A communication module testing method for smart electric energy meters and collection terminals

Abstract

The application discloses a kind of communication module test methods for intelligent electric energy meter and acquisition terminal, it is related to smart grid equipment test technical field, and first communication state data is obtained by conventional non-interference test;Identify critical protocol window period and inject interference, obtain the second communication state data disturbed;Extraction degradation gradient combines dynamic weight and communication failure identification to calculate communication reliability score;Based on the score and multidimensional access boundary intersection verification, complete scene level division;The application realizes protocol level accurate targeted interference and dynamic quantitative scoring, not only effectively prevents the defect module mixed into power grid core node with high score and low ability, but also constructs the test closed loop of echelon security degradation and short board directional tracing, comprehensively improves the security resilience and fine management and control level of new power system bottom communication asset.

Description

Technical Field

[0001] This invention relates to the field of smart grid equipment testing technology, specifically a testing method for communication modules of smart meters and data acquisition terminals. Background Technology

[0002] As a key data interaction component of smart meters and data acquisition terminals, the communication module undertakes the core functions of remote meter reading and issuing business commands. Therefore, it generally requires factory or network access testing using testing equipment to monitor its communication performance. However, existing testing technologies can usually only determine whether the module is connected under ideal static conditions, lacking an effective simulation mechanism for sudden severe interference in the actual power grid. This makes it impossible to accurately quantify the dynamic anti-interference performance boundary of the communication module, resulting in defective modules with substandard anti-interference capabilities being incorrectly approved for use in actual power grid operations. Summary of the Invention

[0003] The purpose of this invention is to provide a testing method for communication modules of smart energy meters and data acquisition terminals, so as to solve the problems raised in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A method for testing communication modules of smart energy meters and data acquisition terminals includes the following steps: obtaining device identification information of the communication module under test, and matching the corresponding communication protocol library and benchmark test case set based on the device identification information; Furthermore, the step of matching the corresponding communication protocol library and benchmark test case set based on the device identification information specifically includes: obtaining the hardware identification code and firmware version number of the communication module under test, and parsing to obtain the communication module type of the communication module under test, wherein the communication module type includes broadband carrier HPLC module, low power wireless module, carrier wireless dual-mode communication module, etc. According to the type of communication module, the corresponding communication protocol is retrieved from the protocol database. The communication protocol includes at least one of the following protocols: DL / T 645, DL / T 698.45, and Q / GDW 1376.1, and the communication protocol library is constructed. Based on the communication module type, typical power grid business test items are selected from a preset test scenario library; and standard test sequences and test judgment criteria associated with the typical power grid business test items are extracted and combined to generate the corresponding benchmark test case set. The benchmark test case set includes network test cases, meter reading business test cases, and status reporting test cases. The preset test scenario library is constructed offline based on the typical power grid access test outlines and real-world business models of the current power grid over the years.

[0005] Based on the communication protocol library, the benchmark test case set is encapsulated into standard service messages, and a test instruction containing the standard service messages is sent to the communication module under test. The response status of the communication module under test is continuously monitored under an interference-free benchmark communication link to obtain the first communication status data. Furthermore, the method for obtaining the first communication status data specifically includes: based on the communication protocol library, sequentially encapsulating the test instructions of the network test cases, meter reading service cases, and status reporting cases at the protocol layer, and adding a verification sequence to generate standard service messages; Under interference-free communication link conditions where the signal-to-noise ratio (SNR) is above a reference threshold, standard service messages are continuously sent to the communication module under test. The interference-free communication link does not refer to an ideal vacuum communication medium with absolutely zero background noise at the physical layer, but rather to a stable operating environment where the current physical SNR of the communication medium is above a preset reference threshold. In actual industrial field or laboratory bench testing environments, basic thermal noise and weak ambient electromagnetic background waves inevitably exist within communication cables or wireless spaces. When the ratio of the effective service signal power captured by the communication receiver to the background noise power is greater than the reference threshold, the underlying physical channel decoder can achieve complete error correction and extremely low bit error rate transmission. Under this channel condition, the upper-layer network protocol stack does not experience any packet loss, packet errors, or passive retransmission due to underlying interference.

