Intelligent monitoring data processing method, system, equipment and medium

Abstract

The invention provides an intelligent monitoring data processing method, system and device and a medium, and belongs to the technical field of intelligent monitoring of power distribution cabinets, and the method comprises the steps: firstly comparing whether environment data of a power distribution cabinet exceeds a limit or not, then comparing whether electrical data of the power distribution cabinet exceeds the limit or not when the environment data exceeds the limit, and carrying out the intelligent judgment of the data in the power distribution cabinet, a reasonable operation state diagnosis strategy of the power distribution cabinet is given, maintenance can be carried out without over-limit alarm, manpower waste can be reduced, and the operation intensity of maintenance personnel can be reduced at the same time.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent monitoring of power distribution cabinets, and more specifically, to an intelligent monitoring data processing method, system, device and medium. Background Art

[0002] With the development of the Internet of Things (IoT) and technological advancements, intelligent power monitoring systems are becoming increasingly intelligent. These systems can centrally monitor a large number of distribution cabinets in different locations and distribution sites, and can identify faults by monitoring the electrical operating parameters of each cabinet.

[0003] Existing intelligent power monitoring systems typically issue an immediate alarm when they detect an electrical parameter exceeding a limit, prompting maintenance personnel to conduct repairs promptly. However, in some cases, the limit exceeding may be a false alarm or may not require repair. If maintenance personnel are called in again, this wastes manpower and increases their workload. Summary of the Invention

[0004] The purpose of this application is to provide an intelligent monitoring data processing method, system, equipment and medium, so as to make intelligent judgments on the data in the distribution cabinet and provide a reasonable distribution cabinet operation status diagnosis strategy, so as to carry out maintenance without over-limit alarm, reduce manpower waste, and reduce the workload of maintenance personnel.

[0005] A first aspect of the embodiments of the present application provides an intelligent monitoring data processing method, comprising: Acquire monitoring data of the power distribution cabinet, the monitoring data including first-category monitoring data and second-category monitoring data, the first-category monitoring data including environmental data of the power distribution cabinet, and the second-category monitoring data including electrical data of the power distribution cabinet; comparing the first type of monitoring data with a preset environmental threshold; If there is first-category monitoring data that exceeds a preset environmental warning threshold, the second-category monitoring data is compared with a preset electrical warning threshold; Based on the comparison results of the second type of monitoring data and the preset electrical warning threshold, the operating status diagnosis strategy of the distribution cabinet is determined.

[0006] A second aspect of the embodiments of the present application provides an intelligent monitoring data processing system, including: A data acquisition module is used to acquire monitoring data of the power distribution cabinet, the monitoring data including first-category monitoring data and second-category monitoring data, the first-category monitoring data including environmental data of the power distribution cabinet, and the second-category monitoring data including electrical data of the power distribution cabinet; A first comparison module, configured to compare the first type of monitoring data with a preset environmental threshold; a second comparison module, configured to compare the second type of monitoring data with a preset electrical warning threshold if there is first type of monitoring data exceeding a preset environmental warning threshold; The strategy determination module is used to determine the operation status diagnosis strategy of the distribution cabinet based on the comparison result of the second type of monitoring data and the preset electrical warning threshold.

[0007] In a third aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned intelligent monitoring data processing method when executing the computer program.

[0008] In a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned intelligent monitoring data processing method are implemented.

[0009] In a fifth aspect of an embodiment of the present application, a computer program product is provided, comprising a computer program or computer executable instructions, which, when executed by a processor, implements the steps of the above-mentioned intelligent monitoring data processing method.

[0010] The beneficial effects of the intelligent monitoring data processing method, system, device, and medium provided by the embodiments of the present application are: The embodiment of the present application first compares whether the environmental data of the distribution cabinet exceeds the limit. If there is an over-limit, it then compares whether the electrical data of the distribution cabinet exceeds the limit. By making an intelligent judgment on the data in the distribution cabinet, a reasonable distribution cabinet operation status diagnosis strategy is given. Repair is performed without the need for an over-limit alarm, which can reduce manpower waste and reduce the workload of maintenance personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only 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.

