Intelligent monitoring method and related device for power field operation risks

Abstract

The present application provides an intelligent monitoring method and related devices for risks in power field operations, the method comprising: acquiring multiple first image data; identifying and analyzing the multiple first image data to obtain multiple risk levels; generating multiple alarm information according to the multiple risk levels; compressing the multiple first image data according to the multiple risk levels to obtain multiple second image data; analyzing the importance of the multiple second image data to obtain multiple importance levels; assigning priorities to the second image data according to the risk level and importance level to obtain multiple priorities; sorting the second image data according to the priority level to obtain a target transmission queue; transmitting, decompressing and analyzing the second image data according to the target transmission queue to obtain a target risk assessment result; and feeding back the alarm information and the target risk assessment result to the terminal device to facilitate timely response to risk events at the power operation site.

Description

Technical Field

[0001] The present application relates to the field of intelligent monitoring technology, and in particular to an intelligent monitoring method and related devices for risks in power field operations. Background Art

[0002] In the power industry, on-site safety monitoring is crucial for preventing accidents and ensuring personnel safety. Traditional video surveillance systems are often limited by network bandwidth when transmitting large amounts of data in real time, resulting in transmission delays and the inability to detect and respond to potential safety risks in a timely manner. This problem is particularly prominent in remote areas or environments with poor network conditions.

[0003] Currently, a ball control system can be deployed at the work site to capture and store video of the site, and then sample and recognize the image information of the video to analyze the area and transmit it to the edge computing module of the monitoring host and the network cloud monitoring platform. However, when faced with different network environments and a large amount of alarm information, problems such as low data transmission efficiency and delayed alarm response may occur.

[0004] Therefore, how to improve the timeliness and accuracy of safety monitoring in response to the risks of power site operations has become an urgent problem to be solved. Summary of the Invention

[0005] The embodiments of the present application provide an intelligent monitoring method and related devices for the risks of power site operations. The methods can compress the image data of the power operation site and assign priorities according to the risk level and importance to ensure that high-risk and important image data are transmitted first, thereby improving transmission efficiency and timely receiving and processing alarm information and risk assessment results to speed up response and improve the timeliness and accuracy of safety monitoring.

[0006] In a first aspect, embodiments of the present application provide an intelligent monitoring method for risks in power field operations, which is applied to an intelligent monitoring system that is communicatively connected to a cloud and a terminal device. The method includes:

[0007] Acquiring a plurality of first image data of a target electric power operation site;

[0008] Identify and analyze each of the plurality of first image data according to a preset risk level standard to obtain a plurality of risk levels;

[0009] Generate multiple warning messages according to the multiple risk levels; each risk level corresponds to one warning message;

[0010] compressing the plurality of first image data according to the plurality of risk levels to obtain a plurality of second image data;

[0011] analyzing the importance of each second image data in the plurality of second image data to obtain a plurality of importance levels;

[0012] Assigning priorities to the plurality of second image data according to the plurality of risk levels and the plurality of importance levels to obtain a plurality of priorities, wherein each second image data corresponds to a priority;

[0013] sorting the plurality of second image data according to the plurality of priorities to obtain a target transmission queue;

[0014] transmitting the plurality of second image data to the cloud according to the target transmission queue;

[0015] receiving the plurality of second image data through the cloud, and decompressing and analyzing the plurality of second image data to obtain a target risk assessment result;

[0016] The multiple alarm information and the target risk assessment results are fed back to the terminal device to facilitate timely response to risk events at the target power operation site.

[0017] In a second aspect, an embodiment of the present application provides an intelligent monitoring device for risks in power field operations, which is applied to an intelligent monitoring system. The intelligent monitoring system is communicatively connected to the cloud and terminal devices. The device includes an acquisition module, an identification module, a generation module, a compression module, an analysis module, an allocation module, a sorting module, a transmission module, and a feedback module, wherein:

[0018] The acquisition module is used to acquire a plurality of first image data of the target power operation site;

[0019] The identification module is configured to identify and analyze each of the plurality of first image data according to a preset risk level standard to obtain a plurality of risk levels;

[0020] The generating module is configured to generate a plurality of warning messages according to the plurality of risk levels; each risk level corresponds to one warning message;

[0021] The compression module is configured to compress the plurality of first image data according to the plurality of risk levels to obtain a plurality of second image data;

[0022] The analysis module is configured to analyze the importance of each second image data in the plurality of second image data to obtain a plurality of importance levels;

[0023] The allocation module is configured to allocate priorities to the plurality of second image data according to the plurality of risk levels and the plurality of importance levels, thereby obtaining a plurality of priorities, wherein each second image data corresponds to a priority;

[0024] The sorting module is configured to sort the plurality of second image data according to the plurality of priorities to obtain a target transmission queue;

[0025] The transmission module is configured to transmit the plurality of second image data to the cloud according to the target transmission queue;

[0026] The analysis module is further configured to receive the plurality of second image data via the cloud, and decompress and analyze the plurality of second image data to obtain a target risk assessment result;

[0027] The feedback module is used to feed back the multiple alarm information and the target risk assessment results to the terminal device, so as to facilitate timely response to risk events at the target power operation site.

[0028] In a third aspect, an embodiment of the present application provides an electronic device comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program comprises instructions for executing the steps of any method of the first aspect of the embodiment of the present application.

[0029] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute part or all of the steps described in any method of the first aspect of the embodiment of the present application.

[0030] In a fifth aspect, embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to execute some or all of the steps described in any method of the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0031] By implementing the embodiments of the present application, the image data of the power operation site can be compressed and assigned priorities according to the risk level and importance, ensuring that high-risk and important image data is transmitted first, thereby improving transmission efficiency, and timely receiving and processing alarm information and risk assessment results to speed up response speed and improve the timeliness and accuracy of safety monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0033] Figure 1 This is a system architecture diagram of an intelligent monitoring system provided by an embodiment of the present application;

[0034] Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0035] Figure 3 This is a flow chart of an intelligent monitoring method for power field operation risks provided by an embodiment of the present application;

[0036] Figure 4 This is a block diagram of the functional modules of an intelligent monitoring device for power field operation risks provided by an embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of this application.

