Resistance detection system and method for distribution network hot-line work

Abstract

The invention provides a resistor detection system and method for distribution network hot-line work, and relates to the field of intelligent resistor detection. A data analysis technology based on artificial intelligence is adopted to respectively carry out time sequence feature association on a time queue of temperature values and a time queue of humidity values in an insulation detection process, and query matching based on principal components is remarkably fused and interacted; on the basis of the temperature time sequence correlation characteristic, the humidity time sequence correlation characteristic and the multi-dimensional correlation representation characteristic between temperature and humidity principal component significant interaction, an insulation resistance measurement compensation factor is intelligently generated, and a compensation insulation resistance value is calculated and obtained on the basis of the compensation factor. Therefore, through an automatic measurement process, the accuracy of measurement data can be ensured, and a more accurate insulation resistance value can be further obtained. And the insulation resistance value is dynamically compensated based on the environment data acquired in real time, so that the performance of the insulation material in a real working environment can be reflected more accurately.

Description

Technical Field

[0001] The present application relates to the field of intelligent resistance detection, and more specifically, to a resistance detection system and method for live working in a distribution network. Background Art

[0002] Live working on distribution network refers to the inspection, maintenance and transformation of power lines without power outage. This operation method can reduce power outage time, improve power supply reliability and reduce economic losses. Therefore, live working has very high requirements for insulation materials, and any decline in insulation performance may lead to safety accidents. Insulation resistance is one of the important indicators to measure the insulation performance of electrical equipment. In live working on distribution network, accurate measurement of insulation resistance value is crucial to ensure the safety of operators and the normal operation of equipment.

[0003] Chinese patent CN113960368A proposes an insulation resistance detection tool for live distribution network operation and its use method. Specifically, the operator first wears insulating gloves, starts the insulation resistance tester, and adjusts the knob to fix the anti-slip gear. Then, hold the insulating handle, adjust the length and rotate the test contact to set the appropriate pole spacing, and finally lock the contact position to prepare for insulation resistance measurement.

[0004] However, the above patent solves the traditional reliance on manual detection by two people for insulation resistance value detection. But it still requires a single operator to perform manual operation and the process is cumbersome and error-prone, which affects the accuracy and reliability of the measurement. In addition, the limitation of this insulation resistance measurement method is that it does not take into account the actual working environment of the resistance measurement, that is, during the resistance measurement process, factors such as ambient temperature and humidity will have a significant impact on the measurement results. Specifically, an increase in temperature may increase the molecular activity of the insulating material, thereby reducing the resistance value; while an increase in humidity may promote current leakage and further reduce the insulation performance. However, the solution in the above patent does not pay attention to the influence of environmental factors, and thus cannot truly reflect the state of the insulating material under actual working conditions, resulting in an underestimation of the true performance of the insulating material, which in turn affects the reliability and safety of the insulation resistance measurement.

[0005] Therefore, an optimized resistance detection solution for live working in distribution networks is desired. Summary of the invention

[0006] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a resistance detection system and method for live working in a distribution network, which uses artificial intelligence-based data analysis technology to perform time series feature association on the time queue of temperature values ​​and the time queue of humidity values ​​in the insulation detection process, respectively, and performs significant fusion interaction based on the query matching of the principal component, thereby intelligently generating an insulation resistance measurement compensation factor based on the multi-dimensional correlation representation characteristics between the temperature time series association characteristics and the humidity time series association characteristics and the significant interaction between the temperature and humidity principal components, and calculating the compensated insulation resistance value based on the compensation factor. In this way, through the automated measurement process, the accuracy of the measurement data can be ensured, thereby obtaining a more accurate insulation resistance value. And dynamically compensating the insulation resistance value based on the real-time collected environmental data can more accurately reflect the performance of the insulating material in the real working environment.

[0007] According to one aspect of the present application, there is provided a resistance detection system for live-line working on a distribution network, comprising: an insulation resistance detection tool for live-line working on a distribution network, a temperature sensor, a humidity sensor, and a resistance measurement compensation corrector; The insulation resistance detection tool for live distribution network operation is used to collect the initial insulation resistance value of the object under test; The temperature sensor and the humidity sensor are used to collect the time queue of the temperature value and the time queue of the humidity value during the insulation detection process respectively; The resistance measurement compensation corrector is used to compensate and correct the initial insulation resistance value based on the time queue of the temperature value and the time queue of the humidity value to obtain a compensated insulation resistance value; Wherein, the resistance measurement compensation corrector comprises: A temperature and humidity time series association module, used for inputting the time queue of the temperature value and the time queue of the humidity value into a sequence encoder to obtain a temperature bidirectional time series association feature vector and a humidity bidirectional time series association feature vector; A temperature-humidity principal component time series significant matching module is used to input the temperature bidirectional time series associated feature vector and the humidity bidirectional time series associated feature vector into a feature principal component significant fusion network to obtain a temperature-humidity principal component time series significant collaborative interaction representation vector; An insulation measurement factor multidimensional association module is used to input the temperature-humidity principal component time series significant collaborative interaction representation vector, the temperature bidirectional time series association feature vector, and the humidity bidirectional time series association feature vector into an insulation measurement factor multidimensional association analyzer to obtain an insulation measurement factor multidimensional association representation vector; An insulation resistance measurement compensation factor generation module, used to obtain an insulation resistance measurement compensation factor based on the insulation measurement factor multi-dimensional association representation vector; The insulation resistance value compensation module is used to calculate the product between the insulation resistance measurement compensation factor and the initial insulation resistance value to obtain the compensated insulation resistance value.

[0008] In the above-mentioned resistance detection system for live working in the distribution network, the temperature and humidity timing association module is used to: input the time queue of the temperature value and the time queue of the humidity value into a sequence encoder based on a bidirectional gated cyclic unit to obtain the temperature bidirectional timing association feature vector and the humidity bidirectional timing association feature vector.

[0009] In the above-mentioned resistance detection system for live working in the distribution network, the temperature-humidity principal component time series significant matching module includes: a temperature and humidity bidirectional time series correlation feature standardization unit, which is used to standardize the temperature bidirectional time series correlation feature vector and the humidity bidirectional time series correlation feature vector to obtain a standardized temperature bidirectional time series correlation feature vector and a standardized humidity bidirectional time series correlation feature vector; a temperature and humidity bidirectional time series correlation sample covariance matrix calculation unit, which is used to calculate the sample covariance matrix of the standardized temperature bidirectional time series correlation feature vector and the standardized humidity bidirectional time series correlation feature vector to obtain a temperature bidirectional time series correlation sample covariance matrix and a humidity bidirectional time series correlation sample covariance matrix; a temperature and humidity bidirectional time series correlation principal component feature extraction unit, which is used to extract feature vectors based on matrix decomposition from the temperature bidirectional time series correlation sample covariance matrix and the humidity bidirectional time series correlation sample covariance matrix to obtain a set of temperature bidirectional time series correlation principal component feature vectors and a set of humidity bidirectional time series correlation principal component feature vectors. A set; a temperature and humidity bidirectional temporal association feature query matching unit, which is used to input the set of the temperature bidirectional temporal association principal component feature vectors and the set of the humidity bidirectional temporal association principal component feature vectors into a maximum approximate query matching network to obtain a set of best matching pairs of the temperature bidirectional temporal association principal component feature vectors and the humidity bidirectional temporal association principal component feature vectors; a temperature-humidity bidirectional temporal association principal component fusion unit, which is used to input the best matching pairs of each temperature bidirectional temporal association principal component feature vector and the humidity bidirectional temporal association principal component feature vector in the set of best matching pairs of the temperature bidirectional temporal association principal component feature vector and the humidity bidirectional temporal association principal component feature vector into a semantic fine-grained gating joint module to obtain a set of temperature-humidity bidirectional temporal association principal component fusion feature vectors; a temperature-humidity principal component temporal significant collaborative interaction representation generation unit, which is used to cascade the set of the temperature-humidity bidirectional temporal association principal component fusion feature vectors to obtain the temperature-humidity principal component temporal significant collaborative interaction representation vector.

