Fault self-checking method, system and equipment for monitoring terminal of modular substation based on electric red-ong, and medium

Abstract

The invention discloses a modular substation monitoring terminal fault self-checking method, system and equipment based on an electric red-ong, and a medium, and belongs to the technical field of substation monitoring, and the method comprises the steps of executing module cooperative self-checking, extracting abnormal characteristic parameters in real time, executing link interactive self-checking, and integrating and actively reporting a self-checking result. The method solves the problems of self-inspection lagging and passive receiving in the prior art, realizes module cooperative detection and active reporting of abnormity, improves the operation reliability of a transformer substation monitoring system, and is suitable for a fault detection scene of a modular transformer substation monitoring terminal.

Description

Technical Field

[0001] This invention relates to the field of substation monitoring technology, specifically to a method, system, equipment, and medium for fault self-testing of a modular substation monitoring terminal based on Dianhong. Background Technology

[0002] Existing modular substation monitoring terminals often employ a single-module independent detection mode for fault self-checking. This means the main control module and the monitoring module each perform their own self-checks, lacking a collaborative mechanism. This results in the inability to promptly detect faults caused by abnormal inter-module interactions. Furthermore, the transmission of self-check results relies on periodic manual queries or passive reception by the monitoring center. When a terminal malfunctions, it cannot proactively push abnormal information to the monitoring center, leading to a significant delay in fault detection. This delay can cause the substation monitoring system to remain in an unsafe operating state even after a fault occurs, increasing the risk of equipment damage and maintenance costs.

[0003] Based on the above problems, there is an urgent need for a fault self-inspection method that can realize module collaborative self-inspection and proactive anomaly reporting, so as to solve the problems of lag and insufficient collaboration of existing self-inspection schemes. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, this invention aims to solve the problems of delayed fault warning, rigid self-testing process with low resource efficiency, and insufficient data integration and transmission security. To solve the above-mentioned technical problems, the present invention provides the following technical solution: a self-diagnosis method for faults in a modular substation monitoring terminal based on Dianhong, comprising, The system performs collaborative self-tests, initiating self-test initialization operations for both the main control module and the monitoring module of the modular substation monitoring terminal. It also extracts abnormal feature parameters in real time, performs link interaction self-tests, and conducts data transmission detection and health assessment of the connection link between the main control module and the monitoring module based on the communication protocol of the Dianhong system. Finally, it integrates the self-test results, combining the module collaborative self-test results, abnormal feature parameter extraction results, and link interaction self-test results into a unified self-test result and actively reports it.

[0006] As a preferred embodiment of the modular substation monitoring terminal fault self-testing method based on Dianhong described in this invention, wherein: before performing the module collaborative self-test, a power-on verification is performed; The Dianhong system sends a self-test start command to the main control module. After receiving the self-test start command, the main control module sends an interaction signal to the monitoring module. After receiving the interaction signal, the monitoring module sends a response signal back to the main control module. The main control module transmits the response signal to the Elec-Tech system, which then determines whether to trigger a collaborative self-test based on the response signal.

[0007] As a preferred embodiment of the fault self-detection method for a modular substation monitoring terminal based on Dianhong described in this invention, the step of extracting abnormal feature parameters includes: extracting abnormal feature parameters in real time and then performing preprocessing on the abnormal feature parameters. Preprocessing includes performing data denoising and data standardization.

[0008] As a preferred embodiment of the fault self-testing method for a modular substation monitoring terminal based on Dianhong as described in this invention, the link interaction self-testing includes: the Dianhong system sending a link detection command to the main control module; the main control module generating a test data packet according to the link detection command; the main control module transmitting the test data packet to the monitoring module; and the monitoring module receiving and parsing the test data packet. The detection feedback data is generated based on the analysis results and transmitted to the main control module. The main control module then transmits the detection feedback data to the Dianhong system, which determines the status of the connection link based on the detection feedback data.

[0009] As a preferred embodiment of the fault self-testing method for a modular substation monitoring terminal based on Dianhong described in this invention, the preprocessing of abnormal feature parameters includes calculating the health values ​​of the main control module and the monitoring module respectively using the module health evaluation formula for the preprocessed abnormal feature parameters. The module health assessment formula combines the weighting of operating parameters, differences in module type, and the impact of runtime to obtain a quantitative value reflecting the module's operating status. Based on the module health value obtained from the module health assessment formula, the main control module and the monitoring module are judged to have potential faults by the abnormal feature fusion discrimination formula. The calculation logic of the abnormal feature fusion discrimination formula is to comprehensively consider the health deviation, parameter change rate deviation and temperature deviation to obtain a discrimination quantification value reflecting the degree of module abnormality.

[0010] The beneficial effects of the preferred technical solution in the embodiments of the present invention are as follows: by quantitatively evaluating the health of the module and judging the multi-dimensional anomalies, accurate early warning of potential module faults is achieved, thereby improving the accuracy and timeliness of fault identification.

[0011] As a preferred embodiment of the fault self-testing method for a modular substation monitoring terminal based on Dianhong described in this invention, the method for obtaining the quantification value reflecting the degree of module abnormality includes: obtaining the module abnormality discrimination value by combining the abnormal feature fusion discrimination formula; and calculating the adaptability coefficient of the connection link between the main control module and the monitoring module by using the link interaction adaptation coefficient formula. The link interaction adaptation coefficient formula is a quantification value reflecting the degree of link adaptability by combining the abnormal state of the module, the link transmission performance and environmental interference. The Elec-Link system determines the self-test frequency of the connection link based on the link interaction adaptation coefficient obtained from the link interaction adaptation coefficient formula. When the link interaction adaptation coefficient is greater than the preset adaptation threshold, the self-test frequency of the connection link is reduced; when the link interaction adaptation coefficient is less than or equal to the preset adaptation threshold, the self-test frequency of the connection link is increased.

[0012] The beneficial effects of the preferred technical solution in the embodiments of the present invention are as follows: the self-test frequency is dynamically adjusted according to the link adaptability coefficient, which optimizes the allocation of system resources and improves the efficiency and adaptability of the self-test process while ensuring the reliability of the connection.

