Storage resource management method and system of substation inspection visual analysis system
By employing a hierarchical directory structure, intelligent analysis and compression technologies, and real-time data cleaning, the problem of insufficient storage capacity in the substation inspection visual analysis system has been solved, achieving efficient management of inspection data and system stability.
Patent Information
- Application Number
- CN202511552147.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-28
- Publication Date
- 2026-01-30
AI Technical Summary
Existing substation inspection visual analysis systems face problems such as insufficient storage capacity and inefficient data management during the data acquisition process, resulting in slow system response or even crashes, making it difficult to meet the rapidly growing storage demands.
The system adopts a hierarchical directory structure to classify and organize patrol data. Combined with intelligent analysis and compression technology, it extracts key information and performs lossy compression on ultra-high-definition data, eliminates low-value data from silent patrol points, and cleans up low-value historical data through real-time calculation, thereby achieving hierarchical storage and automatic cleanup.
It enables efficient management of the entire lifecycle of inspection data, ensures stable system operation, reduces storage space usage, and improves data processing efficiency.
Smart Images
Figure CN121434445A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent substation inspection technology, and in particular to a storage resource management method and system for a substation inspection visual analysis system. Background Technology
[0002] Existing substation inspection visual analysis systems, relying on advanced data acquisition and inspection technologies, have become a crucial support for the safe operation and fault early warning of substations. Currently, these systems are widely used in substations at all levels, achieving real-time monitoring of equipment operating status, environmental conditions, and potential faults through the deployment of numerous inspection points. The systems employ various high-performance acquisition devices such as cameras, robots, and drones, utilizing conventional and ultra-high-definition data acquisition technologies to obtain high-precision, multi-angle inspection data, meeting the application requirements for intelligent analysis and fault identification. Simultaneously, to achieve seamless 24 / 7 monitoring, substations have also introduced silent task monitoring equipment. These silent tasks continuously collect real-time inspection data without interfering with routine inspections, thus constructing a monitoring network with a large volume of diverse data.
[0003] However, with the frequent execution of inspection missions and the continuous upgrading of data acquisition technologies, multi-source data is experiencing explosive growth, posing unprecedented challenges to the system's data storage and management. Firstly, while ultra-high-definition image and video data offer higher recognition accuracy, they also place extremely high demands on storage capacity. Secondly, the continuous accumulation of inspection data generated during silent missions leads to a rapid increase in the overall data volume. In some substations with limited funding or hardware investment, the existing server storage space is often insufficient to meet this rapidly growing storage demand. Inefficient data management strategies result in redundant and low-value data occupying critical storage resources for extended periods, easily leading to serious problems such as storage exhaustion, slow system response, and even system crashes. These problems urgently require intelligent, hierarchical data storage solutions to achieve efficient management of the entire lifecycle of inspection data, ensuring the long-term stable operation of the substation system. Summary of the Invention
[0004] The purpose of this invention is to solve at least one technical problem in the background art and to provide a storage resource management method and system for a substation inspection visual analysis system.
[0005] To achieve the above objectives, the present invention provides a storage resource management method for a substation inspection visual analysis system, comprising: The inspection data is classified and organized so that it can be classified and stored according to time sequence and source. Intelligent analysis and compression of ultra-high-definition patrol data; The data collected from silent inspection points in the inspection data are processed to eliminate low-value data that has not triggered alarms; Based on the actual usage of storage space, low-value historical data is automatically cleaned up through real-time calculations.
[0006] According to one aspect of the present invention, the inspection data is classified and organized as follows: The inspection data is categorized and organized using a hierarchical directory structure; The directory hierarchy is constructed in the format of substation code / year / month / day / inspection task execution code / data type; The data types include visible light images, infrared spectra, videos, and audio. The format of the inspection data is: inspection point code_inspection equipment code_timestamp.file extension.
[0007] According to one aspect of the present invention, the intelligent analysis and compression of ultra-high-definition patrol data includes: The collected ultra-high-definition inspection data is processed to extract key information, including equipment status, meter readings, and abnormal features. Based on the key information, image recognition, image discrimination, and meter identification analysis are performed to generate inspection results. Based on the inspection results, the image data in the original ultra-high-definition inspection data is lossily compressed using the `cv::imwrite(target_path, ori_path, compression_params)` function from the OpenCV library. The `compression_params` parameter controls the compression quality, `target_path` is the target image path, and `ori_path` is the original image path, specifically including: Initially, the compression_params parameter is set to 90, and the compression result is saved as a temporary file; If the size of the temporary file is greater than or equal to 1 MB, the compression_params parameter value is appropriately reduced, and the compression operation is re-performed on the same original image data until the size of the temporary file is less than 1 MB. After compression, the previous original uncompressed image files are deleted, and the final compressed temporary file is renamed and saved with the original filename to enable the analysis and storage of ultra-high-definition patrol data.
