A method, system and device for wireless encrypted transmission of heterogeneous data of distribution network insulator detection and a storage medium

Abstract

The application discloses a kind of wireless encryption transmission methods, systems, equipment and storage medium of distribution network insulator detection heterogeneous data, it is related to power system monitoring technical field, method includes: acquisition distribution network insulator multi-source heterogeneous monitoring data, encapsulation is heterogeneous data set;Type identification and classification are carried out based on data structure characteristics, obtain data type level and extract evaluation features;Through multi-source data fusion analysis, equipment state level is evaluated;Combined with equipment state level and data type level two-dimensional linkage calculation transmission security level;Matching differential encryption strategy generates encrypted data packet;According to the transmission rule of wireless link type matching transmission is carried out and executes exception fault-tolerant processing;The application realizes the dynamic adaptation of encryption protection and transmission demand, reduces terminal power consumption, improves transmission efficiency and data security;Can improve insulator contamination evaluation accuracy and risk early warning timeliness, guarantee the stable transmission of key data under complex wireless environment.

Description

Technical Field

[0001] This invention relates to the field of power system monitoring technology, and in particular to a wireless encrypted transmission method, system, device and storage medium for heterogeneous data detection of distribution network insulators. Background Technology

[0002] With the continuous expansion of distribution network scale and the improvement of its intelligence level, monitoring the operational status of distribution network equipment has become an important means to ensure the safe and stable operation of the power grid. Insulators, as key insulating components in distribution network equipment, directly affect the safety and reliability of the power grid in their operational status. Currently, insulator condition monitoring typically relies on multi-source data acquisition, including electrical parameters, environmental information, pollution levels, and image data, which exhibit heterogeneity.

[0003] Traditional data transmission methods for monitoring distribution network insulators mostly adopt a fixed and unified encryption mode. However, this mode cannot simultaneously meet the requirements of security protection and the limitations of terminal computing power and link bandwidth, resulting in problems such as excessive terminal power consumption, low transmission efficiency, or insufficient protection. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention provides a wireless encrypted transmission method, system, device and storage medium for heterogeneous data detection of distribution network insulators.

[0005] Therefore, the technical problem solved by this invention is that the traditional data transmission method for monitoring insulators in distribution networks adopts a fixed and unified encryption mode, which cannot take into account both security protection requirements and the limitations of terminal computing power and link bandwidth, resulting in excessive terminal power consumption, low transmission efficiency, or insufficient protection.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a wireless encrypted transmission method for detecting heterogeneous data of distribution network insulators, comprising: Collect multi-source heterogeneous monitoring data corresponding to the operating status of distribution network insulators, add an identifier to each set of multi-source heterogeneous monitoring data, and encapsulate them to form a heterogeneous dataset. Based on the data structure characteristics of multi-source heterogeneous monitoring data within a heterogeneous dataset, the types of multi-source heterogeneous monitoring data are identified and classified, the data type levels corresponding to the multi-source heterogeneous monitoring data are obtained, and the operating status evaluation features of distribution network insulators are extracted. Based on the operational status assessment characteristics of distribution network insulators, multi-source data fusion analysis is performed to assess the risk level of distribution network insulator operational status and obtain the corresponding equipment status level of distribution network insulators. By combining equipment status level and data type level for dual-dimensional linkage calculation, the transmission security level corresponding to multi-source heterogeneous monitoring data is determined. Based on a differentiated encryption strategy matching the transmission security level, the encryption processing of multi-source heterogeneous monitoring data is completed, and encrypted data packets are generated. Based on the type of the current wireless communication link, the corresponding transmission rules are matched, the generated encrypted data packets are transmitted to the monitoring platform through the wireless network, and anomaly fault tolerance processing is performed.

[0007] As a preferred embodiment of a wireless encrypted transmission method for heterogeneous data detection in distribution network insulators, wherein: The multi-source heterogeneous monitoring data corresponding to the operating status of distribution network insulators are collected, and each set of multi-source heterogeneous monitoring data is labeled and encapsulated to form a heterogeneous dataset, including: Establish a unified sampling time benchmark and synchronously collect electrical data, environmental data, pollution data, and image data corresponding to distribution network insulators according to a fixed sampling cycle; The integrity of each type of data collected is verified. Data whose actual collected data length is consistent with the preset standard data length and whose actual calculated check code is consistent with the preset standard check code are determined to be valid data. Invalid data that does not meet the conditions are removed to obtain valid multi-source heterogeneous monitoring data. Based on a unified sampling time benchmark, valid multi-source heterogeneous monitoring data within the same collection period are divided into a data group, and each data group is attached with a time identifier of the start time of the corresponding sampling period and a unique device identity identifier of the monitoring terminal that collected the data of the corresponding group. According to the preset unified data frame format, each group of data after the additional identifier is encapsulated. The unified data frame format includes the frame header segment, device identifier segment, timestamp segment, data type segment, data payload segment and frame tail check segment in sequence. The device identifier segment is written with a unique device identity identifier, the timestamp segment is written with a time identifier, the data type segment marks the structure type of the data payload, the data payload segment is written with the monitoring data of the corresponding type, and the frame tail check segment is written with a cyclic redundancy check code calculated based on all the data of the corresponding frame. Integrate all the encapsulated data frames to generate a heterogeneous dataset.

[0008] As a preferred embodiment of a wireless encrypted transmission method for heterogeneous data detection in distribution network insulators, wherein: Based on the data structure characteristics of multi-source heterogeneous monitoring data within a heterogeneous dataset, the data types of multi-source heterogeneous monitoring data are identified and classified to obtain the data type levels corresponding to the multi-source heterogeneous monitoring data. Features for evaluating the operating status of distribution network insulators are extracted, including: Disassemble heterogeneous datasets to separate different types of multi-source heterogeneous monitoring data; For different types of multi-source heterogeneous monitoring data, filtering and noise reduction, outlier removal based on data distribution statistics, and missing data completion based on neighboring values ​​linear interpolation are performed respectively. For two-dimensional matrix data, resolution unification and grayscale normalization are performed. For all data, format unification is performed to generate standardized preprocessed data. Extract the data structure features of the standardized preprocessed multi-source heterogeneous monitoring data, compare the weighted deviation of the extracted data structure features with the standard structure features corresponding to each type in the preset structure classification rules, select the type with the smallest deviation as the identification result, complete the type identification and classification, generate the corresponding data type label, and assign the corresponding data type level to each group of data according to the preset type level mapping rules. According to the preset feature extraction rules based on data type labels, feature parameters required for the operation status assessment of distribution network insulators are extracted in a targeted manner for multi-source heterogeneous monitoring data of different data types. Normalization processing is performed on all extracted feature parameters to map each feature parameter to a unified numerical range and integrate them to generate a feature set for the operation status assessment of distribution network insulators.