[0010] In the above-mentioned resistance detection system for live working in the distribution network, the temperature and humidity bidirectional time series correlation feature standardization unit is used to: respectively calculate the mean and standard deviation of the temperature bidirectional time series correlation feature vector to obtain the mean of the temperature bidirectional time series correlation feature and the standard deviation of the temperature bidirectional time series correlation feature; after subtracting the temperature bidirectional time series correlation feature vector from the mean of the temperature bidirectional time series correlation feature by position, divide the calculated temperature bidirectional time series offset vector by the temperature bidirectional time series correlation feature standard deviation by position to obtain the standardized temperature bidirectional time series correlation feature vector; respectively calculate the mean and standard deviation of the humidity bidirectional time series correlation feature vector to obtain the mean of the humidity bidirectional time series correlation feature and the standard deviation of the humidity bidirectional time series correlation feature; after subtracting the humidity bidirectional time series correlation feature vector from the mean of the humidity bidirectional time series correlation feature by position, divide the calculated humidity bidirectional time series offset vector by the humidity bidirectional time series correlation feature standard deviation by position to obtain the standardized humidity bidirectional time series correlation feature vector.

[0011] In the above-mentioned resistance detection system for live working in distribution network, the temperature and humidity bidirectional time series correlation sample covariance matrix calculation unit includes: a temperature bidirectional time series correlation sample covariance matrix generation subunit, which is used to multiply the transposed vector of the standardized temperature bidirectional time series correlation feature vector with the standardized temperature bidirectional time series correlation feature vector, and then divide the obtained standardized temperature bidirectional time series correlation matrix by the value obtained by subtracting one from the length of the standardized temperature bidirectional time series correlation feature vector by position to obtain the temperature bidirectional time series correlation sample covariance matrix; a humidity bidirectional time series correlation sample covariance matrix generation subunit, which is used to multiply the transposed vector of the standardized humidity bidirectional time series correlation feature vector with the standardized humidity bidirectional time series correlation feature vector, and then divide the obtained standardized humidity bidirectional time series correlation matrix by the value obtained by subtracting one from the length of the standardized humidity bidirectional time series correlation feature vector by position to obtain the humidity bidirectional time series correlation sample covariance matrix.

[0012] In the above-mentioned resistance detection system for live working in distribution network, the temperature and humidity bidirectional time series correlation feature query matching unit is used to: extract a predetermined temperature bidirectional time series correlation principal component feature vector from the set of temperature bidirectional time series correlation principal component feature vectors; calculate the cosine similarity between the predetermined temperature bidirectional time series correlation principal component feature vector and each humidity bidirectional time series correlation principal component feature vector in the set of humidity bidirectional time series correlation principal component feature vectors to obtain a set of matching query similarities; and use the humidity bidirectional time series correlation principal component feature vector corresponding to the largest matching query similarity in the set of matching query similarities and the predetermined temperature bidirectional time series correlation principal component feature vector as the best matching pair of the predetermined temperature bidirectional time series correlation principal component feature vector and the humidity bidirectional time series correlation principal component feature vector.

[0013] In the above-mentioned resistance detection system for live working in distribution network, the temperature-humidity bidirectional time series correlation principal component fusion unit is used to: respectively calculate the position difference, position dot multiplication and position addition between the best matching pairs of the temperature bidirectional time series correlation principal component feature vector and the humidity bidirectional time series correlation principal component feature vector to obtain the temperature-humidity time series correlation principal component difference vector, the temperature-humidity time series correlation principal component dot product vector and the temperature-humidity time series correlation principal component sum vector; cascade the temperature-humidity time series correlation principal component difference vector, the temperature-humidity time series correlation principal component dot product vector and the temperature-humidity time series correlation principal component sum vector, and then perform one-dimensional convolution encoding to obtain the temperature-humidity time series correlation principal component multi-dimensional fusion vector; perform local window-based maximum pooling processing on the temperature-humidity time series correlation principal component multi-dimensional fusion vector to obtain the temperature-humidity bidirectional time series correlation principal component fusion feature vector.

[0014] In the above-mentioned resistance detection system for live working in the distribution network, the insulation measurement factor multidimensional association module is used to: input the temperature-humidity principal component time series significant collaborative interaction representation vector, the temperature bidirectional time series association feature vector and the humidity bidirectional time series association feature vector into the insulation measurement factor multidimensional association analyzer based on the embedding layer to obtain the insulation measurement factor multidimensional association representation vector.

[0015] In the above-mentioned resistance detection system for live working in distribution network, the insulation resistance measurement compensation factor generation module is used to: input the insulation measurement factor multi-dimensional association representation vector into the decoder-based compensation factor generator to obtain the insulation resistance measurement compensation factor.

[0016] According to another aspect of the present application, a resistance detection method for live working in a distribution network is provided, comprising: Collect the initial insulation resistance value of the object under test; Collect the time queue of temperature value and humidity value during insulation detection; Based on the time queue of the temperature value and the time queue of the humidity value, compensating and correcting the initial insulation resistance value to obtain a compensated insulation resistance value; The method of compensating and correcting the initial insulation resistance value based on the time queue of the temperature value and the time queue of the humidity value to obtain a compensated insulation resistance value includes: Inputting the time queue of the temperature value and the time queue of the humidity value into a sequence encoder to obtain a temperature bidirectional time series association feature vector and a humidity bidirectional time series association feature vector; Inputting the temperature bidirectional time series correlation feature vector and the humidity bidirectional time series correlation feature vector into a feature principal component significant fusion network to obtain a temperature-humidity principal component time series significant collaborative interaction representation vector; Input the temperature-humidity principal component time series significant synergistic interaction representation vector, the temperature bidirectional time series association feature vector, and the humidity bidirectional time series association feature vector into an insulation measurement factor multidimensional association analyzer to obtain an insulation measurement factor multidimensional association representation vector; Based on the multi-dimensional association representation vector of the insulation measurement factor, obtaining an insulation resistance measurement compensation factor; The product of the insulation resistance measurement compensation factor and the initial insulation resistance value is calculated to obtain the compensated insulation resistance value.

