Power distribution room monitoring method and device and electronic equipment

Abstract

The invention relates to the technical field of intelligent monitoring, and provides a power distribution room monitoring method and device and electronic equipment, and aims at the current power distribution room environment, if no abnormity is detected, the time interval of the next monitoring is dynamically adjusted according to the real-time operation parameters of a power distribution room, the historical alarm record and a preset interval increasing sequence, and the power distribution room is monitored. And monitoring again after the time interval of the next monitoring is reached, so as to prolong the sleep time of the monitoring equipment to the greatest extent to realize energy conservation when no abnormity is ensured, and if abnormity is detected, controlling the monitoring equipment to continuously monitor the change trend of an abnormal state, so as to immediately enter a continuous monitoring state to ensure safety when the abnormity occurs. Therefore, the monitoring effect can be ensured, the abnormity can be timely and accurately found, and the energy consumption of the equipment can be greatly reduced.

Description

Technical Field

[0001] This application relates to the field of intelligent monitoring technology, and more specifically, to a method, device, and electronic equipment for monitoring a power distribution room. Background Technology

[0002] As a critical node in the power system, the safe and stable operation of power distribution rooms is of paramount importance. Currently, the industry's common solution for monitoring the status of power distribution rooms is a video surveillance system based on network cameras (Internet Protocol Cameras, IPCs).

[0003] Specifically, this solution typically deploys several IPCs in key equipment areas (such as switchgear, transformers, instrument panels, cable joints, etc.) inside the power distribution room. These IPCs provide real-time video streams or periodically captured images for remote viewing by maintenance personnel or can be processed by simple image analysis algorithms to attempt to detect abnormal situations such as abnormal temperatures, instrument readings exceeding limits, and changes in equipment status.

[0004] However, this existing solution has several limitations: First, to ensure imaging quality at night or in low-light conditions, the IPC needs to rely on supplementary lighting. Currently, most supplementary lights operate in a constant-on mode or a high-power mode based on simple photosensitive / motion-triggered operation, resulting in significant energy waste in unattended power distribution rooms, which contradicts the goal of energy conservation and efficiency improvement in power facilities. Second, its inspection strategy has a low level of intelligence, relying heavily on manual timed inspections or fixed-interval image capture. Manual timed inspections are inefficient, and for fixed-interval strategies, too short an interval will generate a large amount of redundant data, occupying storage resources, while too long an interval will easily miss sudden faults. Summary of the Invention

[0005] The purpose of this application is to provide a power distribution room monitoring method, device, and electronic equipment that can ensure monitoring effectiveness, detect anomalies in a timely and accurate manner, and significantly reduce equipment energy consumption.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows: In a first aspect, embodiments of this application provide a method for monitoring a power distribution room, the method comprising: When the set trigger conditions are met, the control monitoring equipment collects monitoring data from the power distribution room; The monitoring data is analyzed to determine whether there are any abnormal conditions in the current power distribution room environment; If the abnormal state does not exist in the current power distribution room environment, the time interval for the next monitoring will be dynamically adjusted according to the real-time operating parameters of the power distribution room, historical alarm records and preset interval increment sequence, and the step of controlling the monitoring equipment to collect the monitoring data of the power distribution room will be returned after the time interval for the next monitoring is reached. If the abnormal state exists in the current power distribution room environment, the monitoring equipment is controlled to continuously collect monitoring data of the power distribution room and perform real-time analysis on the monitoring data to continuously monitor the changing trend of the abnormal state.

[0007] Optionally, the real-time operating parameters include station environmental operating parameters and station electrical operating parameters, and the interval increment sequence includes multiple incrementing set durations and an index for each set duration; The time interval for the next monitoring is dynamically adjusted based on the real-time operating parameters of the power distribution room, historical alarm records, and a preset incremental sequence, including: The dynamic risk coefficient of the power distribution room is determined based on the station's environmental operating parameters, electrical operating parameters, and historical alarm records. The dynamic risk coefficient is positively correlated with the station's environmental operating parameters, electrical operating parameters, and historical alarm records. The number of consecutive times that the abnormal state does not exist in the current power distribution room environment is used as the target index, and the set duration corresponding to the target index is obtained from the interval increment sequence; The ratio of the set duration corresponding to the target index to the dynamic risk coefficient is calculated to obtain the time interval for the next monitoring.

[0008] Optionally, the station's environmental operating parameters include data from multiple environmental sensors, and the station's electrical operating parameters include data from multiple electrical sensors. Based on the station's environmental operating parameters, electrical operating parameters, and historical alarm records, the dynamic risk coefficient of the power distribution room is determined, including: The comprehensive environmental operating parameter evaluation value is obtained by comprehensively evaluating the data from the various environmental sensors. The comprehensive electrical operating parameter evaluation value is obtained by comprehensively evaluating the data from the various types of electrical sensors. Based on the historical alarm records and the pre-built time-attenuation-based weighted model, the historical failure rate of the power distribution room is obtained. The comprehensive environmental operation parameter evaluation value, the comprehensive electrical operation parameter evaluation value, and the historical failure rate are normalized to obtain the normalized comprehensive environmental operation parameter evaluation value, comprehensive electrical operation parameter evaluation value, and historical failure rate. The dynamic risk coefficient is obtained by multiplying the normalized comprehensive environmental operation parameter evaluation value, the comprehensive electrical operation parameter evaluation value, and the historical failure rate.

[0009] Optionally, the historical alarm record includes multiple historical alarm data entries, each of which includes at least one abnormal event; the time decay-based weighted model includes an abnormal event weight table and a time decay model, wherein the abnormal event weight table includes multiple abnormal events and a weight corresponding to each abnormal event, and the weight is positively correlated with the severity of the abnormal event; the time decay model characterizes the weakening of the impact of the abnormal event over time. Based on the historical alarm records and a pre-built time-attenuation-based weighted model, the historical failure rate of the power distribution room is obtained, including: For each piece of historical alarm data, the weight of each abnormal event in the historical alarm data is obtained from the abnormal event weight table, and the time decay coefficient of each abnormal event in the historical alarm data is calculated based on the time decay model. The historical failure rate is obtained by weighting and summing the weights and time decay coefficients of all the aforementioned abnormal events.

