Interference test system for power transmission equipment and wireless communication equipment and test method thereof

Abstract

The invention discloses an interference test system for power transmission equipment and wireless communication equipment and a test method thereof. The system comprises a communication performance acquisition module, an interference environment sensing module, an AI intelligent analysis and anomaly detection module, an abnormal event marking and reporting module, a local cache and breakpoint resume module, a multi-point synchronous monitoring and redundancy verification module and a system control and linkage scheduling module. The communication performance acquisition module and the interference environment sensing module are integrated, so that cooperative monitoring of communication performance and interference source behaviors can be realized, whether power interference is a main cause of communication performance reduction or not is effectively and intelligently identified through AI intelligent analysis, multi-point synchronous monitoring can be realized in cooperation with the arrangement of the multi-point synchronous monitoring and redundancy verification module, and the communication performance can be effectively and intelligently monitored. Redundancy verification and abnormity checking are achieved, the credibility of a test result and the fault-tolerant capability of the system are remarkably improved, and the accuracy and automation level of interference event judgment can be effectively improved in cooperation with AI intelligent analysis and abnormity detection.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly to an interference test system and a test method for power transmission equipment and wireless communication equipment. Background Art

[0002] With the ultra-high voltage and intelligent development of power systems, the operating voltage level of power transmission equipment has been continuously improved. Especially in the context of the large-scale construction of ultra-high voltage transmission lines, strong electric field effects and electromagnetic radiation are easily generated during the operation of transmission equipment, such as corona discharge, partial discharge, lightning induction, etc. These interference sources pose a potential threat to the surrounding wireless communication systems;

[0003] Most of the existing test methods for electromagnetic interference in power systems focus on immunity tests or single-point electromagnetic measurements in laboratory environments, lacking collaborative monitoring means for "communication performance + interference source behavior" in actual operating environments. It is difficult to effectively and intelligently identify whether power interference is the main cause of communication performance degradation. In addition, its test relies on manual interpretation, with low efficiency and large errors, and cannot perform intelligent analysis and judgment. In view of the above situation, the present application proposes an interference test system and a test method for power transmission equipment and wireless communication equipment. Summary of the Invention

[0004] Based on the technical problems existing in the background art, the present invention proposes an interference test system and a test method for power transmission equipment and wireless communication equipment.

[0005] The interference test system for power transmission equipment and wireless communication equipment proposed by the present invention includes a communication performance acquisition module, an interference environment perception module, an AI intelligent analysis and anomaly detection module, an anomaly event marking and reporting module, a local cache and breakpoint resumption module, a multi-point synchronous listening and redundancy verification module, and a system control and linkage scheduling module.

[0006] Preferably, the communication performance acquisition module is used to collect key communication parameters of wireless devices at high frequency. The key communication parameters include communication signal strength (RSSI, SNR, RSRP / RSRQ), signal-to-noise ratio (SNR), bit error rate (BER), delay, and packet loss rate, and support multiple communication protocols;

[0007] The interference environment perception module is used to collect electromagnetic interference characteristics generated during the operation of power equipment. The collected interference characteristic parameters include electromagnetic field strength, spectrum distribution, and interference source type, and achieve time series alignment with communication performance data.

[0008] Preferably, the AI intelligent analysis and anomaly detection module includes an anomaly detector, an interference event classifier, and an anomaly compensator. The AI intelligent analysis and anomaly detection module uses artificial intelligence algorithms to conduct a joint analysis of communication performance fluctuations and interference feature data to achieve the identification, classification, and evaluation of communication anomalies. Its operating logic steps are as follows:

[0009] (1) Clean, synchronize the time, and convert the parameters collected by the communication performance acquisition module and the interference environment perception module into a unified format;

[0010] (2) Extract the feature values meaningful for "interference anomaly identification" from the original data. The feature values include communication-side features, interference-side features, and time features. Communication-side features include mutation amplitude, volatility, trend slope, packet loss rate, peak bit error rate, communication delay jump amplitude, and frequency. Interference-side features include interference frequency distribution, frequency bandwidth, pulse amplitude, duration, interference signal modulation features, and time-domain / frequency-domain energy features. Time features include the sliding window statistical features before and after the occurrence of interference, as well as the duration and interval period of abnormal events;