[0013] As a preferred embodiment of the modular substation monitoring terminal fault self-testing method based on Dianhong described in this invention, the active reporting includes, when performing active reporting, performing an encryption processing step on the unified self-test result, using the encryption algorithm preset by the Dianhong system to encrypt the unified self-test result, and transmitting the encrypted unified self-test result to the substation monitoring center. The test data packet includes a preset checksum and a data identifier. The data identifier contains the module number of the monitoring terminal and the data acquisition time information. The module number is the unique identifier of the modular substation monitoring terminal, and the data acquisition time information is used to mark the generation time of the test data packet.

[0014] The preferred technical solution in the embodiments of the present invention has the following advantages: the use of encrypted transmission and structured data identification enhances the security and traceability of the transmission of self-inspection results, and ensures the integrity and reliability of monitoring data.

[0015] Another objective of this invention is to provide a fault self-diagnosis system for a modular substation monitoring terminal based on Dianhong.

[0016] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a modular substation monitoring terminal fault self-testing system based on Dianhong, comprising: a self-testing module, a detection module, and an anomaly reporting module; The self-test module performs collaborative self-testing, initiating self-test initialization operations for the main control module and monitoring module of the modular substation monitoring terminal, respectively. The detection module extracts abnormal feature parameters in real time, performs link interaction self-test, and performs data transmission detection on the connection link between the main control module and the monitoring module based on the communication protocol of the Elec-Tech system. The anomaly reporting module integrates self-inspection results, combining the module collaborative self-inspection results, anomaly feature parameter extraction results, and link interaction self-inspection results into a unified self-inspection result, and then performs proactive reporting.

[0017] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the aforementioned method for fault self-testing of a modular substation monitoring terminal based on electric power.

[0018] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the aforementioned method for fault self-testing of a modular substation monitoring terminal based on Dianhong.

[0019] The beneficial effects of this invention are as follows: This invention achieves linkage detection between the main control module and the monitoring module through module collaborative self-testing, solving the problem that independent detection by a single module cannot detect interaction anomalies; it automatically transmits information when the self-test result is abnormal through an active reporting mechanism, solving the lag problem of existing solutions relying on manual queries; at the same time, it combines anomaly feature quantitative analysis and link adaptation detection to further improve the accuracy of fault identification and link reliability, effectively ensuring the safe and stable operation of the substation monitoring system and meeting the real-time and collaborative requirements of modular substations for fault self-testing. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 The above is a flowchart of a fault self-testing method for a modular substation monitoring terminal based on Dianhong, provided as an embodiment of the present invention. Detailed Implementation

[0022] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0023] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a fault self-testing method for a modular substation monitoring terminal based on Dianhong, including: S100, Execution module collaborative self-test, initiates self-test initialization operations for the main control module and monitoring module of the modular substation monitoring terminal respectively; S200: Extract abnormal feature parameters in real time, perform link interaction self-check, and perform data transmission detection and health assessment on the connection link between the main control module and the monitoring module based on the communication protocol of the Dianhong system. S300: Integrate self-inspection results, combine module collaborative self-inspection results, abnormal feature parameter extraction results, and link interaction self-inspection results into a unified self-inspection result, and perform proactive reporting; It should be noted that in the existing technology, the fault self-check of modular substation monitoring terminals usually relies on periodic polling or simple status reporting, which has the defects of delayed fault warning, rigid self-check process and low resource efficiency, as well as insufficient data integration and transmission security.

[0024] Therefore, to address the aforementioned issues, steps S100–S300 were used to achieve accurate and proactive early warning of potential module faults. Dynamic strategies were employed to optimize the allocation of self-test resources and system adaptability, and the integrity and security of self-test results during transmission were ensured. This resulted in the construction of an efficient, intelligent, and reliable terminal fault self-testing system.

[0025] Example 2, refer to Figure 1 This is one embodiment of the present invention, which provides a fault self-testing method for a modular substation monitoring terminal based on Dianhong, including: In this embodiment of the invention, S100 performs a collaborative self-test, initiating self-test initialization operations for the main control module and monitoring module of the modular substation monitoring terminal, including the following steps S101-S102: S101. Before the collaborative self-test of the execution module, perform a power-on verification. The Dianhong system sends a self-test start command to the main control module. After receiving the self-test start command, the main control module sends an interaction signal to the monitoring module. After receiving the interaction signal, the monitoring module sends a response signal back to the main control module. The main control module transmits the response signal to the Elec-Tech system, which then determines whether to trigger a collaborative self-test based on the response signal.

[0026] Specifically, the process of performing the power-on verification step before the collaborative self-test of the execution module is as follows: The Elec-Power system first obtains the power supply voltage of the main control module through the voltage detection pin. If the voltage is within the rated range, such as DC 12V ± 5%, it sends a self-test start command to the main control module. The command adopts a binary encoding format, including an 8-bit command header fixed at 0xAA, an 8-bit command type, such as 0x01 to indicate power-on verification start, a 16-bit data length of 0x0004, a 32-bit timestamp accurate to the second, and an 8-bit checksum using XOR verification. After receiving the instruction, the main control module first verifies whether the checksum is correct, and then sends an interaction signal to the monitoring module through the I2C bus. The interaction signal contains the device address of the main control module, such as 0x01, the current power supply voltage value, and the instruction sending time. After receiving the interaction signal, if the monitoring module's own power supply voltage is normal, such as DC 5V±5%, it will generate a response signal. The response signal includes the monitoring module's device address, such as 0x02, its own power supply voltage value, and response time. If the power supply voltage is abnormal or the interaction signal cannot be received, no response signal will be generated. The monitoring module sends the response signal back to the main control module via the same I2C bus. The main control module then summarizes its own status and the response signal from the monitoring module and transmits it to the Elec-Tech system via the SPI bus. When the Dianhong system parses the transmitted data, if it simultaneously obtains the normal power supply voltage of the main control module and the response signal of the monitoring module, it determines that the two modules have the conditions for self-testing and triggers the modules to perform a collaborative self-test. Otherwise, it generates an abnormal prompt, such as the monitoring module not responding, failing to start the self-test, and storing it in the log file.

