Remote monitoring method and device for explosion-proof distribution box

Abstract

The invention discloses a remote monitoring method and device for an explosion-proof distribution box, and relates to the field of explosion-proof distribution boxes. The method comprises the following steps: acquiring monitoring data of a plurality of explosion-proof distribution boxes in a target area; acquiring a priority coefficient of each piece of monitoring data; and compressing the monitoring data based on the priority coefficient, and transmitting the compressed monitoring data to a central server, so that the central server performs remote monitoring on the monitoring data. By implementing the technical scheme provided by the invention, efficient remote monitoring of the explosion-proof distribution box can be realized.

Description

Technical Field

[0001] The present application relates to the field of explosion-proof distribution boxes, and in particular to a remote monitoring method and device for explosion-proof distribution boxes. Background Art

[0002] With the rapid development of industrial automation and intelligence, higher requirements are put forward for the safety monitoring of power equipment in high-risk environments. As a key power equipment in flammable and explosive environments such as chemical plants, oil fields, and mines, the safe operation of explosion-proof distribution boxes is directly related to the stability of the entire production system and the safety of personnel. Therefore, real-time and reliable remote monitoring of explosion-proof distribution boxes has become an important topic in the current industrial safety field.

[0003] At present, common explosion-proof distribution box remote monitoring systems usually use fixed-frequency data collection and transmission methods. This method regularly collects various parameters of the distribution box, such as temperature, voltage, current, etc., and transmits these data to the central monitoring server for analysis and processing. This monitoring method can meet basic monitoring needs when the network is in good condition and the bandwidth is sufficient.

[0004] However, in actual applications, since explosion-proof distribution boxes are often deployed in complex industrial environments, network conditions may be unstable and bandwidth resources are limited. In this case, the traditional fixed-frequency data transmission method may cause important data to fail to be transmitted in time, or non-critical data to occupy a large amount of bandwidth resources, thus affecting the real-time and reliability of the monitoring system. Therefore, how to ensure the timely transmission of key monitoring data under limited network resource conditions while improving the efficiency of data transmission has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present application provides a remote monitoring method and device for an explosion-proof distribution box, which can perform efficient remote monitoring of the explosion-proof distribution box.

[0006] In a first aspect of the present application, the present application provides a remote monitoring method for an explosion-proof distribution box, characterized in that the method is applied to an edge server and includes: Obtain monitoring data of multiple explosion-proof distribution boxes in the target area; Obtaining a priority coefficient of each monitoring data; The monitoring data is compressed based on the priority coefficient, and the compressed monitoring data is transmitted to a central server, so that the central server remotely monitors the monitoring data.

[0007] In a second aspect of the present application, a remote monitoring device for an explosion-proof distribution box is provided, characterized in that it includes: A monitoring data acquisition module is used to acquire monitoring data of multiple explosion-proof distribution boxes in a target area; A priority coefficient determination module, used to obtain the priority coefficient of each monitoring data; The monitoring data compression module is used to compress the monitoring data based on the priority coefficient and transmit the compressed monitoring data to the central server so that the central server can remotely monitor the monitoring data.

[0008] In a third aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the above method steps.

[0009] In a fourth aspect of the present application, an electronic device is provided, comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0010] In a fifth aspect of the present application, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the method steps described above are implemented.

[0011] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: By obtaining the monitoring data of multiple explosion-proof distribution boxes in the target area and their corresponding priority coefficients, and compressing the monitoring data based on these priority coefficients, the problem of data transmission under limited network resources can be effectively solved. This method first distinguishes the importance of different monitoring data, and through the setting of priority coefficients, enables the system to identify and prioritize key data. In the data compression stage, the system adopts different compression strategies for data of different importance according to the priority coefficient, ensuring that high-priority data can be transmitted with a lower compression ratio or even lossless, ensuring the integrity and timeliness of the data. At the same time, for low-priority data, the system can use a higher compression ratio, effectively reducing the amount of data transmission.

[0012] The above-mentioned priority-based dynamic compression mechanism not only improves the efficiency of data transmission, but also ensures that key monitoring data can be transmitted to the central server in priority when the network bandwidth is limited. Compared with the traditional fixed-frequency data transmission method, this method can adapt to complex network environments more flexibly and significantly improve the real-time performance and reliability of the monitoring system. By transmitting the compressed monitoring data to the central server, this method realizes efficient remote monitoring of explosion-proof distribution boxes, effectively solving the technical problem in the prior art that key data cannot be transmitted in a timely manner under the condition of limited network resources, thereby improving the performance and reliability of the entire monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a flow chart of a remote monitoring method for an explosion-proof distribution box provided in an embodiment of the present application; Figure 2 It is a structural schematic diagram of a remote monitoring device for an explosion-proof distribution box provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0014] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0015] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0016] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0017] This application provides a remote monitoring method for explosion-proof distribution boxes, referring to Figure 1 , Figure 1 The present invention discloses a flow chart of a remote monitoring method for an explosion-proof distribution box disclosed in an embodiment of the present application. The remote monitoring method for an explosion-proof distribution box is applied to an edge server.

[0018] Before introducing the remote monitoring method for an explosion-proof distribution box provided in an embodiment of the present application, the system architecture of the application of the method is first described.

[0019] The remote monitoring system for explosion-proof distribution boxes provided in the embodiment of the present application adopts a multi-layer architecture design, aiming to achieve a comprehensive solution from data collection to remote monitoring. The core of the system lies in its hierarchical structure and the close collaboration between the various levels.

[0020] The information flow of the system starts from the data acquisition layer, which is composed of various sensor modules installed in the explosion-proof distribution box, responsible for real-time collection of key operating data such as temperature, current, voltage, humidity and wind speed. These raw data are then transmitted to the edge computing layer for preliminary processing by the edge computing nodes. The edge computing layer not only performs preliminary compression and classification of the data, but also sorts the data according to the preset priority and can dynamically adjust the data compression ratio. Especially when the network bandwidth is limited, the edge computing layer can implement strategies such as differential compression or suspended transmission to ensure the timely transmission of high-priority data.