[0011] (3) Based on historical data and combined with the classifier model, determine whether there is an "abnormal state" or "suspected interference event" in the current time period;

[0012] (4) Calculate the time-delay correlation between interference features and communication performance indicators, determine whether there is a causal relationship between variables, classify interference samples and communication anomaly samples according to similarity, find the corresponding relationship, and determine whether interference is the "main cause" of the decline in communication performance during a certain period;

[0013] When calculating the time-delay correlation between interference features and communication performance indicators, the expression used is:

[0014] where x t is the interference feature sequence, y t is the communication performance indicator sequence, k is the delay time, and are the means of the two sequences, σ x σ y is the standard deviation. By calculating r xy (k) under different k values, find the lag with the largest correlation coefficient, which is the most likely time delay of "interference affecting communication";

[0015] When determining whether there is a causal relationship between variables, the expression used is:

[0016]

[0017] If Var(∈ 2,t )<Var(∈1,t ) then it proves the existence of a causal relationship;

[0018] where y t is the explained variable at time point t, x t-j is the potential causal variable at time point t - j, p is the autoregressive order of the explained variable y, that is, the number of historical terms of y used in the model, q is the lag order of the potential causal variable x, that is, the number of historical terms of x used in the model, a i is the autoregressive coefficient of y at the i-th order, b j is the influence coefficient of x at the j-th order on y, ∈ 1,t and ∈ 2,t are both prediction residuals, Var(∈) is the variance of the residuals, that is, the average prediction error intensity of the model, and t is the current time point index;

[0019] (5), Record and structurally annotate the identified abnormal events for use by the subsequent abnormal event marking and reporting module.

[0020] Preferably, the abnormal event marking and reporting module is used to sort out, visually display and archive the output of the recognition results, generate an interference impact report for analysis and decision-making, and its operation logic steps are as follows:

[0021] (1), Receive the output results of the communication performance acquisition module, the interference environment perception module, and the AI intelligent analysis and anomaly detection module. The output results include interference feature sequences, communication performance index sequences, and anomaly prediction labels;

[0022] (2), Set multi-threshold rules to determine whether an anomaly is formed. The multi-threshold rules include packet loss rate > 10%, RSSI reduction > 20 dBm, and duration > 1 s, and AI anomaly prediction probability > 0.8. If the conditions are met, it is determined as an "abnormal event";

[0023] (3), Extract a time window before and after the occurrence of the anomaly from the time series data to form a complete "interference-response" sample segment;

[0024] (4), Automatically annotate the anomaly type according to the event characteristics and match the known labels. The annotation content includes corona interference, arc discharge, instantaneous interference, and continuous electromagnetic interference;

[0025] (5), Bind the abnormal event with the time stamp, geographical location information, environmental status information, interference source device number, communication device type, and channel number;

[0026] (6), Automatically generate a structured anomaly report according to the information data in (4) and (5).

[0027] Preferably, the local cache and resume interrupted transfer module is used to automatically detect link interruptions and abnormal data collection. When the communication link is interrupted or the data collection system is abnormal, important data will not be lost. After the network is restored, the missing data packets will be automatically retransmitted to ensure data integrity and achieve data reliability guarantee.

