A method, system and storage medium for online monitoring of electricity consumption

Abstract

This application relates to the field of power distribution technology and discloses a method, system, and storage medium for online monitoring of electricity consumption. The method includes: collecting temperature and humidity data of distribution nodes through a three-zone environmental monitoring device to form an environmental data matrix; using a dual threshold algorithm to identify environmental deviations and generate a monitoring termination signal; restoring the environment to the standard range through cooling / heating and humidification / dehumidification adjustments; calculating a compensation coefficient based on the adjustment data; calibrating the electricity meter data for temperature and humidity based on the compensation coefficient; and optimizing power supply quality using a distribution loss environmental correlation algorithm to generate an adaptive scheduling strategy for the distribution network environment. This application solves the problem of inconsistent metering accuracy caused by the lack of an environmental adaptive mechanism in existing distribution network power monitoring technologies, improving the accuracy of power monitoring data under different environmental conditions and the economic efficiency of distribution network operation.

Description

Technical Field

[0001] This application relates to the field of power distribution technology, and in particular to a method, system and storage medium for online monitoring of electricity consumption. Background Technology

[0002] Existing power distribution network monitoring technologies mainly employ traditional electricity meter testing methods. These methods involve installing smart meters at distribution nodes to monitor and collect data on electricity consumption in real time. Traditional monitoring methods typically involve calibrating and testing electricity meters in conventional laboratory or distribution room environments, assuming that relatively stable environmental temperature and humidity conditions have little impact on the accuracy of electricity metering. Current power monitoring systems primarily focus on the metering accuracy and communication functions of the electricity meters themselves, using communication methods such as RS485 and Ethernet to achieve remote data acquisition and transmission. Power distribution network operation and management usually employ fixed scheduling strategies and load allocation schemes based on historical experience and preset parameters for power distribution network operation control. Electricity meter testing devices are typically placed in distribution rooms or dedicated testing laboratories, and the temperature and humidity control of the testing environment relies on the building's air conditioning system; there is no dedicated environmental control equipment specifically for electricity meter testing.

[0003] The main shortcomings of existing technologies are that they ignore the impact of changes in ambient temperature and humidity on the metering accuracy of electricity meters, resulting in a lack of consistency and accuracy in electricity monitoring results under different seasons and environmental conditions. Traditional electricity monitoring methods do not establish a correlation model between environmental factors and electricity metering errors, and therefore cannot effectively compensate for metering deviations caused by environmental changes. Existing distribution network dispatching strategies lack environmental adaptability and cannot dynamically adjust operating parameters and control strategies according to changes in environmental conditions. The temperature and humidity control of the electricity meter detection environment is not precise enough and lacks real-time monitoring and automatic adjustment mechanisms. When environmental conditions do not meet the detection requirements, they cannot be detected and corrected in time. Distribution network loss analysis usually ignores the impact of environmental factors on line resistance and equipment performance, resulting in deviations between loss calculation results and actual conditions.

[0004] Because existing technologies cannot monitor changes in temperature and humidity at distribution nodes in real time and lack environmental deviation identification mechanisms, power monitoring cannot be stopped in time when the temperature and humidity of the standard equipment area and the tested area exceed the normal range. This results in systematic errors in the power data collected under abnormal environmental conditions. Even if environmental anomalies are detected, existing technologies lack adaptive recovery control methods and cannot quickly restore environmental stability through cooling, heating, humidification, and dehumidification. More importantly, existing technologies have not established a mathematical correlation model between environmental adjustment process data and power metering compensation coefficients. They cannot quantitatively calculate compensation coefficients based on the temperature and humidity differences before and after adjustment and form a compensation algorithm library for different environmental deviation situations. This means that even if power meter data is obtained, it cannot be calibrated for temperature and humidity compensation. Ultimately, the distribution network loss analysis ignores the impact of environmental temperature and humidity on changes in distribution line resistance and the efficiency of distribution equipment. It is impossible to formulate an adaptive dispatch strategy for the distribution network environment that takes environmental factors into account, resulting in low operating efficiency and unstable power supply quality of the distribution network under different environmental conditions. Summary of the Invention

[0005] This application provides a method, system, and storage medium for online monitoring of electricity consumption, which solves the problem of inconsistent metering accuracy caused by the lack of environmental adaptation mechanism in existing power distribution network monitoring technology, and improves the accuracy of power monitoring data under different environmental conditions and the economy of power distribution network operation.

[0006] Firstly, this application provides a method for online monitoring of electricity consumption, the method comprising: The temperature and humidity parameters of the standard equipment area and the tested item area of ​​the power distribution node are collected and processed by the three-area environmental monitoring device of the power distribution network to obtain the power distribution environment data matrix. Based on the power distribution environment data matrix, an environmental deviation identification process is performed using a dual threshold judgment algorithm. When the temperature and humidity exceed the preset range, a power distribution monitoring stop signal is obtained. The power distribution monitoring interruption signal is processed by adaptive recovery control, and the environmental parameters are restored to the standard range through cooling and heating and humidification and dehumidification adjustment. The compensation coefficient is calculated based on the temperature and humidity adjustment data during the environmental restoration process. A compensation mathematical model is established based on the temperature and humidity difference before and after adjustment to obtain a set of environmental compensation coefficients. Based on the set of environmental compensation coefficients, the power meter data of the distribution network is calibrated by temperature and humidity compensation to obtain the power consumption data of the distribution network after environmental correction. The power supply quality is optimized by using a power distribution loss environment correlation algorithm to process the environmentally corrected power distribution energy consumption data, thereby obtaining an adaptive scheduling strategy for the power distribution network environment.

[0007] Secondly, this application provides a system for online monitoring of electricity consumption, the system comprising: The data acquisition module is used to collect and process temperature and humidity parameters of the standard equipment area and the tested area of ​​the power distribution node through the three-area environmental monitoring device of the power distribution network, and obtain the power distribution environment data matrix. The identification module is used to perform environmental deviation identification processing based on the power distribution environment data matrix through a dual threshold judgment algorithm, and to obtain a power distribution monitoring stop signal when the temperature and humidity exceed the preset range. The control module is used to adaptively recover the power distribution monitoring interruption signal and restore the environmental parameters to the standard range through cooling and heating and humidification and dehumidification adjustment. The calculation module is used to calculate the compensation coefficient based on the temperature and humidity adjustment data during the environmental restoration process. It establishes a compensation mathematical model based on the temperature and humidity difference before and after adjustment to obtain a set of environmental compensation coefficients. The calibration module is used to perform temperature and humidity compensation calibration on the power distribution network energy meter data according to the set of environmental compensation coefficients to obtain the environmentally corrected power distribution energy consumption data. The optimization module is used to perform power quality optimization processing on the environmentally corrected power distribution energy consumption data through the power distribution loss environment correlation algorithm to obtain the power distribution network environment adaptive scheduling strategy.

[0008] Thirdly, a device for online monitoring of power consumption is provided, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the device for online monitoring of power consumption to perform the above-described method for online monitoring of power consumption.

[0009] Fourthly, a computer-readable storage medium is provided, wherein instructions are stored therein, which, when executed on a computer, cause the computer to perform the above-described method for online monitoring of power consumption.

[0010] In the technical solution provided in this application, a power distribution environment data matrix is ​​obtained by collecting and processing temperature and humidity parameters of the standard equipment area and the tested item area of ​​the power distribution node through a three-zone environmental monitoring device of the power distribution network. A spatial partitioning architecture and data acquisition mechanism for the environmental monitoring of the power distribution node are established. The three-zone spatial layout physically isolates and functionally divides the control area, standard equipment area, and tested item area, so that environmental monitoring can simultaneously cover the operating environment of standard equipment and the testing environment of tested items. Temperature and humidity sensors collect high-precision data with a temperature accuracy of ±0.1℃ and a humidity accuracy of ±1%RH. The environmental parameters of the standard equipment area and the tested item area are synchronously recorded through timestamp matching processing. The establishment of the power distribution environment data matrix provides a complete data foundation for subsequent environmental deviation identification and compensation coefficient calculation. Based on the power distribution environment data matrix, an environmental deviation identification and processing algorithm using dual thresholds is employed. When the temperature and humidity exceed preset ranges, a power distribution monitoring stop signal is generated. The application of the dual threshold algorithm enables accurate identification and categorized processing of environmental deviations. The first threshold setting sets a temperature threshold range of 20℃ to 24℃ and a humidity threshold range of 40%RH to 55%RH for the standard equipment area. The second threshold setting sets a more stringent temperature threshold range of 22℃ to 24℃ and a humidity threshold range of 45%RH to 55%RH for the tested area. The dual threshold mechanism can set differentiated judgment standards according to the environmental characteristics of different areas. The dual threshold logic operation uses an OR operation logic; when the temperature and humidity in either area exceed the corresponding threshold range, an environmental exceedance trigger signal is generated. Deviation source identification processing distinguishes between gradual temperature and humidity fluctuations caused by the heat generated during the operation of standard equipment and abrupt environmental exchanges caused by the opening of doors when the tested item is replaced. Based on the deviation type classification results, a power distribution monitoring stop signal is generated to avoid continuing to collect power data with systematic errors under abnormal environmental conditions.

[0011] The power distribution monitoring interruption signal is processed by adaptive recovery control. Environmental parameters are restored to the standard range through cooling / heating and humidification / dehumidification adjustments. Compensation coefficients are calculated based on temperature and humidity adjustment data during the recovery process. A closed-loop environmental regulation mechanism and a compensation coefficient calculation method based on regulation process data are established. The environmental regulation controller selects to start the cooling / heating unit or humidification / dehumidification unit according to the deviation type label and sets the operating power level of the regulating equipment according to the deviation intensity level. During the environmental regulation process, temperature and humidity changes are collected every 30 seconds to form an environmental regulation effect data stream. An environmental recovery confirmation signal is generated only when three consecutive detection results show that the temperature and humidity have returned to the standard range. The temperature and humidity compensation coefficient calculation is based on the ratio of the temperature and humidity difference before and after regulation to the standard reference value, establishing a compensation mathematical model. An adjustment time correction factor is introduced to consider the ratio of the actual total adjustment time to the standard adjustment time to correct the compensation coefficient. A multi-dimensional index structure compensation algorithm library is established for different environmental deviation situations. Temperature and humidity compensation coefficients are organized and stored according to three dimensions: deviation source type, deviation intensity level, and regulation effect, providing a quantitative correction basis for temperature and humidity compensation calibration of electricity meter data. Based on the set of environmental compensation coefficients, temperature and humidity compensation calibration is performed on the power meter data of the distribution network to obtain environmentally corrected power consumption data. This achieves accurate compensation for the impact of environmental factors and fundamentally solves the problem of inconsistent power metering accuracy caused by environmental changes. The temperature and humidity compensation algorithm queries the corresponding compensation coefficient from the set of environmental compensation coefficients based on the deviation between the current temperature and humidity values ​​and the standard reference values. The temperature deviation is multiplied by the temperature compensation coefficient to obtain the temperature correction amount, and the humidity deviation is multiplied by the humidity compensation coefficient to obtain the humidity correction amount. The temperature correction amount plus the humidity correction amount is obtained as the comprehensive temperature and humidity compensation factor. The measured power value is multiplied by 1 and added to the comprehensive compensation factor to obtain the compensated and calibrated power value. The same compensation process is applied to all power parameters such as instantaneous power, cumulative power, voltage, and current. The data quality verification process verifies the effectiveness of the compensation effect by calculating the statistical characteristics of the power data before and after calibration. The environmentally corrected power consumption data eliminates the systematic error influence of environmental temperature and humidity changes on power meter measurement. An adaptive scheduling strategy for the distribution network environment is obtained by optimizing the power supply quality of environmentally corrected distribution energy consumption data through a distribution loss environment correlation algorithm. The distribution loss environment correlation algorithm establishes a quantitative relationship model between environmental factors and distribution losses by analyzing the impact mechanism of environmental temperature and humidity on the changes in distribution line resistance and the efficiency of distribution equipment. This makes the distribution network loss analysis more accurate and comprehensive. The algorithm features core functions such as calculating distribution loss data in relation to the environment, evaluating distribution load optimization indicators, dynamically selecting power supply paths, and calculating power supply quality parameters. The adaptive scheduling strategy for the distribution network environment can adaptively adjust according to real-time environmental conditions. Compared with the traditional fixed scheduling mode, it significantly reduces distribution losses and improves the operating efficiency of the distribution network while ensuring power supply reliability. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram of one embodiment of the method for online monitoring of electricity consumption in this application. Figure 2 This is a schematic diagram illustrating the temperature and humidity changes during the environmental restoration process in an embodiment of this application. Figure 3 This is a schematic diagram illustrating the relationship between power distribution loss and ambient temperature in an embodiment of this application; Figure 4 This is a schematic diagram of one embodiment of the system for online monitoring of electricity consumption in this application. Figure 5 This is a schematic block diagram of the structure of the device for online monitoring of power consumption in an embodiment of the present invention. Detailed Implementation