[0009] As a preferred embodiment of a wireless encrypted transmission method for heterogeneous data detection in distribution network insulators, wherein: The method involves multi-source data fusion analysis based on the operational status assessment characteristics of distribution network insulators to assess the risk level of distribution network insulator operational status, thereby obtaining the corresponding equipment status level of the distribution network insulators, including: Based on the feature set for evaluating the operating status of distribution network insulators, all features used for evaluating the operating status of distribution network insulators are obtained, and the preset weight values ​​corresponding to each feature are determined. A weighted fusion calculation is performed on the salt density feature corresponding to the pollution data and the pollution area feature corresponding to the image data to obtain the pollution degree assessment feature; According to the preset multi-source data fusion model, all features are weighted and fused to obtain the risk assessment value corresponding to the operating status of distribution network insulators; The obtained risk assessment values ​​are matched with the preset risk level classification range to determine the equipment status level corresponding to the distribution network insulator.

[0010] As a preferred embodiment of a wireless encrypted transmission method for heterogeneous data detection in distribution network insulators, wherein: The method of combining device status level and data type level for dual-dimensional linkage calculation to determine the transmission security level corresponding to multi-source heterogeneous monitoring data includes: Obtain the device status level and data type level corresponding to multi-source heterogeneous monitoring data; Determine the preset weight values ​​corresponding to the device status level and data type level respectively; According to the preset dual-dimensional linkage calculation model, the device status level and data type level are weighted and calculated to obtain the security level quantification value; The security level quantification value is matched with the preset security level division range to determine the transmission security level corresponding to the multi-source heterogeneous monitoring data.

[0011] As a preferred embodiment of a wireless encrypted transmission method for heterogeneous data detection in distribution network insulators, wherein: The differential encryption strategy based on transmission security level matching completes the encryption processing of multi-source heterogeneous monitoring data and generates encrypted data packets, including: Obtain the transmission security level corresponding to multi-source heterogeneous monitoring data; The transmission security level is matched with the preset security level-encryption strategy mapping rules to determine the corresponding national cryptographic algorithm combination; Based on the matched and determined national cryptographic algorithm combination, perform session key negotiation, data encryption and integrity verification operations; Complete the encryption processing of multi-source heterogeneous monitoring data and generate encrypted data packets.

[0012] As a preferred embodiment of a wireless encrypted transmission method for heterogeneous data detection in distribution network insulators, wherein: The step of matching the corresponding transmission rules according to the type of the current wireless communication link, transmitting the generated encrypted data packets to the monitoring platform via the wireless network, and performing anomaly fault tolerance processing includes: Obtain the generated encrypted data packet and the transmission rules corresponding to the current wireless communication link type; The encrypted data packets are processed for packet adaptation according to the matching transmission rules. The transmission priority of the encrypted data packets is adjusted according to the device status level and data type level. The adjusted encrypted data packets are then sent to the monitoring platform via the wireless network. Real-time monitoring of wireless link connectivity and encrypted data packet transmission feedback status to determine if any transmission anomalies occur; In response to the detection of a transmission anomaly, the fault tolerance handling operation corresponding to the anomaly type is executed to complete the transmission control of encrypted data packets.

[0013] Secondly, the present invention provides a wireless encrypted transmission system for detecting heterogeneous data of distribution network insulators, comprising: The multi-source data acquisition and encapsulation module is used to collect multi-source heterogeneous monitoring data corresponding to the operating status of distribution network insulators, add identifiers to each group of multi-source heterogeneous monitoring data, and encapsulate them to form a heterogeneous dataset. The type identification and feature extraction module is used to identify and classify the types of multi-source heterogeneous monitoring data based on the data structure characteristics of multi-source heterogeneous monitoring data within the heterogeneous dataset, obtain the data type level corresponding to the multi-source heterogeneous monitoring data, and extract the operating status evaluation features of distribution network insulators. The integrated risk assessment module is used to perform multi-source data fusion analysis based on the operational status assessment characteristics of distribution network insulators, to assess the risk level of the operational status of distribution network insulators, and to obtain the corresponding equipment status level of the distribution network insulators. The dual-dimensional linkage classification module is used to perform dual-dimensional linkage calculations by combining the device status level and the data type level to determine the transmission security level corresponding to multi-source heterogeneous monitoring data. The differentiated encryption processing module is used to match differentiated encryption strategies based on the transmission security level, complete the encryption processing of multi-source heterogeneous monitoring data, and generate encrypted data packets; The transmission adaptation and fault tolerance module is used to match the corresponding transmission rules according to the type of the current wireless communication link, transmit the generated encrypted data packets to the monitoring platform through the wireless network, and perform abnormal fault tolerance processing.

[0014] Thirdly, the present invention provides a computer device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the wireless encrypted transmission method for heterogeneous data detection of distribution network insulators are implemented.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of a method for wireless encrypted transmission of heterogeneous data for distribution network insulator detection.

[0016] The beneficial effects of this invention are as follows: This invention determines the transmission security level by combining the dual-dimensional linkage calculation of device status level and data type level, and completes data encryption processing by matching differentiated encryption strategies according to the transmission security level. This achieves dynamic adaptation between encryption protection and transmission requirements, thereby improving the problems of traditional distribution network insulator monitoring data transmission methods, which mostly adopt a fixed and unified encryption mode. Due to the inability to take into account both security protection requirements and terminal computing power and link bandwidth limitations, this results in excessive terminal power consumption, low transmission efficiency, or insufficient protection. By identifying and classifying the types of multi-source heterogeneous monitoring data and performing dual-dimensional linkage calculations based on device status level, fine-grained decision-making basis is provided for differentiated encryption strategies, optimizing the allocation of encryption resources. By matching transmission rules according to wireless communication link type, adjusting data transmission priority based on device status level and data type level, and performing fault tolerance processing throughout the transmission process, stable and efficient transmission of monitoring data in complex wireless environments is achieved. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is an overall flowchart of a wireless encrypted transmission method for detecting heterogeneous data of distribution network insulators provided by the present invention.