[0010] Optionally, the time decay model is an exponential decay model or a linear decay model, wherein the exponential decay model satisfies the following formula:

[0011] The linear decay model satisfies the following formula:

[0012] in, This represents the time decay coefficient for the i-th type of abnormal event. Represents the decay rate constant. This indicates the number of days since the occurrence of the abnormal event. Indicates the maximum number of valid days for an abnormal event.

[0013] Optionally, the monitoring device is a combination of a supplementary light and a camera, or an infrared thermal imager; The control and monitoring equipment collects monitoring data from the power distribution room, including: For the combination of a fill light and a camera, the fill light is controlled to flash while the camera is simultaneously controlled to capture images. For an infrared thermal imager, control the infrared thermal imager to perform a full-area thermal imaging scan.

[0014] Optionally, the monitoring device is a combination of a supplementary light and a camera, or an infrared thermal imager; Controlling the monitoring equipment to continuously collect monitoring data from the power distribution room and performing real-time analysis on the monitoring data to continuously monitor the changing trend of the abnormal state includes: For the combination of a fill light and a camera, the fill light is controlled to remain constantly on while the camera is simultaneously controlled to record continuously in real time. The video stream captured by the camera is analyzed in real time to continuously monitor the changing trend of the abnormal state. Alternatively, the fill light is controlled to flash at high frequency while the camera is simultaneously controlled to capture images at high frequency. The images captured by the camera are analyzed in real time to continuously monitor the changing trend of the abnormal state. For the infrared thermal imager, the infrared thermal imager is controlled to scan at high frequency, and the thermal imaging images acquired by the infrared thermal imager are analyzed in real time to continuously monitor the changing trend of the abnormal state.

[0015] Optionally, after controlling the monitoring equipment to continuously collect monitoring data from the power distribution room and performing real-time analysis on the monitoring data to continuously monitor the changing trend of the abnormal state, the method further includes: If the abnormal state disappears and does not reappear within the set time window, then return to the steps of controlling the fill light to flash and controlling the camera to capture images.

[0016] Secondly, embodiments of this application provide a power distribution room monitoring device, the device comprising: The control module is used to control the monitoring equipment to collect monitoring data of the power distribution room when the set trigger conditions are met. The judgment module is used to analyze the monitoring data and determine whether there is an abnormal state in the current power distribution room environment; The first execution module is used to dynamically adjust the time interval of the next monitoring based on the real-time operating parameters of the power distribution room, historical alarm records and a preset interval increment sequence if the abnormal state does not exist in the current power distribution room environment, and return to the step of controlling the monitoring equipment to collect the monitoring data of the power distribution room after the time interval of the next monitoring is reached. The second execution module is used to control the monitoring device to continuously collect monitoring data of the power distribution room if the abnormal state exists in the current power distribution room environment, and to perform real-time analysis of the monitoring data to continuously monitor the changing trend of the abnormal state.

[0017] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory, wherein the memory is used to store a program, and the processor is used to implement the power distribution room monitoring method described in the first aspect when executing the program.

[0018] Compared to existing technologies, the power distribution room monitoring method, device, and electronic equipment provided in this application, when a set trigger condition is met, controls the monitoring equipment to collect monitoring data from the power distribution room, analyzes the monitoring data, and determines whether there is an abnormal state in the current power distribution room environment. If there is no abnormality, the monitoring time interval is dynamically adjusted according to the real-time operating parameters of the power distribution room, historical alarm records, and a preset interval increment sequence, and monitoring is restarted after the next monitoring time interval arrives. If there is an abnormality, the monitoring equipment continuously collects monitoring data from the power distribution room and performs real-time analysis of the monitoring data to continuously monitor the changing trend of the abnormal state. In this way, the monitoring equipment's sleep time is maximized to achieve energy saving when there is no abnormality, while it immediately enters continuous monitoring mode to ensure safety when an abnormality occurs. This ensures both monitoring effectiveness and timely and accurate detection of abnormalities, while significantly reducing equipment energy consumption. Attached Figure Description

[0019] Figure 1 This illustration shows an application diagram of a power distribution room monitoring scenario provided by an embodiment of this application.

[0020] Figure 2 This application provides a schematic flowchart of a power distribution room monitoring method according to an embodiment. Figure 1 .

[0021] Figure 3 This application provides a schematic flowchart of a power distribution room monitoring method according to an embodiment. Figure 2 .

[0022] Figure 4 A block diagram of a power distribution room monitoring device provided in an embodiment of this application is shown.

[0023] Figure 5 A block diagram of an electronic device provided in an embodiment of this application is shown.

[0024] Icons: 100-Power distribution room monitoring device; 101-Control module; 102-Judgment module; 103-First execution module; 104-Second execution module; 10-Electronic device; 11-Processor; 12-Memory; 13-Bus. Detailed Implementation

[0025] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0026] The technical solutions provided in the embodiments of this application can be applied to, for example... Figure 1 The power distribution room monitoring scenario shown includes electronic equipment and monitoring equipment, which are connected via a bus. The electronic equipment is responsible for decision-making and control, while the monitoring equipment performs data acquisition.

[0027] The monitoring equipment can be a combination of a supplementary light and a camera, or an infrared thermal imager. For the supplementary light and camera combination, the two work together to form a complete monitoring system. This system is based on visible light imaging, and its workflow is as follows: When the system needs monitoring (such as scheduled inspections, insufficient light, or detection of moving objects), the electronic equipment first controls the supplementary light to turn on (constantly on or flashing) to provide illumination to the site. Subsequently, the electronic equipment synchronously triggers the camera to capture clear visible light images under sufficient lighting. This is mainly used for viewing instrument readings, identifying equipment status, confirming switch openings and closings, and monitoring personnel activities.