[0008] According to one aspect of the present invention, the processing of data collected from silent inspection points to eliminate low-value data that has not triggered alarms includes: The inspection data collected from the silent inspection points are analyzed, identified, and processed. If the analysis result shows no abnormalities, the analyzed and identified images are deleted. If the analysis result indicates that there are abnormalities, it is further determined whether the current time has exceeded the preset locking time of the silent inspection point. If the locking time has been exceeded, the analyzed and identified images are retained; if not, the analyzed and identified images are deleted.
[0009] According to one aspect of the present invention, the automatic cleaning of low-value historical data based on real-time calculation according to the actual usage of storage space includes: Set a storage space threshold, periodically read the system's storage space usage rate, and automatically trigger a cleanup operation if the current system's storage space usage rate exceeds the set storage space threshold. The cleaning operation includes: first, deleting all folders containing months older than one year from the current time; second, for data older than six months from the current time, deleting audio and video files, and also deleting the original acquisition images of visible light images and infrared spectra, retaining only the images for analysis and identification.
[0010] To achieve the above objectives, the present invention also provides a storage resource management system for a substation inspection visual analysis system, comprising: The data acquisition module categorizes and organizes the patrol data, enabling the data to be categorized and stored according to time sequence and source. The intelligent analysis and compression module performs intelligent analysis and compression on the ultra-high-definition inspection data in the inspection data. The intelligent analysis and processing module processes the data collected from silent inspection points in the inspection data and eliminates low-value data that has not triggered alarms. The storage management module automatically cleans up low-value historical data in real time based on the actual usage of storage space.
[0011] To achieve the above objectives, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the storage resource management method of the substation inspection visual analysis system as described above.
[0012] To achieve the above objectives, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the storage resource management method of the substation inspection visual analysis system as described above.
[0013] According to the present invention, a structured storage scheme is first constructed based on a directory hierarchy to achieve efficient and orderly data classification. Then, for the collected ultra-high-definition patrol data, a strategy of intelligent analysis followed by efficient compression is adopted to ensure optimal data storage after intelligent analysis. Data collected during silent patrols is accurately identified through intelligent analysis, and an automatic elimination strategy is implemented for data that does not trigger alarms, achieving refined management of patrol data. Simultaneously, the system automatically cleans up low-value historical data through real-time calculation based on actual storage space usage. Through the comprehensive application of hierarchical storage, intelligent compression, and automatic cleanup, the present invention can achieve efficient management of patrol data throughout its entire lifecycle. Attached Figure Description
[0014] Figure 1 The flowchart illustrates a method for managing storage resources in a substation inspection visual analysis system according to an embodiment of the present invention. Detailed Implementation
[0015] The invention will now be discussed with reference to exemplary embodiments. It should be understood that the described embodiments are merely intended to enable those skilled in the art to better understand and thus implement the invention, and are not intended to imply any limitation on the scope of the invention.
[0016] As used herein, the term "comprising" and its variations are to be interpreted as open-ended terms meaning "including but not limited to". The term "based on" is to be interpreted as "at least partially based on". The terms "one embodiment" and "an embodiment" are to be interpreted as "at least one embodiment".
[0017] Figure 1 This is a schematic flowchart illustrating a storage resource management method for a substation inspection visual analysis system according to an embodiment of the present invention. Figure 1 As shown, in this embodiment, the storage resource management method of the substation inspection visual analysis system includes: The inspection data is classified and organized so that it can be classified and stored according to time sequence and source. Intelligent analysis and compression of ultra-high-definition patrol data; The data collected from silent inspection points in the inspection data are processed to eliminate low-value data that has not triggered alarms; Based on the actual usage of storage space, low-value historical data is automatically cleaned up through real-time calculations.
[0018] Furthermore, according to one embodiment of the present invention, the inspection data is classified and organized as follows: The inspection data is categorized and organized using a hierarchical directory structure; The directory hierarchy is constructed in the format of substation code / year / month / day / inspection task execution code / data type; this hierarchy can clearly show the temporal relationship of the inspection data; The data types include four types: CCD (visible light image), FIR (infrared spectrum), Video, and Audio. The format of the inspection data is: Inspection Point Code_Inspection Equipment Code_Timestamp.file extension. The timestamp is accurate to the year, month, day, hour, minute, and second, and its specific format is "yyyymmddhhmmss". This naming format can record the data source and ensure the uniqueness of the inspection data.
