Power distribution fire-fighting information management method and system based on Internet of Things

Abstract

The invention discloses a power distribution fire-fighting information management method and system based on the Internet of Things, and relates to the technical field of power distribution fire-fighting information management, and the method specifically comprises the following steps: analyzing the obtained transmission delay information of a current sensor, and generating a data integrity coefficient and a load disturbance index; and constructing a delay influence prediction model based on the generated data integrity coefficient and the load disturbance index, generating a delay prediction coefficient, carrying out analysis after generation, and predicting the influence degree of the delay of the current sensor data on the fault early warning of the power distribution equipment under the high-load operation condition. The influence degree is divided into a low influence degree, a medium influence degree and a high influence degree according to the prediction result; and implementing corresponding management measures for different influence degrees according to a prediction result. According to the method, the problem of inaccurate fault early warning caused by data delay of the current sensor under the condition of high-load operation is solved, the delay influence is accurately predicted, and a data acquisition and management strategy is dynamically optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution fire protection information management, and particularly to a distribution fire protection information management method and system based on the Internet of Things. Background Art

[0002] Distribution fire protection information refers to the fire safety data related to the distribution system, including information such as the operating status of power equipment, fault alarms, overload monitoring, temperature and humidity monitoring, fire hazard warnings, and energy consumption statistics. These information play a crucial role in ensuring the safe and stable operation of the power system. Due to the wide distribution of distribution equipment and its susceptibility to environmental and aging factors, timely and efficient acquisition and processing of this information can prevent potential safety hazards and improve the overall safety and reliability of the power system. Therefore, effective management of distribution fire protection information can not only achieve real-time monitoring of power facilities, ensure the safety of personnel and equipment, but also optimize the operation and maintenance strategies of the distribution system through data analysis, reduce energy consumption and maintenance costs. The Internet of Things technology, with its extensive sensing capabilities, real-time data transmission, and intelligent processing capabilities, provides strong technical support for the efficient management of distribution fire protection information. By using the Internet of Things, remote monitoring, centralized data management, and intelligent warning can be achieved, thereby improving the emergency response ability and accident handling efficiency, and further ensuring the safe operation of the power grid.

[0003] The existing distribution fire protection information management technology based on the Internet of Things mainly relies on sensors (such as temperature sensors, current sensors, smoke sensors, etc.) distributed at various key nodes of the distribution system to monitor the operating status of distribution equipment in real time. These sensors upload the collected data to the central management platform through wireless or wired networks (such as LoRa, NB-IoT, Wi-Fi, or Ethernet). The central management platform usually based on cloud computing or edge computing technology, performs real-time analysis, storage, and processing on the received current, voltage, temperature, humidity and other data to form a global monitoring view of the distribution system. When abnormal indicators (such as too high temperature, current overload, abnormal smoke concentration, etc.) are detected, the system will trigger an alarm mechanism according to the preset alarm threshold to notify the operation and maintenance personnel to conduct inspections or take emergency measures. In addition, this management technology also has functions such as data historical trend analysis, equipment health assessment, remote control, and intelligent alarm linkage, to help managers make preventive maintenance decisions based on data analysis to improve the safety and reliability of the system.

[0004] The existing technology has the following deficiencies: In the power distribution system of a large industrial park, current sensors are deployed on key equipment (such as transformers and switchgear) to monitor the load conditions of the equipment in real time. During the peak power consumption period in this area, the load of the power distribution equipment increases sharply, especially the current of equipment such as transformers fluctuates greatly. Due to the limited data acquisition frequency of the current sensors and the inability of the sensors to quickly respond to the load changes under the condition of severe load fluctuations, this leads to the transmission delay of the sensor data, that is, the system cannot obtain the real-time current data of the equipment in a short time. When the equipment is overloaded or the current is abnormal, the delayed data of the current sensor causes the system to fail to update the load information of the equipment in time, so that it is impossible to accurately judge whether a fault occurs when the equipment load is too high. The existing technology cannot dynamically adjust the data acquisition frequency of the sensors or accelerate the data upload according to the equipment load changes, resulting in the delay problem of the current sensors not being effectively alleviated under the high load operation state, thus affecting the timeliness of fault warning. This data delay causes the fault warning system to fail to capture the abnormal current data in the first time, miss the key warning signal of equipment overload, and thus delay the maintenance and emergency response. Without timely warning, the equipment may continue to operate in an overloaded state, leading to safety accidents such as equipment damage and fire, and increasing the complexity and cost of subsequent maintenance. In severe cases, it may affect the safety and stability of the entire power distribution system.

[0005] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and therefore it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The object of the present invention is to provide a power distribution fire protection information management method and system based on the Internet of Things to solve the problems in the above background art.

[0007] To achieve the above object, the present invention provides the following technical solutions: A power distribution fire protection information management method based on the Internet of Things, specifically including the following steps: During the peak power consumption period, monitor the operating load of the power distribution equipment in real time, and when it is detected that the power distribution equipment is in a high-load operation state, obtain the equipment operating load information of the equipment in real time; Analyze the obtained equipment operating load information, detect in real time whether there is a data transmission delay in the current sensor, and when it is detected that there is a data transmission delay in the current sensor, obtain the current sensor transmission delay information of the sensor in real time; Analyze the obtained current sensor transmission delay information, and generate a data integrity coefficient and a load disturbance index respectively; Construct a delay impact prediction model based on the generated data integrity coefficient and load disturbance index, generate a delay prediction coefficient, and perform analysis after generation to predict the impact degree of the delay of current sensor data on the fault warning of distribution equipment under high-load operation conditions, and divide the impact degree into low impact degree, medium impact degree, and high impact degree according to the prediction results; Implement corresponding management measures according to the prediction results for different impact degrees; Continuously monitor the data transmission status of the current sensor, and after the management measures are executed, track its effect in real time. If it is detected that the delay problem has not improved, adjust the management measures and re-optimize the data acquisition and upload strategy of the sensor.

[0008] Preferably, analyze the obtained current sensor transmission delay information to generate a data integrity coefficient and a load disturbance index respectively, specifically including the following steps: Preprocess the obtained current sensor transmission delay information; Extract the data integrity evaluation information and load disturbance analysis information from the preprocessed current sensor transmission delay information; Analyze the extracted data integrity evaluation information and load disturbance analysis information to generate a data integrity coefficient and a load disturbance index respectively.

[0009] Preferably, the acquisition logic of the data integrity coefficient is as follows: Extract the data integrity evaluation information from the preprocessed current sensor transmission delay information, specifically including the number of lost data points at different moments within a period of time when it is detected that the current sensor has a data transmission delay, the proportion of data repeatedly sent by the current sensor under high-load conditions, and the change range of the current value between adjacent data points, and respectively calibrate them as 、 and , represents the number of lost data points at the moment within a period of time when it is detected that the current sensor has a data transmission delay, represents the proportion of data repeatedly sent by the current sensor under high-load conditions at the moment within a period of time when it is detected that the current sensor has a data transmission delay, represents the change range of the current value between adjacent data points at the moment within a period of time when it is detected that the current sensor has a data transmission delay, , is a positive integer; Calculate the data integrity coefficient, and the specific calculation formula is as follows:

[0010] In the formula, is the data integrity coefficient.