[0017] Compared with the prior art, the resistance detection system and method for live working in distribution network provided by the present application adopts artificial intelligence-based data analysis technology to associate the time series characteristics of the time queue of temperature value and the time queue of humidity value in the insulation detection process respectively, and performs significant fusion interaction based on the query matching of the principal component, so as to intelligently generate the insulation resistance measurement compensation factor based on the multi-dimensional correlation representation characteristics between the temperature time series association characteristics and the humidity time series association characteristics and the significant interaction between the temperature and humidity principal components, and calculate the compensated insulation resistance value based on the compensation factor. In this way, through the automated measurement process, the accuracy of the measurement data can be ensured, and a more accurate insulation resistance value can be obtained. And the insulation resistance value is dynamically compensated based on the real-time collected environmental data, which can more accurately reflect the performance of the insulating material in the real working environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 It is a block diagram of a resistance detection system for live working in a distribution network according to an embodiment of the present application.

[0019] Figure 2 It is a block diagram of a resistance measurement compensation corrector in a resistance detection system for live working in a distribution network according to an embodiment of the present application.

[0020] Figure 3 This is a data flow diagram of a resistance measurement compensation corrector in a resistance detection system for live working in a distribution network according to an embodiment of the present application.

[0021] Figure 4 It is a block diagram of a temperature-humidity main component timing significant matching module in a resistance detection system for live working in a distribution network according to an embodiment of the present application.

[0022] Figure 5 It is a block diagram of a temperature and humidity bidirectional time-series correlation sample covariance matrix calculation unit in a resistance detection system for live working in a distribution network according to an embodiment of the present application.

[0023] Figure 6 It is a flow chart of a resistance detection method for live working in a distribution network according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0025] Live working in distribution networks aims to maintain and transform power lines without interrupting power supply, so as to improve the stability of power supply and reduce economic losses. In this process, accurate measurement of insulation resistance is the key to ensuring work safety and normal operation of equipment. Chinese patent CN113960368A introduces an insulation resistance detection tool and its use method. The insulation resistance detection tool measures insulation resistance through manual operation. Although it improves the convenience of measurement, it still has the problems of complex operation and easy errors. In addition, this method fails to fully consider the influence of environmental factors such as temperature and humidity on the measurement results, which may lead to misjudgment of the performance of the insulation material, thereby affecting the accuracy of the measurement and the safety of the operation.

[0026] Based on this, the present application proposes an optimized resistance detection system for live distribution network operations. Figure 1 1 is a system block diagram of a resistance detection system for live working in a distribution network according to an embodiment of the present application. Figure 1As shown, in the resistance detection system 100 for live working on distribution network, it includes: an insulation resistance detection tool 110 for live working on distribution network, a temperature sensor 120, a humidity sensor 130 and a resistance measurement compensation corrector 140; the insulation resistance detection tool 110 for live working on distribution network is used to collect the initial insulation resistance value of the object under test; the temperature sensor 120 and the humidity sensor 130 are respectively used to collect the time queue of temperature value and the time queue of humidity value in the insulation detection process; the resistance measurement compensation corrector 140 is used to compensate and correct the initial insulation resistance value based on the time queue of temperature value and the time queue of humidity value to obtain a compensated insulation resistance value.

[0027] It should be understood that in order to obtain the benchmark data of the insulating material without considering the influence of environmental factors, it is first necessary to collect the initial insulation resistance value of the object to be measured by the insulation resistance detection tool for live operation of the distribution network. Insulation resistance is one of the key indicators for measuring the performance of insulating materials, and a high insulation resistance value usually means good insulation performance. The initial insulation resistance value collected by the detection tool is the basic value for subsequent environmental factor compensation. Next, considering that temperature and humidity are important environmental factors affecting the performance of insulating materials, different temperature and humidity conditions will cause changes in the insulation resistance value. In order to monitor these parameters in real time to help evaluate the authenticity of the insulation performance, in the technical solution of the present application, it is necessary to use a temperature sensor and a humidity sensor to obtain the temperature value time queue and humidity value time queue data during the insulation detection process. That is, when performing the compensation calculation of the insulation resistance, the changes of the two environmental factors of temperature and humidity are taken into consideration. Finally, in the resistance measurement compensation corrector, the obtained temperature and humidity time queue data are analyzed, and then the initial insulation resistance value is compensated and corrected, so as to obtain the compensated insulation resistance value, so as to improve the accuracy and reliability of the insulation resistance measurement.

[0028] Correspondingly, the resistance measurement compensation corrector uses artificial intelligence-based data analysis and processing technology to associate the time series characteristics of the time queue of the temperature value and the time queue of the humidity value, and performs significant fusion interaction based on the query matching of the principal component, thereby intelligently generating the insulation resistance measurement compensation factor based on the multi-dimensional association representation characteristics between the temperature time series association characteristics and the humidity time series association characteristics and the significant interaction between the temperature and humidity principal components, and calculating the compensated insulation resistance value based on the compensation factor. In this way, environmental data can be collected in real time, and the insulation resistance value can be dynamically compensated based on these data to reflect the performance in the real working environment. At the same time, through the automated measurement process, human operation errors are reduced, the accuracy of the measurement data is ensured, and a more accurate insulation resistance value is obtained.

[0029] Figure 2It is a block diagram of a resistance measurement compensation corrector in a resistance detection system for live working in a distribution network according to an embodiment of the present application. Figure 3 This is a data flow diagram of a resistance measurement compensation corrector in a resistance detection system for live working in a distribution network according to an embodiment of the present application. Figure 2 and Figure 3 As shown, in the resistance measurement compensation corrector 140, it includes: a temperature-humidity time series association module 141, which is used to input the time queue of the temperature value and the time queue of the humidity value into a sequence encoder to obtain a temperature bidirectional time series association feature vector and a humidity bidirectional time series association feature vector; a temperature-humidity principal component time series significant matching module 142, which is used to input the temperature bidirectional time series association feature vector and the humidity bidirectional time series association feature vector into a feature principal component significant fusion network to obtain a temperature-humidity principal component time series significant collaborative interaction representation vector; an insulation measurement factor multidimensional association module 143, which is used to input the temperature-humidity principal component time series significant collaborative interaction representation vector, the temperature bidirectional time series association feature vector and the humidity bidirectional time series association feature vector into an insulation measurement factor multidimensional association analyzer to obtain an insulation measurement factor multidimensional association representation vector; an insulation resistance measurement compensation factor generation module 144, which is used to obtain an insulation resistance measurement compensation factor based on the insulation measurement factor multidimensional association representation vector; an insulation resistance value compensation module 145, which is used to calculate the product between the insulation resistance measurement compensation factor and the initial insulation resistance value to obtain the compensated insulation resistance value.

