end data section panorama evaluation method, device and equipment and a medium. In order to solve the problem of one-sided evaluation, the method comprises the following steps: firstly, collecting a data section containing real-time and historical data, and completing multi-protocol equipment communication configuration and data collection; secondary system defect data are evaluated from multiple dimensions, and cause influences are analyzed; the accuracy and robustness of functional data are evaluated in combination with transmission delay and interference simulation; analyzing the data stream quality of the main and auxiliary devices based on the transmission range, frequency, protocol and the like; and constructing a multi-level monitoring system of a measuring point level, an equipment level and a function level, and evaluating the operation and maintenance data collaboration of a station level, a spacing level and a process level. And finally, integrating results of all dimensions to generate a visual report. According to the scheme, the limitation of single equipment detection is broken through, and through full-link data analysis, multi-dimensional index design and system-level risk modeling, data transmission robustness and cross-layer collaborative evaluation in a complex scene are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of power big data, and particularly relates to a panoramic evaluation method, device, equipment and medium for substation terminal data section. Background Art

[0002] With the deepening of the energy transformation, the large-scale access of new energy power generation, distributed power sources and flexible loads has transformed the power grid structure from the traditional centralized radial type to a complex network form of multi-source interaction. The new energy power generation on the source side is significantly affected by natural conditions such as weather and seasons, and the flexible loads on the load side show dynamic change characteristics due to the diversity of user behaviors. The strong randomness and volatility of the two lead to a more complex operation state of the power grid, posing higher requirements for the real-time monitoring, fault response and optimal control of the power system. As the core node of the smart grid, the integrated monitoring system of the intelligent substation undertakes key functions such as real-time data acquisition, equipment status monitoring, and automatic fault handling, and is the "nerve center" to ensure the safe and stable operation of the power grid. The substation terminal real-time data is not only the "digital mirror" reflecting the operation state of the power grid, but also the basis for carrying out equipment performance analysis, system control strategy verification and power grid health status evaluation. However, with the diversification of equipment types and the complexity of data interaction in the substation, the substation terminal data faces characteristics such as multi-source heterogeneity, complex transmission paths, and strong time-series correlation, and its data quality directly affects the reliability of power grid operation control.

[0003] Currently, the evaluation of substation terminal data in intelligent substations mainly focuses on the isolated detection of single equipment or local functions, such as the measurement accuracy verification of secondary equipment, the compliance check of communication protocols, or simple statistical analysis based on historical data. These methods are difficult to comprehensively reveal the potential risks of data defects to power grid operation. Summary of the Invention

[0004] The purpose of the present invention is to provide a panoramic evaluation method, device, equipment and medium for substation terminal data section, so as to solve the technical problem that the existing substation terminal data evaluation methods are difficult to comprehensively reveal the potential risks of data defects to power grid operation.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions: In the first aspect of the present invention, a panoramic evaluation method for substation terminal data section is provided, including the following steps: Obtain the substation terminal data section of the intelligent substation; Determine the secondary system data according to the substation terminal data section; evaluate the secondary system data, determine the defective data in the secondary system data, and analyze the causes and impacts of the defective data; Determine the transmission process and transmission result of function type data according to the station - side data section; evaluate the function type data based on the transmission process and transmission result to obtain the accuracy and robustness of the function type data; wherein, the function type data includes tagging data, blocking data, substitution data, and out - of - limit data; Determine the data flow transmission characteristic tuple of the primary and secondary equipment according to the station - side data section; evaluate the data flow transmission characteristic tuple to obtain the data flow quality evaluation result; wherein, the data flow transmission characteristic tuple includes the transmission range, content, frequency, and protocol of the data flow; Determine the operation and maintenance management data between the station control layer, bay layer, and process layer in the intelligent substation according to the station - side data section; evaluate the operation and maintenance management data based on a preset multi - level data monitoring status evaluation system to obtain the multi - level operation and maintenance data evaluation result; wherein, the operation and maintenance management data includes full - caliber operation and maintenance data, maintenance data, and joint debugging data; Integrate and display the defect data causes, defect data impacts, accuracy, robustness, data flow quality evaluation result, and multi - level operation and maintenance data evaluation result to obtain a visual display report.

[0006] Furthermore, evaluate the secondary system data, determine the defect data in the secondary system data, and analyze the defect data to obtain the defect data causes and defect data impacts, including: Calculate the value of each evaluation index of the secondary system data under the evaluation index system by using statistical analysis methods according to the pre - constructed evaluation index system; wherein, the evaluation indexes include data integrity, data accuracy, data timeliness, and data consistency; judge the value of each evaluation index based on a preset threshold to determine the defect data in the secondary system data; Use data mining methods to analyze the association rules between the defect data and each data in the station - side data section, and determine the defect data causes according to the association rules; Evaluate the influence degree and influence range of the defect data on the various functions of the secondary system, and evaluate the impact consequences of the defect data on the stable operation of the power grid; determine the defect data impacts according to the influence degree, influence range, and impact consequences.

[0007] Furthermore, determine the transmission process and transmission result of function type data according to the station - side data section; evaluate the function type data based on the transmission process and transmission result to obtain the accuracy and robustness of the function type data, including: Identify the function type data based on the identification, format, and association information of the data in the station - side data section, and extract the function type data in the order of data transmission time; Determine the source device and target device for the transmission of functional type data, as well as the network nodes through which the functional type data passes when transmitted between the source device and the target device, based on network topology information and communication logs; determine the processing time of the functional type data at each network node; compare the functional type data received by the target device with the functional type data sent by the source device to determine the complete reception situation of the functional type data; and, determine the processing result of the target device on the functional type data; determine the transmission process delay based on the processing time of the functional type data at each network node; evaluate the accuracy of the functional type data based on the transmission process delay, complete reception situation, and processing result; Simulate a network interference environment, determine the transmission process and transmission result of the functional type data in the network interference environment, and evaluate the robustness of the functional type data based on the transmission process and transmission result in the network interference environment.

[0008] Furthermore, evaluate the data stream transmission feature tuple to obtain the data stream quality evaluation result, including: Evaluate the transmission range of the data stream, including: checking whether the data is transmitted between specified devices according to a preset standard to obtain a transmission range score; Evaluate the content of the data stream, including: comparing the data with the measured values of actual physical quantities to check whether there are deviations, errors, or omissions in the data to obtain a content score; Evaluate the frequency of the data stream, including: comparing the actual value of the frequency with the preset frequency range to obtain a frequency score; Evaluate the protocol of the data stream, including: checking whether the data stream follows the adopted communication protocol to obtain a protocol score; Perform a weighted sum of the transmission range score, content score, frequency score, and protocol score to obtain a comprehensive data stream score; divide the data stream quality into different levels according to the comprehensive data stream score to obtain the data stream quality evaluation result.