[0027] In existing technologies, the extracted abnormal feature parameters often contain noise data such as instantaneous current fluctuations and signal noise caused by electromagnetic interference. Furthermore, the dimensions and formats of different parameters are not uniform, for example, voltage is in V and time delay is in ms. Directly using these parameters for anomaly judgment will lead to large deviations in the results and make it impossible to accurately identify faults.

[0028] S102, Module Collaborative Self-Test initiates self-test initialization operations for the main control module and monitoring module of the modular substation monitoring terminal, respectively. Specifically, when performing link interaction self-test, in the embodiments of the present invention, based on the industrial Ethernet communication protocol preset by the Elec-Hong system, such as Profinet, the Elec-Hong system first sends a link detection command to the main control module. The command specifies the test data packet size, such as 1024 bytes, and the number of transmissions, such as 3 times. In an optional embodiment, the communication protocol of the core control carrier of the S102 system can be a link interaction self-test based on the Modbus TCP / IP protocol. However, this implementation method is insufficient to support operation in scenarios with complex object model descriptions and extremely high real-time requirements.

[0029] In another optional embodiment, the communication protocol of the core control carrier of the S102 system can also be a link interaction self-test based on the EtherNet / IP CIP protocol. However, this implementation method has a relatively complex protocol stack and requires high processing power and configuration complexity of the terminal device, and is not suitable for the present invention.

[0030] The Dianhong system serves as the core control carrier, first sending a self-test initialization command containing an instruction identifier and a timestamp to the main control module to ensure the uniqueness and timeliness of the command; After receiving the instruction, the main control module first verifies the legality of the instruction through its built-in verification logic, and then generates a coordination signal containing its current power-on status, such as whether the voltage is stable within the rated range, and sends it synchronously to the monitoring module. After receiving the coordination signal, if the monitoring module is in a normal power-on state, it will immediately return a response signal containing its own status code. If it is not powered on or there is a hardware abnormality, it will not return a response or will return an abnormal status code. The Dianhong system only triggers the synchronous execution of self-test initialization operations by both the monitoring module and the main control module after receiving normal response signals simultaneously, thus avoiding interruption of the self-test process due to an abnormality in a single module.

[0031] In this embodiment of the invention, S200 involves real-time extraction of abnormal feature parameters, execution of link interaction self-check, and data transmission detection of the connection link between the main control module and the monitoring module based on the communication protocol of the Elec-Tech system, as well as a health assessment, including the following steps S201-S206: S201. When extracting abnormal feature parameters in real time, the Dianhong system collects parameters through a dedicated data interface with the main control module and monitoring module, such as the RS485 interface. The main control module's operating parameters include input voltage (V), operating current (A), and processor utilization rate (dimensionless, expressed as a percentage). The monitoring module's operating parameters include image acquisition frame rate (fps), lens focal length (mm), and signal reception strength (dBm). The inter-module interaction parameters include data transmission latency (ms), command response rate (times / second), and data packet loss rate (dimensionless, expressed as a percentage). The sampling frequency is set to once per second to ensure the real-time accuracy of the parameters. During the data acquisition process, parameters are temporarily stored using the built-in data caching unit of the Dianhong system to prevent data loss.

[0032] S202. After extracting abnormal feature parameters in real time, perform preprocessing on the abnormal feature parameters, including data denoising and data standardization.

[0033] Specifically, the preprocessing step performed after real-time extraction of abnormal feature parameters is as follows: In an embodiment of the present invention, during the data denoising stage, an adaptive filtering algorithm is employed. First, frequency analysis is performed on the collected abnormal feature parameters. Then, the parameters are transformed to the frequency domain using a Fast Fourier Transform (FFT) to identify the noise type. For high-frequency noise, such as instantaneous current fluctuations with a frequency greater than 10Hz, a moving average filter with a window size of 5 is used. The calculation formula is as follows: in, For the k-th original parameter value, The value of the i-th parameter after filtering is used to quickly eliminate transient noise; For low-frequency noise, such as slow voltage drift with a frequency less than 1 Hz, a moving average filter with a window size of 20 is used to smooth the parameter change trend through a larger window and avoid loss of effective data. During the filtering process, the algorithm adjusts the window size in real time according to the parameter frequency change. For every 5 Hz increase in frequency, the window size decreases by 2, with a minimum window size of 3, to ensure that the filter is adapted to different noise characteristics.

[0034] In an optional embodiment, data denoising in S202 can be achieved by fixed threshold judgment filtering. The Dianhong system presets fixed normal value range thresholds for various abnormal feature parameters. After data acquisition, the system compares each real-time parameter value with the corresponding fixed threshold range. If the parameter value falls within the threshold range, it is determined to be valid data and directly retained. If the parameter value exceeds the threshold range, the data point is immediately marked as noise and replaced with the valid value collected in the previous cycle or the historical average value over a period of time, thereby achieving data denoising. However, this implementation method has poor adaptability, cannot handle fluctuation noise within the threshold range, and misjudges normal parameter drift or sudden faults, and is not applicable to the present invention.

[0035] In another optional embodiment, data denoising in S202 can also be performed as simple statistical filtering. In the data caching unit, the Elec-Tech system maintains a fixed-length recent data queue for each abnormal feature parameter. Each time a new parameter value is collected, the system calculates the arithmetic mean and standard deviation of all data in the corresponding queue. The newly collected value is compared with a dynamic range determined by the mean ± 2 times the standard deviation. If the new value is within this range, it is accepted as valid data and the queue is updated; if the new value exceeds this range, it is determined to be noisy data, and the newly acquired value is replaced with the currently calculated mean, thus completing the denoising. However, this implementation is not effective for filtering instantaneous, short-lived spike noise, and the calculation of statistics can be affected by historical noise data, impacting the filtering accuracy. Therefore, it is not suitable for this invention.