[0011] Preferably, the acquisition logic of the load disturbance index is as follows: Extract the load disturbance analysis information from the pre - processed current sensor transmission delay information, specifically including the fluctuation amplitude of the distribution equipment load at different times within a period when it is detected that the current sensor has data transmission delay, the load change gradient between all data points, and the change rate of the distribution equipment load, and calibrate them as , and , represents the fluctuation amplitude of the distribution equipment load at time within a period when it is detected that the current sensor has data transmission delay, represents the load change gradient between all data points at time within a period when it is detected that the current sensor has data transmission delay, represents the change rate of the distribution equipment load at time within a period when it is detected that the current sensor has data transmission delay, , is a positive integer; Calculate the load disturbance index, and the specific calculation formula is as follows:

[0012] In the formula, is the load disturbance index.

[0013] Preferably, based on the generated data integrity coefficient and the load disturbance index construct a delay impact prediction model, and generate a delay prediction coefficient through weighted summation. The specific calculation formula is as follows:

[0014] In the formula, is the delay prediction coefficient, and are the non - zero weight coefficients of the data integrity coefficient and the load disturbance index respectively, and .

[0015] Preferably, determine the pre - set delay prediction coefficient threshold interval , and after determination, compare it with the generated delay prediction coefficient Compare them, predict the impact degree of the delay of current sensor data on the fault warning of distribution equipment under high-load operation according to the comparison results, and divide the impact degree into low impact degree, medium impact degree and high impact degree according to the prediction results. The specific comparison and analysis are as follows: If , the impact degree of the delay of current sensor data on the fault warning of distribution equipment under high-load operation is high impact degree; If , the impact degree of the delay of current sensor data on the fault warning of distribution equipment under high-load operation is medium impact degree; If , the impact degree of the delay of current sensor data on the fault warning of distribution equipment under high-load operation is low impact degree.

[0016] Preferably, according to the prediction results, corresponding management measures are implemented for different impact degrees, specifically: For the case of high impact degree, the management measures taken are: increasing the data acquisition frequency of the current sensor, optimizing the data upload strategy, using a compensation prediction algorithm to intelligently fill in the missing data, and immediately sending a high-priority warning to the operation and maintenance personnel; For the case of medium impact degree, the management measures taken are: adjusting the data upload strategy of the current sensor, increasing the data acquisition frequency, and at the same time correcting the delayed data through historical data trend prediction, and sending a general-priority warning reminder to the operation and maintenance personnel; For the case of low impact degree, the management measures taken are: keeping the current data acquisition and upload strategy unchanged, continuously monitoring the data transmission situation of the current sensor, and if a trend of increasing delay is detected, dynamically adjusting the acquisition frequency and performing periodic health checks.

[0017] Preferably, the distribution power fire information management system based on the Internet of Things includes a high-load operation monitoring module, a data transmission delay detection module, a data integrity and load disturbance calculation module, a delay impact prediction and evaluation module, an intelligent management measure execution module, and an adaptive optimization and feedback regulation module; The high-load operation monitoring module, during peak electricity consumption periods, monitors the operation load of distribution equipment in real time, and when it detects that the distribution equipment is in a high-load operation state, it obtains the equipment operation load information of the equipment in real time; The data transmission delay detection module analyzes the obtained equipment operation load information, detects in real time whether the current sensor has data transmission delay, and when it detects that the current sensor has data transmission delay, it obtains the current sensor transmission delay information of the sensor in real time; The data integrity and load disturbance calculation module analyzes the transmission delay information of the current sensor obtained, and generates a data integrity coefficient and a load disturbance index respectively; The delay impact prediction and evaluation module constructs a delay impact prediction model based on the generated data integrity coefficient and load disturbance index, generates a delay prediction coefficient, and analyzes it after generation, predicts the impact degree of the delay of the current sensor data on the fault warning of the distribution equipment under high-load operation conditions, and divides the impact degree into low impact degree, medium impact degree and high impact degree according to the prediction results; The intelligent management measure execution module implements corresponding management measures according to the prediction results for different impact degrees; The adaptive optimization and feedback regulation module continuously monitors the data transmission status of the current sensor, and in real time tracks the effect after the management measures are executed. If it is detected that the delay problem has not improved, the management measures are adjusted and the data acquisition and upload strategy of the sensor is re-optimized.

[0018] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. Through multiple technical links such as high-load operation monitoring, data integrity analysis, load disturbance evaluation, delay prediction model construction and adaptive optimization adjustment, the present invention realizes high-precision real-time monitoring of the operation status of distribution equipment, and provides differentiated management measures for the impact degree of data delay, ensuring that the system can accurately predict the operation status of the equipment and issue a fault warning in time. In particular, through the calculation methods of the data integrity coefficient and the load disturbance index, the present invention can quantify the integrity of the sensor data and the stability of the load change, and construct a delay impact prediction model based on these data, calculate the delay prediction coefficient through weighted summation, and finally accurately evaluate the impact degree of the data delay. Compared with the traditional method based on a fixed alarm threshold or simple data comparison, the present invention adopts methods such as mathematical modeling, exponential operation, logarithmic transformation and data trend analysis, enabling the fault warning system to more accurately predict and analyze the risks brought by data delay in a complex distribution network environment, improving the accuracy and timeliness of the warning.

[0019] 2. The present invention provides adaptive management measures for different degrees of data delay impact to ensure that the system can flexibly adjust the acquisition and upload strategies and improve the stability of data transmission. Specifically, in the case of a high impact degree, the system minimizes the impact of data delay on fault warning by increasing the acquisition frequency, optimizing the data transmission protocol, adopting data compensation algorithms, etc. In the case of a medium impact, the data upload strategy is adjusted and trend prediction correction is combined with historical data to ensure data accuracy. In the case of a low impact, the system keeps the current acquisition strategy unchanged and conducts periodic monitoring to ensure the long-term stability of the system operation. In addition, the present invention adopts an adaptive optimization and feedback control module, which can continuously monitor the data transmission status after the management measures are executed. If the delay problem fails to be effectively improved, the system can adjust the management strategy based on the feedback and re-optimize the data acquisition and upload mode, thus forming an intelligent monitoring and management system with dynamic adjustment and closed-loop optimization. Compared with the existing static monitoring and alarm strategies, the present invention realizes an intelligent and dynamic data management mode, which not only improves the adaptability of the system to sudden load changes, but also effectively reduces the risks of false alarms and missed alarms, and improves the safety and stability of the power system.

[0020] 3. The present invention also has good scalability and adaptability, can be applied to different-scale and different-type distribution network environments, and can be combined with advanced technologies such as cloud computing, big data analysis, and AI intelligent optimization to further enhance the self-learning ability of the system, so that the management strategy can be continuously optimized with the accumulation of operation data. Through the modular architecture design, the system of the present invention can be flexibly deployed in different industrial parks, substations, distribution rooms and other scenarios, and can be seamlessly docked with the existing power monitoring system to realize the fusion analysis and comprehensive management of data. In addition, due to the adoption of a series of technical means such as dynamic threshold adjustment, intelligent warning, data compensation, and prediction optimization in the present invention, the system can be flexibly adjusted according to different environments, different devices, and different operation states to ensure that the system always maintains high-precision data monitoring and fault warning capabilities during the long-term operation process. Compared with the traditional system, the present invention not only improves the safety and stability of the distribution system, but also can reduce the maintenance cost and improve the efficiency of fault handling, providing an efficient and reliable technical support for the intelligent operation and maintenance of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0022] Figure 1This is a schematic flow diagram of the distribution fire protection information management method and system based on the Internet of Things according to the present invention.