[0019] Furthermore, according to one embodiment of the present invention, intelligent analysis and compression of ultra-high-definition patrol data includes: The collected ultra-high-definition inspection data is processed to extract key information, including equipment status, meter readings, and abnormal features. Based on the key information, image recognition, image discrimination, and meter identification analysis are performed to generate inspection results. Based on the inspection results, the image data in the original ultra-high-definition inspection data is lossily compressed using the `cv::imwrite(target_path, ori_path, compression_params)` function from the OpenCV library. The `compression_params` parameter controls the compression quality, `target_path` is the target image path, and `ori_path` is the original image path, specifically including: Initially, the compression_params parameter is set to 90, and the compression result is saved as a temporary file; If the size of the temporary file is greater than or equal to 1 MB, the compression_params parameter value is appropriately reduced, and the compression operation is re-performed on the same original image data until the size of the temporary file is less than 1 MB. After compression, the previous original uncompressed image files are deleted, and the final compressed temporary file is renamed and saved with the original filename to enable the analysis and storage of ultra-high-definition patrol data.
[0020] Furthermore, according to one embodiment of the present invention, the data collected at silent inspection points is processed to eliminate low-value data that has not triggered alarms, including: The inspection data collected from the silent inspection points are analyzed, identified, and processed. If the analysis result shows no abnormalities, the analyzed and identified images are deleted. If the analysis result indicates that there are abnormalities, it is further determined whether the current time has exceeded the preset locking time of the silent inspection point. If the locking time has been exceeded, the analyzed and identified images are retained; if not, the analyzed and identified images are deleted.
[0021] Furthermore, according to one embodiment of the present invention, based on the actual usage of storage space, low-value historical data is automatically cleaned up through real-time calculation, including: Set a storage space threshold, periodically read the system's storage space usage rate, and automatically trigger a cleanup operation if the current system's storage space usage rate exceeds the set storage space threshold. The cleaning operation includes: first, deleting all folders containing months older than one year from the current time; second, for data older than six months from the current time, deleting audio and video files, and also deleting the original acquisition images of visible light images and infrared spectra, retaining only the images for analysis and identification.
[0022] According to the above-described scheme of this invention, the invention first constructs a structured storage scheme based on a directory hierarchy to achieve efficient and orderly data classification; then, for the collected ultra-high-definition inspection data, a strategy of intelligent analysis followed by efficient compression is adopted to ensure that the data achieves optimal storage after intelligent analysis; data collected during silent inspections is accurately identified through intelligent analysis, and an automatic elimination strategy is implemented for data that has not triggered alarms, thereby achieving refined management of inspection data; simultaneously, the system automatically cleans up low-value historical data through real-time calculation based on the actual usage of storage space. Through the comprehensive application of hierarchical storage, intelligent compression, and automatic cleanup, this invention can achieve efficient management of the entire lifecycle of inspection data.
[0023] Furthermore, to achieve the above objectives, the present invention also provides a storage resource management system for a substation inspection visual analysis system, comprising: The data acquisition module categorizes and organizes the patrol data, enabling the data to be categorized and stored according to time sequence and source. The intelligent analysis and compression module performs intelligent analysis and compression on the ultra-high-definition inspection data in the inspection data. The intelligent analysis and processing module processes the data collected from silent inspection points in the inspection data and eliminates low-value data that has not triggered alarms. The storage management module automatically cleans up low-value historical data in real time based on the actual usage of storage space.
[0024] In this embodiment, the data acquisition module integrates high-performance acquisition devices, including cameras, robots, and drones. Cameras are deployed in key areas of the substation to capture static and dynamic images; robots possess autonomous navigation capabilities, enabling them to patrol the substation along preset routes and collect multi-dimensional data such as equipment status and ambient temperature; drones are responsible for aerial patrols, providing wide-area overhead images of the substation, helping to discover problems that are difficult to detect on the ground. These acquisition devices work together to ensure the acquisition of high-precision, multi-angle patrol data. Furthermore, the data acquisition and storage module implements a structured storage scheme, enabling the classification and storage of patrol data according to dimensions such as type and time.
[0025] An automatic cleanup mechanism is implemented to periodically remove expired or invalid data, ensuring efficient use of storage space. Through these measures, the storage management module achieves efficient storage and management of inspection data.
[0026] The intelligent analysis and compression module uses an intelligent compression algorithm to compress the inspection data in order to reduce storage space usage.