[0014] This application provides a method, system, and storage medium for online monitoring of electricity consumption. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0015] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the method for online monitoring of electricity consumption in this application includes: Step S101: Collect and process temperature and humidity parameters of the standard equipment area and the tested item area of ​​the power distribution node through the three-area environmental monitoring device of the power distribution network to obtain the power distribution environment data matrix; Step S102: Based on the power distribution environment data matrix, environmental deviation identification is performed using a dual threshold judgment algorithm. When the temperature and humidity exceed the preset range, a power distribution monitoring stop signal is obtained. Step S103: Perform adaptive recovery control processing on the power distribution monitoring stop signal, and restore the environmental parameters to the standard range through cooling and heating and humidification and dehumidification adjustment; Step S104: Calculate the compensation coefficient based on the temperature and humidity adjustment data during the environmental restoration process, establish a compensation mathematical model based on the temperature and humidity difference before and after adjustment, and obtain a set of environmental compensation coefficients. Step S105: Perform temperature and humidity compensation calibration on the power distribution network energy meter data according to the environmental compensation coefficient set to obtain the environmentally corrected power distribution energy consumption data; Step S106: The power supply quality of the environmentally corrected power consumption data is optimized by using the power distribution loss environment correlation algorithm to obtain the power distribution network environment adaptive scheduling strategy.

[0016] It is understood that the executing entity of this application can be a system for online monitoring of electricity consumption, or it can be a terminal or a server; no specific limitation is made here. This application's embodiment uses a server as an example for illustration.

[0017] Specifically, the three-zone environmental monitoring device for the power distribution network establishes independent monitoring environments for a control zone, a standard equipment zone, and a monitored item zone through physical spatial division. The control zone is equipped with cooling / heating units and humidification / dehumidification units as the core of environmental regulation. The standard equipment zone houses standard equipment for electricity meter testing and installs temperature and humidity sensors. The monitored item zone houses the electricity meters to be tested and is equipped with sensors of the same specifications. The temperature and humidity sensors adopt technical specifications of ±0.1℃ temperature accuracy and ±1%RH humidity accuracy, acquiring data once per second via an RS485 bus. The sensors in the standard equipment zone first acquire temperature and humidity values, while the sensors in the monitored item zone acquire data simultaneously after receiving a synchronization signal. The two sets of data are matched using timestamps to form a dual-zone environmental data comparison table. When organizing the data matrix, the original temperature values ​​are rounded to an accuracy of ±0.1℃. For example, the original temperature of 23.14℃ is corrected to 23.1℃. The humidity values ​​are rounded to an accuracy of ±1%RH. The original humidity of 52.6%RH is corrected to 53%RH. The rows of the matrix represent time series, and the columns contain five fields: timestamp, temperature of the standard equipment area, humidity of the standard equipment area, temperature of the tested item area, and humidity of the tested item area, forming a structured power distribution environment data matrix.

[0018] The dual threshold judgment algorithm sets two independent judgment mechanisms to monitor temperature and humidity changes. The first threshold judgment sets a temperature threshold range of 20℃ to 24℃ and a humidity threshold range of 40%RH to 55%RH for the standard equipment area. The algorithm extracts the temperature and humidity values ​​of the standard equipment area row by row from the power distribution environment data matrix. When the temperature value is below 20℃ or above 24℃, it is marked as temperature exceeding the standard; when the humidity value is below 40%RH or above 55%RH, it is marked as humidity exceeding the standard, forming the first layer of environmental deviation judgment results. The second threshold judgment sets a stricter temperature threshold range of 22℃ to 24℃ and a humidity threshold range of 45%RH to 55%RH for the tested area. Based on the timestamp information of the first layer judgment results, it extracts the temperature and humidity data of the tested area at the corresponding time from the power distribution environment data matrix for comparison. When the temperature of the tested area is below 22℃ or above 24℃, it is marked as temperature exceeding the standard; when the humidity is below 45%RH or above 55%RH, it is marked as humidity exceeding the standard, forming the second layer of environmental deviation judgment results. The dual threshold logic operation performs an OR operation on the results of two levels of judgment. If either the first-level temperature deviation state or the second-level temperature deviation state is true, the overall temperature deviation state is true. The overall humidity deviation state uses the same logic. When either the overall temperature deviation state or the overall humidity deviation state is true, an environmental exceedance trigger signal is generated. Deviation source identification is performed by calculating the temperature and humidity change rates and performing pattern matching. The change rate is calculated by subtracting the previous value from the current value and dividing by the time interval. When the temperature change rate is less than 0.5℃ / min and the humidity change rate is less than 2%RH / min, it is identified as a device heating type deviation. When both the temperature and humidity change rates are greater than 1℃ / min and 5%RH / min and the change direction is consistent, it is identified as an environmental exchange type deviation. Based on the deviation type classification result, a power distribution monitoring stop signal is generated.

[0019] After receiving the power distribution monitoring stop signal, the environmental control controller analyzes the deviation type classification result. When the deviation type is equipment heating, the cooling unit is activated first to reduce the temperature of the standard equipment area and the tested area, while the dehumidification unit is activated to control the humidity increase. When the deviation type is environmental exchange, both the cooling / heating unit and the humidification / dehumidification unit are activated for comprehensive adjustment. The adjustment strategy determines the equipment activation sequence and adjustment intensity based on the direction and magnitude of the current temperature and humidity deviation. When the temperature deviation is greater than 2°C, a high-intensity cooling or heating mode is activated; when the humidity deviation is greater than 10%RH, a high-intensity humidification or dehumidification mode is activated. The controller sends equipment activation commands to the corresponding adjustment equipment via the control bus. After the environmental control equipment is activated, the temperature and humidity sensors continue to collect environmental parameters of the two areas every 30 seconds. The data acquisition module compares and analyzes the newly collected data with the baseline data before the adjustment started, calculating the temperature and humidity changes by subtracting the initial values ​​from the current values. The environmental control effect data stream records the temperature and humidity changes within each monitoring cycle. Environmental recovery is assessed using a standard of three consecutive tests. The recovery conditions must be met for all three monitoring cycles within 90 seconds. These conditions include the standard equipment area temperature returning to the range of 20°C to 24°C, the standard equipment area humidity returning to the range of 40%RH to 55%RH, the tested area temperature returning to the range of 22°C to 24°C, and the tested area humidity returning to the range of 45%RH to 55%RH. The algorithm compares each monitoring result with a threshold. When a monitoring result shows that all parameters are within the standard range, a successful recovery is recorded. Environmental parameter recovery is complete when three consecutive monitoring results are recorded as successful recovery.

[0020] The temperature and humidity compensation coefficients are calculated based on a mathematical model established using temperature and humidity data recorded before and after adjustment during the environmental restoration process. The temperature compensation coefficient is calculated by dividing the difference between the pre-adjustment and post-adjustment temperatures by the standard temperature baseline of 23℃. For example, assuming the pre-adjustment temperature of the standard equipment area was 26.2℃ and the post-adjustment temperature recovered to 23.0℃, the temperature difference is 3.2℃, which, when divided by the standard temperature baseline of 23℃, yields a temperature compensation coefficient of 0.139. The humidity compensation coefficient is calculated using the same ratio method: the difference between the pre-adjustment and post-adjustment humidity is divided by the standard humidity baseline of 50%RH. For example, assuming the pre-adjustment humidity of the standard equipment area was 58%RH and the post-adjustment humidity recovered to 50%RH, the humidity difference is 8%RH, which, when divided by the standard humidity baseline of 50%RH, yields a humidity compensation coefficient of 0.16. The compensation mathematical model also considers the adjustment time factor. A longer adjustment time indicates a more severe environmental deviation. The ratio of the adjustment time to the standard adjustment time of 600 seconds is used as a time correction factor. Assuming the actual adjustment time is 720 seconds and the time correction factor is 1.2, the final temperature compensation coefficient equals the base temperature compensation coefficient (0.139) multiplied by the time correction factor 1.2, resulting in 0.167. The final humidity compensation coefficient equals the base humidity compensation coefficient (0.16) multiplied by the time correction factor 1.2, resulting in 0.192. The compensation parameter set is organized by classifying and storing the temperature and humidity compensation coefficients calculated under different environmental deviation conditions according to the deviation source type, deviation intensity level, and adjustment effect. The first dimension, based on the deviation source type, is divided into equipment heating type and environmental exchange type. The second dimension, based on the deviation intensity, is divided into three levels: mild, moderate, and severe. The third dimension, based on the adjustment effect, is divided into three cases: rapid recovery, normal recovery, and slow recovery. Each classification combination corresponds to a specific set of temperature and humidity compensation coefficients, forming a structured set of environmental compensation coefficients.