[0028] Preferably, the multi-point synchronous monitoring and redundancy verification module is used to deploy multiple monitoring points in the test area to collect communication data and interference conditions from multiple angles, improving the accuracy and reliability of anomaly identification;

[0029] Its operation logic steps are as follows:

[0030] (1) Deploy multiple monitoring nodes near the power transmission equipment, and the monitoring nodes are marked with unique IDs and spatial positions;

[0031] (2) All monitoring nodes perform time synchronization through the GPS / Beidou timing module to ensure data alignment accuracy and ensure that the propagation delay of interference signals and the response time of communication events can be accurately compared;

[0032] The mathematical expression used for time synchronization is:

[0033] T i = t + δ i ,T j = t + δ j ;

[0034] where t is the standard reference time, δ i and δ j are the local clock deviations of node ij, and T i and T j are the timestamps actually recorded by the nodes;

[0035] (3) Each monitoring point collects interference spectra, communication performance indicators, and local environmental parameters, and uploads the data of multiple monitoring points to the central node to align the data according to the timestamps;

[0036] (4) Compare the interference signal propagation model with the received strength of each node to verify the consistency of the interference source. If some monitoring points do not detect anomalies, they are marked as possibly locally abnormal. If all monitoring points detect synchronous anomalies, they are marked as high-confidence interference events;

[0037] The formula used for intensity comparison is:

[0038] P r = p t + G t + G r - 20log 10 (d) - 20log 10 (f) - 32.44;

[0039] Among them, P r is the received signal strength, p t is the transmission power of the interference source, G t and G r are the transmitting and receiving antenna gains, d is the distance, f is the frequency, and 32.44 is the spatial loss constant term;

[0040] (5) Perform node redundancy verification on the node data with missing or abnormal values, and mark or downgrade individual "isolated anomalies" as errors;

[0041] (6) Feed back the spatial consistency score of the abnormal event, the direction of the interference signal, and the redundancy verification result to the abnormal event marking and reporting module;

[0042] The formula used when it scores the spatial consistency of abnormal events is:

[0043] Suppose that within the same time window, there are N listening nodes and M detected anomalies, and the consistency score is defined as: S consistency = 1 indicates that all nodes unanimously believe that there is interference, and S consistency <0.5: regarded as a low-confidence event or local anomaly.

[0044] Preferably, the system control and linkage scheduling module is responsible for coordinating the operating states of each module, scheduling the test process, and controlling the on / off operations of external devices.

[0045] The present invention also proposes an interference test method for power transmission equipment and wireless communication equipment, including the following steps:

[0046] S1: Deploy communication terminals, monitor power transmission equipment and interference perception nodes in the test area, start the system control and linkage scheduling module, synchronize the clocks of each listening point, load the parameters of the power transmission equipment, communication protocols, test task plans, and activate the data caching and fault tolerance strategies;

[0047] S2: The communication performance acquisition module starts listening and acquires key indicators such as the communication signal strength (RSSI, SNR, RSRP / RSRQ), signal-to-noise ratio (SNR), bit error rate (BER), delay, and packet loss rate connection status, and simultaneously records the location information of the communication equipment;

[0048] S3: The interference environment perception module parallelly acquires the electromagnetic field strength, spectrum distribution, and interference source type, synchronously records the external environment parameters, and compares with the historical background interference threshold in real time to preliminarily screen possible "abnormal fluctuations";

[0049] S4: The AI intelligent analysis and anomaly detection module conducts multi-dimensional analysis on the real-time data stream, identifies the abnormal trends of communication metrics, and uses time-delay correlation analysis to determine whether interference is likely to be the main cause of communication anomalies, and gives a causal association score for interference-caused communication anomalies.

[0050] S5: Multiple monitoring points upload data simultaneously. The multi-point synchronous monitoring and redundancy verification module performs time alignment and synchronization verification, conducts spatial redundancy comparison, and eliminates isolated anomaly points or hardware false alarms to obtain a set of unified and reliable "high-quality interference communication event pairs".

[0051] S6: The anomaly event marking and reporting module automatically determines whether a "communication performance anomaly event" is constituted, automatically generates an event report, and pushes the report to the local cache and breakpoint resumption module for caching.

[0052] S7: If the event reaches the alarm level, the system control and linkage dispatching module triggers a response.