[0021] The data processed by the edge computing layer then enters the data transmission layer. The transmission module of this layer is responsible for safely and efficiently transmitting the processed data from the edge computing node to the remote monitoring center. The transmission module not only needs to complete the data transmission task, but also needs to monitor the network bandwidth status in real time and dynamically adjust the transmission strategy according to the current network situation. In addition, in order to cope with possible network interruptions, the transmission module also implements the breakpoint resume function to ensure the integrity and continuity of the data.

[0022] The end point of the data flow is the monitoring center layer, which is equipped with a remote monitoring platform to undertake the task of data reception, decompression, analysis and display. The platform can not only display the operating status of the explosion-proof distribution box in real time, but also detect anomalies and issue alarms in time. More importantly, through the analysis of historical data, the monitoring center can predict network fluctuations, thereby adjusting the data transmission strategy in advance, and further improving the system's intelligence level and preventive maintenance capabilities.

[0023] Based on the above system architecture, the remote monitoring method for explosion-proof distribution boxes may specifically include the following steps: Step 101: Acquire monitoring data of multiple explosion-proof distribution boxes in a target area.

[0024] Among them, the target area refers to a specific geographical range or spatial area, in which multiple explosion-proof distribution boxes that need to be remotely monitored are deployed. In the embodiment of the present application, it can be understood as an industrial environment with potential explosion risks, such as chemical plants, oil drilling platforms, mines or other production areas involving flammable and explosive substances. The above-mentioned areas usually have extreme environmental characteristics such as high temperature, high humidity, high dust or corrosive gases, which pose severe challenges to the safe operation of the equipment. The definition of the target area is used to clarify the coverage of the remote monitoring system to ensure that the system can comprehensively monitor the operating status of all explosion-proof distribution boxes in the area and the surrounding environmental parameters. By clearly defining the target area, it is possible to deploy sensor networks in a more targeted manner, optimize data acquisition strategies, and provide accurate spatial references for subsequent data analysis and risk assessment.

[0025] Specifically, the edge server establishes data connections with various sensor modules installed in the explosion-proof distribution box and continuously receives real-time data collected by these sensors. These monitoring data mainly include two categories: one is the first monitoring data directly related to the distribution box, such as key operating parameters such as temperature, current and voltage; the other is the second monitoring data reflecting the environmental conditions of the target area, such as humidity and wind speed. The edge server adopts a high-frequency sampling method to ensure that it can capture instantaneous changes in monitoring data, especially those sudden parameter fluctuations that may affect the safe operation of the equipment. At the same time, in order to cope with possible data transmission interruptions, the edge server is also equipped with a local cache mechanism that can store a large amount of raw data in a short time to prevent data loss.

[0026] Step 102: Obtain the priority coefficient of each monitoring data.

[0027] Among them, the priority coefficient refers to a quantitative indicator used to characterize the importance and urgency of different monitoring data during transmission and processing. In the embodiment of the present application, it can be understood as a dynamic weight value that comprehensively considers data characteristics, system status and network environment. This value determines the processing order and resource allocation ratio of various types of monitoring data under resource-constrained conditions. The priority coefficient is used to achieve intelligent management and optimized transmission of monitoring data in a complex and changeable network environment.

[0028] Based on the above embodiment, as an optional embodiment, the monitoring data includes first monitoring data of different categories related to each distribution box, and second monitoring data related to the environment in the target area. On this basis, in step 102: the step of obtaining the priority coefficient of each monitoring data can also specifically include the following steps: Step 201: Obtain the communication quality between the edge server and the central server.

[0029] Among them, the first monitoring data refers to the key operating parameters directly related to the explosion-proof distribution box. In the embodiment of the present application, it can be understood as real-time data reflecting the core working status of the distribution box, such as temperature, current, voltage, power factor, insulation resistance, etc. The first monitoring data is directly related to the safe operation and performance of the distribution box. The first monitoring data is used to monitor the working status of the distribution box in real time and promptly detect potential safety hazards or performance abnormalities. Through continuous monitoring and analysis of these data, the system can quickly identify dangerous situations such as overheating, overload, and short circuit, providing a key basis for preventive maintenance and emergency intervention.

[0030] Correspondingly, the second monitoring data refers to parameters related to the overall environment of the target area. In the embodiment of the present application, it can be understood as external factors that affect the operating environment of the distribution box, such as ambient temperature, humidity, atmospheric pressure, wind speed, dust concentration, concentration of harmful gases, etc. Although these data do not directly reflect the working status of the distribution box, they have an important impact on its long-term performance and safe operation. The second monitoring data is used to evaluate and predict the potential impact of environmental factors on the distribution system. Through long-term tracking and analysis of these data, the system can establish a correlation model between environmental factors and equipment performance, predict possible environmental risks, such as corrosion, dust accumulation, moisture and other problems, so as to formulate corresponding preventive measures and maintenance strategies.

[0031] By distinguishing and comprehensively analyzing the above two types of data, the remote monitoring system can comprehensively evaluate the operating status and potential risks of the explosion-proof distribution box, paying attention to both the immediate status of the equipment itself and the long-term impact of environmental factors, thereby achieving more intelligent and comprehensive safety management.

[0032] Correspondingly, communication quality refers to the overall performance and reliability evaluation index of the data transmission link between the edge server and the central server. In the embodiment of the present application, it can be understood as a dynamic parameter that comprehensively reflects the network connection status, including network bandwidth, delay, packet loss rate, signal strength and stability.