[0028] Infrared thermal imagers are based on thermal radiation imaging. They do not emit any light themselves, but directly receive the infrared thermal radiation emitted by the electrical equipment. They convert the received thermal radiation signal into an electrical signal and generate a real-time thermal image reflecting the temperature distribution on the object's surface. This image is color, with different colors representing different temperatures. It is primarily used for non-contact detection of overheating hazards inside equipment (such as overheated cable joints, partial discharge in lines, and abnormal transformer temperature rise), enabling early warning of faults.

[0029] Supplemental lighting (especially high-power LEDs) and infrared thermal imagers are both energy-intensive devices. Traditional power distribution room monitoring solutions require them to operate continuously or frequently. While this ensures safety, it comes at a huge energy cost and contradicts the concept of intelligent and green operation of power distribution rooms.

[0030] Based on this, this application provides a method for monitoring a power distribution room. When a set trigger condition is met, the monitoring equipment is controlled to collect monitoring data from the power distribution room. The monitoring data is analyzed to determine whether there is an abnormal state in the current power distribution room environment. If there is no abnormality, the time interval for the next monitoring is dynamically adjusted according to the real-time operating parameters of the power distribution room, historical alarm records, and a preset interval increment sequence. Monitoring is restarted after the next monitoring time interval arrives. If there is an abnormality, the monitoring equipment is controlled to continuously collect monitoring data from the power distribution room and analyze the monitoring data in real time to continuously monitor the changing trend of the abnormal state. In this way, the sleep time of the monitoring equipment is maximized to achieve energy saving when there is no abnormality, while it immediately enters continuous monitoring mode to ensure safety when an abnormality occurs. This ensures both monitoring effectiveness and timely and accurate detection of abnormalities, while significantly reducing equipment energy consumption. A detailed description is provided below with reference to the accompanying drawings.

[0031] The power distribution room monitoring method provided in this application embodiment is applied to... Figure 1The electronic device in the application can be a server, such as a single server or a server cluster, or a terminal, such as a smartphone, laptop, tablet, or desktop computer. It can also be a development board or a chip system. The chip system can be composed of chips or may include chips and other discrete devices. This application does not impose any restrictions on this.

[0032] Please refer to Figure 2 , Figure 2 This paper illustrates a flowchart of a power distribution room monitoring method provided in an embodiment of this application, which may include the following steps: S101, when the set trigger conditions are met, the control monitoring equipment collects monitoring data from the power distribution room.

[0033] S102, Analyze the monitoring data to determine whether there are any abnormal conditions in the current power distribution room environment.

[0034] S103. If there is no abnormal state in the current power distribution room environment, the time interval for the next monitoring is dynamically adjusted according to the real-time operating parameters of the power distribution room, historical alarm records and preset interval increment sequence, and the process returns to step S101 after the time interval for the next monitoring is reached.

[0035] S104 If there is an abnormal state in the current power distribution room environment, the control monitoring equipment continuously collects the monitoring data of the power distribution room and performs real-time analysis on the monitoring data to continuously monitor the changing trend of the abnormal state.

[0036] In step S101, the triggering condition can be, but is not limited to, the device being powered on or the system restarting, the system recovering from an abnormal state (such as a crash, network interruption, etc.), or receiving a remote instruction manually issued by the maintenance personnel (such as an initialization instruction, an immediate inspection instruction, etc.), or reaching a preset fixed time period (for example, 00:00 every day).

[0037] In other words, system startup, system recovery, or remote command triggering activates the monitoring equipment to collect monitoring data from the power distribution room and initiate routine inspections. Alternatively, the monitoring equipment can be controlled to collect monitoring data from the power distribution room at fixed times each day to complete scheduled inspections. This mechanism ensures that the monitoring system can acquire a reliable on-site baseline image in any initial state and proactively detect potential anomalies during unattended periods through scheduled mandatory inspections. Its core is to achieve a hard check of the power distribution room's status through preset, non-skippable monitoring actions.

[0038] It should be noted that, in addition to the examples given above, the triggering conditions can also be flexibly set by the operation and maintenance personnel according to actual needs. For example, threshold triggering (such as temperature > 50℃, voltage fluctuation exceeding ±10%, etc.) and event triggering (such as equipment fault signal, smoke alarm, access control abnormality, etc.). This embodiment does not impose any restrictions on this.

[0039] In one alternative implementation, for a combination of a fill light and a camera, the fill light is controlled to flash while the camera is simultaneously controlled to acquire images; for an infrared thermal imager, the infrared thermal imager is controlled to perform a full-area thermal imaging scan.

[0040] In step S102, for the combination of supplementary lighting and camera, the captured images are analyzed to detect whether there are any abnormal conditions in the current power distribution room environment. These abnormal conditions may include, but are not limited to: small animal intrusion, dripping or stagnant water, smoke or open flame, instrument readings exceeding thresholds, or abnormal appearance.

[0041] Infrared thermal imagers analyze the acquired thermal images to detect any abnormal conditions in the current power distribution room environment. These abnormal conditions may include, but are not limited to, localized overheating, loose connections, and insulation deterioration.

[0042] The process of detecting abnormal conditions in the current power distribution room environment by analyzing captured images or thermal imaging images can employ existing technologies. For example, for captured images, deep learning models can be used for target detection and recognition, and the recognition results can be analyzed to determine whether an abnormal condition exists. For thermal imaging images, abnormal temperature rise areas can be detected by judging whether they exceed set temperature thresholds or temperature difference thresholds, and the presence of abnormal conditions can be determined by combining historical operating data of the equipment. This embodiment does not impose any limitations on this.

[0043] In step S103, if no abnormal state is detected in the current power distribution room environment, the system enters the energy-saving detection mode. In this mode, the time interval for the next monitoring is determined by the real-time operating parameters of the power distribution room, historical alarm records, and a preset interval increment sequence, rather than being fixed. The overall principle is to maximize the sleep time of the monitoring equipment to achieve energy saving when there are no abnormalities.