[0006] Therefore, this invention refers to high-quality communication links that meet the above signal-to-noise ratio conditions as interference-free communication links. The core function of defining these links is to establish an absolutely reliable performance reference anchor point for network response parameters such as baseline network setup time, baseline message success rate, and baseline response delay of the communication module under test. This allows for a scientific and objective comparison with the disturbed state after subsequent injection of severe interference characteristics according to intensity gradients, providing a solid data foundation for the degradation calculation of the communication reliability scoring model.

[0007] The reference threshold is not a single, static, fixed constant, but rather a reference threshold calibration process initiated before the test system formally extracts network response parameters. The system controls a programmable signal generator to produce background white noise of initial intensity and superimposes it onto the communication medium, while simultaneously sending probe messages cyclically to the communication module under test. Subsequently, the system passively improves the physical signal-to-noise ratio of the link by gradually reducing the injected power of the background white noise, and synchronously monitors the physical layer bit error rate and link layer message retransmission rate of the communication module under test in real time.

[0008] When the physical layer bit error rate is detected to have steadily fallen below the protocol tolerance limit for several consecutive detection cycles, and the underlying physical channel decoding is completely correct with no packet loss or obstruction in the upper-layer protocol interaction, the system immediately captures and records the measured physical signal-to-noise ratio (PSNR) value in the communication link at this time. The system then solidifies this PSNR value to generate the baseline threshold specific to this test batch.

[0009] The benchmark threshold determined by this dynamic pre-calibration mechanism can completely shield the individual physical differences in the noise floor of hardware components of communication modules from different manufacturers and batches, ensuring that the first communication status data extracted subsequently is completely within the module's own absolutely ideal working range, thus laying a highly objective and consistent data benchmark for anti-interference degradation assessment.

[0010] The network response parameters of the communication module under test are continuously monitored and extracted in an interference-free state. The network response parameters include the baseline networking time, the baseline message success rate, and the baseline response delay. These parameters are combined to generate the first communication status data.

[0011] During the execution of the test command, interference features are injected into the reference communication link at random time nodes, and the abnormal response behavior of the communication module under test in response to the interference features is monitored to obtain second communication status data. Furthermore, the injection of interference features into the reference communication link at random time nodes specifically includes: real-time analysis of the transmission timing of the standard service messages in the reference communication link and the response status of the communication module under test, and identification of the current communication interaction status of the communication module under test based on the communication protocol library. When the communication interaction state is identified to have entered a critical communication protocol window period, the random time node is randomly generated within the critical communication protocol window period. The critical communication protocol window period includes the network interaction stage, the clock synchronization stage, and the meter reading data transmission stage. At the random time node, interference features with different intensity gradients are injected into the reference communication link.

[0012] Furthermore, the injection of interference features with different intensity gradients into the reference communication link specifically includes: controlling a programmable signal generator to generate noise signals corresponding to the intensity gradient, and physically superimposing the noise signals onto the communication medium where the communication module under test is located through a signal coupler, as physical layer signal interference of the interference features; Before the standard service message is sent, the message is intercepted, and according to the bit error rate corresponding to the strength gradient, the standard service message is tampered with to generate a distorted message, which is then sent to the communication link as data link layer message interference as the interference feature.

[0013] Furthermore, the method for obtaining the second communication status data specifically includes: monitoring the disconnection and reconnection behavior, message retransmission behavior, and system crash behavior of the communication module under test during the interference feature injection at different intensity gradients. Based on the disconnection and reconnection behavior, the time interval from the disconnection of the communication link to the re-completion of the networking interaction stage by the tested communication module is recorded as the disturbed reconnection time. Based on the message retransmission behavior, the total number of standard service messages sent within the preset disturbance period and the number of lost messages that have not received a valid response after reaching the maximum retransmission threshold are counted. The difference between the two is calculated as the ratio of the total number of messages sent, which is used as the success rate of the disturbed message. Record the time taken for a standard service message from its initial transmission, through retransmission, until a valid response is finally received, and calculate the average time taken for all successful interactive messages within a preset disturbance period as the disturbance response delay. Based on the system crash behavior, it is determined whether the communication module under test has experienced an unrecoverable crash, and a corresponding communication failure identifier is generated based on the determination result. The determination method for the communication failure identifier specifically includes: based on software-level link status monitoring, when status probe messages are continuously sent to the communication module under test, and no valid response or link-layer retransmission action is received within multiple consecutive determination periods, it is determined that the link-layer communication is completely lost; based on hardware-level electrical characteristic monitoring, when the operating current of the communication module under test is continuously locked in an extreme range indicating chip hang-up or abnormal heating after interference injection and exceeds a preset time threshold, it is determined that the hardware physical layer is crashed; based on the interception and parsing of the underlying debug logs, when an abnormal trigger code indicating kernel crash, hardware-level error, or watchdog reset failure is matched in the abnormal response log reported by the communication module under test, it is determined that the system-level operation is paralyzed. When any of the above determination criteria are met, the communication failure identifier is generated.