[0009] Furthermore, in the steps of evaluating the operation and maintenance management data based on a preset multi-level data monitoring status evaluation system, the multi-level data monitoring status evaluation system is constructed as follows: In a simulation environment, inject faults into the system according to fault scenarios to obtain the operation and maintenance management data after the faults are injected; Classify the operation and maintenance management data after the faults are injected according to the degree of newness, fault type, and functional type to obtain classified data; analyze the correlation relationship between the operation and maintenance management data of different types of devices and systems based on the classified data, and construct the topological relationship and logical relationship between the operation and maintenance management data according to the correlation relationship; An intelligent substation event model is refined based on the topological and logical relationships among operation and maintenance management data; among them, the intelligent substation event model is used to describe the variation laws and mutual influences of data under different fault scenarios; The intelligent substation event model is classified by level, and a multi-level data monitoring status evaluation system at the measurement point level, equipment level, and function level is divided.

[0010] Furthermore, the operation and maintenance management data is evaluated based on the preset multi-level data monitoring status evaluation system, including: The determined operation and maintenance management data is matched with the event model in the multi-level data monitoring status evaluation system to determine the fault scenario and evaluation index matching the current operation and maintenance management data; According to the matched evaluation index, the operation and maintenance management data is calculated, and based on the calculated index value, the evaluation result of the abnormality of the operation and maintenance management data among the station control layer, bay layer, and process layer is determined; according to the multi-level data monitoring status evaluation system, the abnormality evaluation result is divided into abnormalities at the measurement point level, equipment level, or function level, and the severity level of the abnormality is determined.

[0011] Furthermore, the station-side data section of the intelligent substation is obtained, including: Collect the real-time data and historical data of the station-side data of the intelligent substation; Determine the data section time point, and extract the data corresponding to the data section time point from the real-time data and historical data to form a data section.

[0012] In the second aspect of the present invention, a panoramic evaluation device for the station-side data section is provided, including: A data acquisition module for acquiring the station-side data section of the intelligent substation; A first evaluation module for determining secondary system data according to the station-side data section; evaluating the secondary system data to determine the defective data in the secondary system data, and analyzing the causes and impacts of the defective data; A second evaluation module for determining the transmission process and transmission result of the function type data according to the station-side data section; evaluating the function type data according to the transmission process and transmission result to obtain the accuracy and robustness of the function type data; among them, the function type data includes setting data, blocking data, substitution data, and out-of-limit data; A third evaluation module for determining the data flow transmission characteristic tuple of the main and auxiliary equipment according to the station-side data section; evaluating the data flow transmission characteristic tuple to obtain the data flow quality evaluation result; among them, the data flow transmission characteristic tuple includes the transmission range, content, frequency, and protocol of the data flow; The fourth evaluation module is used to determine the operation and maintenance management data among the station control layer, bay layer, and process layer in the intelligent substation according to the station - end data section; evaluate the operation and maintenance management data based on a preset multi - level data monitoring status evaluation system to obtain multi - level operation and maintenance data evaluation results; among them, the operation and maintenance management data includes full - caliber operation and maintenance data, maintenance data, and joint debugging data. The comprehensive display module is used to integratively display the causes of defect data, the impacts of defect data, accuracy, robustness, data flow quality evaluation results, and multi - level operation and maintenance data evaluation results to obtain a visual display report.

[0013] In the third aspect of the present invention, an electronic device is provided, including a processor and a memory. The processor is used to execute a computer program stored in the memory to implement the station - end data section panoramic evaluation method as described above.

[0014] In the fourth aspect of the present invention, a computer - readable storage medium is provided. The computer - readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the station - end data section panoramic evaluation method as described above is implemented.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: Through full - link data integration, multi - dimensional index design, and system - level risk modeling, this solution realizes the panoramic evaluation of the station - end data quality of the intelligent substation. It not only solves the one - sidedness of traditional methods in single - device detection but also strengthens the evaluation ability in complex scenarios through interference simulation and cross - layer collaborative analysis. Moreover, through data mining and impact quantification, it reveals the deep - layer risks of defect data on power grid operation. Finally, through visual integration, the scattered evaluation results are transformed into intuitive decision - making support, providing key technical support for the reliable operation of intelligent substations in the new - energy background. A station - end data section panoramic evaluation device, electronic device, and computer - readable storage medium provided by the present invention also solve the problems raised in the background art part. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings forming a part of this application are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings: Figure 1 It is a flowchart of a station - end data section panoramic evaluation method according to an embodiment of the present invention; Figure 2 It is a structural block diagram of a station - end data section panoramic evaluation device according to an embodiment of the present invention; Figure 3 It is a structural block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other.

[0018] The following detailed descriptions are all exemplary descriptions, aiming to provide further detailed descriptions of the present invention. Unless otherwise specified, all technical terms adopted by the present invention have the same meaning as commonly understood by those of ordinary skill in the art to which this application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0019] Embodiment 1 Aiming at the problems of multi-source heterogeneity, complex transmission and one-sided quality assessment of substation terminal data in the context of new energy and distributed power access, the present invention proposes a panoramic assessment method for substation terminal data sections.

[0020] As Figure 1 shown, a panoramic assessment method for substation terminal data sections includes the following steps: S1. Obtain the substation terminal data section of the intelligent substation; S2. Determine the secondary system data according to the substation terminal data section; evaluate the secondary system data, determine the defective data in the secondary system data, and analyze the causes and impacts of the defective data; S3. Determine the transmission process and transmission results of the function type data according to the substation terminal data section; evaluate the function type data according to the transmission process and transmission results to obtain the accuracy and robustness of the function type data; wherein, the function type data includes setting data, blocking data, substitution data and out-of-limit data; S4. Determine the data stream transmission characteristic tuple of the primary and auxiliary equipment according to the substation terminal data section; evaluate the data stream transmission characteristic tuple to obtain the data stream quality assessment result; wherein, the data stream transmission characteristic tuple includes the transmission range, content, frequency and protocol of the data stream; S5. Determine the operation and maintenance management data among the station control layer, bay layer and process layer in the intelligent substation according to the substation terminal data section; evaluate the operation and maintenance management data based on a preset multi-level data monitoring status assessment system to obtain a multi-level operation and maintenance data assessment result; wherein, the operation and maintenance management data includes full-caliber operation and maintenance data, maintenance data and joint debugging data; S6. Integrate and display the causes of defective data, the impacts of defective data, accuracy, robustness, data stream quality assessment results and multi-level operation and maintenance data assessment results to obtain a visual display report.

[0021] This solution breaks through the limitations of traditional single-device detection. Through full-link data parsing, multi-dimensional index design, and system-level risk modeling, it realizes a panoramic assessment of data quality, effectively addresses the challenges of data transmission robustness and cross-layer collaboration assessment in complex scenarios, and provides key technical support for the reliable operation of intelligent substations and the secure and stable control of the power grid. This solution realizes the upgrade from single-point detection to panoramic assessment through a full-dimensional assessment system, full-process link analysis, and in-depth risk quantification.