[0033] In a feasible implementation, relevant indicators of communication quality can be obtained by real-time measurement and analysis of network performance parameters, such as using detection packets to measure delay and packet loss rate, evaluating available bandwidth through actual data transmission rate, and combining factors such as wireless signal strength to form a comprehensive communication quality evaluation.

[0034] Furthermore, communication quality is used to realize intelligent data transmission management of remote monitoring system, ensuring reliable transmission of monitoring data in complex and changing network environment. The system dynamically adjusts data transmission strategy according to real-time communication quality.

[0035] Step 202: Determine the priority coefficient of each first monitoring data based on the relative relationship between each first monitoring data, the second monitoring data and the communication quality.

[0036] Among them, the relative relationship refers to the complex relationship of interactive influence and interdependence between different types of data and between data and system status. In the embodiment of the present application, it can be understood as the dynamic interaction and mutual influence between the three key elements of the first monitoring data, the second monitoring data and the communication quality. Specifically, this relative relationship is reflected in multiple dimensions such as the importance weight of the data, the speed of change, the environmental impact and the network status.

[0037] Correspondingly, the priority coefficient refers to a quantitative indicator used to characterize the relative importance and urgency of different monitoring data during transmission and processing. In the embodiment of the present application, it can be understood as a dynamic weight value that comprehensively considers data characteristics, environmental factors and system status. This value determines the processing order and resource allocation ratio of various types of monitoring data under resource-constrained conditions. The priority coefficient is used to realize intelligent management and optimized transmission of monitoring data in complex and changeable network environments and extreme working conditions.

[0038] Specifically, data with a higher priority coefficient will receive more network bandwidth resources and a lower compression ratio, ensuring that these critical data can be transmitted to the monitoring center in a timely and accurate manner. By introducing the priority coefficient, the system can prioritize the real-time transmission of the most important and fastest-changing data when the network bandwidth is limited or unstable, and dynamically adjust the transmission strategy according to environmental changes and system status.

[0039] Based on the above embodiment, as an optional embodiment, in step 202: based on the relative relationship between each first monitoring data, the second monitoring data and the communication quality, the step of determining the priority coefficient of each first monitoring data may further include the following steps: Step 301: Define an importance weight of the first monitoring data and an importance weight of the second monitoring data based on the first monitoring data and the second monitoring data.

[0040] Among them, the importance weight of the first monitoring data refers to the contribution of the key operating parameters directly related to the explosion-proof distribution box to the overall safety and reliability of the system. In the embodiment of the present application, it can be understood as a preset value used to characterize the relative importance of the first monitoring data such as temperature, current, and voltage in evaluating the operating status of the distribution box. For example, current and voltage data may be given a higher weight value because these parameters are directly related to the safe operation and performance of the distribution box.

[0041] In contrast, the importance weight of the second monitoring data refers to the degree of influence of the parameters reflecting the overall environmental conditions of the target area on the long-term stability of the system. In the embodiment of the present application, it can be understood as another preset value, which is used to indicate the relative importance of environmental factors such as humidity and wind speed in evaluating the overall operating environment of the system. Although these parameters do not directly reflect the immediate working status of the distribution box, they are of great significance to its long-term performance and potential risk assessment.

[0042] Furthermore, there is a significant difference between the importance weight of the first monitoring data and the importance weight of the second monitoring data, which reflects the degree of influence of different types of data on the safe operation of the explosion-proof distribution box. In the embodiment of the present application, the importance weight of the first monitoring data is usually much greater than the importance weight of the second monitoring data, which can be expressed as I1>>I2. This relationship can be understood as follows: in extreme environments such as high temperature and high humidity, the operating parameters directly related to the distribution box (such as temperature, current and voltage) have a more significant immediate impact on equipment safety, and are therefore given a higher importance.

[0043] For example, in the explosion-proof distribution box monitoring system of an offshore oil drilling platform, the importance weights of the first monitoring data such as temperature, current and voltage may be set to 0.95, 0.90 and 0.90 respectively, while the importance weights of the second monitoring data such as humidity, salt spray concentration and wind speed may be set to 0.40, 0.35 and 0.30. This weight distribution reflects that in the marine environment, sudden changes in temperature, current and voltage may directly lead to equipment failure or safety accidents, so they are given extremely high weights. In contrast, although environmental factors such as humidity, salt spray concentration and wind speed will affect equipment performance in the long term, they usually change slowly, so the weights are relatively low.

[0044] Similarly, in the scenario of an underground coal mine power distribution system, the methane concentration in the first monitoring data may be assigned the highest weight of 0.98, the weights of temperature, current and voltage may be 0.95 and 0.90, and the weights of the second monitoring data such as dust concentration, humidity and air pressure may be set to 0.50, 0.40 and 0.35. In this scenario, the methane concentration is directly related to the explosion risk of the mine, so it gets the highest weight. Although dust concentration belongs to the second monitoring data, it is given a relatively high weight because it may affect the heat dissipation of equipment and cause dust explosions.

[0045] The above weight setting method enables the system to prioritize the transmission and processing of high-priority data when resources are limited, while not completely ignoring the impact of environmental factors. By flexibly adjusting the weight value, the monitoring system can optimize the data collection, transmission and processing strategies according to different application scenarios and potential risks, thereby improving the overall efficiency of the system while ensuring safety. This method can not only respond to immediate security threats, but also provide necessary data support for the long-term maintenance and performance optimization of the equipment, and realize comprehensive and intelligent monitoring of the operating status of the explosion-proof distribution box.

[0046] Step 302: Substitute each of the first monitoring data, the second monitoring data, and the communication quality into the priority coefficient calculation formula to obtain the priority coefficient of the first monitoring data; The priority coefficient calculation formula is: ; In the formula, Indicates the priority coefficient of the first monitoring data, represents the importance weight of the first monitoring data, represents the importance weight of the second monitoring data, Indicates the change speed of the first monitoring data, Indicates the number of explosion-proof distribution boxes. Indicates the number of first monitoring data of each explosion-proof distribution box, Indicates the signal transmission capability in communication quality. Indicates the load capacity of the central server in the communication quality, They respectively represent the weight coefficients affecting the signal transmission capacity and load capacity.