[0012] Figure 1 A flowchart of an intelligent monitoring data processing method provided in one embodiment of the present application; Figure 2 A structural block diagram of an intelligent monitoring data processing system provided in one embodiment of the present application; Figure 3 A schematic block diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0014] In order to make the purpose, technical solutions and advantages of this application clearer, specific embodiments will be described below with reference to the accompanying drawings.

[0015] The intelligent monitoring data in the embodiments of the present application generally comes from an intelligent power monitoring system.

[0016] In an embodiment of the present application, the power intelligent monitoring system may include a microcomputer integrated automation system and corresponding supporting equipment.

[0017] Specifically, it may include: monitoring, protocol conversion equipment and supporting equipment (including optical fiber, network cable, 485 line and auxiliary facilities), computer monitoring system and other software. Among them, protocol conversion equipment can be a device used to convert between different communication protocols.

[0018] In an embodiment of the present application, the intelligent power monitoring system may also include a fault recording screen, a microcomputer five-protection operation locking system, an online power quality monitoring device, a frequency and voltage emergency control device, a remote power energy collection system and a multi-function power meter.

[0019] Specifically, the fault recorder collects current and voltage signals in real time through sensors connected to the power system and stores the data in internal memory. When a fault occurs in the system, the recorder is immediately triggered and begins recording waveform data over a period of time. This data can be analyzed and displayed using software to help engineers identify the cause of the fault. The microcomputer-based five-protection operation interlock system is the core of the system, responsible for storing operating rules and status information for electrical equipment, receiving operator instructions, and performing logical judgment and interlock control.

[0020] In an embodiment of the present application, the intelligent power monitoring system can centrally monitor a large number of distribution cabinets in different locations and different distribution sites.

[0021] In the embodiment of the present application, passive external temperature measuring points and active fastened temperature measuring points can be configured in the power distribution site to realize temperature monitoring of different areas and locations of the power distribution cabinet.

[0022] The intelligent power monitoring system of the present application can aggregate data such as temperature, current, voltage, and power factor at key nodes within each distribution cabinet. This aggregated data, along with data from all water and steam meters in the plant, can then be transmitted to the mainframe in the electrician's duty room. The mainframe in the electrician's duty room is equipped with a corresponding host control system, which can generate over-temperature, over-voltage, under-voltage, and over-current alarms based on this aggregated data.

[0023] The intelligent power monitoring system of the embodiment of the present application can regularly capture water, electricity and gas data, analyze production energy consumption and generate daily and monthly reports. In addition, it can compare high-voltage and low-voltage data to achieve energy consumption analysis.

[0024] The intelligent power monitoring system of the embodiment of the present application can realize data intercommunication between multiple plant areas, and understand the electrical energy consumption status and data of each plant in real time while ensuring data security.

[0025] Considering that existing power intelligent monitoring systems generally issue direct alarms when detecting electrical parameters exceeding the limit, maintenance personnel are required to carry out repairs in a timely manner, which easily wastes manpower and increases the workload of maintenance personnel. The power intelligent monitoring system of the embodiment of the present application can judge the collected intelligent monitoring data, reduce the probability of false alarms, more reasonably allocate manpower, reduce the workload of maintenance personnel, and improve maintenance efficiency.

[0026] See also Figure 1 , Figure 1 A flowchart of an intelligent monitoring data processing method provided in one embodiment of the present application can be executed by the above-mentioned power intelligent monitoring system. The method may include S101 and S104.

[0027] S101, acquiring monitoring data of a power distribution cabinet, the monitoring data including first-category monitoring data and second-category monitoring data, the first-category monitoring data including environmental data of the power distribution cabinet, the second-category monitoring data including electrical data of the power distribution cabinet.

