5G integrated smart power cabinet near-end wireless inspection system

Abstract

This application relates to the technical field of power cabinet inspection, and discloses a 5G integrated smart power cabinet proximal wireless inspection system, including a data acquisition module, a fault detection module, a maintenance feedback module, and a fault verification module; wherein: the data acquisition module is used to collect the status parameters and environmental parameters of each power cabinet; the fault detection module calculates the fault confidence of each power cabinet based on the status parameters, and identifies abnormal power cabinets based on the fault confidence; the fault verification module corrects the fault confidence of the abnormal power cabinet based on the environmental parameters and status parameters; the maintenance feedback module is used to arrange maintenance for the abnormal power cabinet, and provide feedback optimization for the identification of the abnormal power cabinet based on the maintenance results. This application improves the accuracy of power cabinet fault detection and operation and maintenance efficiency, and achieves a technological breakthrough from passive response to intelligent prevention.

Description

Technical Field

[0001] The present application relates to the technical field of power cabinet inspection, and specifically to a 5G integrated smart power cabinet proximal wireless inspection system. Background Art

[0002] Advances in the Internet of Things (IoT) and edge computing technologies are driving the automation and intelligence of power cabinet inspections, leading to the emergence of a number of remote monitoring systems based on wireless sensor networks. However, existing technologies still have shortcomings in power cabinet inspections.

[0003] Currently, the industry's intelligent inspection systems for power supply cabinets identify power supply cabinet anomalies using preset fixed thresholds. Existing systems often use a unified detection threshold, failing to account for fluctuations in power supply cabinet parameter baselines due to differences in production batches, operating hours, and load, leading to misjudgments. While some existing technologies use algorithms such as SVM and random forests to train historical data for fault classification, they lack causal interpretation for multi-parameter anomalies and are insufficiently adaptable to sudden environmental disturbances. Parameters such as current harmonics, temperature, and fan power are coupled, and existing technologies lack the ability to model the causal transmission between these parameters, making it difficult to identify complex faults.

[0004] For example, the Chinese patent application with publication number CN118100440A discloses a fault detection system for a distributed DC power supply cabinet, which includes multiple DC power supply cabinets, multiple battery packs and a main controller. Multiple data transmission modules are installed inside the multiple DC power supply cabinets, and multiple sensor modules are installed on the inner walls of the multiple DC power supply cabinets and multiple battery packs. The main controller includes a data acquisition module, a data processing module, a fault diagnosis module and a communication module. The fault detection system for the distributed DC power supply cabinet determines the number and layout positions of the sensor modules according to the number of DC power supply cabinets and battery packs, and can determine the algorithm and data processing capabilities of the main controller according to the requirements of fault detection. It adopts a distributed structure to disperse the fault detection function to multiple DC power supply cabinets and battery packs, reducing the load and fault risk of a single device. However, this technical solution still has the problems raised in the background technology of this application: it lacks the ability to model causal transmission between parameters and is difficult to identify complex faults.

[0005] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the application and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to ordinary technicians in this field. Summary of the Invention

[0006] The technical problem to be solved by this application is to overcome the defects of the existing technology, provide a 5G integrated smart power cabinet proximal wireless inspection system, improve the accuracy of power cabinet fault detection and operation and maintenance efficiency, and achieve a technological breakthrough from passive response to intelligent prevention.

[0007] To solve the above technical problems, this application provides the following technical solutions:

[0008] A 5G integrated smart power cabinet proximal wireless inspection system includes a data acquisition module, a fault detection module, a maintenance feedback module, and a fault verification module; wherein:

[0009] The data acquisition module is used to collect the status parameters and environmental parameters of each power cabinet;

[0010] The fault detection module calculates the fault confidence of each power cabinet based on the state parameters, and identifies the abnormal power cabinet based on the fault confidence;

[0011] The fault verification module corrects the fault confidence of the abnormal power cabinet based on the environmental parameters and the state parameters;

[0012] The maintenance feedback module is used to arrange maintenance for abnormal power supply cabinets and provide feedback optimization for the identification of abnormal power supply cabinets based on the maintenance results.