[0022] In a specific embodiment, this solution also provides a method for panoramic assessment of substation-side data sections, including: S100. Obtain the substation-side data section of the intelligent substation.

[0023] In one embodiment, the substation-side data section of the intelligent substation is obtained in the following manner: Collect the real-time data and historical data of the substation-side data of the intelligent substation; determine the data section time point, and extract the data corresponding to the data section time point from the real-time data and historical data to form a data section. Specifically, it includes the following steps: 1). Determine the types of substation-side data to be obtained, including secondary system data, function type data, main and auxiliary equipment data streams, operation and maintenance management data, etc., and at the same time determine the time range of the substation-side data, etc. Sort out the communication interfaces of each device and system in the intelligent substation, including communication protocols (such as IEC 61850, Modbus, etc.), interface types (such as Ethernet, serial port, etc.), and data formats, etc. Check the status of the devices (measurement and control devices, protection devices, switches, etc.) participating in data collection to ensure the normal operation of the devices and the smooth communication link.

[0024] 2). According to the communication interface information of the devices, configure the corresponding communication parameters in the data collection system, such as IP address, port number, baud rate, etc. Establish communication connections with each device and system in the intelligent substation through network or serial port, etc.; for devices using the IEC 61850 protocol, corresponding communication services (such as MMS service) can be used for connection; for Modbus devices, Modbus TCP or RTU protocol can be used for connection. After establishing the connection, send test commands or data to verify whether the connection is normal; if the connection fails, check the communication parameters, device status, and communication link until the connection is successful.

[0025] 3). Data collection, including real-time data collection and historical data collection.

[0026] (1) Real-time data collection: For data acquisition devices that support the polling method, data request commands are sent at preset time intervals (such as 1 second, 5 seconds, etc.) to obtain the real-time data of the devices; for example, for measurement and control devices, analog quantity data such as voltage, current, and power and status quantity data such as switch status can be polled and collected.

[0027] For devices adopting the IEC 61850 protocol, the status changes and sampling data of the devices can be obtained in real time by subscribing to GOOSE (Generic Object Oriented Substation Event) and SV (Sampled Values) messages; for example, by subscribing to the GOOSE messages of the protection device, the protection action information can be obtained in real time.

[0028] (2) Historical data acquisition: For devices with data storage functions (such as protection devices, PMU devices, etc.), the historical data stored inside the devices is read through corresponding communication protocols and commands; for example, the fault recording wave data and action record data of the protection device can be read. If a data storage system (such as a historical database) is set up in the intelligent substation, the required historical data can be obtained from the database through database query statements; when querying, filtering needs to be performed according to conditions such as the time range of the data and the device name.

[0029] 4) Determine the time point of the data section, extract the data corresponding to the time point, and form the data section. Optionally, the data section can be the data at a certain specific moment, or the average value or statistical value of the data within a period of time.

[0030] S200. Determine the secondary system data according to the station-side data section; evaluate the secondary system data, determine the defective data in the secondary system data, and analyze the defective data to obtain the causes and impacts of the defective data.

[0031] In one embodiment, the secondary system of the intelligent substation in this solution includes in-station measurement and control devices, time synchronization devices, protection and measurement integrated devices, PMU devices, and auxiliary control systems, and the secondary system data is the real-time and historical data of the secondary system; this solution evaluates the real-time and historical data in the in-station measurement and control devices, time synchronization devices, protection and measurement integrated devices, PMU devices, and auxiliary control systems, and evaluates the defective data. The defective data is analyzed from the perspectives of defective information classification, defective trend analysis, causes of defective data, impacts of defective data, operation error prevention, intelligent linkage, etc., to obtain the causes and impacts of the defective data.

[0032] In one embodiment, the data of the secondary system is evaluated to determine the defective data in the secondary system data, and the causes and impacts of the defective data are analyzed, including: calculating the values of each evaluation index of the secondary system data under the evaluation index system by using statistical analysis methods according to the pre-constructed evaluation index system; wherein the evaluation indexes include data integrity, data accuracy, data timeliness, and data consistency; judging the values of each evaluation index based on a preset threshold to determine the defective data in the secondary system data; analyzing the association rules between the defective data and each data in the station-side data section by using data mining methods, and determining the causes of the defective data according to the association rules; evaluating the influence degree and scope of the defective data on the various functions of the secondary system, and evaluating the impact consequences of the defective data on the stable operation of the power grid; and determining the impact of the defective data according to the influence degree, influence scope, and impact consequences.

[0033] In one embodiment, step S200 specifically includes: 1). Determining the secondary system data from the station-side data section, including: (1) According to the device identifier and data type in the station-side data section, filtering out the data related to the secondary system, and the data can be real-time data and historical data; for example, for the in-station measurement and control device, filtering out the analog quantities (such as voltage, current), status quantities (such as switch position), etc. collected by it; for the time synchronization device, filtering out the time synchronization accuracy, time signal status, etc. data. Classify and store according to the different components of the secondary system (in-station measurement and control device, time synchronization device, protection and measurement integrated device, PMU device, and auxiliary control system) for convenient subsequent processing.

[0034] (2) Integrating the filtered real-time data and historical data to obtain a data set.

[0035] 2). Evaluation of the secondary system data, including: (1) Formulating an evaluation index system; wherein the evaluation indexes include data integrity, data accuracy, data timeliness, and data consistency.

[0036] Specifically, data integrity includes: checking whether there are missing values in the data; for example, for the analog quantity data collected by the measurement and control device, if the data at a certain moment is missing, it is determined that the data is incomplete; the data missing rate (the number of missing data points / the total number of data points) can be calculated to quantitatively evaluate.

[0037] Specifically, data accuracy includes: comparing the data from different data sources or comparing with the theoretical value; for example, comparing the measured value of the protection and measurement integrated device with the measured value of the standard meter, and if the error exceeds a certain range (such as ±0.5%), the data is considered inaccurate.

[0038] Specifically, data timeliness includes: checking whether the update time of the data meets the requirements; for data with high real-time requirements, such as protection action information, if the update delay exceeds the set threshold (e.g., 100 ms), it is determined that the data timeliness is poor.

[0039] Specifically, data consistency includes: checking whether the logical relationship between relevant data is reasonable; for example, the switch position signal and the corresponding current and voltage changes should conform to the circuit principle, and if there are contradictions, it is determined that the data is inconsistent.

[0040] (2) Adopt statistical analysis methods to calculate and analyze each evaluation index; for example, statistically analyze the average value, maximum value, etc. of the data missing rate over a period of time. Based on the preset threshold, judge the evaluation results of each index to determine whether there are defects in the data; for example, when the data missing rate exceeds 1%, it is determined that there are defects in the integrity of the data.