[0028] The first type of monitoring data may include temperature values ​​in multiple areas of the power distribution cabinet. These areas are key heat dissipation zones or areas of focus within the cabinet. Temperature monitoring of these areas can be achieved by installing passive external temperature measuring points in these areas. Alternatively, active fixed temperature measuring points can be used to monitor the temperature of these areas. The choice can be based on actual circumstances.

[0029] The second type of monitoring data may include voltage and current data from the distribution cabinet. The intelligent power monitoring system can obtain relevant electrical data through the monitoring module in the distribution cabinet. Voltage and current data can be collected from key locations within the distribution cabinet.

[0030] The power intelligent monitoring system in the embodiment of this application can comprehensively collect temperature data of different areas and locations of the distribution cabinet, as well as electrical parameters such as voltage and current. There can be one or more distribution cabinets. This embodiment of the application mainly uses a single distribution cabinet as an example for description, and other situations are similar.

[0031] S102: Compare the first type of monitoring data with a preset environmental threshold.

[0032] The current common practice is to compare all monitoring data with the corresponding warning thresholds, which is rather cumbersome. Generally, the operating status of the distribution cabinet is relatively stable and there is no need for real-time comparison.

[0033] Therefore, this application takes into account that temperature changes are generally caused by the operation of electrical components. When the ambient temperature in a distribution cabinet area rises sharply, it is possible that the electrical components in that area have failed. At this time, determining whether the electrical parameters of the electrical components in that area are out of limit can reduce the possibility of false alarms. This application is mainly aimed at the overvoltage and overcurrent of electrical components in the distribution cabinet, which causes the distribution cabinet temperature to rise and affects the operating reliability of the distribution cabinet.

[0034] The first type of monitoring data may include temperature values ​​of multiple different areas, and the preset environmental threshold may include preset temperature warning values ​​of different areas, or the preset environmental threshold may be the lowest value among the preset temperature warning values ​​of multiple different areas. Specific selection may be made based on actual conditions.

[0035] Optionally, multiple first target data can be filtered from the first category of monitoring data according to the regional importance of different areas, and then the first target data can be compared with the preset environmental threshold corresponding to the area where the first target data is located to determine whether the temperature of the distribution cabinet exceeds the limit.

[0036] Specifically, the target data may be the temperature value corresponding to a region whose regional importance is greater than a preset importance. The regional importance of each region can be determined based on its location within the power distribution cabinet relative to the main input power conversion module. The closer the region is to the main input power conversion module, the greater its regional importance; the farther the region is from the main input power conversion module, the lower its regional importance. The temperature of the main input power conversion module directly affects the operating reliability of the power distribution cabinet.

[0037] Alternatively, each temperature value in the first type of monitoring data can be compared with a corresponding preset temperature warning value to determine whether the power distribution cabinet exceeds the limit. The specific selection can be made based on actual conditions.

[0038] S103: If there is first-category monitoring data exceeding a preset environmental warning threshold, the second-category monitoring data is compared with a preset electrical warning threshold.

[0039] Specifically, if there is a first amount of data in the first type of monitoring data that exceeds the corresponding preset environmental warning value, it can be determined that there is first type of monitoring data that exceeds the preset environmental warning threshold. At this time, it can be determined that the temperature of the distribution cabinet is abnormal, and the second type of monitoring data can be compared with the preset electrical warning threshold to determine the specific fault area or fault type.

[0040] The first number can be set according to actual conditions. The first number is different for each distribution cabinet. The first number can be proportional to the volume of the distribution cabinet, or the first number can be proportional to the number of electrical components in the distribution cabinet.

[0041] S104: Determine an operating status diagnosis strategy for the power distribution cabinet based on a comparison result of the second type of monitoring data and a preset electrical warning threshold.

[0042] In the embodiment of the present application, the second type of monitoring data includes voltage data and current data; the preset electrical warning thresholds include voltage warning values ​​and current warning values. Voltage data at different locations can correspond to different voltage warning values, and current data at different locations can correspond to different current warning values.

