A method for edge protection monitoring based on digital cloud computing

Abstract

The present invention relates to the field of cloud computing analysis technology, and in particular to a method for edge protection monitoring based on digital cloud computing. The method comprises the following steps: obtaining edge operation videos; performing edge operation identification on edge operation videos to generate edge operation standardized data; performing edge manual annotation on edge operation standardized data based on a cloud platform to generate edge manual annotation data; constructing a model based on the edge manual annotation data to generate an edge protection monitoring model; using the edge protection monitoring model to perform status identification on edge operation videos to generate a protection fence status report. The present invention realizes edge protection monitoring through a distributed voltage sensor network and a dynamic monitoring algorithm, which greatly improves the safety and management efficiency of edge operations, effectively reduces the possibility of accidents, and comprehensively ensures the safety of operators and equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing analysis, and in particular to a method for edge protection monitoring based on digital cloud computing. Background Art

[0002] Edge operations often face high risks and complex environments. Traditional safety monitoring methods often rely on manual inspections and limited perception technology, which makes it difficult to accurately capture safety issues in real time, resulting in high accident risks and low management efficiency. Traditional methods have serious limitations in data acquisition, processing and analysis, such as a limited number of monitoring equipment, difficulty in data collection, slow analysis speed and unstable data quality. These problems make it difficult to effectively monitor and manage the status of edge fences, operator behavior and changes in the surrounding environment. In addition, traditional methods cannot achieve centralized processing and real-time analysis of large-scale data, lack the ability to immediately identify and warn of safety hazards, resulting in serious deficiencies in safety management and waste of resources. To solve these problems, there is an urgent need for an edge protection monitoring method based on digital cloud computing to monitor the status of edge fences and the safety of operators in real time, effectively predict and prevent potential safety risks, and provide on-site managers with instant and accurate decision support. Summary of the invention

[0003] Based on this, it is necessary to provide a method for edge protection monitoring based on digital cloud computing to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for edge protection monitoring based on digital cloud computing is provided, the method comprising the following steps:

[0005] Step S1: Acquire edge operation video; perform edge operation recognition on edge operation video to generate edge operation standardized data; perform edge operation manual annotation on edge operation standardized data based on cloud platform to generate edge operation manual annotation data;

[0006] Step S2: construct a model based on the manually labeled data of edge operations to generate an edge protection monitoring model; use the edge protection monitoring model to identify the status of the edge operation video and generate a protection fence status report;

[0007] Step S3: constructing a distributed network topology diagram of the adjacent voltage; using the distributed network topology diagram of the adjacent voltage to detect voltage fluctuations on the voltage sensor on the adjacent fence, and generating a voltage fluctuation detection value; performing a voltage trigger comparison detection on the voltage fluctuation detection value and a preset adjacent voltage threshold, and generating a safety voltage report of the protective fence;

[0008] Step S4: Visually display the protective fence status report and the protective fence safety voltage report to generate a visual status diagram for edge protection monitoring; perform dynamic monitoring algorithm detection based on the visual status diagram for edge protection monitoring, and generate a protective fence dynamic monitoring report to perform dynamic detection operations on the protective fence.

[0009] The present invention first obtains video data of the edge operation area in real time through a camera, and then performs in-depth analysis and processing through advanced image recognition technology to generate detailed standardized data of edge operation. These data are stored and processed through a cloud platform, and combined with fine adjustment of manual annotation, the accuracy and completeness of the data are further improved, laying a solid foundation for the training of subsequent machine learning models. Using these annotated data, an efficient edge protection monitoring model is constructed, which can automatically analyze and identify various edge operation states in the video and instantly generate a detailed protection fence status report. A distributed network topology diagram of edge voltage is constructed, and the voltage changes are monitored in real time through voltage sensors installed on the fence. The detected voltage fluctuation data is compared with the preset edge voltage threshold to generate an accurate protection fence safety voltage report, which effectively prevents potential electric shock risks. Finally, advanced data visualization technology is used to display the protection fence status report and safety voltage report in an intuitive way to generate a highly clear visualization status diagram of edge protection monitoring. Based on this state diagram, a dynamic monitoring algorithm is applied to perform in-depth detection and real-time response, and a comprehensive dynamic monitoring report of the protective fence is generated to ensure that the protective fence remains efficient and safe during operation, minimize potential safety risks, and provide reliable protection and support for operators and equipment. The beneficial effect of the present invention is to achieve comprehensive monitoring and safety assurance of the edge operation area. Through advanced video recognition technology and efficient artificial intelligence models, various situations of edge operations can be accurately identified and recorded, and detailed standardized data and real-time status reports can be generated. At the same time, a distributed voltage sensor network is used to monitor the voltage fluctuations on the edge fence in real time to ensure that the voltage is maintained within a safe range, thereby effectively preventing safety risks such as electric shock. Data visualization technology makes the status and safety of the protective fence clear at a glance through clear and intuitive display, providing timely decision-making support for managers and operators. The application of dynamic monitoring algorithms can not only detect potential safety hazards in a timely manner, but also quickly warn and respond, greatly improving the safety and management efficiency of edge operations, effectively reducing the possibility of accidents, and comprehensively ensuring the safety of operators and equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of the steps of a method for edge protection monitoring based on digital cloud computing;

[0011] Figure 2 for Figure 1Detailed implementation steps of step S2 in the flowchart;

[0012] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0013] Figure 4 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0014] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0015] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0016] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0017] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0018] To achieve this, please refer to Figures 1 to 4 , a method for monitoring edge protection based on digital cloud computing, the method comprising the following steps:

[0019] Step S1: Acquire edge operation video; perform edge operation recognition on edge operation video to generate edge operation standardized data; perform edge operation manual annotation on edge operation standardized data based on cloud platform to generate edge operation manual annotation data;

[0020] Step S2: construct a model based on the manually labeled data of edge operations to generate an edge protection monitoring model; use the edge protection monitoring model to identify the status of the edge operation video and generate a protection fence status report;

[0021] Step S3: constructing a distributed network topology diagram of the adjacent voltage; using the distributed network topology diagram of the adjacent voltage to detect voltage fluctuations on the voltage sensor on the adjacent fence, and generating a voltage fluctuation detection value; performing a voltage trigger comparison detection on the voltage fluctuation detection value and a preset adjacent voltage threshold, and generating a safety voltage report of the protective fence;

[0022] Step S4: Visually display the protective fence status report and the protective fence safety voltage report to generate a visual status diagram for edge protection monitoring; perform dynamic monitoring algorithm detection based on the visual status diagram for edge protection monitoring, and generate a protective fence dynamic monitoring report to perform dynamic detection operations on the protective fence.