[0023] Figure 2 This is a schematic diagram of the modules of the distribution fire protection information management method and system based on the Internet of Things according to the present invention. Specific embodiments

[0024] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.

[0025] The present invention provides a Figure 1 distribution fire protection information management method based on the Internet of Things as shown, specifically including the following steps: During peak power consumption periods, the operating load of distribution equipment is monitored in real time, and when it is detected that the distribution equipment is in a high-load operating state, the equipment operating load information of the equipment is obtained in real time. In order to monitor the operating load of distribution equipment in real time during peak power consumption periods, methods such as Internet of Things sensor data collection, data stream processing, and intelligent analysis algorithms can be used. First, intelligent current sensors, power monitoring modules, etc. are deployed on distribution equipment (such as transformers, distribution cabinets, switchgear, etc.). Through wireless communication methods such as LoRa, NB-IoT, Wi-Fi, or 5G, or through wired networks such as Ethernet and optical fibers, load-related parameters such as current, voltage, and power factor are uploaded to the central management platform in real time. Secondly, the central management platform uses a data stream processing framework (such as Flink, Kafka Streams, etc.) to perform real-time processing and filtering on the load data uploaded by the sensors, remove invalid data, and continuously analyze the change trend of the equipment load. In addition, in order to improve the accuracy of monitoring, historical data within a short time window can be combined for smoothing processing to avoid misjudgment caused by instantaneous fluctuations. In this way, the system can continuously track the operating load status of distribution equipment with millisecond or second-level accuracy, ensuring that the real-time load information of the equipment can be monitored stably and reliably during peak power consumption periods.

[0026] Based on the real-time monitoring of the load of power distribution equipment, the system needs to determine whether the equipment is operating at a high load, and when the high-load condition is met, it needs to obtain the operating load information of the equipment in real time. First, a high-load determination model can be set through the method of dynamic load threshold setting. This model can be based on historical load data and the rated power of the equipment, and adopt an adaptive threshold algorithm (such as the moving average algorithm based on time series or the exponentially weighted moving average (EWMA) algorithm) to dynamically adjust the high-load determination criteria. Second, when the high-load determination is triggered, the system will immediately call the data request module to send data pulling instructions to the current sensor and power monitoring module of the equipment, ensuring that at the moment when the load reaches the high-load state, the system can quickly and completely collect the detailed operating load information of the equipment and store it in a real-time database (such as Redis, InfluxDB) for subsequent analysis. In this way, it can be ensured that when the equipment enters the high-load state, the system can respond immediately, obtain accurate load information, and will not cause untimely response due to data lag or misjudgment.

[0027] The reason for real-time monitoring of the load of power distribution equipment during peak power consumption periods and obtaining the operating load information of the equipment immediately when a high-load state is detected is that the high-load state is the main inducement for data delay problems in current sensors and the key factor leading to delayed fault warnings. Due to the inability of existing technologies to dynamically adjust the data acquisition frequency of sensors, when the load fluctuates violently, the system cannot obtain the actual current state of the equipment in time, resulting in data lag and affecting the accuracy of fault warnings. Through the above method, the system can continuously monitor the load status of the equipment and immediately collect key data when the equipment enters a high-load state, providing complete data support for subsequent data delay detection, impact assessment, and optimization of management measures. In this way, not only can potential data lag problems be discovered in time, but also the data acquisition strategy can be optimized during high-load periods, such as increasing the acquisition frequency and optimizing the data upload method, ultimately ensuring that the fault warning system can respond quickly, reduce the warning lag caused by data delay, and thus improve the safety and stability of the power distribution system.

[0028] Analyze the obtained operating load information of the equipment, detect in real time whether there is a data transmission delay in the current sensor, and when it is detected that there is a data transmission delay in the current sensor, obtain the current sensor transmission delay information of the sensor in real time; Analyze the obtained device operating load information and detect in real time whether there is a data transmission delay in the current sensor, which can be achieved through methods such as timestamp comparison, heartbeat detection, and data integrity analysis. First, when the current sensor uploads data, the system will attach a timestamp to each piece of data and record the reception time of the timestamp in the central management platform. By comparing the deviation between the data upload time and the actual acquisition time, it is judged whether there is a data delay. Second, a heartbeat detection mechanism can be adopted, that is, the system actively sends a status request to the sensor at regular intervals (such as 500 ms or 1 s). If no sensor response or data update is received within the set timeout period, it is judged that the sensor may have a data delay. In addition, data integrity analysis can be used, that is, time series analysis is performed on the continuous data stream of the sensor. If it is found that the time interval between data points is significantly too long, or some data is not uploaded at the predetermined frequency, it is considered that there is a transmission delay in the sensor data. By comprehensively using these methods, the system can accurately and quickly detect whether there is a delay in the data transmission of the current sensor under high load conditions.

[0029] In the case where it is detected that there is a data transmission delay in the current sensor, the current sensor transmission delay information of the sensor can be obtained in real time, which can be achieved through data retransmission mechanisms, sensor operating status queries, and historical data compensation. First, the system can send a data retransmission request to the sensor detected with a delay, asking it to re-upload the latest data to judge the duration of the delay and the data loss situation. Second, the system can call the sensor operating status query interface to obtain information such as the current network status, data cache situation, and signal strength of the sensor, so as to analyze the possible reasons for the delay. To further improve the delay information, the system can also use a historical data compensation algorithm. By querying the historical load data of similar devices, the reasonable range of the current data is speculated, and the possible values of the delayed data are predicted in combination with a machine learning model (such as LSTM time series prediction) to reduce the data gap caused by the delay. Through these methods, the system can comprehensively obtain the transmission delay information of the sensor and provide accurate data support for subsequent predictive analysis.

[0030] Analyze the obtained current sensor transmission delay information and generate a data integrity coefficient and a load disturbance index respectively; In this embodiment, analyze the obtained current sensor transmission delay information and generate a data integrity coefficient and a load disturbance index respectively, which specifically includes the following steps: Preprocess the obtained current sensor transmission delay information; The purpose of preprocessing the obtained current sensor transmission delay information is to improve the accuracy, integrity, and analyzability of the data, ensuring that when calculating the subsequent data integrity coefficient and load disturbance index, it will not be interfered by abnormal data, noise, or format inconsistency problems, thereby improving the reliability of the calculation results. Specifically, the preprocessing includes operations such as outlier removal, data denoising, and data format standardization. First, in terms of outlier removal, abnormal data points far from the data mean can be identified and removed through statistical methods (such as the 3σ principle), or the box plot method (IQR, interquartile range) can be used to detect and remove outliers to ensure that the calculation deviation is not caused by individual extreme values. Second, in terms of data denoising, signal processing algorithms such as moving average filtering or Kalman filtering can be used to smooth the data to reduce data fluctuations caused by network jitter or sampling errors, thereby improving the stability and consistency of the data. In addition, in terms of data format standardization, the system needs to perform time alignment and unit conversion on the data to ensure that all data is stored and processed at the same time step and the measurement units of the data are unified (such as amperes, kilowatts, etc.) so that the data can maintain consistency during subsequent calculations. Through the above preprocessing steps, invalid data can be effectively removed, the quality and stability of the data can be improved, and a reliable data basis can be provided for subsequent analysis.