[0053] Compared with the existing technologies, the beneficial effects of the present invention are as follows:

[0054] By integrating the communication performance acquisition module and the interference environment perception module, the present invention can achieve collaborative monitoring of communication performance + interference source behavior, and effectively and intelligently identify whether power interference is the main cause of communication performance degradation through AI intelligent analysis. With the setting of the multi-point synchronous monitoring and redundancy verification module, multi-point synchronous monitoring can be achieved, redundancy verification and anomaly troubleshooting can be realized, the credibility of test results and the fault tolerance ability of the system can be significantly improved, and with the cooperation of AI intelligent analysis and anomaly detection, the accuracy and automation level of interference event determination can be effectively improved, and manual intervention can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a block diagram of the interference test system for power transmission equipment and wireless communication equipment proposed by the present invention;

[0056] Figure 2 is a flowchart of the interference test method for power transmission equipment and wireless communication equipment proposed by the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0057] The present invention will be further explained below with reference to specific embodiments.

[0058] Embodiment

[0059] Refer to Figure 1-2, this embodiment proposes an interference test system for power transmission equipment and wireless communication equipment, including a communication performance acquisition module, an interference environment perception module, an AI intelligent analysis and anomaly detection module, an anomaly event marking and reporting module, a local cache and breakpoint resumption module, a multi-point synchronous listening and redundancy verification module, and a system control and linkage scheduling module;

[0060] The communication performance acquisition module is used to collect key communication parameters of wireless devices at high frequency. Its key communication parameters include communication signal strength (RSSI, SNR, RSRP / RSRQ), signal-to-noise ratio (SNR), bit error rate (BER), time delay, and packet loss rate, and it supports multiple communication protocols;

[0061] The interference environment perception module is used to collect the electromagnetic interference characteristics generated during the operation of power equipment. The collected interference characteristic parameters include electromagnetic field strength, spectrum distribution, and interference source type, and achieve time series alignment with communication performance data;

[0062] The AI intelligent analysis and anomaly detection module includes an anomaly detector, an interference event classifier, and an anomaly compensator. The AI intelligent analysis and anomaly detection module uses artificial intelligence algorithms to perform joint analysis on communication performance fluctuations and interference characteristic data to achieve the identification, classification, and evaluation of communication anomalies. Its operation logic steps are as follows:

[0063] (1). Clean, time-synchronize, and convert the parameters collected by the communication performance acquisition module and the interference environment perception module into a unified format;

[0064] (2). Extract feature values meaningful for "interference anomaly identification" from the original data. Its feature values include communication-side features, interference-side features, and time features. Communication-side features include mutation amplitude, volatility, trend slope, packet loss rate, peak bit error rate, communication delay jump amplitude, and frequency. Interference-side features include interference frequency distribution, frequency bandwidth, pulse amplitude, duration, interference signal modulation features, and time domain / frequency domain energy features. Time features include sliding window statistical features before and after interference occurrence, as well as the duration and interval period of anomaly events;

[0065] (3). Based on historical data and combined with the classifier model, determine whether there is an "abnormal state" or "suspected interference event" in the current time period;

[0066] (4). Calculate the time delay correlation between interference features and communication performance indicators, determine whether there is a causal relationship between variables, classify interference samples and communication anomaly samples according to similarity, find the corresponding relationship, and determine whether interference is the "main cause" of the decline in communication performance within a certain period of time;

[0067] When calculating the time delay correlation between interference features and communication performance indicators, the expression used is:

[0068] where x t is the interference feature sequence, y t is the communication performance metric sequence, k is the latency time, and are the means of the two sequences, σ x σ y is the standard deviation. By calculating r xy (k) for different values of k, find the lag with the largest correlation coefficient, which is the most likely time delay for "interference affecting communication";

[0069] When judging whether there is a causal relationship between variables, the expression used is:

[0070]

[0071] If Var(∈ 2,t ) < Var(∈ 1,t ), it proves that there is a causal relationship;

[0072] where y t is the explained variable at time point t, x t-j is the potential causal variable at time point t - j, p is the autoregressive order of the explained variable y, that is, the number of historical terms of y used in the model, q is the lag order of the potential causal variable x, that is, the number of historical terms of x used in the model, a i is the autoregressive coefficient of y at the i-th order, b j is the influence coefficient of x on y at the j-th order, ∈ 1,t and ∈ 2,t are both prediction residuals, Var(∈) is the variance of the residuals, that is, the average prediction error intensity of the model, and t is the current time point index;

