Testing methods and systems applicable to various types of primary and secondary integrated equipment

Abstract

This invention discloses a testing method and system applicable to various types of integrated primary and secondary equipment, relating to the field of power grid equipment testing technology. The method includes: constructing an association model representing the mapping relationship between data items to be matched in a point table and function names; receiving a first point table from a first device under test, recommending candidate function names for the data items to be matched in the point table based on the association model, and obtaining user confirmation results for the candidate function names; updating the association model; receiving a second point table from a second device under test, calling the updated association model, automatically establishing a matching relationship between the data items to be matched that meet preset conditions and function names, prompting manual confirmation for those that do not meet the preset conditions, and iteratively updating the association model. This system is used to implement the method. This solution can solve the problem of the testing scheme being unable to adaptively implement due to differences in point tables, while simultaneously improving the accuracy and efficiency of point table matching.

Description

Technical Field

[0001] This invention relates to the field of power grid equipment testing technology, and in particular to a testing method and system applicable to various types of integrated primary and secondary equipment. Background Technology

[0002] As the special project on the integration of primary and secondary power grids continues to advance, existing testing systems can perform most of the testing outlines for electronic and electromagnetic instrument transformers integrated primary and secondary equipment. However, they lack compatibility with digital equipment: the hardware architecture and software platform of the tested equipment are incompatible. For example, traditional testing systems are configured with analog signal input terminals, while digital equipment requires the testing system to have an Ethernet / fiber optic communication interface and support decoding in the system software. Existing testing systems detect continuously changing electrical signals, and their hardware architecture and software platform cannot directly identify, parse, process, and test the networked digital messages output by digital power equipment based on the new generation of international standards (such as IEC 61850). Digital messages are data frames transmitted over the network and following protocols and encapsulated, requiring a translator to decode and extract them. This results in the need for digital equipment to perform testing items item by item or piecemeal in practical applications, leading to low testing efficiency. This fundamental mismatch has caused the testing work of this equipment to regress from automation to manual, which may lead to problems such as low efficiency, error-proneness, and poor reliability, becoming a technical bottleneck restricting the comprehensive advancement of the integration of primary and secondary power grids.

[0003] Meanwhile, according to the relevant testing specifications and factory test requirements of the State Grid Corporation of China, all complete sets of equipment are required to complete primary and secondary integration testing, which means that relevant point number configurations and functional tests need to be carried out according to the requirements of different provinces and cities. In the application of integrated primary and secondary equipment, typical primary equipment includes instrument transformers, circuit breakers, etc., whose core function is to sense primary electrical quantities or operational execution status, while secondary equipment includes distribution terminals (DTUs). The primary equipment (FTU), protection devices, and measurement and control devices, among others, primarily collect signals from primary equipment and perform calculations. This also includes uploading data to the control center (control platform or master station) and receiving control commands. Therefore, the primary equipment outputs raw electrical signals or digital messages. Secondary equipment needs to organize data including voltage, current, other statuses, alarms, and commands. During testing, the primary equipment does not involve point meter processing (when the primary testing platform is in use, its task is to apply standard voltage and current excitation signals to the primary side of the tested transformer and collect analog voltage or digital messages output from its secondary side. Then, by directly comparing the output value with known reference values, it calculates physical accuracy indicators such as ratio difference and angle difference. This process is based on the direct measurement and comparison of physical quantities and does not involve accessing or parsing the internal data addresses of the equipment). Secondary equipment... The system relies on a point table to achieve data interconnection with the testing system. (The tested objects are intelligent secondary devices such as distribution terminals, DTUs, and FTUs. These devices maintain a lookup table (i.e., a point table) that maps up to hundreds of physical quantities or status signals, such as "A-phase voltage," "switch position," and "overcurrent alarm," to specific communication addresses.) In automated testing, the testing system needs to simulate the master station or the device on the other side, interact with the terminal through a communication protocol, read telemetry values, issue remote control commands, and verify whether its various functions meet the specifications. If the point table of the terminal cannot be correctly parsed, for example, the testing system will not know whether "A-phase voltage" is stored at address 4001H or 5000H, or which register and position the "opening command" should be written to, thus making it impossible to communicate effectively with the tested terminal, causing the testing work to be completely paralyzed.

[0004] In the prior art, patent application CN202511101531.4 discloses a general operation and maintenance method and system supporting multiple types of distribution terminals. In this scheme, the point table structure and mapping relationship are established through defined communication parameters and point table structure. Unlike point number configuration in other fields, the joint commissioning of primary and secondary integrated equipment pairs specific primary transformers with specific secondary terminals. According to the differentiated specifications of power companies in various provinces and cities, the point-to-point configuration of information points and the overall functional testing are completed. It is necessary not only to read the point table of the secondary terminal, but also to establish a precise calibration relationship between the standard signal injected from the primary side and the corresponding telemetry point in the secondary terminal point table. The accuracy of the point table directly determines the reliability of the primary and secondary joint commissioning results. The risk of errors and omissions caused by manual point table configuration is magnified in this scenario, becoming the core technical bottleneck restricting the large-scale and efficient testing of primary and secondary integrated equipment.

[0005] Therefore, in order to improve the automation level of primary and secondary integrated testing equipment, it is necessary to further optimize the relevant technologies. Summary of the Invention

[0006] To address the aforementioned issue of improving the automation level of primary and secondary integrated equipment testing, this invention provides a testing method and system applicable to various types of primary and secondary integrated equipment. This solution transforms manual confirmation operations into targeted updates of the weights of specific mapping paths in the association model. This not only solves the problem of testing schemes being unable to adapt due to differences in the secondary equipment point tables of different manufacturers, but also improves the accuracy and efficiency of primary and secondary integrated equipment point table matching.

[0007] To address the above problems, the present invention provides a testing method and system applicable to various types of primary and secondary fusion integrated equipment, which solves the problems through the following technical points: The testing method applicable to various types of primary and secondary fusion integrated equipment includes the following steps: S1: Construct an association model, which is used to characterize the mapping relationship between the data items to be matched in the point table of the device under test and the standardized function names in the association model. The data items to be matched include object address and point number keyword. S2: Receive the first point table of the first device under test, and based on the association model, recommend candidate function names for the data items to be matched in the first point table, and obtain the user's confirmation result of the candidate function names, so as to establish a mapping relationship between the data items to be matched and the function names. S3: Update the mapping relationship in the association model based on the user confirmation result; S4: Receive the second point table of the second device under test, call the updated association model to match the data items to be matched in the second point table, automatically establish a matching relationship between the data items to be matched that meet the preset conditions and the function name, prompt manual confirmation for those that do not meet the preset conditions, and return to S3 to iteratively update the association model.