[0031] Extract the data integrity evaluation information and load disturbance analysis information from the preprocessed current sensor transmission delay information; Extract the data integrity evaluation information and load disturbance analysis information from the pre - processed current sensor transmission delay information, which can be achieved through software methods such as feature selection, data grouping, and statistical analysis to ensure the accurate extraction of key data related to data integrity and load disturbance. First, in terms of feature selection, a rule - based screening method can be used to select variables highly relevant to data integrity and load disturbance from the pre - processed data. For example, features such as the number of missing adjacent data points, the number of repeated adjacent data points, and the amplitude of change between adjacent data points are used to construct data integrity evaluation information, while features such as the amplitude of load fluctuation between adjacent data points, the load change gradient, and the load change rate are used to construct load disturbance analysis information. Secondly, in terms of data grouping, the sliding window technique can be adopted to window the data at a certain time interval (such as 10 seconds or 100 data points), and calculate the features of data integrity and load disturbance within each window respectively to ensure that the extracted information can dynamically reflect the changes in sensor data at different times. Finally, in terms of statistical analysis, statistical indicators such as the mean, standard deviation, maximum value, and minimum value within the window can be calculated to further extract the change trend of the data. For example, the mean of the adjacent data point loss rate is calculated to evaluate data integrity, or the variance of the load fluctuation gradient is calculated to measure the severity of load disturbance. Through the above methods, the system can accurately extract the data integrity evaluation information and load disturbance analysis information, providing reliable data support for the subsequent calculation of the data integrity coefficient and the load disturbance index.

[0032] Analyze the extracted data integrity evaluation information and load disturbance analysis information to generate a data integrity coefficient and a load disturbance index respectively.

[0033] In this embodiment, the acquisition logic of the data integrity coefficient is as follows: Extract the data integrity evaluation information from the pre - processed current sensor transmission delay information, specifically including the number of data points lost at different times within a period when a data transmission delay of the current sensor is detected, the proportion of data repeatedly sent by the current sensor under high - load conditions, and the amplitude of change in the current value between adjacent data points, and calibrate them respectively as 、 and , represents the number of data points lost at time within a period when a data transmission delay of the current sensor is detected, represents the proportion of data repeatedly sent by the current sensor under high - load conditions at time within a period when a data transmission delay of the current sensor is detected, represents within a period when a data transmission delay of the current sensor is detected The change amplitude of the current value between adjacent data points at a moment , is a positive integer; In the case where data transmission delay of the current sensor is detected, the number of lost data points, the proportion of data repeatedly sent by the current sensor, and the change amplitude of the current value between adjacent data points can be obtained in real time through methods such as data packet monitoring, time series analysis, and statistical calculation, and it is ensured that the obtained data can accurately reflect the real-time working state of the sensor. First, the number of lost data points can be obtained by comparing data packets with timestamps. The system records the timestamp of each data packet when receiving data, and calculates the time interval between adjacent data points. If the interval exceeds the normal sampling period of the sensor, it indicates that there is data loss in the middle. The system can calculate the loss amount according to the difference between the theoretically expected number of data points and the actual number of received data points. Secondly, the proportion of data repeatedly sent by the current sensor under high load conditions can be obtained through the hash deduplication algorithm and data redundancy detection. After receiving the data, the system converts it into a hash value and stores it in the short-term cache, and compares it when new data arrives. If the same hash value is found, it is determined that the data is repeated, and the proportion of repeated data points is calculated. Finally, the change amplitude of the current value between adjacent data points can be obtained in real time through time series difference calculation. The system performs a sliding window process on the continuous data stream, calculates the numerical difference between each adjacent data point, and stores the real-time results of all change amplitudes. Through these methods, the system can accurately and real-time obtain the integrity information of the sensor data, provide high-quality data input for subsequent calculations and analyses, and thus ensure the accuracy of the data integrity coefficient.

[0034] Calculate the data integrity coefficient, and the specific calculation formula is as follows:

[0035] In the formula, is the data integrity coefficient.

[0036] The reason why the calculation formula of this data integrity coefficient adopts this form is to comprehensively measure the data integrity of the current sensor under high load operating conditions, and through the mathematical properties of logarithmic and exponential operations, ensure that the calculation result can reasonably reflect the comprehensive impact of data loss, repetition, and change amplitude on data integrity. First, taking the natural logarithm of the number of lost data points ( ) and the proportion of data repeatedly sent ( ) respectively is because logarithmic operation can stretch the data, making the influence of small value changes more obvious, while the influence of large value changes gradually flattens, avoiding extreme values from overly dominating the overall calculation, and at the same time ensuring that the data distribution is smoother. Secondly, using exponential operation for the denominator part of data integrity Processing the current value variation between adjacent data points ( ), because the larger the data change, the stronger the data volatility and the lower the data integrity. Exponential operation can amplify this effect, making larger current fluctuations contribute more to the overall data integrity coefficient, thereby reducing the final Finally, take the average of the calculated values ​​at all times to ensure It can stably reflect the data integrity within the entire time window, and will not affect the overall calculation results due to extreme conditions at individual moments. In summary, this calculation method can comprehensively consider the impact of data loss, duplicate data and data fluctuations, and use the characteristics of logarithmic and exponential operations to make It can accurately measure the data integrity of the current sensor under high load conditions and provide highly reliable input data for subsequent fault prediction.

[0037] Data integrity factor The size of directly affects the degree of influence of the delay of current sensor data on the fault warning of power distribution equipment under high load operation. The core relationship is: The lower the value, the more serious the data loss, the more duplicate data, or the more severe the current fluctuation, which leads to a decrease in data quality and makes the system unable to accurately identify load anomalies, thereby increasing the risk of false alarms or missed alarms of fault warnings; conversely, The higher the value, the more complete and stable the data is, and the system can more accurately assess the equipment status and improve the reliability of fault warning. When the value is low, there may be a large amount of missing data transmitted by the sensor, making it difficult for the system to continuously track changes in the equipment load, resulting in abnormal load conditions being difficult to detect in a timely manner. In addition, if the proportion of duplicate data is high, it means that the sensor may upload the same data multiple times due to network congestion or cache retention problems, and fail to provide the latest equipment status, resulting in a delay in updating the actual load of the equipment, affecting the early warning system's ability to respond quickly to faults. At the same time, if the current change amplitude of adjacent data points is too large, indicating abnormal data fluctuations, it may be due to instantaneous measurement errors caused by unstable data transmission, further reducing the system's ability to accurately judge the load status. Therefore, As a comprehensive parameter to measure data integrity, the lower its value, the greater the impact of data delay on fault warning, which may lead to misjudgment or missed judgment; The higher the value, the better the data quality. The system can more accurately evaluate the equipment operating status under high load conditions and improve the reliability and response speed of fault warnings.