[0038] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0039] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document indicates that the associated objects are in an "or" relationship. The "plurality" appearing in the embodiments of this application refers to two or more.

[0040] In the embodiments of the present application, "at least one item" or similar expressions refers to any combination of these items, including any combination of single items or plural items, and refers to one or more, and multiple refers to two or more. For example, at least one item (item) of a, b, or c can represent the following seven situations: a, b, c, a and b, a and c, b and c, a, b, and c. Among them, each of a, b, and c can be an element or a set containing one or more elements.

[0041] The "connection" appearing in the embodiments of the present application refers to various connection methods such as direct connection or indirect connection to achieve communication between devices, and the embodiments of the present application do not impose any limitations on this.

[0042] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0043] The following are the explanations of the relevant terms involved in this application:

[0044] Yolo-V7 (You Only Look Once-version 7) model: refers to an object detection model that optimizes model reparameterization, dynamic label assignment, and auxiliary head training, improving the model's detection accuracy and speed. It is widely used in computer vision tasks.

[0045] Long Short-Term Memory (LSTM) model: refers to a special type of recurrent neural network that is used to process and predict long-distance dependencies in time series data and is particularly effective for processing long sequence data.

[0046] JPEG2000 algorithm: refers to an image compression standard that uses a compression method based on wavelet transform. Compared with traditional image compression algorithms, it provides higher compression efficiency and image quality, and supports both lossless and lossy compression.

[0047] In the power industry, on-site safety monitoring is crucial for preventing accidents and ensuring personnel safety. Traditional video surveillance systems are often limited by network bandwidth when transmitting large amounts of data in real time, resulting in transmission delays and the inability to detect and respond to potential safety risks in a timely manner. This problem is particularly prominent in remote areas or environments with poor network conditions.

[0048] Currently, a surveillance system can be deployed at the work site to capture and store video footage. This system then samples and recognizes the video image information to analyze the area and transmit it to the monitoring host's edge computing module and network cloud monitoring platform. However, when faced with varying network environments and a large volume of alarm information, data transmission efficiency can be low and alarm response can be delayed. Therefore, improving the timeliness and accuracy of safety monitoring to address the risks of power site operations has become a pressing issue.

[0049] To solve the above problems, the embodiment of the present application provides an intelligent monitoring method and related devices for the risks of power site operations, which are applied to an intelligent monitoring system, wherein the intelligent monitoring system is connected to the cloud and terminal devices for communication; a plurality of first image data of the target power operation site are obtained; each of the plurality of first image data is identified and analyzed according to a preset risk level standard to obtain a plurality of risk levels; a plurality of alarm messages are generated according to the plurality of risk levels; each risk level corresponds to an alarm message; the plurality of first image data are compressed according to the plurality of risk levels to obtain a plurality of second image data; the importance of each of the plurality of second image data is analyzed; The system analyzes the plurality of second image data to obtain multiple levels of importance; assigns priorities to the plurality of second image data according to the plurality of risk levels and the plurality of levels of importance to obtain multiple priorities; each second image data corresponds to a priority; sorts the plurality of second image data according to the plurality of priorities to obtain a target transmission queue; transmits the plurality of second image data to the cloud according to the target transmission queue; receives the plurality of second image data via the cloud, decompresses and analyzes the plurality of second image data to obtain a target risk assessment result; and feeds back the plurality of alarm information and the target risk assessment result to the terminal device to facilitate timely response to risk events at the target power operation site. By compressing the image data at the power operation site and assigning priorities according to risk levels and levels of importance, it is possible to ensure that high-risk and important image data is transmitted first, thereby improving transmission efficiency, and timely receiving and processing alarm information and risk assessment results to accelerate response speed, thereby improving the timeliness and accuracy of safety monitoring.

[0050] The following combination Figure 1 The system architecture of an intelligent monitoring method for power field operation risks in an embodiment of the present application is described. Figure 1 This is a system architecture diagram of an intelligent monitoring system provided in an embodiment of the present application. The intelligent monitoring system includes multiple control balls and a control center, and is connected to the cloud and terminal devices through the control center.

[0051] In one possible embodiment, the control center selects the number and installation angles of surveillance cameras based on the needs of the power operation site to ensure coverage of every critical area. These critical areas include, but are not limited to, high-voltage equipment areas, personnel operating areas, and potentially hazardous areas, though these are not specifically defined here. Each surveillance camera can then be configured with a specific viewing angle and perspective to maximize its field of view. Furthermore, each surveillance camera is equipped with a high-resolution camera capable of capturing clear image data, ensuring clear image details and facilitating subsequent analysis and evaluation. This allows the surveillance cameras to capture real-time image data from the power operation site, including both static and dynamic scenes, to ensure that information from critical areas is fully captured. The control center then performs a preliminary assessment of the image data based on pre-set risk assessment criteria to generate a risk assessment result. Based on the risk assessment results, the image data is compressed, with data in high-risk areas being compressed more efficiently to preserve more detail, while data in low-risk areas is compressed more efficiently, thereby optimizing storage and transmission efficiency. The compressed image data is then transmitted to the cloud by the control center for further decompression and analysis, resulting in the risk assessment result and real-time alert information. The risk assessment results and real-time alarm information are fed back to the user's terminal device so that operators or managers can take prompt action to ensure the safety of the power operation site.

[0052] It can be seen that through the above system architecture, efficient and comprehensive safety monitoring of power operation sites can be achieved, and potential risks can be responded to in a timely manner, providing strong protection for the safety of power operations.

[0053] The following combination Figure 2 The electronic device in the embodiment of the present application is described. Figure 2 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the electronic device includes one or more processors, a memory, a communication interface and one or more programs, and the processor is communicatively connected with the memory and the communication interface via an internal communication bus.