[0021] The power distribution network energy meter data acquisition module establishes a communication connection with the smart energy meter via RS485 or Ethernet interface, acquiring four main parameters once per second: instantaneous power, cumulative energy, voltage amplitude, and current amplitude. The data acquisition module arranges the raw data obtained from different energy meters in chronological order to form a raw power distribution energy consumption data stream. Synchronization timestamp processing extracts the acquisition time of each data point in the raw data stream and converts it to a standard timestamp in Coordinated Universal Time (UTC) format with millisecond-level accuracy. A synchronization processing algorithm performs time calibration on the data from different energy meters to eliminate device clock deviations. The processed data is grouped according to the same timestamp, with each time group containing complete energy parameters from all monitoring devices at that moment, forming a time-series correlated energy parameter data group. The environmental parameter matching process extracts environmental parameter data that is the same as or closest to the power data timestamp from the power distribution environment data matrix. When the power data timestamp and environmental data timestamp are completely identical, exact matching is used. When there is a slight difference in timestamps, linear interpolation is used to calculate the corresponding temperature and humidity values. The interpolation calculation is a weighted average of the temperature and humidity values ​​and time differences between two consecutive environmental data points. After matching, an environment-power correlation data pair is formed. Each data pair contains complete information including timestamp, standard equipment area temperature, standard equipment area humidity, measured object area temperature, measured object area humidity, instantaneous power, cumulative power, voltage, and current. The temperature and humidity compensation algorithm selects the corresponding temperature compensation coefficient and humidity compensation coefficient from the environmental compensation coefficient set based on the current environmental deviation. The temperature deviation is calculated by subtracting the standard temperature reference value of 23℃ from the current temperature. Assuming the current measured object area temperature is 24.8℃, the temperature deviation is 1.8℃. The humidity deviation is calculated by subtracting the standard humidity reference value of 50%RH from the current humidity. Assuming the current measured object area humidity is 53%RH, the humidity deviation is 3%RH. The temperature compensation value equals the temperature deviation of 1.8℃ multiplied by the corresponding temperature compensation coefficient of 0.167, resulting in 0.301. The humidity compensation value equals the humidity deviation of 3%RH multiplied by the corresponding humidity compensation coefficient of 0.192, resulting in 0.576. The final compensation coefficient is calculated by weighting the temperature and humidity compensation values. Assuming a temperature weight of 0.6 and a humidity weight of 0.4, the final compensation coefficient equals 0.301 multiplied by 0.6 plus 0.576 multiplied by 0.4, resulting in 0.411. The calibration calculation applies the final compensation coefficient to the original power data. The calibrated instantaneous power equals the original instantaneous power multiplied by 1 plus the final compensation coefficient of 0.411. Assuming the original instantaneous power is 1000 watts, the calibrated instantaneous power is 1000 watts multiplied by 1.411, resulting in 1411 watts. The accumulated power, voltage, and current parameters are processed using the same correction method to form the compensated and calibrated power value.The data quality verification process calculates the difference and proportion of electrical energy data before and after calibration to assess the accuracy of the compensation algorithm. The compensation effect is quantified by calculating statistical indicators such as the change in standard deviation, mean shift, and correlation coefficient of the data before and after compensation. When the statistical indicators show that the data quality has been significantly improved after compensation, the compensation process is confirmed to be effective. The verified data forms the environmentally corrected power distribution energy consumption data.

[0022] The power distribution network loss analysis and processing takes the environmentally corrected power distribution energy consumption data as the basic input, extracts electrical parameters such as current, voltage, and power, and calculates the real-time change in line resistance by combining the corresponding ambient temperature and humidity data. The resistance change is calculated using the temperature coefficient method. The difference between the current temperature and the standard temperature of 23℃ is multiplied by the temperature resistivity of the conductor material copper, which is 0.00393, to obtain the resistance increment rate. Assuming the current temperature is 25.5℃, the temperature difference is 2.5℃, and the resistance increment rate is 2.5℃ multiplied by 0.00393, which equals 0.0098. The original line resistance is assumed to be 0.5 ohms, and the real-time resistance is equal to 0.5 ohms multiplied by 1 plus 0.0098, which equals 0.505 ohms. The impact of humidity is corrected using the insulation loss factor, which is calculated based on the deviation of the humidity from the standard humidity of 50%RH. For every 10% RH increase in humidity deviation, the insulation loss factor increases by 0.005. Assuming the current humidity is 56%RH and the humidity deviation is 6%RH, the insulation loss factor is 0.003. After correction, the real-time resistance equals 0.505 ohms multiplied by 1 plus 0.003, which equals 0.506 ohms. Line loss is calculated using the method of multiplying the square of the current by the real-time resistance. Assuming the line current is 10 amperes, the line loss equals 10 amperes squared multiplied by 0.506 ohms, which equals 50.6 watts. This forms the environmentally related distribution loss data considering the influence of environmental factors. The load distribution balance assessment calculates the load proportion and load change rate of each distribution branch to identify additional losses caused by uneven load distribution. Assuming the distribution network has three branches: the first branch has an actual load of 800 kW, the second branch has an actual load of 600 kW, and the third branch has an actual load of 400 kW, for a total load of 1800 kW. An ideal uniform load distribution is 600 kW per branch. The load deviation for the first branch is 200 kW, the second branch has 0 kW, and the third branch has 200 kW. The load deviation coefficient is calculated by taking the square root of the sum of the squares of the load deviations of each branch and dividing by the total load. The load deviation coefficient equals 200² + 0² + 200² divided by 1800, which equals 0.157. The impact of environmental changes on load stability is analyzed by calculating the standard deviation and rate of change of load fluctuations under different environmental conditions to form distribution load optimization indicators. The power supply path selection process calculates the loss and reliability indices of different power supply path combinations based on the power distribution load optimization index. Path selection takes into account the power distribution network topology and the switching status of switchgear. Environmental condition adaptability analysis evaluates the operating performance of different power supply paths under the current temperature and humidity conditions. In high-temperature environments, lines with smaller resistance temperature coefficients are given priority. The dynamic adjustment mechanism re-evaluates the merits of power supply paths in real time based on load and environmental changes to form the optimal power distribution path scheme.The power quality parameter calculation applies the optimal distribution path scheme to the distribution network operation analysis. Power flow analysis is used to input environmentally corrected load data and line parameters into the power flow calculation model. Voltage stability is calculated by analyzing the deviation of each node's voltage from its rated voltage. Frequency deviation analysis monitors the fluctuation range of the distribution network frequency relative to the standard frequency. Power factor calculation considers the impact of ambient temperature and humidity on the power factor of electrical equipment, forming a power quality assessment result. The dispatch strategy generation analyzes the distribution network operation characteristics and potential risks under different environmental conditions based on the power quality assessment results. Under normal environmental conditions, dispatch rules focus on economic operation and loss minimization, reducing operating costs by optimizing load allocation and equipment switching status. Under abnormal environmental conditions, dispatch rules focus on safe and reliable power supply. When environmental parameters exceed normal ranges, protective dispatch measures are activated. Emergency response measures include load transfer, backup equipment deployment, and line switching. Emergency measure selection is based on dynamic decision-making based on the type and severity of environmental changes. Predictive dispatch function anticipates distribution network operation risks based on environmental change trends and takes preventative measures in advance. Environmental adaptability is reflected in the dispatch strategy automatically adjusting operating parameters and control strategies based on environmental monitoring data, forming an environmentally adaptive dispatch strategy for the distribution network.

[0023] In one specific embodiment, the process of performing step S101 may specifically include the following steps: The temperature and humidity parameters of the standard equipment area and the tested item area of ​​the power distribution node are collected and processed by the three-area environmental monitoring device of the power distribution network to obtain the power distribution environment data matrix. Based on the power distribution environment data matrix, an environmental deviation identification process is performed using a dual threshold judgment algorithm. When the temperature and humidity exceed the preset range, a power distribution monitoring stop signal is obtained. The power distribution monitoring interruption signal is processed by adaptive recovery control, and the environmental parameters are restored to the standard range through cooling and heating and humidification and dehumidification adjustment. The compensation coefficient is calculated based on the temperature and humidity adjustment data during the environmental restoration process. A compensation mathematical model is established based on the temperature and humidity difference before and after adjustment to obtain a set of environmental compensation coefficients. Based on the set of environmental compensation coefficients, the distribution network energy meter data is calibrated by temperature and humidity compensation to obtain the environmentally corrected distribution energy consumption data. By using the distribution loss environment correlation algorithm to optimize the power supply quality of the environmentally corrected distribution energy consumption data, an adaptive scheduling strategy for the distribution network environment is obtained.

[0024] Specifically, the environmental monitoring device for the three zones of the power distribution network adopts a physical spatial isolation design. The control zone is equipped with cooling and heating units and humidification and dehumidification units. The standard equipment zone houses standard equipment for testing electricity meters and is equipped with temperature and humidity sensors. The monitored area houses the electricity meters to be monitored and is equipped with the same sensors. The temperature and humidity sensors adopt technical specifications of ±0.1℃ for temperature and ±1%RH for humidity, and transmit data via RS485 bus. The sensors in the standard equipment zone collect temperature and humidity data once per second. The sensors convert the physical quantities of temperature and humidity into electrical signals, which are then converted into digital signals by an analog-to-digital converter. After receiving the signals, the data processing unit verifies them, removes outliers, and then performs a moving average filter with a filtering window of 5 data points. The current data point is added to the previous two data points and the two data points before and after it, for a total of 5 values, and the average value is obtained by dividing by 5. The filtered data is stored in chronological order, and each data point includes a timestamp, temperature value, and humidity value. The sensors in the monitored area receive a unified clock signal and collect data synchronously, with time jitter controlled within 1 millisecond. Timestamp matching is performed to extract timestamps from the data of the two regions. Data with matching timestamps are directly paired; for those with discrepancies, linear interpolation is used to calculate the corresponding environmental parameters. The paired data is organized into a dual-region environmental data comparison table, including timestamp, standard zone temperature, standard zone humidity, test zone temperature, and test zone humidity. For data matrix organization, temperature data is rounded to ±0.1℃ accuracy by multiplying the original temperature value by 10, rounding, and then dividing by 10. Humidity data is directly rounded to ±1%RH accuracy. The formatted data is stored in matrix form, forming a power distribution environment data matrix.

[0025] The dual threshold judgment algorithm establishes a two-layer judgment mechanism. The first threshold judgment module extracts temperature and humidity data for the standard equipment area from the matrix, comparing the temperature value with a range of 20℃ to 24℃ and the humidity value with a range of 40%RH to 55%RH. If the conditions are met, it is recorded as normal; otherwise, it is recorded as a deviation. The second threshold judgment module extracts temperature and humidity data for the tested area, with a temperature threshold of 22℃ to 24℃ and a humidity threshold of 45%RH to 55%RH. When the first layer of judgment indicates a deviation in the standard area, the second layer of judgment temporarily tightens the thresholds for the test area, adjusting the temperature to 22.2℃ to 23.8℃ and the humidity to 46%RH to 54%RH. The dual threshold logical operation aligns the results of the two layers of judgment by timestamp and then performs an OR operation. The first-layer temperature deviation is logically ORed with the second-layer temperature deviation, and the first-layer humidity deviation is logically ORed with the second-layer humidity deviation. The combined temperature deviation and the combined humidity deviation are then logically ORed again to obtain the environmental exceedance trigger signal. Deviation source identification determines the deviation type by analyzing the rate of change of temperature and humidity. The rate of change is the current value minus the previous value divided by the time interval. The temperature change rate is compared to 0.5℃ / min, and the humidity change rate is compared to 2%RH / min. A temperature change rate less than 0.5℃ / min and a humidity change rate less than 2%RH / min are identified as equipment heating type deviations. A temperature change rate greater than 1℃ / min and a humidity change rate greater than 5%RH / min with the same sign are identified as environmental exchange type deviations. A stop command is generated based on the deviation type, and a response strategy is formulated accordingly. For equipment heating type deviations, a gradual stop command is generated; for environmental exchange type deviations, an immediate stop command is generated, forming a power distribution monitoring stop signal.

