High-voltage cable intelligent inspection method and system based on multi-mode identification

Abstract

The invention relates to a high-voltage cable intelligent inspection method and system based on multimode identification, and the method comprises the steps: deploying an intelligent identifier and cable monitoring equipment according to the equipment characteristics and environmental conditions in a high-voltage cable line; reading identification data of the intelligent identification through a double-frequency reading device, and collecting cable state monitoring data through a cable monitoring device; the information is uploaded to the Internet of Things cloud platform; carrying out fusion analysis on the received data in the cloud platform, evaluating the health condition of the cable in combination with a historical record and a fault recognition model, and generating an inspection result; and displaying the inspection result through the intelligent monitoring center, calculating the risk score of each device according to the identification data, the cable state monitoring data and the fault risk prediction result, and dynamically adjusting the optimal inspection route and the inspection frequency. Compared with the prior art, unique identification and multi-dimensional information storage of the cable and accessory equipment of the cable are achieved, and the fineness and comprehensiveness of cable operation state monitoring are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cable inspection, and in particular to a high-voltage cable intelligent inspection method and system based on multi-mode identification. Background Art

[0002] As an important part of the power system, high-voltage cables are responsible for long-distance and large-capacity power transmission. Their operating status directly affects the stability and power supply reliability of the power system. However, high-voltage cable lines are often widely distributed and have complex environments. Traditional manual inspection methods have problems such as low efficiency, limited coverage, inaccurate data recording, and delayed fault detection. It is difficult to meet the needs of modern power systems for efficient operation and maintenance and rapid response to faults.

[0003] However, the existing intelligent inspection technology for high-voltage cables still has certain defects. The existing intelligent inspection of high-voltage cables relies on a single identification technology and lacks the ability to identify and store all-round and multi-dimensional information of cables and their ancillary equipment, resulting in incomplete data collection and failure to meet the inspection needs in complex environments. It lacks integrated management of deep-level information such as equipment historical maintenance records and fault history data, which is not conducive to deep fault analysis and prevention. It does not have a dual-frequency reading function and cannot take into account both close-range and remote identification needs, affecting inspection efficiency and accuracy. The sensor configuration is single or the data collection capability is limited, and it may not be able to monitor the key state parameters of the cable such as thermal distribution, temperature fluctuation, mechanical vibration, and insulation performance in real time and accurately, making it difficult to achieve refined monitoring of the cable operation status. The information display on the monitoring interface is not intuitive enough and the real-time update capability is poor. It cannot clearly display the overall inspection progress, the location of the inspection personnel, and the cable status changes, which is not conducive to the operation and maintenance personnel to quickly grasp the on-site situation. It does not have the accurate fault location and navigation function based on the GIS system, which affects the operation and maintenance personnel to quickly reach the fault site for disposal. Therefore, a high-voltage cable intelligent inspection method system based on multi-mode identification is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a high-voltage cable intelligent inspection method and system based on multi-mode identification in order to overcome the defects of the above-mentioned prior art that the data collection is single and incomplete, and it is difficult to achieve refined monitoring of the cable operation status.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A high-voltage cable intelligent inspection method based on multi-mode identification includes the following steps:

[0007] Deploy smart identification according to the equipment characteristics and environmental conditions in the high-voltage cable lines; deploy cable monitoring equipment in the high-voltage cable lines;

[0008] The identification data of the intelligent identification is read through the dual-frequency reading device, and the cable status monitoring data is collected through the cable monitoring device;

[0009] Upload the collected identification data and cable status monitoring data to the IoT cloud platform for data cleaning;

[0010] The received data is integrated and analyzed in the cloud platform, combined with historical records and fault identification models to evaluate the health status of the cable, identify potential fault risks, and generate inspection results;

[0011] Receiving and displaying the inspection results through the intelligent monitoring center;

[0012] The intelligent monitoring center calculates the risk score of each device based on identification data, cable status monitoring data and fault risk prediction results, thereby generating a risk level list for each device and dividing the devices into several risk level intervals, and dynamically adjusting the optimal inspection route and inspection frequency of each device accordingly.