[0047] Among them, the change rate of the first monitoring data refers to the change range of the key operating parameters directly related to the explosion-proof distribution box within a unit time. In the embodiment of the present application, it can be understood as a dynamically calculated value, which is used to quantify the fluctuation degree of the first monitoring data such as temperature, current, voltage, etc. in a short period of time. Specifically, this change rate can be obtained by calculating the ratio of the difference between two adjacent sampling data and the sampling time interval.

[0048] For example, for temperature data, its change rate can be expressed as the temperature change in degrees per second; for current data, it can be expressed as the current change in amperes per second. This parameter is used to reflect the dynamic characteristics and timeliness of the data in the priority calculation process. Data with a higher rate of change usually means that the status of the equipment is changing significantly, which may indicate potential failures or safety hazards. Therefore, by incorporating the change rate into the priority coefficient calculation formula.

[0049] Among them, the number of explosion-proof distribution boxes refers to the total number of explosion-proof distribution boxes deployed in the target area that need to be remotely monitored. In the embodiment of the present application, it can be understood as an integer value reflecting the monitoring range of the system, representing the number of devices covered by the entire monitoring network. This parameter is used to measure the overall scale of the system and balance the relative importance of individual distribution box data in priority calculations.

[0050] Correspondingly, the number of first monitoring data of each explosion-proof distribution box refers to the number of key operating parameters that need to be monitored in real time in each explosion-proof distribution box. In the embodiment of the present application, it can be understood as an integer value representing the monitoring complexity of a single distribution box, reflecting the amount of data generated by each device. This parameter is used to quantify the data generation capacity of a single distribution box and consider the diversity of data sources in priority calculation.

[0051] The above two parameters are used together to adjust the relative importance of data in the priority calculation formula. By introducing these parameters, the system can allocate network resources more fairly and efficiently. For example, when the number of explosion-proof distribution boxes increases, the relative importance of each individual data point will decrease accordingly, which reflects the practical need to balance the overall monitoring effect in large-scale systems. Similarly, when the number of first monitoring data for each distribution box increases, the system will automatically adjust to ensure that key data will not be ignored due to the increase in data volume.

[0052] Among them, signal transmission capability refers to the ability of network infrastructure to reliably transmit data per unit time. In the embodiment of the present application, it can be understood as a dynamically changing value used to quantify the data transmission rate, stability and reliability under the current network environment. This parameter can be comprehensively evaluated by measuring indicators such as network bandwidth, delay and packet loss rate in real time. For example, high signal transmission capability may correspond to a network state with high bandwidth, low delay and low packet loss rate.

[0053] Accordingly, the load capacity of the central server refers to the ability of the server of the remote monitoring center to process and analyze data per unit time. In the embodiment of the present application, it can be understood as a value reflecting the current computing resource occupancy of the server, which is used to indicate the server's ability to handle additional data load. This parameter can be evaluated by monitoring the server's CPU usage, memory usage, disk I / O and other performance indicators. High load capacity means that the server has enough resources to process more or more complex data.

[0054] The above two parameters are used together to dynamically adjust the data transmission and processing strategies in the priority calculation formula. By introducing the above two parameters, the system can intelligently adjust the data transmission priority according to the current network conditions and server load conditions. For example, when the signal transmission capacity is low, the system may increase the data compression ratio and prioritize the transmission of the most critical monitoring data to ensure that core functions are maintained under limited bandwidth. Similarly, when the central server load is high, the system may reduce the transmission frequency of non-critical data to avoid server overload.

[0055] Furthermore, α and β are weight coefficients used to adjust the influence of signal transmission capacity and central server load capacity in priority calculation. The reasonable setting of these two coefficients is crucial for the optimal operation of the system under different network environments and server load conditions. In actual applications, the adjustment of α and β needs to be dynamically adjusted according to the specific deployment environment and operation requirements. Below we use two typical application scenarios to illustrate how to adjust these two weight coefficients: For example, in an explosion-proof distribution box monitoring system on an offshore oil platform, due to its remote location, network communication conditions are usually poor, and local edge computing capabilities may be relatively limited. In this case, we may set a higher α value (such as α=0.7) and a lower β value (such as β=0.3).

[0056] A high α value reflects the system's high reliance on network transmission capabilities. This means that when signal transmission conditions are poor, the system will more actively compress data or delay the transmission of non-critical data to ensure that the most important monitoring information can be transmitted to the central server in a timely manner. For example, when a storm is approaching and the network is unstable, the system will prioritize the transmission of current and voltage data that are directly related to safety, and temporarily reduce the transmission frequency of environmental parameters.

[0057] The relatively low β value indicates that the system has a high tolerance for the central server load. This is because on offshore platforms, local processing capabilities are limited, and more data processing and analysis tasks need to rely on central servers. Even if the central server load is high, the system will continue to transmit important data to ensure the continuity and comprehensiveness of monitoring.

[0058] It should be noted that in actual operation, the above weight coefficients may need to be further refined and adjusted. For example, the α and β values ​​can be dynamically adjusted according to different time periods of the day, seasonal changes, or special events (such as equipment maintenance). The system can also automatically optimize these weight coefficients by analyzing historical data through machine learning algorithms to adapt to long-term environmental changes and the evolution of system requirements.

[0059] By flexibly adjusting α and β, the remote monitoring system can maintain efficient and reliable operation in various complex industrial environments, ensuring the timely transmission and processing of key data and optimizing the utilization of system resources, thus providing strong protection for the safe operation of explosion-proof distribution boxes.