[0041] 3) Determine the defective data, including: (1) Mark the data found to be defective during the evaluation process, record the defect type (such as integrity defect, accuracy defect, etc.) and the relevant evaluation index values. Establish a defective data list, which contains information such as device name, data type, defect type, defect occurrence time, etc.

[0042] (2) Classify according to the severity of the defects, such as emergency defects (seriously affecting the normal operation of the system, such as protection device misoperation data), major defects (having a greater impact on the system operation, such as seriously inaccurate data of the measurement and control device), and general defects (having a smaller impact on the system operation, such as slight data missing). According to the defect information classification standard, further subdivide the defective data, such as classifying the accuracy defect into excessive measurement error, data jump, etc.

[0043] 4) Analyze the causes of defective data, including: Analyze the correlation between the defective data and other relevant data; for example, if the data of the time synchronization device is abnormal, check whether it is related to data such as GPS signal strength and the clock accuracy of the device itself. Data mining techniques can be used, such as association rule mining, to find potential factors that may lead to defective data.

[0044] 5) Analyze the impact of defective data, including: (1) Evaluate the degree of impact of the defective data on the various functions of the secondary system; for example, if the data of the measurement and control device is inaccurate, it may cause incorrect information to be displayed in the remote monitoring system, affecting the decision-making of dispatchers; if the data of the protection device is abnormal, it may cause protection misoperation or refusal to operate, endangering the safety of the power grid. Analyze the impact scope of the defective data on different functional modules, and determine the specific functions and business processes affected.

[0045] (2)From the perspective of power grid operation, analyze the possible consequences brought by the defect data; for example, inaccurate time synchronization devices may lead to inconsistent time across the network, affecting the accuracy of fault location and accident analysis; abnormal data of PMU devices may affect the stable monitoring and control of the power grid. Evaluate the risk level of the defect data to the safe and stable operation of the power grid, and provide a basis for formulating countermeasures.

[0046] S300. Determine the transmission process and transmission results of the function type data according to the substation end data section; evaluate the function type data according to the transmission process and transmission results to obtain the accuracy and robustness of the function type data; among them, the function type data includes tagging data, blocking data, substitution data, and out-of-limit data.

[0047] In one embodiment, from the perspectives of measurement and monitoring, remote monitoring, network security, penetration access, device alarm, accident recollection, etc., evaluate the accuracy and robustness of the transmission process and transmission results of the function type data such as tagging data, blocking data, substitution data, and out-of-limit data.

[0048] In one embodiment, determine the transmission process and transmission results of the function type data according to the substation end data section; evaluate the function type data according to the transmission process and transmission results to obtain the accuracy and robustness of the function type data, including: identifying the function type data based on the identification, format, and association information of the data in the substation end data section, and extracting the function type data in the order of data transmission time; determining the source device and target device of the function type data transmission according to the network topology information and communication logs, as well as the network nodes passed by the function type data between the source device and the target device; determining the processing time of the function type data at each network node; comparing the function type data received by the target device with the function type data sent by the source device to determine the complete reception situation of the function type data; and determining the processing result of the target device on the function type data; determining the transmission process delay according to the processing time of the function type data at each network node; evaluating the accuracy of the function type data according to the transmission process delay, complete reception situation, and processing result; simulating the network interference environment, determining the transmission process and transmission results of the function type data in the network interference environment, and evaluating the robustness of the function type data according to the transmission process and transmission results in the network interference environment.

[0049] In one embodiment, step S300 specifically includes: 1). Determine the transmission process and transmission results of the function type data from the substation end data section, including: (1)Identify functional type data such as signage data, blocking data, alternative data, and out-of-limit data based on the identification, format, and association information of the data in the station-side data section. For example, different types of data are distinguished according to specific protocol identification fields. Extract the functional type data from the station-side data section in the chronological order of data transmission to prepare for subsequent analysis.

[0050] (2)With the help of network topology information and communication logs, determine the source device and target device of the functional type data. For example, view the port connection information of the switch and the IP address of the device to clarify the start and end points of the data. Record the network nodes passed by the data during transmission, such as routers, switches, etc., as well as the processing time and operations of each node. Analyze the communication protocol and transmission method adopted during data transmission, such as whether a reliable transmission protocol (e.g., TCP) or an unreliable transmission protocol (e.g., UDP) is used, and the data encapsulation format and encryption method.

[0051] (3)Obtain the functional type data received by the target device and compare it with the data sent by the source device to determine whether the data is completely received. It is possible to judge whether there is data loss or damage by checking information such as the length and checksum of the data. Record the processing results of the functional type data by the target device, such as whether the signage operation is successfully executed and whether the blocking command takes effect. It is possible to understand the actual application effect of the data by viewing the operation records and status feedback of the device.

[0052] 2) Evaluate the functional type data, including: (1)Compare the content of the functional type data sent by the source device with the data received by the target device to check for data deviation. For example, for signage data, check whether information such as the name and status of the sign is consistent. Analyze whether the numerical values in the data conform to the business logic and the expected range. For example, whether the threshold setting in the out-of-limit data is reasonable and whether the actual measured value is within the allowable error range. Evaluate whether the time delay of data transmission is within an acceptable range. For functional type data with high real-time requirements, such as the blocking data of the protection device, a large time delay will affect the response speed and security of the system. Check whether the timestamp of the data is accurate and consistent with the actual occurrence time, and judge the accuracy of the timestamp by comparing it with the standard time source.

[0053] (2)Simulate a network interference environment, such as packet loss, error code, delay, etc., and observe the transmission and processing of functional type data under interference; network simulation tools can be used to simulate different degrees of interference. Statistically analyze the success rate of data transmission and the correct rate of processing in the interference environment, and evaluate the anti-interference ability of the data; for example, calculate the proportion of data successfully transmitted and correctly processed when the packet loss rate is 10%. Deliberately introduce incorrect data or abnormal situations, such as sending misformatted tag data or abnormal out-of-limit data, and check the system's ability to handle these errors. Observe whether the system can promptly detect and correct errors, or take appropriate measures for fault tolerance processing, such as resending data, ignoring incorrect data, etc. After the network fails or the system recovers from an anomaly, check whether the transmission and processing of functional type data can quickly return to normal, and record the recovery time and data accuracy during the recovery process. Evaluate whether data loss or incorrect processing occurs during the recovery process of the system to ensure that the system has good recovery ability.

[0054] 3), Comprehensive evaluation and result presentation (1)Give a comprehensive score for the transmission process and result of functional type data according to various evaluation indicators of accuracy and robustness; the weighted average method can be used to allocate corresponding weights according to the importance of different indicators. Score different types of functional type data separately to more clearly understand the evaluation of each type of data.