[0030] In an embodiment of the present application, the temperature and humidity time series association module 141 is used to input the time queue of the temperature value and the time queue of the humidity value into a sequence encoder to obtain a temperature bidirectional time series association feature vector and a humidity bidirectional time series association feature vector. Specifically, in an embodiment of the present application, the temperature and humidity time series association module is used to: input the time queue of the temperature value and the time queue of the humidity value into a sequence encoder based on a bidirectional gated cyclic unit to obtain the temperature bidirectional time series association feature vector and the humidity bidirectional time series association feature vector. It should be understood that in resistance detection, temperature and humidity are constantly changing with time, and the current temperature and humidity values ​​may be associated with the previous and next values, and these changes in the time dimension may have a significant impact on the insulation resistance value. Those of ordinary skill in the art should know that the gated cyclic unit (GRU) is a variant of a recurrent neural network that controls the flow of information by introducing a gating mechanism (update gate and reset gate), thereby effectively capturing long-distance dependencies in the input sequence. The bidirectional gated cyclic unit is an extension of the gated cyclic unit, which can simultaneously process information from the forward and reverse directions of the input sequence data. This means that for the input sequence data, the model not only considers the previous information (past context), but also the later information (future context). This bidirectional characteristic enables the model to more comprehensively understand the context of the input sequence data, and thus effectively characterize the temporal characteristics of the input sequence data. Based on this, in the technical solution of the present application, the time queue of the temperature value and the time queue of the humidity value are input into a sequence encoder based on a bidirectional gated recurrent unit to capture and mine the temporal variation law and characteristic information of the temperature and humidity, and obtain a temperature bidirectional temporal association feature vector and a humidity bidirectional temporal association feature vector.

[0031] In an embodiment of the present application, the temperature-humidity principal component time series significant matching module 142 is used to input the temperature bidirectional time series association feature vector and the humidity bidirectional time series association feature vector into a feature principal component significant fusion network to obtain a temperature-humidity principal component time series significant collaborative interaction representation vector. Accordingly, considering that both the temperature bidirectional time series association feature vector and the humidity bidirectional time series association feature vector contain important time series data features, and there is a key interaction between the two in the time dimension. In order to perform significant fusion based on the key feature information of the two in time series, so as to provide a more reliable basis for the generation of subsequent compensation factors, in the technical solution of the present application, the temperature bidirectional time series association feature vector and the humidity bidirectional time series association feature vector are input into a feature principal component significant fusion network to obtain a temperature-humidity principal component time series significant collaborative interaction representation vector.

[0032] It should be understood that the feature principal component significant fusion network is a technology that integrates feature dimension reduction, principal component analysis and key feature collaborative interaction, aiming to create a prominent and concise feature association map for extracting the core interaction between feature vectors. In detail, firstly, the temperature bidirectional time series association feature vector and the humidity bidirectional time series association feature vector are standardized to eliminate the influence of different feature vectors due to different dimensions, so that the features are comparable, thereby obtaining the standardized temperature bidirectional time series association feature vector and the standardized humidity bidirectional time series association feature vector. Then, the respective sample covariance matrices are calculated based on the standardized features, providing data support for the subsequent principal component feature extraction. Then, the sample covariance matrix is ​​subjected to matrix-based feature vector extraction to obtain a set of temperature bidirectional time series association principal component feature vectors and a set of humidity bidirectional time series association principal component feature vectors. In particular, the feature vector extraction here uses the principal component analysis algorithm to extract the key features of the features. That is, principal component analysis (PCA) is a commonly used data dimension reduction technology that can extract the main components in temperature and humidity data, that is, those features that can best explain the data changes. Subsequently, the set of temperature bidirectional temporal correlation principal component feature vectors and the set of humidity bidirectional temporal correlation principal component feature vectors are matched based on the maximum approximation value to obtain the best matching pair. Specifically, the temperature bidirectional temporal correlation principal component feature vector and the humidity bidirectional temporal correlation principal component feature vector corresponding to the largest cosine value are selected as the best matching pair by calculating the cosine similarity of all humidity bidirectional temporal correlation principal component feature vectors in each set of temperature bidirectional temporal correlation principal component feature vector and humidity bidirectional temporal correlation principal component feature vector. Afterwards, the semantic fine-grained gated joint module inputted by each best matching pair is fine-grainedly analyzed and processed to more deeply understand the semantic interaction content between temperature and humidity, improve the grasp of data meaning and the ability to capture key significant features, and obtain a set of temperature-humidity bidirectional temporal correlation principal component fusion feature vectors. Finally, the set of principal component fusion feature vectors is cascaded to integrate the global interaction information in the entire time domain, and the temperature-humidity principal component temporal significant collaborative interaction representation vector is obtained.

[0033] Specifically, Figure 4 FIG. 1 is a block diagram of a temperature-humidity main component timing significant matching module in a resistance detection system for live working in a distribution network according to an embodiment of the present application. Figure 4As shown, the temperature-humidity principal component time series significant matching module 142 includes: a temperature-humidity bidirectional time series correlation feature standardization unit 1421, which is used to standardize the temperature bidirectional time series correlation feature vector and the humidity bidirectional time series correlation feature vector to obtain a standardized temperature bidirectional time series correlation feature vector and a standardized humidity bidirectional time series correlation feature vector; a temperature-humidity bidirectional time series correlation sample covariance matrix calculation unit 1422, which is used to calculate the sample covariance matrix of the standardized temperature bidirectional time series correlation feature vector and the standardized humidity bidirectional time series correlation feature vector to obtain a temperature bidirectional time series correlation sample covariance matrix and a humidity bidirectional time series correlation sample covariance matrix; a temperature-humidity bidirectional time series correlation principal component feature extraction unit 1423, which is used to extract feature vectors based on matrix decomposition from the temperature bidirectional time series correlation sample covariance matrix and the humidity bidirectional time series correlation sample covariance matrix to obtain a set of temperature bidirectional time series correlation principal component feature vectors and a set of humidity bidirectional time series correlation principal component feature vectors; A bidirectional temporal association feature query matching unit 1424 is used to input the set of the temperature bidirectional temporal association principal component feature vectors and the set of the humidity bidirectional temporal association principal component feature vectors into a maximum approximate query matching network to obtain a set of best matching pairs of the temperature bidirectional temporal association principal component feature vectors and the humidity bidirectional temporal association principal component feature vectors; a temperature-humidity bidirectional temporal association principal component fusion unit 1425 is used to input the best matching pairs of each temperature bidirectional temporal association principal component feature vector and the humidity bidirectional temporal association principal component feature vector in the set of best matching pairs of the temperature bidirectional temporal association principal component feature vector and the humidity bidirectional temporal association principal component feature vector into a semantic fine-grained gating joint module to obtain a set of temperature-humidity bidirectional temporal association principal component fusion feature vectors; a temperature-humidity principal component temporal significant collaborative interaction representation generation unit 1426 is used to cascade the set of the temperature-humidity bidirectional temporal association principal component fusion feature vectors to obtain the temperature-humidity principal component temporal significant collaborative interaction representation vector.