[0027] The intelligent analysis and processing module employs image recognition algorithms to perform real-time analysis and intelligent identification of the inspection data collected by the data acquisition module. The algorithm can automatically identify potential problems such as equipment anomalies, temperature anomalies, and foreign object intrusion, and generate alarm information. Through machine learning technology, the intelligent analysis module can continuously optimize the identification accuracy and improve inspection efficiency. The intelligent analysis and processing module processes data collected from silent inspection points, discarding low-value data that has not triggered alarms. The storage management module's real-time monitoring module is responsible for monitoring storage space occupancy and system performance. It monitors storage space utilization in real time and issues warnings when storage space is nearing saturation, prompting administrators to expand capacity or clean up data. Simultaneously, it monitors the system's operational status, including performance metrics for each stage of data acquisition, analysis, and storage. Upon detecting system anomalies or performance degradation, the real-time monitoring module immediately triggers an alarm mechanism to ensure the long-term stable operation of the system.
[0028] Furthermore, according to one embodiment of the present invention, the inspection data is classified and organized as follows: The inspection data is categorized and organized using a hierarchical directory structure; The directory hierarchy is constructed in the format of substation code / year / month / day / inspection task execution code / data type; this hierarchy can clearly show the temporal relationship of the inspection data; The data types include four types: CCD (visible light image), FIR (infrared spectrum), Video, and Audio. The format of the inspection data is: Inspection Point Code_Inspection Equipment Code_Timestamp.file extension. The timestamp is accurate to the year, month, day, hour, minute, and second, and its specific format is "yyyymmddhhmmss". This naming format can record the data source and ensure the uniqueness of the inspection data.
[0029] Furthermore, according to one embodiment of the present invention, intelligent analysis and compression of ultra-high-definition patrol data includes: The collected ultra-high-definition inspection data is processed to extract key information, including equipment status, meter readings, and abnormal features. Based on the key information, image recognition, image discrimination, and meter identification analysis are performed to generate inspection results. Based on the inspection results, the image data in the original ultra-high-definition inspection data is lossily compressed using the `cv::imwrite(target_path, ori_path, compression_params)` function from the OpenCV library. The `compression_params` parameter controls the compression quality, `target_path` is the target image path, and `ori_path` is the original image path, specifically including: Initially, the compression_params parameter is set to 90, and the compression result is saved as a temporary file; If the size of the temporary file is greater than or equal to 1 MB, the compression_params parameter value is appropriately reduced, and the compression operation is re-performed on the same original image data until the size of the temporary file is less than 1 MB. After compression, the previous original uncompressed image files are deleted, and the final compressed temporary file is renamed and saved with the original filename to enable the analysis and storage of ultra-high-definition patrol data.
[0030] Furthermore, according to one embodiment of the present invention, the data collected at silent inspection points is processed to eliminate low-value data that has not triggered alarms, including: The inspection data collected from the silent inspection points are analyzed, identified, and processed. If the analysis result shows no abnormalities, the analyzed and identified images are deleted. If the analysis result indicates that there are abnormalities, it is further determined whether the current time has exceeded the preset locking time of the silent inspection point. If the locking time has been exceeded, the analyzed and identified images are retained; if not, the analyzed and identified images are deleted.
[0031] Furthermore, according to one embodiment of the present invention, based on the actual usage of storage space, low-value historical data is automatically cleaned up through real-time calculation, including: Set a storage space threshold, periodically read the system's storage space usage rate, and automatically trigger a cleanup operation if the current system's storage space usage rate exceeds the set storage space threshold. The cleaning operation includes: first, deleting all folders containing months older than one year from the current time; second, for data older than six months from the current time, deleting audio and video files, and also deleting the original acquisition images of visible light images and infrared spectra, retaining only the images for analysis and identification.
[0032] According to the above-described scheme of this invention, the invention first constructs a structured storage scheme based on a directory hierarchy to achieve efficient and orderly data classification; then, for the collected ultra-high-definition inspection data, a strategy of intelligent analysis followed by efficient compression is adopted to ensure that the data achieves optimal storage after intelligent analysis; data collected during silent inspections is accurately identified through intelligent analysis, and an automatic elimination strategy is implemented for data that has not triggered alarms, thereby achieving refined management of inspection data; simultaneously, the system automatically cleans up low-value historical data through real-time calculation based on the actual usage of storage space. Through the comprehensive application of hierarchical storage, intelligent compression, and automatic cleanup, this invention can achieve efficient management of the entire lifecycle of inspection data.
[0033] Furthermore, to achieve the above objectives, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the storage resource management method of the substation inspection visual analysis system as described above.
[0034] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the storage resource management method of the substation inspection visual analysis system as described above.