[0044] In one optional implementation, real-time operating parameters may include station environmental operating parameters and station electrical operating parameters. The station environmental operating parameters include data from various environmental sensors, such as real-time data collected from various environmental sensors within the power distribution room (e.g., temperature and humidity sensors, noise sensors, hazardous gas sensors, smoke sensors, water immersion sensors, cable trench water level sensors, etc.), which includes data on temperature, humidity, noise, hazardous gas concentration, smoke concentration, station building water accumulation rate, and cable trench water level. The station electrical operating parameters include data from various electrical sensors within the power distribution room, such as real-time data collected from various electrical sensors (e.g., switchgear temperature sensors, transient ground voltage amplitude sensors, operating current sensors, etc.), which includes data on switchgear temperature, transient ground voltage amplitude, and operating current.

[0045] An incrementing interval sequence includes multiple incrementing preset durations and an index for each preset duration, for example, ,in, This represents n incrementing duration settings. This represents the index for each set duration. Optionally, the interval increasing sequence can use a geometric progression, such as 1s, 2s, 4s, 8s..., or it can use a Fibonacci progression or other non-linear increasing strategies. This embodiment does not impose any restrictions on this.

[0046] Historical alarm records can be alarm records from the power distribution room within a recent period (e.g., within 30 days) retrieved from the platform's database. The query duration can be flexibly set by maintenance personnel according to actual needs. For example, maintenance personnel can select a fixed "last 30 days" through the platform's calendar component, or customize a specific time period such as "March 1, 2024 to April 15, 2024" to accurately obtain the required historical alarm records for analysis. The core purpose is to balance the comprehensiveness of the data with retrieval efficiency.

[0047] The process of dynamically adjusting the time interval for the next monitoring based on the real-time operating parameters of the power distribution room, historical alarm records, and a preset interval increment sequence in step S103 may include sub-steps S1031 to S1033.

[0048] S1031. Based on the station's environmental operating parameters, electrical operating parameters, and historical alarm records, determine the dynamic risk coefficient of the power distribution room. The dynamic risk coefficient is positively correlated with the station's environmental operating parameters, electrical operating parameters, and historical alarm records. S1032, take the number of times that there is no abnormal state in the current power distribution room environment as the target index, and obtain the set duration corresponding to the target index from the interval increment sequence; S1033, calculate the ratio of the set duration corresponding to the target index to the dynamic risk coefficient to obtain the time interval for the next monitoring.

[0049] In sub-step S1031, the dynamic risk coefficient is a real number greater than 1. Its value is determined by the operating parameters of the substation environment, the electrical operating parameters of the substation, and the historical alarm records. Specifically, the dynamic risk coefficient is positively correlated with the operating parameters of the substation environment (including temperature, humidity, noise, concentration of harmful gases, smoke concentration, water accumulation rate in the substation building, water level in the cable trench, etc.), the electrical operating parameters of the substation (including switchgear temperature, transient ground voltage amplitude, operating current, etc.) and the historical alarm records. That is, the worse the environmental condition, the worse the electrical condition, and the more frequent the historical faults, the greater the dynamic risk coefficient.

[0050] In one optional implementation, the process of determining the dynamic risk coefficient of the substation based on the substation's environmental operating parameters, electrical operating parameters, and historical alarm records may include: comprehensively evaluating multiple types of environmental sensor data to obtain a comprehensive environmental operating parameter evaluation value; comprehensively evaluating multiple types of electrical sensor data to obtain a comprehensive electrical operating parameter evaluation value; obtaining the historical failure rate of the substation based on historical alarm records and a pre-built time-attenuation-based weighted model; normalizing the comprehensive environmental operating parameter evaluation value, the comprehensive electrical operating parameter evaluation value, and the historical failure rate to obtain normalized comprehensive environmental operating parameter evaluation values, comprehensive electrical operating parameter evaluation values, and the historical failure rate; and multiplying the normalized comprehensive environmental operating parameter evaluation value, the comprehensive electrical operating parameter evaluation value, and the historical failure rate to obtain the dynamic risk coefficient. The comprehensive evaluation of data from multiple environmental sensors refers to the calculation based on real-time data collected from various environmental sensors (such as temperature and humidity sensors, noise sensors, harmful gas sensors, smoke sensors, water immersion sensors, and cable trench water level sensors) within the power distribution room. This data includes parameters such as temperature, humidity, noise, harmful gas concentration, smoke concentration, water accumulation rate in the station building, and cable trench water level. For example, the collected values ​​from each environmental sensor are first normalized to a preset range (e.g., 0 to 1). The normalization function can be set according to parameter characteristics (e.g., threshold, safety range), with higher values ​​indicating worse environmental conditions. Then, a comprehensive environmental operation parameter evaluation value is calculated using methods such as weighted averaging or taking the maximum value.

[0051] Comprehensive evaluation of data from multiple electrical sensors refers to calculations based on real-time data collected from various electrical sensors in the power distribution room (such as switchgear temperature sensors, transient ground voltage amplitude sensors, and operating current sensors). For example, the collected values ​​from each electrical sensor are first normalized to a preset range (e.g., 0 to 1). The normalization function can be set according to parameter characteristics (e.g., threshold, safety range), with higher values ​​indicating worse electrical conditions. Then, a comprehensive evaluation value of the electrical operating parameters is calculated through weighted averaging or taking the maximum value.

[0052] An anomaly event weight table and a time decay-based weighted model are used. The anomaly event weight table includes various anomalies and their corresponding weights. The weight of the i-th anomaly event can be expressed as... The weights are positively correlated with the severity of the anomalous event; that is, the more severe the anomalous event, the higher its weight. Different weights are assigned to different types of anomalous events to distinguish their severity. For example, a smoke and open flame event has a weight of 10, an instrument malfunction has a weight of 5, a small animal intrusion has a weight of 3, and a dripping water event has a weight of 2, indicating that the smoke and open flame event has the highest severity, and the dripping water event has the lowest. The time decay model characterizes the weakening of the impact of an anomalous event over time. The time decay model is used to calculate the time decay coefficient of an anomalous event. The time decay coefficient of the i-th anomalous event can be expressed as... .