[0023] The present invention first obtains video data of the edge operation area in real time through a camera, and then performs in-depth analysis and processing through advanced image recognition technology to generate detailed standardized data of edge operation. These data are stored and processed through a cloud platform, and combined with fine adjustment of manual annotation, the accuracy and completeness of the data are further improved, laying a solid foundation for the training of subsequent machine learning models. Using these annotated data, an efficient edge protection monitoring model is constructed, which can automatically analyze and identify various edge operation states in the video and instantly generate a detailed protection fence status report. A distributed network topology diagram of edge voltage is constructed, and the voltage changes are monitored in real time through voltage sensors installed on the fence. The detected voltage fluctuation data is compared with the preset edge voltage threshold to generate an accurate protection fence safety voltage report, which effectively prevents potential electric shock risks. Finally, advanced data visualization technology is used to display the protection fence status report and safety voltage report in an intuitive way to generate a highly clear visualization status diagram of edge protection monitoring. Based on this status diagram, a dynamic monitoring algorithm is applied to perform in-depth detection and real-time response to generate a comprehensive dynamic monitoring report for the protective fence, ensuring that the protective fence remains efficient and safe during operation, minimizing potential safety risks, and providing reliable protection and support for operators and equipment.

[0024] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a method for monitoring edge protection based on digital cloud computing of the present invention. In this example, the method for monitoring edge protection based on digital cloud computing includes the following steps:

[0025] Step S1: Acquire edge operation video; perform edge operation recognition on edge operation video to generate edge operation standardized data; perform edge operation manual annotation on edge operation standardized data based on cloud platform to generate edge operation manual annotation data;

[0026] In an embodiment of the present invention, video data of the edge operation site is obtained in real time by a high-resolution camera device to ensure the clarity and continuity of the video image. The collected video is processed and analyzed using advanced image recognition technology, specifically including image preprocessing, feature extraction and scene recognition steps. Through these processes, various edge operation scenes and key activities in the video can be accurately identified, thereby generating standardized edge operation data. These standardized data include detailed information such as the actions of the operators, the use of tools, and changes in the operation area. Subsequently, these standardized data are uploaded to the cloud platform, and the data is further processed and optimized using the powerful storage and computing capabilities of the cloud platform. The standardized data is manually annotated, and the annotators refine the data according to the video content to ensure that each operation scene and action is accurately recorded. Then, the annotated data is trained and optimized using an artificial intelligence algorithm to improve the accuracy and completeness of the data. The artificial intelligence algorithm convolutional neural network (CNN), through the training of a large amount of annotated data, the model can automatically learn and optimize the recognition accuracy. Finally, detailed edge operation manual annotated data is generated. These data not only contain detailed descriptions of each work scene and action, but also include information such as timestamps and location tags, forming a complete data set.

[0027] Step S2: construct a model based on the manually labeled data of edge operations to generate an edge protection monitoring model; use the edge protection monitoring model to identify the status of the edge operation video and generate a protection fence status report;

[0028] In an embodiment of the present invention, the manually annotated data obtained from the edge operation is preprocessed and feature extracted to provide input for building a machine learning model. The machine learning model uses a convolutional neural network (CNN) to learn and identify various states and dynamic changes in edge operations. During the model training process, a supervised learning method is used to optimize the accuracy and generalization ability of the model using annotated data. The trained edge protection monitoring model is used to analyze and identify the edge operation video collected in real time. After the video data is processed by the model, it can quickly and accurately identify various activities and states in the edge operation area, including personnel location, movements, and tool usage. The generated protective fence status report describes the monitored situation in detail.

[0029] Step S3: constructing a distributed network topology diagram of the adjacent voltage; using the distributed network topology diagram of the adjacent voltage to detect voltage fluctuations on the voltage sensor on the adjacent fence, and generating a voltage fluctuation detection value; performing a voltage trigger comparison detection on the voltage fluctuation detection value and a preset adjacent voltage threshold, and generating a safety voltage report of the protective fence;

[0030] In the embodiment of the present invention, a distributed network topology diagram with comprehensive coverage is constructed by deploying multiple high-precision voltage sensors at key positions of the edge fence. These voltage sensors are interconnected using wireless communication technology to form a real-time monitoring network. Each sensor collects voltage fluctuation data at its location in real time to ensure high-frequency data collection and high-precision measurement. The collected voltage fluctuation data is transmitted to the central data processing system via a wireless network. The central data processing system is equipped with a high-performance computing unit and an advanced data analysis algorithm, which can perform real-time processing and accurate analysis of the data transmitted by the sensor. First, the raw data is preprocessed to filter out noise and abnormal values, and then an advanced voltage fluctuation detection algorithm is used to generate an accurate voltage fluctuation detection value. The generated voltage fluctuation detection value is compared and analyzed with the preset edge voltage safety threshold. The preset voltage threshold is set according to safety standards and actual site conditions to ensure that the voltage fluctuation does not exceed the safety range under any circumstances. Once the voltage fluctuation is detected to exceed the safety threshold, the voltage abnormality alarm is immediately triggered. The alarm system notifies the on-site operators and managers through various means such as sound and light alarms, text messages, and emails to ensure that relevant personnel can take measures in a timely manner. A detailed safety voltage report of the protective fence will be automatically generated. The report includes key information such as the specific time of abnormal detection, location and voltage value, and describes the abnormal voltage situation in detail. The report data not only facilitates on-site managers to conduct risk assessment and decision-making, but can also be archived for future analysis and improvement.

[0031] Step S4: Visually display the protective fence status report and the protective fence safety voltage report to generate a visual status diagram for edge protection monitoring; perform dynamic monitoring algorithm detection based on the visual status diagram for edge protection monitoring, and generate a protective fence dynamic monitoring report to perform dynamic detection operations on the protective fence.