[0013] As a preferred solution of the 5G integrated smart power cabinet proximal wireless inspection system described in this application, wherein: the fault detection module includes a data processing unit and a fault identification unit;

[0014] The data processing unit constructs a state feature vector for each power cabinet based on the state parameters; specifically, the data processing unit standardizes each state parameter and arranges them in a specified order to form a state feature vector for the corresponding power cabinet;

[0015] The fault identification unit is configured with a reference feature vector for each power supply cabinet; the reference feature vector is established as follows: each status parameter is continuously collected during the stable operation of the power supply cabinet; for each status parameter, the mean is calculated after removing outliers to obtain the reference status parameter of the corresponding item of the power supply cabinet; each reference status parameter of each power supply cabinet is standardized and arranged into a reference feature vector for each power supply cabinet.

[0016] As a preferred solution of the 5G integrated smart power cabinet proximal wireless inspection system described in this application, wherein: the fault identification unit is also configured with a fault identification strategy; the fault identification strategy is used to calculate the fault confidence based on the state feature vector and the reference feature vector of each power cabinet, specifically including: for any power cabinet, calculating the Mahalanobis distance between the state feature vector and the reference feature vector; mapping the Mahalanobis distance to the fault confidence through a nonlinear activation function; the value range of the fault confidence is ;

[0017] The fault identification unit is further configured with a confidence threshold; the fault identification strategy further includes: if the fault confidence of any power cabinet is higher than the confidence threshold, the corresponding power cabinet is an abnormal power cabinet.

[0018] As a preferred solution of the 5G integrated smart power cabinet proximal wireless inspection system described in this application, wherein: the fault verification module includes a first correction unit; the first correction unit is configured with a first correction strategy for correcting the fault confidence of the abnormal power cabinet according to the state parameter;

[0019] The first correction strategy specifically includes:

[0020] Establish a causal correlation matrix for describing the causal correlation between state parameters;

[0021] The causal association matrix is ​​a matrix with N rows and N columns, where N is the number of state parameters; the element in the i-th row and j-th column of the causal association matrix is ​​denoted as , the value range of i, j is 1, 2, ..., N; represents the causal strength coefficient of the j-th state parameter to the i-th state parameter;

[0022] Identify abnormal status parameters in abnormal power cabinets;

[0023] Determining the correlation parameters of each abnormal state parameter based on the causal correlation matrix;

[0024] Based on the associated parameters of each abnormal state parameter, it is identified whether fault conduction exists in the abnormal power supply cabinet; if fault conduction exists in the abnormal power supply cabinet, the fault confidence is set to 1.

[0025] As a preferred solution of the 5G integrated smart power cabinet proximal wireless inspection system described in the present application, the method of identifying abnormal status parameters in the abnormal power cabinet is as follows: the difference between each status parameter of the abnormal power cabinet and the corresponding reference status parameter is calculated item by item as the deviation of each status parameter; the first correction unit is further configured with a deviation threshold for each status parameter; the status parameter whose deviation is greater than the deviation threshold is the abnormal status parameter in the abnormal power cabinet;

[0026] The first correction unit is further configured with a causal strength threshold; based on the causal association matrix, determining the association parameter of each abnormal state parameter, specifically including: the causal strength coefficient in the corresponding row of any abnormal state parameter in the causal association matrix is ​​the association causal strength coefficient of the abnormal state parameter; the association parameters of any abnormal state parameter include all state parameters corresponding to the association causal strength coefficients greater than the causal strength threshold;

[0027] Based on the associated parameters of each abnormal status parameter, identify whether there is fault conduction in the abnormal power supply cabinet, specifically including: if any associated parameter B of any abnormal status parameter A in the abnormal power supply cabinet is also an abnormal status parameter, and the deviation amount of A and the deviation amount of B meet the fault conduction mode between A and B, then fault conduction exists in the abnormal power supply cabinet.

[0028] As a preferred solution of the 5G integrated smart power cabinet proximal wireless inspection system described in this application, the causal association matrix is ​​established as follows:

[0029] Synchronously and continuously collect each state parameter and establish a time series for each state parameter;

[0030] Perform hypothesis testing on any two state parameters based on the time series and assign values ​​to the corresponding elements in the causal association matrix, including:

[0031] make Represents the time series of the p-th state parameter, let Represents the time series of the qth state parameter, the value range of p and q is 1, 2, ..., N; based on 、 Construct a vector autoregressive model of the p-th state parameter; make assumptions about the vector autoregressive model as follows: calculate the value of any positive integer t corresponding to When , in the vector autoregressive model The weights of the previous k consecutive elements are all 0;

[0032] The hypothesis is tested by chi-square test, and the joint significance is calculated, which is denoted as R; based on the joint significance R is the corresponding element in the causal association matrix Assign values, including: If R is less than 0.05, then The value of is 1-R; otherwise, The value of is 0.