[0043] The electrical warning threshold for each location can be obtained by weighting the initial warning threshold based on the importance of the location. The initial warning threshold is the electrical warning threshold of the electrical component at that location. For example, it can be the overcurrent value or overvoltage value of the component.

[0044] The comparison result of the second type of monitoring data with the preset electrical warning threshold may include an overcurrent state or an overvoltage state, which are the main factors affecting the stable operation and temperature of the distribution cabinet.

[0045] After obtaining the corresponding comparison results, the corresponding operating status diagnostic strategy can be determined based on the different comparison results. For example, if the overcurrent state is detected, the overcurrent detection frequency is increased and the overcurrent threshold is lowered to more accurately identify the fault location. At the same time, the corresponding overcurrent treatment measures and the required overcurrent repair equipment can be sent to the maintenance personnel's work terminal, eliminating the need for reconfirmation by the maintenance personnel, thereby improving the maintenance personnel's overcurrent repair efficiency.

[0046] For example, if an overvoltage condition occurs, the frequency of overvoltage detection is increased and the overvoltage threshold is lowered to more accurately identify the fault location. Furthermore, the corresponding overvoltage treatment measures and the required overvoltage repair equipment can be sent to the maintenance personnel's work terminal, eliminating the need for reconfirmation by the maintenance personnel, thereby improving the maintenance personnel's overvoltage repair efficiency.

[0047] In an embodiment of the present application, the method may further include: If the number of times the second type of monitoring data is compared with the preset electrical warning threshold exceeds a preset number within a preset period, the preset environmental threshold is adjusted based on the operating status diagnosis strategy of the distribution cabinet.

[0048] If the temperature exceeds a certain limit more than a certain number of times in a short period of time, or if the overvoltage and overcurrent in the distribution cabinet exceed a certain number of times, and if the components have not been replaced, it indicates that the distribution cabinet is prone to failure. In this case, the preset environmental threshold can be adjusted to lower it to increase the detection frequency of the distribution cabinet and achieve reliable monitoring of the distribution cabinet.

[0049] The embodiment of the present application first compares whether the environmental data of the distribution cabinet exceeds the limit. If there is an over-limit, it then compares whether the electrical data of the distribution cabinet exceeds the limit. By making an intelligent judgment on the data in the distribution cabinet, a reasonable distribution cabinet operation status diagnosis strategy is given. Repair is performed without the need for an over-limit alarm, which can reduce manpower waste and reduce the workload of maintenance personnel.

[0050] In an embodiment of the present application, the first type of monitoring data includes temperature values ​​of multiple areas of the power distribution cabinet; the preset environmental warning values ​​include preset temperature warning values ​​corresponding to multiple areas of the power distribution cabinet; Accordingly, the first type of monitoring data is compared with the preset environmental thresholds, including: The temperature value of each area is compared with the preset temperature warning value corresponding to the area.

[0051] Accordingly, if there is first-category monitoring data that exceeds the preset environmental warning threshold, the second-category monitoring data is compared with the preset electrical warning threshold, including: If the number of areas where the temperature exceeds the corresponding preset temperature warning value is greater than the target number, the second type of monitoring data is compared with the preset electrical warning threshold.

[0052] The embodiment of the present application triggers the comparison of electrical appliance parameters only when the number of temperature abnormality areas exceeds a certain threshold, which not only reduces false alarms but also reduces unnecessary computing resource consumption.

[0053] In an embodiment of the present application, the second type of monitoring data includes voltage data and current data; the preset electrical warning threshold includes a voltage warning value and a current warning value; Comparing the second type of monitoring data with preset electrical warning thresholds includes: comparing the voltage data with a voltage warning value, and comparing the current data with a current warning value; Accordingly, based on the comparison result of the second type of monitoring data with the preset electrical warning threshold, the operation status diagnosis strategy of the distribution cabinet is determined, including: marking the number of voltage data exceeding the voltage warning value as a first number; marking the number of current data exceeding the current warning value as a second number; Based on the size relationship between the first quantity and the second quantity, an operation status diagnosis strategy of the power distribution cabinet is determined.