[0036] In the embodiments of this invention, during the data standardization stage, parameter mapping rules are established: for continuous numerical parameters such as voltage (V) with a value range of 5-15V and current (A) with a value range of 0.1-2A, a linear normalization formula is adopted: in, This is the minimum rated value of the parameter. To obtain the maximum rated value of the parameter, convert the parameter into dimensionless data in the range of 0-1; For count-type parameters such as frame rate (fps, range 15-30fps) and response rate (Hz, range 10-50Hz), first calculate the ratio to the rated value. For example, the formula for calculating the frame rate ratio is: in, The parameter is set to a fixed value, and then the ratio is mapped to the 0-1 range; The standardized data are all stored as 32-bit floating-point numbers, retaining 6 decimal places to ensure the accuracy of subsequent quantization calculations.

[0037] In an optional embodiment, data standardization in S202 can be based on threshold segmentation mapping. According to a preset rated threshold range, the abnormal feature parameters are divided into three segments: normal, warning, and abnormal. The parameter values ​​in each segment are independently linearly mapped: the parameters in the normal segment are mapped to the 0.3-0.7 range, the warning segment is mapped to the 0-0.3 or 0.7-1 range, and the abnormal segment is fixedly mapped to 0 or 1. The mapped data is uniformly converted to a 32-bit floating-point format, retaining 6 decimal places, and stored in the Dianhong system. However, this implementation relies on fixed threshold segments. When the parameters are affected by temporary fluctuations due to interference from the field environment, the mapping results will frequently change, reducing the stability of standardization.

[0038] In another optional embodiment, data standardization in S202 can also be based on data standardization using a sliding window dynamic benchmark. Parameters are dynamically collected using a sliding time window, and the average value of the parameters within the window is calculated in real time as the dynamic benchmark value. The real-time parameters are compared with the dynamic benchmark value: if the parameter value is within the preset offset range of the benchmark value, it is directly mapped to 0.5; if it exceeds the offset range, it is linearly mapped to the 0-0.5 or 0.5-1 range according to the excess ratio. The larger the offset, the closer the mapped value is to 0 or 1. During the mapping process, the update log of the dynamic benchmark value is recorded synchronously. The standardization result is stored as a 32-bit floating-point number, retaining 6 decimal places for use by the health assessment module. However, in this implementation, the dynamic benchmark value drifts along with the parameters as they drift continuously and slowly, causing abnormal parameters to be mismapped as normal, reducing the sensitivity of fault identification.

[0039] In existing technologies, link self-testing only checks the connectivity of the link by determining whether feedback data has been received. It does not verify the integrity and accuracy of data transmission, and cannot detect data loss, errors, or excessive delays in the link transmission. As a result, hidden link faults cannot be identified, affecting the reliability of data interaction between modules.

[0040] S203. The main control module transmits data packets to the monitoring module via the connection link. After receiving the data packets, the monitoring module first calculates the data packet checksum and compares it with the header checksum. Then, it extracts the data identifier to confirm the terminal's ownership. If the checksum matches and the identifier matches, it generates detection feedback data containing the checksum result and the reception time. Otherwise, it marks the error type, such as checksum failure or identifier mismatch, and generates feedback data. The monitoring module sends the feedback data back to the main control module. The main control module summarizes the data and transmits it to the Dianhong system. The Dianhong system combines the feedback data from the three transmissions. If there are no errors, the link is considered normal. If there is one or more errors, the link is considered abnormal.

[0041] Specifically, the process of performing a self-check on the execution link interaction is as follows: The Elec-Tech system sends a link detection command to the main control module via the UDP protocol. The link detection command includes a detection period of, for example, 10 seconds, a number of test data packets of, for example, 5, and a valid data length of each data packet of, for example, 512 bytes. After receiving the instruction, the main control module starts the data packet generation thread. The structure of each test data packet is as follows: The header 16 bytes contain an 8-byte terminal identifier and an 8-byte data packet sequence number. The middle 32 bytes contain a preset checksum generated by calculating the valid data using the CRC-32 algorithm. The middle 512 bytes of valid data are randomly generated integers from 0 to 255. The middle part is a data identifier containing a 12-bit terminal number, with the first 6 bits being the substation number and the last 6 bits being the terminal device number. The 8-byte data packet at the end generates a timestamp accurate to milliseconds.

[0042] Furthermore, the main control module transmits the generated test data packets sequentially to the monitoring module via a connection link, such as an RJ45 interface, with a transmission interval of 2 seconds to avoid data packet congestion. The monitoring module receives data packets through the same connection link. It first extracts the terminal identifier and data packet sequence number from the header and verifies whether the data packet is a valid data packet of the target terminal. If the terminal identifier does not match or the sequence number is missing, the data packet is discarded. Then extract the middle check code, recalculate the CRC-32 check value for the valid data, and compare it with the preset check code. If they match, the data is considered complete; otherwise, the check fails. Simultaneously, the data packet reception timestamp is recorded, and the difference between the received timestamp and the generated timestamp is calculated to obtain the link transmission delay; The monitoring module generates detection feedback data based on the verification result of each data packet (complete / failed / missing sequence number) and transmission delay. The feedback data includes the status of each of the five data packets, the average transmission delay, and the maximum transmission delay. The monitoring module transmits feedback data to the main control module via the SPI bus. The main control module then aggregates the data, adds its own device identifier, and transmits it back to the Elec-Tech system via the UDP protocol. The Dianhong system analyzes the feedback data. If all 5 data packets are complete and the average transmission delay is less than 50ms, the link is considered to be normal. If one or more data packets fail to be verified or the average transmission delay is greater than 100ms, the link is considered abnormal, and the abnormality type is marked, such as data verification failure or excessive transmission delay.

[0043] In existing technologies, the assessment of module health status often relies on subjective human judgment, such as judging by the color of indicator lights or by a single parameter, such as whether the voltage is normal. This cannot quantitatively reflect the overall operating status of the module, resulting in inaccurate health assessment and easy to miss potential faults or misjudge normal status.