[0032] In the embodiment of the present invention, data in the protection fence status report and the protection fence safety voltage report are graphically processed through data visualization technology to generate an easy-to-understand edge protection monitoring visualization status diagram. These graphical data displays include but are not limited to line graphs, heat maps, bar graphs, etc., so that managers can intuitively view and analyze the real-time status and voltage safety of the fence. Based on the generated edge protection monitoring visualization status diagram, dynamic monitoring algorithms are applied to further analyze and process the data. These dynamic monitoring algorithms include time series analysis and anomaly detection algorithms, which are used to continuously monitor the operating status of the protection fence. Through continuous analysis and comparison of real-time data, potential risks and abnormal situations can be detected. Finally, a dynamic monitoring report for the protection fence is generated, which records the detected dynamic changes and risk assessment results in detail. This report provides key information such as specific time, location, and change trend, helping managers and operators to understand the operating status of the protection fence in a timely manner and take necessary maintenance and adjustment measures.

[0033] Preferably, step S1 comprises the following steps:

[0034] Step S11: using a camera to obtain edge operation video data;

[0035] Step S12: performing key frame interception processing on the edge operation video to generate an edge operation key frame image;

[0036] Step S13: performing edge computing on the edge operation key frame image to generate edge computing data;

[0037] Step S14: performing weak edge removal on the edge operation key frame image according to the edge edge calculation data to generate an edge weak edge removal image; performing edge operation recognition on the edge weak edge removal image to generate edge operation recognition data;

[0038] Step S15: performing data standardization processing on the edge operation identification data to generate edge operation standardized data;

[0039] Step S16: manually annotating the edge operation standardized data on the cloud platform to generate manually annotated edge operation data.

[0040] The present invention realizes comprehensive monitoring and data processing of the edge operation area through a series of advanced technical means, and improves the safety management level of the operation site. Use high-resolution cameras to obtain video data in real time to ensure that all key operation details are captured; use image processing technology to intercept key frames and extract representative images to ensure the validity and representativeness of the data; use edge computing technology to process key frame images to generate accurate edge computing data, and perform weak edge removal to filter out unimportant noise information to obtain clearer and more important images; use image recognition technology to analyze and identify weak edge removal images to generate accurate operation recognition data; standardize the recognition data to ensure the consistency of data format and the standardization of content, and generate high-quality standardized data; finally, use the storage and computing power of the cloud platform to further manually annotate the standardized data, use artificial intelligence algorithms to improve the accuracy and completeness of the data, and generate detailed manually annotated data. Through this series of steps, it is possible to efficiently and accurately carry out operation monitoring and safety management, timely identify and warn potential risks, improve the safety and management efficiency of edge operations, effectively reduce the possibility of accidents, and ensure the safety of operators and equipment.

[0041] In an embodiment of the present invention, a high-resolution camera is used to obtain video data of edge operations in real time to ensure the clarity and continuity of the video image, thereby accurately capturing all dynamic details of the work site. The video data is processed by advanced image processing technology, specifically including inter-frame difference method and background subtraction method, to extract representative edge operation key frame images. These key frame images can effectively represent the important moments and actions in the entire operation process. These key frame images are processed by edge computing technology, specifically including steps such as image segmentation, edge detection and feature extraction, to generate edge edge computing data. According to these edge computing data, the key frame images are weakly edged, and the Canny edge detection algorithm is used to filter out the unimportant and noisy edge information in the image, so as to obtain a clearer and more important edge weak edge eliminated image. The weak edge eliminated image is analyzed and identified by image recognition technology, specifically using a convolutional neural network (CNN) deep learning model to automatically identify the edge operation scene and key actions in the image, and generate edge operation recognition data. These recognition data include information such as the specific actions of the operator, the use of tools, and changes in the working environment. These identification data are standardized, and technical means such as data normalization and data cleaning are used to ensure that the data format is consistent and the content is standardized, thereby generating standardized data for edge operations. These standardized data can be further used for analysis and processing to ensure data consistency and reliability. Using the storage and computing capabilities of the cloud platform, these standardized data are uploaded to the cloud for further manual labeling. Manual labelers finely label the data based on the video content to ensure that every operation scene and action is accurately recorded, and ultimately generate detailed manual labeling data for edge operations. These manually labeled data provide a solid data foundation for subsequent edge operation monitoring and management, ensuring efficient and accurate operation monitoring and safety management.

[0042] Preferably, step S16 includes the following steps:

[0043] Step S161: Upload the standardized operation data of the edge operation to the cloud platform for repeated data screening to generate preliminary edge screening data;

[0044] Step S162: Manually annotate the preliminary screening data of the edge to generate an edge annotation data set, wherein the manual annotation includes fence status, operator behavior and environmental conditions;

[0045] Step S163: performing data consistency integration on the edge annotation data set to generate edge operation manual annotation data.

[0046] The present invention further improves the accuracy and reliability of edge operation data through sophisticated data processing and manual labeling. Upload the standardized edge operation data to the cloud platform for duplicate data screening, remove redundant and duplicate data, ensure the simplicity and efficiency of the data set, and generate preliminary screening data; manually label the preliminary screening data, and the labeling content includes key factors such as fence status, operator behavior, and environmental conditions to ensure that every important detail is accurately recorded; finally, integrate the labeled data set for data consistency to ensure the consistency of format and content of data from different sources and at different times, and generate high-quality manually labeled edge operation data. Through these steps, more accurate and comprehensive operation data can be obtained, providing a solid data foundation for subsequent analysis, monitoring, and management, thereby improving the safety and management efficiency of edge operations, effectively reducing the possibility of accidents, and ensuring the safety of operators and equipment.

[0047] In an embodiment of the present invention, the standardized data of the edge operation is uploaded to the cloud platform through the efficient HTTP / 2 data transmission protocol to ensure the speed and reliability of data transmission; on the cloud platform, the MD5 hash algorithm deduplication algorithm is used to screen the uploaded data, automatically detect and remove redundant and repeated data, and generate the edge preliminary screening data to ensure the simplicity and effectiveness of the data; with the help of the powerful computing power and artificial intelligence auxiliary tools of the cloud platform, the preliminary screening data is manually annotated, and the annotation content includes key factors such as fence status, operator behavior and environmental conditions, and the accuracy and comprehensiveness of the data are ensured through detailed annotation; finally, the ETL tool data consistency integration technology is used to normalize the annotated data set to ensure the consistency of the format, structure and content of data from different sources and time, and generate high-quality edge operation manual annotation data. These technical means are combined with each other, through efficient data transmission, accurate deduplication, detailed manual annotation and strict data consistency integration, to achieve efficient processing and accurate annotation of edge operation data, and provide a solid data foundation for subsequent analysis, monitoring and management.