[0008] A further technical solution to the testing method applicable to various types of primary and secondary fusion complete sets of equipment is as follows: The association model stores the confidence weights of each mapping relationship, wherein: The specific steps for recommending candidate function names in S2 are as follows: calculate the confidence score of each candidate function name based on the confidence weight, and push at least one candidate function name with the highest confidence score to the user for confirmation; The specific method for updating the mapping relationship described in S3 is to use the mapping relationship between the object address, dot keyword and function name confirmed by the user as a feedback training sample and increase the confidence weight of the mapping relationship. The preset condition in S4 is: there is a unique candidate function name whose confidence weight exceeds a preset threshold; when the preset condition is not met, a limited candidate list generated from multiple candidate function names arranged in descending order of confidence score will be provided to the user for selection and confirmation.

[0009] The confidence score for each candidate function name calculated based on confidence weight in S2 adopts a multi-level retrieval strategy, including: First-level retrieval: Determine the object address of the data item to be matched, retrieve the candidate set of function names in the association model that match the encoding features of the object address, and use them as the first candidate set; Second-level search: Within the first candidate set obtained from the first-level search, keyword matching is performed based on the dot keyword of the data item to be matched to obtain the second-level candidate set; Third-level search: If the candidate set obtained by the first-level search or the second-level search is empty or the number of candidates is lower than the preset value, the search scope is expanded to the function name candidate set corresponding to other address ranges in the association model for supplementary search; The candidate function names obtained from each level of retrieval are sorted in descending order of confidence score to generate the final list of candidate function names.

[0010] The first-level retrieval specifically involves: extracting the high-order code of the object address of the data item to be matched, and retrieving the set of candidate function names corresponding to the address range that matches the high-order code in the association model; The second-level retrieval specifically involves: performing string matching between the dot-matrix keywords of the data items to be matched and the dot-matrix keywords associated with each function name in the first candidate set obtained from the first-level retrieval, retaining the candidate function names with a matching degree exceeding a preset value, and forming the second-level candidate set; The third-level retrieval specifically involves: if the candidate set obtained from the first-level retrieval is empty, or the number of candidate function names retained after the second-level retrieval is zero, then the address range limitation is removed, and global keyword matching is performed using the dot keyword within the function name range corresponding to all address ranges in the association model to obtain a supplementary candidate set.

[0011] Before receiving the first point table and the second point table, a unified semantic layer mapping step is also included, specifically: The output signal of the primary or secondary device under test is acquired through a signal source conversion device. The type of the output signal is at least one of analog signal, digital switch signal or networked digital message following standard communication protocol. Feature extraction is performed on the output signal, and the extracted effective information is uniformly encapsulated into a standardized detection intermediate semantic message that is decoupled from the underlying physical interface and communication protocol on which the association model depends, for use in subsequent detection steps.

[0012] For the output signal of type analog signal or digital switch signal, the acquisition module inside the signal source conversion device quantizes it into feature data with physical dimensions and parameter values, and encapsulates it into a standardized detection intermediate semantic message, which is then used as a response semantic message. For the output signal of type networked digital message, the protocol parsing module inside the signal source switching device parses it to application layer data, extracts information after stripping the frame header and encoding rules of the communication protocol, and encapsulates the extracted information into a standardized detection intermediate semantic message, which is then used as a response semantic message. The detection excitation commands issued by the detection system are uniformly expressed as standardized detection intermediate semantic messages, which are subsequently used as excitation semantic messages. These excitation semantic messages are used to record the expected value of the excitation signal applied by the detection system to the device under test. The detection system compares the stimulus semantic message with the response semantic message returned from the device under test in real time to verify the consistency between the response of the device under test and the expected value.

[0013] The method is based on an integrated testing system, which includes a control cabinet, a primary testing platform, a secondary testing platform, and a testing management system. Following the unified semantic layer mapping step, the method further includes a complete equipment testing step, specifically: The primary device to be tested is connected to the primary testing platform, the secondary device to be tested is connected to the secondary testing platform, and a communication connection is established between the testing management system and the secondary device to be tested through a communication device. The device information of the device to be tested is entered, the testing management system calibrates the consistency of the device information, and calls the generated testing plan according to the testing function; The detection management system adjusts the power output module in the primary detection platform through the control cabinet to output standard voltage and standard current signals with adjustable angle, amplitude and frequency to the primary device under test. The detection management system collects the actual values ​​of the standard voltage and standard current signals through the standard measurement module in the primary detection platform, and uses them as a comparison benchmark. The testing management system reads the test results of the secondary equipment under test through the secondary testing platform, and compares them with the comparison benchmark, response semantic message, and excitation semantic message to determine whether the test items are qualified. After the testing plan is completed, the testing management system controls the self-discharge module in the primary testing platform to automatically discharge the residual charge on the primary equipment under test. After the discharge is completed, it automatically exits and prompts for replacement of the equipment under test, while generating a test report.

[0014] When only secondary equipment is tested individually, the specific steps for testing the entire set of equipment are as follows: The secondary device under test is connected to the secondary testing platform through the signal source adapter, and a communication connection is established between the testing management system and the secondary device under test through the communication equipment. The device information of the secondary device to be tested is entered and the transformer type is selected. The detection management system calibrates the consistency of the device information and controls the signal source conversion device to switch to the signal mode corresponding to the transformer type. The detection management system calls the generated detection plan based on the detection point table; The detection management system controls the standard source in the secondary detection platform to output standard voltage and standard current signals to the secondary device under test; The detection management system collects the actual value of the standard source output through the standard meter and high-speed waveform recording device in the secondary detection platform, and determines whether the output meets the detection requirements. The testing management system calls the corresponding testing modules to perform functional tests according to the testing plan and generates a testing report.