[0026] After receiving the power distribution monitoring stop signal, the environmental control controller analyzes the deviation type identifier and selects the control strategy. For equipment with a heating type, the cooling unit is activated first, with a target temperature set at 23℃. The cooling unit adjusts its power based on the temperature difference. It operates at maximum power when the temperature difference is greater than 2℃, at 70% power when the difference is between 1℃ and 2℃, and at 40% power when the difference is less than 1℃. Simultaneously, the dehumidification unit is activated, with a target humidity of 50%RH. For environmental exchange types, both cooling / heating and humidification / dehumidification units are activated simultaneously. Cooling is activated when the temperature is above the standard value, heating when the temperature is below the standard value, dehumidification when the humidity is above the standard value, and humidification when the humidity is below the standard value, thus generating an environmental control execution command. After the environmental control equipment is activated, the sensor acquisition frequency increases to once every 30 seconds, continuously monitoring changes in environmental parameters. The temperature and humidity data collected in each monitoring cycle are compared with the initial data, and the temperature change is equal to the current temperature minus the initial temperature; the humidity change is equal to the current humidity minus the initial humidity. The environmental conditioning effect data stream records the timestamp, standard zone temperature change, standard zone humidity change, test zone temperature change, and test zone humidity change for each 30-second cycle. Environmental recovery assessment verifies the continuity of the data stream, requiring the standard zone temperature to return to 20℃-24℃, the standard zone humidity to return to 40%RH-55%RH, the test zone temperature to return to 22℃-24℃, and the test zone humidity to return to 45%RH-55%RH. An environmental recovery confirmation signal is generated when all temperature and humidity parameters monitored for three consecutive times meet the threshold range.

[0027] The temperature and humidity compensation coefficients are calculated by extracting the initial temperature and humidity data before and after adjustment. The temperature compensation coefficient uses a ratio method, with a standard temperature reference value of 23℃. The difference between the temperature before and after adjustment is divided by the standard temperature reference value to obtain the temperature compensation coefficient. The humidity compensation coefficient also uses a ratio method, with a standard humidity reference value of 50%RH. The adjustment time is the time span from issuing the adjustment command to receiving the recovery signal. The standard adjustment time is 300 seconds, and the ratio of the actual adjustment time to the standard adjustment time is used as a time correction factor. The final temperature and humidity compensation coefficients are multiplied by the time correction factor for correction. A multi-dimensional index structure is established to organize the compensation parameter set. The first dimension is divided into two categories based on deviation type: equipment heating and environmental exchange. The second dimension is divided into three levels based on deviation intensity: mild, moderate, and severe. Mild is a temperature deviation of less than 1℃ and a humidity deviation of less than 5%RH; moderate is a temperature deviation of 1℃ to 2℃ or a humidity deviation of 5%RH to 10%RH; and severe is a temperature deviation of greater than 2℃ or a humidity deviation of greater than 10%RH. The third dimension categorizes recovery based on its effectiveness into rapid recovery, normal recovery, and slow recovery. Rapid recovery is defined as a recovery time of less than 200 seconds, normal recovery as 200 to 400 seconds, and slow recovery as greater than 400 seconds. Each category is combined with a set of temperature and humidity compensation coefficients, forming a compensation algorithm library for 18 scenarios, constituting an environmental compensation coefficient set.

[0028] The power distribution network energy meter data acquisition module connects to the smart energy meter via RS485 or Ethernet interface to read real-time operating data. Data reading employs timed polling with a polling cycle of 1 second, acquiring instantaneous power, cumulative energy, voltage amplitude, and current amplitude each time. The acquired data is arranged chronologically, with each record containing a timestamp, device identifier, instantaneous power value, cumulative energy value, voltage value, and current value, forming the raw power distribution energy consumption data stream. Timestamp processing converts the acquisition time to Coordinated Universal Time (UTC) format with millisecond-level accuracy. Time synchronization processing uses Network Time Protocol (NTP) to calibrate clocks, eliminating clock deviations between devices. After processing, the data is grouped by timestamp; all device data within a 1-second window is grouped together, forming a time-series correlated energy parameter data group. Environmental parameter matching extracts temperature and humidity values ​​corresponding to the energy data timestamps from the power distribution environment data matrix. Precise matching is achieved for timestamps that match exactly; for time differences less than 5 seconds, linear interpolation is used. After matching, each electrical energy data record is associated with the standard area temperature, standard area humidity, test area temperature, and test area humidity, forming an environment-electrical energy data pair. The temperature and humidity compensation algorithm queries the corresponding compensation coefficient from the environmental compensation coefficient set, using a three-dimensional index based on deviation type, deviation intensity, and adjustment effect. The temperature deviation is equal to the current temperature minus 23℃, and the humidity deviation is equal to the current humidity minus 50%RH. The temperature compensation value is equal to the temperature deviation multiplied by the temperature compensation coefficient, and the humidity compensation value is equal to the humidity deviation multiplied by the humidity compensation coefficient. The comprehensive temperature and humidity compensation factor is the sum of the temperature compensation value and the humidity compensation value. The instantaneous power after calibration is equal to the original instantaneous power multiplied by 1 plus the comprehensive compensation factor. The cumulative electrical energy, voltage, and current are corrected using the same method to obtain the compensated and calibrated electrical energy value. Data quality verification verifies the compensation effect by comparing and analyzing data before and after calibration, calculating the difference and difference ratio, and performing numerical consistency checks and trend consistency checks. Data that passes verification forms the environmentally corrected power distribution energy consumption data.

[0029] The distribution loss environment correlation algorithm uses environmentally corrected distribution energy consumption data as input to analyze the impact of ambient temperature and humidity on the resistance changes of distribution lines and the efficiency of distribution equipment. Increased temperature leads to increased conductor resistance and increased line losses, while humidity changes affect the electrical performance of insulation materials. The algorithm extracts current, voltage, and power parameters and calculates the real-time changes in line resistance based on ambient temperature and humidity. The resistance change is calculated using the temperature coefficient method; the difference between the current temperature and the standard temperature is multiplied by the temperature resistivity coefficient to obtain the resistance increment. The influence of humidity is corrected using the insulation loss coefficient. Line losses are calculated by multiplying the square of the current by the real-time resistance, forming environment-correlated distribution loss data. The load distribution balance assessment quantitatively analyzes the load distribution of each branch, calculates the load ratio and load change rate, and identifies the additional losses caused by uneven load distribution. By comparing the actual load with the ideal uniformly distributed load, the load deviation coefficient and load variance are calculated to quantify the degree of unevenness. Overloaded lines show a sharp increase in losses according to the relationship between the square of the current and resistance, while the proportion of fixed losses increases in lightly loaded lines. The standard deviation and rate of change of load fluctuations under different environmental conditions are calculated to assess the interference of environmental factors on stable load operation and form distribution load optimization indicators. Power supply path selection is based on distribution load optimization indicators to formulate distribution circuit switching strategies. Loss and reliability indicators for different power supply path combinations are calculated according to environmental conditions and load demand. Path selection considers the distribution network topology and switching status of switching equipment, reconfiguring power supply paths by changing the on / off states of key nodes. In high-temperature environments, lines with lower resistance temperature coefficients are preferred; in high-humidity environments, equipment with poor insulation performance is avoided, resulting in the optimal distribution path scheme. Power quality parameter calculation applies the optimal path scheme to distribution network operation analysis, calculating voltage stability, frequency deviation, and power factor performance under environmental changes. Voltage stability assesses the distribution network's ability to maintain voltage stability by analyzing the deviation of each node's voltage from the rated voltage. Frequency deviation monitors the fluctuation amplitude and trend of the distribution network frequency relative to the standard frequency. Power factor considers the impact of ambient temperature and humidity on the power factor of electrical equipment, forming a power quality assessment result. Dispatch strategy generation formulates distribution network operation dispatch rules and emergency response measures based on the power quality assessment results. Under normal environmental conditions, the focus is on economic operation and loss minimization, reducing operating costs by optimizing load allocation and equipment switching status. Under abnormal environmental conditions, the focus is on ensuring safe and reliable power supply. Protective dispatching measures are activated when environmental parameters exceed normal ranges. Emergency response measures include load transfer, activation of backup equipment, and line switching. The selection of emergency measures is based on dynamic decision-making according to the type and severity of environmental changes. The dispatching strategy automatically adjusts operating parameters and control strategies based on environmental monitoring data, forming an adaptive dispatching strategy for the distribution network environment.

[0030] In one specific embodiment, the process of performing step S102 may specifically include the following steps: The power distribution environment data matrix is ​​input into the first threshold judgment module for comparison of the temperature and humidity range of the standard equipment area to obtain the first layer of environmental deviation judgment results. Based on the first-layer environmental deviation judgment result, the temperature and humidity threshold of the tested sample area are processed by the second threshold judgment to obtain the second-layer environmental deviation judgment result. Based on the first-level environmental deviation judgment result and the second-level environmental deviation judgment result, a dual threshold logic operation is performed to obtain an environmental exceedance trigger signal when the temperature and humidity of any area exceed the corresponding threshold range. The environmental exceedance trigger signal is processed to identify the source of deviation, and the temperature and humidity fluctuations caused by the heat generated during the operation of the standard equipment and the environmental exchange caused by the opening of the door when the tested item is replaced are distinguished to obtain the deviation type classification result. Based on the deviation type classification results, the power distribution network monitoring equipment is processed to generate a stop command, resulting in a power distribution monitoring stop signal.

[0031] Specifically, after the power distribution environment data matrix is ​​input into the first threshold judgment module, the module extracts the temperature and humidity columns of the standard equipment area from the matrix. The standard equipment area refers to the physical space where the standard equipment for detecting electricity meters is placed. The temperature and humidity in this area are mainly affected by the heat generated by the operation of the standard equipment. The first threshold judgment module sets the temperature threshold range for the standard equipment area to 20℃ to 24℃ and the humidity threshold range to 40%RH to 55%RH. The judgment process iterates through the power distribution environment data matrix row by row, comparing the temperature value of the standard equipment area at each time point with 20℃ and 24℃. If the temperature value is less than 20℃ or greater than 24℃, the temperature deviation status at that time point is recorded as true, indicating that the temperature exceeds the standard range. If the temperature value is between 20℃ and 24℃, the deviation status is recorded as false. The humidity judgment uses the same logic, comparing the humidity value of the standard equipment area with 40%RH and 55%RH. If it exceeds the range, it is recorded as true; if it is within the range, it is recorded as false. The first-level environmental deviation judgment result forms a data record sequence containing a timestamp, the temperature deviation status of the standard equipment area, and the humidity deviation status of the standard equipment area.

[0032] After receiving the environmental deviation judgment result from the first layer, the second threshold judgment module extracts the temperature and humidity data of the tested area at the corresponding time from the power distribution environment data matrix based on the timestamp information in the result. The tested area refers to the sealed space where the energy meter to be tested is placed. The temperature and humidity of this area are affected not only by the standard equipment but also by the environmental exchange caused by opening the door when the tested item is replaced. The second threshold judgment sets the temperature threshold range for the tested area to 22℃ to 24℃ and the humidity threshold range to 45%RH to 55%RH. The judgment standard is stricter than that of the first layer to ensure the environmental stability of the tested area. The judgment module compares the temperature value of the tested area with 22℃ and 24℃. A temperature below 22℃ or above 24℃ is recorded as true, and a temperature within the range is recorded as false. The same judgment logic is used to compare the humidity value with 45%RH and 55%RH. The second threshold judgment also introduces a correlation judgment mechanism. When the standard equipment area shows a deviation (true) in the first-level judgment result, the second-level judgment lowers the tolerance threshold of the tested area. Specifically, it narrows the temperature threshold range of the tested area to 22.5℃ to 23.5℃ and the humidity threshold range to 47%RH to 53%RH. By narrowing the threshold range, the sensitivity to environmental anomalies in the tested area is improved. The second-level environmental deviation judgment result includes a timestamp, the temperature deviation status of the tested area, the humidity deviation status of the tested area, and a correlation identifier with the first-level result.

