Non-standard ammeter anomaly detection system and method based on parameterized protocol model

Abstract

The invention discloses a non-standard ammeter anomaly detection method based on a parameterized protocol model, and the method comprises the steps: firstly constructing a parameterized protocol model which comprises test point models corresponding to a plurality of test points; loading the parameterized protocol model, and determining a detection execution sequence; generating a request frame for any test point according to communication information configured in the parameterized protocol model, sending the request frame to a tested ammeter, performing communication interaction with the tested ammeter, and receiving a response frame returned by the tested ammeter; performing verification on the response data, and performing field-level analysis and data conversion according to a verification result to obtain an actual detection value of the test point; and performing fault judgment according to the actual detection value and generating a detection report. According to the method, non-standard electricity meters of different models and different protocol formats can be adapted under the condition that detection program codes do not need to be modified.

Description

Technical Field

[0001] This invention relates to the field of electricity meter testing and maintenance diagnosis technology, specifically a non-standard electricity meter anomaly detection system and method based on a parameterized protocol model. Background Technology

[0002] With the rapid development of smart grid construction and power Internet of Things (IoT) technology, electricity meters, as key equipment for power system metering and monitoring, are increasingly employing diverse communication methods and protocols. In practical deployments, in addition to widely used standard communication protocols such as DL / T645 and Modbus, many manufacturers often adopt proprietary or non-standard communication protocols for reasons such as device function customization, security isolation, or technical protection. These protocols often exhibit significant differences in frame structure, field definitions, verification methods, and data encoding rules (such as byte order and data type representation), forming a complex protocol ecosystem characterized by "one protocol per manufacturer and one specification per type."

[0003] Currently, fault detection and maintenance diagnosis of electricity meters mainly rely on detection systems based on standard communication protocols or fixed parsing rules. These systems typically hard-code the communication command construction, data parsing logic, verification algorithms, and judgment rules into the program. When faced with non-standard protocols, existing technical solutions reveal the following significant shortcomings: High adaptation costs and long cycles: Each time a new protocol or new meter model is integrated, technicians need to thoroughly analyze its protocol manual and rewrite or extensively modify the communication module, parsing code, and verification logic. This process not only requires developers to have professional protocol analysis capabilities but also leads to more software maintenance branches and more complex version management, resulting in longer system deployment cycles and difficulty in quickly responding to on-site testing needs.

[0004] Poor flexibility and scalability: The hard-coded detection logic is deeply coupled with the specific protocol, resulting in a lack of versatility in the core detection program of the system. Any minor protocol change (such as field position offset or verification algorithm adjustment) may trigger program modifications, making it difficult for the system to adapt to protocol version iterations or subtle differences between different batches of devices, resulting in weak field adaptability.

[0005] Inefficient Batch Testing and Result Management: In practical scenarios such as power operation and maintenance and metering inspection, it is often necessary to perform automated batch testing on multiple parameter points such as voltage, current, and power of hundreds or thousands of meters, and to obtain overall pass rate, classification statistics, and a clear list of anomalies. Traditional methods lack a unified protocol description and task scheduling mechanism, making it difficult to efficiently organize batch tasks for multiple test points. At the same time, test results are often output in the form of raw data or simple logs, lacking structured status summaries (such as "communication anomaly", "parsing anomaly", "value exceeding limits") and cause records. Operation and maintenance personnel need to manually screen and judge fault points, resulting in low fault location efficiency and easy omissions.

[0006] Difficulties in knowledge accumulation and reuse: The parsing code and verification rules developed for each non-standard protocol are scattered throughout the program, failing to form a reusable and configurable protocol knowledge base. This not only leads to the loss of knowledge assets but also requires repeated development for subsequent function upgrades and protocol extensions, making it impossible to achieve the agile detection goal of "configuration once, reuse everywhere".

[0007] Therefore, existing technologies lack a universal method that can abstract and model the differences between non-standard protocols, and on this basis, decouple the testing process from specific protocols. There is an urgent need for a solution that can quickly adapt to multiple non-standard protocols without modifying the core program code, while supporting automated batch testing at multiple test points and outputting structured test reports and accurate fault location information, in order to improve the intelligence level and work efficiency of electricity meter testing and maintenance. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the prior art and propose a non-standard electricity meter anomaly detection system and method based on a parameterized protocol model. This method models and parameterizes the request instruction structure, response frame field position, verification rules, data parsing method, numerical conversion rules and fault judgment criteria involved in the communication process of non-standard electricity meters, thereby decoupling the detection process from the specific protocol. As a result, it can adapt to non-standard electricity meters of different models and protocol formats without modifying the detection program code.