[0013] Furthermore, the smart identification includes a QR code, NFC and UHF tag;

[0014] The identification data in the smart identification includes device ID, basic information and maintenance record data.

[0015] Furthermore, the cable monitoring device is integrated with a multi-sensor for real-time monitoring of the cable's thermal distribution imaging, temperature, mechanical vibration, and insulation degradation status data.

[0016] Furthermore, the method starts the dual-frequency reading device through the mobile inspection terminal to read the identification data. The dual-frequency reading device activates the chip in the smart identification to transmit data by emitting a signal of a specific frequency, obtains the identification data returned by the smart identification, and stores it in the local database of the mobile inspection terminal;

[0017] The mobile inspection terminal obtains the cable thermal distribution imaging, temperature data and mechanical vibration data collected in real time by the cable monitoring equipment, so as to identify the hot spots, abnormal cold spot temperatures, abnormal vibrations and insulation degradation data, and store them in the local database of the mobile inspection terminal.

[0018] Furthermore, during the data uploading process of the identification data and the cable status monitoring data, the collected identification data and the cable status monitoring data are time-stamped and synchronized in the mobile inspection terminal, and the cable status monitoring data is preliminarily verified. The synchronized and verified identification data and the cable status monitoring data are integrated into a data packet, which is accompanied by the equipment location information and the inspection timestamp metadata. The data packet is sent to the IoT cloud platform in real time through the IoT communication technology.

[0019] Furthermore, the processing process of the cloud platform includes: receiving data packets and performing data parsing to obtain real-time data of corresponding equipment; according to the equipment ID corresponding to the data packet, retrieving the historical identification data, status monitoring data and maintenance record history information corresponding to the equipment from a preset database as the corresponding historical data, arranging the historical data and real-time data in chronological order, constructing time series data, thereby extracting key features reflecting the operating status of the equipment, fusing the extracted real-time features with the historical features, and inputting them into a pre-trained data model to obtain the health score of the cable;

[0020] According to the health score of the cable, the preset health threshold is used to determine whether the cable is in the normal working range. If it is not in the normal working range, the cable fault type is identified based on the extracted real-time and historical features, and the failure probability is given; thus, the cable is divided into different risk levels according to the health score, failure probability and cable failure type of the cable.

[0021] Furthermore, the processing process of the cloud platform also includes: for the identified cable fault type, using a pre-trained time series prediction model to predict the change of the cable status in the future period of time.

[0022] Furthermore, the intelligent monitoring center continuously receives inspection result data, equipment status information and fault prediction results from the IoT cloud platform through an API interface and a message queue, and performs data analysis, converts the data into a unified data format within the intelligent monitoring center, and integrates the data into the same data structure;

[0023] The integrated data is mapped to the visual interface of the intelligent monitoring center, and the completed and ongoing inspection tasks marked on the GIS map, as well as the real-time location of the inspection personnel or inspection robot, are updated in real time. The identification data and cable status monitoring data are combined to locate the specific location of the fault, which is marked on the GIS map and provides navigation guidance.

[0024] Furthermore, the process of adjusting the optimal inspection route by the intelligent monitoring center is specifically as follows:

[0025] The geographical location information of each device is imported into the geographic information system of the intelligent monitoring center to form an equipment distribution map, set the constraints for inspection route planning, take the risk level list of each device as input, and calculate multiple optimal inspection routes in combination with the constraints.