[0054] Specifically, if the first number is greater than the second number, and the second number is less than a preset number, the power distribution cabinet's operating status diagnostic strategy is determined to be an overvoltage state diagnostic strategy. In this case, locations in the power distribution cabinet that are prone to overvoltage can be specifically marked. These areas or locations that are prone to overvoltage can be determined based on historical overvoltage locations of similar power distribution cabinets.

[0055] If the first number is less than the second number, and the first number is less than a preset number, the power distribution cabinet's operating status diagnostic strategy is determined to be an overcurrent status diagnostic strategy. In this case, locations in the power distribution cabinet prone to overcurrent can be specifically identified. These areas or locations prone to overcurrent can be determined based on historical overcurrent locations of similar power distribution cabinets.

[0056] If the first number is equal to the second number, and both the first number and the second number are greater than the preset number, then the distribution cabinet operating status diagnostic strategy is determined to be an overvoltage-overcurrent diagnostic strategy. In this case, the locations in the distribution cabinet that are prone to overvoltage-overcurrent can be marked. The areas or locations prone to overcurrent can be determined based on the historical overvoltage-overcurrent locations of the same type of distribution cabinet. In an embodiment of the present application, the preset number can be determined based on the number of historical faults.

[0057] The embodiment of the present application determines the operating status diagnosis strategy of different distribution cabinets by combining the number of voltage overruns and the number of current overruns, which is beneficial for maintenance personnel to quickly locate the fault location and perform fault repair according to different operating status diagnosis strategies, thereby improving the work efficiency of maintenance personnel.

[0058] In an embodiment of the present application, before comparing the first type of monitoring data with the preset environmental threshold, the intelligent monitoring data processing method further includes: Obtain temperature parameters and usage parameters of multiple key components in the area corresponding to the first type of monitoring data; Determine the temperature over-limit threshold of each key component based on the temperature parameters and usage parameters of the key component; The preset environmental threshold for the area is determined based on the temperature exceeding the limit threshold of each key component.

[0059] In an embodiment of the present application, a first model may be constructed, and the first model is used to determine a temperature exceeding a limit threshold of a key component.

[0060] Specifically, the first model can be a trained linear fitting model. First, a linear fit can be performed on the historical temperature parameters, historical usage parameters, and actual operating temperature alarm thresholds of a key component in other distribution cabinets to obtain the first model. The historical temperature parameters and historical usage parameters can be placed as a parameter pair on the horizontal axis, and the vertical axis is the temperature alarm threshold of the key component. In actual use, the temperature parameters and usage parameters of the corresponding key component can be input into the first model to obtain the corresponding temperature alarm threshold, which can be used as the temperature over-limit threshold of the key component.

[0061] Each key component may correspond to a different first model, and a temperature over-limit threshold for each key component may be obtained through the model output. After obtaining the temperature over-limit threshold for each key component, a minimum temperature over-limit threshold may be selected from the temperature over-limit thresholds for each key component as the preset environmental threshold for the area.

[0062] Before comparing the second type of monitoring data with the preset electrical warning threshold, the intelligent monitoring data processing method further includes: Obtaining out-of-limit parameters and usage parameters of multiple electrical components corresponding to the second type of monitoring data; Determine the electrical over-limit threshold of each electrical component based on the over-limit parameters and usage parameters of the electrical component; A preset electrical warning threshold is determined based on the electrical over-limit threshold of each electrical component.

[0063] In the embodiments of the present application, the temperature parameter of a key component may include the maximum operating temperature of the key component, and the usage parameter of a key component may include the operating time of the key component. The over-limit parameter of an electrical component may include the maximum withstand voltage or current value of the electrical component, and the usage parameter of an electrical component may include the operating time of the electrical component.

[0064] In an embodiment of the present application, a second model may be constructed, and the second model is used to determine the electrical over-limit threshold of a certain electrical component.