[0053] Based on this, the process of obtaining the historical failure rate of the power distribution room based on historical alarm records and a pre-built time decay-based weighted model can include: for each historical alarm data, obtaining the weight of each abnormal event in the historical alarm data from the abnormal event weight table, calculating the time decay coefficient of each abnormal event in the historical alarm data based on the time decay model; and weighting and summing the weights and time decay coefficients of all abnormal events to obtain the historical failure rate.

[0054] In other words, the weight of each type of abnormal event in each historical alarm data is obtained from the abnormal event weight table. Meanwhile, based on the time decay model, the time decay coefficient of each abnormal event in each historical alarm data is calculated. Then, the weights and time decay coefficients of all abnormal events are weighted and summed to obtain the historical failure rate, i.e., .

[0055] Optionally, the time decay model can be an exponential decay model or a linear decay model, wherein the exponential decay model satisfies the following formula:

[0056] The linear decay model satisfies the following formula:

[0057] in, This represents the time decay coefficient for the i-th type of abnormal event. This represents the decay rate constant, for example, 0.1; This indicates the number of days since the occurrence of the abnormal event. This indicates the maximum number of valid days for an abnormal event. For example, 30 indicates that the impact drops to zero after this number of days. The specific values ​​can be flexibly adjusted by the operations and maintenance personnel according to the actual situation, and there are no restrictions on this.

[0058] For example, suppose a power distribution room experienced an "instrument malfunction" 7 days ago (weight=5) and an "animal intrusion" 3 days ago (weight=3). Using a linear decay model to calculate the time decay coefficient, where... The historical failure rate H is calculated as H = 5*(1-7 / 30) + 3*(1-3 / 30) ≈ 6.53.

[0059] After calculating the historical failure rate H of the power distribution room, in order to unify and coordinate the three parameters with different dimensions—the station's environmental operating parameters, the station's electrical operating parameters, and the historical failure rate—they can be normalized to the same numerical range (e.g., 1.0 to 3.0). Then, the normalized station environmental operating parameters, station electrical operating parameters, and historical failure rate are multiplied together to obtain the dynamic risk coefficient.

[0060] This time-attenuation weighted method can more accurately reflect the actual fault risk status of the power distribution room, taking into account both the cumulative impact of historical alarms and the higher importance of recent alarms, thus providing a scientific basis for operation and maintenance decisions.

[0061] In sub-step S1032, based on the interval-increasing sequence The target index is the number of consecutive times no abnormal conditions are detected in the current power distribution room environment. The set duration corresponding to the target index is obtained from an incrementing interval sequence. For example, when no abnormality is detected for the first time, the time interval is... Get from Used to calculate the time interval for the next monitoring. If no abnormality is detected the second time, from [date missing]... Get from Used to calculate the time interval for the next monitoring. When no abnormality is detected on the third monitoring, from [date / time]... Get from This is used to calculate the time interval for the next monitoring, and so on, until the set maximum interval is reached. If no abnormalities are detected thereafter, then maintain... The time interval for the next monitoring is calculated without changing the time.

[0062] In sub-step S1033, after obtaining the dynamic risk coefficient through S1031 and the set duration through S1032, the formula is followed. Calculate the time interval for the next monitoring, where, Indicates the time interval for the next monitoring. This indicates the duration for which index i is set, for example, This indicates the set duration for index 1. This represents the dynamic risk coefficient.

[0063] In other words, the time interval for the next monitoring is calculated using the formula mentioned above. This formula indicates that the interval is shortened when the risk is high and extended when the risk is low. The system calculates the time interval for the next monitoring based on the formula above, and returns to step S101 to perform the next monitoring after the time interval for the next monitoring is reached. If there are no abnormalities continuously, the index i is incremented according to the interval increment sequence to calculate a new longer interval. However, the final calculated time interval for the next monitoring is always modulated by the dynamic risk coefficient.

[0064] In this way, the dynamic risk coefficient is first calculated based on the station's environmental operating parameters, electrical operating parameters, and historical alarm records. The dynamic risk coefficient is positively correlated with the station's environmental operating parameters, electrical operating parameters, and historical alarm records. That is, the worse the environmental condition, the worse the electrical condition, and the more frequent the historical faults, the greater the dynamic risk coefficient. Then, based on the dynamic risk coefficient and the preset interval increment sequence, the time interval of the next monitoring is adaptively adjusted to achieve high-frequency monitoring of high-risk periods and energy-saving monitoring of low-risk periods.

[0065] At the same time, once any abnormality is detected, the system immediately exits the energy-saving detection mode and enters the continuous monitoring mode, that is, it executes step S104.

[0066] In step S104, if an abnormal state is detected in the current power distribution room environment, the continuous monitoring mode is entered. In this mode, the monitoring equipment is controlled to continuously collect monitoring data of the power distribution room and perform real-time analysis on the monitoring data to continuously monitor the changing trend of the abnormal state.

[0067] In one possible implementation, for the combination of a supplementary light and a camera, the supplementary light is controlled to remain constantly on while the camera is simultaneously controlled to record continuously in real time. The video stream captured by the camera is then analyzed in real time to continuously monitor the changing trends of abnormal states. Alternatively, the supplementary light is controlled to flash at high frequency while the camera is simultaneously controlled to capture images at high frequency. The images captured by the camera are then analyzed in real time to continuously monitor the changing trends of abnormal states. For an infrared thermal imager, the infrared thermal imager is controlled to scan at high frequency, and the thermal images captured by the infrared thermal imager are analyzed in real time to continuously monitor the changing trends of abnormal states.