[0034] More specifically, in an embodiment of the present application, the temperature and humidity bidirectional time series association feature standardization unit is used to: respectively calculate the mean and standard deviation of the temperature bidirectional time series association feature vector to obtain the mean of the temperature bidirectional time series association feature and the standard deviation of the temperature bidirectional time series association feature; after positionally subtracting the temperature bidirectional time series association feature vector from the temperature bidirectional time series association feature mean, the calculated temperature bidirectional time series offset vector and the temperature bidirectional time series association feature standard deviation are divided by position to obtain the standardized temperature bidirectional time series association feature vector; respectively calculate the mean and standard deviation of the humidity bidirectional time series association feature vector to obtain the mean of the humidity bidirectional time series association feature and the standard deviation of the humidity bidirectional time series association feature; after positionally subtracting the humidity bidirectional time series association feature vector from the humidity bidirectional time series association feature mean, the calculated humidity bidirectional time series offset vector and the humidity bidirectional time series association feature standard deviation are divided by position to obtain the standardized humidity bidirectional time series association feature vector.

[0035] More specifically, Figure 5 FIG. 1 is a block diagram of a temperature and humidity bidirectional time series correlation sample covariance matrix calculation unit in a resistance detection system for live working in a distribution network according to an embodiment of the present application. Figure 5 As shown, the temperature and humidity bidirectional time series associated sample covariance matrix calculation unit 1422 includes: a temperature bidirectional time series associated sample covariance matrix generation subunit 14221, which is used to multiply the transposed vector of the standardized temperature bidirectional time series associated feature vector with the standardized temperature bidirectional time series associated feature vector, and then divide the obtained standardized temperature bidirectional time series associated matrix by a value obtained by subtracting one from the length of the standardized temperature bidirectional time series associated feature vector to obtain the temperature bidirectional time series associated sample covariance matrix; a humidity bidirectional time series associated sample covariance matrix generation subunit 14222, which is used to multiply the transposed vector of the standardized humidity bidirectional time series associated feature vector with the standardized humidity bidirectional time series associated feature vector, and then divide the obtained standardized humidity bidirectional time series associated matrix by a value obtained by subtracting one from the length of the standardized humidity bidirectional time series associated feature vector to obtain the humidity bidirectional time series associated sample covariance matrix.

[0036] More specifically, in an embodiment of the present application, the temperature and humidity bidirectional time series association feature query matching unit is used to: extract a predetermined temperature bidirectional time series association main component feature vector from the set of temperature bidirectional time series association main component feature vectors; calculate the cosine similarity between the predetermined temperature bidirectional time series association main component feature vector and each humidity bidirectional time series association main component feature vector in the set of humidity bidirectional time series association main component feature vectors to obtain a set of matching query similarities; and use the humidity bidirectional time series association main component feature vector corresponding to the largest matching query similarity in the set of matching query similarities and the predetermined temperature bidirectional time series association main component feature vector as the best matching pair of the predetermined temperature bidirectional time series association main component feature vector and the humidity bidirectional time series association main component feature vector.

[0037] More specifically, in an embodiment of the present application, the temperature-humidity bidirectional time series association principal component fusion unit is used to: respectively calculate the position difference, position dot multiplication and position addition between the best matching pairs of the temperature bidirectional time series association principal component feature vector and the humidity bidirectional time series association principal component feature vector to obtain the temperature-humidity time series association principal component difference vector, the temperature-humidity time series association principal component dot product vector and the temperature-humidity time series association principal component sum vector; cascade the temperature-humidity time series association principal component difference vector, the temperature-humidity time series association principal component dot product vector and the temperature-humidity time series association principal component sum vector, and then perform one-dimensional convolution encoding to obtain a temperature-humidity time series association principal component multi-dimensional fusion vector; perform local window-based maximum pooling processing on the temperature-humidity time series association principal component multi-dimensional fusion vector to obtain the temperature-humidity bidirectional time series association principal component fusion feature vector.

[0038] In the embodiment of the present application, specifically, the temperature-humidity principal component time series significant matching module is used to: input the temperature bidirectional time series association feature vector and the humidity bidirectional time series association feature vector into the feature principal component significant fusion network, and process them with the following significant fusion formula to obtain the temperature-humidity principal component time series significant collaborative interaction representation vector; wherein the significant fusion formula is: in, and represent the temperature bidirectional time series association feature vector and the humidity bidirectional time series association feature vector respectively, and are respectively the means of the temperature bidirectional time series association feature vector and the humidity bidirectional time series association feature vector, and are the standard deviations of the temperature bidirectional time series association feature vector and the humidity bidirectional time series association feature vector, respectively, and are respectively the standardized temperature bidirectional time series association feature vector and the standardized humidity bidirectional time series association feature vector, and They are and The transposed vector of and are respectively the lengths of the standardized temperature bidirectional time series association feature vector and the standardized humidity bidirectional time series association feature vector, and are respectively the temperature bidirectional time series associated sample covariance matrix and the humidity bidirectional time series associated sample covariance matrix, and They are respectively the orthogonal matrix of the principal component of the temperature bidirectional time series correlation and the orthogonal matrix of the principal component of the humidity bidirectional time series correlation, and They are the temperature bidirectional time series correlation diagonal matrix and the humidity bidirectional time series correlation diagonal matrix, The diagonal elements of the matrix are The temperature bidirectional time series correlation diagonal matrix, are the weight values ​​of the principal component eigenvectors of the bidirectional time series association of each temperature, The diagonal elements of the matrix are The humidity bidirectional time series correlation diagonal matrix, are the weight values ​​of the principal component eigenvectors of each humidity bidirectional time series association, and They are and The transposed matrix of is each temperature bidirectional time series associated principal component eigenvector in the set of temperature bidirectional time series associated principal component eigenvectors, is each humidity bidirectional time series associated principal component eigenvector in the set of humidity bidirectional time series associated principal component eigenvectors, To calculate the and The vector inner product between To calculate the one-norm of a vector, To return the maximum value value, is the maximum approximate matching value, , and They are position difference, position dot multiplication and position addition respectively. For cascade processing, is a one-dimensional convolutional coding operation, is the maximum pooling operation, is the first in the set of the temperature-humidity bidirectional time series correlation principal component fusion feature vectors. temperature-humidity bidirectional time series correlation principal component fusion feature vector, is the number of feature vectors in the set of the temperature-humidity bidirectional time series correlation principal component fusion feature vectors, is the vector representing the significant synergistic interaction of the temperature-humidity principal component time series.

[0039] In an embodiment of the present application, the insulation measurement factor multidimensional association module 143 is used to input the temperature-humidity principal component time series significant collaborative interaction representation vector, the temperature bidirectional time series association feature vector, and the humidity bidirectional time series association feature vector into the insulation measurement factor multidimensional association analyzer to obtain the insulation measurement factor multidimensional association representation vector. Specifically, in an embodiment of the present application, the insulation measurement factor multidimensional association module is used to: input the temperature-humidity principal component time series significant collaborative interaction representation vector, the temperature bidirectional time series association feature vector, and the humidity bidirectional time series association feature vector into the insulation measurement factor multidimensional association analyzer based on the embedding layer to obtain the insulation measurement factor multidimensional association representation vector. It should be understood that in order to further integrate and analyze the information in temperature, humidity and temperature and humidity interaction characteristics to generate a multidimensional feature that can comprehensively reflect the impact of environmental conditions on insulation resistance measurement, in the technical solution of the present application, the temperature-humidity principal component time series significant collaborative interaction representation vector, the temperature bidirectional time series correlation feature vector and the humidity bidirectional time series correlation feature vector are input into the insulation measurement factor multidimensional correlation analyzer based on the embedding layer to capture the complex time series relationship between each feature, and obtain the insulation measurement factor multidimensional correlation representation vector.