[0044] It should be noted that the specific implementation process of the data identification is as follows: The data identification adopts a two-field structure of a unique terminal number and a millisecond-level timestamp, with a total length of 20 bytes, or 160 bits. The first 12 bytes are the unique number of the monitoring terminal, and the encoding rule is as follows: The first 6 bytes are the substation number in ASCII code, for example, GZ0001 represents a substation in Guizhou. The last 6 bytes are the terminal device number in decimal code, for example, 000001 represents the first terminal. This ensures that each terminal has a unique number in the entire substation monitoring system, with no duplicates. The last 8 bytes contain the data acquisition time information, using UTC time encoding, in the format YYYYMMDDHHMMSSmmm, where YYYY represents the year, MM the month, DD the day, HH the hour, MM the minute, SS the second, and mmm the millisecond. For example, 20250520143025123 represents 14:30:25:123 on May 20, 2025. The time precision is accurate to the millisecond level, ensuring that the generation time of each data packet can be accurately traced.

[0045] The data identifier is embedded in the header of the test data packet immediately after the data packet instruction type field, and is stored in big-endian byte order to facilitate parsing by processors of different architectures such as ARM and x86. During the link interaction self-test process, after receiving a data packet, the monitoring module first extracts the terminal number from the data identifier and compares it with its own preset terminal number. If they do not match, the data packet is discarded to avoid receiving interference data packets from other terminals. If they match, the timestamp is extracted and compared with the current system time. The transmission delay is calculated by subtracting the timestamp from the current time, and the data packet number and timestamp are recorded in the local log. When a link anomaly occurs, such as data packet loss, the specific generation time and terminal of the lost data packet can be determined by comparing the logs of the sending and receiving ends and based on the timestamp in the data identifier. This allows for quick location of the time node where the fault occurred and the associated terminal, improving the efficiency of fault diagnosis. Meanwhile, the data identifier is also used for data packet deduplication. If the monitoring module receives data identifiers with the same terminal number and timestamp within 1 second, it is determined to be a duplicate data packet, and only the first data packet received is retained to avoid resource waste caused by repeated processing.

[0046] In existing technologies, the unified self-inspection results reported proactively are mostly transmitted in plaintext without encryption. In the complex network environment of substations, there is a risk that the data may be illegally stolen, such as maliciously destroying or tampering with fault information after obtaining it, for example, changing abnormal results to normal results. This affects the authenticity and security of the information received by the monitoring center and threatens the stable operation of the substation monitoring system.

[0047] S204. The specific process for calculating the health value of the preprocessed abnormal feature parameters module is as follows: First, a weighted summation method is used to calculate the comprehensive health contribution value of multiple parameters to reflect the overall impact of each parameter on health. Then, a module type correction coefficient is introduced to compensate for the differences in functional complexity of different modules. Finally, a runtime correction coefficient is used to reflect the impact of module aging on health status. The three factors are multiplied together to obtain a quantitative health value. The module health assessment formula used is: in, This is the module health value, which is dimensionless and ranges from 0 to 1. The closer it is to 1, the better the module's health status. This is a module type identifier, dimensionless. Corresponding main control module, Corresponding monitoring module; For the first The weighting coefficients of each operating parameter, dimensionless, are determined using the Analytic Hierarchy Process (AHP) based on the parameter's influence on module operation—in the main control module, the input voltage weighting... Operating current weight Processor utilization weight Memory usage weight Heat dissipation temperature weight and In the monitoring module, the image acquisition frame rate weight Signal reception strength weight Lens focal length stability weight Data transmission success rate weight Heat dissipation temperature weight and ; For the first The preprocessed values ​​of each operating parameter are dimensionless and fall within the range of 0-1. For the first The preprocessed values ​​corresponding to the standard rated values ​​of each operating parameter are dimensionless and take the value 1.0. Since the preprocessing has already mapped the rated values ​​to 1.0, therefore... ; A dimensionless adjustment coefficient is used to adjust the module type. It is set according to the functional complexity of the module. The main control module has more complex functions because it needs to handle the collaborative logic of multiple modules. The monitoring module has relatively simple functions. ; This is a dimensionless correction factor for module runtime, based on the module's cumulative runtime. The dimension h is obtained through piecewise function mapping: when hour, That is, the impact of module aging is small; when hour, That is, the effects of aging increase linearly; when hour, That is, the effects of aging tend to stabilize.

[0048] In actual calculations, taking the main control module as an example, if the preprocessed input voltage... Operating current Processor utilization Memory usage Heat dissipation temperature Cumulative runtime ,but , , Therefore This indicates that the main control module is in good health.

[0049] In existing technologies, the judgment of potential module failures is based on a single health indicator, without considering the trend of parameter changes, such as whether the parameter is rapidly decreasing, and environmental factors such as the module's operating temperature. This makes it easy to miss potential failures where the health status is normal but the parameters are rapidly deteriorating or the temperature is too high, and it is impossible to provide early warning of module failure risks.

[0050] S205. The specific process for judging potential faults based on module health values ​​is as follows: First, the square of the deviation of each dimension is calculated to eliminate the problem of positive and negative deviations canceling each other out and highlight the magnitude of the deviation. Then, the importance of each dimension is reflected by the weight coefficient. The health deviation has the highest weight, followed by the temperature and parameter change rate deviations. Finally, the square root of the weighted result is taken to normalize the result to the 0-1 range, which is convenient for the classification of abnormal levels. The anomaly feature fusion discrimination formula used is: in, This is the module's anomaly detection value. It is dimensionless and ranges from 0 to 1. The closer it is to 1, the higher the degree of anomaly. This is the weight for health deviation, dimensionless, and takes a value of 0.5 because health is the core indicator and has the highest weight. The module health standard threshold is dimensionless and takes the value of 0.8. According to the long-term operation data of the module, the probability of failure increases significantly when the health is below 0.8. This is the deviation weight for the parameter change rate, dimensionless, and takes a value of 0.25. This represents the real-time rate of change of the module's operating parameters, with dimensions of 1 / h. It is calculated by taking the difference between the maximum and minimum values ​​of the preprocessed parameters within the past hour and dividing by the time interval of 1 hour. For example, if the main control module's input voltage drops from 0.95 to 0.85 within the past hour, then... ; This is the standard rate of change threshold for the module's operating parameters. It is dimensionless and takes a value of 0.05. It is set according to the requirements for stable operation of the parameters. If it exceeds this value, it indicates that the parameters are changing too quickly. This is the module temperature deviation weight, dimensionless, with a value of 0.25; The real-time operating temperature of the module is measured in °C and is acquired by a built-in temperature sensor, such as the DS18B20 temperature sensor, with an accuracy of ±0.5 °C. This is the standard operating temperature threshold for the module, measured in °C, with a value of 45 °C, which is the highest safe operating temperature designed for the module.