[0048] Preferably, step S2 comprises the following steps:

[0049] Step S21: constructing a model based on the manually labeled data of edge operations to generate an edge protection monitoring model;

[0050] Step S22: using the edge protection monitoring model to perform status recognition on the edge operation video to generate protection fence status data, wherein the status recognition includes installation integrity, position stability, and material aging degree;

[0051] Step S23: Upload the protective fence status data to the cloud platform for data storage and generate a protective fence status report.

[0052] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0053] Step S21: constructing a model based on the manually labeled data of edge operations to generate an edge protection monitoring model;

[0054] In an embodiment of the present invention, a manually annotated dataset of edge operations is used, and through the process of data preprocessing and feature extraction, an advanced machine learning algorithm, convolutional neural network (CNN), is used to extract key features and patterns from the data. These features and patterns are crucial for identifying and classifying various situations in edge operations. Next, a supervised learning method is used to train the model, and the generalization ability and accuracy of the model are improved through cross-validation technology and parameter tuning to ensure that the model can effectively respond to challenges in different scenarios. Finally, through an iterative training and verification process, the performance of the model is optimized and a high-precision edge protection monitoring model is generated. This model can monitor safety hazards in edge operations in real time, and issue warnings in a timely manner, providing real-time support and safety guarantees for on-site operations, thereby improving work efficiency and reducing potential risks.

[0055] Step S22: using the edge protection monitoring model to perform status recognition on the edge operation video to generate protection fence status data, wherein the status recognition includes installation integrity, position stability, and material aging degree;

[0056] In an embodiment of the present invention, the model prepares the video data through preprocessing steps, including frame difference, optical flow calculation and other techniques, in order to capture dynamic changes and important features in the video. Secondly, a deep learning model such as a convolutional neural network (CNN) is used to analyze and process the video frame sequence to extract key features such as the installation integrity, position stability and material aging of the fence. The patterns and context information learned during the model training phase are used for recognition to generate detailed protective fence status data.

[0057] Step S23: Upload the protective fence status data to the cloud platform for data storage and generate a protective fence status report.

[0058] In the embodiment of the present invention, the status data obtained from the edge protection monitoring model is transmitted to the cloud platform using an efficient data transmission protocol and security mechanism. The cloud platform receives and stores the data through a storage service to ensure that the data is stored and backed up safely and reliably. The uploaded data is processed and analyzed using the powerful processing power of cloud computing, including data cleaning, formatting and structuring, to generate a protection fence status report.

[0059] The beneficial effects of the present invention are that by using the manually annotated data of edge operations to construct an edge protection monitoring model, the key states of the protective fence, such as installation integrity, position stability and material aging, can be accurately analyzed and understood. It is applied to the state recognition of edge operation videos to generate detailed protective fence state data, which not only includes static properties, but also captures dynamic changes and potential safety hazards. These key data are uploaded to the cloud platform for centralized storage and processing, allowing managers to access and analyze the real-time status of the protective fence anytime and anywhere. By generating a clear protective fence status report, it can provide a comprehensive visual display and data analysis to help managers identify potential problems in a timely manner and take necessary preventive measures, thereby maximizing the safety and operational efficiency of the edge operation area. This comprehensive technical solution not only improves the response speed and accuracy of safety management, but also effectively reduces the probability of accidents through continuous monitoring and data-driven decision support, and ensures the overall safety of operators and facilities.

[0060] Preferably, step S22 includes the following steps:

[0061] Step S221: performing installation integrity identification on the edge operation video through the edge protection monitoring model to generate edge installation integrity data;

[0062] Step S222: using a fence displacement sensor to obtain edge fence position information data; using an edge protection monitoring model to perform fence displacement analysis on the edge protection fence position information data to generate edge displacement change data;

[0063] Step S223: acquiring infrared equipment detection data; using the edge protection monitoring model to perform material wear assessment on the infrared equipment detection data to generate edge wear assessment data;

[0064] Step S224: merging the edge installation integrity data, the edge displacement change data and the generated edge wear assessment data into edge status data to generate protective fence status data.

[0065] The present invention uses the edge protection monitoring model to identify the installation integrity of the edge operation video, which can accurately capture the actual situation of the fence installation and any potential integrity problems, and generate detailed edge installation integrity data. The fence position information data is obtained by using the fence displacement sensor, and then combined with the fence displacement analysis technology of the edge protection monitoring model to generate edge displacement change data, effectively monitoring the stability and safety of the fence position. The material detection data is obtained by using infrared equipment, and the wear degree of the material is evaluated by the model to generate edge wear assessment data, which reflects the actual condition of the fence material after long-term use. Finally, the installation integrity data, displacement change data and wear assessment data are comprehensively analyzed and merged to generate comprehensive protection fence status data. These data not only provide a detailed description of the static and dynamic properties of the fence, but also provide timely decision support for managers through data integration and analysis, helping them to take preventive measures and maintenance strategies, thereby ensuring the safety and operating efficiency of the edge operation area. This comprehensive monitoring and evaluation system effectively reduces the risk of potential accidents, improves the response speed and accuracy of management, and provides reliable protection and support for edge operations.

[0066] In an embodiment of the present invention, the edge protection monitoring model is used to identify the installation integrity of the edge operation video. This process uses advanced image recognition and analysis technology to accurately identify the actual situation of the fence installation and any potential integrity problems, thereby generating detailed edge installation integrity data. The position information data of the edge fence is obtained using a fence displacement sensor. At the same time, these position information data are analyzed and processed by the edge protection monitoring model to achieve dynamic monitoring of the fence position and displacement change analysis, and generate edge displacement change data. This step can capture any changes in the fence position in a timely manner to ensure the stability and safety of the fence. Infrared equipment detection data is obtained, which reflects the actual wear of the fence material. The infrared equipment detection data is analyzed and evaluated by the edge protection monitoring model to generate edge wear assessment data. These data provide an important reference for the service life and health of the fence material. Finally, the edge installation integrity data, edge displacement change data and edge wear assessment data obtained above are comprehensively analyzed and integrated.