[0073] (5) Record and structurally annotate the identified abnormal events for use by the subsequent abnormal event marking and reporting module;

[0074] The abnormal event marking and reporting module is used to organize, visually display, and archive the output of the identification results, generate an interference impact report for analysis and decision-making, and its operating logic steps are as follows:

[0075] (1) Receive the output results of the communication performance collection module, the interference environment perception module, and the AI intelligent analysis and anomaly detection module. Its output results include the interference feature sequence, the communication performance metric sequence, and the anomaly prediction label;

[0076] (2) Set multiple threshold rules to determine whether an anomaly is constituted. The multiple threshold rules include packet loss rate > 10%, RSSI decrease > 20 dBm, and duration > 1 s, and AI anomaly prediction probability > 0.8. If the conditions are met, it is determined as an "abnormal event".

[0077] (3) Extract a time window before and after the occurrence of the anomaly from the time series data to form a complete "interference - response" sample segment.

[0078] (4) Match known labels according to the event characteristics and automatically label the anomaly type. The labeled content includes corona interference, arc discharge, instantaneous interference, and continuous electromagnetic interference.

[0079] (5) Bind the abnormal event with the timestamp, geographical location information, environmental status information, interference source device number, communication device type, and channel number.

[0080] (6) Automatically generate a structured anomaly report based on the information data in (4) and (5).

[0081] The local cache and breakpoint resumption module is used to automatically detect link interruptions and acquisition anomalies. When the communication link is interrupted or the acquisition system is abnormal, important data is not lost, and the missing data packets are automatically re - transmitted after the network is restored to ensure data integrity and achieve data reliability guarantee.

[0082] The multi - point synchronous listening and redundancy verification module is used to deploy multiple listening points in the test area to collect communication data and interference situations from multiple angles, improving the accuracy and reliability of anomaly recognition.

[0083] Its operation logic steps are as follows:

[0084] (1) Deploy multiple listening nodes near the power transmission equipment, and the listening nodes are marked with unique IDs and spatial positions.

[0085] (2) All listening nodes perform time synchronization through the GPS / Beidou timing module to ensure data alignment accuracy and ensure that the propagation delay of interference signals and the response time of communication events can be accurately compared.

[0086] The mathematical expression used for time synchronization is:

[0087] T i = t + δ i ,T j = t + δ j ;

[0088] where t is the standard reference time, δ i and δ j are the local clock deviations of node ij, T i and Tj is the timestamp actually recorded by the node;

[0089] (3) Each monitoring point collects interference spectrum, communication performance indicators, and local environment parameters, uploads the data of multiple monitoring points to the central node, and aligns the data according to the timestamp;

[0090] (4) Compare with the received strength of each node using the interference signal propagation model to verify the consistency of the interference source. If no abnormality is detected at some monitoring points, it is marked as a possible local abnormality. If synchronous abnormalities are detected at all monitoring points, it is marked as a high-confidence interference event;

[0091] The formula used for the strength comparison is:

[0092] P r = p t + G t + G r - 20log 10 (d) - 20log 10 (f) - 32.44;

[0093] Among them, P r is the received signal strength, p t is the transmission power of the interference source, G t , G r are the transmitting and receiving antenna gains, d is the distance, f is the frequency, and 32.44 is the spatial loss constant term;

[0094] (5) Perform node redundancy check on the node data with missing or abnormal values, and mark or downgrade individual "isolated abnormalities" as errors;

[0095] (6) Feed back the spatial consistency score of the abnormal event, the direction of the interference signal, and the redundancy check result to the abnormal event marking and reporting module;

[0096] The formula used for its spatial consistency score of the abnormal event is:

[0097] Suppose there are N monitoring nodes within the same time window, and M abnormalities are detected. Define the consistency score: S consistency = 1 indicates that all nodes unanimously believe that there is interference, S consistency < 0.5: regarded as a low-confidence event or local abnormality;

[0098] The system control and linkage scheduling module is responsible for coordinating the operating states of each module, scheduling the test process, and controlling the on / off operations of external devices.