[0051] In practical applications, taking a monitoring module as an example, if and , and , and ,but , , Substituting into the formula, we get A value of 1.0 indicates that the monitoring module has a serious potential for failure and requires immediate warning.

[0052] In existing technologies, link adaptability assessment relies solely on the link's own performance, such as transmission rate and bandwidth usage, without considering the abnormal states of the modules at both ends. This fails to reflect the impact of module abnormalities, such as processor overload of the main control module, on link interaction, resulting in a one-sided assessment of link adaptability and an inability to accurately evaluate the collaborative working status of the module and the link.

[0053] S206. Based on this, the specific process for calculating the link interaction adaptability coefficient is as follows: First, the average of the anomaly detection values ​​of the two modules is calculated to reflect the overall impact of module anomalies on link interaction. The more severe the module anomaly, the larger this value and the smaller the adaptation coefficient. Next, the ratio of real-time transmission rate to maximum transmission rate is calculated to reflect link transmission efficiency. The higher the efficiency, the larger the adaptation coefficient. Then, the ratio of actual bandwidth usage to total bandwidth capacity is calculated to reflect link resource utilization. Moderate usage results in good adaptation, while excessively high or low usage affects adaptability. Here, the actual usage directly reflects the current utilization status. Finally, an environmental interference correction coefficient is introduced to compensate for the weakening of link performance by electromagnetic interference. The stronger the interference, the smaller the correction coefficient and the smaller the adaptation coefficient. Multiplying these four factors together yields the link interaction adaptability coefficient, enabling collaborative judgment of multiple factors including modules, links, and the environment. The formula for the link interaction adaptation coefficient is as follows: in, This is the link interaction adaptability coefficient, which is dimensionless and ranges from 0 to 1. The closer it is to 1, the better the adaptability. The main control module's anomaly detection value is dimensionless and similar to the values ​​described in the above embodiments. If the definitions are consistent, the calculation results can be directly referenced. The anomaly detection value of the monitoring module is dimensionless and is calculated using the same method as in the above embodiment. The real-time transmission rate of the link is measured in Mbps. It is calculated by transmitting 1024 bytes of data in three test data packets, recording the transmission time. The dimension is s, and the average value is taken. ,but Convert bytes to megabits; This refers to the maximum transmission rate of the link, measured in Mbps, and is set according to the link hardware specifications, such as an RJ45 Ethernet link. ; The actual bandwidth usage of the link is measured in Mbps. It is collected in real time using the bandwidth monitoring tool built into the Dianhong system, with a collection frequency of once per second, and the average value of the last 10 seconds is taken. The total bandwidth capacity of the link, measured in Mbps, is... Consistency means the total bandwidth of the link, which is the bandwidth corresponding to the maximum transmission rate. This is a dimensionless correction factor for link environmental interference. It is derived by mapping the electromagnetic interference intensity within the substation, collected by an electromagnetic interference detector, to a dimension of dBμV / m. When the interference value is ≤50dBμV / m, That is, the interference is small; when 50dBμV / m < interference value ≤ 80dBμV / m, That is, the interference is moderate; when the interference value is >80dBμV / m, That is, there is a lot of interference.

[0054] In actual calculations, if , , , , , , ,but , , Therefore This indicates poor link interaction adaptability, and the self-test strategy needs to be adjusted.

[0055] In existing technologies, the data identifier of the test data packets for link interaction self-test only contains a simple terminal number and lacks data packet generation time information. It is impossible to trace the transmission delay of the data packets and it is also difficult to distinguish data packets from different detection periods. This is not conducive to the location of link faults, such as the inability to determine whether the data packets are lost in a certain period or have been lost for a long time, and it also affects the efficiency of troubleshooting.

[0056] In an embodiment of the present invention, step S300 integrates the self-test results, combining the module collaborative self-test results, the abnormal feature parameter extraction results, and the link interaction self-test results into a unified self-test result, and performs active reporting, including the following steps S301-S303: S301. When integrating self-inspection results and performing proactive reporting, a three-level fusion logic of data layer, feature layer, and decision layer is adopted: The data layer will unify the format of the module collaboration self-check results (such as whether the two modules have passed initialization), the abnormal feature parameter extraction results (such as the real-time values ​​of each parameter), and the link interaction self-check results (such as whether the link is abnormal) and convert them into JSON format data. The feature layer performs consistency checks on the unified data. For example, if the module's collaborative self-test shows normal but the processor utilization rate in the abnormal feature parameters continues to exceed 90%, it is marked as an anomaly to be confirmed. The decision-making level integrates the verified data into a unified self-inspection result based on preset anomaly level rules, such as a single parameter anomaly being a minor anomaly and a link anomaly being a severe anomaly. When the unified self-inspection result is marked as abnormal, the Dianhong system automatically transmits the result to the substation monitoring center through an encrypted communication channel using the SSL / TLS protocol. During the transmission process, the channel status is monitored in real time. If the transmission fails, a retransmission mechanism is triggered, with a maximum of 3 retransmissions to ensure that abnormal information is delivered in a timely manner.

[0057] In existing technologies, the initial state of the modules is not effectively verified before the collaborative self-test. If the main control module or monitoring module is in a state of being unpowered, offline, or experiencing hardware failure, directly starting the self-test will lead to self-test failure and may also misjudge the module as normal, affecting the accuracy of subsequent fault detection.