[0040] In an embodiment of the present application, the insulation resistance measurement compensation factor generation module 144 and the insulation resistance value compensation module 145 are respectively used to obtain the insulation resistance measurement compensation factor based on the multi-dimensional association representation vector of the insulation measurement factor and to calculate the product between the insulation resistance measurement compensation factor and the initial insulation resistance value to obtain the compensated insulation resistance value. Specifically, in an embodiment of the present application, the insulation resistance measurement compensation factor generation module is used to: input the insulation measurement factor multi-dimensional association representation vector into a compensation factor generator based on a decoder to obtain the insulation resistance measurement compensation factor. That is, the insulation measurement factor multi-dimensional association representation vector obtained by multi-dimensional association analysis using the temperature-humidity principal component time series significant collaborative interaction representation vector, the temperature bidirectional time series association feature vector and the humidity bidirectional time series association feature vector is used for decoding processing, so as to intelligently generate the insulation resistance measurement compensation factor, and calculate the compensated insulation resistance value based on the compensation factor. In this way, environmental data can be collected in real time, and the insulation resistance value can be dynamically compensated according to these data to reflect the performance in a real working environment. At the same time, through the automated measurement process, human operation errors are reduced, the accuracy of the measurement data is ensured, and a more accurate insulation resistance value is obtained.

[0041] Preferably, it is considered that the temperature-humidity principal component time series significant collaborative interaction representation vector, the temperature bidirectional time series association feature vector and the humidity bidirectional time series association feature vector represent the temperature-humidity time series collaborative feature, the temperature bidirectional local time series association feature and the humidity bidirectional local time series association feature respectively. Considering the possible feature redundancy in the feature set composed of the temperature-humidity principal component time series significant collaborative interaction representation vector, the temperature bidirectional time series association feature vector and the humidity bidirectional time series association feature vector, when aligning the multidimensional association analysis of the insulation measurement factor based on the embedding layer, there will be a multimodal semantic fusion offset caused by the unbalanced distribution of attention weights under feature redundancy, so as to further improve the multimodal semantic fusion regression comprehensibility of the insulation measurement factor multidimensional association representation vector, thereby improving the accuracy of the insulation resistance measurement compensation factor obtained by inputting the compensation factor generator based on the decoder.

[0042] Therefore, in a preferred embodiment, when the insulation measurement factor multi-dimensional association representation vector is input into a decoder-based compensation factor generator to obtain the insulation resistance measurement compensation factor, the insulation measurement factor multi-dimensional association representation vector is optimized, including the steps of: Arrange the eigenvalues ​​of the insulation measurement factor multi-dimensional association representation vector in ascending order to form an insulation measurement factor multi-dimensional association sequence encoding vector; In response to the insulation measurement factor multi-dimensional associated sequential encoding vector The eigenvalue and The covariance between eigenvalues ​​is less than or equal to the distance difference hyperparameter , calculate the The eigenvalues ​​are similar to the The weighted sum between the eigenvalues ​​is the optimized Eigenvalues: in, and Respectively represent the first The eigenvalue and Eigenvalues, is the characteristic mean of the multi-dimensional associated order encoding vector of the insulation measurement factor, represents the distance difference hyperparameter, and and is the weight hyperparameter, Represents the optimized Eigenvalue; Calculate the hyperbolic sine function value of the sum of squares of all eigenvalues ​​of the multi-dimensional association representation vector of the insulation measurement factor: in, represents the length of the multi-dimensional association representation vector of the insulation measurement factor, represents the hyperbolic sine function, Represents the value of the hyperbolic sine function; And calculate its exponential value with the natural constant as the base and then divide it by the square of the length of the multi-dimensional association representation vector of the insulation measurement factor to obtain the multi-dimensional association potential manifold value of the insulation measurement factor ,in, represents a natural constant, Represents the multi-dimensional associated potential manifold value of insulation measurement factors; In response to the insulation measurement factor multi-dimensional associated sequential encoding vector The eigenvalue and The absolute value of the covariance between the eigenvalues ​​is greater than the distance difference hyperparameter , multiply the multidimensional associated potential manifold value of the insulation measurement factor by the first After the eigenvalue, calculate the product with the first The weighted reduction between the eigenvalues ​​is the optimized Eigenvalues: in, and Respectively represent the first The eigenvalue and Eigenvalues, represents the multi-dimensional associated potential manifold value of the insulation measurement factor, and is the weight hyperparameter, Represents the optimized Eigenvalue; On the basis of keeping the first eigenvalue of the multi-dimensional associated order coding vector of the insulation measurement factor unchanged, the first eigenvalue of the combined optimization is The eigenvalues ​​are used to obtain the optimized multi-dimensional correlation representation vector of the insulation measurement factor.

[0043] Therefore, for the semantic feature set of the multi-dimensional association representation vector of the insulation measurement factor under the preset semantic space trajectory distribution condition, the phenomenon of cross-domain semantic positioning efficiency attenuation mainly stems from the long-range action distance beyond the local association window. By adopting the nonlinear manifold potential unit representation method based on covariance tensor integration, the multimodal association topological structure implicit in its parameter space is analyzed, and by constructing a nonlinear manifold potential unit model with scale adaptability, the adaptive association representation mechanism between the parameters of the temporal association mapping matrix of the real-time interactive scene is re-established. Therefore, through this reconstruction strategy of the physical state evolution trajectory, the continuous state migration modeling under the long-range association of parameters is effectively realized, and the semantic field reconstruction efficiency is significantly improved. At the same time, the original encoding representation characteristics of the multi-dimensional association representation vector of the insulation measurement factor are maintained, and the accuracy of the insulation resistance measurement compensation factor obtained by the compensation factor generator based on the decoder of the multi-dimensional association representation vector of the insulation measurement factor is improved. In this way, environmental data can be collected in real time, and the insulation resistance value can be dynamically compensated according to these data to reflect the performance in the real working environment. At the same time, through the automated measurement process, human operation errors are reduced, the accuracy of the measurement data is ensured, and a more accurate insulation resistance value is obtained.

[0044] In summary, the resistance detection system 100 for live working in the distribution network based on the embodiment of the present application is explained, which uses artificial intelligence-based data analysis technology to associate the time queue of temperature values ​​and the time queue of humidity values ​​in the insulation detection process with time series features and significantly fuse the interaction based on the query matching of the principal component, thereby intelligently generating the insulation resistance measurement compensation factor based on the multi-dimensional correlation representation characteristics between the temperature time series correlation characteristics and the humidity time series correlation characteristics and the significant interaction between the temperature and humidity principal components, and calculating the compensated insulation resistance value based on the compensation factor. In this way, the accuracy of the measurement data can be ensured through the automated measurement process, thereby obtaining a more accurate insulation resistance value. And the insulation resistance value is dynamically compensated based on the environmental data collected in real time, which can more accurately reflect the performance of the insulating material in the real working environment.