[0015] This solution also relates to a testing system applicable to various types of primary and secondary fusion equipment sets, the testing system comprising: A signal source conversion device is used to acquire the output signal of the primary and / or secondary device under test. The type of the output signal is at least one of analog signal, digital switch signal or networked digital message following standard communication protocol, and the output signal is uniformly encapsulated into a standardized detection intermediate semantic message. The memory is used to store the association model, which is used to characterize the mapping relationship between the data items to be matched in the point table of the device under test and the standardized function names. The data items to be matched include object addresses and point number keywords. The human-computer interaction interface is used to push candidate function names to users and receive confirmation commands from users; The processor is configured to execute any of the methods described above.

[0016] A further technical solution to the testing system applicable to various types of primary and secondary integrated equipment is: it also includes a primary testing platform and a secondary testing platform; The primary testing platform includes a power output module, a standard measurement module, and a self-discharge module. The power output module is used to output standard voltage and standard current signals with adjustable angle, amplitude, and frequency to the primary equipment. The standard measurement module is used to collect the actual values ​​of the standard voltage and standard current signals as a comparison benchmark. The self-discharge module is used to automatically discharge the residual charge of the primary equipment after the test is completed and automatically exit after the discharge is completed. The secondary testing platform includes the signal source conversion device, the standard source, the standard meter, and the high-speed waveform recording device; the standard source is used to output standard voltage and standard current signals to the secondary equipment, and the standard meter and the high-speed waveform recording device are used to collect the output signal of the standard source for comparison and judgment. The secondary testing platform also includes an adjustable AC voltage regulator, a programmable electronic load, an electrical parameter tester, a control signal indicator, and a line loss module. The adjustable AC voltage regulator is used to power the device under test and perform power-related tests. The programmable electronic load is used for power load testing. The electrical parameter tester is used for power consumption testing. The control signal indicator is used for control output and status input display. The line loss module is used to generate standard power pulses and clock pulses for power detection.

[0017] The present invention has the following beneficial effects: This solution constructs an association model mapping data items to be matched to standardized function names and introduces a self-learning mechanism for confidence weights based on manual verification. This enables adaptive matching and automatic generation of detection schemes for heterogeneous point tables from different manufacturers, effectively avoiding the inefficiency and error-prone nature of manually verifying point markers item by item when testing integrated primary and secondary equipment from different manufacturers. Furthermore, through a unified semantic layer mapping, heterogeneous signals such as analog quantities, digital switch quantities, and networked digital messages are uniformly encapsulated into standardized intermediate semantic messages, eliminating physical differences in equipment at the signal level. This allows a single detection system to be compatible with various types of integrated primary and secondary equipment, including electronic, electromagnetic, and digital systems. The association model and unified semantic layer mapping work together to automate the entire process from point table configuration to signal processing, and from scheme generation to detection execution, thereby improving the efficiency, accuracy, and reliability of integrated primary and secondary equipment testing and adapting to the testing of heterogeneous equipment from multiple manufacturers. Attached Figure Description

[0018] Figure 1 This is a flowchart of a specific embodiment of the testing method applicable to various types of primary and secondary fusion equipment described in this solution. Detailed Implementation

[0019] The following is in conjunction with the instruction manual appendix. Figure 1 The present invention will be further described in detail below with reference to the following embodiments, but the present invention is not limited to the following embodiments: Example 1:

[0020] A testing method applicable to various types of primary and secondary fusion equipment includes the following steps: S1: Construct an association model, which is used to characterize the mapping relationship between the data items to be matched in the point table of the device under test and the standardized function names in the association model. The data items to be matched include object address and point number keyword. S2: Receive the first point table of the first device under test, and based on the association model, recommend candidate function names for the data items to be matched in the first point table, and obtain the user's confirmation result of the candidate function names, so as to establish a mapping relationship between the data items to be matched and the function names. S3: Update the mapping relationship in the association model based on the user confirmation result; S4: Receive the second point table of the second device under test, call the updated association model to match the data items to be matched in the second point table, automatically establish a matching relationship between the data items to be matched that meet the preset conditions and the function name, prompt manual confirmation for those that do not meet the preset conditions, and return to S3 to iteratively update the association model.

[0021] In response to the aforementioned issues regarding the integration and commissioning of primary and secondary integrated equipment, the point tables of integrated equipment from different manufacturers differ in address arrangement and naming conventions. Traditional methods require testing personnel to manually verify the configuration item by item, which is inefficient and prone to errors. If a fixed template is used for matching, it cannot adapt to the needs of new manufacturers or new models. This solution employs a mechanism of manual confirmation, self-learning of the association model based on the confirmation results, and automatic matching based on the updated association model. This allows the association model to obtain a more accurate mapping relationship between the data items to be matched and the function names in the association model in each manual confirmation. This not only solves the problem of the testing scheme being unable to adapt to the differences in the point tables of secondary equipment from different manufacturers, but also improves the accuracy and efficiency of the point table matching for primary and secondary integrated equipment, solving the problem that fixed templates cannot adapt to heterogeneous point tables from multiple manufacturers.

[0022] Specifically, this solution provides a testing method applicable to various types of primary and secondary integrated equipment. In this solution, the association model is used to record the correspondence between the equipment point table and the standard function name. When the first device under test is connected to the system, the system provides matching suggestions between the data items to be matched and the candidate function names based on the association model. The results are then manually confirmed to establish a more accurate mapping relationship. The confirmation results are then applied to the association model to update the mapping relationship in the association model. When subsequent devices are connected to the system, the updated association model is used to automatically complete the matching of the data items to be matched and the function names. The matching items that do not meet the preset conditions and the uncertain parts of the function point table are handed over to manual confirmation. The manual confirmation results are then fed back to the association model for updating the association model, thereby realizing the development of the automated matching capability of the association model towards greater and greater accuracy.

[0023] In one specific embodiment, the detection system first executes S1 to construct an initial association model. This association model pre-defines a framework for the correspondence between address ranges, point number keywords, and function names commonly used in the field of power distribution automation. For example, within the telemetry address range (4000H-4FFFH), a mapping relationship is pre-defined between common point number keywords such as "UA", "Ua", and "A-phase voltage" and the function name "A-phase voltage". Within the telemetry address range (6000H-6FFFH), a mapping relationship is pre-defined between common point number keywords such as "switch position", "CB_Status", and "BreakerPos" and the function name "switch position". The confidence weights of all mapping relationships are set to initial values.