[0033] The dual threshold logic operation matches the first-level environmental deviation judgment results and the second-level environmental deviation judgment results according to timestamps. The logic operation uses an OR operation. The temperature deviation logic operation performs an OR operation on the temperature deviation status of the first-level standard equipment area and the temperature deviation status of the second-level tested product area. If either status is true, the overall temperature deviation status is set to true. The humidity deviation logic operation uses the same processing, performing an OR operation on the humidity deviation status of the first-level standard equipment area and the humidity deviation status of the second-level tested product area to obtain the overall humidity deviation status. The generation of the environmental exceedance trigger signal performs another OR operation on the overall temperature deviation status and the overall humidity deviation status. When either the overall temperature deviation status or the overall humidity deviation status is true, the environmental exceedance trigger signal is activated. The signal records the specific exceedance type, including temperature exceedance, humidity exceedance, or simultaneous exceedance of temperature and humidity. It also records the specific area where the exceedance occurred: the standard equipment area, the tested product area, or both areas exceeding the limit simultaneously.

[0034] The analysis of deviation sources reveals the underlying mechanisms of environmental exceedance trigger signals. Standard equipment operating heat causes gradual temperature and humidity fluctuations, with a smooth, continuous temperature rise curve and a small rate of change. Humidity changes are relatively delayed and limited in magnitude. However, environmental exchange caused by opening a door during equipment replacement exhibits abrupt changes. The entry of external air at the moment of door opening leads to rapid temperature and humidity changes within a short period, with both temperatures and humidity fluctuating significantly. The identification algorithm calculates the rate of change in temperature and humidity to quantify these fluctuations. The temperature rate of change is calculated by subtracting the previous temperature value from the current temperature value and then dividing by the time interval. The humidity rate of change is calculated using the same method. Threshold judgment is applied by comparing the calculated temperature rate of change to 0.5℃ / min and the humidity rate of change to 2%RH / min. When the temperature rate of change is less than 0.5℃ / min and the humidity rate of change is less than 2%RH / min, it is identified as a deviation in equipment heating type. When the temperature rate of change is greater than 1℃ / min and the humidity rate of change is greater than 5%RH / min, and the directions of temperature and humidity changes are consistent, it is identified as a deviation in environmental exchange type. The deviation type classification results include deviation source type labels, deviation intensity level, and affected area range information.

[0035] The termination command generation and processing system formulates differentiated monitoring termination strategies based on the deviation type classification results. For equipment heating type deviations, a gradual termination strategy is adopted. First, a warning signal is sent to the distribution network power monitoring equipment to indicate that environmental conditions are beginning to deviate from the standard range. Then, the power monitoring frequency is gradually reduced from once per second to once every 5 seconds to reduce the amount of data collected during environmental fluctuations. When the deviation lasts for more than 5 minutes and the temperature and humidity deviation continues to worsen, a formal termination command is issued to stop power monitoring. For environmental exchange type deviations, an immediate termination strategy is adopted. Due to the unpredictable and drastic fluctuations in temperature and humidity caused by door opening operations, the termination command generator immediately sends a stop monitoring command to the distribution network power monitoring equipment upon detecting an environmental exchange type deviation, suspending all current power data acquisition activities. The distribution monitoring termination signal includes a command type field indicating four levels: warning, frequency reduction, pause, or stop; an execution timing field recording the specific time when the termination command takes effect; and a recovery condition field setting the time requirement for sustained stability after the environmental parameters return to the standard range.

[0036] In one specific embodiment, the power distribution monitoring stop signal is subjected to adaptive recovery control processing. Environmental parameters are restored to the standard range through cooling / heating and humidification / dehumidification adjustments. Compensation coefficients are calculated based on temperature and humidity adjustment data during the environmental recovery process, including: The power distribution monitoring stop signal is input into the environmental control controller for adjustment strategy selection. Based on the deviation type, the corresponding cooling / heating unit or humidification / dehumidification unit is activated to obtain the environmental control execution command. According to the environmental control execution command, the environmental control equipment is started to carry out environmental control treatment on the standard equipment area and the test sample area. During the environmental control treatment process, the environmental control area and the test sample area are continuously monitored. The changes in environmental parameters are detected at fixed intervals to obtain the environmental control effect data stream. Environmental recovery is assessed based on the data stream of environmental regulation effects. When three consecutive test results show that the temperature and humidity have returned to the standard range, an environmental recovery confirmation signal is obtained. The environmental recovery confirmation signal and the data stream of environmental regulation effect are processed by calculating the temperature and humidity compensation coefficient. A compensation mathematical model is established based on the temperature and humidity difference before and after regulation to obtain the temperature compensation coefficient and humidity compensation coefficient. The compensation parameter set is organized and processed based on the temperature compensation coefficient and humidity compensation coefficient, and a compensation algorithm library is established for different environmental deviation conditions to obtain the environmental compensation coefficient set.

[0037] Specifically, after receiving the power distribution monitoring stop signal, the environmental control controller first parses the deviation type classification result field and the deviation intensity level field in the signal. The deviation type classification result includes two labels: equipment heating type and environmental exchange type. The deviation intensity level is divided into three levels: mild, moderate, and severe. The control strategy selection process executes different equipment startup logic based on the deviation type label. When the deviation type is equipment heating, the controller determines whether the temperature or humidity in the standard equipment area exceeds the standard. If the temperature exceeds the standard, the cooling unit is activated to lower the ambient temperature in both the standard equipment area and the tested area. If the humidity exceeds the standard, the dehumidification unit is activated to lower the air humidity in both areas. When the deviation type is environmental exchange, the controller simultaneously activates the cooling / heating unit and the humidification / dehumidification unit. This is because environmental exchange caused by opening a door can cause both temperature and humidity to change unpredictably, requiring comprehensive control of the equipment to work together to restore environmental stability. The deviation intensity level determines the operating power level of the control equipment: mild deviation starts at a low power level (30% of rated power), moderate deviation starts at a medium power level (60% of rated power), and severe deviation starts at a high power level (100% of rated power). The environmental adjustment execution command includes a device start type field indicating cooling, heating, humidification, or dehumidification; an adjustment intensity level field indicating low, medium, or high power levels; and an adjustment duration field initially set to continuous operation until the environment is restored.

[0038] The environmental control execution command is sent via the control bus to the cooling / heating unit and the humidification / dehumidification unit in the control area. The cooling / heating unit refers to the temperature control equipment equipped with a compressor and condenser, while the humidification / dehumidification unit refers to the humidity control equipment equipped with an ultrasonic atomizer and a dehumidifier. Upon receiving the start command, the compressor in the cooling unit begins operation, circulating refrigerant to absorb heat from the standard equipment area and the tested area. Cool air is then delivered to both areas through ventilation ducts to lower the ambient temperature. Upon receiving the start command, the dehumidification unit begins operation, with the dehumidifier condensing moisture into water droplets in the air, which are then discharged. Dry air is returned to the standard equipment area and the tested area to reduce humidity. After the environmental control equipment is started, the data acquisition frequency of the temperature and humidity sensors is adjusted from once per second to once every 30 seconds for more intensive monitoring. Each monitoring cycle collects four parameters: temperature and humidity in the standard equipment area, temperature and humidity in the tested area, and humidity in the tested area. After each acquisition, the difference between the current value and the initial value at the start of the control is immediately calculated to obtain the change in temperature and humidity. The temperature change in the standard equipment area is equal to the current temperature of the standard equipment area minus the temperature of the standard equipment area at the start of regulation. The humidity change in the standard equipment area is equal to the current humidity of the standard equipment area minus the humidity of the standard equipment area at the start of regulation. The temperature and humidity changes in the tested area are calculated using the same subtraction method. The environmental regulation effect data stream records the timestamp, temperature change, humidity change, tested area temperature change, and tested area humidity change for each monitoring cycle in chronological order. The data stream shows a trend where the temperature and humidity changes gradually decrease and tend towards zero over time.

[0039] The environmental recovery assessment process compares data from each monitoring cycle in the environmental regulation effect data stream with thresholds. The judgment logic compares the current monitoring cycle's standard equipment area temperature value with the standard equipment area temperature threshold range of 20℃ to 24℃, the measured sample area temperature value with the measured sample area temperature threshold range of 22℃ to 24℃, the standard equipment area humidity value with the standard equipment area humidity threshold range of 40%RH to 55%RH, and the measured sample area humidity value with the measured sample area humidity threshold range of 45%RH to 55%RH. When all temperature and humidity values ​​for a given monitoring cycle are within their respective threshold ranges, that monitoring cycle is marked as successfully recovered. If any temperature or humidity value exceeds its corresponding threshold range, that monitoring cycle is marked as unsuccessfully recovered. The environmental recovery assessment process requires continuous monitoring of temperature and humidity data for three cycles (90 seconds). An environmental recovery confirmation signal is generated only when all three consecutive monitoring cycles are marked as successfully recovered. If any intermediate monitoring cycle is marked as unsuccessfully recovered, the counting restarts, waiting for three consecutive successful recoveries. The environmental recovery confirmation signal includes a recovery completion timestamp recording the time of the last monitoring cycle that satisfies three consecutive successful recoverys, final temperature and humidity value recording the specific values ​​of the standard equipment area temperature, standard equipment area humidity, measured item area temperature, and measured item area humidity at the time of recovery completion, and total adjustment time recording the time interval from the time the environmental adjustment execution command is issued to the time when the environmental recovery confirmation signal is generated.

[0040] The temperature and humidity compensation coefficient calculation process receives the environmental recovery confirmation signal and the environmental regulation effect data stream as input data. It extracts the initial temperature and humidity values ​​at the start of regulation and the final temperature and humidity values ​​from the environmental regulation effect data stream. The temperature compensation coefficient is calculated using the difference ratio method. The temperature difference in the standard equipment area equals the standard equipment area temperature at the start of regulation minus the standard equipment area temperature at the completion of recovery. The temperature difference in the measured item area equals the measured item area temperature at the start of regulation minus the measured item area temperature at the completion of recovery. The standard temperature reference value is set to 23℃, representing the ideal operating temperature of the electricity meter. The temperature compensation coefficient for the standard equipment area is equal to the temperature difference in the standard equipment area divided by the standard temperature reference value of 23℃. The temperature compensation coefficient for the measured item area is equal to the temperature difference in the measured item area divided by the standard temperature reference value of 23℃. The humidity compensation coefficient is calculated using the same difference ratio method. The humidity difference in the standard equipment area equals the humidity of the standard equipment area at the start of adjustment minus the humidity of the standard equipment area at the completion of recovery. The humidity difference in the tested area equals the humidity of the tested area at the start of adjustment minus the humidity of the tested area at the completion of recovery. The standard humidity reference value is set at 50%RH to represent the ideal operating humidity of the energy meter. The humidity compensation coefficient for the standard equipment area is equal to the humidity difference in the standard equipment area divided by the standard humidity reference value of 50%RH. The humidity compensation coefficient for the tested area is equal to the humidity difference in the tested area divided by the standard humidity reference value of 50%RH. The compensation mathematical model also introduces an adjustment time correction factor. The adjustment time correction factor equals the actual total adjustment time divided by the standard adjustment time. The standard adjustment time is preset according to the deviation intensity level: 5 minutes for mild deviation, 10 minutes for moderate deviation, and 15 minutes for severe deviation. The final temperature compensation coefficient is equal to the initially calculated temperature compensation coefficient multiplied by the adjustment time correction factor, and the final humidity compensation coefficient is equal to the initially calculated humidity compensation coefficient multiplied by the adjustment time correction factor.