[0065] Specifically, the second model can be a trained linear fitting model. First, a linear fit can be performed based on the historical over-limit parameters and usage parameters of a particular electrical component in other distribution cabinets to generate the second model. In actual use, the over-limit parameters and usage parameters of the corresponding electrical component can be input into the second model to obtain the corresponding electrical alarm threshold, which serves as the electrical over-limit threshold for the electrical component.

[0066] Each electrical component may correspond to a different second model, and the electrical alarm threshold of each electrical component may be obtained through the model output. After obtaining the electrical alarm threshold of each electrical component, the minimum electrical alarm threshold may be selected from the electrical alarm thresholds of each electrical component as the preset electrical warning threshold.

[0067] In the embodiments of the present application, the intelligent power monitoring system can pre-enter the temperature parameters, over-limit parameters, and usage parameters of each electrical component. For example, the initial usage time and material parameter information of each electrical component can be pre-recorded. During operation, the operating time of each electrical component can be determined based on the operating time and operating status of the power distribution cabinet. The operating status can include the startup state or the dormant state. Dormant components are not included in the operating time calculation.

[0068] In an embodiment of the present application, the temperature exceeding limit threshold of a certain area can be calculated according to the following formula:

[0069] Where Q represents the temperature exceeding the limit threshold of a certain area, k i represents the weight of the i-th key component in the region, t i represents the working time weight of the i-th key component in the area, n represents the number of key components in the area, T n Indicates the maximum factory operating temperature of the nth key component in the area. As the operating time increases, the operating performance of each component gradually decreases. i Determined based on experimental data or historical data statistics.

[0070] In the embodiment of the present application, the ambient temperature varies in different seasons, which has different effects on the heat dissipation and performance of the electrical components in the distribution cabinet. Therefore, the embodiment of the present application can dynamically adjust the weight coefficient k in the threshold calculation formula according to the season. i .

[0071] In summer, the ambient temperature is high and the heat dissipation conditions of electrical components are relatively poor. Therefore, the weight coefficient k can be increased. i In winter, the ambient temperature is low and the heat dissipation of electrical components is relatively good, so the weight coefficient k can be appropriately reduced. i to ensure the reliability of threshold calculation.

[0072] In the embodiment of the present application, the weight coefficient ki can change according to the change of the external ambient temperature of the power distribution cabinet. The weight coefficient ki increases with the increase of the external ambient temperature and decreases with the decrease of the external ambient temperature. Basically, the weight coefficient k increases with each increase of 1 degree Celsius in the external ambient temperature. i Compared to the initial coefficient, it increases by 5%.

[0073] In the embodiment of the present application, the electrical over-limit threshold of the electrical component decreases as the working time of the electrical component increases. The specific attenuation coefficient can be determined according to actual conditions.

[0074] In addition, the embodiment of the present application can also determine the electrical over-limit threshold of the electrical component according to the aging degree of the electrical component, and can be adjusted according to actual conditions.

[0075] This application takes into account that a power distribution cabinet may operate in different working environments, which have different requirements for the operating reliability of the power distribution cabinet and may also affect the over-limit threshold of the power distribution cabinet in different working environments. Therefore, by setting a dynamically changing threshold, the embodiments of this application can improve the adaptability of the power distribution cabinet in different working environments.

[0076] Corresponding to an intelligent monitoring data processing method of the above embodiment, Figure 2 This is a structural block diagram of an intelligent monitoring data processing system provided by an embodiment of the present application. For ease of explanation, only the parts related to the embodiment of the present application are shown. Figure 2 , the intelligent monitoring data processing system 20 includes: The data acquisition module 201 is used to acquire monitoring data of the power distribution cabinet, the monitoring data including first-category monitoring data and second-category monitoring data, the first-category monitoring data including environmental data of the power distribution cabinet, and the second-category monitoring data including electrical data of the power distribution cabinet; A first comparison module 202 is used to compare the first type of monitoring data with a preset environmental threshold; A second comparison module 203 is configured to compare the second type of monitoring data with a preset electrical warning threshold if there is first type of monitoring data that exceeds a preset environmental warning threshold; The strategy determination module 204 is configured to determine an operation status diagnosis strategy of the power distribution cabinet based on a comparison result between the second type of monitoring data and a preset electrical warning threshold.