[0024] When the first secondary device from Manufacturer A is connected to the detection system, S2 is executed: The system reads its point table, which contains the data item to be matched, "Object Address = 4001H, Point Number Keyword = UA". The association model locates the telemetry function range based on the high-order code "4" of address 4001H. Within this range, it retrieves two candidate function names associated with the point number keyword "UA", namely "A-phase voltage" and "unbalanced voltage". After sorting them according to the current confidence score, the system pushes them to the user. When the user confirms and selects "A-phase voltage", the system establishes a mapping relationship of "4001H (object address) + UA (point number keyword) pointing to A-phase voltage (function name)". Then, S3 is executed: The system uses the confirmation result as a feedback training sample and increases the confidence weight of the mapping path "4001H + UA → A-phase voltage" in the association model.

[0025] When a second device of the same model from Manufacturer A is connected, S4 is executed: the system reads the same "4001H+UA" from its point table, calls the association model updated by S3 for matching. At this time, the confidence weight of the mapping path has exceeded the preset threshold and the candidate is unique. The system automatically establishes the matching relationship between the data item to be matched "4001H+UA" and the function name "A phase voltage" without manual intervention.

[0026] When the first device from vendor B is connected, its point table includes a data item to be matched: "Object address 5000H, point number keyword PhA_Volt". The system executes S2, which locates the telemetry address range based on the high-order code "5" of address 5000H in the first-level search. In the second-level search, the point number keyword "PhA_Volt" is matched with the point number keywords associated with each function name in the range. Since the matching degree of the point number keyword "Volt" associated with "A phase voltage" exceeds the preset value, "A phase voltage" is pushed to the user as the first choice after being sorted by confidence score. After the user confirms, S3 is executed to update the association model.

[0027] When a second device or a new model of the same series from Manufacturer B is connected, the system executes S4, and the data item to be matched, “5000H+PhA_Volt”, is automatically matched to the function name “A-phase voltage”.

[0028] Example 2:

[0029] This embodiment is a further refinement of embodiment 1: The association model stores the confidence weights of each mapping relationship, wherein: The specific steps for recommending candidate function names in S2 are as follows: calculate the confidence score of each candidate function name based on the confidence weight, and push at least one candidate function name with the highest confidence score to the user for confirmation; The specific method for updating the mapping relationship described in S3 is to use the mapping relationship between the object address, dot keyword and function name confirmed by the user as a feedback training sample and increase the confidence weight of the mapping relationship. The preset condition in S4 is: there is a unique candidate function name whose confidence weight exceeds a preset threshold; when the preset condition is not met, a limited candidate list generated from multiple candidate function names arranged in descending order of confidence score will be provided to the user for selection and confirmation.

[0030] The above provides a technical solution for automatically matching data items and function names based on confidence weights. Specifically, in S2, when providing candidate function name recommendations to the user, the confidence score of each candidate function name is calculated based on the confidence weights of each current mapping path, and the candidate with the highest score is pushed to the user. In S3, based on the user's confirmation, the system uses the mapping relationship as a feedback training sample to increase the confidence weight of the mapping path. In S4, automatic matching is completed when there is a unique candidate function name. When there are other candidate function names, in order to avoid incorrect matching, a limited candidate list is provided for the user to select and confirm.

[0031] This solution introduces a confidence weight, assigning a measurable weight value to each mapping relationship: when a mapping relationship is manually confirmed, the system increases the confidence weight of that mapping relationship to complete the update, so that the reliability of the mapping relationship is quantified. In subsequent matching, whether the confidence weight exceeds a preset threshold is used as the trigger condition for automatic matching.

[0032] In one specific embodiment, after the keyword "IA" for the secondary equipment of Manufacturer A is manually confirmed as the function name "A-phase current", the association model increases the confidence weight of the mapping relationship by a set increment based on the original value. When the confidence weight of the mapping path has not yet exceeded the preset threshold, subsequent encounters with the keyword "IA" will still recommend candidate function names for user confirmation. The weight continues to increase after each confirmation until it exceeds the threshold. When "PhA_Volt" from Manufacturer B is matched for the first time, since the mapping path weight is the initial value and has not exceeded the preset threshold, the system generates a restricted candidate list for user selection and confirmation. After confirmation, the weight of the path increases. When subsequent devices of the same model are connected, the weight has exceeded the threshold and the candidate is unique, so the system automatically establishes a matching relationship.

[0033] Example 3:

[0034] This embodiment is a further refinement of embodiment 2: The confidence score for each candidate function name calculated based on confidence weight in S2 adopts a multi-level retrieval strategy, including: First-level retrieval: Determine the object address of the data item to be matched, retrieve the candidate set of function names in the association model that match the encoding features of the object address, and use them as the first candidate set; Second-level search: Within the first candidate set obtained from the first-level search, keyword matching is performed based on the dot keyword of the data item to be matched to obtain the second-level candidate set; Third-level search: If the candidate set obtained by the first-level search or the second-level search is empty or the number of candidates is lower than the preset value, the search scope is expanded to the function name candidate set corresponding to other address ranges in the association model for supplementary search; The candidate function names obtained from each level of retrieval are sorted in descending order of confidence score to generate the final list of candidate function names.

[0035] The above provides a specific scheme for implementing confidence scoring of candidate function names based on a multi-level retrieval strategy: The first-level retrieval uses the object address of the data item to be matched to retrieve a set of candidate function names that match the encoding features of the object address in the association model, and uses this as the first candidate set; the second-level retrieval, based on the first candidate set, further obtains a second-level candidate set through keyword matching based on dot-major keywords; the third-level retrieval serves as a supplementary mechanism, expanding the retrieval scope to conduct supplementary retrieval when the candidate sets obtained from the first two levels are empty or the number of candidates is lower than a preset value; finally, the candidate function names obtained after retrieval are sorted in descending order of confidence score to generate the final list of candidate function names.