[0038] In this embodiment, the logic for obtaining the load disturbance index is as follows: Extract the load disturbance analysis information from the preprocessed current sensor transmission delay information, specifically including the fluctuation amplitude of the distribution equipment load at different moments within a period of time when a data transmission delay of the current sensor is detected, the load change gradient between all data points, and the change rate of the distribution equipment load, and calibrate them respectively as 、 and , represents the fluctuation amplitude of the distribution equipment load at the moment within a period of time when a data transmission delay of the current sensor is detected, represents the load change gradient between all data points at the moment within a period of time when a data transmission delay of the current sensor is detected, represents the change rate of the distribution equipment load at the moment within a period of time when a data transmission delay of the current sensor is detected, , is a positive integer; When a data transmission delay of the current sensor is detected, the fluctuation amplitude of the distribution equipment load, the load change gradient between all data points, and the change rate of the distribution equipment load can be obtained in real time through software methods such as data stream processing, differential calculation, and statistical analysis to accurately evaluate the load disturbance situation. First, the fluctuation amplitude of the distribution equipment load can be achieved through sliding window analysis. The system calculates the difference between the maximum and minimum values of the equipment load (current, voltage, or power) within a period window (such as 10s or 100 data points) to obtain the load fluctuation amplitude within this period, thereby reflecting the degree of load change. Second, the load change gradient between all data points can be achieved through first-order difference calculation, that is, performing adjacent point difference calculation on time series data. The gradient value of each data point can be expressed as the change amplitude between its previous and subsequent data points, thereby quantifying the direction and intensity of load change. Finally, the change rate of the distribution equipment load can be obtained through multi-step difference and normalization calculation, that is, calculating the data change amount of adjacent multiple time steps and dividing it by the corresponding time interval to obtain the load change rate per unit time. This method can identify the severity of load change and normalize data of different load levels to make them comparable. Through the above methods, the system can monitor the dynamic change situation of the distribution equipment load in real time, provide high-precision data support for the calculation of the load disturbance index (LDI), ensure accurate evaluation of the equipment load status even in the case of data delay, and optimize the reliability of fault warning.

[0039] Calculate the load disturbance index, and the specific calculation formula is as follows:

[0040] In the formula, is the load disturbance index.

[0041] Load disturbance index The design logic of the calculation formula is to comprehensively evaluate the influence degree of the current sensor data delay on the load fluctuation of the distribution equipment, and through the mathematical characteristics of exponential operation and logarithmic operation, ensure that the calculation result can reasonably reflect the influence of the load change gradient, load change rate and load fluctuation amplitude on the load disturbance. First, The calculation method of this part reflects the dynamic relationship between the load change gradient ( ) and the load change rate ( ), where represents the trend of the load change at the current moment, and amplifies the influence of the load change rate through exponential operation, so that when the load change rate is high, the value of this item increases rapidly, indicating that the system load changes violently and is easily affected by data delay, thus increasing value. Second, The part is used to perform logarithmic operation on the load fluctuation amplitude ( ), so that smaller load fluctuations can be reflected to a greater extent, while the influence of larger fluctuation amplitudes tends to be gentle, avoiding the overinfluence of a single extreme value on the overall calculation. In addition, taking the average value of the calculated values at all times ensures that can stably reflect the load disturbance degree within the entire time window, and will not cause the calculation result to be distorted due to the violent fluctuation at a single time point. To sum up, this calculation method can fully consider the comprehensive influence of the load change gradient, load change rate and load fluctuation amplitude on the disturbance, and utilize the mathematical characteristics of exponential and logarithmic operations, so that can accurately measure the influence degree of the current sensor data delay on the load disturbance of the distribution equipment under high load conditions, providing high-precision data support for subsequent fault warnings.

[0042] Load disturbance index The magnitude directly affects the influence degree of the current sensor data delay on the fault warning of the distribution equipment under high load operation. The core relationship is as follows: The higher the value, the more violently the load of the distribution equipment changes, the greater the influence of the current sensor data delay on the equipment operation state, resulting in the system being difficult to accurately judge the load abnormality, thus increasing the risk of false alarms or missed alarms in the fault warning; on the contrary, The lower the value, the more stable the equipment load is. Even if there is data delay, the system can still reliably identify the load trend and ensure the accuracy of the fault warning. Specifically, when When the value is relatively high, it means that the load change gradient is relatively large, the load fluctuation amplitude is relatively strong, and the load change rate is relatively fast. In this case, if there is a delay in the data of the current sensor, the system may not be able to obtain the latest load status in a timely manner, resulting in missing key load abnormal signals, thereby affecting the timely response of the early warning system. In addition, high also means that the operating state of the power distribution equipment is more sensitive to external factors (such as sudden load increase, temperature change, etc.). Once the data delay fails to synchronously reflect these changes, it may cause the early warning system to fail at a critical moment, increasing the risk of equipment failure. On the contrary, when the value is relatively low, it indicates that the load change of the equipment is relatively stable. Even if there is a certain delay in data transmission, the system can still predict the operating state of the equipment relatively accurately and will not affect the early warning decision due to short-term data lag. Therefore, as a parameter measuring the load change stability, the higher its value, the greater the impact of data delay on fault early warning; the lower the value, the smaller the impact of data delay on fault early warning, and the higher the reliability of the early warning system.

[0043] Based on the generated data integrity coefficient and load disturbance index, a delay impact prediction model is constructed to generate a delay prediction coefficient, and after generation, it is analyzed to predict the impact degree of the delay of the current sensor data on the fault early warning of the power distribution equipment under high load operation conditions, and according to the prediction results, the impact degree is divided into low impact degree, medium impact degree and high impact degree; In this embodiment, based on the generated data integrity coefficient and load disturbance index a delay impact prediction model is constructed, and a delay prediction coefficient is generated by weighted summation. The specific calculation formula is as follows:

[0044] In the formula, is the delay prediction coefficient, and are the non-zero weight coefficients of the data integrity coefficient and load disturbance index respectively, and .

[0045] The implementation of this method mainly includes three key steps: constructing a delay impact prediction model, calculating and generating a delay prediction coefficient by weighted summation ( ), and optimizing the non-zero weight coefficients ( and ). First, a delay impact prediction model is constructed based on the data integrity coefficient and load disturbance index. The model inputs and The two parameters measure the degree of data integrity and load disturbance respectively. Then, the delay prediction coefficient is calculated by weighted summation ( ),in Characterize the integrity of sensor data, The higher the value, the better the data quality and the less impact of delay on the early warning system; Characterizes the degree of load fluctuation, The higher the value, the more severe the load change, and the greater the impact of data delay on early warning. use This calculation method uses As a positive factor, As a negative factor, the impact of data delay on fault warning is comprehensively evaluated. Finally, the weight coefficient and The way to determine is crucial, they need to satisfy the constraints that they are non-zero and sum to 1 ( ), weights can usually be adjusted dynamically through data-driven adaptive optimization methods (such as least squares regression, gradient descent optimization, etc.) to ensure that the weights can adapt to the data characteristics under different load conditions. For example, based on historical operating data, Minimize the error between the calculated results and the actual warning situation, and adjust adaptively and The value of is used to optimize the calculation accuracy of the delay prediction coefficient, so that it can more accurately reflect the impact of data delay on fault warning.