[0019] Figure 2 This is a flowchart illustrating the dual-dimensional linkage calculation of transmission security level in a wireless encrypted transmission method for detecting heterogeneous data of distribution network insulators, provided by the present invention.

[0020] Figure 3 This is a flowchart illustrating the identification and classification of multi-source heterogeneous monitoring data types in a wireless encrypted transmission method for detecting heterogeneous data of distribution network insulators, provided by the present invention. Detailed Implementation

[0021] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0022] Example 1, referring to Figure 1This is the first embodiment of the present invention, which provides a wireless encrypted transmission method for heterogeneous data detection of distribution network insulators, comprising: S1: Collect multi-source heterogeneous monitoring data corresponding to the operating status of distribution network insulators, add an identifier to each set of multi-source heterogeneous monitoring data, and encapsulate them to form a heterogeneous dataset; S2: Based on the data structure characteristics of multi-source heterogeneous monitoring data within the heterogeneous dataset, perform type identification and classification of multi-source heterogeneous monitoring data, obtain the data type level corresponding to the multi-source heterogeneous monitoring data, and extract the operating status evaluation features of distribution network insulators. S3: Based on the operational status assessment characteristics of distribution network insulators, multi-source data fusion analysis is performed to assess the risk level of distribution network insulator operational status and obtain the corresponding equipment status level of distribution network insulators; S4: Combine device status level and data type level for dual-dimensional linkage calculation to determine the transmission security level of multi-source heterogeneous monitoring data; S5: Based on the transmission security level matching differentiated encryption strategy, complete the encryption processing of multi-source heterogeneous monitoring data and generate encrypted data packets; S6: Match the corresponding transmission rules according to the type of the current wireless communication link, transmit the generated encrypted data packet to the monitoring platform through the wireless network, and perform fault tolerance processing.

[0023] It should be noted that, through steps S1-S6, the present invention realizes dynamic adaptation of the entire process of heterogeneous monitoring data of distribution network insulators from collection, classification, evaluation to encrypted transmission. It can flexibly adjust the encryption strength and transmission strategy according to the device status and data type, effectively reduce terminal energy consumption and bandwidth occupation, improve the protection level and transmission reliability of high-risk data, and significantly improve the security and timeliness of monitoring data in complex wireless environments.

[0024] Example 2, refer to Figures 1-3 As an embodiment of the present invention, based on the previous embodiment, a wireless encrypted transmission method for heterogeneous data detection of distribution network insulators is provided, comprising: In this embodiment, the multi-source heterogeneous monitoring data corresponding to the operating status of distribution network insulators collected in step S1 above, the identification of each group of multi-source heterogeneous monitoring data, and the encapsulation to form a heterogeneous dataset include: Establish a unified sampling time benchmark; According to a fixed sampling period, electrical data, environmental data, pollution data and image data corresponding to distribution network insulators are collected synchronously. Perform integrity verification on each type of data collected; Invalid data that fails the verification is removed to obtain valid multi-source heterogeneous monitoring data.

[0025] Specifically, establishing a unified sampling time benchmark includes: synchronizing and calibrating the clock of the sampling system with the standard clock of the distribution network monitoring system; Set a fixed sampling period Sampling period Based on the accuracy requirements for monitoring the operating status of distribution network insulators, a unified sampling time benchmark is pre-set, and a fixed sampling period is followed. The process of collecting multiple types of data is started simultaneously, collecting electrical data, environmental data, pollution data and image data corresponding to distribution network insulators. Among them, electrical data is leakage current time-domain sampling data during the operation of distribution network insulators, environmental data is ambient temperature and relative humidity sampling data at the insulator installation location, pollution data is equivalent salt density sampling data on the surface of insulators, and image data is visible light image and infrared thermal image data of the insulator body. After completing a single synchronous data acquisition, an integrity check is performed on each type of data acquired. Collect data of this type, and assume its preset standard data length is... The actual length of the collected data is The default standard check code is The check code calculated based on the actual collected data is: When satisfied and If the data is in the correct state, it is considered valid; otherwise, it is considered invalid. Invalid data obtained from the verification process is removed to obtain valid multi-source heterogeneous monitoring data.

[0026] Based on the same sampling time reference, multi-source heterogeneous monitoring data within the same collection period are divided into the same data group; Add time stamps and device identification marks to each group of multi-source heterogeneous monitoring data after the data is divided; Each set of multi-source heterogeneous monitoring data, after being marked with an additional identifier, is encapsulated according to a unified data frame format; Integrate all the encapsulated data frames to generate a heterogeneous dataset.

[0027] Specifically, based on a pre-established unified sampling time benchmark, the input consists of valid multi-source heterogeneous monitoring data obtained after integrity verification and invalid data removal, as well as the sampling period of the corresponding acquisition process. With sampling timestamp The sampling period For a pre-defined fixed sampling interval, the sampling timestamp To correspond to the start time of the collection period, the sampling timestamp is... Completely consistent multi-source heterogeneous monitoring data within the same acquisition cycle are grouped into the same data group to complete the time synchronization grouping of multi-source heterogeneous monitoring data. Each set of multi-source heterogeneous monitoring data is assigned a time stamp and a device identification stamp, where the time stamp is the sampling timestamp corresponding to that set of data. The device identification is the unique device code corresponding to the monitoring terminal that collects this set of data. Unique device code It is pre-stored in the storage unit of the corresponding monitoring terminal, corresponding to the monitoring terminal; According to the preset unified data frame format, each set of multi-source heterogeneous monitoring data with additional identifiers is encapsulated. The unified data frame format consists of, in order, a frame header segment, a device identifier segment, a timestamp segment, a data type segment, a data payload segment, and a frame tail check segment. The device identifier segment contains a unique device code. The timestamp segment is written with the sampling timestamp. The data type segment indicates the structure type of the corresponding data payload. The data payload segment is filled with multi-source heterogeneous monitoring data of the corresponding type. The frame tail check segment is filled with the cyclic redundancy check code calculated based on all the data in the frame. Cyclic Redundancy Check Code Calculated using a preset 16-bit cyclic redundancy check algorithm; Integrate all encapsulated standard data frames within the same acquisition cycle to generate a complete heterogeneous dataset.