[0036] The above scheme utilizes the inherent characteristic that different functional types of data in the secondary equipment point table are usually distributed in specific address ranges. First, it quickly narrows down the candidate range by using the encoding characteristics of the object address. For example, it locates the telemetry address range by using the object address 4001H and uses the function name of the telemetry class as the first candidate set. Then, it performs keyword matching within the narrowed range. For example, the second-level search matches the keyword "UA" in the first candidate set and obtains two candidates, "A-phase voltage" and "AB line voltage". The number of candidates is sufficient, so the third-level search is not triggered. The global supplementary search is only triggered when the results of the first two levels of search are insufficient (this situation is not common. For example, if a manufacturer sets the object address of common data such as "A-phase voltage" to a region that is not located in the common telemetry range, then a valid first candidate set cannot be obtained in the first-level search). While ensuring the accuracy of the search, it effectively narrows down the candidate range and improves the search efficiency and the accuracy of the confidence score.

[0037] Example 4:

[0038] This embodiment is a further refinement of embodiment 3: The first-level retrieval specifically involves: extracting the high-order code of the object address of the data item to be matched, and retrieving the set of candidate function names corresponding to the address range that matches the high-order code in the association model; The second-level retrieval specifically involves: performing string matching between the dot-matrix keywords of the data items to be matched and the dot-matrix keywords associated with each function name in the first candidate set obtained from the first-level retrieval, retaining the candidate function names with a matching degree exceeding a preset value, and forming the second-level candidate set; The third-level retrieval specifically involves: if the candidate set obtained from the first-level retrieval is empty, or the number of candidate function names retained after the second-level retrieval is zero, then the address range limitation is removed, and global keyword matching is performed using the dot keyword within the function name range corresponding to all address ranges in the association model to obtain a supplementary candidate set.

[0039] The above provides a specific implementation of a three-level retrieval strategy. Specifically, it involves extracting a candidate set of function names based on high-order encoding, obtaining a second-level candidate set based on string matching, and performing a global search to obtain a supplementary candidate set after failures in the first and second-level searches. This approach simultaneously ensures retrieval efficiency, matching accuracy, and compatibility with non-standard addresses. The characteristics of this retrieval strategy are: First, it uses the high-order encoding of the object address as the first-level filtering condition. Since the high-order encoding of the object address in the secondary equipment point table usually implicitly indicates the function type of the data, extracting the high-order encoding can quickly locate the address range of the corresponding function type without traversing the entire function name database. Second, within the finite candidate set (function name candidate set) limited by the address range, string matching is used as the second-level filtering method. A quantitative matching degree threshold is used to control the candidate quality, avoiding the introduction of irrelevant candidates due to partial keyword overlap. Finally, whether the results of the first two levels of retrieval are empty is used as the trigger condition for the third-level retrieval. While ensuring necessary global searches of non-standard address data, this minimizes the trigger frequency of global searches, balancing retrieval efficiency and recall. However, when dotted keywords are directly applied to global matching of function name databases, not only is the search scope large, but also, due to the easy occurrence of similar keywords for remote signaling and telemetry, mismatches across function types are likely to occur.

[0040] Example 5:

[0041] This embodiment is a further refinement of embodiment 1: Before receiving the first point table and the second point table, a unified semantic layer mapping step is also included, specifically: The output signal of the primary or secondary device under test is acquired through a signal source conversion device. The type of the output signal is at least one of analog signal, digital switch signal or networked digital message following standard communication protocol. Feature extraction is performed on the output signal, and the extracted effective information is uniformly encapsulated into a standardized detection intermediate semantic message that is decoupled from the underlying physical interface and communication protocol on which the association model depends, for use in subsequent detection steps.

[0042] The above provides a technical solution for unified preprocessing of potentially heterogeneous signals before point-to-point matching. Specifically, the output signal of the primary or secondary device under test is acquired through a signal source conversion device. The output signal type is at least one of analog signals, digital switch signals, or networked digital messages following standard communication protocols. Feature extraction is performed on the output signal, and the extracted effective information is uniformly encapsulated into standardized intermediate semantic messages for use in subsequent detection steps. The characteristic of this solution is that the obtained standardized intermediate semantic messages are completely decoupled from the underlying physical interface and communication protocol on which the association model depends. Regardless of whether the original signal is analog voltage or current, digital switch signal, or networked digital message, it is converted into a unified format semantic message after processing by the signal source conversion device. The system detection logic does not need to be aware of the physical form and protocol differences of the underlying signals. This solution uses a signal source conversion device to uniformly access and extract features from all possible signal types, and encapsulates the effective information into standardized intermediate semantic messages for detection. This eliminates subsequent detection compatibility issues caused by physical differences in equipment at the signal input level, enabling the detection system to not only address the detection needs of multiple types of primary and secondary fusion equipment at the point table heterogeneity level, but also to be compatible with multiple types of primary and secondary fusion equipment at the signal type level, providing a unified data foundation for subsequent detection steps.

[0043] Example 6:

[0044] This embodiment is a further refinement of embodiment 5: For the output signal of type analog signal or digital switch signal, the acquisition module inside the signal source conversion device quantizes it into feature data with physical dimensions and parameter values, and encapsulates it into a standardized detection intermediate semantic message, which is then used as a response semantic message. For the output signal of type networked digital message, the protocol parsing module inside the signal source switching device parses it to application layer data, extracts information after stripping the frame header and encoding rules of the communication protocol, and encapsulates the extracted information into a standardized detection intermediate semantic message, which is then used as a response semantic message. The detection excitation commands issued by the detection system are uniformly expressed as standardized detection intermediate semantic messages, which are subsequently used as excitation semantic messages. These excitation semantic messages are used to record the expected value of the excitation signal applied by the detection system to the device under test. The detection system compares the stimulus semantic message with the response semantic message returned from the device under test in real time to verify the consistency between the response of the device under test and the expected value.

[0045] The above provides a technical solution for specifying the unified semantic layer mapping steps and establishing a closed-loop verification mechanism. In this solution, for analog signals or digital switch signals, the acquisition module inside the signal source conversion device quantizes them into feature data with physical dimensions and parameter values ​​and encapsulates them into response semantic messages; for networked digital messages, the protocol parsing module extracts the effective information after stripping the frame header and encoding rules of the communication protocol and encapsulates it into response semantic messages. As those skilled in the art will understand, since the system needs to be compatible with digital devices, the standardized intermediate semantic messages for testing are preferably digital signals. When the device under test outputs an analog signal, it is encapsulated into a standardized intermediate semantic message for testing after A / D conversion and quantization. When the device under test outputs digital messages such as SV, it is necessary to strip the communication protocol packaging, including but not limited to Ethernet frame headers, IP / UDP headers, and ASN.1 encoding rules, extract valid information such as sampled values, quality bits, and timestamps, and then encapsulate it to obtain a standardized intermediate semantic message for testing. On this basis, the testing excitation commands issued by the testing system are uniformly expressed as excitation semantic messages, which are used to record the expected value of the excitation signal. The system compares the excitation semantic messages with the response semantic messages returned from the device under test in real time to verify the consistency between the response of the device under test and the expected value. This provides a technical basis for comprehensively judging whether the test item is qualified by combining the comparison benchmark with the comparison results of the response semantic messages and the excitation semantic messages.