[0054] Among them, the processor is mainly used for:

[0055] Acquiring a plurality of first image data of a target electric power operation site;

[0056] Identify and analyze each of the plurality of first image data according to a preset risk level standard to obtain a plurality of risk levels;

[0057] Generate multiple warning messages based on multiple risk levels; each risk level corresponds to one warning message;

[0058] compressing the plurality of first image data according to the plurality of risk levels to obtain a plurality of second image data;

[0059] analyzing the importance of each second image data in the plurality of second image data to obtain a plurality of importance levels;

[0060] assigning priorities to the plurality of second image data according to the plurality of risk levels and the plurality of importance levels, thereby obtaining a plurality of priorities; each second image data corresponds to a priority;

[0061] sorting the plurality of second image data according to a plurality of priorities to obtain a target transmission queue;

[0062] transmitting the plurality of second image data to the cloud according to the target transmission queue;

[0063] receiving a plurality of second image data through the cloud, and decompressing and analyzing the plurality of second image data to obtain a target risk assessment result;

[0064] Feedback of multiple alarm information and target risk assessment results to terminal devices facilitates timely response to risk events at the target power operation site.

[0065] The one or more programs are stored in the above-mentioned memory and are configured to be executed by the above-mentioned processor, and the one or more programs include instructions for executing any step in the above-mentioned method embodiment.

[0066] Among them, the processor can be, for example, a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute the various exemplary logic blocks, units and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like. The communication unit can be a communication interface, a transceiver, a transceiver circuit, etc., and the storage unit can be a memory.

[0067] The memory may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Among them, the non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DR RAM).

[0068] It is understood that the electronic device may include more or fewer structural elements than those in the above structural block diagram, for example, including a power module, physical buttons, Wi-Fi module, speaker, Bluetooth module, sensor, display module, etc., which are not limited here. It is understood that the electronic device may be equipped with Figure 1 The system architecture described.

[0069] After understanding the software and hardware architecture of this application, Figure 3 An intelligent monitoring method for power field operation risks in an embodiment of the present application is described. Figure 3 A flowchart of an intelligent monitoring method for power field operation risks provided in an embodiment of the present application is applied to an intelligent monitoring system that is in communication with the cloud and terminal devices, specifically including the following steps:

[0070] Step S301: Acquire a plurality of first image data of a target power operation site.

[0071] Specifically, the intelligent monitoring system also includes multiple surveillance cameras deployed at different locations at the target power operation site to form a multi-perspective, high-density monitoring network, ensuring that the cameras of the surveillance cameras provide comprehensive, no-blind-angle coverage of the target power operation site. Each surveillance camera is equipped with a high-definition camera, capable of capturing image data of the target power operation site. When the multiple surveillance cameras capture images of the target power operation site, multiple first image data sets are generated, each corresponding to a surveillance camera.

[0072] Step S302 : identifying and analyzing each of the plurality of first image data according to a preset risk level standard to obtain a plurality of risk levels.

[0073] Among them, a preset risk factor library and an assessment weight group are obtained; the risk factor library includes multiple risk factors; the assessment weight group includes multiple assessment weights; each risk factor corresponds to an assessment weight; the risk factor set corresponding to the reference image data is determined according to the risk factor library to obtain a reference risk factor set; the reference image data is any one of the multiple first image data; according to the mapping relationship between the preset risk factors and the risk initial values, the risk initial value corresponding to each reference risk factor in the reference risk factor set is determined to obtain a reference risk initial value group; the reference risk initial value group is weightedly summed according to the assessment weight group to obtain a reference risk assessment value; the risk level corresponding to the reference risk assessment value is determined according to the risk level standard to obtain the risk level of the reference image data.

[0074] Specifically, the preset risk factor library includes multiple risk factors, including but not limited to human error, human safety, and environmental anomalies, which are not specifically limited here. Human error includes unauthorized equipment operation, violation of safety operating procedures, and improper use of tools or equipment; human safety includes not wearing a safety helmet, work clothes, or safety rope; and environmental anomalies include fire, smoke, and oil leaks. The assessment weighting set includes multiple assessment weights, with each risk factor corresponding to one assessment weight. For example, the assessment weight corresponding to human error is 0.3, the assessment weight corresponding to human safety is 0.3, and the assessment weight corresponding to environmental anomalies is 0.4, which are not specifically limited here. Any one of the multiple first image data sets is determined as reference image data, and then the risk factor set corresponding to the reference image data is determined based on the risk factor library to obtain a reference risk factor set. Based on the mapping relationship between the preset risk factors and initial risk values, the initial risk value corresponding to each reference risk factor in the reference risk factor set is determined to obtain a reference risk initial value set. The reference risk initial value set is weighted and summed according to the assessment weighting set to obtain a reference risk assessment value. The risk level corresponding to the reference risk assessment value is determined based on the risk level standard to obtain the risk level of the reference image data. Pre-set risk level standards include low risk, medium risk, and high risk. Furthermore, a quantitative assessment of the power field operation environment can be performed using methods such as fuzzy comprehensive evaluation in combination with the risk level standard, which is not specifically limited here.

[0075] The detection target corresponding to the reference image data includes at least one of the following: personnel, equipment, and environment. The risk factor set corresponding to the reference image data is determined according to the risk factor library to obtain the reference risk factor set. Specifically, the steps include:

[0076] When the reference image data is a static scene, and the detection target corresponding to the reference image data includes at least one of the following: a person, a device, and an environment, the reference image data is detected according to a preset first model to determine a first risk factor corresponding to the detection target and the risk factor library; and the reference risk factor set is determined based on the first risk factor;

[0077] or,

[0078] When the reference image data is a dynamic scene, and the detection targets corresponding to the reference image data include not only personnel but also equipment and / or environment, the steps of detecting the reference image data according to a preset first model to determine the first risk factor corresponding to the detection target and the risk factor library are performed; analyzing the personnel in the adjacent image data of the specified time period corresponding to the reference image data according to a preset second model to obtain an action sequence, and identifying the action sequence to obtain the second risk factor corresponding to the action sequence and the risk factor library; and determining the reference risk factor set based on the first risk factor and the second risk factor.