[0033] As a preferred solution of the 5G integrated smart power cabinet proximal wireless inspection system described in this application, the vector autoregressive model is used to calculate Any element in The tth element in , The tth element in , t is a positive integer, then the vector autoregressive model is based on middle, The previous k consecutive elements and middle, Calculate the weighted sum of the previous k consecutive elements , k is the lag order.

[0034] As a preferred solution of the 5G integrated smart power cabinet proximal wireless inspection system described in this application, wherein: the fault verification module also includes a second correction unit;

[0035] The second correction unit is configured with a second correction strategy for correcting the fault confidence of the abnormal power cabinet according to the environmental parameters and the status parameters of the adjacent power cabinets;

[0036] The second correction unit is further configured with a distance threshold; the second correction unit calculates the straight-line distance between each power cabinet and the abnormal power cabinet, and marks the power cabinets whose straight-line distance is less than the distance threshold as adjacent power cabinets of the abnormal power cabinet.

[0037] As a preferred solution of the 5G integrated smart power cabinet proximal wireless inspection system described in this application, the second correction strategy specifically includes:

[0038] Marking each state parameter corresponding to the abnormal state parameter in the adjacent power cabinet as a potential abnormal parameter; calculating the deviation of each potential abnormal parameter in each adjacent power cabinet, and identifying whether fault conduction exists in each adjacent power cabinet; the second correction unit is also configured with an environmental detection model for detecting whether the environmental parameter is abnormal;

[0039] If there is no fault conduction between the abnormal power cabinet and any adjacent power cabinet, and the deviation of each abnormal state parameter and each potential abnormal parameter is less than the deviation threshold, and an abnormal environmental parameter is detected, the fault confidence of the abnormal power cabinet is reduced;

[0040] If fault conduction occurs in at least one adjacent power cabinet, and the deviation of each potential abnormal parameter of the adjacent power cabinet with fault conduction has the same positive or negative sign as the deviation of the corresponding abnormal status parameter of the abnormal power cabinet, the fault confidence of the abnormal power cabinet is increased.

[0041] The method for the second correction unit to detect whether the environmental parameters are abnormal based on the environmental detection model is as follows:

[0042] Continuously obtain environmental parameters and form a time series for each environmental parameter;

[0043] Inputting the time series of each environmental parameter into the environmental detection model;

[0044] The environmental detection model calculates and outputs the detection results, including abnormal environmental parameters and normal environmental parameters.

[0045] As a preferred solution of the 5G integrated smart power cabinet proximal wireless inspection system described in this application, wherein: the maintenance feedback module includes a maintenance recording unit and a feedback optimization unit;

[0046] The maintenance recording unit is used to arrange maintenance for the abnormal power supply cabinet and record the maintenance results; the maintenance results include whether the abnormal power supply cabinet has a fault or not;

[0047] The feedback optimization unit is configured with a feedback optimization strategy; the feedback optimization strategy is used to perform feedback optimization on the identification of abnormal power cabinets, specifically including performing feedback optimization on the confidence threshold;

[0048] The feedback optimization strategy is as follows:

[0049] Record the inspection results and corresponding confidence thresholds for each inspection;

[0050] Calculate the true positive rate and false positive rate corresponding to each confidence threshold based on the inspection results;

[0051] Determining an optimal confidence threshold; the optimal confidence threshold is the confidence threshold that maximizes the difference between the true positive rate and the false positive rate;

[0052] Update the confidence threshold to the optimal confidence threshold.

[0053] Compared with the prior art, the beneficial effects achieved by this application are as follows:

[0054] This application establishes a dynamic health baseline for each device, effectively distinguishing normal parameter fluctuations from true faults and improving the accuracy of fault identification. The introduction of a multi-parameter correlation analysis mechanism can identify complex faults caused by causal transmission within the device, addressing the blind spots of traditional single-parameter detection.