[0046] In this embodiment, the preset delay prediction coefficient threshold interval is determined , and after determining the generated delay prediction coefficient A comparison is made, and the influence of the delay of current sensor data on the fault warning of power distribution equipment under high load operation is predicted according to the comparison results. The influence degree is divided into low influence degree, medium influence degree and high influence degree according to the prediction results. The specific comparison and analysis are as follows: like ,The influence degree of the delay of the current sensor data on the ,distribution equipment fault warning under high load operation is high ; This situation indicates that the data integrity of the current sensor is poor, and the load of the power distribution equipment fluctuates violently, resulting in a very high degree of impact of the sensor data delay on fault warning, that is, a high degree of impact. In this case, there may be relatively serious data loss or duplication in the current sensor, making it difficult for the system to accurately obtain the real-time load condition of the equipment, resulting in the warning system being unable to respond in a timely manner when the equipment load is abnormal. In addition, due to the frequent and large fluctuations in the load, even if the system receives lagged data, it is difficult to accurately infer the current equipment state through prediction algorithms, further exacerbating the risk of false alarms or missed alarms. Ultimately, this high degree of impact may lead to the failure to detect equipment overload faults in a timely manner, and may even cause serious accidents such as equipment damage and electrical fires, while increasing the emergency response pressure of maintenance personnel and affecting the overall safety and stability of the power grid.

[0047] If , the degree of impact of the current sensor data delay on the fault warning of the power distribution equipment under high-load operation is a medium degree of impact; This situation indicates that the data integrity of the current sensor and the load fluctuation are at a medium level, that is, a medium degree of impact, which means that the impact of data delay on the fault warning system still exists, but to a certain extent, it can be partially corrected through data compensation or algorithm optimization. In this case, although there is a certain degree of data loss or duplication in the sensor data, it can still provide certain effective information. The system can correct the data through interpolation algorithms, trend prediction or machine learning models, enabling the warning system to still have a certain judgment ability. However, due to the data not being completely reliable, there may still be certain deviations when the system predicts the equipment operation state, especially when the load changes suddenly within a short period of time, there may be slight false alarms or lagged responses, resulting in a decrease in the warning accuracy. Nevertheless, the system in this state can still identify equipment operation abnormalities in most cases and provide relatively reliable warning information, enabling maintenance personnel to conduct inspections and maintenance within a reasonable time window.

[0048] If , the degree of impact of the current sensor data delay on the fault warning of the power distribution equipment under high-load operation is a low degree of impact.

[0049] This situation indicates that the data integrity of the current sensor is relatively high, and the load change of the power distribution equipment is relatively stable, that is, the low impact level, which means that the data delay has little impact on the fault warning, and the system can work normally. In this case, the data quality uploaded by the current sensor is relatively high, and the loss rate and repetition rate are relatively low, ensuring that the system can obtain relatively complete equipment operation status information. At the same time, due to the small load fluctuation, even if there is a slight data delay, it will not have an obvious impact on the warning ability of the system. The system can still accurately judge the operation status of the equipment, ensuring that the warning system can respond in a timely manner when the equipment is overloaded, fails or is in an abnormal state. Therefore, in this state, the fault warning accuracy of the system is relatively high, and the operation and maintenance personnel can carry out effective intervention based on accurate warning information, thereby reducing the risk of equipment damage or grid instability and improving the safety and reliability of the overall power grid.

[0050] The pre-set delay prediction coefficient threshold interval can be determined by combining historical data analysis, machine learning modeling and statistical methods to ensure that this interval can accurately reflect the impact of different data delay degrees on the fault warning system. First, the historical operation data analysis method can be used to extract a large amount of current sensor data under high-load operation conditions, calculate the corresponding data integrity coefficient and load disturbance index, and calculate the delay prediction coefficient of a large number of historical samples based on these data to form a data distribution model. Then, through statistical analysis methods, such as Quantile Analysis or K-Means Clustering, cluster analysis is performed on all historical DPC data to automatically divide different levels of data delay impact, and then determine the interval boundaries. Specifically, the DPC range of low impact level can be set as the upper quantile of the data distribution (such as 90% and above), the DPC range of medium impact level can be set as the middle interval (such as 10% - 90%), and the DPC range of high impact level can be set as the lower quantile (such as below 10%), so as to ensure that the divided threshold interval is based on the actual data distribution and can effectively distinguish different delay impact levels. In addition, a machine learning model (such as a decision tree or Support Vector Machine SVM) can be combined, and a classification model is trained using historical data to enable it to automatically adjust the threshold interval based on historical DPC data to adapt to the dynamic changes of different operating environments. In this way, the system can dynamically adjust the threshold interval based on real data to ensure its applicability to different high-load operating conditions and improve the stability and accuracy of the fault warning system.

[0051] According to the prediction results, corresponding management measures are implemented for different impact levels; In this embodiment, according to the prediction results, corresponding management measures are implemented for different impact levels, specifically: For high-impact situations, the management measures are as follows: increase the data acquisition frequency of current sensors, optimize the data upload strategy, use a compensation prediction algorithm to intelligently fill in missing data, and immediately send a high-priority warning to the operation and maintenance personnel to ensure timely intervention in fault troubleshooting; For high-impact situations, the impact of data latency on fault warning can be reduced by dynamically adjusting the data acquisition frequency, optimizing the data upload strategy, and intelligent data compensation. First, the system can increase the data acquisition frequency of current sensors based on an adaptive data acquisition adjustment algorithm (such as load-adaptive sampling). When severe data latency is detected, the system will automatically shorten the sampling interval of the sensor to collect data more frequently, thus reducing information loss caused by data lag. Second, in terms of data upload, the data transmission priority management mechanism can be optimized. That is, when the network bandwidth is limited or there is data backlog, critical data (such as abnormal load, current mutation, etc.) is preferentially transmitted. At the same time, the edge computing preprocessing method is adopted to analyze data on the local device side first, reducing redundant information to be uploaded and improving the transmission efficiency. Finally, to compensate for information loss caused by data latency, the system can use time series prediction algorithms (such as the LSTM model) or Kalman filtering algorithms to intelligently fill in missing data points based on the received data trend, thereby enhancing the system's perception of the device load situation. In addition, the system will immediately trigger a high-priority alarm mechanism to push notifications to the operation and maintenance personnel through the cloud platform or mobile device to ensure that emergency response measures can be taken within the shortest time to reduce the risk of equipment damage and improve the safety and reliability of the power distribution system.