[0079] It should be noted that the preset first model can be a Yolo-V7 model, and the preset second model can be an LSTM model, which are not specifically limited here. Among them, the Yolo-V7 model is used to identify people, equipment, and environments in images, providing preliminary identification of static risk factors, and the LSTM model is used to analyze people's movements and behaviors in time series. It can capture people's dynamic behavior patterns and facilitate the identification of behavioral risks such as unauthorized operation of equipment or incorrect use of tools, thereby compensating for the shortcomings of static detection alone.

[0080] In one possible embodiment, the reference image data is a static scene, that is, the scene in the reference image data is fixed and does not change dynamically. The Yolo-V7 model can be used to detect the personnel, equipment, and environment in the reference image data to determine whether the personnel, equipment, and environment match the first risk factor in the risk factor library, for example, a person not wearing a helmet, a fire in the environment, etc., and the matching first risk factors are integrated into a reference risk factor set.

[0081] In one possible embodiment, the reference image data is a dynamic scene, that is, the scene in the reference image data changes over time. The Yolo-V7 model can be used to detect the personnel, equipment, and environment in the reference image data to obtain a first risk factor, and the LSTM model can be used to further detect the personnel in the reference image data. The personnel in the adjacent image data of the specified time period corresponding to the reference image data can be analyzed to obtain an action sequence. Then, the action sequence is identified to obtain a second risk factor corresponding to the action sequence and the risk factor library, such as unauthorized operation of equipment by personnel, incorrect use of tools or equipment by personnel, etc., and the first risk factor and the second risk factor are integrated into a reference risk factor set.

[0082] It can be seen that by real-time analysis and detection of risk factors in static and dynamic scenarios, a more comprehensive and accurate identification of risk factors in power operation sites can be achieved, thereby enhancing the overall safety and reliability of the system.

[0083] Step S303: Generate multiple warning messages according to the multiple risk levels; each risk level corresponds to one warning message.

[0084] Specifically, the alarm information includes but is not limited to risk level, risk description, occurrence time, specific location, preliminary suggestions, response time, and responsible department, which are not specifically limited here.

[0085] Step S304 : compressing the plurality of first image data according to the plurality of risk levels to obtain a plurality of second image data.

[0086] Among them, according to the mapping relationship between the preset risk level and the compression parameter, the reference compression parameter corresponding to the reference risk level is determined; the reference compression parameter includes a reference quantization step and a reference quantization value; wherein, the higher the risk level, the smaller the quantization step and the lower the quantization value; according to the reference quantization step and the reference quantization value, the reference image data is compressed to obtain reference second image data; the reference second image data is the second image data corresponding to the reference first image data among the multiple second image data.

[0087] It should be noted that a higher risk level requires greater detail in the image data, necessitating a smaller quantization step size and lower quantization value to ensure higher image quality. The quantization step size represents the precision of quantization during the data compression process; smaller step sizes result in higher quantization precision. The quantization value is the calibration value used when quantizing image data. Lower values ​​indicate less data loss during quantization, resulting in higher image fidelity.

[0088] In one possible embodiment, image data is efficiently compressed using a preset image compression algorithm. This significantly reduces the amount of image data transmitted while maintaining image quality, thereby lowering bandwidth usage and improving data transmission efficiency. The image compression algorithm may be the JPEG2000 algorithm, which is not specifically limited herein. The image data compression process may include encoding through steps such as preprocessing, discrete wavelet transform, quantization, entropy coding, and bitstream organization. Among them, in the preprocessing step, various preprocessing operations can be performed on the original image data, such as color space conversion, denoising, etc., to improve the compression efficiency; in the discrete wavelet transform step, the image data is decomposed into wavelet coefficients at different resolution levels, and the wavelet coefficients contain different detail information of the image data; in the quantization step, the wavelet coefficients generated by the discrete wavelet transform are quantized to reduce the amount of data and realize the compression of the image data; in the entropy coding step, the quantized wavelet coefficients are entropy coded to further reduce the amount of data; in the code stream organization step, the encoded data is organized into a code stream for easy storage or transmission, and the code stream organization step also includes operations such as code stream segmentation and packet header information addition to improve transmission efficiency and error recovery capability.

[0089] It should be noted that in the quantization step, the quantization step size or quantization table can be dynamically adjusted according to the risk level to control the degree of reduction in image data accuracy. Among them, a smaller step size is used in high-risk areas to retain more details; a larger step size is used in low-risk areas to improve the compression ratio. During the quantization process, the corresponding quantization step size is applied according to the risk level of the area. A quantization table suitable for different areas is generated according to the importance of the image data, and a lower quantization value is used for important areas to retain higher image quality; a higher quantization value is used for unimportant areas to increase the compression effect. During the quantization process, the quantization values ​​in the quantization table are updated to ensure appropriate quantization of different areas. Among them, the parameters of the quantization step can also be adjusted according to the preset adjustment rules to ensure that the amount of data is reduced as much as possible while maintaining key information.

[0090] It can be seen that by analyzing the risk level of image data, the compression strategy of image data can be adjusted efficiently and dynamically to balance image quality and compression ratio, thereby improving data transmission efficiency without affecting the image effect.

[0091] Step S305 : analyzing the importance of each second image data in the plurality of second image data to obtain a plurality of importance levels.

[0092] Specifically, the following steps may be performed for each second image data: features of the second image data may be extracted using a preset computer vision technique to obtain edge information, texture information, and a color histogram of the second image data. Computer vision techniques include, but are not limited to, edge detection, texture analysis, and color histograms, which are not specifically limited herein. A comprehensive score is then assigned to the edge information, texture information, and color histogram according to a preset scoring criteria to obtain a target score. A target importance corresponding to the target score is then determined based on a preset mapping relationship between the score and importance.