[0028] In this embodiment, step S2 above involves identifying and classifying the types of multi-source heterogeneous monitoring data based on the data structure characteristics of the multi-source heterogeneous monitoring data within the heterogeneous dataset, obtaining the data type levels corresponding to the multi-source heterogeneous monitoring data, and extracting the operating status assessment features of distribution network insulators, including: like Figure 3 As shown, the heterogeneous dataset is first decomposed to separate different types of multi-source heterogeneous monitoring data; For different types of multi-source heterogeneous monitoring data, respectively perform filtering and noise reduction, outlier removal, and missing data completion operations; Perform resolution unification and grayscale normalization on multi-source heterogeneous monitoring data of two-dimensional matrix type; All processed multi-source heterogeneous monitoring data are formatted to generate standardized preprocessed data.

[0029] Specifically, for the heterogeneous dataset generated by the encapsulation, the heterogeneous dataset is decomposed according to the preset unified data frame format, the frame structure of each data frame is analyzed, and different types of multi-source heterogeneous monitoring data are separated, including time series electrical data, scalar sampling environmental data and pollution data, and two-dimensional matrix image data. For different types of multi-source heterogeneous monitoring data, filtering and noise reduction, outlier removal, and missing data completion operations are performed respectively. For time-series electrical data, a sliding window with a length of [missing information] is used. The moving average filter completes the noise reduction process, with a moving window length of [missing information]. According to the sampling period Pre-set, filtered first The value of each sampling point Through formula The calculation yielded, where For the sampled values ​​at corresponding positions in the original acquired electrical data sequence, outlier removal was performed using the three-standard-deviation criterion for all types of multi-source heterogeneous monitoring data, and the mean of the corresponding data sequence was calculated. with standard deviation , where the mean Through formula The standard deviation was calculated. Through formula Calculations show that The effective data length of the corresponding data sequence. For the data sequence of the first One valid data point, the value is outside the range. The data was identified as outliers and removed. For the missing data remaining after outlier removal, linear interpolation was used to complete the replacement. The sampling positions corresponding to the missing data were then... The effective sampling positions adjacent to it are and The corresponding valid values ​​are and The completed value Through formula Calculated; For multi-source heterogeneous monitoring data of two-dimensional matrix type, resolution unification processing is first performed to scale all two-dimensional matrix data to a preset standard resolution. ;in To preset standard width, To preset a standard height, grayscale normalization is then performed. The normalized pixel positions... Corresponding value Through formula The calculation yielded, where This represents the value at the corresponding position in the original two-dimensional matrix. This represents the maximum value of the original two-dimensional matrix. To obtain the minimum value of the original two-dimensional matrix, the format of all processed multi-source heterogeneous monitoring data is unified to generate standardized preprocessed data.

[0030] Extracting the data structure features of standardized preprocessed multi-source heterogeneous monitoring data; The extracted data structure features are matched with preset structure classification rules to complete the type identification of multi-source heterogeneous monitoring data; The multi-source heterogeneous monitoring data are classified and aggregated according to the identified types, and corresponding data type labels are generated. Based on the preset type-level mapping rules, the corresponding data type level is assigned to the multi-source heterogeneous monitoring data.

[0031] Specifically, based on the standardized preprocessed multi-source heterogeneous monitoring data, the data structure features of each set of multi-source heterogeneous monitoring data are extracted. The core structural features extracted include data dimensions. Number of valid elements in a single set of data Data arrangement format identifier Among them, data dimensions The data dimension of one-dimensional time series data is obtained by analyzing the storage structure of the corresponding data. Two-dimensional matrix data corresponds to data dimensions Scalar sampling data corresponds to data dimensions The number of valid elements in a single set of data The data arrangement format identifier corresponds to the total number of valid values ​​contained in a single data set. Encode the corresponding data in the preset storage format; The extracted data structure features are matched with preset structure classification rules, which are predefined mapping relationships between structure features and data types. The degree of matching between the extracted structure features and the standard features of each type in the preset classification rules is calculated. Matching degree Through formula The calculation yielded, where , , These are the preset weight coefficients corresponding to data dimension, number of valid elements, and arrangement format identifier, respectively. , , For each of the standard structural feature parameters corresponding to the data type in the preset classification rules, select the matching degree. The preset data type corresponding to the minimum value is used as the identification result to complete the type identification of multi-source heterogeneous monitoring data; All multi-source heterogeneous monitoring data are classified and aggregated according to the identified data types, and corresponding data type labels are generated for each category of aggregated multi-source heterogeneous monitoring data. Data type tags Corresponding to the identified data types, and based on the preset type level mapping rules, each category is labeled with a data type label. Assign corresponding data type levels to multi-source heterogeneous monitoring data Data type level Based on a pre-defined hierarchy of the importance of the data for assessing the insulator's operational status, the data type level corresponding to the output multi-source heterogeneous monitoring data is determined. With data type tags The preset type level mapping rule is a predefined correspondence between data type labels and data type levels.

[0032] Match the corresponding preset feature extraction rules according to the data type labels of the multi-source heterogeneous monitoring data; For different types of multi-source heterogeneous monitoring data, characteristic parameters corresponding to the operation status assessment of distribution network insulators are extracted in a targeted manner. Among them, salt density features are extracted from pollution data, and surface pollution area features and thermal anomaly area features of insulators are extracted from image data. Normalize all extracted feature parameters. The integrated and processed feature parameters are used to generate a feature set for evaluating the operating status of distribution network insulators.