[0046] Example 7:

[0047] This embodiment is a further refinement of embodiment 6: The method is based on an integrated testing system, which includes a control cabinet, a primary testing platform, a secondary testing platform, and a testing management system. Following the unified semantic layer mapping step, the method further includes a complete equipment testing step, specifically: The primary device to be tested is connected to the primary testing platform, the secondary device to be tested is connected to the secondary testing platform, and a communication connection is established between the testing management system and the secondary device to be tested through a communication device. The device information of the device to be tested is entered, the testing management system calibrates the consistency of the device information, and calls the generated testing plan according to the testing function; The detection management system adjusts the power output module in the primary detection platform through the control cabinet to output standard voltage and standard current signals with adjustable angle, amplitude and frequency to the primary device under test. The detection management system collects the actual values ​​of the standard voltage and standard current signals through the standard measurement module in the primary detection platform, and uses them as a comparison benchmark. The testing management system reads the test results of the secondary equipment under test through the secondary testing platform, and compares them with the comparison benchmark, response semantic message, and excitation semantic message to determine whether the test items are qualified. After the testing plan is completed, the testing management system controls the self-discharge module in the primary testing platform to automatically discharge the residual charge on the primary equipment under test. After the discharge is completed, it automatically exits and prompts for replacement of the equipment under test, while generating a test report.

[0048] The above provides a technical solution for the integrated testing of primary and secondary fusion equipment by combining point table self-learning matching and unified semantic layer mapping. In this solution, after completing the unified semantic layer mapping step, the equipment testing steps are performed: the primary and secondary equipment to be tested are connected to the corresponding platforms and a communication connection is established. The equipment information is entered and the system is calibrated for consistency. Then, the generated testing scheme is called. The power output module in the primary platform is adjusted by the control cabinet to inject standard voltage and current signals into the primary equipment. The standard measurement module in the primary platform collects the actual output value as a comparison benchmark. The secondary platform reads the test results of the secondary equipment. The test items are judged comprehensively by combining the comparison benchmark with the comparison results of the response semantic message and the excitation semantic message. After the test is completed, the self-discharge module automatically discharges the residual voltage and prompts to replace the equipment to be tested, and a test report is generated at the same time.

[0049] Unlike existing integrated primary and secondary testing systems that typically operate in a separate manner, this solution addresses the shortcomings of traditional methods. First, it automatically matches the standard signal values ​​injected from the primary side with the corresponding telemetry points in the secondary terminal point table, relying entirely on manual establishment. This is inefficient and prone to mismatches. Second, it addresses the inability to automatically fuse the physical benchmark comparison results from the primary side with the communication message comparison results from the secondary side, requiring testing personnel to manually review the results from both systems and make a comprehensive judgment, thus hindering automated testing. This solution organically integrates the aforementioned point table self-learning matching mechanism with a unified semantic layer mapping and closed-loop verification mechanism into a complete testing solution. The solution employs the following approach: First, it automatically matches the point table through self-learning or manual verification. The system efficiently maps each data item in the secondary equipment checklist to the standardized function names of the testing system, eliminating the need for manual configuration. Secondly, the testing system simultaneously utilizes the physical reference values ​​collected by the standard measurement module of the primary testing platform (as the primary side accuracy reference) and the closed-loop comparison results of the excitation and response semantic messages (as the secondary side communication and function reference). By comprehensively applying these two different comparison results to the qualification determination of the testing items, the reliability of the qualification determination results can be effectively guaranteed. Finally, the entire testing process can achieve closed-loop automated operation, thereby achieving the goal of automated complete testing of integrated primary and secondary equipment.

[0050] Example 8:

[0051] This embodiment is a further refinement of embodiment 7: When only secondary equipment is tested individually, the specific steps for testing the entire set of equipment are as follows: The secondary device under test is connected to the secondary testing platform through the signal source adapter, and a communication connection is established between the testing management system and the secondary device under test through the communication equipment. The device information of the secondary device to be tested is entered and the transformer type is selected. The detection management system calibrates the consistency of the device information and controls the signal source conversion device to switch to the signal mode corresponding to the transformer type. The detection management system calls the generated detection plan based on the detection point table; The detection management system controls the standard source in the secondary detection platform to output standard voltage and standard current signals to the secondary device under test; The detection management system collects the actual value of the standard source output through the standard meter and high-speed waveform recording device in the secondary detection platform, and determines whether the output meets the detection requirements. The testing management system calls the corresponding testing modules to perform functional tests according to the testing plan and generates a testing report.

[0052] The above describes an application requiring only the testing of secondary equipment. In this solution, the secondary equipment under test is connected to the secondary testing platform via a signal source adapter and a communication connection is established. When entering equipment information, the transformer type is selected simultaneously. After the system calibration information is consistent, the signal source adapter is switched to the signal mode corresponding to the selected transformer type. Based on the test point table, the generated test scheme is invoked, and a standard signal is directly injected into the secondary equipment from a standard source. The actual output value of the standard source is collected by a standard meter and a high-speed waveform recorder to determine whether the output meets the test requirements. Finally, the corresponding test module is invoked to perform functional testing and generate a report. This solution is suitable for applications such as: factory testing of power distribution terminals, incoming inspection, and on-site maintenance diagnosis. These testing scenarios do not include the testing of primary equipment. This solution achieves compatibility between complete set testing and individual secondary equipment testing on the same testing system through process replacement, avoiding the need for two separate testing systems to handle all testing scenarios.