[0093] Step S306 : assigning priorities to the plurality of second image data according to the plurality of risk levels and the plurality of importance levels to obtain a plurality of priorities.

[0094] Each second image data corresponds to a priority, the priority including an integer part and a decimal part, and the steps of assigning priorities to the plurality of second image data according to the plurality of risk levels and the plurality of importance levels to obtain a plurality of priorities include:

[0095] Allocate the integer part of the priority to the multiple second image data according to the multiple risk levels to obtain the integer part of the multiple priorities; the higher the risk level, the greater the value of the integer part of the priority; allocate the decimal part of the priority to the multiple second image data according to the multiple importance levels to obtain the decimal part of the multiple priorities; the higher the importance, the greater the value of the decimal part of the priority; integrate the integer part of the multiple priorities and the decimal part of the multiple priorities to obtain the multiple priorities.

[0096] Specifically, an integer portion of the priority is assigned to each second image data according to its risk level. For example, if the risk level of image data A is high, the integer portion of its priority can be 6.0, and if the risk level of image data B is low, the integer portion of its priority can be 2.0. There is no specific limitation here, and the higher the risk level, the larger the integer portion of the priority. A decimal portion of the priority is assigned to each second image data according to its importance. For example, if the importance of image data A is low, the decimal portion of its priority can be 0.2, and if the importance of image data B is high, the integer portion of its priority can be 0.6. There is no specific limitation here, and the higher the importance, the larger the decimal portion of the priority. The integer and decimal portions of the priority are integrated to obtain the final priority of each second image data. For example, the final priority of image data A is 6.2, and the final priority of image data B is 2.6. There is no specific limitation here.

[0097] It can be seen that assigning priorities according to risk level and importance can ensure that higher-risk and important data are processed first, thereby improving overall processing efficiency and focusing more resources on more critical data to avoid waste of resources.

[0098] Step S307 : sorting the plurality of second image data according to the plurality of priorities to obtain a target transmission queue.

[0099] Specifically, the plurality of second image data are sorted according to their corresponding priorities, with the image data with higher priorities being placed in front. Then, the target transmission queue is determined based on the sorted second image data to ensure that the image data with higher priorities is transmitted first.

[0100] In one possible embodiment, the current network status is obtained; the current network status includes any one of the following: a first-level congestion status, a second-level congestion status, and a smooth status; if the current network status is the first-level congestion status, the multiple second image data are transmitted to the cloud at a preset first transmission rate; if the current network status is the second-level congestion status, the transmission of low-priority second image data among the multiple second image data is suspended according to a preset time interval, and / or the resolution of high-priority second image data among the multiple second image data is reduced according to a preset ratio; if the current network status is the smooth status, the multiple second image data are transmitted to the cloud at a preset second transmission rate; the first transmission rate is less than the second transmission rate.

[0101] Specifically, the network status can be obtained during the transmission process. When the network status is a level 1 congestion state, all the second image data are transmitted to the cloud at a preset first transmission rate. The first transmission rate is relatively low and can adapt to the level 1 congestion condition of the network. When the network status is a level 2 congestion state, the transmission of the low-priority second image data can be suspended according to a preset time interval, or the resolution or frame rate of the high-priority second image data can be reduced according to a preset ratio to reduce the amount of transmitted data and adapt to the level 2 congestion condition of the network. When the network status is a smooth state, all the second image data are transmitted to the cloud at a preset second transmission rate, wherein the second transmission rate is relatively high and suitable for a smooth network.

[0102] As can be seen, by adjusting the transmission rate and image data resolution, we can better adapt to network conditions and avoid transmission delays caused by congestion. When the network is congested, pausing the transmission of low-priority data or reducing the resolution of high-priority data can facilitate the rational use of network bandwidth and reduce the risk of data loss.

[0103] In one possible embodiment, the effectiveness of transmission strategies can be evaluated and system performance optimized in different network environments by tracking and analyzing key metrics during the transmission process. Key metrics include, but are not limited to, success rate, latency, bandwidth usage, and packet loss rate, which are not specifically defined here. Machine learning algorithms can also be used to analyze historical data and real-time feedback to optimize transmission strategies. By predicting network trends and automatically adjusting transmission parameters, risk identification and importance assessment models can be gradually optimized, thereby improving the accuracy and efficiency of the overall system.

[0104] It can be seen that through real-time feedback and adaptive optimization, more efficient transmission and more accurate risk identification can be achieved.

[0105] Step S308 : transmitting the plurality of second image data to the cloud according to the target transmission queue.

[0106] Specifically, according to the target transmission queue, the second image data is transmitted to the cloud in queue order, ensuring that important or urgent image data is uploaded first, and optimizing resource allocation and efficiency during the transmission process.

[0107] Step S309: receiving the plurality of second image data through the cloud, and decompressing and analyzing the plurality of second image data to obtain a target risk assessment result.

[0108] Among them, the multiple second image data are decompressed to obtain multiple third image data; the risk level corresponding to each third image data in the multiple third image data is determined to obtain multiple target risk levels; the multiple target risk levels are analyzed to obtain multiple response measures suggestions; each target risk level corresponds to a response measure suggestion; the target risk assessment result is determined based on the multiple target risk levels and the multiple response measure suggestions.

[0109] Specifically, the second image data is decompressed, that is, the compressed image is restored to its original state for further analysis. Then, the risk level of each image data can be evaluated again to obtain its corresponding target risk level. Each target risk level is analyzed to identify the response measures required for different levels. For example, image data A shows that device A shows signs of overheating, which corresponds to a high risk level. The corresponding response measures are "it is recommended to check the equipment immediately, take cooling measures, and arrange emergency repairs." Image data B shows that device B shows obvious wear and tear, which corresponds to a medium risk level. The corresponding response measures are "it is recommended to arrange regular inspections and plan to replace worn parts within the next maintenance cycle." The target risk assessment result is determined based on the target risk level and the response measures. For example, the target risk assessment result of image data A is "high risk level" and "it is recommended to check the equipment immediately, take cooling measures, and arrange emergency repairs."