[0055] This application integrates collaborative analysis of equipment status and environmental parameters to intelligently identify anomalies caused by the external environment, significantly reducing the false alarm rate. Each inspection result is converted into system knowledge to achieve automatic correction and optimization of inspection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be derived from these drawings without inventive effort. Among them:

[0057] Figure 1 A schematic diagram of the structure of the 5G integrated smart power cabinet proximal wireless inspection system provided for this application;

[0058] Figure 2 Flowchart of the first revision strategy provided for this application. DETAILED DESCRIPTION

[0059] The technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Unless there is a conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0060] This application embodiment introduces a 5G integrated smart power cabinet near-end wireless inspection system. Figure 1 The system includes a data acquisition module, a fault detection module, a maintenance feedback module, and a fault verification module; wherein:

[0061] The data acquisition module is used to collect the status parameters and environmental parameters of each power cabinet;

[0062] The data acquisition module includes a state parameter unit and an environment parameter unit;

[0063] The state parameter unit is used to collect each state parameter of each power supply cabinet; the preferred state parameters in this embodiment include: current harmonic distortion rate, which reflects the abnormality of the power supply filter circuit and load, and has a causal relationship with the capacitor aging rate; capacitor equivalent series resistance, which is the core indicator of capacitor degradation; the temperature difference between the inside and outside of the power supply cabinet, which reflects the interference of the environment on the state of the power supply cabinet; the power change rate of the cooling fan, which captures the attenuation of the heat dissipation efficiency; the internal temperature gradient, such as the temperature difference between the air inlet, the capacitor area, and the air outlet, which reflects the local temperature anomaly; the DC output ripple voltage, which reflects the stability of the DC voltage.

[0064] The environmental parameter unit is used to collect environmental parameters. Preferred environmental parameters in this embodiment include ambient temperature, grid voltage fluctuation data, and wireless signal interference data, such as the bit error rate and signal-to-noise ratio of 5G base stations. These preferred environmental parameters are strongly correlated with the accuracy of the power cabinet status parameters, helping to determine whether an abnormality in the status parameters is due to a power cabinet failure or a misjudgment caused by environmental interference.

[0065] The fault detection module calculates the fault confidence of each power cabinet based on the state parameters, and identifies the abnormal power cabinet based on the fault confidence;

[0066] The fault detection module includes a data processing unit and a fault identification unit;

[0067] The data processing unit constructs a state feature vector for each power cabinet based on the state parameters; specifically, it includes: standardizing each state parameter separately, and arranging them in a specified order to form a state feature vector of the corresponding power cabinet; the standardization method includes but is not limited to Z-score standardization, using logarithmic transformation to compress the dimension, etc.

[0068] The fault identification unit is configured with a reference feature vector for each power supply cabinet; the reference feature vector is established as follows: each status parameter is continuously collected during the stable operation of the power supply cabinet; for each status parameter, the mean is calculated after removing outliers to obtain the reference status parameter of the corresponding item of the power supply cabinet; each reference status parameter of each power supply cabinet is standardized and arranged into a reference feature vector for each power supply cabinet.

[0069] The fault identification unit is also equipped with a fault identification strategy; the fault identification strategy is used to calculate the fault confidence based on the state feature vector of each power cabinet and the reference feature vector, specifically including: for any power cabinet, calculating the Mahalanobis distance between the state feature vector and the reference feature vector; mapping the Mahalanobis distance to the fault confidence through a nonlinear activation function; the value range of the fault confidence is In this embodiment, the extended form of the Sigmoid function is preferably used as a nonlinear activation function to map the Mahalanobis distance between the state feature vector and the reference feature vector to the fault confidence of the corresponding power cabinet. The formula is as follows:

[0070] ;

[0071] Among them, C is the fault confidence, D is the Mahalanobis distance, is an exponential function. The general form of the Sigmoid function is , this embodiment introduces adjustable parameters and To improve the accuracy of the fault confidence level in representing the actual fault probability. and Determined by fitting historical fault data.

[0072] The fault identification unit is further configured with a confidence threshold; the fault identification strategy further includes: if the fault confidence of any power cabinet is higher than the confidence threshold, the corresponding power cabinet is an abnormal power cabinet.

[0073] The fault verification module corrects the fault confidence of the abnormal power cabinet based on the environmental parameters and the state parameters;

[0074] The fault verification module includes a first correction unit and a second correction unit;

[0075] The first correction unit is configured with a first correction strategy for correcting the fault confidence of the abnormal power cabinet according to the state parameter;

[0076] Reference Figure 2 , the first correction strategy specifically includes:

[0077] Establish a causal correlation matrix for describing the causal correlation between state parameters;

[0078] The causal association matrix is ​​a matrix with N rows and N columns, where N is the number of state parameters; the element in the i-th row and j-th column of the causal association matrix is ​​denoted as , the value range of i, j is 1, 2, ..., N; represents the causal strength coefficient of the j-th state parameter to the i-th state parameter; that is, Indicates the probability that the i-th state parameter will be triggered when the j-th state parameter is abnormal.