[0026] The present invention also provides a high-voltage cable intelligent inspection system based on the high-voltage cable intelligent inspection method based on multi-mode identification as described above, comprising:

[0027] Intelligent identification module, used to deploy intelligent identification to uniquely identify cables and their ancillary equipment and facilities, and store basic equipment information, maintenance records, and fault history data;

[0028] Dual-frequency reading module, used to read different types of smart identification based on dual-frequency reading equipment and obtain identification data;

[0029] Mobile inspection terminal module, used to carry dual-frequency reading module;

[0030] IoT platform, used to receive identification data and cable status monitoring data and perform data cleaning;

[0031] The data fusion analysis module is used to perform fusion analysis on the data received by the IoT platform, combine historical records and fault identification models, evaluate the health status of the cable, identify potential fault risks, and generate inspection results;

[0032] The intelligent monitoring center is used to receive and display the inspection results; calculate the risk score of each device based on the identification data, cable status monitoring data and fault risk prediction results, thereby generating a risk level list for each device, and dividing the devices into several risk level intervals, and dynamically adjusting the optimal inspection route and the inspection frequency of each device accordingly.

[0033] Compared with the prior art, the present invention has the following advantages:

[0034] (1) The present invention deploys intelligent identification on high-voltage cable lines, extracts basic information and maintenance record data of equipment, and combines the cable monitoring status data collected by cable monitoring equipment to achieve multi-dimensional information storage of cables and their ancillary equipment; subsequently, the cloud platform can perform comprehensive cable health status assessment and fault prediction based on historical data and real-time data, greatly improving the accuracy and predictability of fault identification; the intelligent monitoring center can realize intuitive display of data, facilitate grasping the overall inspection situation, and conduct risk scoring and risk level division, dynamically optimize and adjust the inspection route and training frequency, and improve the pertinence and efficiency of the inspection work.

[0035] (2) The present invention realizes the unique identification and multi-dimensional information storage of cables and their ancillary equipment by adopting dual-frequency intelligent identification, thereby enhancing the comprehensiveness and compatibility of data collection. The dual-frequency reading equipment can flexibly respond to short-range or long-distance identification needs, ensuring that inspection personnel can obtain detailed equipment information efficiently and accurately. The mobile inspection terminal is equipped with a variety of sensors that can collect multi-dimensional status monitoring data such as thermal distribution imaging, temperature, mechanical vibration, insulation degradation, etc. of the cable in real time, thereby improving the precision and comprehensiveness of cable operation status monitoring.

[0036] (3) The present invention can receive, integrate, clean and preliminarily analyze a large amount of real-time data from inspection terminals through the cloud platform, thereby achieving effective data management and preliminary insight. Through deep mining and fusion analysis of historical identification data, status monitoring data, maintenance records, etc., the system can construct time series data of equipment status and extract key features reflecting the operating status of equipment. Through advanced analysis technologies such as deep learning, the fused feature vectors are used to evaluate the cable health status, and the cable health score, fault probability, risk level and fault prediction are output, which greatly improves the accuracy and predictability of fault identification.

[0037] (4) The present invention provides a real-time and intuitive data display interface through the intelligent monitoring center, and updates the inspection task progress, inspection personnel location, and cable status icon color in real time through the GIS map, so that operation and maintenance personnel can grasp the overall inspection situation. Combined with equipment identification information and status monitoring data, the system can accurately mark the location of the fault on the GIS map and provide navigation guidance, which helps operation and maintenance personnel to quickly arrive at the scene for disposal.

[0038] (5) The present invention calculates the risk score of each device based on the device identification data, status monitoring data, and fault prediction results, and classifies the devices into risk levels accordingly. It also plans multiple optimal inspection routes in combination with the GIS system. At the same time, it dynamically adjusts the inspection frequency based on the current risk level of the equipment and its changes, ensuring that resources are first invested in the inspection of high-risk equipment, thereby improving the pertinence and efficiency of the inspection work. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A schematic diagram of a process flow of a high-voltage cable intelligent inspection method based on multi-mode identification provided in an embodiment of the present invention;

[0040] Figure 2 The present invention provides a schematic diagram of the structure of a high-voltage cable intelligent inspection system based on multi-mode identification in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0042] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0044] Example 1

[0045] like Figure 1 As shown, this embodiment provides a high-voltage cable intelligent inspection method based on multi-mode identification, comprising the following steps:

[0046] S1: Deploy smart markers based on the equipment characteristics and environmental conditions in the high-voltage cable lines and regularly check their integrity; deploy cable monitoring equipment in the high-voltage cable lines;