[0110] It can be seen that by systematically combining risk levels with response measures, the efficiency and accuracy of risk management can be improved.

[0111] Step S310: Feedback the multiple alarm information and the target risk assessment results to the terminal device to facilitate timely response to risk events at the target power operation site.

[0112] Specifically, real-time alarm information is transmitted to the user's terminal devices, such as mobile phones, tablets, and computers, through various channels, such as sound, pop-up windows, and text messages. This ensures that users receive alarm information in the shortest possible time and understand its severity and specific content, so that they can respond promptly to risk events at the target power operation site. At the same time, the latest risk assessment report is automatically generated and fed back to the terminal device regularly or when major changes occur. The risk assessment report includes but is not limited to the analysis results of each alarm, the overall risk assessment, and response recommendations. Historical data can be archived to facilitate user query of historical alarm records, risk assessment results, and detailed information on treatment measures. Filter queries can be performed by time range, alarm type, device ID, etc. By analyzing historical data, potential safety trends and issues can be identified, allowing preventive measures to be taken in advance, such as optimizing equipment maintenance plans and adjusting monitoring strategies.

[0113] It can be seen that through effective analysis and processing of data, on-site risks can be managed efficiently, emergencies can be responded to quickly, and long-term trends can be analyzed, thereby improving the overall safety management level.

[0114] The above mainly introduces the solution of the embodiment of the present application from the perspective of the execution process of the method side. It is understandable that, in order to realize the above functions, the electronic device includes a hardware structure and / or software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiment provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0115] The embodiment of the present application can divide the functional units of the electronic device according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0116] In the case of dividing each functional module into corresponding functional modules, Figure 4 This is a functional module block diagram of an intelligent monitoring device for power field operation risks provided in an embodiment of the present application, which is applied to an intelligent monitoring system. The intelligent monitoring system is connected to the cloud and terminal devices for communication. The intelligent monitoring device 400 for power field operation risks includes an acquisition module 410, an identification module 420, a generation module 430, a compression module 440, an analysis module 450, an allocation module 460, a sorting module 470, a transmission module 480, and a feedback module 490, wherein:

[0117] The acquisition module 410 is used to acquire a plurality of first image data of a target power operation site;

[0118] The identification module 420 is configured to identify and analyze each of the plurality of first image data according to a preset risk level standard to obtain a plurality of risk levels;

[0119] The generating module 430 is configured to generate a plurality of warning messages according to the plurality of risk levels; each risk level corresponds to one warning message;

[0120] The compression module 440 is configured to compress the plurality of first image data according to the plurality of risk levels to obtain a plurality of second image data;

[0121] The analysis module 450 is configured to analyze the importance of each second image data in the plurality of second image data to obtain a plurality of importance levels;

[0122] The allocation module 460 is configured to allocate priorities to the plurality of second image data according to the plurality of risk levels and the plurality of importance levels, thereby obtaining a plurality of priorities; each second image data corresponds to a priority;

[0123] The sorting module 470 is configured to sort the plurality of second image data according to the plurality of priorities to obtain a target transmission queue;

[0124] The transmission module 480 is configured to transmit the plurality of second image data to the cloud according to the target transmission queue;

[0125] The analysis module 450 is further configured to receive the plurality of second image data through the cloud, and decompress and analyze the plurality of second image data to obtain a target risk assessment result;

[0126] The feedback module 490 is configured to feed back the multiple alarm information and the target risk assessment result to the terminal device, so as to facilitate timely response to risk events at the target power operation site.

[0127] Optionally, in the aspect of performing identification and analysis on each of the plurality of first image data according to a preset risk level standard to obtain a plurality of risk levels, the identification module 420 is specifically configured to:

[0128] Obtain a preset risk factor library and an assessment weight group; the risk factor library includes multiple risk factors; the assessment weight group includes multiple assessment weights; each risk factor corresponds to an assessment weight;

[0129] Determine a risk factor set corresponding to the reference image data according to the risk factor library to obtain a reference risk factor set; the reference image data is any one of the multiple first image data;

[0130] Determining the initial risk value corresponding to each reference risk factor in the reference risk factor set according to a mapping relationship between preset risk factors and initial risk values, to obtain a reference risk initial value group;

[0131] Performing weighted summation on the reference risk initial value group according to the assessment weight group to obtain a reference risk assessment value;

[0132] The risk level corresponding to the reference risk assessment value is determined according to the risk level standard to obtain the risk level of the reference image data.

[0133] Optionally, the detection target corresponding to the reference image data includes at least one of the following: a person, a device, and an environment. In determining the risk factor set corresponding to the reference image data according to the risk factor library to obtain the reference risk factor set, the identification module 420 is further specifically configured to:

[0134] When the reference image data is a static scene, and the detection target corresponding to the reference image data includes at least one of the following: a person, a device, and an environment, the reference image data is detected according to a preset first model to determine the first risk factor corresponding to the detection target and the risk factor library;

[0135] determining the reference risk factor set according to the first risk factor;

[0136] or,

[0137] When the reference image data is a dynamic scene, and when the detection target corresponding to the reference image data includes not only a person but also a device and / or an environment, performing the step of detecting the reference image data according to a preset first model to determine the first risk factor corresponding to the detection target and the risk factor library;

[0138] Analyzing the persons in the adjacent image data of the specified time period corresponding to the reference image data according to a preset second model to obtain an action sequence, and identifying the action sequence to obtain a second risk factor corresponding to the action sequence and the risk factor library;

[0139] The reference risk factor set is determined according to the first risk factor and the second risk factor.

[0140] Optionally, in the aspect of compressing the plurality of first image data according to the plurality of risk levels to obtain the plurality of second image data, the compression module 440 is specifically configured to:

[0141] Determining a reference compression parameter corresponding to the reference risk level according to a preset mapping relationship between the risk level and the compression parameter; the reference compression parameter includes a reference quantization step size and a reference quantization value; wherein, the higher the risk level, the smaller the quantization step size and the lower the quantization value;

[0142] The reference image data is compressed according to a reference quantization step size and a reference quantization value to obtain reference second image data; the reference second image data is second image data corresponding to the reference first image data among the multiple second image data.