[0052] For medium-impact situations, the management measures are as follows: adjust the data upload strategy of current sensors, increase the data acquisition frequency, correct the delayed data through historical data trend prediction, and send a warning reminder with general priority to the operation and maintenance personnel to ensure maintenance can be carried out within a reasonable time; For the case of medium impact, the impact of data latency can be reduced by moderately optimizing the data upload strategy, using trend prediction to correct latency data, and sending general-priority warnings, while avoiding excessive consumption of system resources. First, the system can dynamically adjust the data upload mode, adopting a strategy that combines event-triggered and regular uploads. When the sensor detects slight data latency, it can temporarily increase the data upload frequency, but after the latency returns to normal, it resumes the standard upload mode to optimize data transmission efficiency. Second, to reduce the impact of data latency on fault warnings, sliding window regression analysis can be used to calculate the load change trend in combination with historical data and correct the latency data to ensure that the system can still reasonably infer the current device load even if the data is lagged. In addition, the system sends warnings of general priority to the operation and maintenance personnel to remind them of possible data latency problems, but does not trigger an emergency response to reduce unnecessary intervention. This strategy ensures that the data latency problem can still maintain the basic accuracy of the warning system without seriously affecting the operation of the device, and can be adjusted when necessary to improve the intelligent level of power distribution management.

[0053] For the case of low impact, the management measures taken are as follows: Keep the current data collection and upload strategy unchanged, and continuously monitor the data transmission status of the current sensor. If a trend of increasing latency is detected, dynamically adjust the collection frequency and perform periodic health checks to ensure the long-term stable operation of the system.

[0054] For the case of low impact, the long-term stable operation of the power distribution system can be ensured by continuously monitoring the data transmission status, dynamically adjusting the data collection strategy, and performing regular health checks, without imposing an additional burden on normal data collection and transmission. First, the system starts the data transmission health monitoring module to continuously track the data upload status of the current sensor and determines whether the data continuously remains within the normal range by counting indicators such as the data packet loss rate, data transmission jitter, and average data transmission delay. Second, the system adopts an adaptive collection frequency management mechanism. When it detects that the data transmission status remains good, it maintains the current data collection and upload frequency unchanged to ensure the efficient use of system resources. However, if it finds that the latency situation of the sensor has deteriorated, it dynamically adjusts the collection interval to intervene in potential problems in advance. In addition, to further ensure the long-term stability of the system, the system performs periodic health check tasks, such as regularly analyzing the data latency trend over a period of time, identifying whether there are potential anomalies, and performing data verification to prevent the occurrence of potential failures. Through these measures, the system can ensure the stability of data transmission while maintaining the existing strategy, and at the same time has the ability to adaptively adjust to possible future problems, thereby improving the overall reliability and operation efficiency of the power distribution system.

[0055] Continuously monitor the data transmission status of the current sensor, and after the management measures are implemented, track its effects in real time. If it is detected that the latency problem has not improved, adjust the management measures and re-optimize the data acquisition and upload strategy of the sensor.

[0056] To continuously monitor the data transmission status of the current sensor, it can be achieved through real-time data stream analysis, anomaly detection algorithms, and dynamic transmission performance evaluation, etc., to ensure that the changes in data transmission can be accurately tracked after the management measures are implemented. First, the system can establish a data stream monitoring module. Using a sliding time window analysis, it can statistically analyze the number of data packets uploaded by the sensor, data transmission intervals, loss rates, and repetition rates in real time, and record the timestamps of the data packets to identify whether there are long-term or sudden latency problems. Second, anomaly detection algorithms (such as Z-score analysis, DBSCAN clustering, or autoregressive anomaly detection) can be adopted. When the data transmission status is abnormal, it can automatically mark the data lag of the sensor, and establish a benchmark model based on historical data to determine whether the current latency exceeds the normal fluctuation range. In addition, to ensure the accuracy of monitoring, the system can be based on dynamic transmission performance evaluation methods, such as calculating data throughput, average network latency, and jitter degree, and use these to quantify the data transmission status of the sensor, so as to ensure the continuity and accuracy of data monitoring and provide real-time feedback for subsequent management optimization.

[0057] After the management measures are implemented, if it is detected that the latency problem has not improved, adjust the management measures and re-optimize the data acquisition and upload strategy of the sensor, which can be achieved through adaptive control algorithms, feedback closed-loop regulation, and dynamic parameter optimization, etc. First, the system can be based on adaptive control algorithms (such as PID control, reinforcement learning, or fuzzy control), and dynamically adjust the data acquisition frequency and upload interval according to the severity of the sensor latency, so that the system can automatically adapt to the current network and device status. Second, adopt a feedback closed-loop regulation mechanism. After implementing a certain management measure, the system will continuously monitor its effects, and calculate the data latency change trend within a set time window (such as 5 minutes or 100 data points). If the latency fails to return to the normal threshold, the system will automatically select other optimization strategies, such as switching the data transmission protocol (from TCP to UDP), adjusting the edge computing priority, compressing data packets to reduce bandwidth occupancy, etc. Finally, through the dynamic parameter optimization mechanism, combined with historical data and the current transmission status, optimize the data upload strategy, such as adjusting the batch size of data transmission, increasing the transmission priority of key data fields, and even switching to a backup sensor or redundant network channel in extreme cases to ensure the timeliness of data. Through these measures, the system can autonomously optimize the data transmission strategy when it detects that the management measures are ineffective, ensure that the data latency of the current sensor is effectively controlled, and improve the reliability and response ability of the fault warning system.

[0058] such asFigure 2 The shown Internet of Things-based distribution fire protection information management system includes a high-load operation monitoring module, a data transmission delay detection module, a data integrity and load disturbance calculation module, a delay impact prediction and evaluation module, an intelligent management measure execution module, and an adaptive optimization and feedback regulation module; The high-load operation monitoring module, during peak electricity consumption periods, monitors the operation load of distribution equipment in real time, and when it monitors that the distribution equipment is in a high-load operation state, it obtains the equipment operation load information of this equipment in real time; The data transmission delay detection module analyzes the obtained equipment operation load information, detects in real time whether there is a data transmission delay in the current sensor, and when it detects that there is a data transmission delay in the current sensor, it obtains the current sensor transmission delay information of this sensor in real time; The data integrity and load disturbance calculation module analyzes the obtained current sensor transmission delay information and generates a data integrity coefficient and a load disturbance index respectively; The delay impact prediction and evaluation module constructs a delay impact prediction model based on the generated data integrity coefficient and load disturbance index, generates a delay prediction coefficient, and after generating it, analyzes it to predict the impact degree of the delay of the current sensor data on the distribution equipment fault warning under high-load operation conditions, and divides the impact degree into a low impact degree, a medium impact degree, and a high impact degree according to the prediction result; The intelligent management measure execution module, according to the prediction result, implements corresponding management measures for different impact degrees; The adaptive optimization and feedback regulation module continuously monitors the data transmission status of the current sensor, and after the management measure is executed, it tracks its effect in real time. If it detects that the delay problem has not improved, it adjusts the management measure and re-optimizes the data collection and upload strategy of the sensor.

[0059] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by technicians in this field according to the actual situation.

[0060] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0061] It should be understood that in various embodiments of the present application, the order of the above processes does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0062] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0063] In several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the above-described embodiments are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be an indirect coupling or communication connection through some interfaces, devices or units, and can be in an electrical, mechanical or other form.