[0099] This embodiment also proposes an interference test method for power transmission equipment and wireless communication equipment, including the following steps:

[0100] S1: Deploy communication terminals, monitor power transmission equipment and interference perception nodes in the test area, start the system control and linkage dispatching module, synchronize the clocks of each monitoring point, load power transmission equipment parameters, communication protocols, test task plans, and activate data caching and fault tolerance strategies;

[0101] S2: The communication performance acquisition module starts monitoring and acquires key indicators such as communication signal strength (RSSI, SNR, RSRP / RSRQ), signal-to-noise ratio (SNR), bit error rate (BER), latency, and packet loss rate connection status, and simultaneously records the location information of communication devices;

[0102] S3: The interference environment perception module concurrently acquires electromagnetic field strength, spectrum distribution, and interference source types, synchronously records external environment parameters, and compares with historical background interference thresholds in real time to preliminarily screen for possible "abnormal fluctuations";

[0103] S4: The AI intelligent analysis and anomaly detection module performs multi-dimensional analysis on real-time data streams, identifies abnormal trends in communication metrics, and uses time-delay correlation analysis to determine whether interference may be the main cause of communication anomalies, and gives a causal association score for interference communication anomalies;

[0104] S5: Multiple monitoring points upload data simultaneously. The multi-point synchronous monitoring and redundancy verification module performs time alignment and synchronization verification, conducts spatial redundancy comparison, and eliminates isolated anomaly points or hardware false alarms to obtain a set of unified and reliable "high-quality interference communication event pairs";

[0105] S6: The anomaly event marking and reporting module automatically determines whether a "communication performance anomaly event" is constituted, automatically generates an event report, and pushes the report to the local cache and breakpoint resumption module for caching;

[0106] S7: If the event reaches the warning level, the system control and linkage dispatching module triggers a response;

[0107] Through the integration of the communication performance acquisition module and the interference environment perception module, this embodiment can achieve collaborative monitoring of communication performance + interference source behavior, and through AI intelligent analysis, effectively and intelligently identify whether power interference is the main cause of communication performance degradation. With the setting of the multi-point synchronous monitoring and redundancy verification module, multi-point synchronous monitoring can be achieved, redundancy verification and anomaly troubleshooting can be realized, significantly improving the credibility of test results and the fault tolerance ability of the system. Moreover, with the cooperation of AI intelligent analysis and anomaly detection, the accuracy and automation level of interference event determination can be effectively improved, reducing manual intervention.

[0108] The above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes, shall be covered by the protection scope of the present invention.

Claims

1. An interference test system for a power transmission device and a wireless communication device, characterized in that, It includes a communication performance acquisition module, an interference environment perception module, an AI intelligent analysis and anomaly detection module, an anomaly event marking and reporting module, a local cache and breakpoint resumption module, a multi-point synchronous listening and redundancy verification module, and a system control and linkage scheduling module.

2. The interference test system for a power transmission device and a wireless communication device according to claim 1, wherein The communication performance acquisition module is used to collect key communication parameters of wireless devices at high frequency. The key communication parameters include communication signal strength, signal-to-noise ratio, bit error rate, time delay, and packet loss rate, and it supports multiple communication protocols. The interference environment perception module is used to collect electromagnetic interference characteristics generated during the operation of power equipment. The collected interference characteristic parameters include electromagnetic field strength, spectrum distribution, and interference source type, and it realizes time series alignment with communication performance data.