[0009] To achieve the above objectives, the technical solution specifically adopted by the present invention is as follows: A method for detecting anomalies in non-standard electricity meters based on a parameterized protocol model includes the following steps: Step S1: Construct a parameterized protocol model, which includes test point models corresponding to multiple test points. Each test point model includes at least: basic information, category information, communication information, parsing information, conversion information, verification information, and judgment criterion information. Step S2: Load the parameterized protocol model, initialize test result objects for multiple test points respectively, generate batch detection tasks based on the multiple test points, and organize and schedule the detection execution order of multiple test points according to the batch detection tasks; Step S3: Under the scheduling of the batch testing task, for any test point, a request frame is generated according to the communication information configured in the parameterized protocol model, and the request frame is sent to the meter under test. Communication interaction is performed with the meter under test and a response frame returned by the meter under test is received. Step S4: Perform verification on the response data, and perform field-level parsing and data conversion based on the verification results to obtain the actual detection value of the test point; Step S5: Based on the fault judgment criteria configured in the parameterized protocol model, the actual detection value is judged, and the detection result status of the current test point is generated. The detection result status includes at least detection success, detection failure, and abnormal status. Step S6: Under the scheduling of the batch detection task, repeat steps S3 to S5 until the detection of all test points is completed. Based on the detection result status of all test points, generate a detection report containing overall statistical results and a list of abnormal items.

[0010] Preferably, the parsed information includes the starting position, length, data type, and byte order of the data field in the response frame.

[0011] Preferably, in step S4, the verification method is as follows: based on the verification type configured in the verification information, perform verification value calculation on the response frame data segment other than the verification field according to the verification type, compare the calculated verification value with the verification field in the response frame, and when the comparison fails, set the detection result status of the test point to parsing abnormal; if the comparison is successful, perform subsequent operations.

[0012] Preferably, in step S4, the field-level parsing method is as follows: based on the frame field annotations and parsing information in the parameterized protocol model, perform field-level parsing on the response frame to extract the original values.

[0013] Preferably, the field-level parsing is performed based on the data field positions determined by the frame field annotations and in conjunction with the parsing information.

[0014] Preferably, in step S4, the data conversion method is to multiply the original value by a scaling factor and then add an offset.

[0015] Preferably, the verification type includes at least one of cyclic redundancy check, summation check, or XOR check.

[0016] Preferably, the method for annotating the frame field is to perform semantic annotation on the byte segments in the request frame and / or response frame, wherein the semantic annotation includes at least the field name, starting position, length, and display format.

[0017] Preferably, when saving the frame field annotation, out-of-bounds checks and field overlap detection are performed. The out-of-bounds checks are used to limit the sum of the starting position and the length to not exceed the frame length, and the field overlap detection is used to limit the overlap of any field coverage area.

[0018] Preferably, the fault determination criteria include a minimum threshold and a maximum threshold; step S5 specifically includes: comparing the actual detection value with the minimum threshold and the maximum threshold; if the actual detection value is between the two, the detection is determined to be successful; if the actual detection value is less than the minimum threshold or greater than the maximum threshold, the detection is determined to be unsuccessful.

[0019] Preferably, in step S2, the multiple test points are grouped according to predefined categories; in step S6, the tests are performed in the order of grouping or in the order within the group; the generated test report also includes the classification test results statistically analyzed by category.

[0020] This invention also provides a non-standard electricity meter anomaly detection system based on a parameterized protocol model, comprising: The protocol model building module is used to configure and generate parameterized protocol models; The model management module is used to store and load the parameterized protocol model; The batch detection scheduling module is used to organize a queue of detection tasks for multiple test points based on the loaded parameterized protocol model. The communication execution module is used to generate a request command based on the parameterized protocol model and to communicate with the meter under test to obtain response data. The verification processing module is used to verify the response data according to the verification rules configured in the parameterized protocol model. The data parsing and conversion module is used to process the response data to obtain the actual detection value according to the parsing and conversion rules configured in the parameterized protocol model after the verification is passed. The status determination module is used to determine the actual detection value according to the fault determination criteria configured in the parameterized protocol model, and output the detection result status. The report generation module is used to summarize the detection results status of all test points and generate a detection report.