[0060] In summary, the priority coefficient calculation formula can quantitatively evaluate the relative importance and transmission urgency of various types of data in the explosion-proof distribution box monitoring system. In an embodiment of the present application, the formula can dynamically calculate and adjust the priority of various types of monitoring data in the explosion-proof distribution box remote monitoring system. Through this formula, the system can achieve intelligent data management and resource allocation. First, it can determine which data is the most critical at the current moment and needs to be processed first based on the preset data importance and the real-time data change speed. This ensures that the system can promptly capture rapidly changing data that may indicate equipment abnormalities or safety hazards.

[0061] Through the priority coefficient calculation formula, the system can realize intelligent data priority management in various complex industrial environments. Whether in harsh environments such as offshore oil platforms or in complex systems such as large chemical parks, it can ensure that the most critical and urgent data are processed and transmitted first. This not only improves the system's ability to respond quickly to potential risks, but also optimizes the utilization efficiency of network and computing resources.

[0062] Step 203: Use the priority coefficient of each first monitoring data as the priority coefficient of the monitoring data.

[0063] Specifically, the edge server maintains a dynamically updated priority queue. After each round of priority calculation, the system will immediately update the queue and sort the first monitoring data according to its newly calculated priority coefficient. The sorted queue will be directly used to guide subsequent data compression, transmission and processing strategies.

[0064] On the basis of the above-mentioned embodiment, as an optional embodiment, in order to further improve the adaptability and reliability of the explosion-proof distribution box remote monitoring system, the present application also introduces a network fluctuation coefficient and dynamically adjusts the priority coefficient based on the parameter.

[0065] Specifically, the method may further include the following steps: obtaining network fluctuation coefficients of the edge server and the central server, and adjusting the priority coefficient based on the network fluctuation coefficients.

[0066] The network fluctuation coefficient refers to a dynamic indicator that quantifies the stability and predictability of network connections. In the embodiments of the present application, it can be understood as a value that comprehensively reflects the degree of fluctuation in network performance, which is calculated by continuously monitoring and analyzing key parameters such as network delay, packet loss rate, and bandwidth changes.

[0067] Specifically, the network fluctuation coefficient can be expressed as the standardized value of the standard deviation of these network performance indicators within a specific time window. For example, the system can calculate the standard deviation of network delay in the past hour at regular intervals (such as 5 minutes) and map it to a standardized value between 0 and 1 as the current network fluctuation coefficient.

[0068] Furthermore, the network fluctuation coefficient is used in the explosion-proof distribution box remote monitoring system to dynamically evaluate the stability of the network environment and provide a basis for adjusting the data transmission strategy. A higher network fluctuation coefficient means that the network environment is unstable, there may be frequent connection interruptions or drastic fluctuations in bandwidth, which will directly affect the real-time transmission of monitoring data and the overall reliability of the system. By introducing the network fluctuation coefficient, the system can more accurately perceive changes in network conditions, thereby achieving smarter and more flexible data management and transmission optimization in complex and changing industrial environments.

[0069] Specifically, the edge server first obtains the network fluctuation coefficient between the edge server and the central server, which reflects the stability and predictability of the network connection. The network fluctuation coefficient can be obtained by continuously monitoring indicators such as network delay, packet loss rate, and bandwidth changes, and combining historical data analysis. For example, the system can calculate the standard deviation of network performance indicators within a certain time window and standardize it as the network fluctuation coefficient. A higher network fluctuation coefficient indicates an unstable network environment, and there may be frequent connection interruptions or bandwidth fluctuations.

[0070] After obtaining the network fluctuation coefficient, the system will dynamically adjust the previously calculated priority coefficient based on the coefficient. The key point of the adjustment is to further increase the priority of critical data and reduce the priority of non-critical data when the network environment is unstable.

[0071] Exemplarily, the above adjustment can be achieved by a nonlinear function: P adjusted =P*(1+k*log(1+N F )); Among them, P adjusted Represents the adjusted priority coefficient, P is the original priority coefficient, N F is the network fluctuation coefficient, and k is the adjustment factor. This formula ensures that when the network fluctuation is large, the priority of high-priority data will be further improved, while the priority of low-priority data will be moderately reduced.

[0072] Step 103: compress the monitoring data based on the priority coefficient, and transmit the compressed monitoring data to the central server so that the central server can remotely monitor the monitoring data.

[0073] After acquiring the monitoring data and calculating the priority coefficient of each data, the remote monitoring method of the explosion-proof distribution box in the embodiment of the present application enters the critical data processing and transmission stage. This step is intended to compress the monitoring data based on the priority coefficient and transmit the compressed data to the central server to achieve effective remote monitoring of the explosion-proof distribution box. Thereby, under the condition of limited network resources, it is ensured that the most important monitoring data can be transmitted to the central server in a timely and accurate manner, while optimizing the overall data transmission efficiency.

[0074] Based on the above embodiment, as an optional embodiment, in step 103: the step of compressing the monitoring data based on the priority coefficient may further include the following steps: Step 401: Determine the compression ratio of the monitoring data based on the priority coefficient.

[0075] Specifically, after determining the priority coefficients of each monitoring data, the system needs to allocate appropriate compression ratios to different data based on these coefficients. This maximizes transmission efficiency and monitoring effects under limited network resource conditions. The system will use a dynamic compression ratio allocation mechanism that maps the priority coefficients to the preset compression ratio range, thereby determining the most suitable compression ratio for each monitoring data.

[0076] The compression ratio refers to the ratio of the original data size to the compressed data size. In the embodiments of the present application, it can be understood as a value indicating the degree of compression of the monitoring data, usually expressed as a real number greater than 1. For example, a compression ratio of 2:1 means that the original data is compressed to half of its original size. A higher compression ratio means that the data is compressed more, while a lower compression ratio means that the data retains more of the original information.