[0068] Meanwhile, in continuous monitoring mode, once an abnormal state is confirmed by analysis, an alarm message is immediately generated and the alarm message and monitoring data are uploaded to the remote monitoring platform. The monitoring data may include, but is not limited to: real-time video stream, captured images, thermal imaging images, equipment operating parameters, etc.

[0069] In this embodiment, when the monitoring data analysis shows that the abnormal state has disappeared, the monitoring interval can be reset to the initial value (e.g., 1s) after a set time (e.g., 30 minutes), and the process can return to step S101 to restart the adaptive detection loop.

[0070] Therefore, in Figure 2 Based on this, please refer to Figure 3 After step S104, the power distribution room monitoring method provided in this application embodiment may further include step S105.

[0071] S105. If the abnormal state disappears and does not reappear within the set time window, return to step S101.

[0072] That is, when the analysis results in the continuous monitoring mode indicate that the anomaly has disappeared (such as small animals leaving, water dripping stopping, smoke going out, etc.), the monitoring interval will be reset to the initial value (e.g., 1 second) after a certain period of time (e.g., 30 minutes) has passed since the anomaly disappeared, and the process will return to step S101 to restart the adaptive detection loop, so as to ensure a rapid review of the environmental recovery status.

[0073] In this embodiment, resetting the monitoring interval to its initial value does not simply mean setting the monitoring interval to a fixed value, but rather resetting the monitoring interval parameter to its initial value (e.g., ...). =1s), and at the same time, the index i in the abnormal event weight table is set to the initial state (e.g., i=1) so that the monitoring interval can be gradually extended when there are no abnormalities. The purpose of this setting is that after the abnormality disappears, the system does not immediately enter the energy-saving detection mode, but first uses high-frequency detection to confirm that the environment is stable, so as to prevent false judgments and missed detections.

[0074] Meanwhile, once the abnormal state disappears and does not reappear within the set time window, the system returns to step S101. The purpose of this is to: 1. Prevent misjudgment, as the abnormality may only be temporarily alleviated (e.g., a small animal leaves and then returns); 2. Ensure environmental stability, as some abnormalities may have a lag (e.g., the equipment has cooled down, but there are potential faults that have not been completely eliminated); 3. Avoid frequent mode switching, reduce the number of times the system switches back and forth between energy-saving detection mode and continuous monitoring mode, and improve system stability and energy-saving effect.

[0075] Compared with the prior art, the power distribution room monitoring method provided in this embodiment has the following advantages: First, considering the current power distribution room environment, if no abnormalities are detected, the time interval for the next monitoring will be dynamically adjusted based on the real-time operating parameters of the power distribution room, historical alarm records, and a preset interval increment sequence. After the next monitoring interval is reached, monitoring will be restarted. If an abnormality is detected, the monitoring equipment will be controlled to continuously monitor the changing trend of the abnormal state. This ensures both effective monitoring and timely and accurate detection of abnormalities, while also significantly reducing equipment energy consumption.

[0076] Secondly, the time interval for the next monitoring is determined by the real-time operating parameters of the power distribution room, historical alarm records, and a preset interval increment sequence, rather than being fixed. This allows for maximizing the sleep time of the monitoring equipment to achieve energy saving when there are no abnormalities.

[0077] Third, existing video surveillance systems collect data at short intervals with fixed intervals, resulting in the collection of a large amount of data and putting a lot of pressure on the storage of on-site or back-end equipment. In contrast, the power distribution room monitoring method provided in this solution can achieve high-frequency monitoring during high-risk periods and energy-saving monitoring during low-risk periods, thereby significantly reducing the amount of data and alleviating the pressure on data storage.

[0078] Fourth, monitoring is resumed only after the abnormal state disappears and does not reappear within the set time window. This not only prevents misjudgments and missed detections, but also reduces the number of times the system switches back and forth between energy-saving detection mode and continuous monitoring mode, thereby improving system stability and energy-saving effect.

[0079] In order to perform the corresponding steps in the above method embodiments and various possible implementations, an implementation method of a power distribution room monitoring device is given below.

[0080] Please refer to Figure 4 , Figure 4 A block diagram of a power distribution room monitoring device 100 provided in an embodiment of this application is shown. The power distribution room monitoring device 100 is applied to electronic equipment and includes: a control module 101, a judgment module 102, a first execution module 103, and a second execution module 104.

[0081] The control module 101 is used to control the monitoring equipment to collect monitoring data from the power distribution room when the set trigger conditions are met.

[0082] The judgment module 102 is used to analyze the monitoring data and determine whether there is an abnormal state in the current power distribution room environment.

[0083] The first execution module 103 is used to dynamically adjust the time interval of the next monitoring based on the real-time operating parameters of the power distribution room, historical alarm records and a preset interval increment sequence if there is no abnormal state in the current power distribution room environment, and return to the execution control monitoring equipment to collect monitoring data of the power distribution room after the time interval of the next monitoring is reached.

[0084] The second execution module 104 is used to control the monitoring equipment to continuously collect monitoring data of the power distribution room if there is an abnormal state in the current power distribution room environment, and to perform real-time analysis of the monitoring data to continuously monitor the changing trend of the abnormal state.

[0085] Optionally, the real-time operating parameters include station environmental operating parameters and station electrical operating parameters, and the interval increment sequence includes multiple incrementing set durations and an index for each set duration; The first execution module 103 executes a method to dynamically adjust the next monitoring time interval based on the real-time operating parameters of the power distribution room, historical alarm records, and a preset interval increment sequence. This includes: determining the dynamic risk coefficient of the power distribution room based on the station's environmental operating parameters, electrical operating parameters, and historical alarm records. The dynamic risk coefficient is positively correlated with the station's environmental operating parameters, electrical operating parameters, and historical alarm records. The number of consecutive times there are no abnormal states in the current power distribution room environment is used as the target index, and the set duration corresponding to the target index is obtained from the interval increment sequence. The ratio of the set duration corresponding to the target index to the dynamic risk coefficient is calculated to obtain the next monitoring time interval.