[0067] Preferably, step S3 comprises the following steps:

[0068] Step S31: constructing a topological structure using the position information data of the adjacent fence to generate an adjacent voltage distributed network topology map; using the adjacent voltage distributed network topology map to perform voltage fluctuation detection on the voltage sensor on the adjacent fence to generate a voltage fluctuation detection value;

[0069] Step S32: Perform voltage trigger comparison detection on the voltage fluctuation detection value and the preset voltage threshold of the adjacent edge to generate a voltage fluctuation detection value; when the voltage fluctuation detection value is greater than the preset voltage fluctuation threshold, generate voltage fluctuation abnormality data; initiate the adjacent edge warning measures based on the voltage fluctuation abnormality data, and generate adjacent edge fence voltage abnormality warning data, wherein the adjacent edge warning measures include alarm notification and automatic power-on reminder;

[0070] Step S33: Upload the abnormal voltage warning data of the edge fence to the cloud platform for data storage and generate a protective fence report.

[0071] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0072] Step S31: constructing a topological structure using the position information data of the adjacent fence to generate an adjacent voltage distributed network topology map; using the adjacent voltage distributed network topology map to perform voltage fluctuation detection on the voltage sensor on the adjacent fence to generate a voltage fluctuation detection value;

[0073] In an embodiment of the present invention, the position information data of the adjacent fence is used to construct a topological structure, and a distributed network topology diagram of the adjacent voltage is generated. By collecting the position information of the adjacent fence, a spatial distribution structure diagram of the fence is established to ensure that each fence position is accurately represented in the network topology diagram, which provides basic data for subsequent voltage fluctuation monitoring. When the voltage sensor on the adjacent fence is used to detect voltage fluctuations using the distributed network topology diagram of the adjacent voltage, the voltage fluctuation on the fence is monitored in real time. These voltage sensors are responsible for collecting voltage data at various positions of the fence and transmitting the collected data to the central data processing system. By analyzing these voltage fluctuation data, accurate voltage fluctuation detection values ​​are generated. These detection values ​​reflect the actual changes in the voltage on the fence, including periodic fluctuations or abnormal fluctuations, and are compared and analyzed with the preset adjacent voltage threshold.

[0074] Step S32: Perform voltage trigger comparison detection on the voltage fluctuation detection value and the preset voltage threshold of the adjacent edge to generate a voltage fluctuation detection value; when the voltage fluctuation detection value is greater than the preset voltage fluctuation threshold, generate voltage fluctuation abnormality data; initiate the adjacent edge warning measures based on the voltage fluctuation abnormality data, and generate adjacent edge fence voltage abnormality warning data, wherein the adjacent edge warning measures include alarm notification and automatic power-on reminder;

[0075] In an embodiment of the present invention, the voltage fluctuation detection value is compared and detected with the preset adjacent voltage threshold. The voltage fluctuation detection value is data obtained by real-time monitoring of the voltage sensor on the adjacent fence, which is used to reflect the actual fluctuation of the fence voltage. When the voltage fluctuation detection value exceeds the preset voltage fluctuation threshold, voltage fluctuation abnormality data will be generated. Based on the voltage fluctuation abnormality data, the adjacent early warning measures are initiated. These early warning measures include operations such as alarm notifications and automatic power-on reminders. The alarm notification can be sent to relevant personnel or monitoring centers through various communication methods such as SMS, email, and mobile phone App notifications to promptly remind relevant personnel that the fence voltage is abnormal. Automatic power-on means that when the voltage fluctuation is abnormal in the fence, the fence is automatically powered on, so that the contactor can feel the weak current and remind the staff to stay away from the dangerous area.

[0076] Step S33: Upload the abnormal voltage warning data of the edge fence to the cloud platform for data storage and generate a protective fence report.

[0077] In an embodiment of the present invention, the real-time acquired voltage anomaly warning data of the edge fence is uploaded to the cloud platform through an efficient data transmission protocol to ensure the timeliness and integrity of the data. On the cloud platform, these data are stored and data processing and analysis algorithms are applied to generate detailed protection fence reports. These reports include key information such as the time when the voltage anomaly occurred, the specific location, the voltage fluctuation, and the implementation of the early warning measures.

[0078] The beneficial effects of the present invention are to construct a topological structure by using the position information data of the adjacent fence to generate an adjacent voltage distributed network topology map, realize the voltage fluctuation detection of the voltage sensor on the adjacent fence, generate an accurate voltage fluctuation detection value, so that the voltage abnormality can be discovered and recorded in time. By comparing the voltage fluctuation detection value with the preset voltage threshold, when the detection value exceeds the threshold, the voltage fluctuation abnormality data is generated, and the adjacent early warning measures are started based on this data, including alarm notification and automatic power-on reminder, to ensure the safety and timely early warning function of the adjacent operation area. The alarm notification notifies the relevant personnel immediately through SMS, email or sound alarm, while the automatic power-on reminder reminds the contactor to stay away from the dangerous area through a weak current, effectively preventing the occurrence of potential accidents. The generated voltage abnormality warning data is uploaded to the cloud platform for data storage, and a protective fence report is generated, which further enhances the monitoring and management of voltage abnormalities. The cloud platform not only provides reliable data storage, but also uses powerful computing power to conduct in-depth analysis and processing of data, and generates a detailed protective fence report, including key information such as the time, location, and voltage value of the abnormality. These reports are intuitively displayed through visualization technology, which helps managers quickly understand and evaluate voltage anomalies and formulate corresponding safety measures. This method improves the safety monitoring efficiency and management level of the edge operation area, reduces the possibility of safety hazards and accidents, and ensures the safety of personnel and equipment. Through accurate voltage monitoring and timely early warning mechanisms, the real-time monitoring capability of the safety status of the edge fence is significantly improved, allowing managers to more effectively maintain and manage the edge operation environment and ensure the safety and smooth progress of the operation process.