[0033] Specifically, based on the completion of type identification and classification, and with corresponding data type labels. Standardized preprocessed multi-source heterogeneous monitoring data, including data type labels Generated by the preceding type identification and classification process, corresponding to the types of multi-source heterogeneous monitoring data, first according to the data type labels corresponding to the multi-source heterogeneous monitoring data. It matches the preset feature extraction rules, which are pre-defined feature extraction logic and calculation methods corresponding to each data type, and the data type labels. Mapping; For different types of multi-source heterogeneous monitoring data, characteristic parameters corresponding to the operational status assessment of distribution network insulators are extracted in a targeted manner. For electrical data, the effective value of current is extracted. Peak current Current fluctuation rate The effective value of the current Through formula Calculations show that The number of valid sampling points in the electrical data sequence. For the first Instantaneous leakage current value at each sampling point, peak current value The absolute maximum value in the electrical data sequence, current fluctuation rate. Through formula The calculation yielded the average relative humidity within the sampling period for the environmental data. With temperature change rate Average relative humidity The arithmetic mean of all humidity samples within the monitoring period, and the rate of temperature change. To monitor the difference between the maximum and minimum temperatures within a monitoring period, salt density features are extracted from the contamination data. Salt density characteristics Based on the standardized preprocessed equivalent salt density sampling values, the surface contamination area features of the insulator are extracted from the image data. Characteristics of thermal anomaly regions Pollution area characteristics The proportion of the pixel area of ​​the contaminated area on the insulator surface in a visible light image to the total pixel area of ​​the effective insulator region obtained after image segmentation, representing the thermal anomaly region characteristics. This refers to the proportion of pixel area in the infrared thermal image where the temperature exceeds a preset threshold to the total pixel area of ​​the effective insulator region obtained through image segmentation. Normalization is performed on all extracted feature parameters. For the first... Each feature parameter has an original value of 1. The upper limit of the preset value corresponding to this feature parameter is The preset lower limit of the value is Normalized eigenvalues Through formula The calculations show that by mapping the values ​​of all characteristic parameters to a unified range of 0 to 1, and integrating all normalized characteristic parameters, a feature set for evaluating the operating status of distribution network insulators is generated.

[0034] In this embodiment, step S3 above involves multi-source data fusion analysis based on the operational status assessment characteristics of distribution network insulators to assess the risk level of the distribution network insulator's operational status, resulting in the following equipment status levels for the distribution network insulators: Obtain all features used for evaluating the operating status of distribution network insulators and determine the preset weight values ​​for each feature; A weighted fusion calculation is performed on the salt density feature corresponding to the pollution data and the pollution area feature corresponding to the image data to obtain the pollution degree assessment feature; According to the preset multi-source data fusion model, all features are weighted and fused to obtain the risk assessment value corresponding to the operating status of distribution network insulators; The obtained risk assessment values ​​are matched with the preset risk level classification range to determine the equipment status level corresponding to the distribution network insulator.

[0035] Specifically, based on all the normalized feature parameters used for evaluating the operating status of distribution network insulators, and the preset weight values ​​corresponding to each feature parameter, the total feature parameters include four categories: electrical features, environmental features, pollution features, and image features. The preset weight values ​​corresponding to each feature are pre-set according to the degree of influence of the feature on the operating risk of the insulator. The sum of all weight values ​​is 1. First, all features used for evaluating the operating status of distribution network insulators are obtained, and the preset weight values ​​corresponding to each feature are determined. Salt density features corresponding to filth data Pollution area features corresponding to image data Perform weighted fusion calculation to obtain pollution level assessment characteristics. Among them, salt density characteristics The preceding steps extract and normalize the feature parameters from the contaminated data, including the contaminated area feature. These are the feature parameters extracted and normalized from the visible light image data in the preceding steps. These are the preset weighting coefficients corresponding to the salt density feature. The preset weighting coefficients corresponding to the pollution area characteristics satisfy... Filth assessment characteristics Through formula The calculated pollution level assessment characteristics are then incorporated into the feature set used for risk assessment. Based on the preset multi-source data fusion model, all features are weighted and fused to obtain the risk assessment value corresponding to the operating status of distribution network insulators. The preset multi-source data fusion model is a weighted linear fusion model, and the total number of features used for risk assessment is assumed to be... , No. The normalized values ​​of the features are , No. The preset weight coefficients corresponding to each feature are: ,satisfy Risk assessment value Through formula Calculated; The obtained risk assessment value The system matches the equipment against a preset risk level range. This preset range consists of pre-defined numerical intervals corresponding to different equipment status levels. The range is positively correlated with the degree of risk in equipment operation; a higher risk assessment value corresponds to a higher equipment status level. Based on the risk assessment value... The range in which the insulator falls determines the corresponding equipment status level of the distribution network insulator. .

[0036] In this embodiment, step S4 above combines device status level and data type level for dual-dimensional linkage calculation to determine the transmission security level corresponding to multi-source heterogeneous monitoring data, including: like Figure 2 As shown, firstly, the device status level and data type level corresponding to the multi-source heterogeneous monitoring data are obtained; Determine the preset weight values ​​corresponding to the device status level and data type level respectively; According to the preset dual-dimensional linkage calculation model, the device status level and data type level are weighted and calculated to obtain the security level quantification value; The security level quantification value is matched with the preset security level division range to determine the transmission security level corresponding to the multi-source heterogeneous monitoring data.

[0037] Specifically, the equipment status level based on multi-source heterogeneous monitoring data. With data type level Among them, equipment status level Calculated using the risk level assessment process for distribution network insulator operation status, the value is a pre-quantified positive integer used to characterize the current operational risk level of the distribution network insulator. A higher value corresponds to a higher operational risk. (Data type: Level) Generated by a multi-source heterogeneous monitoring data type identification and classification process, the values ​​are pre-quantified positive integers used to characterize the importance of the corresponding multi-source heterogeneous monitoring data to the insulator operating status assessment. The higher the value, the higher the importance of the data. First, the equipment status level corresponding to the multi-source heterogeneous monitoring data is obtained. With data type level ; Determine the preset weight values ​​corresponding to the device status level and data type level, where the preset weight coefficient corresponding to the device status level is: The preset weighting coefficient corresponding to the data type level is The weighting coefficients are pre-set based on the degree of impact of the corresponding dimension on the data transmission security protection requirements, to meet the requirements. ; Based on the preset dual-dimensional linkage calculation model, the equipment status level is determined. With data type level A weighted calculation is performed to obtain the quantitative value of the security level. Security level quantification value Through formula Calculated; The calculated security level quantification value Matching with a preset security level range, which is a pre-defined range of values ​​corresponding to the transmission security level, based on the quantified security level value. The corresponding interval into which the multi-source heterogeneous monitoring data falls determines the transmission security level. .