[0047] S2: During the inspection process, the mobile inspection terminal reads the identification data of the intelligent identification through the dual-frequency reading device and collects the cable status monitoring data through the cable monitoring device;

[0048] S3: Upload the collected identification data and cable status monitoring data to the IoT cloud platform for data cleaning;

[0049] S4: Perform fusion analysis on the received data in the cloud platform, combine historical records and fault identification models, evaluate the health status of the cable, identify potential fault risks, and generate inspection results;

[0050] S5: The inspection results are received and displayed through the intelligent monitoring center, an alarm is triggered, and fault location, cause analysis, and disposal suggestion information are provided to assist operation and maintenance personnel in making decisions;

[0051] S6: The intelligent monitoring center calculates the risk score of each device based on the identification data, cable status monitoring data and fault risk prediction results, thereby generating a risk level list for each device and dividing the devices into several risk level intervals, and dynamically adjusting the optimal inspection route and inspection frequency of each device accordingly.

[0052] Specifically, in step S1, according to the equipment characteristics and environmental conditions, a dual-frequency smart identification including equipment ID, basic information, and maintenance record data is deployed. The dual-frequency smart identification includes multiple identification methods such as QR code, NFC, and UHF tag.

[0053] The cable monitoring device is integrated with a multi-sensor for real-time monitoring of the cable's thermal distribution imaging, temperature, mechanical vibration, and insulation degradation status data.

[0054] In step S2, during the inspection process, the mobile inspection terminal starts the dual-frequency reading device, remotely senses the smart tag, and the reading device transmits a signal of a specific frequency to activate the chip in the tag to transmit data. The dual-frequency reading device receives and parses the information returned by the smart tag, including the device unique identifier, factory information, last maintenance record, and key parameters. After confirming that the data is complete and correct, the read smart tag information is stored in the local database of the mobile inspection terminal, ready to be merged with the status monitoring data and uploaded;

[0055] The cable monitoring equipment can collect real-time thermal distribution imaging and temperature data of the cable, identify abnormal temperatures at hot and cold spots, monitor mechanical vibrations during cable operation, capture abnormal vibrations, and insulation degradation data, which are stored in the local database of the mobile inspection terminal.

[0056] In step S3, the collected identification data and cable status monitoring data are time-stamped and synchronized in the mobile inspection terminal, and the status monitoring data is preliminarily verified. The synchronized and verified identification data and cable status monitoring data are integrated into a data packet with device location information and inspection timestamp metadata. The data packet is sent to the IoT cloud platform in real time through IoT communication technology.

[0057] In step S4, the cloud platform receives and integrates the real-time identification data and status monitoring data uploaded from the inspection terminal, and performs data processing on the received data; the cloud platform retrieves the historical identification data, status monitoring data, and maintenance record history information of the equipment from a preset database according to the equipment ID corresponding to the data packet, and uses them as the corresponding historical data, and arranges the historical data and the real-time data in chronological order to construct time series data of the equipment status. For the time series data, the key features reflecting the operating status of the equipment are extracted, and the extracted real-time features are fused with the historical features. The data model is trained with historical data, and the fused feature vector is used as input to evaluate the health status of the cable;

[0058] The model outputs the health score of the cable and sets a threshold to determine whether the cable is in the normal working range. The model identifies the possible fault types of the cable based on the feature vector and gives the fault probability. The cable is divided into different risk levels based on the equipment health score, fault probability and fault severity. For the identified potential faults, the time series prediction model is used to predict the time window for the occurrence of the fault, analyze the changing trends of the key indicators of the equipment over time, and predict the changes in the cable status in the future.

[0059] Preferably, the processing process of the cloud platform also includes: for the identified cable fault type, using a pre-trained time series prediction model to predict changes in the cable status within a future period of time.