[0045] As described above, the resistance detection system 100 for live-line working of a distribution network according to the embodiment of the present application can be implemented in various terminal devices, such as a server for resistance detection for live-line working of a distribution network. In one example, the resistance detection system 100 for live-line working of a distribution network according to the embodiment of the present application can be integrated into the terminal device as a software module and / or a hardware module. For example, the resistance detection system 100 for live-line working of a distribution network can be a software module in the operating system of the terminal device, or can be an application developed for the terminal device; of course, the resistance detection system 100 for live-line working of a distribution network can also be one of the many hardware modules of the terminal device.

[0046] Alternatively, in another example, the resistance detection system 100 for live working in the distribution network and the terminal device may be separate devices, and the resistance detection system 100 for live working in the distribution network may be connected to the terminal device via a wired and / or wireless network and transmit interactive information in accordance with an agreed data format.

[0047] Figure 6 Flow chart of a resistance detection method for live working in a distribution network according to an embodiment of the present application. Figure 6As shown, in the resistance detection method for live distribution network operation, the method includes: S110, collecting the initial insulation resistance value of the object to be measured; S120, collecting the time queue of the temperature value and the time queue of the humidity value in the insulation detection process; S130, based on the time queue of the temperature value and the time queue of the humidity value, compensating and correcting the initial insulation resistance value to obtain a compensated insulation resistance value; wherein, in step S130, it includes: inputting the time queue of the temperature value and the time queue of the humidity value into a sequence encoder to obtain a temperature bidirectional time series association feature vector and a humidity bidirectional time series association feature vector; the temperature bidirectional time series association feature vector The vector and the humidity bidirectional time series correlation feature vector are input into a feature principal component significant fusion network to obtain a temperature-humidity principal component time series significant collaborative interaction representation vector; the temperature-humidity principal component time series significant collaborative interaction representation vector, the temperature bidirectional time series correlation feature vector and the humidity bidirectional time series correlation feature vector are input into an insulation measurement factor multidimensional correlation analyzer to obtain an insulation measurement factor multidimensional correlation representation vector; based on the insulation measurement factor multidimensional correlation representation vector, an insulation resistance measurement compensation factor is obtained; the product between the insulation resistance measurement compensation factor and the initial insulation resistance value is calculated to obtain the compensated insulation resistance value.

[0048] Here, those skilled in the art can understand that the specific operations of each step in the above-mentioned distribution network live working resistance detection method have been referred to above. Figures 1 to 5 The invention has been introduced in detail in the description of the resistance detection system for live working in distribution network, and therefore, its repeated description will be omitted.

[0049] In summary, the resistance detection method for live working in the distribution network based on the embodiment of the present application is explained, which uses artificial intelligence-based data analysis technology to associate the time series characteristics of the time queue of the temperature value and the time queue of the humidity value in the insulation detection process respectively, and significantly fuses and interacts based on the query matching of the principal component, thereby intelligently generating the insulation resistance measurement compensation factor based on the multi-dimensional correlation representation characteristics between the temperature time series correlation characteristics and the humidity time series correlation characteristics and the significant interaction between the temperature and humidity principal components, and calculating the compensated insulation resistance value based on the compensation factor. In this way, through the automated measurement process, the accuracy of the measurement data can be ensured, and a more accurate insulation resistance value can be obtained. And the insulation resistance value is dynamically compensated based on the real-time collected environmental data, which can more accurately reflect the performance of the insulating material in the real working environment.

[0050] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0051] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0052] In addition, each functional module in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0053] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application.

Claims

1. A resistance detection system for live working in a distribution network, characterized in that: include: Insulation resistance detection tools, temperature sensors, humidity sensors and resistance measurement compensation correctors for live distribution network work; The insulation resistance detection tool for live distribution network operation is used to collect the initial insulation resistance value of the object under test; The temperature sensor and the humidity sensor are used to collect the time queue of the temperature value and the time queue of the humidity value during the insulation detection process respectively; The resistance measurement compensation corrector is used to compensate and correct the initial insulation resistance value based on the time queue of the temperature value and the time queue of the humidity value to obtain a compensated insulation resistance value; Wherein, the resistance measurement compensation corrector comprises: A temperature and humidity time series association module, used for inputting the time queue of the temperature value and the time queue of the humidity value into a sequence encoder to obtain a temperature bidirectional time series association feature vector and a humidity bidirectional time series association feature vector; A temperature-humidity principal component time series significant matching module is used to input the temperature bidirectional time series associated feature vector and the humidity bidirectional time series associated feature vector into a feature principal component significant fusion network to obtain a temperature-humidity principal component time series significant collaborative interaction representation vector; An insulation measurement factor multidimensional association module is used to input the temperature-humidity principal component time series significant collaborative interaction representation vector, the temperature bidirectional time series association feature vector, and the humidity bidirectional time series association feature vector into an insulation measurement factor multidimensional association analyzer to obtain an insulation measurement factor multidimensional association representation vector; An insulation resistance measurement compensation factor generation module, used to obtain an insulation resistance measurement compensation factor based on the insulation measurement factor multi-dimensional association representation vector; The insulation resistance value compensation module is used to calculate the product between the insulation resistance measurement compensation factor and the initial insulation resistance value to obtain the compensated insulation resistance value.

2. The resistance detection system for live working in distribution network according to claim 1, characterized in that: The temperature and humidity time series association module is used to: input the time queue of the temperature value and the time queue of the humidity value into a sequence encoder based on a bidirectional gated cyclic unit to obtain the temperature bidirectional time series association feature vector and the humidity bidirectional time series association feature vector.