[0021] Preferably, the data parsing and conversion module includes: The parsing unit is used to extract the original value of a specified field from the response data based on the field annotation model and parsing information in the parameterized protocol model. The conversion unit is used to convert the original numerical value into an actual detection value with engineering units based on the conversion information in the parameterized protocol model.

[0022] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the above-described non-standard electricity meter anomaly detection method based on a parameterized protocol model.

[0023] This invention has the following characteristics and beneficial effects: In the technical solution of this invention, a parameterized protocol model is first constructed. This model is used to uniformly describe the detection rules for multiple test points. Each test point is associated with at least its communication command or command template, field annotation information, verification method, parsing rules, conversion rules, and judgment criteria. Before the detection begins, the parameterized protocol model is loaded, and test result objects are initialized for multiple test points, generating batch detection tasks. During the detection process, for any test point, a corresponding request command is generated based on the parameterized protocol model and communicates with the meter under test. After receiving the response data, the response data is verified according to the verification rules configured in the model. If the verification passes, the response data is parsed at the field level according to the field annotations and parsing rules to obtain the original value. Then, the original value is converted into an actual detection value with engineering significance according to the conversion rules. Subsequently, the actual detection value is compared with the judgment criteria pre-configured in the parameterized protocol model, thereby generating a detection result status of successful detection, failed detection, or abnormal for the test point. The abnormal status includes at least communication abnormality and parsing abnormality.

[0024] After completing the testing of multiple test points, this invention further performs statistical analysis on the test results to generate a test report containing overall test results, categorized test results, and a list of abnormal items. The overall test results include at least the total number of test points, the number of completed tests, the number of successful tests, the number of failed tests, the number of abnormal tests, and the pass rate. The categorized test results reflect the test status under different parameter categories. The list of abnormal items lists the test points that failed or were abnormal, along with their corresponding causes and test data. Through the above technical solution, this invention can achieve batch fault detection of multiple test points on electricity meters in complex and ever-changing non-standard protocol scenarios, and output test results and fault information in a structured manner, thereby significantly improving the universality, testing efficiency, and fault location efficiency of non-standard electricity meter testing. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the overall system architecture of the present invention.

[0026] Figure 2 This is a schematic diagram of the instruction editing and configuration interface of the present invention.

[0027] Figure 3 This is a schematic diagram of the field annotation configuration interface for the present invention.

[0028] Figure 4 This is a schematic diagram of the detection execution interface of the present invention.

[0029] Figure 5 This is a schematic diagram of the test report interface of the present invention.

[0030] Figure 6 This is a schematic diagram of the parameterized protocol model structure of the present invention. Detailed Implementation

[0031] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0032] Example 1 The non-standard electricity meter anomaly detection system based on a parameterized protocol model provided in this embodiment, such as... Figure 1 As shown, it includes the following modules: Protocol model building module: Used to configure and generate parameterized protocol models, supporting visual or configuration-based editing of communication commands, frame field annotations, verification rules, parsing rules, conversion rules, and fault judgment criteria; Model management module: used for storing, loading, and versioning parameterized protocol models, supporting model reuse and updates; Batch detection scheduling module: responsible for organizing the test point queue according to the loaded model and scheduling detection tasks by category or priority; Communication execution module: Communicates with the meter under test based on the request commands generated by the model, and supports common communication interfaces such as RS-485, infrared, and low-power wireless. Verification processing module: Verifies the response data according to the verification type configured in the model (such as CRC, summation, XOR, etc.); Data parsing and transformation module: includes a parsing unit and a transformation unit, which are responsible for extracting the original field values ​​from the response data and converting them into engineered values, respectively; Status determination module: Determines the status of the detection result based on the fault determination criteria (such as threshold range) preset in the model; Report generation module: Summarizes the test results of each test point and generates a test report containing statistical data and an anomaly list.