[0038] In this embodiment, step S5 above, which involves matching differentiated encryption strategies based on transmission security levels to complete the encryption processing of multi-source heterogeneous monitoring data and generating encrypted data packets, includes: Obtain the transmission security level corresponding to multi-source heterogeneous monitoring data; The transmission security level is matched with the preset security level-encryption strategy mapping rules to determine the corresponding national cryptographic algorithm combination. The ordinary security level is matched with the SM4 symmetric encryption algorithm, the medium security level is matched with the SM4 encryption algorithm and the SM3 hash verification algorithm, and the high security level is matched with the SM2 asymmetric encryption algorithm, the SM4 encryption algorithm and the SM3 hash verification algorithm. According to the matched and determined combination of national cryptographic algorithms, the session key negotiation, data encryption and integrity verification operations are performed, among which the SM2 asymmetric encryption algorithm is only used for session key negotiation; Complete the encryption processing of multi-source heterogeneous monitoring data and generate encrypted data packets.

[0039] Specifically, the transmission security level based on multi-source heterogeneous monitoring data. And the standardized preprocessed multi-source heterogeneous monitoring data to be encrypted, with a transmission security level of The pre-defined level identifiers corresponding to data security protection requirements are generated by a preceding two-dimensional linkage calculation process. The multi-source heterogeneous monitoring data to be encrypted is the valid multi-source heterogeneous monitoring data obtained from S1 after integrity verification and invalid data removal. First, the transmission security level corresponding to the multi-source heterogeneous monitoring data is obtained. ; Transmission security level Match the security level with the preset security level-encryption strategy mapping rules to determine the corresponding combination of national cryptographic algorithms. The ordinary security level matches the SM4 symmetric encryption algorithm, the medium security level matches the SM4 encryption algorithm and the SM3 hash verification algorithm, and the high security level matches the SM2 asymmetric encryption algorithm, the SM4 encryption algorithm and the SM3 hash verification algorithm. According to the matched and determined national cryptographic algorithm combination, session key negotiation, data encryption, and integrity verification operations are performed. The SM2 asymmetric encryption algorithm is used only for session key negotiation. For advanced security levels, the SM2 asymmetric encryption algorithm's key negotiation mechanism is used based on the pre-configured SM2 public keys at the sending and receiving ends. With SM2 private key Negotiate and generate session keys For both standard and medium security levels, pre-configured fixed session keys are used. The plaintext of the multi-source heterogeneous monitoring data to be encrypted is obtained by using the SM4 symmetric encryption algorithm. Perform encryption to obtain encrypted ciphertext data. For medium and high security levels, the SM3 hash verification algorithm is used to verify the encrypted ciphertext data. Perform a hash calculation to obtain the integrity check value. ; Complete the encryption processing of multi-source heterogeneous monitoring data, and convert the encrypted data... The integrity verification value generated by the matching algorithm is integrated and encapsulated with the corresponding key identification information to generate an encrypted data packet. This encrypted data packet will be used as the transmission object in the subsequent wireless transmission process and sent to the monitoring platform through the wireless network.

[0040] In this embodiment, step S6 above, which matches the corresponding transmission rules according to the type of the current wireless communication link, transmits the generated encrypted data packet to the monitoring platform through the wireless network, and performs anomaly fault tolerance processing, includes: Obtain the generated encrypted data packet and the transmission rules corresponding to the current wireless communication link type; The encrypted data packets are processed for packet adaptation according to the matching transmission rules. The transmission priority of the encrypted data packets is adjusted according to the device status level and data type level. The adjusted encrypted data packets are then sent to the monitoring platform via the wireless network. Real-time monitoring of wireless link connectivity and encrypted data packet transmission feedback status to determine if any transmission anomalies occur; When a transmission anomaly is detected, the fault tolerance handling operation corresponding to the anomaly type is executed to complete the transmission control of encrypted data packets.

[0041] Specifically, based on the generated encrypted data packet, the current wireless communication link type obtained through real-time wireless link status detection, and the device status level corresponding to the generated encrypted data packet... With data type level The encrypted data packet is the data to be transmitted, which is encapsulated after being encrypted using the national cryptographic algorithm. The current wireless communication link type is obtained through real-time link signal detection and network type identification, and the device status level is also specified. This data type is generated by the distribution network insulator operation status risk level assessment process and is used to characterize the current operational risk level of the distribution network insulators. It is generated by the type identification and classification process of multi-source heterogeneous monitoring data, and is used to characterize the importance of the corresponding monitoring data to the insulator operation status assessment. First, the encrypted data packet and the transmission rules corresponding to the current wireless communication link type are obtained. The preset transmission rules are the transmission parameter configurations mapped to each wireless communication link type, including preset values ​​for maximum transmission unit, packet length, transmission interval, and maximum retransmission count. According to the matching transmission rules, packet adaptation processing is performed on the encrypted data packets. The maximum transmission unit corresponding to the current wireless communication link is... The total length of the encrypted data packet to be transmitted is Number of subcontractors Through formula The calculation shows that the encrypted data packet is split into... A length not exceeding The sub-data packets are processed to complete packet adaptation, based on the device status level. With data type level Adjust the transmission priority of encrypted data packets; transmission priority quantization value. Through formula The calculation yielded, where This refers to the priority weight coefficient corresponding to the device status level. The priority weight coefficients corresponding to the data type levels are pre-set based on the degree of impact of the corresponding dimension on the timeliness of data transmission, satisfying the following requirements. Transmission priority quantization value The higher the priority, the higher the transmission scheduling priority. The encrypted data packets that have completed priority adjustment and packet adaptation will be sent to the monitoring platform through the wireless network. Real-time monitoring of wireless link connectivity and encrypted data packet transmission feedback status, and monitoring of the received signal strength of the link. (Received Signal Strength Indicator), Data Packet Delivery Confirmation Indicator (Acknowledgement), Cumulative number of retransmissions To determine if a transmission anomaly has occurred, check the received signal strength. The connection is below the preset link connectivity threshold, or no delivery confirmation flag has been received for the corresponding data packet. And the cumulative number of retransmissions When the preset maximum number of retransmissions is reached, a transmission anomaly is determined. Specifically, if the Received Signal Strength Index (RSSI) is lower than the preset link connectivity threshold, it is considered a link disconnection anomaly. This occurs when no ACK (Acknowledgment of Delivery) for the corresponding data packet is received and the cumulative retransmission count exceeds the threshold. Reaching the preset maximum number of retransmissions is considered a data packet transmission failure exception. When a transmission anomaly is detected, fault-tolerant processing operations corresponding to the anomaly type are executed. For link disconnection anomalies, offline data caching is performed, storing the encrypted data packet to be transmitted in a local non-volatile storage unit. After the link is restored, a retransmission operation is performed. For data packet loss and transmission timeout anomalies, the corresponding sub-data packet is retransmitted. For data verification failure anomalies, the complete data packet is retransmitted. This completes the full-process transmission control of the encrypted data packet and finally outputs a confirmation result of the encrypted data packet transmission completion. This result will be used for closed-loop recording of this data transmission process and will also provide a reference for the dynamic optimization of subsequent wireless transmission rules.