3. The interference test system for the power transmission device and the wireless communication device according to claim 1, characterized in that, The AI intelligent analysis and anomaly detection module includes an anomaly detector, an interference event classifier, and an anomaly compensator. The AI intelligent analysis and anomaly detection module uses artificial intelligence algorithms to conduct joint analysis on communication performance fluctuations and interference characteristic data to realize the identification, classification, and evaluation of communication anomalies. Its operation logic steps are as follows: (1). Clean, synchronize time, and convert the parameters collected by the communication performance acquisition module and the interference environment perception module into a unified format. (2). Extract eigenvalue meaningful for "interference anomaly identification" from the original data. The eigenvalues include communication-side characteristics, interference-side characteristics, and time characteristics. Communication-side characteristics include mutation amplitude, volatility, trend slope, packet loss rate, peak bit error rate, communication delay jump amplitude, and frequency. Interference-side characteristics include interference frequency distribution, bandwidth, pulse amplitude, duration, interference signal modulation characteristics, and time domain / frequency domain energy characteristics. Time characteristics include sliding window statistical characteristics before and after interference occurrence, as well as the duration and interval period of anomaly events. (3). According to historical data and combined with the classifier model, judge whether there is an "abnormal state" or "suspected interference event" in the current time period. (4). Calculate the time delay correlation between interference characteristics and communication performance indicators, judge whether there is a causal relationship between variables, classify interference samples and communication anomaly samples according to similarity, find the corresponding relationship, and judge whether interference is the "main cause" of the decline in communication performance during a certain period. When calculating the time-delay correlation between the interference feature and the communication performance index, the expression used is: where x t is the interference feature sequence, y t is the communication performance metric sequence, k is the latency time, and are the means of the two sequences, σ x σ y is the standard deviation. By calculating r xy (k) for different k values, the lag with the maximum correlation coefficient is found, which is the most likely time delay for "interference affecting communication". When judging whether there is a causal relationship between variables, the expression used is: If Var(∈ 2,t ) < Var(∈ 1,t ), then it is proved that there is a causal relationship; where y t is the explained variable at time point t, x t-j is the potential dependent variable at time point t-j, p is the autoregressive order of the explained variable y, i.e., the number of historical terms of y used in the model, q is the lag order of the potential dependent variable x, i.e., the number of historical terms of x used in the model, a i is the autoregressive coefficient of y at the i-th order, b j is the influence coefficient of x at the j-th order on y, ∈ 1,t and ∈ 2,t are both prediction residuals, Var(∈) is the variance of the residuals, i.e., the average prediction error intensity of the model, and t is the index of the current time point; (5). Record and structurally annotate the identified anomaly events for use by the subsequent anomaly event marking and reporting module.

4. The interference test system for the power transmission device and the wireless communication device according to claim 1, wherein, The anomaly event marking and reporting module is used to sort out, visually display, and archive and output the recognition results, and generate an interference impact report for analysis and decision-making. Its operation logic steps are as follows: (1). Receive the results output by the communication performance acquisition module, the interference environment perception module, and the AI intelligent analysis and anomaly detection module. The output results include interference characteristic sequences, communication performance index sequences, and anomaly prediction labels. (2). Set multi-threshold rules to judge whether an anomaly is formed. The multi-threshold rules include packet loss rate > 10%, RSSI decrease > 20 dBm, and duration > 1 s, and AI anomaly prediction probability > 0.

8. If the conditions are met, it is determined as an "anomaly event". (3) Extract a time window before and after the occurrence of an anomaly from the time-series data to form a complete "interference-response" sample segment; (4) Automatically annotate the anomaly type by matching known tags according to event characteristics, and the annotation content includes corona interference, arc discharge, instantaneous interference, and continuous electromagnetic interference; (5) Bind the anomaly event with the timestamp, geographical location information, environmental status information, interference source device number, communication device type, and channel number; (6) Automatically generate a structured anomaly report based on the information data in (4) and (5).

5. The interference test system for a power transmission device and a wireless communication device according to claim 1, wherein The local cache and resume-on-break module is used to automatically detect link interruptions and acquisition anomalies. When the communication link is interrupted or the acquisition system is abnormal, important data is not lost, and missing data packets are automatically retransmitted after the network is restored to ensure data integrity and achieve data reliability guarantee.