[0055] (2)Organize the data and results in the evaluation process to generate a detailed evaluation report; the report includes the purpose, method, process, results, conclusions and suggestions of the evaluation. Present the evaluation results in intuitive charts and tables, such as bar charts of accuracy and robustness scores, line charts of data transmission success rates under different interference conditions, etc.

[0056] S400. Determine the data flow transmission characteristic tuple of the main and auxiliary equipment according to the station-side data section; evaluate the data flow transmission characteristic tuple to obtain the data flow quality evaluation result; among them, the data flow transmission characteristic tuple includes the transmission range, content, frequency and protocol of the data flow.

[0057] In one embodiment, the present solution systematically analyzes the data flow range, content, frequency and protocol of the main and auxiliary equipment in the substation, identifies the data flow quality, and obtains the data flow quality evaluation result.

[0058] In the preferred embodiment, the best scheme for collecting data flow is further optimized according to the data flow quality evaluation result, providing support for the optimal configuration and design scheme of substation equipment.

[0059] In one embodiment, evaluating the data flow transmission characteristic tuple to obtain the data flow quality evaluation result includes: Evaluate the transmission range of the data stream, including: checking whether the data is transmitted between specified devices according to a preset standard to obtain a transmission range score; Evaluate the content of the data stream, including: comparing the data with the measured values of actual physical quantities, checking whether there are deviations, errors or omissions in the data to obtain a content score; Evaluate the frequency of the data stream, including: comparing the actual value of the frequency with a preset frequency range to obtain a frequency score; Evaluate the protocol of the data stream, including: checking whether the data stream follows the adopted communication protocol to obtain a protocol score; Perform a weighted sum of the transmission range score, content score, frequency score and protocol score to obtain a comprehensive data stream score; divide the data stream quality into different levels according to the comprehensive data stream score to obtain the data stream quality evaluation result.

[0060] In one embodiment, step S400 specifically includes: 1). Determine the data stream transmission characteristic tuples of the main and auxiliary devices from the slave - end data section, including: (1) Based on the device identifiers and related information in the slave - end data section, distinguish the main devices (such as transformers, circuit breakers, etc.) and auxiliary devices (such as measurement and control devices, protection devices, etc.) in the substation. Screen out the data streams related to the main and auxiliary devices from the slave - end data section. These data streams involve different communication links and protocols. Classify and store the screened data streams according to device types and data uses. For example, classify the current and voltage data streams of transformers into one category, and the status monitoring data streams of measurement and control devices into another category.

[0061] (2) Define the starting point and ending point of the data stream, determine the data transmission path and the involved devices; for example, determine that a certain current data stream is transmitted from a current transformer to a measurement and control device and then to a monitoring system. Analyze the specific content in the data stream, identify the data type (such as analog quantity, status quantity), format and meaning; for example, analyze the numerical unit, accuracy and actual physical meaning represented by the data in the voltage data stream. Statistically calculate the transmission frequency of the data stream, that is, the number of data transmissions per unit time; the transmission frequency can be obtained by recording the timestamps of the data and calculating the time interval between two adjacent data transmissions. Determine the communication protocol adopted by the data stream, such as IEC 61850, Modbus, etc.; determine the protocol type by analyzing the data message format, header information and specific identifiers.

[0062] 2). Evaluate the data stream transmission characteristic tuples, including: (1) Evaluate whether the transmission range of the data stream meets the design requirements and actual business needs; this can be done by checking whether the data is transmitted between the specified devices and whether there is unnecessary cross-device or cross-system transmission. Verify whether the content in the data stream is accurate; this can be done by comparing the data with the measured values of actual physical quantities to check whether there are deviations, errors, or omissions in the data; for example, check whether the values in the current data stream match the actually measured current values. Analyze the stability of the data stream transmission frequency; this can be done by calculating the fluctuation range and standard deviation of the frequency to determine whether the frequency is within a reasonable fluctuation range and whether there are frequent mutations or anomalies. Check whether the data stream strictly follows the adopted communication protocol; this can be done by verifying whether the message format, field definitions, communication process, etc. of the data conform to the protocol standards and whether there are protocol violations.

[0063] (2) Compare the actual data stream transmission characteristic tuples with the preset standard values or reference values; for example, compare the actual value of the frequency with the frequency range required by the design to determine whether it meets the standard. Use statistical methods to analyze the data of the characteristic tuples, such as calculating statistical quantities such as the average value, median, and variance; through statistical analysis, the overall characteristics and change trends of the data stream can be understood. Use anomaly detection algorithms to identify abnormal situations in the data stream; for example, use a machine learning-based anomaly detection model to detect abnormal frequencies, abnormal content, etc. in the data stream.

[0064] 3) Obtain the evaluation results of the data stream quality, including: Based on the scores of each evaluation index, give a comprehensive score to the quality of the data stream; the weighted average method can be used to assign corresponding weights according to the importance of different indexes. According to the comprehensive score, divide the data stream quality into different levels, such as excellent, good, qualified, unqualified, etc.; clarify the score range of each level and the corresponding data stream quality characteristics.

[0065] S500. Determine the operation and maintenance management data between the station control layer, bay layer, and process layer in the intelligent substation according to the station-side data section; evaluate the operation and maintenance management data based on a preset multi-level data monitoring status evaluation system to obtain multi-level operation and maintenance data evaluation results; among them, the operation and maintenance management data includes full-caliber operation and maintenance data, maintenance data, and joint debugging data.

[0066] In one embodiment, the present solution evaluates and analyzes the full-caliber operation and maintenance data, maintenance data, and joint debugging data between the station control layer, bay layer, and process layer inside the intelligent substation from the perspective of operation and maintenance; mainly evaluate the operation and maintenance management data according to the pre-constructed multi-level data monitoring status evaluation system to obtain multi-level operation and maintenance data evaluation results.

[0067] In one embodiment, the multi-level data monitoring status evaluation system is constructed as follows: By injecting typical fault event cases into the system, the operation and maintenance management data is classified according to the degree of newness and oldness, fault types, and function types, forming the topological and logical relationships between the operation and maintenance management data of different types of devices and systems. Based on the topological and logical relationships between the operation and maintenance management data, an intelligent substation event model is refined, and the formed intelligent substation event model is classified by level, forming a multi-level data monitoring status evaluation system including the measurement point level, device level, and function level.

[0068] In one embodiment, in the step of evaluating the operation and maintenance management data based on the preset multi-level data monitoring status evaluation system, the multi-level data monitoring status evaluation system is constructed as follows: In a simulation environment, faults are injected into the system according to fault scenarios, and the operation and maintenance management data after the injection of faults is obtained; The operation and maintenance management data after the injection of faults is classified according to the degree of newness and oldness, fault types, and function types to obtain classified data; according to the classified data, the correlation relationships between the operation and maintenance management data of different types of devices and systems are analyzed, and based on the correlation relationships, the topological and logical relationships between the operation and maintenance management data are constructed; An intelligent substation event model is refined based on the topological and logical relationships between the operation and maintenance management data; wherein, the intelligent substation event model is used to describe the change rules and mutual influences of data under different fault scenarios; The intelligent substation event model is classified by level, and a multi-level data monitoring status evaluation system at the measurement point level, device level, and function level is divided.