[0033] Furthermore, such as Figure 6 As shown, the parameterized protocol model is the core configuration entity, and its structure includes: a test point model. Each test point model contains the following information: Basic information: Name, code, description, unit; Category information: Used for grouping statistics, such as "voltage category", "current category", "power category", etc.; Communication information: Request instructions or instruction templates, which may include variable placeholders; Parsing information: the starting position of the data in the response frame, its length, data type (such as integer or floating-point), and byte order (big-endian / little-endian); Conversion information: Scale factor and offset, used to convert the original value to the actual value. The conversion formula is: Actual value = Original value × Scale factor + Offset; Verification information: verification type, verification field position (e.g., frame end checksum byte); Judgment criteria information: minimum threshold, maximum threshold, which can also be extended to tolerance, segmented threshold, etc.

[0034] Furthermore, the field annotation model is used for semantic annotation of communication frames, such as... Figure 3 As shown, it includes: field name, starting position, length, display format; optional annotations for whether to participate in validation and whether they are critical fields; when saving annotations, out-of-bounds validation (ensuring that starting position + length ≤ frame length) and field overlap detection (ensuring that field ranges do not overlap).

[0035] Example 2 This embodiment provides a non-standard electricity meter anomaly detection method based on a parameterized protocol model, which is described below in conjunction with... Figure 4 (Detection execution interface) Explains the specific detection steps: Step S1: Construct a parameterized protocol model, which includes test point models corresponding to multiple test points. Each test point model includes at least: basic information, category information, communication information, parsing information, conversion information, verification information, and judgment criterion information.

[0036] Specifically, each test point model contains the following information: Basic information: Name, code, description, unit; Category information: Used for grouping statistics, such as "voltage category", "current category", "power category", etc.; Communication information: Request instructions or instruction templates, which may include variable placeholders; Parsing information: the starting position of the data in the response frame, its length, data type (such as integer or floating-point), and byte order (big-endian / little-endian); Conversion information: Scale factor and offset, used to convert the original value to the actual value. The conversion formula is: Actual value = Original value × Scale factor + Offset; Verification information: verification type, verification field position (e.g., frame end checksum byte); Judgment criteria information: minimum threshold, maximum threshold, which can also be extended to tolerance, segmented threshold, etc.

[0037] In this embodiment, the user through Figure 2 , Figure 3The configuration interface shown models a non-standard electricity meter protocol. For example, configuring the "Phase A voltage" test point: Communication information: The request frame is 68 AA BB CC DD 68 01 02 43 C3 A6 16; Parsing information: The data in the response frame starts at the 10th byte, is 2 bytes long, is an integer, and is big-endian. Conversion information: Scale factor 0.01, offset 0, unit "V"; Verification information: CRC16 checksum, the verification field is located in the last 2 bytes of the response frame; Judgment criteria: minimum threshold 210V, maximum threshold 250V.

[0038] Step S2: Load the parameterized protocol model, initialize test result objects for multiple test points respectively, generate batch detection tasks based on the multiple test points, and organize and schedule the detection execution order of multiple test points according to the batch detection tasks.

[0039] The system loads the parameterized protocol model, initializes the detection result objects for all test points, and generates a batch detection task queue by category.

[0040] Step S3: Under the scheduling of the batch testing task, for any test point, a request frame is generated according to the communication information configured in the parameterized protocol model, and the request frame is sent to the meter under test. Communication interaction is performed with the meter under test and a response frame returned by the meter under test is received.

[0041] In this embodiment, for the "A-phase voltage" test point, the communication execution module generates a request frame based on the communication information, sends it to the meter via RS-485, and receives a response frame.

[0042] Step S4: Perform verification on the response data, and perform field-level parsing and data transformation based on the verification results to obtain the actual detection value of the test point.

[0043] Specifically, the verification method is as follows: based on the verification type configured in the verification information, the verification value is calculated for the response frame data segment excluding the verification field according to the verification type. The calculated verification value is compared with the verification field in the response frame. When the comparison fails, the detection result status of the test point is set to parsing abnormal. If the comparison is successful, subsequent field-level parsing and data conversion operations are performed.

[0044] Furthermore, the field-level parsing method is as follows: based on the frame field annotations and parsing information in the parameterized protocol model, field-level parsing is performed on the response frame to extract the original values.