[0079] Identify abnormal status parameters in an abnormal power supply cabinet; the method is as follows: calculate the difference between each status parameter of the abnormal power supply cabinet and the corresponding reference status parameter item by item, as the deviation of each status parameter; the first correction unit is also configured with a deviation threshold value for each status parameter; a status parameter with a deviation greater than the deviation threshold value is an abnormal status parameter of the abnormal power supply cabinet;

[0080] Based on the causal association matrix, the associated parameters of each abnormal state parameter are determined; the first correction unit is further configured with a causal strength threshold; in the causal association matrix, the causal strength coefficient in the corresponding row of any abnormal state parameter is the associated causal strength coefficient of the abnormal state parameter; the associated parameters of any abnormal state parameter include all state parameters corresponding to the associated causal strength coefficients greater than the causal strength threshold; for example, the fifth state parameter is the abnormal state parameter, and If it is greater than the causal strength threshold, the first state parameter is the associated parameter of the fifth state parameter.

[0081] Identify whether fault conduction exists in the abnormal power cabinet based on the associated parameters of each abnormal state parameter; if fault conduction exists in the abnormal power cabinet, set the fault confidence level to 1;

[0082] If any associated parameter B of any abnormal state parameter A in the abnormal power supply cabinet is also an abnormal state parameter, and the deviation of A and the deviation of B satisfy the fault conduction mode between A and B, then fault conduction exists in the abnormal power supply cabinet.

[0083] In this embodiment, the fault conduction mode between any state parameter A and its associated parameter B is preferably determined as follows: based on historical fault data, the positive and negative relationship between the deviations of state parameters A and B when both are abnormal state parameters is analyzed, and the relationship is used as the fault conduction mode between A and B. For example, the fault conduction mode is such that when the deviation of A is positive, the deviation of B is also positive.

[0084] The causal association matrix can be used to identify the fault transmission paths between various state parameters. If B is a correlated parameter of A, then a fault transmission path exists where an abnormality in B causes an abnormality in A. For example, A represents the current harmonic distortion rate, and B represents the equivalent series resistance of the capacitor. Capacitor aging causes an abnormal equivalent series resistance of the capacitor, which in turn leads to an abnormal current harmonic distortion rate. If the deviation between A and B satisfies the fault transmission pattern between A and B, it indicates that the fault propagated along this path. The anomalies in A and B are not caused by sensor system errors or communication errors, but rather by a typical fault in the power cabinet, indicating that fault transmission has occurred. Therefore, the fault confidence is set to 1.

[0085] The causal relationship matrix is ​​established as follows:

[0086] When the power cabinet is running stably, each status parameter is collected synchronously and continuously, and a time series of each status parameter is established; the timestamps of the time series of all status parameters are strictly aligned;

[0087] Perform hypothesis testing on any two state parameters based on the time series and assign values ​​to the corresponding elements in the causal association matrix, including:

[0088] make Represents the time series of the p-th state parameter, let Represents the time series of the qth state parameter, the value range of p and q is 1, 2, ..., N; based on 、 Construct a vector autoregressive model of the pth state parameter; the vector autoregressive model is used to calculate Any element in The tth element in , The tth element in , t is a positive integer, then the vector autoregressive model is based on middle, The previous k consecutive elements and middle, Calculate the weighted sum of the previous k consecutive elements , k is the lag order; the value of k is determined by information criterion method, etc.;

[0089] The vector autoregressive model is assumed as follows: Calculate the value of any positive integer t corresponding to When , in the vector autoregressive model The weights of the previous k consecutive elements are all 0;

[0090] The hypothesis is tested by chi-square test, and the joint significance is calculated, which is denoted as R; based on the joint significance R is the corresponding element in the causal association matrix Assign values, including: If R is less than 0.05, then The value of is 1-R; otherwise, The value of is 0.