[0077] Furthermore, in the explosion-proof distribution box remote monitoring system, the compression ratio is used to balance data transmission efficiency and information fidelity. By dynamically adjusting the compression ratio of different monitoring data, the system can optimize the data transmission strategy under limited network bandwidth conditions. For high-priority critical data, such as rapidly changing temperature or current readings, the system will assign a lower compression ratio to ensure data integrity and accuracy. This ensures that important information that may indicate equipment abnormalities or safety hazards can be transmitted to the monitoring center in a timely and accurate manner. Conversely, for lower-priority data, such as slowly changing environmental parameters, the system will assign a higher compression ratio, significantly reducing the amount of data transmission while retaining basic information.

[0078] Exemplarily, the above process can convert the priority coefficient into a compression ratio through a nonlinear mapping function. This mapping function can be expressed as: C R =C Rmax -(C Rmax-C Rmin )*(P / P max )^γ; Among them, C R represents the calculated compression ratio, C Rmax and C Rmin They represent the maximum and minimum compression ratios allowed by the system, P is the priority coefficient of the current data, and P max is the maximum priority coefficient of all data, and γ is an adjustable curve steepness parameter. By adjusting the γ value, the system can flexibly control the relationship curve between compression ratio and priority to adapt to different application scenarios and network conditions.

[0079] Specifically, for monitoring data with a higher priority coefficient, the system will assign a lower compression ratio to ensure the integrity and accuracy of the data. For example, when the key first monitoring data such as temperature and current undergo a sharp change, it may be assigned a priority coefficient close to 1. At this time, the system will assign it a priority coefficient close to C. Rmin The system uses a low compression ratio to ensure that important data that may indicate equipment abnormalities can be transmitted with the highest quality. Conversely, for lower priority data, such as slowly changing environmental parameters, the system assigns a higher compression ratio to significantly reduce the amount of data while ensuring the transmission of basic information.

[0080] Furthermore, the system can also dynamically adjust C according to the current network conditions. Rmax and C Rmin For example, when a sudden drop in network bandwidth is detected, the system can increase C Rmin The value of C is set to ensure that even high-priority data can be transmitted in a timely manner under limited bandwidth. At the same time, the system will also increase C accordingly. Rmax , further compressing low-priority data to free up bandwidth for the transmission of critical data.

[0081] Step 402: Compress the monitoring data based on a compression mode corresponding to the compression ratio, where the compression mode includes at least one of lossless compression, differential compression, and lossy compression.

[0082] Specifically, after determining the compression ratio of each monitoring data, the system needs to select an appropriate compression mode to actually compress the data. This is to maximize transmission efficiency while ensuring data quality. The compression modes in the embodiments of the present application include lossless compression, differential compression, and lossy compression. These three modes have their own characteristics and are suitable for monitoring data of different types and priorities.

[0083] Among them, lossless compression refers to the ability to reduce the data volume without losing any original information. In the embodiment of the present application, it can be understood as a way to compress high-priority monitoring data, and the compressed data can be completely restored to the original data without causing any information loss. Lossless compression is used to process key monitoring parameters that require extremely high accuracy. Through lossless compression, the system can reduce the amount of data transmission while ensuring that these important data directly related to the safe operation of the explosion-proof distribution box will not be distorted due to compression.

[0084] Among them, differential compression refers to a compression method that only records and transmits the amount of data changes. In the embodiment of the present application, it can be understood as a way to compress monitoring data that changes relatively slowly but still needs to maintain a certain degree of accuracy. Differential compression significantly reduces the amount of data by only transmitting the data difference between adjacent sampling points, rather than the complete value. This method is used to process parameters that do not change drastically but need to be continuously monitored, such as certain environmental data or equipment status indicators. Through differential compression, the system can significantly reduce the amount of data transmission while still maintaining an accurate grasp of data change trends.

[0085] Among them, lossy compression refers to a data compression method that allows some non-critical information to be lost during the compression process. In the embodiment of the present application, it can be understood as a way to compress low-priority monitoring data. The compressed data cannot be completely restored to the original data, but the main information is still retained. Lossy compression is used to process non-critical monitoring parameters that have relatively low accuracy requirements, such as certain background environmental data. Through lossy compression, the system can significantly reduce the amount of data transmitted, and when network resources are tight, more bandwidth can be freed up for the transmission of high-priority data.

[0086] Specifically, the system will select the most suitable compression mode based on the previously calculated compression ratio, combined with the type and change characteristics of the data. For high-priority data with a compression ratio close to 1, the system will give priority to lossless compression mode. Lossless compression can reduce the data volume without losing any information, and is very suitable for processing key first monitoring data such as temperature, current and voltage. This ensures that these important parameters directly related to the safe operation of the explosion-proof distribution box can be transmitted to the monitoring center with the highest accuracy.

[0087] For data with a moderate compression ratio, the system may choose differential compression mode. Differential compression is particularly suitable for data that changes relatively slowly but still needs to maintain a certain degree of accuracy. In this mode, the system only transmits the data changes between adjacent sampling points, rather than the complete values. This method can significantly reduce the amount of transmission while ensuring data quality. For example, for slowly changing ambient humidity data, the system can transmit a complete value at regular intervals, and only transmit the difference from the previous sampling in the middle. This method not only ensures data continuity, but also greatly reduces the amount of data.

[0088] When the compression ratio is high, the system will use lossy compression mode. Lossy compression is suitable for low-priority data that allows a certain degree of information loss, such as some non-critical environmental parameters. In this mode, the system may reduce the sampling rate of the data or reduce the precision of the values. Although this will result in the loss of some detailed information, it can greatly reduce the amount of data, which is especially useful when network resources are tight.

[0089] The system can also dynamically switch compression modes based on real-time changes in data. For example, when a parameter that usually changes slowly suddenly experiences a dramatic fluctuation, the system can immediately switch its compression mode from lossy to lossless to capture this important change that may indicate an abnormality in the device. This dynamic adjustment mechanism ensures that the system can maintain sensitivity to critical information in all situations.