[0058] S302. The specific process of encrypting the unified self-test results during proactive reporting is as follows: The encryption algorithm used is the pre-set asymmetric encryption algorithm of the Dianhong system, namely RSA-2048. The public key and private key pairing generation rules of this algorithm are as follows: During the initialization phase, the Dianhong system generates a large prime number of 2048 bits using a random number generator. and Calculate the modulus: Euler's totient function: Select public key index satisfy: and and Coprime, here we take Calculate the private key index satisfy: The public key is The private key is The public key is pre-stored in the encryption chip of each monitoring terminal, such as ATECC608A, while the private key is only stored in the encryption server of the substation monitoring center. The private key is stored in hardware encryption and cannot be read by software, thus ensuring key security.

[0059] The encryption process is as follows: First, the unified self-test result in JSON format is converted into a byte stream encoded in UTF-8, and the SHA-256 hash value of the byte stream is calculated for subsequent integrity verification. The combined data of byte stream and hash value is then encrypted using the RSA public key in the encryption chip of the monitoring terminal. The encryption process uses OAEP padding mode to avoid chosen plaintext attacks, generating 256 bytes of ciphertext data. The encrypted ciphertext data is appended with a 16-byte message authentication code (MAC), which uses the HMAC-SHA256 algorithm. The key is a symmetric key pre-negotiated between the monitoring terminal and the monitoring center, used to verify whether the data transmission has been tampered with.

[0060] The encrypted unified self-test results are transmitted through the dedicated communication channel of the Elec-Hong system based on the VLAN isolation channel of industrial Ethernet. Only the monitoring terminal and the monitoring center are allowed to communicate and transmit the data. The TCP protocol is used during the transmission to ensure that the data arrives reliably. After receiving encrypted data, the monitoring center first verifies the MAC value by calculating the MAC value of the received data using the same symmetric key and comparing it with the received MAC value. If they match, the data has not been tampered with. The encrypted data is then decrypted using the RSA private key in the encryption server to obtain the original unified self-test result byte stream and hash value; Finally, the SHA-256 hash value of the byte stream is calculated and compared with the hash value obtained from decryption. If they match, the data is intact; otherwise, the data is considered corrupted, and the monitoring terminal is requested to retransmit it.

[0061] In existing technologies, the link self-test frequency is mostly a fixed value, such as once every 5 minutes, and is not dynamically adjusted according to the link interaction adaptability status. For links with good adaptability, such as normal modules, stable transmission, and low interference, fixed high-frequency self-tests will waste the computing resources of the monitoring terminal and the link bandwidth. For links with poor adaptability, such as abnormal modules, large transmission delays, and strong interference, fixed low-frequency self-tests cannot detect new faults in time, which can easily lead to the expansion of faults.

[0062] S303. Based on this, the specific process by which the Elec-Tech system determines the self-test frequency of the connection link according to the link interaction adaptability coefficient is as follows: The frequency adjustment command is issued, and the Elec-tron system generates a frequency adjustment command containing a new self-test frequency and the number of test data packets, which is then transmitted to the main control module via the SPI bus. After receiving the instruction, the main control module updates its own link detection timer period, for example, by adjusting it to 15 minutes, and simultaneously sends a frequency synchronization instruction to the monitoring module to ensure that the self-test frequencies of the two modules are consistent. After receiving the synchronization command, the monitoring module updates its own detection cycle and sends a confirmation signal back to the main control module. The main control module sends a confirmation signal back to the Elec-Hong system. The Elec-Hong system records the frequency adjustment time and the adjusted parameters to a log file for easy traceability later.

[0063] The preset adaptation threshold is set to 0.8 dimensionless, which was obtained through statistical analysis of a large amount of link operation data. When the adaptation coefficient is greater than 0.8, the link failure probability is less than 1%; when it is less than or equal to 0.8, the failure probability increases significantly. The self-test frequency adjustment level is divided into three levels: The first-level frequency, or low frequency, is 15 minutes per cycle; the second-level frequency, or default frequency, is 5 minutes per cycle; and the third-level frequency, or high frequency, is 1 minute per cycle.

[0064] The frequency adjustment logic is as follows: The Elec-Tech system recalculates the link interaction adaptability coefficient every 10 minutes. , like If the link adaptability is good, the self-test frequency will be adjusted to the first-level frequency, i.e., once every 15 minutes, and the number of test data packets will be reduced from 5 to 2 to reduce resource consumption. like If the link adaptability is average, the self-test frequency is maintained at the secondary frequency, i.e., 5 minutes / time, and the number of test data packets remains unchanged at 5. like If the link adaptability is poor, the self-test frequency will be adjusted to a level 3 frequency, i.e., once per minute, and the number of test data packets will be increased from 5 to 8 to improve the sensitivity of fault detection.

[0065] In practical applications, if a certain link... In other words, with good adaptability, the self-test frequency decreases from 5 minutes / time to 15 minutes / time, the number of test packets decreases from 5 to 2, and the link bandwidth consumed per self-test decreases from approximately 40kb (5×8kb / packet) to 16kb (2×8kb / packet), significantly reducing resource consumption; if If the adaptability is poor, the self-test frequency is increased to once per minute and the number of data packets is increased to 8, which can quickly detect new faults in the link, such as a sudden increase in transmission delay.

[0066] Example 3 is an embodiment of the present invention. The above is an illustrative scheme of a fault self-testing method for a modular substation monitoring terminal based on Dianhong. It should be noted that the technical solution of a fault self-testing system for a modular substation monitoring terminal based on Dianhong and the above-described fault self-testing method for a modular substation monitoring terminal based on Dianhong belong to the same concept. Details not described in detail in the technical solution of the fault self-testing system for a modular substation monitoring terminal based on Dianhong in this embodiment can be found in the description of the above-described fault self-testing method for a modular substation monitoring terminal based on Dianhong.