[0014] The second communication status data is generated by combining the disrupted reconnection time, disrupted message success rate, disrupted response delay, and communication failure identifier.

[0015] The evaluation dimension parameters are extracted from the first communication status data and the second communication status data, and the evaluation dimension parameters are correlated based on the injection intensity of the interference features to obtain the communication reliability score of the communication module under test. Furthermore, the method for obtaining the communication reliability score of the communication module under test specifically includes: pairing the baseline networking time, baseline message success rate, and baseline response delay in the first communication status data with the corresponding disrupted re-networking time, disrupted message success rate, and disrupted response delay in the second communication status data as the evaluation dimension parameters. Calculate the difference in the change of the evaluation dimension parameters of each pair between the disturbed state and the undisturbed state to obtain the degradation gradient of each evaluation dimension parameter; The intensity gradient levels of the interference features are extracted, and a preset dynamic weight mapping table is queried based on the intensity gradient levels to determine the dynamic weight coefficients corresponding to each evaluation dimension parameter. The dynamic weight mapping table is generated by the system extracting a large number of test samples of the communication modules under test under different interference intensity levels from the historical test database, and using statistical methods to calculate the Pearson correlation coefficient between the degradation gradient of each evaluation dimension parameter and the final communication failure state, so as to accurately quantify the contribution of the fluctuation of each parameter to the module failure under different interference conditions. Subsequently, the correlation coefficient matrix calculated under each intensity level is normalized by absolute value and directly mapped to the dynamic weight coefficients of each evaluation dimension parameter under that intensity level, and finally solidified to form the preset dynamic weight mapping table.

[0016] The degradation gradient of each evaluation dimension parameter is weighted and calculated with its corresponding dynamic weight coefficient to obtain the corresponding weighted product term, which is then summed. Combined with the communication failure identifier in the second communication status data, a deduction calculation is performed to output the communication reliability score of the tested communication module.

[0017] The formula for the communication reliability score is: ; Where S is the communication reliability score, S base The preset benchmark full score is usually set to 100, ΔP i Let n be the degradation gradient of each evaluation dimension parameter, and ω be the number of terms in the degradation gradient. i C represents the dynamic weighting coefficient, where λ is the penalty coefficient, typically a larger value is used to strengthen the influence of communication failure identification. fail This is the communication failure identifier.

[0018] Based on the communication reliability score, the tested communication module is classified into the corresponding power grid application scenario level.

[0019] Furthermore, the step of classifying the communication module under test into the corresponding power grid application scenario level specifically includes: presetting multiple tiered power grid application scenario levels, and configuring corresponding multi-dimensional access boundaries for each power grid application scenario level. The multi-dimensional access boundaries include a comprehensive scoring threshold, a tolerance degradation upper limit for each evaluation dimension, and a failure rejection red line. Obtain the communication reliability score, the degradation gradient of each evaluation dimension parameter, and the communication failure identifier; The communication reliability score is compared with the comprehensive score threshold of each level to initially determine the candidate application scenario level of the communication module under test. The tolerance degradation limit corresponding to the candidate application scenario level is cross-validated with each degradation gradient, and it is determined whether the communication failure identifier touches the failure rejection red line of the candidate application scenario level. If the cross-validation passes and the failure rejection line is not triggered, the communication module under test will be formally classified into the candidate application scenario level. If any of the degradation gradients exceeds the tolerance degradation limit or touches the failure rejection red line, a degradation protection mechanism is triggered, and the communication module under test is downgraded to a secondary power grid application scenario level that meets the multi-dimensional access boundary. Once the partitioning is complete, the modules are sorted based on the numerical values ​​of the weighted product terms. The evaluation dimension parameter with the largest value is extracted as the core vulnerability feature, and an evaluation report of the tested communication module is output.