[0041] The compensation parameter set is organized and processed to establish a multi-dimensional index structure to store the temperature and humidity compensation coefficients under different environmental deviations. The first-dimensional index is divided into two branches based on the type of deviation source: equipment heating type index and environmental exchange type index. The second-dimensional index is divided into three sub-branches based on the deviation intensity level: mild index, moderate index, and severe index. The third-dimensional index is divided into three categories based on the adjustment effect: rapid recovery index, normal recovery index, and slow recovery index. Rapid recovery refers to the actual total adjustment time being less than the standard adjustment time; normal recovery refers to the actual total adjustment time being equal to the standard adjustment time; and slow recovery refers to the actual total adjustment time being greater than the standard adjustment time. After each environmental deviation occurs and recovery adjustment is completed, the position of the first-dimensional index is determined based on the deviation type classification result, the position of the second-dimensional index is determined based on the deviation intensity level, and the position of the third-dimensional index is determined based on the comparison result of the actual total adjustment time and the standard adjustment time. The calculated temperature and humidity compensation coefficients are then stored in the corresponding three-dimensional index nodes. As the distribution network operates over a long period, the environmental compensation coefficient set accumulates more and more compensation coefficient data. Each three-dimensional index node stores multiple sets of historical compensation coefficient values. When it is necessary to perform temperature and humidity compensation calibration on the electricity meter data, the compensation coefficient value of the corresponding index node is obtained by querying the compensation algorithm library based on the three-dimensional characteristics of the current environmental deviation.

[0042] Figure 2 This is a schematic diagram illustrating the temperature and humidity changes during the environmental restoration process in an embodiment of this application. Figure 2 As shown, the temperature and humidity parameters change over time during the environmental recovery process, illustrating the dynamic process from the occurrence of environmental deviation to complete recovery. The temperature in the standard equipment area gradually decreases from an initial 26.2℃ to 23.0℃, while the temperature in the tested area decreases from 25.8℃ to 22.8℃. The humidity in the standard equipment area decreases from 58%RH to 48%RH, and the humidity in the tested area decreases from 56%RH to 46%RH. The 180-second mark in the figure signifies that the environmental parameters first enter the standard range and recovery judgment begins. The 240-second mark signifies that three consecutive measurements show the temperature and humidity returning to the standard range, confirming successful environmental recovery. The entire adjustment process uses a high-intensity monitoring frequency of once every 30 seconds to continuously track changes in environmental parameters. This curve intuitively reflects the technical effect of the adaptive recovery control process described in this application, which restores environmental parameters to the standard range through cooling, heating, humidification, and dehumidification adjustments.

[0043] In one specific embodiment, the process of executing step S105 may specifically include the following steps: The temperature compensation coefficient and humidity compensation coefficient in the environmental compensation coefficient set are input into the power meter data acquisition module of the distribution network for real-time power parameter acquisition and processing to obtain the raw power distribution energy consumption data stream. Based on the original power distribution energy consumption data stream, the instantaneous power, cumulative energy, voltage, and current parameters are synchronously timestamped to obtain a time-series associated energy parameter data set; Environmental parameter matching processing is performed on the power parameter data group based on time-series correlation, and the temperature and humidity values ​​at the corresponding time are correlated with the power data to obtain environmental-power correlation data pairs. The environmental-electricity correlation data is used to calibrate the input temperature and humidity compensation algorithm. The compensation coefficients are calculated according to the temperature deviation and humidity deviation and applied to the original electrical energy data to obtain the compensated and calibrated electrical energy value. Data quality verification is performed based on the compensated and calibrated power consumption values. The effectiveness of the compensation effect is verified by comparing and analyzing the data before and after calibration, and the power distribution energy consumption data after environmental correction is obtained.

[0044] Specifically, after receiving the environmental compensation coefficient set, the data acquisition module of the distribution network energy meter establishes a communication connection with the smart energy meter at the distribution node through an RS485 bus or Ethernet interface. The smart energy meter is a new type of energy metering device with digital communication capabilities and multi-parameter measurement capabilities. The data acquisition module sends read commands to the energy meter once per second to obtain four energy parameters: instantaneous power, cumulative energy, voltage amplitude, and current amplitude. Instantaneous power reflects the immediate energy consumption rate at the current moment, measured in watts; cumulative energy records the total energy consumption from the start of monitoring to the current moment, measured in kilowatt-hours; voltage amplitude displays the voltage of the power supply line, measured in volts; and current amplitude reflects the load current, measured in amperes. The raw distribution energy consumption data stream organizes all raw data collected by different energy meters at different times into a continuous data stream in chronological order. Each record in the data stream contains six fields: timestamp, device identifier, instantaneous power, cumulative energy, voltage, and current.

[0045] The synchronization timestamp marking process standardizes the timestamps of each record in the original power distribution energy consumption data stream. The original timestamps use the local time format recorded by the internal clocks of each energy meter. Slight deviations between clocks of different devices lead to inconsistent timestamps. The marking process first converts all local time formats to Coordinated Universal Time (UTC). The conversion process extracts the year, month, day, hour, minute, second, and millisecond information of the local time and adds the time zone offset to obtain UTC, maintaining timestamp accuracy at the millisecond level. The time calibration algorithm eliminates clock deviations from data from different energy meters. The algorithm periodically obtains a standard time reference from the time server, calculates the deviation between each energy meter's clock and the standard time reference, and subtracts the deviation from the energy meter's timestamp to obtain the calibrated unified timestamp. Time-series associated energy parameter data groups group records with the same timestamp or a time difference of less than 50 milliseconds into the same time group. Each time group contains the complete energy parameters of all monitored energy meters at that moment.

[0046] The environmental parameter matching process extracts temperature and humidity data corresponding to the timestamps of the power parameter data groups from the power distribution environment data matrix. The power distribution environment data matrix contains five columns: timestamp, standard equipment area temperature, standard equipment area humidity, tested item area temperature, and tested item area humidity. The matching algorithm traverses each time group in the time-series associated power parameter data group, extracts the unified timestamp value of the time group, and then searches for environmental data records with the same timestamp in the power distribution environment data matrix. When the power data timestamp and environmental data timestamp are completely consistent, the four temperature and humidity values ​​in the environmental data record are directly associated with the power data. When the timestamps do not completely match, a linear interpolation method is used to calculate the temperature and humidity values ​​at the corresponding time. Linear interpolation finds the two closest environmental data records before and after the power data timestamp, and calculates the interpolation result proportionally based on the time interval and the difference in temperature and humidity values. The environment-power data pair merges the power parameters of each time group with the matched temperature and humidity parameters. The associated data pair contains a unified timestamp, four temperature and humidity values, the number of devices, and an array of power parameters.

[0047] The temperature and humidity compensation algorithm selects the corresponding temperature and humidity compensation coefficients from the environmental compensation coefficient set based on the current environmental deviation. The selection process compares the temperature and humidity values ​​in the environmental-electricity data pair with the standard reference values ​​to determine the type and intensity of the deviation. The standard temperature reference value is set to 23℃, and the standard humidity reference value is set to 50%RH. The current temperature deviation equals the temperature of the standard equipment area in the data pair minus the standard temperature reference value; the current humidity deviation equals the humidity of the standard equipment area in the data pair minus the standard humidity reference value. The compensation algorithm retrieves the temperature and humidity compensation coefficients from the environmental compensation coefficient set. The temperature compensation value equals the temperature deviation multiplied by the temperature compensation coefficient, and the humidity compensation value equals the humidity deviation multiplied by the humidity compensation coefficient. The comprehensive temperature and humidity compensation factor equals the temperature compensation value plus the humidity compensation value, reflecting the combined impact of temperature and humidity on the accuracy of electricity metering. The calibration calculation applies a comprehensive compensation factor to the original power data. The instantaneous power after compensation and calibration is equal to the original instantaneous power multiplied by 1 plus the comprehensive compensation factor. The cumulative power after compensation and calibration is equal to the original cumulative power multiplied by 1 plus the comprehensive compensation factor. The voltage and current parameters use the same multiplication correction method.

[0048] Data quality verification processing calculates the statistical characteristics of electrical energy data before and after calibration to quantify the compensation effect. Numerical consistency testing calculates the absolute difference in instantaneous power before and after calibration. The absolute difference equals the instantaneous power after compensation and calibration minus the original instantaneous power. The mean and standard deviation of the absolute difference reflect the magnitude and stability of the compensation correction. Trend consistency testing calculates the Pearson correlation coefficient of the instantaneous power time series before and after calibration. A correlation coefficient close to 1 indicates a high degree of consistency in the data trends before and after calibration. Anomaly detection calculates the deviation of the compensated and calibrated electrical energy values ​​from the normal range. The detection algorithm sets the normal fluctuation range of instantaneous power to plus or minus three standard deviations of the mean. When the instantaneous power exceeds the normal fluctuation range at a certain moment after compensation and calibration, it is marked as an anomaly data point. Environmentally corrected distribution energy consumption data includes a complete set of electrical energy parameters that have undergone temperature and humidity compensation and quality verification.

[0049] In one specific embodiment, the environmental-electrical energy correlation data is used to calibrate the input temperature and humidity compensation algorithm. Compensation coefficients are calculated based on temperature and humidity deviations and applied to the original electrical energy data to obtain the compensated and calibrated electrical energy value, including: The temperature values ​​in the environmental-electrical energy correlation data pair are processed by standard temperature reference deviation calculation. The temperature deviation is obtained by subtracting the standard temperature reference value from the current temperature value. The humidity value is processed by standard humidity reference deviation based on the temperature deviation. The humidity deviation is obtained by subtracting the standard humidity reference value from the current humidity value. The temperature and humidity deviations are multiplied by a temperature and humidity compensation factor. The temperature deviation is multiplied by a preset temperature compensation coefficient, and the humidity deviation is multiplied by a preset humidity compensation coefficient. The results are then summed to obtain the comprehensive temperature and humidity compensation factor. The temperature and humidity comprehensive compensation factor is used to correct the power value of the power data. The measured power value is multiplied by the comprehensive compensation factor to obtain the single-term corrected power value. Batch data correction processing is performed based on the individual corrected energy values. The same compensation processing is applied to all energy parameters in the associated data pairs to obtain the compensated and calibrated energy values.

[0050] Specifically, the standard temperature reference deviation calculation process extracts temperature values ​​from the environmental-electricity energy correlation data pair. These temperature values ​​originate from real-time data collected by temperature and humidity sensors in the tested area or the standard equipment area. The standard temperature reference value is set at 23℃, representing the ideal temperature environment for normal operation of the electricity meter. The deviation calculation uses a subtraction operation to subtract the standard temperature reference value of 23℃ from the current temperature value to obtain the temperature deviation. The positive or negative value of the temperature deviation indicates the direction of deviation of the current ambient temperature from the standard conditions; a positive value indicates that the temperature is higher than the standard value, and a negative value indicates that the temperature is lower than the standard value. The deviation calculation process processes each time point in the correlation data pair independently, ensuring that each temperature value has a corresponding deviation record. After the temperature deviation is calculated, it is stored in a time series to form time-series data of temperature deviation.