[0079] Preferably, step S31 includes the following steps:

[0080] Step S311: constructing a topological structure using the location information data of the adjacent fence to generate an adjacent voltage distributed network topology map; collecting voltage data based on the adjacent voltage distributed network topology map to obtain real-time voltage data of the voltage sensor;

[0081] Step S312: performing position voltage association between the real-time voltage data of the voltage sensor and the position information data of the adjacent fence based on the adjacent voltage distributed network topology diagram, and generating a sensor position-voltage data pair;

[0082] Step S313: using a distributed network protocol to perform real-time voltage fluctuation calculations on the sensor position-voltage data pair to generate a voltage fluctuation detection value.

[0083] The present invention constructs a topological structure by using the position information data of the adjacent fence to generate a distributed network topology map of the adjacent voltage, thereby realizing the accurate collection and management of voltage data. Based on the topology map, voltage data is collected to obtain real-time voltage data of the voltage sensor, and these data are associated with the fence position information to generate sensor position-voltage data pairs. The voltage fluctuation is calculated in real time on these data pairs through a distributed network protocol to generate voltage fluctuation detection values, so that the voltage state of each position of the fence can be monitored in real time. This method ensures the efficiency and accuracy of voltage monitoring, can timely discover voltage abnormalities, and quickly take corresponding early warning and protective measures, improves the safety monitoring level of the adjacent operation area, reduces the possibility of safety hazards and accidents, and ensures the safety of operators and equipment. Through the accurate management and analysis of voltage data, reliable data support can be provided for subsequent maintenance and repair, the workload and cost of manual inspection can be reduced, and the overall operational efficiency can be improved. The application of real-time voltage monitoring and early warning system enables management personnel to understand the voltage state of the fence in the first time, make quick responses, effectively prevent and deal with potential electrical faults, and further improve the safety and stability of the working environment.

[0084] In an embodiment of the present invention, the position information data of the adjacent fence is used to construct an adjacent voltage distributed network topology map through an advanced topology algorithm to ensure that the position of each voltage sensor is accurately mapped on the network topology map, so that the distribution and connection relationship of the sensors can be intuitively displayed; based on the constructed network topology map, voltage data collection is performed, and the voltage data of the fence is obtained in real time through high-precision voltage sensors to ensure the real-time and accuracy of the data. These sensors can continuously monitor the voltage changes of the fence and capture any subtle voltage fluctuations; by associating the real-time collected voltage data with the position information data of the fence, a sensor position-voltage data pair is generated, so that each voltage data can correspond to a specific sensor position, and accurate positioning and management of the data can be achieved; finally, a distributed network protocol is used to perform real-time operations on these position-voltage data pairs, and accurate voltage fluctuation detection values ​​are generated through dynamic analysis and processing of the voltage data. These operations include filtering and denoising of the voltage data and comparative analysis with historical data to identify abnormal voltage fluctuations, thereby achieving real-time monitoring of the voltage status of the adjacent fence.

[0085] Preferably, step S32 includes the following steps:

[0086] Step S321: calculating the voltage difference between the voltage fluctuation detection value and the preset edge voltage threshold to generate the voltage fluctuation detection value;

[0087] Step S322: when the voltage fluctuation detection value is greater than a preset voltage fluctuation threshold, generating voltage fluctuation abnormal data;

[0088] Step S323: Initiate edge warning measures based on the abnormal voltage fluctuation data to generate abnormal voltage warning data for the edge fence, wherein the edge warning measures include alarm notification and automatic power-on reminder.

[0089] The present invention can accurately monitor the voltage state of each position of the fence and detect abnormal conditions in time by calculating the voltage difference between the voltage fluctuation detection value and the preset edge voltage threshold. When the voltage fluctuation detection value exceeds the preset threshold, voltage fluctuation abnormal data is generated and early warning measures are initiated. The early warning measures include alarm notification and automatic power-on reminder, wherein the automatic power-on reminder can make the contact feel a weak electric shock, so as to stay away from the fence and avoid danger. This process ensures real-time monitoring and timely early warning of the fence voltage state, effectively improves the safety of the edge operation area, reduces the possibility of safety hazards and accidents, and ensures the safety of operators and equipment. At the same time, the automated early warning system enables managers to quickly respond to voltage abnormalities, improving overall management efficiency and emergency response capabilities. Through this comprehensive monitoring mechanism, not only can real-time data and accurate early warnings be provided, but also historical data analysis can be performed to help managers conduct long-term safety assessments and improvements, further improving the reliability and effectiveness of the protection system. This automated voltage monitoring and early warning system can reduce the frequency and intensity of manual inspections, reduce management costs, improve resource utilization efficiency, and make the edge operation environment safer and more efficient.

[0090] In an embodiment of the present invention, after the voltage sensor collects the voltage data of each position of the fence in real time, the data is processed using an advanced voltage difference calculation algorithm to accurately calculate the actual voltage fluctuation detection value. This process involves real-time data analysis and a complex threshold comparison algorithm to ensure that abnormal conditions can be detected accurately and in a timely manner. Once the calculated voltage fluctuation detection value exceeds the preset voltage fluctuation threshold, voltage fluctuation abnormal data will be generated immediately and the corresponding early warning measures will be triggered. The early warning measures include two key aspects: alarm notification and automatic power-on reminder. The alarm notification uses the advanced communication technology of the wireless communication module to send the abnormal warning data to the equipment of the relevant management personnel in a timely manner, such as a mobile phone or computer, to ensure that the relevant personnel can respond and handle the abnormal situation quickly. At the same time, in order to further improve the safety protection effect, an automatic power-on reminder measure is also implemented. When an abnormal voltage fluctuation is detected, the control circuit will automatically power on and provide a weak electric shock to the contactor to warn him to stay away from the adjacent fence, effectively preventing potential safety risks and accidents.

[0091] Preferably, step S4 comprises the following steps:

[0092] Step S41: reading edge protection data from the protection fence status report and the protection fence safety inspection report to generate edge protection data;

[0093] Step S42: Performing dynamic detection of the edge fence on the edge protection data and generating a dynamic monitoring report of the protection fence;

[0094] Step S43: Perform dynamic detection operations based on the dynamic monitoring report of the protective fence.