[0042] Example 3: The above is an illustrative scheme of a wireless encrypted transmission method for heterogeneous data detection of distribution network insulators according to this embodiment. It should be noted that the technical solution of a wireless encrypted transmission system for heterogeneous data detection of distribution network insulators belongs to the same concept as the above-described wireless encrypted transmission method for heterogeneous data detection of distribution network insulators. Details not described in detail in the technical solution of the wireless encrypted transmission system for heterogeneous data detection of distribution network insulators in this embodiment can be found in the description of the above-described wireless encrypted transmission method for heterogeneous data detection of distribution network insulators.

[0043] This embodiment also provides a wireless encrypted transmission system for detecting heterogeneous data of distribution network insulators, including: The multi-source data acquisition and encapsulation module is used to collect multi-source heterogeneous monitoring data corresponding to the operating status of distribution network insulators, add identifiers to each group of multi-source heterogeneous monitoring data, and encapsulate them to form a heterogeneous dataset. The type identification and feature extraction module is used to identify and classify the types of multi-source heterogeneous monitoring data based on the data structure characteristics of multi-source heterogeneous monitoring data within the heterogeneous dataset, obtain the data type level corresponding to the multi-source heterogeneous monitoring data, and extract the operating status evaluation features of distribution network insulators. The integrated risk assessment module is used to perform multi-source data fusion analysis based on the operational status assessment characteristics of distribution network insulators, to assess the risk level of the operational status of distribution network insulators, and to obtain the corresponding equipment status level of the distribution network insulators. The dual-dimensional linkage classification module is used to perform dual-dimensional linkage calculations by combining the device status level and the data type level to determine the transmission security level corresponding to multi-source heterogeneous monitoring data. The differentiated encryption processing module is used to match differentiated encryption strategies based on the transmission security level, complete the encryption processing of multi-source heterogeneous monitoring data, and generate encrypted data packets; The transmission adaptation and fault tolerance module is used to match the corresponding transmission rules according to the type of the current wireless communication link, transmit the generated encrypted data packets to the monitoring platform through the wireless network, and perform abnormal fault tolerance processing.

[0044] This embodiment also provides a computer device applicable to a wireless encrypted transmission method for heterogeneous data detection of distribution network insulators, including: The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement a wireless encrypted transmission method for heterogeneous data detection of distribution network insulators, as proposed in the above embodiments.

[0045] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements a wireless encrypted transmission method for heterogeneous data detection of distribution network insulators as proposed in the above embodiments.

[0046] The storage medium proposed in this embodiment belongs to the same inventive concept as the wireless encrypted transmission method for heterogeneous data detection of distribution network insulators proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0047] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for wireless encrypted transmission of heterogeneous data from distribution network insulator detection, characterized in that, include: Collect multi-source heterogeneous monitoring data corresponding to the operating status of distribution network insulators, add an identifier to each set of multi-source heterogeneous monitoring data, and encapsulate them to form a heterogeneous dataset. Based on the data structure characteristics of multi-source heterogeneous monitoring data within a heterogeneous dataset, the types of multi-source heterogeneous monitoring data are identified and classified, the data type levels corresponding to the multi-source heterogeneous monitoring data are obtained, and the operating status evaluation features of distribution network insulators are extracted. Based on the operational status assessment characteristics of distribution network insulators, multi-source data fusion analysis is performed to assess the risk level of distribution network insulator operational status and obtain the corresponding equipment status level of distribution network insulators. By combining equipment status level and data type level for dual-dimensional linkage calculation, the transmission security level corresponding to multi-source heterogeneous monitoring data is determined. Based on a differentiated encryption strategy matching the transmission security level, the encryption processing of multi-source heterogeneous monitoring data is completed, and encrypted data packets are generated. Based on the type of the current wireless communication link, the corresponding transmission rules are matched, the generated encrypted data packets are transmitted to the monitoring platform through the wireless network, and anomaly fault tolerance processing is performed.

2. The wireless encrypted transmission method for heterogeneous data detection of distribution network insulators as described in claim 1, characterized in that, The multi-source heterogeneous monitoring data corresponding to the operating status of distribution network insulators are collected, and each set of multi-source heterogeneous monitoring data is labeled and encapsulated to form a heterogeneous dataset, including: Establish a unified sampling time benchmark and synchronously collect electrical data, environmental data, pollution data, and image data corresponding to distribution network insulators according to a fixed sampling cycle; The integrity of each type of data collected is verified. Data whose actual collected data length is consistent with the preset standard data length and whose actual calculated check code is consistent with the preset standard check code are determined to be valid data. Invalid data that does not meet the conditions are removed to obtain valid multi-source heterogeneous monitoring data. Based on a unified sampling time benchmark, valid multi-source heterogeneous monitoring data within the same collection period are divided into a data group, and each data group is attached with a time identifier of the start time of the corresponding sampling period and a unique device identity identifier of the monitoring terminal that collected the data of the corresponding group. According to the preset unified data frame format, each group of data after the additional identifier is encapsulated. The unified data frame format includes the frame header segment, device identifier segment, timestamp segment, data type segment, data payload segment and frame tail check segment in sequence. The device identifier segment is written with a unique device identity identifier, the timestamp segment is written with a time identifier, the data type segment marks the structure type of the data payload, the data payload segment is written with the monitoring data of the corresponding type, and the frame tail check segment is written with a cyclic redundancy check code calculated based on all the data of the corresponding frame. Integrate all the encapsulated data frames to generate a heterogeneous dataset.