3. The resistance detection system for live working in distribution network according to claim 2, characterized in that: The temperature-humidity main component time series significant matching module includes: A temperature and humidity bidirectional time series correlation feature standardization unit, used for performing standardization processing on the temperature bidirectional time series correlation feature vector and the humidity bidirectional time series correlation feature vector to obtain a standardized temperature bidirectional time series correlation feature vector and a standardized humidity bidirectional time series correlation feature vector; A temperature and humidity bidirectional time series correlation sample covariance matrix calculation unit, used to calculate the sample covariance matrix of the standardized temperature bidirectional time series correlation feature vector and the standardized humidity bidirectional time series correlation feature vector to obtain a temperature bidirectional time series correlation sample covariance matrix and a humidity bidirectional time series correlation sample covariance matrix; A temperature and humidity bidirectional time series correlation principal component feature extraction unit, used for performing matrix decomposition-based feature vector extraction on the temperature bidirectional time series correlation sample covariance matrix and the humidity bidirectional time series correlation sample covariance matrix to obtain a set of temperature bidirectional time series correlation principal component feature vectors and a set of humidity bidirectional time series correlation principal component feature vectors; A temperature and humidity bidirectional time series correlation feature query matching unit, used to input the set of temperature bidirectional time series correlation principal component feature vectors and the set of humidity bidirectional time series correlation principal component feature vectors into a maximum approximate query matching network to obtain a set of best matching pairs of temperature bidirectional time series correlation principal component feature vectors and humidity bidirectional time series correlation principal component feature vectors; A temperature-humidity bidirectional time series correlation principal component fusion unit is used to input each best matching pair of the temperature bidirectional time series correlation principal component feature vector and the humidity bidirectional time series correlation principal component feature vector in the set of best matching pairs of the temperature bidirectional time series correlation principal component feature vector and the humidity bidirectional time series correlation principal component feature vector into a semantic fine-grained gating joint module to obtain a set of temperature-humidity bidirectional time series correlation principal component fusion feature vectors; The temperature-humidity principal component time series significant collaborative interaction representation generation unit is used to cascade the set of the temperature-humidity bidirectional time series associated principal component fusion feature vectors to obtain the temperature-humidity principal component time series significant collaborative interaction representation vector.

4. The resistance detection system for live working in distribution network according to claim 3, characterized in that: The temperature and humidity bidirectional time series correlation feature standardization unit is used to: Calculating the mean and standard deviation of the temperature bidirectional time series association feature vector respectively to obtain the temperature bidirectional time series association feature mean and the temperature bidirectional time series association feature standard deviation; After subtracting the temperature bidirectional time series association feature vector from the temperature bidirectional time series association feature mean by position, the calculated temperature bidirectional time series offset vector and the temperature bidirectional time series association feature standard deviation are divided by position to obtain the standardized temperature bidirectional time series association feature vector; Calculating the mean and standard deviation of the humidity bidirectional time series correlation feature vector respectively to obtain the humidity bidirectional time series correlation feature mean and the humidity bidirectional time series correlation feature standard deviation; After subtracting the humidity bidirectional time series association feature vector from the humidity bidirectional time series association feature mean by position, the calculated humidity bidirectional time series offset vector is divided by the humidity bidirectional time series association feature standard deviation by position to obtain the standardized humidity bidirectional time series association feature vector.

5. The resistance detection system for live working in distribution network according to claim 4, characterized in that: The temperature and humidity bidirectional time series correlation sample covariance matrix calculation unit includes: a temperature bidirectional time series association sample covariance matrix generating subunit, configured to multiply the transposed vector of the standardized temperature bidirectional time series association feature vector by the standardized temperature bidirectional time series association feature vector, and then divide the obtained standardized temperature bidirectional time series association matrix by a value obtained by subtracting one from the length of the standardized temperature bidirectional time series association feature vector to obtain the temperature bidirectional time series association sample covariance matrix; The humidity bidirectional time series correlation sample covariance matrix generating subunit is used to multiply the transposed vector of the standardized humidity bidirectional time series correlation feature vector by the standardized humidity bidirectional time series correlation feature vector, and then divide the obtained standardized humidity bidirectional time series correlation matrix by the value obtained by subtracting one from the length of the standardized humidity bidirectional time series correlation feature vector to obtain the humidity bidirectional time series correlation sample covariance matrix.

6. The resistance detection system for live working in distribution network according to claim 5, characterized in that: The temperature and humidity bidirectional time series correlation feature query and matching unit is used to: Extracting a predetermined temperature bidirectional time series correlation principal component feature vector from the set of temperature bidirectional time series correlation principal component feature vectors; Calculating the cosine similarity between the predetermined temperature bidirectional time series association principal component feature vector and each humidity bidirectional time series association principal component feature vector in the set of humidity bidirectional time series association principal component feature vectors to obtain a set of matching query similarities; The humidity bidirectional time series association principal component feature vector corresponding to the largest matching query similarity in the set of matching query similarities and the predetermined temperature bidirectional time series association principal component feature vector are taken as the best matching pair of the predetermined temperature bidirectional time series association principal component feature vector and the humidity bidirectional time series association principal component feature vector.

7. The resistance detection system for live working in distribution network according to claim 6, characterized in that: The temperature-humidity bidirectional time series correlation principal component fusion unit is used to: Respectively calculating the position difference, position dot product and position addition between the best matching pairs of the temperature bidirectional time series association principal component eigenvector and the humidity bidirectional time series association principal component eigenvector to obtain a temperature-humidity time series association principal component difference vector, a temperature-humidity time series association principal component dot product vector and a temperature-humidity time series association principal component sum vector; The temperature-humidity time series associated principal component difference vector, the temperature-humidity time series associated principal component dot product vector and the temperature-humidity time series associated principal component sum vector are cascaded and then one-dimensional convolutional coding is performed to obtain a temperature-humidity time series associated principal component multi-dimensional fusion vector; The temperature-humidity time series correlation principal component multi-dimensional fusion vector is subjected to a local window-based maximum pooling process to obtain the temperature-humidity bidirectional time series correlation principal component fusion feature vector.

8. The resistance detection system for live working in distribution network according to claim 7, characterized in that: The insulation measurement factor multidimensional association module is used to: input the temperature-humidity principal component time series significant collaborative interaction representation vector, the temperature bidirectional time series association feature vector and the humidity bidirectional time series association feature vector into the insulation measurement factor multidimensional association analyzer based on the embedding layer to obtain the insulation measurement factor multidimensional association representation vector.

9. The resistance detection system for live-line working in distribution network according to claim 8, characterized in that: The insulation resistance measurement compensation factor generating module is used to: input the insulation measurement factor multi-dimensional association representation vector into a compensation factor generator based on a decoder to obtain the insulation resistance measurement compensation factor.

10. A resistance detection method for live working in a distribution network, characterized in that: include: Collect the initial insulation resistance value of the object under test; Collect the time queue of temperature value and humidity value during insulation detection; Based on the time queue of the temperature value and the time queue of the humidity value, compensating and correcting the initial insulation resistance value to obtain a compensated insulation resistance value; The method of compensating and correcting the initial insulation resistance value based on the time queue of the temperature value and the time queue of the humidity value to obtain a compensated insulation resistance value includes: Inputting the time queue of the temperature value and the time queue of the humidity value into a sequence encoder to obtain a temperature bidirectional time series association feature vector and a humidity bidirectional time series association feature vector; Inputting the temperature bidirectional time series correlation feature vector and the humidity bidirectional time series correlation feature vector into a feature principal component significant fusion network to obtain a temperature-humidity principal component time series significant collaborative interaction representation vector; Input the temperature-humidity principal component time series significant synergistic interaction representation vector, the temperature bidirectional time series association feature vector, and the humidity bidirectional time series association feature vector into an insulation measurement factor multidimensional association analyzer to obtain an insulation measurement factor multidimensional association representation vector; Based on the multi-dimensional association representation vector of the insulation measurement factor, obtaining an insulation resistance measurement compensation factor; The product of the insulation resistance measurement compensation factor and the initial insulation resistance value is calculated to obtain the compensated insulation resistance value.

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