[0095] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes:

[0096] Step S41: reading edge protection data from the protection fence status report and the protection fence safety inspection report to generate edge protection data;

[0097] In an embodiment of the present invention, the protective fence status report and protective fence safety detection report stored in the cloud are accessed and read through the data storage service of the cloud platform. These reports cover various types of data collected and processed in the previous steps, including fence position status, safety equipment operation status, and voltage fluctuation detection data. Using efficient data reading and parsing algorithms, data files stored in the cloud are obtained through the data storage interface of the cloud computing platform. These data files are stored in the object structured format of database records, file storage, or cloud storage buckets. Once the data is obtained, data integration and merging operations will be performed to integrate data from different sources and formats into a unified edge protection data set. This process involves technologies such as data cleaning, format conversion, and data association to ensure that the generated data set is complete, accurate, and can be used for further analysis and application. The final generated edge protection data set will serve as the basis for decision support and management, and will be used to evaluate the overall safety performance of the edge fence and provide real-time monitoring reports and management recommendations.

[0098] Step S42: Performing dynamic detection of the edge fence on the edge protection data and generating a dynamic monitoring report of the protection fence;

[0099] In an embodiment of the present invention, complex event processing (CEP) system data stream processing technology is used to receive, process and analyze data from various sensors and monitoring devices in real time. During the dynamic detection process, predefined safety indicators and rules are used for real-time evaluation, such as fence position stability, voltage fluctuations, and equipment operating status. These indicators and rules are customized and optimized through machine learning algorithms or rule-based systems to adapt to different edge working environments and needs. Once an anomaly or violation is found, a dynamic monitoring report will be automatically generated, including a detailed description of the anomaly, a timestamp, and recommended countermeasures. The report can be generated and transmitted to relevant managers and operators in real time through the cloud platform's data storage and analysis services to ensure timely response and handling of problems.

[0100] Step S43: Perform dynamic detection operations based on the dynamic monitoring report of the protective fence.

[0101] In an embodiment of the present invention, advanced data analysis and processing technology of complex event processing (CEP) is used to continuously monitor the data streams from protective fence sensors and monitoring equipment. These data include key information such as fence location, voltage fluctuations, and equipment status. According to preset safety rules and indicators, the rule-based system is used to compare the difference between the current status and the expected standard, and dynamically analyze and evaluate the data collected in real time. Based on the abnormal conditions and recommended measures in the dynamic monitoring report, the corresponding dynamic detection operations are initiated. These operations include on-site inspections, equipment maintenance adjustments, and alarm system testing, aiming to immediately respond to potential safety risks and fence operation anomalies.

[0102] The beneficial effects of the present invention are to generate detailed edge protection data by integrating the protective fence status report and the safety inspection report. These data not only cover the physical status of the fence, such as installation status and material integrity, but also include the operating status and real-time data of the equipment, providing a comprehensive information basis for subsequent monitoring and analysis. This comprehensive data integration enables managers to obtain a comprehensive view of the operating status of the fence on a single platform, so as to formulate management strategies and decisions more effectively. Advanced monitoring technology is used to dynamically detect the edge protection data and generate accurate dynamic monitoring reports for protective fences. These reports can reflect the operating status and environmental changes of the fence in real time, and identify potential safety risks and abnormal situations. Through real-time data analysis and anomaly detection algorithms, operational problems of the fence, such as material aging, equipment failure or abnormal operation, can be discovered in a timely manner, providing managers with timely intervention and adjustment suggestions, which helps to reduce the risk of accidents. Finally, dynamic detection operations are performed based on the dynamic monitoring report, including on-site inspections, equipment maintenance, and safety adjustments. These operations not only help prevent the occurrence of safety accidents, but also improve operating efficiency and the overall reliability of the fence. Through timely inspection and maintenance, the long-term operation and safety of the fence facilities can be guaranteed, and operators and equipment can be ensured to operate in a safe working environment, thereby improving overall work efficiency and safety levels.

[0103] Preferably, step S42 includes the following steps:

[0104] Step S421: performing smooth short-term fluctuation analysis on the edge protection data to generate protection fence status trend data;

[0105] Step S422: predicting the future voltage change trend of the edge protection data to generate safe voltage prediction data;

[0106] Step S423: Perform Z-score anomaly detection on the safety voltage prediction data and the protective fence status trend data, and generate a protective fence dynamic monitoring report.

[0107] The present invention can accurately capture the long-term trend changes of the fence state by smoothing short-term fluctuation analysis of the edge protection data and generating the protection fence state trend data. This enables managers to timely understand the evolution of the fence operation state, so as to take preventive maintenance and management measures to ensure the long-term stable operation of the fence facilities. By predicting the future voltage change trend of the edge protection data and generating the safety voltage prediction data, the future fence voltage change trend can be predicted. This prediction capability enables managers to prepare countermeasures in advance, such as adjusting equipment parameters, increasing the inspection frequency or replacing equipment, to prevent safety problems caused by voltage anomalies. By performing Z-score anomaly detection on the safety voltage prediction data and the protection fence state trend data, a protection fence dynamic monitoring report is generated. These reports can quickly identify abnormal situations and issue warnings in time to help managers respond to and deal with potential safety risks in the shortest time. The comprehensive use of these technical means can effectively improve the safety monitoring capability of the fence, reduce the possibility of safety accidents, optimize the operating efficiency and equipment service life, and ensure that operators and equipment operate in a safe working environment.

[0108] In an embodiment of the present invention, the edge protection data is subjected to a smooth short-term fluctuation analysis. This process uses a moving average statistical method to eliminate noise and short-term fluctuations in the data, thereby obtaining more stable and reliable protection fence status trend data, and effectively capturing the long-term change trend of the fence status. The edge protection data is subjected to a prediction of future voltage change trends to generate safe voltage prediction data. This process relies on the autoregressive moving average model (ARMA) time series analysis to predict future fence voltage change trends. The prediction results provide managers with expected information about voltage trends, enabling them to take appropriate preventive maintenance measures to prevent potential voltage anomalies. Z-score anomaly detection is performed on the safe voltage prediction data and the protection fence status trend data to generate a dynamic monitoring report for the protection fence. Z-score anomaly detection is a commonly used statistical method for identifying abnormal conditions in data that significantly deviate from normal conditions. Through this process, the system can quickly and accurately detect voltage anomalies or fence status anomalies, and generate detailed dynamic monitoring reports.