[0077] In one embodiment of the present application, the first type of monitoring data includes temperature values ​​of multiple areas of the power distribution cabinet; the preset environmental warning values ​​include preset temperature warning values ​​corresponding to multiple areas of the power distribution cabinet; The first comparison module 202 is specifically configured to compare the temperature value of each area with a preset temperature warning value corresponding to the area.

[0078] In one embodiment of the present application, the second comparison module 203 is specifically configured to compare the second type of monitoring data with a preset electrical warning threshold if the number of regions with temperature values ​​exceeding the corresponding preset temperature warning value is greater than a target number.

[0079] In one embodiment of the present application, the second type of monitoring data includes voltage data and current data; the preset electrical warning threshold includes a voltage warning value and a current warning value; In one embodiment of the present application, the second comparison module 203 is specifically configured to compare the voltage data with the voltage warning value, and to compare the current data with the current warning value; Accordingly, the strategy determination module 204 is specifically configured to mark the number of voltage data exceeding the voltage warning value as a first number; marking the number of current data exceeding the current warning value as a second number; Based on the size relationship between the first quantity and the second quantity, an operation status diagnosis strategy of the power distribution cabinet is determined.

[0080] In one embodiment of the present application, the system further includes: A first threshold adjustment module is configured to obtain temperature parameters and usage parameters of a plurality of key components in an area corresponding to the first type of monitoring data before comparing the first type of monitoring data with a preset environmental threshold; Determine the temperature over-limit threshold of each key component based on the temperature parameters and usage parameters of the key component; The preset environmental threshold for the area is determined based on the temperature exceeding the limit threshold of each key component.

[0081] In one embodiment of the present application, the system further includes: a second threshold adjustment module, configured to obtain, before comparing the second type of monitoring data with a preset electrical warning threshold, over-limit parameters and usage parameters of a plurality of electrical components corresponding to the second type of monitoring data; Determine the electrical over-limit threshold of each electrical component based on the over-limit parameters and usage parameters of the electrical component; A preset electrical warning threshold is determined based on the electrical over-limit threshold of each electrical component.

[0082] In one embodiment of the present application, the system further includes: The third threshold adjustment module is used to adjust the preset environmental threshold based on the operating status diagnosis strategy of the distribution cabinet if the number of times the second type of monitoring data is compared with the preset electrical warning threshold exceeds the preset number within the preset period.

[0083] See also Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided in one embodiment of the present application. Figure 3The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules / units in the above-mentioned system embodiments, such as Figure 2 The functions of the data acquisition module 201, the first comparison module 202, the second comparison module 203 and the strategy determination module 204 are shown.

[0084] It should be understood that in the embodiment of the present application, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0085] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0086] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301 .

[0087] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiment of the present application can execute the implementation method described in the intelligent monitoring data processing method provided in the embodiment of the present application, and can also execute the implementation method of the electronic device described in the embodiment of the present application, which will not be repeated here.

[0088] In another embodiment of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0089] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0090] An embodiment of the present application provides a computer program product, which includes computer-executable instructions or a computer program, and the computer-executable instructions or computer program are stored in a computer-readable storage medium. The processor of the electronic device reads the computer-executable instructions from the computer-readable storage medium, and the processor executes the computer-executable instructions, so that the electronic device performs the intelligent monitoring data processing method described above in the embodiment of the present application.

[0091] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software 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.

[0092] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0093] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or can be an electrical, mechanical or other form of connection.