[0045] The field-level parsing is performed based on the data field positions determined by the frame field annotations and in conjunction with the parsing information. The frame field annotation method involves semantically annotating byte segments in the request frame and / or response frame. The semantic annotation includes at least the field name, start position, length, and display format. When saving the frame field annotations, boundary checks and field overlap detection are performed. Boundary checks limit the sum of the start position and length from exceeding the frame length, and field overlap detection prevents overlap between any two field coverage areas.

[0046] The data conversion method is to multiply the original value by a scaling factor and then add an offset.

[0047] It should be noted that the verification type includes at least one of cyclic redundancy check, cumulative sum check, or XOR check.

[0048] In this embodiment, the verification process involves the verification processing module extracting the data segments from the response frame excluding the verification field, calculating the CRC16 value, and comparing it with the verification field in the response frame. If they do not match, the point is marked as "parsing error" and the reason is recorded.

[0049] Field-level parsing: Based on the parsing information, the parsing unit extracts 2 bytes of data starting from the 10th byte of the response frame and converts them into integer raw values ​​in big-endian order.

[0050] Data conversion: The conversion unit multiplies the original value by 0.01 to obtain the actual voltage value (unit: V).

[0051] Step S5: Based on the fault judgment criteria configured in the parameterized protocol model, the actual detection value is judged, and the detection result status of the current test point is generated. The detection result status includes at least detection success, detection failure, and abnormal status.

[0052] Specifically, the fault determination criteria include a minimum threshold and a maximum threshold; step S5 specifically includes: comparing the actual detection value with the minimum threshold and the maximum threshold; if the actual detection value is between the two, it is determined that the detection is successful; if the actual detection value is less than the minimum threshold or greater than the maximum threshold, it is determined that the detection is unsuccessful.

[0053] In this embodiment, the state determination module compares the actual voltage value with the threshold range [210V, 250V]: If the actual value is within this range, mark it as "detection successful"; If the voltage is below 210V or above 250V, mark it as "detection failed" and record the abnormal reason as "below the lower limit" or "above the upper limit".

[0054] Step S6: Under the scheduling of the batch detection task, repeat steps S3 to S5 until the detection of all test points is completed. Based on the detection result status of all test points, generate a detection report containing overall statistical results and a list of abnormal items.

[0055] In this embodiment, after all test points have been tested, the report generation module generates a report as follows: Figure 5 The test report shown includes: Overall statistics: total number of test points, number of successes, number of failures, number of exceptions, pass rate; Categorical statistics: Number of test points and pass rate for each category; List of abnormal items: Lists the name, actual value, threshold, cause of the abnormality, response time, etc. of all failed or abnormal test points.

[0056] It should be noted that multiple protocol adaptation: multiple parameterized protocol models can be loaded in the same system, each corresponding to different models or manufacturers of electricity meters, enabling a single system to detect multiple non-standard protocol electricity meters.

[0057] Dynamic scheduling strategy: The batch detection scheduling module supports execution by category order, priority order, or condition triggering, and can be configured with retry mechanism and timeout strategy.

[0058] Judgment criteria expansion: In addition to threshold judgment, judgment criteria can also be configured as tolerance criteria (such as the deviation between the actual value and the standard value not exceeding ±5%), segmented thresholds (different thresholds for different ranges) or statistical window criteria (judgment of results from multiple consecutive tests).

[0059] Field annotation validation participation: Field annotations can mark a field as "participating in validation" or "not participating in validation", and the system will automatically skip the corresponding field during validation calculation.

[0060] Example 3 This embodiment takes a three-phase non-standard protocol electricity meter as an example, configuring test points such as "Phase A voltage", "Phase A current", and "total active power". After batch testing: If all actual values ​​are within the threshold, the report shows an overall pass rate of 100%. If the detected value of "Phase A voltage" is 205V (below the lower limit of 210V), the point is marked as "detection failed". The anomaly list includes the information of the point and the reason "below the lower limit", which helps maintenance personnel to quickly locate the abnormal phase A voltage.