[0109] The beneficial effect of the present invention is to achieve comprehensive monitoring and safety protection of the edge operation area. Through advanced video recognition technology and efficient artificial intelligence models, various situations of edge operations can be accurately identified and recorded, and detailed standardized data and real-time status reports can be generated. At the same time, a distributed voltage sensor network is used to monitor voltage fluctuations on the edge fence in real time to ensure that the voltage is maintained within a safe range, thereby effectively preventing safety risks such as electric shock. Data visualization technology uses clear and intuitive displays to make the status and safety of the protective fence clear at a glance, providing timely decision-making support for managers and operators. The application of dynamic monitoring algorithms can not only detect potential safety hazards in a timely manner, but also quickly warn and respond, greatly improving the safety and management efficiency of edge operations, effectively reducing the possibility of accidents, and comprehensively ensuring the safety of operators and equipment.

[0110] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0111] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for edge protection monitoring based on digital cloud computing, characterized in that: The following steps are involved: Step S1: Acquire edge work videos; perform edge work recognition on edge work videos to generate edge work standardized data, wherein the standardized data includes actions of operators, tool usage, and changes in the work area; perform edge work manual annotation on the edge work standardized data based on the cloud platform to generate edge work manual annotation data, wherein the manual annotation includes fence status, operator behavior, and environmental conditions; Step S2: construct a model based on the manually labeled data of the edge operation to generate an edge protection monitoring model; use the edge protection monitoring model to identify the state of the edge operation video and generate a protection fence status report; wherein step S2 includes the following steps: Step S21: constructing a model based on the manually labeled data of edge operations to generate an edge protection monitoring model; Step S22: using the edge protection monitoring model to perform status recognition on the edge operation video to generate protection fence status data, wherein the status recognition includes installation integrity, position stability and material aging degree; wherein step S22 includes the following steps: Step S221: performing installation integrity identification on the edge operation video through the edge protection monitoring model to generate edge installation integrity data; Step S222: using a fence displacement sensor to obtain edge fence position information data; using an edge protection monitoring model to perform fence displacement analysis on the edge protection fence position information data to generate edge displacement change data; Step S223: acquiring infrared equipment detection data; using the edge protection monitoring model to perform material wear assessment on the infrared equipment detection data to generate edge wear assessment data; Step S224: merging the edge installation integrity data, the edge displacement change data and the edge wear assessment data into edge status data to generate protective fence status data; Step S23: uploading the protection fence status data to the cloud platform for data storage, and generating a protection fence status report; Step S3: constructing a distributed network topology diagram of the adjacent voltage; using the distributed network topology diagram of the adjacent voltage to detect voltage fluctuations on the voltage sensor on the adjacent fence, and generating a voltage fluctuation detection value; comparing and analyzing the voltage fluctuation detection value with a preset adjacent voltage safety threshold, and generating a protective fence safety voltage report; wherein step S3 includes the following steps: Step S31: constructing a topological structure using the position information data of the adjacent fence to generate an adjacent voltage distributed network topology map; using the adjacent voltage distributed network topology map to perform voltage fluctuation detection on the voltage sensor on the adjacent fence to generate a voltage fluctuation detection value; wherein step S31 includes the following steps: Step S311: constructing a topological structure using the location information data of the adjacent fence to generate an adjacent voltage distributed network topology map; collecting voltage data based on the adjacent voltage distributed network topology map to obtain real-time voltage data of the voltage sensor; Step S312: performing position voltage association between the real-time voltage data of the voltage sensor and the position information data of the adjacent fence based on the adjacent voltage distributed network topology diagram, and generating a sensor position-voltage data pair; Step S313: using a distributed network protocol to perform real-time voltage fluctuation calculations on the sensor position-voltage data pair to generate a voltage fluctuation detection value; Step S32: Compare and analyze the voltage fluctuation detection value and the preset voltage safety threshold of the edge voltage. When the voltage fluctuation detection value is greater than the preset voltage safety threshold, generate voltage fluctuation abnormal data, start early warning measures, and generate a protective fence safety voltage report, wherein the edge early warning measures include alarm notification and automatic power-on reminder, and the protective fence safety voltage report includes the specific time of abnormal detection, the location of occurrence, and the voltage value; Step S4: Visually display the protection fence status report and the protection fence safety voltage report to generate a visual state diagram of edge protection monitoring; perform dynamic monitoring algorithm detection based on the visual state diagram of edge protection monitoring, and generate a protection fence dynamic monitoring report to perform dynamic detection operations on the protection fence; wherein step S4 includes the following steps: Step S41: reading edge protection data from the protection fence status report and the protection fence safety voltage report to generate edge protection data; Step S42: Performing dynamic detection of the edge fence on the edge protection data and generating a dynamic monitoring report of the protection fence; Step S421: performing smooth short-term fluctuation analysis on the edge protection data to generate protection fence status trend data; Step S422: predicting the future voltage change trend of the edge protection data to generate safe voltage prediction data; Step S423: Perform Z-score anomaly detection on the safety voltage prediction data and the protection fence status trend data, and generate a protection fence dynamic monitoring report; Step S43: Perform dynamic detection operations based on the dynamic monitoring report of the protective fence.

2. The edge protection monitoring method based on digital cloud computing according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: using a camera to obtain edge operation video data; Step S12: performing key frame interception processing on the edge operation video to generate an edge operation key frame image; Step S13: performing edge computing on the edge operation key frame image to generate edge computing data; Step S14: performing weak edge removal on the edge operation key frame image according to the edge edge calculation data to generate an edge weak edge removal image; performing edge operation recognition on the edge weak edge removal image to generate edge operation recognition data; Step S15: performing data standardization processing on the edge operation identification data to generate edge operation standardized data; Step S16: manually annotating the edge operation standardized data on the cloud platform to generate manually annotated edge operation data.

3. The edge protection monitoring method based on digital cloud computing according to claim 2 is characterized in that: Step S16 includes the following steps: Step S161: Upload the standardized operation data of the edge operation to the cloud platform for repeated data screening to generate preliminary edge screening data; Step S162: manually label the preliminary edge screening data to generate an edge labeling data set; Step S163: performing data consistency integration on the edge annotation data set to generate edge operation manual annotation data.

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