[0094] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0095] The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. An intelligent monitoring data processing method, characterized in that: include: Acquire monitoring data of the power distribution cabinet, the monitoring data including first-category monitoring data and second-category monitoring data, the first-category monitoring data including environmental data of the power distribution cabinet, and the second-category monitoring data including electrical data of the power distribution cabinet; comparing the first type of monitoring data with a preset environmental threshold; If there is first-category monitoring data that exceeds the preset environmental warning threshold, comparing the second-category monitoring data with the preset electrical warning threshold; Based on the comparison result of the second type of monitoring data and the preset electrical warning threshold, the operation status diagnosis strategy of the distribution cabinet is determined.

2. The intelligent monitoring data processing method according to claim 1, wherein: The first type of monitoring data includes temperature values ​​of multiple areas of the power distribution cabinet; the preset environmental warning values ​​include preset temperature warning values ​​corresponding to multiple areas of the power distribution cabinet; Accordingly, comparing the first type of monitoring data with the preset environmental threshold includes: The temperature value of each area is compared with the preset temperature warning value corresponding to the area.

3. The intelligent monitoring data processing method according to claim 2, wherein: If there is first-category monitoring data exceeding the preset environmental warning threshold, comparing the second-category monitoring data with the preset electrical warning threshold includes: If the number of areas where the temperature exceeds the corresponding preset temperature warning value is greater than the target number, the second type of monitoring data is compared with the preset electrical warning threshold.

4. The intelligent monitoring data processing method according to claim 1, wherein: The second type of monitoring data includes voltage data and current data; The preset electrical warning thresholds include a voltage warning value and a current warning value; Comparing the second type of monitoring data with a preset electrical warning threshold value includes: comparing the voltage data with the voltage warning value, and comparing the current data with the current warning value; Accordingly, the operation status diagnosis strategy of the power distribution cabinet is determined based on the comparison result of the second type of monitoring data with the preset electrical warning threshold, including: marking the number of voltage data exceeding the voltage warning value as a first number; marking the number of current data exceeding the current warning value as a second number; Based on the size relationship between the first quantity and the second quantity, an operation status diagnosis strategy of the power distribution cabinet is determined.

5. The intelligent monitoring data processing method according to claim 1, wherein: Before comparing the first type of monitoring data with the preset environmental threshold, it also includes: Obtain temperature parameters and usage parameters of multiple key components in the area corresponding to the first type of monitoring data; Determine the temperature over-limit threshold of each key component based on the temperature parameters and usage parameters of the key component; The preset environmental threshold for the area is determined based on the temperature exceeding the limit threshold of each key component.

6. The intelligent monitoring data processing method according to claim 1, wherein: Before comparing the second type of monitoring data with the preset electrical warning threshold, it also includes: Obtaining out-of-limit parameters and usage parameters of multiple electrical components corresponding to the second type of monitoring data; Determine the electrical over-limit threshold of each electrical component based on the over-limit parameters and usage parameters of the electrical component; A preset electrical warning threshold is determined based on the electrical over-limit threshold of each electrical component.

7. The intelligent monitoring data processing method according to claim 1, wherein: Also includes: If the number of times the second type of monitoring data is compared with the preset electrical warning threshold exceeds a preset number within a preset period, the preset environmental threshold is adjusted based on the operating status diagnosis strategy of the distribution cabinet.

8. An intelligent monitoring data processing system, characterized in that: include: A data acquisition module is used to acquire monitoring data of the power distribution cabinet, wherein the monitoring data includes first-category monitoring data and second-category monitoring data, wherein the first-category monitoring data includes environmental data of the power distribution cabinet, and the second-category monitoring data includes electrical data of the power distribution cabinet; A first comparison module, configured to compare the first type of monitoring data with a preset environmental threshold; a second comparison module, configured to compare the second type of monitoring data with a preset electrical warning threshold if there is first type of monitoring data exceeding the preset environmental warning threshold; The strategy determination module is used to determine the operation status diagnosis strategy of the distribution cabinet based on the comparison result of the second type of monitoring data and the preset electrical warning threshold.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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