[0060] In step S5, the intelligent monitoring center continuously receives the inspection result data, equipment status information and fault prediction results from the IoT cloud platform through the API interface and message queue, parses the received raw data, converts it into a unified data format within the monitoring center, integrates the equipment identification data, status monitoring data and key information of fault prediction results into the same data structure, maps the integrated data to the visualization interface of the intelligent monitoring center, updates the cable icon color in real time, marks the completed and ongoing inspection tasks and the real-time location of the inspection personnel or inspection robot on the GIS map, and compares the real-time received data in real time according to the set alarm rules. Once it is found that the data exceeds the threshold or meets the alarm condition, an alarm is triggered immediately;

[0061] Based on the GIS system, combined with equipment identification information and status monitoring data, the specific location of the fault can be accurately located, marked on the map and provided with navigation guidance.

[0062] In step S6, the intelligent monitoring center calculates the risk score of each device based on the identification data, the current status monitoring data of the device, and the fault prediction results, sorts all the devices to be inspected in descending order according to the risk score, generates a risk level list, imports the geographical location information of the device into the geographic information system of the intelligent monitoring center, forms an equipment distribution map, sets the constraints for inspection route planning, takes the equipment risk level list as input, and calculates multiple optimal inspection routes in combination with the constraints;

[0063] According to the risk score, the equipment is divided into several risk level intervals, and the corresponding inspection frequency adjustment rules are set for each risk level interval. The inspection frequency is dynamically adjusted according to the current risk level of the equipment and its changes in the risk level list.

[0064] Example 2

[0065] like Figure 2 As shown, this embodiment provides a high-voltage cable intelligent inspection system based on a high-voltage cable intelligent inspection method for multi-mode identification as in Embodiment 1, including:

[0066] Intelligent identification module, used to deploy intelligent identification to uniquely identify cables and their ancillary equipment and facilities, and store basic equipment information, maintenance records, and fault history data;

[0067] Dual-frequency reading module, used to read different types of smart identification based on dual-frequency reading equipment and obtain identification data;

[0068] Mobile inspection terminal module, used to carry dual-frequency reading module;

[0069] IoT platform, used to receive identification data and cable status monitoring data and perform data cleaning;

[0070] The data fusion analysis module is used to perform fusion analysis on the data received by the IoT platform, combine historical records and fault identification models, evaluate the health status of the cable, identify potential fault risks, and generate inspection results;

[0071] The intelligent monitoring center is used to receive and display the inspection results; calculate the risk score of each device based on the identification data, cable status monitoring data and fault risk prediction results, thereby generating a risk level list for each device, and dividing the devices into several risk level intervals, and dynamically adjusting the optimal inspection route and the inspection frequency of each device accordingly.

[0072] Specifically, during the inspection process, the mobile inspection terminal starts the dual-frequency reading device, remotely senses the smart tag, and the reading device emits a signal of a specific frequency to activate the chip in the tag to transmit data. The dual-frequency reading device receives and parses the information returned by the smart tag, including the device's unique identifier, factory information, last maintenance record, and key parameters. After confirming that the data is complete and correct, the read smart tag information is stored in the local database of the mobile inspection terminal, ready to be merged with the status monitoring data and uploaded;

[0073] The cable monitoring equipment can collect real-time thermal distribution imaging and temperature data of the cable, identify abnormal temperatures at hot and cold spots, monitor mechanical vibrations during cable operation, capture abnormal vibrations, and capture insulation degradation data.

[0074] The cloud platform receives and integrates the real-time identification data and status monitoring data uploaded from the inspection terminal, and processes the received data. Based on the device ID, the cloud platform retrieves the historical identification data, status monitoring data, and maintenance record information of the device from the database, arranges the historical and real-time data in chronological order, and constructs the time series data of the device status. From the time series data, the key features that reflect the operating status of the device are extracted, and the extracted real-time features are fused with the historical features. The data model is trained through the historical data, and the fused feature vector is used as input to evaluate the health status of the cable.