[0053] Example 9:

[0054] Based on Embodiment 1, this embodiment provides a detection system applicable to various types of primary and secondary fusion equipment sets. The detection system includes: A signal source conversion device is used to acquire the output signal of the primary and / or secondary device under test. The type of the output signal is at least one of analog signal, digital switch signal or networked digital message following standard communication protocol, and the output signal is uniformly encapsulated into a standardized detection intermediate semantic message. The memory is used to store the association model, which is used to characterize the mapping relationship between the data items to be matched in the point table of the device under test and the standardized function names. The data items to be matched include object addresses and point number keywords. The human-computer interaction interface is used to push candidate function names to users and receive confirmation commands from users; The processor is configured to perform the method described in Example 1.

[0055] As is readily understood, this solution provides a detection system for implementing the method. This system includes a signal source conversion device, a memory, a human-machine interface, and a processor. The signal source conversion device acquires the output signal of the device under test and encapsulates it into a standardized intermediate semantic message, eliminating physical differences between devices at the signal input layer. The memory stores an association model that records the mapping relationship between object addresses and point number keywords in the point table and standardized function names, along with their confidence weights. The human-machine interface pushes candidate function names to the user and receives confirmation commands, enabling human-machine collaboration between manual confirmation and model self-learning. The processor executes all detection steps, from association model construction, point table learning and matching, confidence weight updates to semantic layer mapping and closed-loop verification. This system integrates the two core mechanisms of point table self-learning matching and unified semantic layer mapping into physical components, forming an integrated and deployable detection system.

[0056] Example 10: This embodiment is a further refinement of embodiment 9: It also includes primary testing platforms and secondary testing platforms; The primary testing platform includes a power output module, a standard measurement module, and a self-discharge module. The power output module is used to output standard voltage and standard current signals with adjustable angle, amplitude, and frequency to the primary equipment. The standard measurement module is used to collect the actual values ​​of the standard voltage and standard current signals as a comparison benchmark. The self-discharge module is used to automatically discharge the residual charge of the primary equipment after the test is completed and automatically exit after the discharge is completed. The secondary testing platform includes the signal source conversion device, the standard source, the standard meter, and the high-speed waveform recording device; the standard source is used to output standard voltage and standard current signals to the secondary equipment, and the standard meter and the high-speed waveform recording device are used to collect the output signal of the standard source for comparison and judgment. The secondary testing platform also includes an adjustable AC voltage regulator, a programmable electronic load, an electrical parameter tester, a control signal indicator, and a line loss module. The adjustable AC voltage regulator is used to power the device under test and perform power-related tests. The programmable electronic load is used for power load testing. The electrical parameter tester is used for power consumption testing. The control signal indicator is used for control output and status input display. The line loss module is used to generate standard power pulses and clock pulses for power detection.

[0057] As is easily understood, this solution, based on the aforementioned testing system, further clarifies the specific hardware components of the primary and secondary testing platforms: The primary testing platform includes a power output module, a standard measurement module, and a self-discharge module. The power output module outputs standard voltage and current signals with adjustable angle, amplitude, and frequency to the primary equipment. The standard measurement module uses high-precision acquisition of actual output values ​​as a physical comparison benchmark. The self-discharge module automatically discharges residual charge from the primary equipment after testing to ensure operational safety. The secondary testing platform, in addition to a signal source adapter, standard source, standard meter, and high-speed waveform recording device, integrates an adjustable AC voltage regulator, a programmable electronic load, an electrical parameter tester, a control signal indicator, and a line loss module. These modules respectively handle functions such as sample power supply, power load testing, power consumption measurement, control output and status display, and power pulse generation. By integrating all hardware modules required for primary-side accuracy verification and secondary-side functional testing into a single system, this solution achieves fully automated testing covering everything from the physical accuracy of the primary equipment to the communication and functionality of the secondary equipment. This avoids the redundant investment and compatibility issues associated with discrete testing systems that require multiple sets of equipment.

[0058] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific embodiments of the present invention are limited to these descriptions. For those skilled in the art, other embodiments derived without departing from the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. A testing method applicable to various types of primary and secondary fusion integrated equipment, characterized in that, Includes the following steps: S1: Construct an association model, which is used to characterize the mapping relationship between the data items to be matched in the point table of the device under test and the standardized function names in the association model. The data items to be matched include object address and point number keyword. S2: Receive the first point table of the first device under test, and based on the association model, recommend candidate function names for the data items to be matched in the first point table, and obtain the user's confirmation result of the candidate function names, so as to establish a mapping relationship between the data items to be matched and the function names. S3: Update the mapping relationship in the association model based on the user confirmation result; S4: Receive the second point table of the second device under test, call the updated association model to match the data items to be matched in the second point table, automatically establish a matching relationship between the data items to be matched that meet the preset conditions and the function name, prompt manual confirmation for those that do not meet the preset conditions, and return to S3 to iteratively update the association model; the association model stores the confidence weights of each mapping relationship, wherein: The specific steps for recommending candidate function names in S2 are as follows: calculate the confidence score of each candidate function name based on the confidence weight, and push at least one candidate function name with the highest confidence score to the user for confirmation; The specific method for updating the mapping relationship described in S3 is to use the mapping relationship between the object address, dot keyword and function name confirmed by the user as a feedback training sample and increase the confidence weight of the mapping relationship. The preset condition in S4 is: there is a unique candidate function name whose confidence weight exceeds a preset threshold; when the preset condition is not met, a limited candidate list generated from multiple candidate function names arranged in descending order of confidence score will be provided to the user for selection and confirmation. The confidence score for each candidate function name calculated based on confidence weight in S2 adopts a multi-level retrieval strategy, including: First-level retrieval: Determine the object address of the data item to be matched, retrieve the candidate set of function names in the association model that match the encoding features of the object address, and use them as the first candidate set; Second-level search: Within the first candidate set obtained from the first-level search, keyword matching is performed based on the dot keyword of the data item to be matched to obtain the second-level candidate set; Third-level search: If the candidate set obtained by the first-level search or the second-level search is empty or the number of candidates is lower than the preset value, the search scope is expanded to the function name candidate set corresponding to other address ranges in the association model for supplementary search; The candidate function names obtained from each level of retrieval are sorted in descending order of confidence score to generate the final list of candidate function names; The first-level retrieval specifically involves: extracting the high-order code of the object address of the data item to be matched, and retrieving the set of candidate function names corresponding to the address range that matches the high-order code in the association model; The second-level retrieval specifically involves: performing string matching between the dot-matrix keywords of the data items to be matched and the dot-matrix keywords associated with each function name in the first candidate set obtained from the first-level retrieval, retaining the candidate function names with a matching degree exceeding a preset value, and forming the second-level candidate set; The third-level retrieval specifically involves: if the candidate set obtained from the first-level retrieval is empty, or the number of candidate function names retained after the second-level retrieval is zero, then the address range limitation is removed, and global keyword matching is performed using the dot keyword within the function name range corresponding to all address ranges in the association model to obtain a supplementary candidate set.