[0020] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention overcomes the limitations of traditional static environment testing by injecting interference features with different intensity gradients, effectively reproducing the sudden severe interference conditions in the actual power grid, and increasing the probability of detecting deep communication defects such as communication module disconnection and crash.

[0021] 2. This invention calculates the degradation gradient by acquiring state data before and after the communication link is disturbed, and combines it with dynamic weight coefficients generated based on historical samples for quantitative scoring, which can more accurately evaluate the performance of the communication module.

[0022] 3. This invention performs multi-dimensional evaluation of communication modules, which can effectively prevent defective modules from entering the core business of the actual power grid; at the same time, by combining the reverse sorting of weighted product terms to extract core vulnerability features, it can provide intuitive guidance for the subsequent research and development optimization of communication modules. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the structure of a communication module testing method for a smart energy meter and a data acquisition terminal according to the present invention; Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Example: Figure 1 As shown, the present invention provides a technical solution for testing the communication module of a smart energy meter and a data acquisition terminal, comprising the following steps: The testing system first obtains the hardware identifier and firmware version number of the communication module under test, and then parses it to determine the specific communication module type. Based on this type, it retrieves the corresponding communication protocol from the protocol database to build a communication protocol library, and selects typical power grid business test items from a preset test scenario library. Next, it extracts the standard test timing sequences and test judgment criteria associated with the typical power grid business test items, and combines them to generate a benchmark test case set containing network test cases, meter reading business test cases, and status reporting test cases. The system then encapsulates this benchmark test case set at the protocol layer based on the communication protocol library and adds verification sequences to generate standard business messages. Under interference-free communication link conditions where the signal-to-noise ratio is above a preset benchmark threshold, the system continuously sends standard business messages to the communication module under test and continuously monitors its network response parameters under interference-free conditions to generate the first communication status data.

[0026] In this embodiment, the test system parses and confirms that the communication module under test is a broadband carrier communication module. The system automatically retrieves the corresponding power grid communication protocol and generates a set of benchmark test cases. Under an interference-free ideal communication link, the system measures the following first communication status data: benchmark networking time is 5 seconds, benchmark message success rate is 100%, and benchmark response delay is 50 milliseconds.

[0027] During the execution of the test commands, the system analyzes in real time the timing of the standard service messages sent in the benchmark communication link and the response status of the communication module under test, and identifies the current communication interaction status of the communication module under test based on the communication protocol library. When the system identifies that the communication interaction status has entered a critical communication protocol window period, including the network interaction stage, the clock synchronization stage, and the meter reading data transmission stage, random time nodes are randomly generated within the critical communication protocol window period. At the random time nodes, interference features are injected into the benchmark communication link according to different intensity gradients. The injection methods include controlling the programmable signal generator to generate noise signals that are physically superimposed onto the communication medium, and intercepting and tampering with the frame check sequence and data field of the standard service messages to generate distorted messages for transmission.

[0028] In this embodiment, when the system recognizes that the broadband carrier communication module has entered the critical communication protocol window period of meter reading data transmission, the system triggers the physical layer interference injection mechanism to inject a broadband white noise signal with a high intensity gradient into the power line communication medium.

[0029] During interference feature injections of varying intensity gradients, the system monitors the disconnection and reconnection behavior, message retransmission behavior, and system crash behavior of the communication module under test. Based on the disconnection and reconnection behavior, the system records the reconnection time after disruption; based on the message retransmission behavior and the maximum retransmission threshold, it calculates the success rate of disrupted messages and records the average time of each interruption as the disruption response delay. Simultaneously, based on the system crash behavior, the system determines whether the communication module under test has experienced an unrecoverable crash and generates a corresponding communication failure flag based on the determination result. Finally, the system combines the above-mentioned disruption parameters with the communication failure flag to generate second communication status data.

[0030] In this embodiment, the module did not experience an unrecoverable crash during the period of severe broadband white noise interference, therefore the communication failure flag generated by the system was set to a status value of 0. The second communication status data extracted by the system is as follows: the time for reconnection after interference is 15 seconds, the success rate of the interfered message is 80%, and the delay for response to interference is 300 milliseconds.