[0064] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0065] In addition, each functional unit in various embodiments of the present application may be integrated in a processing unit, may exist separately as individual physical units, or two or more units may be integrated in one unit.

[0066] As mentioned above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A power distribution fire protection information management method based on the Internet of Things, characterized in that: The specific steps include: During peak power consumption, the operating load of the power distribution equipment is monitored in real time, and when the power distribution equipment is detected to be in a high-load operating state, the equipment operating load information of the equipment is obtained in real time; Analyze the acquired equipment operation load information, detect in real time whether the current sensor has data transmission delay, and if the current sensor is detected to have data transmission delay, acquire the current sensor transmission delay information of the sensor in real time; Analyze the acquired current sensor transmission delay information to generate data integrity coefficient and load disturbance index respectively; Based on the generated data integrity coefficient and load disturbance index, a delay impact prediction model is constructed to generate a delay prediction coefficient, and after the generation, an analysis is performed to predict the impact of the delay of current sensor data on the fault warning of the distribution equipment under high-load operation, and the impact degree is divided into low impact degree, medium impact degree and high impact degree according to the prediction results; According to the forecast results, corresponding management measures are implemented according to different impact levels; Continuously monitor the data transmission status of the current sensor, and track the effect of management measures in real time after they are implemented. If it is detected that the delay problem has not improved, adjust the management measures and re-optimize the sensor's data collection and upload strategy.

2. The power distribution fire protection information management method based on the Internet of Things according to claim 1 is characterized in that: The acquired current sensor transmission delay information is analyzed to generate a data integrity coefficient and a load disturbance index, respectively, which specifically includes the following steps: Preprocessing the acquired current sensor transmission delay information; extracting data integrity assessment information and load disturbance analysis information from the preprocessed current sensor transmission delay information; The extracted data integrity assessment information and load disturbance analysis information are analyzed to generate a data integrity coefficient and a load disturbance index, respectively.

3. The power distribution fire protection information management method based on the Internet of Things according to claim 2 is characterized in that: The logic for obtaining the data integrity coefficient is as follows: The data integrity assessment information is extracted from the preprocessed current sensor transmission delay information, including the number of data points lost at different times within a period of time when the current sensor detects data transmission delay, the proportion of data repeatedly sent by the current sensor under high load, and the current value change amplitude between adjacent data points, and is calibrated as , and , Indicates the period of time when a current sensor is detected to have data transmission delay The number of missing data points at a time, Indicates the period of time when a current sensor is detected to have data transmission delay The proportion of data repeatedly sent by the current sensor under high load conditions at the moment, Indicates the period of time when a current sensor is detected to have data transmission delay The current value change amplitude between adjacent data points at the same time, , is a positive integer; Calculate the data integrity coefficient. The specific calculation formula is as follows: In the formula, is the data completeness coefficient.

4. The power distribution fire protection information management method based on the Internet of Things according to claim 3 is characterized in that: The acquisition logic of the load disturbance index is as follows: Extract the load disturbance analysis information from the preprocessed current sensor transmission delay information, including the fluctuation amplitude of the distribution equipment load at different times within a period of time when the current sensor detects data transmission delay, the load change gradient between all data points, and the change rate of the distribution equipment load, and calibrate them as , and , Indicates the period of time when a current sensor is detected to have data transmission delay The fluctuation range of the load of the power distribution equipment at any moment, Indicates the period of time when a current sensor is detected to have data transmission delay The load change gradient between all data points at the moment, Indicates the period of time when a current sensor is detected to have data transmission delay The rate of change of the load of the power distribution equipment at any moment, , is a positive integer; Calculate the load disturbance index. The specific calculation formula is as follows: In the formula, is the load disturbance index.

5. The power distribution fire protection information management method based on the Internet of Things according to claim 4 is characterized in that: Based on the generated data complete coefficients and load disturbance index Construct a delay impact prediction model and generate a delay prediction coefficient through weighted summation. The specific calculation formula is as follows: In the formula, is the delay prediction coefficient, and The data integrity coefficients are and load disturbance index The non-zero weight coefficient of .

6. The power distribution fire protection information management method based on the Internet of Things according to claim 5 is characterized in that: Determine the preset delay prediction coefficient threshold interval , and after determining the generated delay prediction coefficient A comparison is made, and the influence of the delay of current sensor data on the fault warning of power distribution equipment under high load operation is predicted according to the comparison results. The influence degree is divided into low influence degree, medium influence degree and high influence degree according to the prediction results. The specific comparison and analysis are as follows: like ,The influence degree of the delay of the current sensor data on the ,distribution equipment fault warning under high load operation is high ; like ,Under high-load operation, the delay of current sensor data has a medium impact on the fault warning of distribution equipment; like ,Under high load operation, the delay of current sensor data has a low impact on the ,distribution equipment fault warning.

7. The power distribution fire protection information management method based on the Internet of Things according to claim 6 is characterized in that: According to the forecast results, corresponding management measures are implemented according to different impact levels, specifically: For situations with high impact, the management measures taken are: increase the data collection frequency of current sensors, optimize data upload strategies, use compensation prediction algorithms to intelligently fill in missing data, and immediately send high-priority warnings to operation and maintenance personnel; For situations with medium impact, the management measures taken are: adjust the data upload strategy of the current sensor, increase the frequency of data collection, correct the delayed data through historical data trend prediction, and send general priority warning reminders to operation and maintenance personnel; For situations with low impact, the management measures taken are: keep the current data collection and upload strategy unchanged, while continuously monitoring the data transmission of the current sensor. If a trend of increasing delay is detected, dynamically adjust the collection frequency and perform periodic health checks.

8. A power distribution fire information management system based on the Internet of Things, used to implement the power distribution fire information management method based on the Internet of Things as described in any one of claims 1 to 7, characterized in that: It includes a high-load operation monitoring module, a data transmission delay detection module, a data integrity and load disturbance calculation module, a delay impact prediction and evaluation module, an intelligent management measure execution module, and an adaptive optimization and feedback control module; The high-load operation monitoring module monitors the operating load of the power distribution equipment in real time during peak power consumption, and obtains the equipment operating load information of the equipment in real time when the power distribution equipment is detected to be in a high-load operating state; The data transmission delay detection module analyzes the acquired equipment operation load information, detects in real time whether the current sensor has data transmission delay, and acquires the current sensor transmission delay information of the sensor in real time if the current sensor has data transmission delay. The data integrity and load disturbance calculation module analyzes the acquired current sensor transmission delay information and generates a data integrity coefficient and a load disturbance index respectively; The delay impact prediction and evaluation module builds a delay impact prediction model based on the generated data integrity coefficient and load disturbance index, generates a delay prediction coefficient, and performs analysis after generation to predict the impact of the delay of current sensor data on the fault warning of distribution equipment under high-load operation, and divides the impact degree into low impact degree, medium impact degree and high impact degree according to the prediction results; Intelligent management measures execution module, which implements corresponding management measures for different impact levels based on the prediction results; The adaptive optimization and feedback control module continuously monitors the data transmission status of the current sensor and tracks the effect of the management measures in real time after they are implemented. If it is detected that the delay problem has not been improved, the management measures are adjusted and the data collection and upload strategy of the sensor is re-optimized.

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