[0090] Through this intelligent compression mode selection based on compression ratio, the explosion-proof distribution box remote monitoring system can optimize data transmission under limited network resources. High-priority data is transmitted in a lossless or low-loss manner to ensure the accuracy of monitoring; medium-priority data is transmitted through differential compression while reducing the transmission volume while ensuring quality; low-priority data is compressed with lossy compression to significantly reduce the data volume. This method not only improves the overall transmission efficiency of the system, but also ensures that the most critical monitoring information can reach the monitoring center in a timely and accurate manner under various network conditions.

[0091] In the actual operation of the explosion-proof distribution box remote monitoring system, network conditions often fluctuate. In order to ensure the timely transmission of key data when network resources are limited, the system adopts methods such as differential compression and lossy compression. However, these compression methods may cause the loss of some data accuracy. In order to restore the integrity of these data when conditions permit, the embodiment of the present application introduces a data recovery mechanism based on communication quality.

[0092] Based on the above embodiment, as an optional embodiment, the process may further include the following steps: When it is determined that the communication quality between the edge server and the central server is greater than the quality threshold, a data recovery instruction is triggered. The data recovery instruction is used to repair the monitoring data using differential compression and lossy compression, and return the repaired monitoring data to the central server.

[0093] Among them, the data recovery instruction refers to a system-level operation command used to trigger and manage the reconstruction and update process of data. In the embodiment of the present application, it can be understood as a special instruction initiated by the edge server, which starts a series of operation processes for recovering the monitoring data previously transmitted using differential compression or lossy compression. This instruction not only contains the trigger signal of the recovery operation, but also includes specific recovery strategies, data ranges, priorities and other information.

[0094] Furthermore, the data recovery instruction is used to automatically start the data integrity recovery process when the network conditions improve. This instruction is triggered when the edge server detects that the communication quality with the central server exceeds the preset threshold. The instruction first guides the edge server to extract the original high-precision data from the local storage, and at the same time starts the reconstruction process of the differential data. In this process, the system combines the previously transmitted differential data with the reference data points to recalculate the complete data sequence. Subsequently, the data recovery instruction coordinates the data transmission between the edge server and the central server to ensure that the restored high-precision data can be transmitted to the central server in an orderly manner.

[0095] On the central server side, the data recovery instructions instruct how to process the received recovery data, including updating the corresponding historical data records and adjusting the data storage structure. In the above process, the instructions also manage the priority of the recovery process to ensure that when multiple data sets need to be restored, the recovery order can be reasonably arranged according to importance and timeliness. Finally, when the recovery process is completed, the data recovery instructions trigger the edge server to clean up the local cache data that has been successfully transmitted and updated, thereby optimizing the use of storage resources.

[0096] Specifically, the system continuously monitors the communication quality between the edge server and the central server. The communication quality can be comprehensively evaluated by multiple indicators, such as network bandwidth, latency, packet loss rate, etc. When the system detects that the communication quality exceeds the preset quality threshold, it triggers the data recovery instruction. This quality threshold represents a standard of good network conditions and is usually set to a level that can support a large amount of data transmission without affecting real-time monitoring.

[0097] After the data recovery command is triggered, the system will prioritize the monitoring data that was previously compressed with differential compression and lossy compression. For differentially compressed data, the system will reconstruct the complete data sequence. This process involves combining the previously transmitted differential data with the baseline data point and recalculating the actual value at each time point. For lossy compressed data, the system will extract the original high-precision data from the local storage of the edge server. While the original data is compressed and transmitted, it will be temporarily stored in the local storage of the edge server for subsequent recovery.

[0098] After the data recovery process is completed, the system will retransmit the repaired high-precision data to the central server. This process usually adopts batch transmission. At the same time, the system will mark the recovered data with special marks so that the central server can identify and update the corresponding historical data records.

[0099] The above-mentioned data recovery mechanism based on communication quality allows the system to adopt a more aggressive compression strategy when network conditions are poor, ensuring that the most critical monitoring data can be transmitted in a timely manner without having to worry too much about temporary loss of data accuracy. Secondly, when network conditions improve, the system can automatically restore the previously compressed and transmitted data to ensure the integrity and accuracy of long-term data records. This is crucial for subsequent data analysis, trend prediction, and equipment maintenance planning.

[0100] More importantly, the above mechanism improves the overall adaptability of the system. In complex industrial environments, network conditions may change frequently. By dynamically adjusting data transmission and recovery strategies, the system can maintain efficient operation under various network conditions. When network conditions are poor, it prioritizes the real-time performance of core functions; when network conditions are good, it can supplement and improve the detailed information of the data.

[0101] In addition, this approach helps optimize the use of storage resources. The edge server only needs to temporarily store the original data, and once the recovery and transmission are completed, this part of the storage space can be released. This enables the system to manage historical data over a longer time span with limited storage resources.

[0102] Reference Figure 2 , the present application also provides a remote monitoring device for an explosion-proof distribution box, comprising: A monitoring data acquisition module is used to acquire monitoring data of multiple explosion-proof distribution boxes in a target area; A priority coefficient determination module, used to obtain the priority coefficient of each monitoring data; The monitoring data compression module is used to compress the monitoring data based on the priority coefficient and transmit the compressed monitoring data to the central server so that the central server can remotely monitor the monitoring data.

[0103] On the basis of the above embodiment, as an optional embodiment, the priority coefficient determination module is also used to obtain the communication quality between the edge server and the central server; based on the relative relationship between each of the first monitoring data, the second monitoring data and the communication quality, determine the priority coefficient of each of the first monitoring data; and use the priority coefficient of each of the first monitoring data as the priority coefficient of the monitoring data.