3. The wireless encrypted transmission method for heterogeneous data detection of distribution network insulators as described in claim 2, characterized in that, Based on the data structure characteristics of multi-source heterogeneous monitoring data within a heterogeneous dataset, the data types of multi-source heterogeneous monitoring data are identified and classified to obtain the data type levels corresponding to the multi-source heterogeneous monitoring data. Features for evaluating the operating status of distribution network insulators are extracted, including: Disassemble heterogeneous datasets to separate different types of multi-source heterogeneous monitoring data; For different types of multi-source heterogeneous monitoring data, filtering and noise reduction, outlier removal based on data distribution statistics, and missing data completion based on neighboring values ​​linear interpolation are performed respectively. For two-dimensional matrix data, resolution unification and grayscale normalization are performed. For all data, format unification is performed to generate standardized preprocessed data. Extract the data structure features of the standardized preprocessed multi-source heterogeneous monitoring data, compare the weighted deviation of the extracted data structure features with the standard structure features corresponding to each type in the preset structure classification rules, select the type with the smallest deviation as the identification result, complete the type identification and classification, generate the corresponding data type label, and assign the corresponding data type level to each group of data according to the preset type level mapping rules. According to the preset feature extraction rules based on data type labels, feature parameters required for the operation status assessment of distribution network insulators are extracted in a targeted manner for multi-source heterogeneous monitoring data of different data types. Normalization processing is performed on all extracted feature parameters to map each feature parameter to a unified numerical range and integrate them to generate a feature set for the operation status assessment of distribution network insulators.

4. The wireless encrypted transmission method for heterogeneous data detection of distribution network insulators as described in claim 3, characterized in that, The method involves multi-source data fusion analysis based on the operational status assessment characteristics of distribution network insulators to assess the risk level of distribution network insulator operational status, thereby obtaining the corresponding equipment status level of the distribution network insulators, including: Based on the feature set for evaluating the operating status of distribution network insulators, all features used for evaluating the operating status of distribution network insulators are obtained, and the preset weight values ​​corresponding to each feature are determined. A weighted fusion calculation is performed on the salt density feature corresponding to the pollution data and the pollution area feature corresponding to the image data to obtain the pollution degree assessment feature; According to the preset multi-source data fusion model, all features are weighted and fused to obtain the risk assessment value corresponding to the operating status of distribution network insulators; The obtained risk assessment values ​​are matched with the preset risk level classification range to determine the equipment status level corresponding to the distribution network insulator.

5. The wireless encrypted transmission method for heterogeneous data detection of distribution network insulators as described in claim 4, characterized in that, The method of combining device status level and data type level for dual-dimensional linkage calculation to determine the transmission security level corresponding to multi-source heterogeneous monitoring data includes: Obtain the device status level and data type level corresponding to multi-source heterogeneous monitoring data; Determine the preset weight values ​​corresponding to the device status level and data type level respectively; According to the preset dual-dimensional linkage calculation model, the device status level and data type level are weighted and calculated to obtain the security level quantification value; The security level quantification value is matched with the preset security level division range to determine the transmission security level corresponding to the multi-source heterogeneous monitoring data.

6. The wireless encrypted transmission method for heterogeneous data detection of distribution network insulators as described in claim 5, characterized in that, The differential encryption strategy based on transmission security level matching completes the encryption processing of multi-source heterogeneous monitoring data and generates encrypted data packets, including: Obtain the transmission security level corresponding to multi-source heterogeneous monitoring data; The transmission security level is matched with the preset security level-encryption strategy mapping rules to determine the corresponding national cryptographic algorithm combination; Based on the matched and determined national cryptographic algorithm combination, perform session key negotiation, data encryption and integrity verification operations; Complete the encryption processing of multi-source heterogeneous monitoring data and generate encrypted data packets.

7. A wireless encrypted transmission method for heterogeneous data detection of distribution network insulators as described in claim 6, characterized in that, The step of matching the corresponding transmission rules according to the type of the current wireless communication link, transmitting the generated encrypted data packets to the monitoring platform via the wireless network, and performing anomaly fault tolerance processing includes: Obtain the generated encrypted data packet and the transmission rules corresponding to the current wireless communication link type; The encrypted data packets are processed for packet adaptation according to the matching transmission rules. The transmission priority of the encrypted data packets is adjusted according to the device status level and data type level. The adjusted encrypted data packets are then sent to the monitoring platform via the wireless network. Real-time monitoring of wireless link connectivity and encrypted data packet transmission feedback status to determine if any transmission anomalies occur; In response to the detection of a transmission anomaly, the fault tolerance handling operation corresponding to the anomaly type is executed to complete the transmission control of encrypted data packets.

8. A wireless encrypted transmission system for heterogeneous data detection of distribution network insulators, using the method described in any one of claims 1 to 7, characterized in that, include: The multi-source data acquisition and encapsulation module is used to collect multi-source heterogeneous monitoring data corresponding to the operating status of distribution network insulators, add identifiers to each group of multi-source heterogeneous monitoring data, and encapsulate them to form a heterogeneous dataset. The type identification and feature extraction module is used to identify and classify the types of multi-source heterogeneous monitoring data based on the data structure characteristics of multi-source heterogeneous monitoring data within the heterogeneous dataset, obtain the data type level corresponding to the multi-source heterogeneous monitoring data, and extract the operating status evaluation features of distribution network insulators. The integrated risk assessment module is used to perform multi-source data fusion analysis based on the operational status assessment characteristics of distribution network insulators, to assess the risk level of the operational status of distribution network insulators, and to obtain the corresponding equipment status level of the distribution network insulators. The dual-dimensional linkage classification module is used to perform dual-dimensional linkage calculations by combining the device status level and the data type level to determine the transmission security level corresponding to multi-source heterogeneous monitoring data. The differentiated encryption processing module is used to match differentiated encryption strategies based on the transmission security level, complete the encryption processing of multi-source heterogeneous monitoring data, and generate encrypted data packets; The transmission adaptation and fault tolerance module is used to match the corresponding transmission rules according to the type of the current wireless communication link, transmit the generated encrypted data packets to the monitoring platform through the wireless network, and perform abnormal fault tolerance processing.

9. A computer device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer-executable instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

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