[0069] In one embodiment, evaluating the operation and maintenance management data based on the preset multi-level data monitoring status evaluation system includes: Matching the determined operation and maintenance management data with the event model in the multi-level data monitoring status evaluation system to determine the fault scenario and evaluation index matching the current operation and maintenance management data; According to the matched evaluation index, calculate the operation and maintenance management data, and based on the calculated index value, determine the evaluation result of the abnormality of the operation and maintenance management data between the station control layer, bay layer, and process layer; according to the multi-level data monitoring status evaluation system, divide the abnormality evaluation result into abnormalities at the measurement point level, device level, or function level, and determine the severity level of the abnormality.

[0070] In one embodiment, step S500 specifically includes: 1). Determine the operation and maintenance management data from the station-side data section, including: Determine the device composition and functions of the station control layer (such as monitoring hosts and telecontrol devices), bay layer (such as protection devices and measurement and control devices), and process layer (such as merging units and intelligent terminals) of the intelligent substation, and clarify the characteristics of data interaction between each layer. In the station-side data section, filter out the data related to operation and maintenance management between the station control layer, bay layer, and process layer according to information such as the device identification, communication address, and data type of the data; for example, find the communication connection information between devices of each layer from the communication logs, and extract the operation status and fault alarm information from the device status data.

[0071] 2) Build a multi-level data monitoring status evaluation system, including: (1) Design typical fault event cases according to the historical fault records and common fault types of the intelligent substation, such as communication interruption between the station control layer and the bay layer, and abnormal process layer device data. In the simulation environment, inject fault events into the system according to the designed fault scenarios, and observe the changes in operation and maintenance management data.

[0072] (2) Classify the operation and maintenance management data collected after injecting faults according to the degree of newness (such as newly generated data, historical data), fault types (communication faults, equipment faults, etc.), and function types (monitoring functions, control functions, etc.). Analyze the correlations between the operation and maintenance management data of different types of devices and systems, and build the topological relationship and logical relationship between them; for example, establish the topological structure of the communication link and the logical relationship of fault propagation by analyzing the data interaction between devices of each layer during communication faults.

[0073] (3) Based on the topological relationship and logical relationship between the operation and maintenance management data, extract the intelligent substation event model, which can describe the change rules and mutual influences of data under different fault scenarios. Classify the formed intelligent substation event model, and divide it into a multi-level data monitoring status evaluation system at the measurement point level (such as data abnormality of a single sensor), device level (such as a certain protection device failure), and function level (such as the failure of the entire monitoring function).

[0074] 3) Evaluate the operation and maintenance management data based on the evaluation system (1) Match the sorted operation and maintenance management data with the event model in the multi-level data monitoring status evaluation system to find the corresponding fault scenarios and evaluation indicators for the current data. Calculate and analyze the operation and maintenance management data according to the matched evaluation indicators; for example, calculate indicators such as the duration of communication interruption and the occurrence frequency of equipment faults.

[0075] (2)Based on the calculated index values, evaluate the status of the operation and maintenance management data between devices at each layer, and determine whether there are any abnormal situations. According to the multi-level data monitoring status evaluation system, classify the evaluation results into abnormalities at the measurement point level, device level, or function level, and determine the severity level of the abnormalities.

[0076] 4). Obtain the multi-level operation and maintenance data evaluation results Summarize the evaluation results of the operation and maintenance management data between devices at each layer to form a comprehensive multi-level operation and maintenance data evaluation report.

[0077] S600. Integrate and display the causes of defect data, the impacts of defect data, accuracy, robustness, the evaluation results of data flow quality, and the multi-level operation and maintenance data evaluation results to obtain a visual display report.

[0078] In one embodiment, step S600 specifically includes: 1). Data collection and collation, including: (1) Collect data such as the causes of defect data, the impacts of defect data, the evaluation results of the accuracy and robustness of function type data, the evaluation results of data flow quality, and the multi-level operation and maintenance data evaluation results.

[0079] (2) Classify the collected data. For example, classify the causes and impacts related to defect data into one category, and the evaluation results of function type data into one category, etc. Unify the format and unit of the data to make the data from different sources comparable; for example, unify all evaluation scores into a 100-point system, or unify the time-related data into the same time format.

[0080] 2). Determine the display dimensions and indicators, including: (1) Identify the audience of the visual display report, such as operation and maintenance personnel, management personnel, etc., and understand their concerns and needs regarding the data. Based on the audience's needs and the evaluation purpose, determine the key dimensions and indicators to be displayed; for example, for operation and maintenance personnel, they may be more concerned about the specific causes of defect data and the evaluation results at the device level; for management personnel, they may be more concerned about the overall evaluation scores and trends.

[0081] (2) Based on the analysis results, determine the specific display content under each dimension. For example, in terms of defect data, display the cause distribution and impact scope of different types of defects; in terms of the evaluation of function type data, display the scores of various indicators for accuracy and robustness.

[0082] 3). Select the visualization method, including: Select appropriate visualization charts for different types of data; for example, for the distribution of the causes of defect data, pie charts or bar charts can be used; for the trend analysis of evaluation results, line charts can be used. For multi-dimensional data or data with a hierarchical structure, tree charts, heat maps, radar charts, etc. can be used for display.

[0083] 4), Generate a visualization report, including: Use professional visualization tools (such as Tableau, PowerBI, etc.) or programming languages (such as the Matplotlib and Seaborn libraries in Python) to draw charts. Necessary text descriptions and annotations can be added next to the charts to explain the meaning of the charts, the definitions of the metrics, and the sources of the data. Integrate all the charts, text descriptions, and analysis content into a report and arrange them in a logical order. Export the report in common formats, such as PDF, PPT, or HTML, etc.

[0084] For the above solution, for the data of the secondary system, systematically screen for data defects of devices such as measurement and control devices and time synchronization devices through indicators such as integrity and accuracy; for functional type data, evaluate its accuracy and robustness in combination with transmission delay, processing results, and interference simulation; analyze the data flow quality of the main and auxiliary devices from dimensions such as transmission range, frequency, and protocol, avoiding isolated analysis of single indicators.

[0085] Multi-level collaborative evaluation: Establish an operation and maintenance management data evaluation system including the measurement point level, device level, and function level. Build an event model by injecting typical fault events, analyze the topology and logical relationships of cross-layer data interactions, and achieve hierarchical correlation evaluation from device anomalies to system function failures, solving the problem of missing cross-layer collaborative evaluation.