[0086] Optionally, the station's environmental operating parameters include data from multiple environmental sensors, and the station's electrical operating parameters include data from multiple electrical sensors. The first execution module 103 executes a method to determine the dynamic risk coefficient of the power distribution room based on the station's environmental operating parameters, electrical operating parameters, and historical alarm records. This includes: comprehensively evaluating the data from multiple environmental sensors to obtain a comprehensive environmental operating parameter evaluation value; comprehensively evaluating the data from multiple electrical sensors to obtain a comprehensive electrical operating parameter evaluation value; obtaining the historical failure rate of the power distribution room based on historical alarm records and a pre-built time-attenuation-based weighted model; normalizing the comprehensive environmental operating parameter evaluation value, the comprehensive electrical operating parameter evaluation value, and the historical failure rate to obtain normalized comprehensive environmental operating parameter evaluation values, comprehensive electrical operating parameter evaluation values, and the historical failure rate; and multiplying the normalized comprehensive environmental operating parameter evaluation value, comprehensive electrical operating parameter evaluation value, and the historical failure rate to obtain the dynamic risk coefficient.

[0087] Optionally, the historical alarm record includes multiple historical alarm data, each of which includes at least one abnormal event; the time decay-based weighted model includes an abnormal event weight table and a time decay model. The abnormal event weight table includes multiple abnormal events and the weight corresponding to each abnormal event. The weight is positively correlated with the severity of the abnormal event; the time decay model characterizes the impact of abnormal events as time goes by. The first execution module 103 executes a method to obtain the historical failure rate of the power distribution room based on historical alarm records and a pre-built time decay-based weighted model, including: for each historical alarm data, obtaining the weight of each abnormal event in the historical alarm data from the abnormal event weight table, calculating the time decay coefficient of each abnormal event in the historical alarm data based on the time decay model; and weighting and summing the weights and time decay coefficients of all abnormal events to obtain the historical failure rate.

[0088] Optionally, the monitoring device is a combination of a supplementary light and a camera, or an infrared thermal imager; the control module 101 executes the method of controlling the monitoring device to collect monitoring data of the power distribution room, including: for the combination of a supplementary light and a camera, controlling the supplementary light to flash and simultaneously controlling the camera to collect images; for the infrared thermal imager, controlling the infrared thermal imager to perform a full-area thermal imaging scan.

[0089] Optionally, the monitoring device is a combination of a supplementary light and a camera, or an infrared thermal imager; the second execution module 104 executes a method to control the monitoring device to continuously collect monitoring data from the power distribution room and to perform real-time analysis of the monitoring data to continuously monitor the changing trend of abnormal states, including: for the supplementary light and camera combination, controlling the supplementary light to remain on and simultaneously controlling the camera to continuously record in real time, and performing real-time analysis of the video stream collected by the camera to continuously monitor the changing trend of abnormal states; or, controlling the supplementary light to flash at high frequency and simultaneously controlling the camera to capture images at high frequency, and performing real-time analysis of the images collected by the camera to continuously monitor the changing trend of abnormal states; for the infrared thermal imager, controlling the infrared thermal imager to scan at high frequency, and performing real-time analysis of the thermal imaging images collected by the infrared thermal imager to continuously monitor the changing trend of abnormal states.

[0090] Optionally, the second execution module 104 is further configured to: if the abnormal state disappears and does not reappear within the set time window, return to the step of controlling the fill light to flash and controlling the camera to capture images.

[0091] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the power distribution room monitoring device 100 described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0092] Please refer to Figure 5 , Figure 5 A block diagram of an electronic device 10 provided in an embodiment of this application is shown. The electronic device 10 includes a processor 11, a memory 12, and a bus 13. The processor 11 is connected to the memory 12 via the bus 13.

[0093] The memory 12 is used to store programs. After receiving an execution instruction, the processor 11 executes the programs to implement the power distribution room monitoring method disclosed in the above embodiments.

[0094] The memory 12 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0095] Processor 11 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed through integrated logic circuits in the hardware of processor 11 or through software instructions. Processor 11 can be a general-purpose processor, including a Central Processing Unit (CPU), a Microcontroller Unit (MCU), a Complex Programmable Logic Device (CPLD), a Field Programmable Gate Array (FPGA), embedded ARM chips, etc.

[0096] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by the processor 11, implements the power distribution room monitoring method disclosed in the above embodiments.

[0097] In summary, the power distribution room monitoring method, device, and electronic equipment provided in this application, when a set trigger condition is met, controls the monitoring equipment to collect monitoring data. Based on the monitoring data, it determines whether there is an abnormal state in the current power distribution room environment. If there is no abnormality, the monitoring time interval is dynamically adjusted according to the real-time operating parameters of the power distribution room, historical alarm records, and a preset interval increment sequence. Monitoring resumes after the next monitoring time interval arrives. If there is an abnormality, the monitoring equipment continuously collects monitoring data from the power distribution room and performs real-time analysis of the monitoring data to continuously monitor the changing trend of the abnormal state. In this way, the monitoring equipment's sleep time is maximized to achieve energy saving when there is no abnormality, while it immediately enters continuous monitoring mode to ensure safety when an abnormality occurs. This ensures both effective monitoring and timely and accurate detection of abnormalities, while significantly reducing equipment energy consumption.

[0098] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method of monitoring an electrical distribution room, characterized by, The method comprises: When a set triggering condition is met, the monitoring device is controlled to collect monitoring data of the power distribution room; The monitoring data is analyzed to determine whether an abnormal state exists in the current power distribution room environment; If the abnormal state does not exist in the current power distribution room environment, the time interval of the next monitoring is dynamically adjusted according to real-time operation parameters, historical alarm records and a preset interval increasing sequence of the power distribution room, and after the time interval of the next monitoring is reached, the step of controlling the monitoring device to collect the monitoring data of the power distribution room is returned to be executed; If the abnormal state exists in the current power distribution room environment, the monitoring device is controlled to continuously collect the monitoring data of the power distribution room, and the monitoring data is analyzed in real time to continuously monitor the change trend of the abnormal state.