2. The testing method applicable to various types of primary and secondary fusion complete sets of equipment according to claim 1, characterized in that, Before receiving the first point table and the second point table, a unified semantic layer mapping step is also included, specifically: The output signal of the primary or secondary device under test is acquired through a signal source conversion device. The type of the output signal is at least one of analog signal, digital switch signal or networked digital message following standard communication protocol. Feature extraction is performed on the output signal, and the extracted effective information is uniformly encapsulated into a standardized detection intermediate semantic message that is decoupled from the underlying physical interface and communication protocol on which the association model depends, for use in subsequent detection steps.

3. The testing method applicable to various types of primary and secondary fusion complete sets of equipment according to claim 2, characterized in that, For the output signal of type analog signal or digital switch signal, the acquisition module inside the signal source conversion device quantizes it into feature data with physical dimensions and parameter values, and encapsulates it into a standardized detection intermediate semantic message, which is then used as a response semantic message. For the output signal of type networked digital message, the protocol parsing module inside the signal source switching device parses it to application layer data, extracts information after stripping the frame header and encoding rules of the communication protocol, and encapsulates the extracted information into a standardized detection intermediate semantic message, which is then used as a response semantic message. The detection excitation commands issued by the detection system are uniformly expressed as standardized detection intermediate semantic messages, which are subsequently used as excitation semantic messages. These excitation semantic messages are used to record the expected value of the excitation signal applied by the detection system to the device under test. The detection system compares the stimulus semantic message with the response semantic message returned from the device under test in real time to verify the consistency between the response of the device under test and the expected value.

4. The testing method applicable to various types of primary and secondary fusion complete sets of equipment according to claim 3, characterized in that, The method is based on an integrated testing system, which includes a control cabinet, a primary testing platform, a secondary testing platform, and a testing management system. Following the unified semantic layer mapping step, the method further includes a complete equipment testing step, specifically: The primary device to be tested is connected to the primary testing platform, the secondary device to be tested is connected to the secondary testing platform, and a communication connection is established between the testing management system and the secondary device to be tested through a communication device. The device information of the device to be tested is entered, the testing management system calibrates the consistency of the device information, and calls the generated testing plan according to the testing function; The detection management system adjusts the power output module in the primary detection platform through the control cabinet to output standard voltage and standard current signals with adjustable angle, amplitude and frequency to the primary device under test. The detection management system collects the actual values ​​of the standard voltage and standard current signals through the standard measurement module in the primary detection platform, and uses them as a comparison benchmark. The testing management system reads the test results of the secondary equipment under test through the secondary testing platform, and compares them with the comparison benchmark, response semantic message, and excitation semantic message to determine whether the test items are qualified. After the testing plan is completed, the testing management system controls the self-discharge module in the primary testing platform to automatically discharge the residual charge on the primary equipment under test. After the discharge is completed, it automatically exits and prompts for replacement of the equipment under test, while generating a test report.

5. The testing method applicable to various types of primary and secondary fusion complete sets of equipment according to claim 4, characterized in that, When only secondary equipment is tested individually, the specific steps for testing the entire set of equipment are as follows: The secondary device under test is connected to the secondary testing platform through the signal source adapter, and a communication connection is established between the testing management system and the secondary device under test through the communication equipment. The device information of the secondary device to be tested is entered and the transformer type is selected. The detection management system calibrates the consistency of the device information and controls the signal source conversion device to switch to the signal mode corresponding to the transformer type. The detection management system calls the generated detection plan based on the detection point table; The detection management system controls the standard source in the secondary detection platform to output standard voltage and standard current signals to the secondary device under test; The detection management system collects the actual value of the standard source output through the standard meter and high-speed waveform recording device in the secondary detection platform, and determines whether the output meets the detection requirements. The testing management system calls the corresponding testing modules to perform functional tests according to the testing plan and generates a testing report.

6. A testing system applicable to various types of primary and secondary integrated equipment, characterized in that, The detection system includes: A signal source conversion device is used to acquire the output signal of the primary and / or secondary device under test. The type of the output signal is at least one of analog signal, digital switch signal or networked digital message following standard communication protocol, and the output signal is uniformly encapsulated into a standardized detection intermediate semantic message. The memory is used to store the association model, which is used to characterize the mapping relationship between the data items to be matched in the point table of the device under test and the standardized function names. The data items to be matched include object addresses and point number keywords. The human-computer interaction interface is used to push candidate function names to users and receive confirmation commands from users; The processor is configured to perform the method according to any one of claims 1 to 5.

7. The detection system applicable to various types of primary and secondary fusion integrated equipment as described in claim 6, characterized in that, It also includes primary testing platforms and secondary testing platforms; The primary testing platform includes a power output module, a standard measurement module, and a self-discharge module. The power output module is used to output standard voltage and standard current signals with adjustable angle, amplitude, and frequency to the primary equipment. The standard measurement module is used to collect the actual values ​​of the standard voltage and standard current signals as a comparison benchmark. The self-discharge module is used to automatically discharge the residual charge of the primary equipment after the test is completed and automatically exit after the discharge is completed. The secondary testing platform includes the signal source conversion device, the standard source, the standard meter, and the high-speed waveform recording device; the standard source is used to output standard voltage and standard current signals to the secondary equipment, and the standard meter and the high-speed waveform recording device are used to collect the output signal of the standard source for comparison and judgment. The secondary testing platform also includes an adjustable AC voltage regulator, a programmable electronic load, an electrical parameter tester, a control signal indicator, and a line loss module. The adjustable AC voltage regulator is used to power the device under test and perform power-related tests. The programmable electronic load is used for power load testing. The electrical parameter tester is used for power consumption testing. The control signal indicator is used for control output and status input display. The line loss module is used to generate standard power pulses and clock pulses for power detection.

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