[0086] Full-process data link parsing to cope with complex scenario challenges: Panoramic acquisition of data sections: Integrate real-time and historical data, support communication configuration and connection verification of multi-protocol devices such as IEC 61850 and Modbus, ensure data integrity through polling and subscription mechanisms, and provide a unified and reliable data basis for full-process evaluation.

[0087] In-depth analysis of the transmission process: Trace the data transmission path based on the network topology and communication logs, calculate the node processing time to identify the delay bottleneck; simulate interference environments such as packet loss and bit errors, and count the data transmission success rate and processing correct rate to verify the anti-interference ability under complex network conditions, filling the evaluation gap of traditional methods in extreme scenarios.

[0088] Deep attribution and impact quantification to improve the risk identification ability: Defect cause mining: Use data mining techniques (such as association rules) to analyze the potential relationships between defect data, device status, and environmental parameters, and locate the deep - seated incentives (such as the association between abnormal protection device data and GPS signal interruption) to avoid superficial fault location.

[0089] System - level impact assessment: From two aspects, namely the functions of the secondary system (such as incorrect data of the measurement and control device leading to deviation in dispatching decisions) and the stability of power grid operation (such as abnormal PMU data affecting the whole - network stability monitoring), quantify the scope and consequences of defect data, establish a risk - level classification standard, and provide accurate basis for formulating operation and maintenance strategies.

[0090] Embodiment 2 As Figure 2 shown, based on the same inventive concept as the above - mentioned embodiment, the present invention also provides a panoramic evaluation device for the station - end data section, including: A data acquisition module, used to acquire the station - end data section of the intelligent substation; A first evaluation module, used to determine the secondary system data according to the station - end data section; evaluate the secondary system data, determine the defect data in the secondary system data, and analyze the defect data to obtain the defect data causes and defect data impacts; A second evaluation module, used to determine the transmission process and transmission results of the function - type data according to the station - end data section; evaluate the function - type data according to the transmission process and transmission results to obtain the accuracy and robustness of the function - type data; wherein, the function - type data includes setting data, blocking data, substitution data, and out - of - limit data; A third evaluation module, used to determine the data - flow transmission characteristic tuples of the main and auxiliary equipment according to the station - end data section; evaluate the data - flow transmission characteristic tuples to obtain the data - flow quality evaluation results; wherein, the data - flow transmission characteristic tuples include the transmission range, content, frequency, and protocol of the data flow; A fourth evaluation module, used to determine the operation and maintenance management data among the station control layer, bay layer, and process layer in the intelligent substation according to the station - end data section; evaluate the operation and maintenance management data based on a preset multi - level data monitoring status evaluation system to obtain multi - level operation and maintenance data evaluation results; wherein, the operation and maintenance management data includes full - caliber operation and maintenance data, maintenance data, and joint - debugging data; A comprehensive display module, used to integrally display the defect data causes, defect data impacts, accuracy, robustness, data - flow quality evaluation results, and multi - level operation and maintenance data evaluation results to obtain a visual display report.

[0091] Embodiment 3 As Figure 3 shown, the present invention also provides an electronic device 100 for implementing the panoramic evaluation method of the station - end data section; The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0092] The memory 101 can be used to store the computer program 103. By running or executing the computer program stored in the memory 101 and invoking the data stored in the memory 101, the processor 102 implements the steps of the method for panoramic evaluation of the station - end data section in Embodiment 1.

[0093] The memory 101 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the electronic device 100 (such as audio data, etc.). In addition, the memory 101 can include non - volatile memory, such as a hard disk, a memory, a plug - in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other non - volatile solid - state storage devices.

[0094] The at least one processor 102 can be a Central Processing Unit (CPU), or can also be other general - purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field - Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 can be a microprocessor or the processor 102 can also be any conventional processor, etc. The processor 102 is the control center of the electronic device 100, connecting all parts of the entire electronic device 100 through various interfaces and lines.

[0095] The memory 101 in the electronic device 100 stores multiple instructions to implement a method for panoramic evaluation of the station - end data section. The processor 102 can execute the multiple instructions to implement: Obtain the station - end data section of the intelligent substation; Determine the secondary system data according to the station - end data section; evaluate the secondary system data, determine the defective data in the secondary system data, and analyze the defective data to obtain the causes and impacts of the defective data; Determine the transmission process and transmission result of functional type data according to the station - side data section; evaluate the functional type data based on the transmission process and transmission result to obtain the accuracy and robustness of the functional type data; wherein, the functional type data includes tagging data, blocking data, substitution data, and out - of - limit data. Determine the data stream transmission characteristic tuples of the main and auxiliary equipment according to the station - side data section; evaluate the data stream transmission characteristic tuples to obtain the data stream quality evaluation result; wherein, the data stream transmission characteristic tuples include the transmission range, content, frequency, and protocol of the data stream. Determine the operation and maintenance management data between the station control layer, bay layer, and process layer in the intelligent substation according to the station - side data section; evaluate the operation and maintenance management data based on a preset multi - level data monitoring status evaluation system to obtain the multi - level operation and maintenance data evaluation result; wherein, the operation and maintenance management data includes full - caliber operation and maintenance data, maintenance data, and joint debugging data. Integrate and display the defect data causes, defect data impacts, accuracy, robustness, data stream quality evaluation result, and multi - level operation and maintenance data evaluation result to obtain a visual display report.

[0096] Embodiment 4 If the modules / units integrated in the electronic device 100 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer - readable storage medium. Based on this understanding, to implement all or part of the processes in the above - mentioned embodiment methods of the present invention, it can also be completed by a computer program instructing related hardware. The computer program can be stored in a computer - readable storage medium. When the computer program is executed by a processor, the steps of the above - mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer - readable medium can include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, and read - only memory (ROM, Read - Only Memory).

[0097] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer - usable storage media (including but not limited to disk storage, CD - ROM, optical storage, etc.) containing computer - usable program code.

[0098] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices generate means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0099] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0101] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A panoramic evaluation method for the data section of the station end, characterized in that Including the following steps: Obtain the station - end data section of the intelligent substation; Determine the secondary system data according to the station - end data section; Evaluate the secondary system data, determine the defective data in the secondary system data, and analyze the defective data to obtain the causes and impacts of the defective data; Determine the transmission process and transmission results of the function - type data according to the station - end data section; Evaluate the function - type data based on the transmission process and transmission results to obtain the accuracy and robustness of the function - type data; Among them, the function - type data includes tagging data, blocking data, substitution data, and out - of - limit data; Determine the data - flow transmission characteristic tuples of the primary and auxiliary equipment according to the station - end data section; Evaluate the data - flow transmission characteristic tuples to obtain the data - flow quality evaluation results; Among them, the data - flow transmission characteristic tuples include the transmission range, content, frequency, and protocol of the data flow; Determine the operation and maintenance management data among the station control layer, bay layer, and process layer in the intelligent substation according to the station - end data section; Evaluate the operation and maintenance management data based on a preset multi - level data monitoring status evaluation system to obtain the multi - level operation and maintenance data evaluation results; Among them, the operation and maintenance management data includes full - caliber operation and maintenance data, maintenance data, and joint - debugging data; Integrate and display the causes of defective data, the impacts of defective data, accuracy, robustness, data - flow quality evaluation results, and multi - level operation and maintenance data evaluation results to obtain a visual display report.