[0091] This embodiment assumes that the calculation of the corresponding When , in the vector autoregressive model The weights of the previous k consecutive elements are all 0; if the assumption is true, it means that the time series For calculation There is no help for any element value in The corresponding p-th state parameter is There is no causal relationship between the corresponding qth state parameters. If the assumption is not true, it means that the qth state parameter is the correlation parameter of the pth state parameter, and the smaller the joint significance R, the stronger the causal relationship. The larger the value of .

[0092] The second correction unit is configured with a second correction strategy for correcting the fault confidence of the abnormal power cabinet according to the environmental parameters and the status parameters of the adjacent power cabinets;

[0093] The second correction unit is further configured with a distance threshold; the second correction unit calculates the straight-line distance between each power cabinet and the abnormal power cabinet, and marks the power cabinets whose straight-line distance is less than the distance threshold as adjacent power cabinets of the abnormal power cabinet.

[0094] The second correction strategy specifically includes:

[0095] Marking each state parameter corresponding to the abnormal state parameter in the adjacent power cabinet as a potential abnormal parameter; calculating the deviation of each potential abnormal parameter in each adjacent power cabinet, and identifying whether fault conduction exists in each adjacent power cabinet; the second correction unit is also configured with an environmental detection model for detecting whether the environmental parameter is abnormal;

[0096] If there is no fault transmission between the abnormal power supply cabinet and any adjacent power supply cabinets, and the deviation between each abnormal status parameter and each potential abnormal parameter is less than the deviation threshold, and an abnormal environmental parameter is detected, the fault confidence level of the abnormal power supply cabinet is lowered; for example, the fault confidence level may be lowered by 50%. At this point, the status parameter fluctuations in adjacent power supply cabinets are all within the normal threshold range, and the environmental parameter is abnormal. The status parameter fluctuations are determined to be caused by environmental interference. At the same time, no fault transmission is detected in adjacent power supply cabinets, further confirming that the probability of a fault in the power supply cabinet is low.

[0097] If at least one adjacent power supply cabinet experiences fault conduction, and the deviations for each potential abnormal parameter of the adjacent power supply cabinet with fault conduction have the same sign as the deviations for the corresponding abnormal status parameters of the abnormal power supply cabinet, the fault confidence level of the abnormal power supply cabinet is increased. For example, the fault confidence level can be set to 1. In this case, if fault conduction occurs in a nearby power supply cabinet and similar status parameter fluctuations occur as in the abnormal power supply cabinet, it is determined that the abnormal power supply cabinet and the adjacent power supply cabinet with fault conduction have potential faults caused by environmental factors or other reasons (such as batch power supply defects).

[0098] The method for the second correction unit to detect whether the environmental parameters are abnormal based on the environmental detection model is as follows:

[0099] Continuously obtain environmental parameters and form a time series for each environmental parameter;

[0100] Inputting the time series of each environmental parameter into the environmental detection model;

[0101] The environmental detection model calculates and outputs the detection results, including abnormal environmental parameters and normal environmental parameters.

[0102] This embodiment preferably uses the LSTM-Autoencoder model as the environmental detection model. This model combines a long short-term memory network and an autoencoder to automatically learn and identify abnormal situations from the time series of environmental parameters.

[0103] The maintenance feedback module is used to arrange maintenance for abnormal power supply cabinets and provide feedback optimization for the identification of abnormal power supply cabinets based on the maintenance results.

[0104] The maintenance feedback module includes a maintenance recording unit and a feedback optimization unit;

[0105] The maintenance recording unit is used to arrange maintenance for the abnormal power supply cabinet and record the maintenance results; the maintenance results include whether the abnormal power supply cabinet has a fault or not;

[0106] The feedback optimization unit is configured with a feedback optimization strategy; the feedback optimization strategy is used to perform feedback optimization on the identification of abnormal power cabinets, specifically including performing feedback optimization on the confidence threshold;

[0107] The feedback optimization strategy is as follows:

[0108] Record the inspection results and corresponding confidence thresholds for each inspection;

[0109] Calculate the true positive rate and false positive rate corresponding to each confidence threshold based on the inspection results;

[0110] Determining an optimal confidence threshold; the optimal confidence threshold is the confidence threshold that maximizes the difference between the true positive rate and the false positive rate;

[0111] Update the confidence threshold to the optimal confidence threshold.