[0051] The standard humidity baseline deviation calculation is based on the temperature deviation calculation, and a similar deviation calculation is performed on the humidity value. The humidity value is also derived from the humidity sensor data at the corresponding time point in the environment-energy correlation data pair. The standard humidity baseline value is set at 50%RH, representing the ideal humidity conditions of the environment detected by the energy meter. The humidity deviation is calculated by subtracting the standard humidity baseline value of 50%RH from the current humidity value. The numerical range and positive / negative nature of the humidity deviation are the same as those of the temperature deviation: a positive value indicates that the current humidity is higher than the standard condition, and a negative value indicates that the humidity is lower than the standard condition. The humidity deviation calculation process is performed synchronously with the temperature deviation calculation to ensure that the temperature and humidity deviation values ​​at each time point are completely corresponding, and the time series data of the humidity deviation and the time series data of the temperature deviation form a paired relationship.

[0052] The temperature and humidity compensation factor product operation multiplies the temperature deviation and humidity deviation with their corresponding preset compensation coefficients, which are derived from a previously established set of environmental compensation coefficients. The temperature compensation coefficient reflects the impact of temperature changes on the accuracy of electricity metering, while the humidity compensation coefficient reflects the impact of humidity changes. The product operation calculates the product of the temperature deviation and the temperature compensation coefficient, and the product of the humidity deviation and the humidity compensation coefficient, respectively. These two products represent the correction amounts for the electricity data caused by temperature and humidity factors. The summation operation adds the temperature and humidity corrections to obtain the comprehensive temperature and humidity compensation factor, which comprehensively considers the combined effects of these two environmental parameters on electricity metering.

[0053] The power value correction process applies a comprehensive temperature and humidity compensation factor to the calculation of electricity data correction. The correction algorithm uses a product operation to adjust the measured power value. The measured power value comes from the instantaneous power data in the environment-electricity correlation data pair, which reflects the original measurement result of the electricity meter under the current environmental conditions. The product operation multiplies the measured power value by the comprehensive temperature and humidity compensation factor. The calculation process uses the form of multiplying the measured power value by 1 and adding the comprehensive compensation factor to ensure that the compensation factor serves as the correction coefficient. The corrected power value eliminates the influence of environmental temperature and humidity changes on the metering accuracy of the electricity meter. The single-item corrected electricity value includes the corrected instantaneous power value, the corresponding timestamp, and environmental condition information.

[0054] Batch data correction extends the calculation method for individual energy values ​​to all energy parameters in a correlated data pair, including instantaneous power, cumulative energy, voltage, current, and other energy monitoring parameters. Batch processing uses the same comprehensive temperature and humidity compensation factor to uniformly correct different types of energy parameters. The correction of cumulative energy data uses the same product operation method as instantaneous power, while the correction of voltage and current data considers their different sensitivities to temperature and humidity changes. The correction process processes each data record in the correlated data pair sequentially, and the compensated and calibrated energy values ​​contain a complete dataset of energy parameters after environmental compensation.

[0055] In one specific embodiment, the process of executing step S106 may specifically include the following steps: After environmental correction, the power distribution energy consumption data is processed by power distribution network loss analysis. The influence of environmental temperature and humidity factors on the change of power line resistance and the efficiency of power distribution equipment is combined to obtain environmentally related power distribution loss data. Based on environmentally related power distribution loss data, the load distribution of each power distribution branch is evaluated for balance. The additional losses caused by uneven load and the impact of environmental changes on load stability are analyzed to obtain power distribution load optimization indicators. Power supply path selection is performed based on power distribution load optimization indicators. The switching status of power distribution circuits is dynamically adjusted according to environmental conditions and load demand to obtain the optimal power distribution path scheme. The optimal power distribution path scheme is processed by calculating power quality parameters, and the performance of voltage stability, frequency deviation and power factor under environmental changes is analyzed to obtain power quality assessment results. Based on the power supply quality assessment results, the dispatch strategy is generated and processed, and the operation and dispatch rules and emergency response measures for the distribution network under different environmental conditions are formulated to obtain the adaptive dispatch strategy for the distribution network environment.

[0056] Specifically, the distribution network loss analysis uses environmentally corrected distribution energy consumption data as the basic input, focusing on the impact of environmental temperature and humidity factors on the changes in distribution line resistance and the efficiency of distribution equipment. Distribution line resistance changes with ambient temperature; increased temperature leads to increased conductor resistance, thus increasing line transmission losses. Humidity changes affect the electrical performance of insulation materials. The analysis algorithm extracts electrical parameters such as current, voltage, and power from the environmentally corrected distribution energy consumption data and calculates the real-time change in line resistance by combining it with the corresponding ambient temperature and humidity data. The resistance change is calculated using the temperature coefficient method, multiplying the difference between the current temperature and the standard temperature by the temperature resistivity of the conductor material to obtain the resistance increment. The influence of humidity is corrected using the insulation loss coefficient. Line loss is calculated by multiplying the square of the current by the real-time resistance to obtain an accurate loss value. The distribution equipment efficiency analysis considers the efficiency changes of transformers, switching equipment, etc., under different temperature and humidity conditions. The environmentally correlated distribution loss data includes line loss values, equipment efficiency losses, corresponding environmental conditions, and loss change trends.

[0057] The load distribution balance assessment process quantitatively analyzes the load allocation of each branch of the distribution network based on environmentally relevant distribution loss data. Distribution branches refer to the various branches branching off from the main distribution line, each connecting different electrical loads. The assessment algorithm calculates the load proportion and load change rate of each distribution branch. The load proportion equals the load power of a single branch divided by the total load power of all branches, and the load change rate equals the load power at the current moment minus the load power at the previous moment, divided by the time interval. Load unevenness analysis calculates the load deviation coefficient by comparing the actual load of each branch with the ideal uniformly distributed load. The square root of the sum of the squares of the load deviation coefficients of all branches yields the load variance, quantifying the degree of unevenness. Additional loss calculation considers the combined loss impact of overload on some lines and light load on others caused by load unevenness. Distribution load optimization indicators comprehensively consider multiple dimensions such as load balance, additional losses, and stability to form quantified optimization target parameters.

[0058] The power supply path selection process formulates dynamic distribution circuit switching strategies based on distribution load optimization indicators. Distribution circuit switching refers to changing the power transmission path by controlling the on / off state of switching equipment to achieve load transfer and line switching. The selection algorithm calculates the loss and reliability indicators of different power supply path combinations based on current environmental conditions and load demand. Path selection considers the distribution network topology and the switching status of switching equipment. Environmental condition adaptability analysis evaluates the operating performance of different power supply paths under current temperature and humidity conditions. In high-temperature environments, lines with lower resistance temperature coefficients are preferred, and in high-humidity environments, equipment with poor insulation performance is avoided. Load demand matching analysis calculates the degree of matching between the power supply capacity of each candidate path and the actual load demand. The optimal distribution path scheme includes specific switching operation sequences, expected loss reduction, and reliability improvement indicators.

[0059] The power quality parameter calculation and processing applies the optimal distribution path scheme to the distribution network operation analysis, focusing on the performance of three key quality indicators—voltage stability, frequency deviation, and power factor—under environmental changes. Voltage stability calculation assesses the distribution network's ability to maintain voltage stability under different environmental conditions and loads by analyzing the deviation of voltage at each node from the rated voltage. Voltage stability is equal to the absolute value of the difference between the actual voltage and the rated voltage, divided by the rated voltage. Frequency deviation analysis monitors the fluctuation range of the distribution network frequency relative to the standard frequency. Frequency deviation is equal to the actual frequency minus 50 Hz of the standard frequency. Power factor calculation considers the impact of ambient temperature and humidity on the power factor of electrical equipment. Power factor is equal to active power divided by apparent power, reflecting energy utilization efficiency. The power quality assessment results reflect the operational quality level of the distribution network in the form of quantitative indicators.

[0060] The dispatch strategy generation process formulates distribution network operation dispatch rules and emergency response measures based on power quality assessment results. The strategy generation algorithm analyzes the operating characteristics and potential risks of the distribution network under different environmental conditions. Dispatch rules under normal environmental conditions primarily focus on economic operation and loss minimization, reducing overall operating costs by optimizing load distribution and equipment switching status. These rules stipulate that load transfer operations are initiated when load imbalance exceeds a threshold, and line losses are switched to low-loss power supply paths when line losses exceed preset limits. Dispatch rules under abnormal environmental conditions prioritize safe and reliable power supply. Protective dispatch measures are activated when environmental parameters exceed normal ranges; line load rates are reduced to prevent equipment overheating when temperatures exceed limits, and equipment inspection frequency is increased to prevent insulation failures when humidity exceeds limits. Emergency response measures include load transfer, backup equipment deployment, and line switching, with the selection of emergency measures based on the type and severity of environmental changes through dynamic decision-making. The distribution network environment adaptive dispatch strategy includes detailed dispatch rules, operation instructions, execution conditions, and other dispatch schemes.

[0061] Figure 3 This is a schematic diagram illustrating the relationship between power distribution loss and ambient temperature in an embodiment of this application. Figure 3As shown in the figure, the relationship between distribution line losses and ambient temperature under different load current conditions is illustrated. The curves for loads of 800A, 600A, and 400A represent the line loss characteristics under high, medium, and low load operating conditions, respectively. The figure shows that at a standard temperature of around 23℃ (marked by the red dashed line), the line losses under the three load conditions are relatively low and the differences are moderate. When the ambient temperature exceeds 24℃ and enters the high-temperature region (pink shaded area), the line losses show an accelerated growth trend with increasing temperature. The loss increase is most significant for the high-load 800A curve, increasing from approximately 50W at the standard temperature to approximately 58W at 30℃, an increase of 16%. The loss increase for the low-load 400A curve is relatively gradual. This fully verifies the necessity of considering the influence of the temperature coefficient in the distribution loss environment correlation algorithm proposed in this application, providing a quantitative basis for the formulation of adaptive scheduling strategies for distribution networks.

[0062] The method for online monitoring of power consumption in the embodiments of this application has been described above. The system for online monitoring of power consumption in the embodiments of this application is described below. Please refer to [link / reference]. Figure 4 One embodiment of the system for online monitoring of electricity consumption in this application includes: The data acquisition module is used to collect and process temperature and humidity parameters of the standard equipment area and the tested area of ​​the power distribution node through the three-area environmental monitoring device of the power distribution network, and obtain the power distribution environment data matrix. The identification module is used to perform environmental deviation identification processing based on the power distribution environment data matrix through a dual threshold judgment algorithm, and to obtain a power distribution monitoring stop signal when the temperature and humidity exceed the preset range. The control module is used to adaptively recover the power distribution monitoring interruption signal and restore the environmental parameters to the standard range through cooling and heating and humidification and dehumidification adjustment. The calculation module is used to calculate the compensation coefficient based on the temperature and humidity adjustment data during the environmental restoration process. It establishes a compensation mathematical model based on the temperature and humidity difference before and after adjustment to obtain a set of environmental compensation coefficients. The calibration module is used to perform temperature and humidity compensation calibration on the power distribution network energy meter data according to the set of environmental compensation coefficients to obtain the environmentally corrected power distribution energy consumption data. The optimization module is used to perform power quality optimization processing on the environmentally corrected power distribution energy consumption data through the power distribution loss environment correlation algorithm to obtain the power distribution network environment adaptive scheduling strategy.