[0067] This embodiment provides a modular substation monitoring terminal fault self-testing system based on Dianhong, including: a self-testing module, a detection module, and an anomaly reporting module; The self-test module performs collaborative self-tests with the execution module, initiating self-test initialization operations for the main control module and monitoring module of the modular substation monitoring terminal, respectively. The detection module extracts abnormal feature parameters in real time, performs link interaction self-test, and performs data transmission detection on the connection link between the main control module and the monitoring module based on the communication protocol of the Dianhong system. The anomaly reporting module integrates self-inspection results, combining the results of module collaborative self-inspection, anomaly feature parameter extraction, and link interaction self-inspection into a unified self-inspection result, and then performs proactive reporting.

[0068] This embodiment also provides an electronic device applicable to a fault self-testing method for a modular substation monitoring terminal based on Dianhong, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the fault self-testing method for a modular substation monitoring terminal based on Dianhong proposed in the above embodiment.

[0069] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a fault self-testing method for a modular substation monitoring terminal based on Dianhong, as proposed in the above embodiment.

[0070] The storage medium proposed in this embodiment and the method for self-diagnosing faults of a modular substation monitoring terminal based on electric power proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0071] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0072] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A fault self-diagnosis method for a modular substation monitoring terminal based on Dianhong, characterized in that: include, The execution module performs a collaborative self-test, initiating self-test initialization operations for the main control module and monitoring module of the modular substation monitoring terminal, respectively. Real-time extraction of abnormal feature parameters, execution of link interaction self-check, data transmission detection of the connection link between the main control module and the monitoring module based on the communication protocol of the Dianhong system, and health assessment; The self-inspection results are integrated, combining the module collaborative self-inspection results, abnormal feature parameter extraction results, and link interaction self-inspection results into a unified self-inspection result, and then actively reporting it.

2. The method for fault self-testing of a modular substation monitoring terminal based on Dianhong as described in claim 1, characterized in that: The module collaborative self-test is performed before power-on verification is performed. The Dianhong system sends a self-test start command to the main control module. After receiving the self-test start command, the main control module sends an interaction signal to the monitoring module. After receiving the interaction signal, the monitoring module sends a response signal back to the main control module. The main control module transmits the response signal to the Elec-Tech system, which then determines whether to trigger a collaborative self-test based on the response signal.

3. The self-diagnosis method for faults in a modular substation monitoring terminal based on Dianhong as described in claim 2, characterized in that: The extraction of abnormal feature parameters includes performing preprocessing on the abnormal feature parameters after real-time extraction. Preprocessing includes performing data denoising and data standardization.

4. The self-diagnosis method for faults in a modular substation monitoring terminal based on Dianhong as described in claim 3, characterized in that: The link interaction self-test includes: the Elec-Hong system sending a link detection command to the main control module; the main control module generating a test data packet according to the link detection command; the main control module transmitting the test data packet to the monitoring module; and the monitoring module receiving and parsing the test data packet. The detection feedback data is generated based on the analysis results and transmitted to the main control module. The main control module then transmits the detection feedback data to the Dianhong system, which determines the status of the connection link based on the detection feedback data.

5. The self-diagnosis method for faults in a modular substation monitoring terminal based on Dianhong as described in claim 4, characterized in that: The preprocessing of abnormal feature parameters includes calculating the health values ​​of the main control module and the monitoring module respectively using the module health assessment formula for the preprocessed abnormal feature parameters. The module health assessment formula combines the weighting of operating parameters, differences in module type, and the impact of runtime to obtain a quantitative value reflecting the module's operating status. Based on the module health value obtained from the module health assessment formula, the main control module and the monitoring module are judged to have potential faults by the abnormal feature fusion discrimination formula. The calculation logic of the abnormal feature fusion discrimination formula is to comprehensively consider the health deviation, parameter change rate deviation and temperature deviation to obtain a discrimination quantification value reflecting the degree of module abnormality.

6. The self-diagnosis method for faults in a modular substation monitoring terminal based on Dianhong as described in claim 5, characterized in that: The obtained quantification value reflecting the degree of module abnormality includes: a module abnormality discrimination value obtained by combining the abnormal feature fusion discrimination formula; and the adaptation coefficient of the connection link between the main control module and the monitoring module calculated by the link interaction adaptation coefficient formula. The link interaction adaptation coefficient formula is a quantification value reflecting the degree of link adaptation that combines the abnormal state of the module, the link transmission performance and environmental interference. The Elec-Link system determines the self-test frequency of the connection link based on the link interaction adaptation coefficient formula. When the link interaction adaptation coefficient is greater than the preset adaptation threshold, the self-test frequency of the connection link is reduced; when the link interaction adaptation coefficient is less than or equal to the preset adaptation threshold, the self-test frequency of the connection link is increased.

7. The self-diagnosis method for faults in a modular substation monitoring terminal based on Dianhong as described in claim 6, characterized in that: The active reporting includes, when performing active reporting, performing an encryption process on the unified self-inspection results, using the encryption algorithm preset by the Dianhong system to encrypt the unified self-inspection results, and transmitting the encrypted unified self-inspection results to the substation monitoring center. The test data packet includes a preset checksum and a data identifier. The data identifier contains the module number of the monitoring terminal and the data acquisition time information. The module number is the unique identifier of the modular substation monitoring terminal, and the data acquisition time information is used to mark the generation time of the test data packet.

8. A fault self-diagnosis system for modular substation monitoring terminals based on Dianhong, employing the fault self-diagnosis method for modular substation monitoring terminals based on Dianhong as described in any one of claims 1 to 7, characterized in that, include: Self-test module, detection module, and anomaly reporting module; The self-test module performs collaborative self-testing, initiating self-test initialization operations for the main control module and monitoring module of the modular substation monitoring terminal, respectively. The detection module extracts abnormal feature parameters in real time, performs link interaction self-check, and performs data transmission detection on the connection link between the main control module and the monitoring module based on the communication protocol of the Dianhong system, and performs health assessment. The anomaly reporting module integrates self-inspection results, combining the module collaborative self-inspection results, anomaly feature parameter extraction results, and link interaction self-inspection results into a unified self-inspection result, and then performs proactive reporting.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the self-diagnosis method for faults of a modular substation monitoring terminal based on Dianhong, as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the self-diagnosis method for faults of a modular substation monitoring terminal based on Dianhong, as described in any one of claims 1 to 7.

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