[0112] This embodiment preferably uses the confidence threshold that maximizes the difference between the true positive rate and the false positive rate as the optimal confidence threshold. The difference between the true positive rate and the false positive rate is the Youden index; the Youden index can measure the confidence threshold's ability to discriminate power supply cabinet faults. The larger the Youden index, the better the corresponding confidence threshold's discriminative ability. The true positive rate is the ratio of the number of maintenance records for power supply cabinets that are faulty and identified as abnormal to the total number of maintenance records for power supply cabinets that are faulty; the false positive rate is the ratio of the number of maintenance records for power supply cabinets that are not faulty but identified as abnormal to the total number of maintenance records for power supply cabinets that are not faulty.

[0113] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0114] The above describes the embodiments of the present application in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose and scope of protection of this application, all of which are protected by this application.

Claims

1. 5G integrated smart power cabinet proximal wireless inspection system, featuring: It includes data acquisition module, fault detection module, maintenance feedback module and fault verification module; among which: The data acquisition module is used to collect the status parameters and environmental parameters of each power cabinet; The fault detection module calculates the fault confidence of each power cabinet based on the state parameters, and identifies the abnormal power cabinet based on the fault confidence; The fault verification module corrects the fault confidence of the abnormal power cabinet based on the environmental parameters and the state parameters; The fault verification module includes a first correction unit; the first correction unit is configured with a first correction strategy for correcting the fault confidence of the abnormal power cabinet according to the state parameter; The first correction strategy specifically includes: Establish a causal correlation matrix for describing the causal correlation between state parameters; The causal association matrix is ​​a matrix with N rows and N columns, where N is the number of state parameters; the element in the i-th row and j-th column of the causal association matrix is ​​denoted as , the value range of i, j is 1, 2, ..., N; represents the causal strength coefficient of the j-th state parameter to the i-th state parameter; Identify abnormal status parameters in abnormal power cabinets; Determining the correlation parameters of each abnormal state parameter based on the causal correlation matrix; Identify whether fault conduction exists in the abnormal power cabinet based on the associated parameters of each abnormal state parameter; if fault conduction exists in the abnormal power cabinet, set the fault confidence level to 1; The maintenance feedback module is used to arrange maintenance for abnormal power supply cabinets and provide feedback optimization for the identification of abnormal power supply cabinets based on the maintenance results.

2. The 5G integrated smart power cabinet proximal wireless inspection system according to claim 1, characterized in that: The fault detection module includes a data processing unit and a fault identification unit; The data processing unit constructs a state feature vector for each power cabinet based on the state parameters; specifically, the data processing unit standardizes each state parameter and arranges them in a specified order to form a state feature vector for the corresponding power cabinet; The fault identification unit is configured with a reference feature vector for each power supply cabinet; the reference feature vector is established as follows: each status parameter is continuously collected during the stable operation of the power supply cabinet; for each status parameter, the mean is calculated after removing outliers to obtain the reference status parameter of the corresponding item of the power supply cabinet; each reference status parameter of each power supply cabinet is standardized and arranged into a reference feature vector for each power supply cabinet.

3. The 5G integrated smart power cabinet proximal wireless inspection system according to claim 2, characterized in that: The fault identification unit is also configured with a fault identification strategy; the fault identification strategy is used to calculate the fault confidence based on the state feature vector of each power cabinet and the reference feature vector, specifically including: for any power cabinet, calculating the Mahalanobis distance between the state feature vector and the reference feature vector; mapping the Mahalanobis distance to the fault confidence through a nonlinear activation function; the value range of the fault confidence is ; The fault identification unit is further configured with a confidence threshold; the fault identification strategy further includes: if the fault confidence of any power cabinet is higher than the confidence threshold, the corresponding power cabinet is an abnormal power cabinet.

4. The 5G integrated smart power cabinet proximal wireless inspection system according to claim 3, characterized in that: The method for identifying abnormal state parameters in an abnormal power supply cabinet is as follows: the difference between each state parameter of the abnormal power supply cabinet and the corresponding reference state parameter is calculated item by item as the deviation of each state parameter; the first correction unit is further configured with a deviation threshold value for each state parameter; a state parameter whose deviation value is greater than the deviation threshold value is considered an abnormal state parameter in the abnormal power supply cabinet; The first correction unit is also configured with a causal strength threshold; Based on the causal association matrix, determining the association parameter of each abnormal state parameter, specifically including: in the causal association matrix, the causal strength coefficient in the corresponding row of any abnormal state parameter is the association causal strength coefficient of the abnormal state parameter; the association parameter of any abnormal state parameter includes all state parameters corresponding to the association causal strength coefficients greater than the causal strength threshold; Based on the associated parameters of each abnormal status parameter, identify whether there is fault conduction in the abnormal power supply cabinet, specifically including: if any associated parameter B of any abnormal status parameter A in the abnormal power supply cabinet is also an abnormal status parameter, and the deviation of A and the deviation of B satisfy the fault conduction mode between A and B, then fault conduction exists in the abnormal power supply cabinet.