[0063] above Figure 4 The system for online monitoring of power consumption in this embodiment of the invention is described in detail from the perspective of modular functional entities. The device for online monitoring of power consumption in this embodiment of the invention is described in detail from the perspective of hardware processing.

[0064] Reference Figure 5 This invention also provides a device for online monitoring of power consumption. This device can be a server, and its internal structure can be as follows: Figure 5 As shown, the online power consumption monitoring device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor, designed as a computer, provides computing and control capabilities. The memory of the online power consumption monitoring device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the online power consumption monitoring device stores the data corresponding to this embodiment. The network interface of the online power consumption monitoring device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0065] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the online monitoring of power consumption applied thereto.

[0066] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the method for online monitoring of power consumption.

[0067] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0068] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a device for online monitoring of power consumption (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0069] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for online monitoring of electricity consumption, characterized in that, The method includes: The temperature and humidity parameters of the standard equipment area and the tested item area of ​​the power distribution node are collected and processed by the three-area environmental monitoring device of the power distribution network to obtain the power distribution environment data matrix. Based on the power distribution environment data matrix, an environmental deviation identification process is performed using a dual threshold judgment algorithm. When the temperature and humidity exceed the preset range, a power distribution monitoring stop signal is obtained. The power distribution monitoring interruption signal is processed by adaptive recovery control, and the environmental parameters are restored to the standard range through cooling and heating and humidification and dehumidification adjustment. The compensation coefficient is calculated based on the temperature and humidity adjustment data during the environmental restoration process. A compensation mathematical model is established based on the temperature and humidity difference before and after adjustment to obtain a set of environmental compensation coefficients. Based on the set of environmental compensation coefficients, the power meter data of the distribution network is calibrated by temperature and humidity compensation to obtain the power consumption data of the distribution network after environmental correction. The power supply quality is optimized by using a power distribution loss environment correlation algorithm to process the environmentally corrected power distribution energy consumption data, thereby obtaining an adaptive scheduling strategy for the power distribution network environment.

2. The method for online monitoring of electricity consumption according to claim 1, characterized in that, The method involves collecting and processing temperature and humidity parameters in the standard equipment area and the tested area of ​​the power distribution node using a three-area environmental monitoring device in the power distribution network, resulting in a power distribution environment data matrix, including: Based on the temperature and humidity control equipment within the control area, the environmental monitoring range of the standard equipment area and the tested item area is divided to obtain a three-zone spatial layout of the control area, the standard equipment area, and the tested item area. The temperature and humidity sensors in the standard equipment area of ​​the three-zone spatial layout are used to collect and process real-time data to obtain the environmental parameter sequence of the standard equipment area. Based on the environmental parameter sequence of the standard equipment area, the temperature and humidity sensor of the tested area is synchronously acquired and processed to obtain the environmental parameter sequence of the tested area. Based on the time stamp matching process of the standard equipment area environmental parameter sequence and the test sample area environmental parameter sequence, a dual-area environmental data comparison table is obtained. The dual-region environmental data comparison table is organized into a data matrix according to temperature ±0.1℃ accuracy and humidity ±1%RH accuracy to obtain the power distribution environment data matrix.

3. The method for online monitoring of electricity consumption according to claim 1, characterized in that, The step of performing environmental deviation identification processing based on the power distribution environment data matrix using a dual threshold judgment algorithm, and obtaining a power distribution monitoring stop signal when the temperature and humidity exceed a preset range, includes: The power distribution environment data matrix is ​​input into the first threshold judgment module for comparison of the temperature and humidity range of the standard equipment area to obtain the first layer of environmental deviation judgment result. Based on the first layer environmental deviation judgment result, the temperature and humidity threshold of the tested sample area is processed by the second threshold judgment to obtain the second layer environmental deviation judgment result. Based on the first-layer environmental deviation judgment result and the second-layer environmental deviation judgment result, a dual threshold logic operation is performed. When the temperature and humidity of any area exceed the corresponding threshold range, an environmental exceedance trigger signal is obtained. The environmental exceedance trigger signal is processed to identify the source of deviation, distinguishing between temperature and humidity fluctuations caused by the heat generated during the operation of standard equipment and environmental exchanges caused by opening the door when the tested item is replaced, and thus obtaining the deviation type classification result. Based on the deviation type classification results, the power distribution network monitoring equipment is processed to generate a stop command, resulting in a power distribution monitoring stop signal.

4. The method for online monitoring of electricity consumption according to claim 1, characterized in that, The power distribution monitoring stop signal is subjected to adaptive recovery control processing. Environmental parameters are restored to the standard range through cooling / heating and humidification / dehumidification adjustments. Compensation coefficients are calculated based on temperature and humidity adjustment data during the environmental recovery process, including: The power distribution monitoring stop signal is input into the environmental regulation controller for regulation strategy selection. The corresponding cooling / heating unit or humidification / dehumidification unit is activated according to the deviation type to obtain the environmental regulation execution command. According to the environmental regulation execution command, the environmental regulation equipment is started to perform environmental regulation processing on the standard equipment area and the test sample area. During the environmental regulation processing, the standard equipment area and the test sample area are continuously monitored. The changes in environmental parameters are detected at fixed intervals to obtain the environmental regulation effect data stream. Based on the environmental regulation effect data stream, environmental recovery judgment processing is performed. When three consecutive test results show that the temperature and humidity have returned to the standard range, an environmental recovery confirmation signal is obtained. The environmental recovery confirmation signal and the environmental adjustment effect data stream are processed by calculating the temperature and humidity compensation coefficients. A compensation mathematical model is established based on the temperature and humidity difference before and after adjustment to obtain the temperature compensation coefficient and humidity compensation coefficient. Based on the temperature compensation coefficient and humidity compensation coefficient, a set of compensation parameters is organized and processed, and a compensation algorithm library is established for different environmental deviation conditions to obtain a set of environmental compensation coefficients.

5. The method for online monitoring of electricity consumption according to claim 1, characterized in that, The step of performing temperature and humidity compensation calibration on the power distribution network energy meter data based on the environmental compensation coefficient set to obtain environmentally corrected power distribution energy consumption data includes: The temperature compensation coefficient and humidity compensation coefficient in the set of environmental compensation coefficients are input into the power distribution network energy meter data acquisition module for real-time energy parameter acquisition and processing to obtain the original power distribution energy consumption data stream. Based on the original power distribution energy consumption data stream, the instantaneous power, cumulative energy, voltage, and current parameters are synchronously timestamped to obtain a time-series associated energy parameter data set; Based on the time-series associated power parameter data set, environmental parameter matching processing is performed, and the temperature and humidity values ​​at the corresponding time are associated with the power data to obtain environmental-power associated data pairs. The environmental-electrical energy correlation data is used to calibrate the input temperature and humidity compensation algorithm. Compensation coefficients are calculated according to temperature deviation and humidity deviation and applied to the original electrical energy data to obtain the compensated and calibrated electrical energy value. Data quality verification processing is performed based on the compensated and calibrated electrical energy values. The effectiveness of the compensation effect is verified by comparing and analyzing the data before and after calibration, and the power distribution energy consumption data after environmental correction is obtained.

6. The method for online monitoring of electricity consumption according to claim 5, characterized in that, The step of calibrating the input temperature and humidity compensation algorithm using the environmental-electrical energy correlation data, calculating compensation coefficients according to temperature and humidity deviations respectively, and applying them to the original electrical energy data to obtain the compensated and calibrated electrical energy value includes: The temperature values ​​in the environmental-electrical energy correlation data pair are processed by standard temperature reference deviation calculation. The temperature deviation is obtained by subtracting the standard temperature reference value from the current temperature value. The humidity value is calculated based on the temperature deviation, and the humidity deviation is obtained by subtracting the standard humidity reference value from the current humidity value. Based on the temperature deviation and humidity deviation, a temperature and humidity compensation factor product operation is performed. The temperature deviation is multiplied by a preset temperature compensation coefficient, and the humidity deviation is multiplied by a preset humidity compensation coefficient, and then the results are summed to obtain the comprehensive temperature and humidity compensation factor. The temperature and humidity comprehensive compensation factor is used to correct the power value of the power data. The measured power value is multiplied by the comprehensive compensation factor to obtain the single-item corrected power value. Based on the single-item corrected energy value, batch data correction processing is performed, and the same compensation processing is applied to all energy parameters in the associated data pair to obtain the compensated and calibrated energy value.

7. The method for online monitoring of electricity consumption according to claim 1, characterized in that, The step involves optimizing the power supply quality of the environmentally corrected power consumption data using a power distribution loss environment correlation algorithm to obtain an adaptive scheduling strategy for the power distribution network environment, including: The environmentally corrected power distribution energy consumption data is processed by power distribution network loss analysis. The influence of environmental temperature and humidity factors on the change of power line resistance and the efficiency of power distribution equipment is combined to obtain environmentally related power distribution loss data. Based on the environmentally related power distribution loss data, the load distribution of each power distribution branch is evaluated for balance. The additional losses caused by uneven load and the impact of environmental changes on load stability are analyzed to obtain power distribution load optimization indicators. Based on the aforementioned power distribution load optimization index, power supply path selection is performed, and the switching status of the power distribution circuit is dynamically adjusted according to environmental conditions and load demand to obtain the optimal power distribution path scheme. The optimal power distribution path scheme is processed by calculating power quality parameters, and the performance of voltage stability, frequency deviation and power factor under environmental changes is analyzed to obtain power quality assessment results. Based on the power supply quality assessment results, a scheduling strategy is generated, and operation scheduling rules and emergency response measures for the distribution network under different environmental conditions are formulated to obtain an adaptive scheduling strategy for the distribution network environment.

8. A system for online monitoring of electricity consumption, characterized in that, A method for implementing online monitoring of power consumption as described in any one of claims 1-7, wherein the system for online monitoring of power consumption comprises: The data acquisition module is used to collect and process temperature and humidity parameters of the standard equipment area and the tested area of ​​the power distribution node through the three-area environmental monitoring device of the power distribution network, and obtain the power distribution environment data matrix. The identification module is used to perform environmental deviation identification processing based on the power distribution environment data matrix through a dual threshold judgment algorithm, and to obtain a power distribution monitoring stop signal when the temperature and humidity exceed the preset range. The control module is used to adaptively recover the power distribution monitoring interruption signal and restore the environmental parameters to the standard range through cooling and heating and humidification and dehumidification adjustment. The calculation module is used to calculate the compensation coefficient based on the temperature and humidity adjustment data during the environmental restoration process. It establishes a compensation mathematical model based on the temperature and humidity difference before and after adjustment to obtain a set of environmental compensation coefficients. The calibration module is used to perform temperature and humidity compensation calibration on the power distribution network energy meter data according to the set of environmental compensation coefficients to obtain the environmentally corrected power distribution energy consumption data. The optimization module is used to perform power quality optimization processing on the environmentally corrected power distribution energy consumption data through the power distribution loss environment correlation algorithm to obtain the power distribution network environment adaptive scheduling strategy.

9. A device for online monitoring of electricity consumption, characterized in that, The method includes a memory and a processor, the memory storing a computer program that can run on the processor, and the processor executing the computer program to implement the method for online monitoring of power consumption as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it causes the processor to perform the method for online monitoring of power consumption as described in any one of claims 1 to 7.

Images