[0031] The system pairs the evaluation dimension parameters in the first communication state data with those in the second communication state data, calculates the difference in change of each paired parameter between the disturbed state and the interference-free state, and obtains the degradation gradient of each evaluation dimension parameter. Simultaneously, the system extracts the intensity gradient level of the currently injected interference feature and queries a preset dynamic weight mapping table to determine the dynamic weight coefficients corresponding to each evaluation dimension parameter.

[0032] In this embodiment, the test system calculates the degradation gradient for three evaluation dimensions: a network setup time difference of 10 seconds results in a degradation gradient of 10; a packet success rate difference of 20% results in a degradation gradient of 20; and a response latency difference of 250 milliseconds results in a degradation gradient of 250. The system queries the dynamic weight mapping table corresponding to the severe interference level and obtains a dynamic weight coefficient of 0.5 for network setup time, 1.2 for packet success rate, and 0.04 for response latency.

[0033] The system calculates the corresponding weighted product terms by weighting the degradation gradient of each evaluation dimension parameter with its corresponding dynamic weight coefficient, and sums them up. Then, it calculates the deduction score by combining the communication failure identifier in the second communication status data, and outputs the communication reliability score of the tested communication module.

[0034] In this embodiment, the system calculates various weighted product terms: the network setup time weighted product term is 0.5 multiplied by 10, equaling 5 points; the message success rate weighted product term is 1.2 multiplied by 20, equaling 24 points; and the response delay weighted product term is 0.04 multiplied by 250, equaling 10 points. Summing these three weighted product terms yields a total attenuation deduction of 39 points. Since the communication failure flag status value is 0, the system multiplies this value with a preset communication failure penalty coefficient of 40 points, resulting in a failure penalty deduction of 0 points. The test system uses a preset maximum score of 100 points as the baseline, subtracts the total attenuation deduction of 39 points and the failure penalty deduction of 0 points, and finally outputs a communication reliability score of 61 points for the tested communication module.

[0035] The system pre-defines multiple tiered distribution levels of power grid application scenarios and configures multi-dimensional access boundaries including a comprehensive scoring threshold, a tolerance degradation upper limit, and a failure rejection red line. The system compares the communication reliability score with the comprehensive scoring threshold to initially determine candidate application scenario levels, and cross-validates various degradation gradients and communication failure indicators with the multi-dimensional access boundaries of the candidate application scenario levels. If the validation passes, the system formally classifies the application scenario; if the tolerance degradation upper limit is triggered or the failure rejection red line is reached, a degradation protection mechanism is activated to allocate the application scenario to a lower level. After classification, the system performs reverse sorting based on the numerical value of the weighted product terms, extracts the parameter with the largest value as the core vulnerability feature, and outputs an evaluation report.

[0036] The power grid application scenario level is scientifically calibrated and tiered based on the business importance, data concurrency throughput, and network topology hierarchy of different communication nodes in the smart grid advanced measurement system. Specifically, for core backbone nodes in the network topology aggregation layer, such as distribution area concentrators or distribution transformer smart terminals, their core business characteristics are carrying the concurrent aggregation of data from a massive number of underlying devices under their jurisdiction, as well as high-real-time fee control commands and power outage assessment commands issued by the power grid master station. Once such nodes experience communication paralysis, it will directly lead to the failure of metering and control services over a large area, causing serious cascading business failures. Therefore, the system classifies them as the application scenario level with the highest security level, and configures the most stringent comprehensive scoring threshold, extremely low tolerance for degradation, and a zero-tolerance failure rejection red line accordingly.

[0037] In contrast, for ordinary edge nodes located at the absolute end of the network topology, such as single-phase residential smart meters or environmental sensing sensors, their communication characteristics are characterized by low data interaction frequency, small single message payload, and extremely low sensitivity of overall services to communication latency. At this level, even if the communication module experiences occasional long-delay retransmissions or brief link breaks and network reassemblies under severe interference, it remains entirely within the fault tolerance range of the underlying services and will not affect the continuity of the overall macro-level power grid services. Therefore, the system classifies this as a basic tolerance level application scenario, configuring a correspondingly low comprehensive scoring threshold, while assigning it a relatively broad tolerance degradation upper limit and a failure rejection red line for conditionally tolerant states.