2. The method of claim 1, wherein, The real-time operation parameters include station environment operation parameters and station electrical operation parameters, and the interval increasing sequence includes a plurality of increasing set time lengths and indexes of each set time length; The time interval of the next monitoring is dynamically adjusted according to the real-time operation parameters, the historical alarm records and the preset interval increasing sequence of the power distribution room, which comprises determining a dynamic risk coefficient of the power distribution room according to the station environment operation parameters, the station electrical operation parameters and the historical alarm records, the dynamic risk coefficient being positively correlated with the station environment operation parameters, the station electrical operation parameters and the historical alarm records; The number of times that the abnormal state does not exist continuously in the current power distribution room environment is taken as a target index, and a set time length corresponding to the target index is obtained from the interval increasing sequence; The ratio of the set time length corresponding to the target index to the dynamic risk coefficient is calculated to obtain the time interval of the next monitoring.

3. The method of claim 2, wherein, The station environment operation parameters include a plurality of types of environment sensor data, and the station electrical operation parameters include a plurality of types of electrical sensor data; The dynamic risk coefficient of the power distribution room is determined according to the station environment operation parameters, the station electrical operation parameters and the historical alarm records, which comprises: The plurality of types of environment sensor data are comprehensively evaluated to obtain a comprehensive environment operation parameter evaluation value; The plurality of types of electrical sensor data are comprehensively evaluated to obtain a comprehensive electrical operation parameter evaluation value; The historical failure rate of the power distribution room is obtained based on the historical alarm records and a pre-constructed time-decay-based weighting model; The comprehensive environment operation parameter evaluation value, the comprehensive electrical operation parameter evaluation value and the historical failure rate are normalized to obtain normalized comprehensive environment operation parameter evaluation value, comprehensive electrical operation parameter evaluation value and historical failure rate; The normalized comprehensive environment operation parameter evaluation value, the comprehensive electrical operation parameter evaluation value and the historical failure rate are multiplied to obtain the dynamic risk coefficient.

4. The method of claim 3, wherein, The historical alarm record includes a plurality of historical alarm data, and each of the historical alarm data includes at least one abnormal event; the time-decay-based weighting model includes an abnormal event weight table and a time-decay model, the abnormal event weight table includes a plurality of abnormal events and a weight corresponding to each of the abnormal events, and the weight is positively correlated with the severity of the abnormal event; The time-decay model represents that the influence of the abnormal event decreases over time; The historical failure rate of the power distribution room is obtained based on the historical alarm record and the pre-constructed time-decay-based weighting model, including: For each of the historical alarm data, the weight of each abnormal event in the historical alarm data is obtained from the abnormal event weight table, and a time-decay coefficient of each abnormal event in the historical alarm data is calculated based on the time-decay model; The weights and time-decay coefficients of all the abnormal events are summed to obtain the historical failure rate.

5. The method of claim 4, wherein, The time-decay model is an exponential decay model or a linear decay model, the exponential decay model satisfies the following formula: The linear decay model satisfies the following formula: wherein, represents the time decay coefficient of the i-th abnormal event, represents the decay rate constant, represents the number of days since the occurrence of the abnormal event, represents the maximum effective days of the abnormal event.

6. The method of claim 1, wherein, The monitoring device is a combination of a light supplement lamp and a camera, or an infrared thermal imager; The monitoring device is controlled to collect monitoring data of the power distribution room, including: For the combination of the light supplement lamp and the camera, the light supplement lamp is controlled to flash and the camera is controlled to collect images synchronously; For the infrared thermal imager, the infrared thermal imager is controlled to perform a full-area thermal imaging scan.

7. The method of claim 1, wherein, The monitoring device is a combination of a light supplement lamp and a camera, or an infrared thermal imager; The monitoring device is controlled to continuously collect monitoring data of the power distribution room, and the monitoring data is analyzed in real time to continuously monitor the change trend of the abnormal state, including: For the combination of the light supplement lamp and the camera, the light supplement lamp is controlled to be always on and the camera is controlled to continuously record videos synchronously, and the video stream collected by the camera is analyzed in real time to continuously monitor the change trend of the abnormal state; or, the light supplement lamp is controlled to flash at a high frequency and the camera is controlled to capture images at a high frequency synchronously, and the images collected by the camera are analyzed in real time to continuously monitor the change trend of the abnormal state; For the infrared thermal imager, the infrared thermal imager is controlled to scan at a high frequency, and the thermal imaging images collected by the infrared thermal imager are analyzed in real time to continuously monitor the change trend of the abnormal state.

8. The method of claim 1, wherein, After the monitoring device is controlled to continuously collect monitoring data of the power distribution room, and the monitoring data is analyzed in real time to continuously monitor the change trend of the abnormal state, the method further includes: If the abnormal state disappears and does not reappear within a set time window, the step of controlling the light supplement lamp to flash and the camera to collect images is returned to be executed.

9. A power distribution room monitoring device characterized by comprising: The device includes: A control module configured to control a monitoring device to collect monitoring data of the power distribution room when a set triggering condition is met; A judgment module configured to analyze the monitoring data and determine whether there is an abnormal state in the current power distribution room environment; The first execution module is configured to, if the abnormal state does not exist in the current power distribution room environment, dynamically adjust a time interval of next monitoring according to real-time operation parameters of the power distribution room, historical alarm records and a preset interval increasing sequence, and return to execute the step of controlling the monitoring device to collect monitoring data of the power distribution room when the time interval of next monitoring arrives. The second execution module is configured to, if the abnormal state exists in the current power distribution room environment, control the monitoring device to continuously collect monitoring data of the power distribution room, and perform real-time analysis on the monitoring data to continuously monitor a change trend of the abnormal state.

10. An electronic device, comprising: The power distribution room monitoring method comprises a processor and a memory. The memory is configured to store a program. The processor is configured to implement the power distribution room monitoring method according to any one of claims 1-8 when executing the program.

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