[0061] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A non-standard meter anomaly detection method based on a parameterized protocol model, characterized in that, The method comprises the following steps: Step S1, constructing a parameterized protocol model, the parameterized protocol model comprising test point models corresponding to a plurality of test points respectively, each test point model comprising at least basic information, category information, communication information, parsing information, conversion information, verification information and judgment standard information; Step S2, loading the parameterized protocol model, initializing test result objects for the plurality of test points respectively, generating a batch detection task based on the plurality of test points, and organizing and scheduling detection execution sequences of the plurality of test points according to the batch detection task; Step S3, under the scheduling of the batch detection task, for any test point, generating a request frame according to the communication information configured in the parameterized protocol model, and sending the request frame to the meter under test, communicating with the meter under test and receiving a response frame returned by the meter under test; Step S4, performing verification and validation on the response data, and performing field-level parsing and data conversion according to the verification result to obtain actual detection values of the test point; Step S5, judging the actual detection values according to the fault judgment standard configured in the parameterized protocol model to generate a detection result state of the current test point, the detection result state comprising at least detection success, detection failure and abnormal state; Step S6, repeating steps S3 to S5 under the scheduling of the batch detection task until the detection of all test points is completed, and generating a detection report comprising overall statistical results and an abnormal item list based on the detection result states of all test points.

2. The method of claim 1, wherein, The parsing information comprises a starting position, a length, a data type and a byte sequence of a data field in the response frame.

3. The method of claim 1, wherein, In step S4, the verification and validation method is as follows: according to the verification type configured in the verification information, a verification value is calculated for a data segment of the response frame except the verification field, the calculated verification value is compared with the verification field in the response frame, when the comparison fails, the detection result state of the test point is set to be abnormal in parsing, and if the comparison succeeds, subsequent operations are performed.

4. The method of claim 1, wherein, In step S4, the field-level parsing method is as follows: according to the frame field annotation and the parsing information in the parameterized protocol model, field-level parsing is performed on the response frame to extract original numerical values.

5. The method of claim 4, wherein, The field-level parsing is performed according to the data field position determined by the frame field annotation and the parsing information.

6. The method of claim 4, wherein, In step S4, the data conversion method is as follows: the original numerical value is multiplied by a proportionality coefficient and then an offset is added.

7. The method of claim 3, wherein, The verification type comprises at least any one of cyclic redundancy check, cumulative sum check or exclusive or check.

8. The method of claim 4, wherein, The frame field annotation method is as follows: a byte segment in the request frame and / or the response frame is semantically annotated, and the semantic annotation comprises at least a field name, a starting position, a length and a display format.

9. The method of claim 8, wherein, When saving the frame field annotation, a boundary check and a field overlap detection are performed, the boundary check is used to limit that the sum of the starting position and the length does not exceed the frame length, and the field overlap detection is used to limit that any field coverage interval does not overlap.

10. The method of claim 1 or 2, wherein, The failure determination criterion includes a minimum threshold value and a maximum threshold value; the step S5 specifically includes: comparing the actual detection value with the minimum threshold value and the maximum threshold value; if the actual detection value is between the two, it is determined that the detection is successful; if the actual detection value is less than the minimum threshold value or greater than the maximum threshold value, it is determined that the detection fails.

11. The method of claim 1, wherein, In the step S2, the plurality of test points are grouped according to predefined categories; in the step S6, the detection is performed in the order of grouping or in the order within the group; and the generated detection report further includes the classified detection results counted according to the categories.

12. A non-standard electricity meter anomaly detection system based on a parameterized protocol model, characterized in that, The method comprises: a protocol model construction module configured to configure and generate a parameterized protocol model; a model management module configured to store and load the parameterized protocol model; a batch detection scheduling module configured to organize a detection task queue of a plurality of test points based on the loaded parameterized protocol model; a communication execution module configured to generate a request instruction according to the parameterized protocol model and perform communication interaction with the meter under test to obtain response data; a verification processing module configured to verify the response data according to a verification rule configured in the parameterized protocol model; a data analysis and conversion module configured to, after the verification passes, process the response data according to an analysis rule and a conversion rule configured in the parameterized protocol model to obtain an actual detection value; a state determination module configured to determine the actual detection value according to a failure determination criterion configured in the parameterized protocol model and output a detection result state; a report generation module configured to summarize the detection result states of all test points and generate a detection report.

13. The system of claim 12, wherein, The data analysis and conversion module comprises: an analysis unit configured to extract original numerical values of specified fields from the response data according to a field annotation model and analysis information in the parameterized protocol model; a conversion unit configured to convert the original numerical values into actual detection values with engineering units according to conversion information in the parameterized protocol model.

14. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, implements the non-standard meter anomaly detection method based on the parameterized protocol model according to any one of claims 1-11.

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