[0038] In this embodiment, the system presets multi-dimensional admission boundaries at two levels: core backbone nodes and ordinary edge boxes. The comprehensive scoring threshold for the ordinary edge box level is greater than or equal to 60 points, the maximum tolerance for message success rate degradation is 25%, and the failure rejection red line is conditionally tolerated. The system compares the communication reliability score of 61 points with the comprehensive scoring threshold, initially identifying the candidate application scenario as the ordinary edge box level. During the cross-validation phase, the module's message success rate degradation gradient of 20 did not exceed the 25% tolerance limit, and the communication failure identifier with a state value of 0 did not touch the failure rejection red line. The system officially classifies this module into the ordinary edge box application scenario level. Subsequently, the system reverse-sorts the three weighted product terms: message success rate deduction of 24 points is greater than response latency deduction of 10 points, which is greater than network setup time deduction of 5 points. The system extracts the message success rate with the highest value as the core vulnerability feature of this communication module and outputs this feature in the evaluation report to guide subsequent R&D optimization.

[0039] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A testing method for a communication module of a smart energy meter and a data acquisition terminal, characterized in that, Includes the following steps: Obtain the device identification information of the communication module under test, and match the corresponding communication protocol library and benchmark test case set based on the device identification information; Based on the communication protocol library, the benchmark test case set is encapsulated into standard service messages, and a test instruction containing the standard service messages is sent to the communication module under test. The response status of the communication module under test is continuously monitored under the benchmark communication link to obtain the first communication status data. During the execution of the test command, interference features are injected into the reference communication link at random time nodes, and the abnormal response behavior of the communication module under test in response to the interference features is monitored to obtain second communication status data. The evaluation dimension parameters are extracted from the first communication status data and the second communication status data, and the evaluation dimension parameters are correlated based on the injection intensity of the interference features to obtain the communication reliability score of the communication module under test. Based on the communication reliability score, the tested communication module is classified into the corresponding power grid application scenario level.

2. The method for testing the communication module of a smart energy meter and data acquisition terminal according to claim 1, characterized in that, The step of matching the corresponding communication protocol library and benchmark test case set based on the device identification information specifically includes: obtaining the hardware identification code and firmware version number of the communication module under test, and parsing to obtain the communication module type of the communication module under test; Based on the type of the communication module, the corresponding communication protocol is retrieved from the protocol database to construct the communication protocol library; Based on the communication module type, select the corresponding typical power grid business test items from the preset test scenario library; and extract the standard test sequence and test judgment criteria associated with the typical power grid business test items, and combine them to generate the corresponding benchmark test case set, which includes network test cases, meter reading business test cases and status reporting test cases.

3. The method for testing the communication module of a smart energy meter and data acquisition terminal according to claim 1, characterized in that, The method for obtaining the first communication status data specifically includes: extracting the message format from the communication protocol library; According to the message format, the test instructions of the network test cases, meter reading service cases and status reporting cases are sequentially encapsulated at the protocol layer, and a verification sequence is added to generate standard service messages. In the reference communication link where the signal-to-noise ratio is above the reference threshold, the standard service message is continuously sent to the communication module under test; The network response parameters of the communication module under test are continuously monitored and extracted in an interference-free state. The network response parameters include the baseline networking time, the baseline message success rate, and the baseline response delay. These parameters are combined to generate the first communication status data.

4. The method for testing the communication module of a smart energy meter and data acquisition terminal according to claim 1, characterized in that, The injection of interference features into the reference communication link at random time nodes specifically includes: real-time analysis of the transmission timing of the standard service messages in the reference communication link and the response status of the communication module under test, and identification of the current communication interaction status of the communication module under test; When the communication interaction state is identified to have entered a critical communication protocol window period, the random time node is randomly generated within the critical communication protocol window period. The critical communication protocol window period includes the network interaction stage, the clock synchronization stage, and the meter reading data transmission stage. At the random time node, interference features with different intensity gradients are injected into the reference communication link.