[0143] Optionally, the priority includes an integer part and a decimal part. In assigning priorities to the plurality of second image data according to the plurality of risk levels and the plurality of importance levels to obtain the plurality of priorities, the assigning module 460 is specifically configured to:

[0144] assigning integer parts of priorities to the plurality of second image data according to the plurality of risk levels to obtain integer parts of the plurality of priorities; the higher the risk level, the greater the value of the integer part of the priority;

[0145] assigning decimal parts of priorities to the plurality of second image data according to the plurality of importance levels to obtain decimal parts of the plurality of priorities; the smaller the importance level, the greater the value of the decimal part of the priority level;

[0146] The multiple priorities are obtained by integrating the integer parts of the multiple priorities and the decimal parts of the multiple priorities.

[0147] Optionally, the transmission module 480 is further configured to:

[0148] Obtaining the current network status; the current network status includes any one of the following: first-level congestion state, second-level congestion state, and unblocked state;

[0149] If the current network state is the first-level congestion state, transmitting the plurality of second image data to the cloud at a preset first transmission rate;

[0150] If the current network state is the second-level congestion state, suspending transmission of low-priority second image data among the plurality of second image data according to a preset time interval, and / or reducing resolution of high-priority second image data among the plurality of second image data according to a preset ratio;

[0151] If the current network state is the unblocked state, the plurality of second image data are transmitted to the cloud at a preset second transmission rate; the first transmission rate is lower than the second transmission rate.

[0152] Optionally, in the aspect of receiving the plurality of second image data through the cloud, decompressing and analyzing the plurality of second image data, and obtaining the target risk assessment result, the analysis module 450 is further specifically configured to:

[0153] Decompressing the plurality of second image data to obtain a plurality of third image data;

[0154] determining a risk level corresponding to each of the plurality of third image data to obtain a plurality of target risk levels;

[0155] Analyze the multiple target risk levels to obtain multiple response measures suggestions; each target risk level corresponds to a response measure suggestion;

[0156] The target risk assessment result is determined according to the multiple target risk levels and the multiple response measure suggestions.

[0157] It can be seen that the comprehensive use of risk assessment, data compression, priority allocation and cloud analysis has improved the response speed and processing capabilities of the overall system, making the monitoring and management of power operation sites more efficient.

[0158] It should be noted that the specific implementation of each operation can adopt the corresponding description of the method embodiment shown above. The intelligent monitoring device 400 for the risks of power field operations can be used to execute the above method embodiment of this application, which will not be repeated here.

[0159] An embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0160] The present application also provides a computer program product comprising a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may comprise an electronic device.

[0161] It should be noted that, for the above-mentioned various embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations. Those skilled in the art should know that this application is not limited by the order of the actions described, because some steps in the embodiments of the present application can be performed in other orders or simultaneously. In addition, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions, steps, modules or units involved are not necessarily required by the embodiments of the present application.

[0162] In the above embodiments, the embodiments of the present application have different focuses on the description of each embodiment. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0163] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0164] The steps of the method or algorithm described in the embodiments of the present application can be implemented in hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, which can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and storage medium can be located in an ASIC. In addition, the ASIC can be located in a terminal device or a management device. Of course, the processor and storage medium can also be present in a terminal device or a management device as discrete components.

[0165] Those skilled in the art will appreciate that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0166] The modules / units included in the devices and products described in the above embodiments may be software modules / units, hardware modules / units, or partly software modules / units and partly hardware modules / units. For example, for the devices and products applied to or integrated in the chip, the modules / units included therein may all be implemented in the form of hardware such as circuits, or at least part of the modules / units may be implemented in the form of software programs, which run on the processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits; for the devices and products applied to or integrated in the chip module, the modules / units included therein may all be implemented in the form of hardware such as circuits, and different modules / units may be located in the same component (such as chip, circuit module, etc.) or different components of the chip module, or at least part of the modules / units may be It is implemented in the form of a software program, which runs on the processor integrated inside the chip module, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits; for various devices and products applied to or integrated in the terminal equipment, the various modules / units contained therein can be implemented in the form of hardware such as circuits, and different modules / units can be located in the same component (for example, chip, circuit module, etc.) or different components in the terminal equipment, or, at least some modules / units can be implemented in the form of a software program, which runs on the processor integrated inside the terminal equipment, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits.

[0167] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the embodiments of the present application. It should be understood that the above description is only a specific implementation method of the embodiments of the present application and is not intended to limit the scope of protection of the embodiments of the present application. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the scope of protection of the embodiments of the present application.

Claims

1. An intelligent monitoring method for power field operation risks, characterized in that: Applied to an intelligent monitoring system, the intelligent monitoring system is connected to the cloud and terminal devices in communication, and the method includes: Acquiring a plurality of first image data of a target electric power operation site; Identify and analyze each of the plurality of first image data according to a preset risk level standard to obtain a plurality of risk levels; Generate multiple warning messages according to the multiple risk levels; each risk level corresponds to one warning message; compressing the plurality of first image data according to the plurality of risk levels to obtain a plurality of second image data; analyzing the importance of each second image data in the plurality of second image data to obtain a plurality of importance levels; Assigning priorities to the plurality of second image data according to the plurality of risk levels and the plurality of importance levels to obtain a plurality of priorities, wherein each second image data corresponds to a priority; sorting the plurality of second image data according to the plurality of priorities to obtain a target transmission queue; transmitting the plurality of second image data to the cloud according to the target transmission queue; receiving the plurality of second image data through the cloud, and decompressing and analyzing the plurality of second image data to obtain a target risk assessment result; The multiple alarm information and the target risk assessment results are fed back to the terminal device to facilitate timely response to risk events at the target power operation site.