[0104] Based on the above embodiment, as an optional embodiment, the priority coefficient determination module is further used to define the importance weight of the first monitoring data and the importance weight of the second monitoring data based on the first monitoring data and the second monitoring data; the importance weight of the first monitoring data and the importance weight of the second monitoring data and the communication quality are substituted into the priority coefficient calculation formula to obtain the priority coefficient of the first monitoring data; The priority coefficient calculation formula is: ; In the formula, Indicates the priority coefficient of the first monitoring data, represents the importance weight of the first monitoring data, represents the importance weight of the second monitoring data, Indicates the change speed of the first monitoring data, Indicates the number of explosion-proof distribution boxes. Indicates the number of first monitoring data of each explosion-proof distribution box, Indicates the signal transmission capability in communication quality. Indicates the load capacity of the central server in the communication quality, They respectively represent the weight coefficients affecting the signal transmission capacity and load capacity.

[0105] Based on the above embodiment, as an optional embodiment, the priority coefficient determination module is further used to obtain the network fluctuation coefficient between the edge server and the central server, and adjust the priority coefficient based on the network fluctuation coefficient.

[0106] Based on the above embodiments, as an optional embodiment, the monitoring data compression module is also used to determine the compression ratio of the monitoring data based on the priority coefficient; compress the monitoring data based on the compression mode corresponding to the compression ratio, and the compression mode includes at least one of lossless compression, differential compression and lossy compression.

[0107] Based on the above embodiment, as an optional embodiment, the monitoring data compression module is also used to trigger a data recovery instruction when it is determined that the communication quality between the edge server and the central server is greater than a quality threshold. The data recovery instruction is used to repair the monitoring data using differential compression and lossy compression, and return the repaired monitoring data to the central server.

[0108] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0109] The embodiment of the present application also provides a computer storage medium, which can store multiple instructions. The instructions are suitable for being loaded by a processor and executed by a remote monitoring method for an explosion-proof distribution box such as the above-mentioned embodiment. The specific execution process can refer to the specific description of the embodiment shown, which will not be repeated here.

[0110] On the other hand, the present application also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute a remote monitoring method for an explosion-proof distribution box provided by the above methods.

[0111] The present application also discloses an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0112] The communication bus 302 is used to realize the connection and communication between these components.

[0113] The user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0114] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0115] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface diagrams and applications, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0116] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program for a remote monitoring method of an explosion-proof distribution box.

[0117] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program stored in the memory 305 for a remote monitoring method for an explosion-proof distribution box. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0118] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0119] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0120] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0121] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0122] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0123] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0124] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A remote monitoring method for an explosion-proof distribution box, characterized in that: Applicable to edge servers include: Obtain monitoring data of multiple explosion-proof distribution boxes in the target area; Obtaining a priority coefficient of each monitoring data; The monitoring data is compressed based on the priority coefficient, and the compressed monitoring data is transmitted to a central server, so that the central server remotely monitors the monitoring data.

2. The remote monitoring method for explosion-proof distribution box according to claim 1, characterized in that: The monitoring data includes first monitoring data of different categories related to each of the distribution boxes, and second monitoring data related to the environment in the target area, The obtaining of the priority coefficient of each monitoring data includes: Obtaining the communication quality between the edge server and the central server; Determining a priority coefficient of each first monitoring data based on a relative relationship between each first monitoring data, the second monitoring data and the communication quality; The priority coefficient of each of the first monitoring data is used as the priority coefficient of the monitoring data.

3. The remote monitoring method for explosion-proof distribution box according to claim 2, characterized in that: The determining the priority coefficient of each first monitoring data based on the relative relationship between each first monitoring data, the second monitoring data and the communication quality includes: defining an importance weight of the first monitoring data and an importance weight of the second monitoring data based on the first monitoring data and the second monitoring data; Substituting the importance weight of the first monitoring data, the importance weight of the second monitoring data and the communication quality into a priority coefficient calculation formula to obtain a priority coefficient of the first monitoring data; The priority coefficient calculation formula is: ； In the formula, Indicates the priority coefficient of the first monitoring data, represents the importance weight of the first monitoring data, represents the importance weight of the second monitoring data, Indicates the change speed of the first monitoring data, Indicates the number of explosion-proof distribution boxes. Indicates the number of first monitoring data of each explosion-proof distribution box, Indicates the signal transmission capability in communication quality. Indicates the load capacity of the central server in the communication quality, They respectively represent the weight coefficients affecting the signal transmission capacity and load capacity.

4. The remote monitoring method for explosion-proof distribution box according to claim 3 is characterized in that: The method further comprises: A network fluctuation coefficient between the edge server and the central server is obtained, and the priority coefficient is adjusted based on the network fluctuation coefficient.

5. The remote monitoring method for explosion-proof distribution box according to claim 1, characterized in that: The compressing the monitoring data based on the priority coefficient includes: determining a compression ratio of the monitoring data based on the priority coefficient; The monitoring data is compressed based on a compression mode corresponding to the compression ratio, where the compression mode includes at least one of lossless compression, differential compression, and lossy compression.

6. The remote monitoring method for explosion-proof distribution box according to claim 5, characterized in that: The method further comprises: When it is determined that the communication quality between the edge server and the central server is greater than the quality threshold, a data recovery instruction is triggered, and the data recovery instruction is used to repair the monitoring data using differential compression and lossy compression, and return the repaired monitoring data to the central server.

7. A remote monitoring device for an explosion-proof distribution box, characterized in that: include: A monitoring data acquisition module is used to acquire monitoring data of multiple explosion-proof distribution boxes in a target area; A priority coefficient determination module, used to obtain the priority coefficient of each monitoring data; The monitoring data compression module is used to compress the monitoring data based on the priority coefficient and transmit the compressed monitoring data to the central server so that the central server can remotely monitor the monitoring data.

8. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-6.

9. A computer storage medium, characterized in that The computer storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 6 is performed.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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