[0035] Those skilled in the art will recognize that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented using electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.
[0036] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and equipment can be referred to the corresponding process in the foregoing method implementation, and will not be repeated here.
[0037] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.
[0038] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the objectives of the embodiments of the present invention, depending on actual needs.
[0039] In addition, the functional modules in the embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.
[0040] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the sending / receiving methods of various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.
[0041] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in this application.
[0042] It should be understood that the sequence number of each step in the invention and its embodiments does not absolutely imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
Claims
1. A method for managing storage resources of a substation inspection visual analysis system, characterized in that, The application relates to a method for managing patrol data. The method comprises the following steps: Classifying and organizing patrol data to enable the patrol data to be classified and stored according to a time sequence and a source; Intelligently analyzing and compressing super-high-definition patrol data in the patrol data; Processing data collected by a silent patrol point in the patrol data, and eliminating low-value data that does not trigger an alarm; 2. The substation patrol visual analysis system storage resource management method of claim 1, wherein, According to an actual use condition of a storage space, automatically cleaning low-value historical data through real-time calculation. The classification and organization of the patrol data are as follows: The patrol data are classified and organized through a directory hierarchical structure; The directory hierarchical structure is constructed in the format of a substation code / year / month / day / patrol task execution code / data type; The data type comprises a visible light picture, an infrared spectrum, a video and audio; 3. The substation patrol visual analytics system storage resource management method of claim 1, wherein, The format of the patrol data is as follows: patrol point code_patrol equipment code_timestamp.file suffix. The intelligent analysis and compression of the super-high-definition patrol data comprise the following steps: Processing the collected super-high-definition patrol data, extracting key information contained in the super-high-definition patrol data, and performing analysis and processing of image recognition, image identification and meter identification based on the key information, to generate patrol results; Based on the patrol results, using a cv::imwrite(target_path, ori_path, compression_params) function of an OpenCV library to perform lossy compression on image data in the original super-high-definition patrol data, wherein the compression_params parameter is used to control the compression quality, the target_path parameter is a target image path, and the ori_path parameter is an original image path, and the specific steps comprise the following steps: Initially, the compression_params parameter is set to 90, and the compression result is saved as a temporary file; The size of the temporary file is read, if the size is greater than or equal to 1MB, the compression_params parameter value is appropriately reduced, and the compression operation is re-executed on the same original image data until the size of the temporary file is less than 1MB; 4. The substation patrol visual analytics system storage resource management method of claim 1, wherein, After the compression is completed, the previous original uncompressed image file is deleted, and the final compressed temporary file is renamed and saved as an original file name, so as to realize the analysis and storage of the super-high-definition patrol data. The processing of the data collected by the silent patrol point and the elimination of the low-value data that does not trigger an alarm comprise the following steps:
5. The substation patrol visual analysis system storage resource management method according to any one of claims 1-4, characterized in that, The patrol data collected by the silent patrol point are analyzed, identified and processed, if the analysis and identification result is that no abnormality is found, the analysis and identification picture is deleted; if the analysis and identification result indicates that there is an abnormality, it is further judged whether the current time has exceeded the preset locking time of the silent patrol point, if the locking time has been exceeded, the analysis and identification picture is retained; if the locking time has not been exceeded, the analysis and identification picture is deleted. The automatic cleaning of the low-value historical data according to the actual use condition of the storage space through real-time calculation comprises the following steps: A storage space threshold is set, the storage space usage of the system is read in time, and if the current storage space usage of the system exceeds the set storage space threshold, a cleaning operation is automatically triggered; The cleaning operation includes: first, deleting all the month folder which is more than 1 year away from the current time; second, for the data which is more than 6 months away from the current time, deleting the audio and video files, and deleting the original collection pictures of the visible light pictures and infrared pictures, and only keeping the analysis and identification pictures.
6. A substation inspection visual analytics system storage resource management system, characterized by, Comprise: The data acquisition module classifies and organizes the patrol data, so that the patrol data can be classified and stored according to the time sequence relationship and the source; The intelligent analysis and compression module intelligently analyzes and compresses the ultra-high definition patrol data in the patrol data; The intelligent analysis and processing module processes the data collected by the silent patrol point in the patrol data, and eliminates the low-value data which does not trigger the alarm; The storage management module automatically cleans up the low-value historical data according to the actual use of the storage space through real-time calculation.
7. An electronic device, characterized by The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the substation inspection visual analysis system storage resource management method in any one of claims 1-5.
8. A computer readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the substation inspection visual analysis system storage resource management method in any one of claims 1-5.