2. The method according to claim 1, wherein The identifying and analyzing each of the plurality of first image data according to a preset risk level standard to obtain a plurality of risk levels includes: Obtain a preset risk factor library and an assessment weight group; the risk factor library includes multiple risk factors; the assessment weight group includes multiple assessment weights; each risk factor corresponds to an assessment weight; Determine a risk factor set corresponding to the reference image data according to the risk factor library to obtain a reference risk factor set; the reference image data is any one of the multiple first image data; Determining the initial risk value corresponding to each reference risk factor in the reference risk factor set according to a mapping relationship between preset risk factors and initial risk values, to obtain a reference risk initial value group; Performing weighted summation on the reference risk initial value group according to the assessment weight group to obtain a reference risk assessment value; The risk level corresponding to the reference risk assessment value is determined according to the risk level standard to obtain the risk level of the reference image data.

3. The method according to claim 2, wherein The detection target corresponding to the reference image data includes at least one of the following: personnel, equipment, and environment. The risk factor set corresponding to the reference image data is determined according to the risk factor library to obtain the reference risk factor set, including: When the reference image data is a static scene, and the detection target corresponding to the reference image data includes at least one of the following: a person, a device, and an environment, the reference image data is detected according to a preset first model to determine the first risk factor corresponding to the detection target and the risk factor library; determining the reference risk factor set according to the first risk factor; or, When the reference image data is a dynamic scene, and when the detection target corresponding to the reference image data includes not only a person but also a device and / or an environment, performing the step of detecting the reference image data according to a preset first model to determine the first risk factor corresponding to the detection target and the risk factor library; Analyzing the persons in the adjacent image data of the specified time period corresponding to the reference image data according to a preset second model to obtain an action sequence, and identifying the action sequence to obtain a second risk factor corresponding to the action sequence and the risk factor library; The reference risk factor set is determined according to the first risk factor and the second risk factor.

4. The method according to claim 2 or 3, wherein: The compressing the plurality of first image data according to the plurality of risk levels to obtain the plurality of second image data includes: Determining a reference compression parameter corresponding to a reference risk level based on a preset mapping relationship between risk levels and compression parameters; the reference compression parameter includes a reference quantization step size and a reference quantization value; wherein, the higher the risk level, the smaller the quantization step size and the lower the quantization value; The reference image data is compressed according to a reference quantization step size and a reference quantization value to obtain reference second image data; the reference second image data is second image data corresponding to the reference image data among the plurality of second image data.

5. The method according to claim 1, wherein The priority includes an integer part and a decimal part, and the priorities are assigned to the plurality of second image data according to the plurality of risk levels and the plurality of importance levels to obtain a plurality of priorities, including: assigning integer parts of priorities to the plurality of second image data according to the plurality of risk levels to obtain integer parts of the plurality of priorities; the higher the risk level, the greater the value of the integer part of the priority; assigning decimal parts of priorities to the plurality of second image data according to the plurality of importance levels to obtain decimal parts of the plurality of priorities; the higher the importance level, the greater the value of the decimal part of the priority level; The multiple priorities are obtained by integrating the integer parts of the multiple priorities and the decimal parts of the multiple priorities.

6. The method according to claim 1, wherein The method further comprises: Obtaining the current network status; the current network status includes any one of the following: first-level congestion state, second-level congestion state, and unblocked state; If the current network state is the first-level congestion state, transmitting the plurality of second image data to the cloud at a preset first transmission rate; If the current network state is the second-level congestion state, suspending transmission of low-priority second image data among the plurality of second image data according to a preset time interval, and / or reducing resolution of high-priority second image data among the plurality of second image data according to a preset ratio; If the current network state is the unblocked state, the plurality of second image data are transmitted to the cloud at a preset second transmission rate; the first transmission rate is lower than the second transmission rate.

7. The method according to claim 1, wherein The receiving the plurality of second image data through the cloud, and decompressing and analyzing the plurality of second image data to obtain a target risk assessment result, includes: Decompressing the plurality of second image data to obtain a plurality of third image data; determining a risk level corresponding to each of the plurality of third image data to obtain a plurality of target risk levels; Analyze the multiple target risk levels to obtain multiple response measures suggestions; each target risk level corresponds to a response measure suggestion; The target risk assessment result is determined according to the multiple target risk levels and the multiple response measure suggestions.

8. An intelligent monitoring device for risks in power field operations, characterized in that: Applied to an intelligent monitoring system, the intelligent monitoring system is connected to the cloud and terminal devices in communication, the device includes an acquisition module, an identification module, a generation module, a compression module, an analysis module, an allocation module, a sorting module, a transmission module and a feedback module, wherein: The acquisition module is used to acquire a plurality of first image data of the target power operation site; The identification module is configured to identify and analyze each of the plurality of first image data according to a preset risk level standard to obtain a plurality of risk levels; The generating module is configured to generate a plurality of warning messages according to the plurality of risk levels; each risk level corresponds to one warning message; The compression module is configured to compress the plurality of first image data according to the plurality of risk levels to obtain a plurality of second image data; The analysis module is configured to analyze the importance of each second image data in the plurality of second image data to obtain a plurality of importance levels; The allocation module is configured to allocate priorities to the plurality of second image data according to the plurality of risk levels and the plurality of importance levels, thereby obtaining a plurality of priorities, wherein each second image data corresponds to a priority; The sorting module is configured to sort the plurality of second image data according to the plurality of priorities to obtain a target transmission queue; The transmission module is configured to transmit the plurality of second image data to the cloud according to the target transmission queue; The analysis module is further configured to receive the plurality of second image data via the cloud, and decompress and analyze the plurality of second image data to obtain a target risk assessment result; The feedback module is used to feed back the multiple alarm information and the target risk assessment results to the terminal device, so as to facilitate timely response to risk events at the target power operation site.

9. An electronic device, characterized in that: include: a processor, a memory, a communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, wherein the programs include instructions for executing the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 7.

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