[0075] The model outputs the health score of the cable and sets a threshold to determine whether the cable is in the normal working range. The model identifies the possible fault types of the cable based on the feature vector and gives the fault probability. The cable is divided into different risk levels based on the equipment health score, fault probability and fault severity. For the identified potential faults, the time series prediction model is used to predict the time window for the occurrence of the fault, analyze the changing trends of the key indicators of the equipment over time, and predict the changes in the cable status in the future.

[0076] Working principle: According to the characteristics of the equipment and environmental conditions, dual-frequency smart tags containing equipment ID, basic information, maintenance records and other data are selected and deployed. The integrity of the smart tags is checked regularly so that the data can be accurately read. During the inspection process, the mobile inspection terminal starts the dual-frequency reading device, remotely senses the smart tags and reads the information in them. At the same time, the integrated multi-sensor monitors the cable's thermal distribution imaging, temperature, mechanical vibration, insulation degradation and other status data in real time;

[0077] In the mobile inspection terminal, the read intelligent identification information and status monitoring data are synchronized and preliminarily verified with the timestamp, integrated into a unified data package containing the equipment location and inspection timestamp, and uploaded to the IoT cloud platform in real time through the IoT communication technology. The IoT cloud platform receives and integrates the real-time uploaded data, retrieves the historical information of the equipment, and constructs the time series data of the equipment status. Through data mining and deep learning algorithms, feature extraction and fusion analysis are performed on the time series data to evaluate the health status of the cable, and output health scores, failure probability, risk levels and failure prediction results. The intelligent monitoring center continuously receives data push from the cloud platform through API interfaces and message queues, parses and integrates data, and updates the inspection task progress, inspection personnel location, and cable status icon color on the GIS map in real time. According to the set alarm rules, the real-time data is monitored, the alarm is triggered, and the fault location is accurately marked on the map to provide navigation guidance. The intelligent monitoring center calculates the equipment risk score based on the equipment identification data, current status monitoring data and fault prediction results, generates a risk level list, and combines the GIS system to plan multiple optimal inspection routes according to the equipment risk level and preset constraints, and dynamically adjusts the inspection frequency according to changes in the equipment risk level.

[0078] It should be noted that other specific contents and beneficial effects of the system of the present application can be found in the above-mentioned method embodiments and will not be repeated here.

[0079] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A high-voltage cable intelligent inspection method based on multi-mode identification, characterized in that: The following steps are involved: Deploy smart identification based on equipment characteristics and environmental conditions in high-voltage cable lines; Deploy cable monitoring equipment in high-voltage cable lines; The identification data of the intelligent identification is read through the dual-frequency reading device, and the cable status monitoring data is collected through the cable monitoring device; Upload the collected identification data and cable status monitoring data to the IoT cloud platform for data cleaning; The received data is integrated and analyzed in the cloud platform, combined with historical records and fault identification models to evaluate the health status of the cable, identify potential fault risks, and generate inspection results; Receiving and displaying the inspection results through the intelligent monitoring center; The intelligent monitoring center calculates the risk score of each device based on identification data, cable status monitoring data and fault risk prediction results, thereby generating a risk level list for each device and dividing the devices into several risk level intervals, and dynamically adjusting the optimal inspection route and inspection frequency of each device accordingly.

2. The high-voltage cable intelligent inspection method based on multi-mode identification according to claim 1 is characterized in that: The smart identification includes QR code, NFC and UHF tag; The identification data in the smart identification includes device ID, basic information and maintenance record data.

3. The high-voltage cable intelligent inspection method based on multi-mode identification according to claim 1 is characterized in that: The cable monitoring device is integrated with a multi-sensor for real-time monitoring of the cable's thermal distribution imaging, temperature, mechanical vibration, and insulation degradation status data.

4. The high-voltage cable intelligent inspection method based on multi-mode identification according to claim 1 is characterized in that: The method starts a dual-frequency reading device through a mobile inspection terminal to read identification data. The dual-frequency reading device activates the chip in the smart identification to transmit data by emitting a signal of a specific frequency, obtains the identification data returned by the smart identification, and stores it in a local database of the mobile inspection terminal; The mobile inspection terminal obtains the cable thermal distribution imaging, temperature data and mechanical vibration data collected in real time by the cable monitoring equipment, so as to identify the hot spots, abnormal cold spot temperatures, abnormal vibrations and insulation degradation data, and store them in the local database of the mobile inspection terminal.