5. A testing method for a communication module of a smart energy meter and a data acquisition terminal according to claim 4, characterized in that, The injection of interference features with different intensity gradients into the reference communication link specifically includes: controlling a programmable signal generator to generate noise signals corresponding to the intensity gradients, and physically superimposing the noise signals onto the communication medium where the communication module under test is located through a signal coupler, as physical layer signal interference of the interference features; Before the standard service message is sent, the message is intercepted, and according to the bit error rate corresponding to the strength gradient, the standard service message is tampered with to generate a distorted message, which is then sent to the communication link as data link layer message interference as the interference feature.

6. A testing method for a communication module of a smart energy meter and a data acquisition terminal according to claim 1, characterized in that, The method for obtaining the second communication status data specifically includes: monitoring the disconnection and reconnection behavior, message retransmission behavior, and system crash behavior of the communication module under test during the injection of interference features at different intensity gradients. Based on the disconnection and reconnection behavior, the time interval from the disconnection of the communication link to the re-completion of the networking interaction stage by the tested communication module is recorded as the disturbed reconnection time. Based on the message retransmission behavior, the total number of standard service messages sent within the preset disturbance period and the number of lost messages that have not received a valid response after reaching the maximum retransmission threshold are counted. The difference between the two is calculated as the ratio of the total number of messages sent, which is used as the success rate of the disturbed message. Record the time taken for a standard service message from its initial transmission, through retransmission, until a valid response is finally received, and calculate the average time taken for all successful interactive messages within a preset disturbance period as the disturbance response delay. Based on the system crash behavior, determine whether the communication module under test has experienced an unrecoverable crash, and generate a corresponding communication failure identifier based on the determination result; The second communication status data is generated by combining the disrupted reconnection time, disrupted message success rate, disrupted response delay, and communication failure identifier.

7. A testing method for a communication module of a smart energy meter and a data acquisition terminal according to claim 6, characterized in that, The method for obtaining the communication reliability score of the communication module under test specifically includes: pairing the baseline networking time, baseline message success rate, and baseline response delay in the first communication status data with the corresponding disrupted re-networking time, disrupted message success rate, and disrupted response delay in the second communication status data as the evaluation dimension parameters. Calculate the difference in the change of the evaluation dimension parameters of each pair between the disturbed state and the undisturbed state, and obtain the degradation gradient of each evaluation dimension parameter; Extract the intensity gradient level of the interference feature, query the preset dynamic weight mapping table, and determine the dynamic weight coefficients corresponding to each evaluation dimension parameter based on the intensity gradient level. The degradation gradient of each evaluation dimension parameter is weighted and calculated with its corresponding dynamic weight coefficient to obtain the corresponding weighted product term, which is then summed. Combined with the communication failure identifier in the second communication status data, a deduction calculation is performed to output the communication reliability score of the tested communication module.

8. A testing method for a communication module of a smart energy meter and a data acquisition terminal according to claim 1, characterized in that, The process of classifying the communication module under test into the corresponding power grid application scenario level specifically includes: presetting multiple tiered power grid application scenario levels, and configuring a corresponding multi-dimensional access boundary for each power grid application scenario level. The multi-dimensional access boundary includes a comprehensive scoring threshold, a tolerance degradation upper limit for each evaluation dimension, and a failure rejection red line. Obtain the communication reliability score, the degradation gradient of each evaluation dimension parameter, and the communication failure identifier; The communication reliability score is compared with the comprehensive score threshold of each level. When the communication reliability score is greater than or equal to the comprehensive score threshold of a certain tier of power grid application scenario level, the highest tier of power grid application scenario level that meets this condition is initially locked as the candidate application scenario level of the communication module under test. The tolerance degradation limit corresponding to the candidate application scenario level is cross-validated with each degradation gradient, and it is determined whether the communication failure identifier touches the failure rejection red line of the candidate application scenario level. If the cross-validation passes and the failure rejection line is not triggered, the communication module under test will be formally classified into the candidate application scenario level. If any of the degradation gradients exceeds the tolerance degradation limit or touches the failure rejection red line, a degradation protection mechanism is triggered, and the communication module under test is downgraded to a secondary power grid application scenario level that meets the multi-dimensional access boundary. Once the partitioning is complete, the modules are sorted based on the numerical values ​​of the weighted product terms. The evaluation dimension parameter with the largest value is extracted as the core vulnerability feature, and an evaluation report of the tested communication module is output.

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