5. The 5G integrated smart power cabinet proximal wireless inspection system according to claim 4, characterized in that: The causal relationship matrix is ​​established as follows: Synchronously and continuously collect each state parameter and establish a time series for each state parameter; Perform hypothesis testing on any two state parameters based on the time series and assign values ​​to the corresponding elements in the causal association matrix, including: make Represents the time series of the p-th state parameter, let Represents the time series of the qth state parameter, the value range of p and q is 1, 2, ..., N; based on 、 Construct a vector autoregressive model of the p-th state parameter; make assumptions about the vector autoregressive model as follows: calculate the value of any positive integer t corresponding to When , in the vector autoregressive model The weights of the previous k consecutive elements are all 0; The hypothesis is tested by chi-square test, and the joint significance is calculated, which is denoted as R; based on the joint significance R is the corresponding element in the causal association matrix Assign values, including: If R is less than 0.05, then The value of is 1-R; otherwise, The value of is 0.

6. The 5G integrated smart power cabinet proximal wireless inspection system according to claim 5, characterized in that: The vector autoregressive model is used to calculate Any element in The tth element in , The tth element in , t is a positive integer, then the vector autoregressive model is based on middle, The previous k consecutive elements and middle, Calculate the weighted sum of the previous k consecutive elements , k is the lag order.

7. The 5G integrated smart power cabinet proximal wireless inspection system according to claim 6, characterized in that: The fault verification module further includes a second correction unit; The second correction unit is configured with a second correction strategy for correcting the fault confidence of the abnormal power cabinet according to the environmental parameters and the status parameters of the adjacent power cabinets; The second correction unit is also configured with a distance threshold; The second correction unit calculates the straight-line distance between each power cabinet and the abnormal power cabinet, and marks the power cabinets whose straight-line distance is less than a distance threshold as adjacent power cabinets of the abnormal power cabinet.

8. The 5G integrated smart power cabinet proximal wireless inspection system according to claim 7, characterized in that: The second correction strategy specifically includes: Marking each state parameter corresponding to the abnormal state parameter in the adjacent power cabinet as a potential abnormal parameter; calculating the deviation of each potential abnormal parameter in each adjacent power cabinet, and identifying whether fault conduction exists in each adjacent power cabinet; the second correction unit is also configured with an environmental detection model for detecting whether the environmental parameter is abnormal; If there is no fault conduction between the abnormal power cabinet and any adjacent power cabinet, and the deviation of each abnormal state parameter and each potential abnormal parameter is less than the deviation threshold, and an abnormal environmental parameter is detected, the fault confidence of the abnormal power cabinet is reduced; If fault conduction occurs in at least one adjacent power cabinet, and the deviation of each potential abnormal parameter of the adjacent power cabinet with fault conduction has the same positive or negative sign as the deviation of the corresponding abnormal status parameter of the abnormal power cabinet, the fault confidence of the abnormal power cabinet is increased.

9. The 5G integrated smart power cabinet proximal wireless inspection system according to claim 8, characterized in that: The maintenance feedback module includes a maintenance recording unit and a feedback optimization unit; The maintenance recording unit is used to arrange maintenance for the abnormal power supply cabinet and record the maintenance results; the maintenance results include whether the abnormal power supply cabinet has a fault or not; The feedback optimization unit is configured with a feedback optimization strategy; the feedback optimization strategy is used to perform feedback optimization on the identification of abnormal power cabinets, specifically including performing feedback optimization on the confidence threshold; The feedback optimization strategy is as follows: Record the inspection results and corresponding confidence thresholds for each inspection; Calculate the true positive rate and false positive rate corresponding to each confidence threshold based on the inspection results; Determining an optimal confidence threshold; the optimal confidence threshold is the confidence threshold that maximizes the difference between the true positive rate and the false positive rate; Update the confidence threshold to the optimal confidence threshold.

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