5. The high-voltage cable intelligent inspection method based on multi-mode identification according to claim 4 is characterized in that: During the data uploading process of the identification data and the cable status monitoring data, the collected identification data and the cable status monitoring data are time-stamped and synchronized in the mobile inspection terminal, and the cable status monitoring data is preliminarily verified. The synchronized and verified identification data and the cable status monitoring data are integrated into a data packet, which is accompanied by the equipment location information and the inspection timestamp metadata. The data packet is sent to the IoT cloud platform in real time through the IoT communication technology.

6. The high-voltage cable intelligent inspection method based on multi-mode identification according to claim 1 is characterized in that: The processing process of the cloud platform includes: receiving data packets and performing data parsing to obtain real-time data of corresponding equipment; according to the equipment ID corresponding to the data packet, retrieving the historical identification data, status monitoring data and maintenance record history information corresponding to the equipment from a preset database as the corresponding historical data, arranging the historical data and real-time data in chronological order, constructing time series data, thereby extracting key features reflecting the operating status of the equipment, fusing the extracted real-time features with the historical features, and inputting them into a pre-trained data model to obtain the health score of the cable; According to the health score of the cable, the preset health threshold is used to determine whether the cable is in the normal working range. If it is not in the normal working range, the cable fault type is identified based on the extracted real-time and historical features, and the failure probability is given; thus, the cable is divided into different risk levels according to the health score, failure probability and cable failure type of the cable.

7. The high-voltage cable intelligent inspection method based on multi-mode identification according to claim 6 is characterized in that: The processing process of the cloud platform also includes: for the identified cable fault type, using a pre-trained time series prediction model to predict the change of the cable status in the future period of time.

8. The high-voltage cable intelligent inspection method based on multi-mode identification according to claim 1 is characterized in that: The intelligent monitoring center continuously receives inspection result data, equipment status information and fault prediction results from the IoT cloud platform through API interfaces and message queues, and performs data analysis, converts the data into a unified data format within the intelligent monitoring center, and integrates the data into the same data structure; The integrated data is mapped to the visual interface of the intelligent monitoring center, and the completed and ongoing inspection tasks marked on the GIS map, as well as the real-time location of the inspection personnel or inspection robot, are updated in real time. The identification data and cable status monitoring data are combined to locate the specific location of the fault, which is marked on the GIS map and provides navigation guidance.

9. The high-voltage cable intelligent inspection method based on multi-mode identification according to claim 1 is characterized in that: The process of adjusting the optimal inspection route by the intelligent monitoring center is specifically as follows: The geographical location information of each device is imported into the geographic information system of the intelligent monitoring center to form an equipment distribution map, set the constraints for inspection route planning, take the risk level list of each device as input, and calculate multiple optimal inspection routes in combination with the constraints.

10. A high-voltage cable intelligent inspection system according to any one of claims 1 to 9, characterized in that: include: Intelligent identification module, used to deploy intelligent identification to uniquely identify cables and their ancillary equipment and facilities, and store basic equipment information, maintenance records, and fault history data; Dual-frequency reading module, used to read different types of smart identification based on dual-frequency reading equipment and obtain identification data; Mobile inspection terminal module, used to carry dual-frequency reading module; The IoT platform is used to receive identification data and cable status monitoring data and perform data cleaning; The data fusion analysis module is used to perform fusion analysis on the data received by the IoT platform, combine historical records and fault identification models, evaluate the health status of the cable, identify potential fault risks, and generate inspection results; An intelligent monitoring center, used to receive and display the inspection results; Based on the identification data, cable status monitoring data and fault risk prediction results, the risk score of each device is calculated to generate a risk level list for each device, and the devices are divided into several risk level intervals. The optimal inspection route and inspection frequency of each device are dynamically adjusted accordingly.

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