6. The interference test system for the power transmission device and the wireless communication device according to claim 1, characterized in that, The multi-point synchronous listening and redundancy verification module is used to deploy multiple listening points in the test area to collect communication data and interference conditions from multiple perspectives, improving the accuracy and reliability of anomaly recognition; Its operation logic steps are as follows: (1) Deploy multiple listening nodes near the power transmission equipment, and the listening nodes are marked with unique IDs and spatial positions; (2) All listening nodes perform time synchronization through the GPS / Beidou timing module to ensure data alignment accuracy and ensure that the propagation delay of interference signals and the response time of communication events can be accurately compared; The mathematical expression used during its time synchronization is: T i = t + δ i ,T j = t + δ j ; where t is the standard reference time, δ i and δ j are the local clock deviations of node ij, T i and T j are the timestamps actually recorded by the node; (3) Each listening point collects interference spectra, communication performance indicators, and local environmental parameters, and uploads the data of multiple listening points to the central node to align the data according to timestamps; (4) Use the interference signal propagation model to compare with the received intensity of each node to verify the consistency of interference sources. If some listening points do not detect anomalies, they are marked as possible local anomalies. If all listening points detect synchronous anomalies, they are marked as high-confidence interference events; The formula used during its intensity comparison is: P r = p t + G t + G r - 20 log 10 (d) - 20 log 10 (f) - 32.44; Among them, P r is the received signal strength, p t is the transmission power of the interference source, G t and G r are the transmitting and receiving antenna gains, d is the distance, f is the frequency, and 32.44 is the spatial loss constant term; (5) Perform node redundancy verification on the node data with missing or abnormal data, and mark or downgrade individual "isolated anomalies" as errors; (6) Feed back the spatial consistency score of the anomaly event, the direction of the interference signal, and the redundancy verification result to the anomaly event marking and reporting module; (6) The formula used for its spatial consistency score of the anomaly event is: Suppose that within the same time window, there are N listening nodes, and M of them detect anomalies. Define the consistency score: S consistency = 1 indicates that all nodes unanimously believe that there is interference. S consistency <0.5: regarded as a low-confidence event or a local anomaly.

7. The interference test system for the power transmission device and the wireless communication device according to claim 1, wherein The system control and linkage scheduling module is responsible for coordinating the operating states of each module, scheduling the test process, and controlling the on / off operations of external devices.

8. Interference test method for power transmission equipment and wireless communication equipment, characterized in that It includes the following steps: S1: Deploy communication terminals, monitor power transmission equipment, and interference sensing nodes in the test area, start the system control and linkage scheduling module, synchronize the clocks of each listening point, load power transmission equipment parameters, communication protocols, test task plans, and activate the data cache and fault tolerance strategy; S2: The communication performance acquisition module starts listening and acquires key indicators such as communication signal strength, signal-to-noise ratio, bit error rate, delay, and packet loss rate connection status, and simultaneously records the location information of the communication device; S3: The interference environment perception module simultaneously collects the electromagnetic field intensity, spectrum distribution, and interference source type, synchronously records the external environment parameters, and compares them with the historical background interference threshold in real time to preliminarily screen for possible "abnormal fluctuations". S4: The AI intelligent analysis and anomaly detection module performs multi-dimensional analysis on the real-time data stream, identifies the abnormal trend of communication metrics, uses time-delay correlation analysis to determine whether the interference may be the main cause of communication anomalies, and gives a causal association score for interference communication anomalies. S5: Multiple listening points upload data simultaneously. The multi-point synchronous listening and redundancy verification module performs time alignment and synchronization verification, conducts spatial redundancy comparison, and eliminates isolated abnormal points or hardware false alarms to obtain a set of unified and credible "high-quality interference communication event pairs". S6: The abnormal event marking and reporting module automatically determines whether a "communication performance abnormal event" is constituted, automatically generates an event report, and pushes the report to the local cache and breakpoint resumption module for caching. S7: When the event reaches the alarm level, the system control and linkage dispatching module triggers a response.

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