2. The method according to claim 1, wherein Evaluate the secondary system data, determine the defective data in the secondary system data, and analyze the defective data to obtain the causes and impacts of the defective data, including: Calculate the values of each evaluation index of the secondary system data under the evaluation index system by using statistical analysis methods according to the pre - constructed evaluation index system; Among them, the evaluation indexes include data integrity, data accuracy, data timeliness, and data consistency; Judge the values of each evaluation index based on a preset threshold to determine the defective data in the secondary system data; Use data mining methods to analyze the association rules between the defective data and each data in the station - end data section, and determine the causes of the defective data according to the association rules; Evaluate the influence degree and scope of the defective data on the various functions of the secondary system, as well as evaluate the impact consequences of the defective data on the stable operation of the power grid; Determine the impacts of the defective data according to the influence degree, influence scope, and impact consequences.

3. The method according to claim 1, wherein Determine the transmission process and transmission results of the function - type data according to the station - end data section; Evaluate the function - type data based on the transmission process and transmission results to obtain the accuracy and robustness of the function - type data, including: Identify the function - type data based on the identification, format, and association information of the data in the station - end data section, and extract the function - type data in the order of data transmission time; Determine the source device and the target device for the transmission of functional type data, as well as the network nodes through which the functional type data passes when transmitted between the source device and the target device, based on network topology information and communication logs; determine the processing time of the functional type data at each network node; compare the functional type data received by the target device with the functional type data sent by the source device to determine the complete reception situation of the functional type data; and, determine the processing result of the target device on the functional type data; determine the transmission process delay based on the processing time of the functional type data at each network node; evaluate the accuracy of the functional type data based on the transmission process delay, the complete reception situation, and the processing result; Simulate a network interference environment, determine the transmission process and transmission result of the functional type data in the network interference environment, and evaluate the robustness of the functional type data based on the transmission process and transmission result in the network interference environment.

4. The method according to claim 1, wherein Evaluate the data stream transmission feature tuple to obtain the data stream quality evaluation result, including: Evaluate the transmission range of the data stream, including: check whether the data is transmitted between specified devices according to a preset standard to obtain a transmission range score; Evaluate the content of the data stream, including: compare the data with the measured value of the actual physical quantity to check whether there are deviations, errors, or omissions in the data to obtain a content score; Evaluate the frequency of the data stream, including: compare the actual value of the frequency with the preset frequency range to obtain a frequency score; Evaluate the protocol of the data stream, including: check whether the data stream follows the adopted communication protocol to obtain a protocol score; Perform a weighted sum of the transmission range score, the content score, the frequency score, and the protocol score to obtain a comprehensive data stream score; divide the data stream quality into different levels according to the comprehensive data stream score to obtain the data stream quality evaluation result.

5. The method according to claim 1, wherein In the steps of evaluating the operation and maintenance management data based on a preset multi-level data monitoring status evaluation system, the multi-level data monitoring status evaluation system is constructed as follows: In a simulation environment, inject faults into the system according to fault scenarios to obtain the operation and maintenance management data after the faults are injected; Classify the operation and maintenance management data after the faults are injected according to the degree of newness, the type of fault, and the functional type to obtain classified data; Analyze the correlation relationship between the operation and maintenance management data of different types of devices and systems based on the classified data, and construct the topological relationship and logical relationship between the operation and maintenance management data according to the correlation relationship; Extract an intelligent substation event model based on the topological relationship and logical relationship between the operation and maintenance management data; among them, the intelligent substation event model is used to describe the change rules and mutual influences of data under different fault scenarios; Classify the intelligent substation event model into levels to divide a multi-level data monitoring status evaluation system at the measurement point level, device level, and functional level.

6. The method according to claim 5, characterized in that, Evaluate the operation and maintenance management data based on a preset multi-level data monitoring status evaluation system, including: Match the determined operation and maintenance management data with the event model in the multi-level data monitoring status evaluation system to determine the fault scenario and evaluation indicators matching the current operation and maintenance management data; Calculate the operation and maintenance management data according to the matching evaluation indicators, and determine the evaluation results of the abnormal conditions of the operation and maintenance management data among the station control layer, bay layer, and process layer based on the calculated indicator values; according to the multi-level data monitoring status evaluation system, classify the evaluation results of the abnormal conditions into point-level, device-level, or function-level abnormalities, and determine the severity level of the abnormalities.

7. The method according to claim 1, wherein Obtain the station-side data section of the intelligent substation, including: Collect the real-time data and historical data of the station-side data of the intelligent substation; Determine the data section time point, and extract the data corresponding to the data section time point from the real-time data and historical data to form a data section.

8. A panoramic evaluation device for the data section of the station end, characterized in that, Including: A data acquisition module for obtaining the station-side data section of the intelligent substation; A first evaluation module for determining the secondary system data according to the station-side data section; Evaluate the secondary system data, determine the defective data in the secondary system data, and analyze the causes and impacts of the defective data; A second evaluation module for determining the transmission process and transmission results of the function type data according to the station-side data section; Evaluate the function type data according to the transmission process and transmission results to obtain the accuracy and robustness of the function type data; among them, the function type data includes tagging data, blocking data, substitution data, and overlimit data; A third evaluation module for determining the data flow transmission characteristic tuple of the main and auxiliary equipment according to the station-side data section; evaluate the data flow transmission characteristic tuple to obtain the data flow quality evaluation result; among them, the data flow transmission characteristic tuple includes the transmission range, content, frequency, and protocol of the data flow; A fourth evaluation module for determining the operation and maintenance management data among the station control layer, bay layer, and process layer in the intelligent substation according to the station-side data section; evaluate the operation and maintenance management data based on the preset multi-level data monitoring status evaluation system to obtain the multi-level operation and maintenance data evaluation result; among them, the operation and maintenance management data includes full-caliber operation and maintenance data, maintenance data, and joint commissioning data; A comprehensive display module for integrating and displaying the causes of defective data, the impacts of defective data, accuracy, robustness, the data flow quality evaluation result, and the multi-level operation and maintenance data evaluation result to obtain a visual display report.

9. An electronic device, characterized in that, Including a processor and a memory, the processor is used to execute the computer program stored in the memory to implement the station-side data section panoramic evaluation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, the station-side data section panoramic evaluation method according to any one of claims 1 to 7 is implemented.

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