An electrical box management method based on the Internet of Things

Abstract

The present invention relates to the field of Internet of Things data management technology, specifically to an electrical box management method based on the Internet of Things, the method comprising: based on the deployment of monitoring equipment inside and outside the electrical box, collecting power consumption, voltage changes, and temperature fluctuation parameters, analyzing parameter time series changes to identify operating trends and abnormal patterns, and generating real-time monitoring records. In the present invention, by deploying monitoring equipment inside and outside the electrical box to collect key parameters and using a moving window method for data analysis, high-precision identification of electrical operating trends and abnormal patterns is achieved, and by generating real-time monitoring records and abnormal trend identifiers, fault cause analysis and response strategy formulation are effectively guided, and historical data comparison and analysis methods are used to evaluate the stability and efficiency of the electrical network. Through the implementation of a comprehensive network performance optimization plan, the method's adaptive adjustment capability is improved to ensure that the electrical network maintains efficient and stable operation in a cyclically changing environment.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things data management, and in particular to an electrical box management method based on the Internet of Things. Background Art

[0002] The field of IoT data management technology covers strategies and solutions for collecting, storing, processing and analyzing data collected from a variety of physical devices and sensors using IoT technology. The core purpose of this field is to achieve intelligent management of equipment and improve efficiency, safety and reliability through remote monitoring and data analysis. IoT data management technology connects devices to the Internet, enabling them to send and receive data, and realizes functions such as real-time monitoring of device status, predictive maintenance, energy management and optimization. It is essential for the realization of smart cities, smart homes, industrial automation and many applications, relying on advanced data analysis tools and algorithms, as well as cloud computing and edge computing technologies to process and analyze batches of data.

[0003] Among them, the electrical box management method based on the Internet of Things refers to a strategy that uses the Internet of Things technology to monitor and manage electrical boxes in real time. The main purpose of this method is to remotely monitor the status of the electrical box to achieve real-time monitoring and fault prediction of the electrical system, reduce downtime, and improve system reliability and safety. Through this management method, abnormal conditions of the electrical box, such as overheating, abnormal current, or equipment failure, can be discovered in time, and preventive measures or timely repairs can be taken to avoid serious equipment damage or safety accidents.

[0004] Traditional electrical box management methods are deficient in real-time and accuracy. Traditional methods rely on intermittent data collection and subsequent analysis, lack effective real-time monitoring and rapid response mechanisms, resulting in delays in fault prediction and processing, increasing electrical box downtime, affecting overall operating efficiency and safety, and failing to promptly identify electrical box overheating or abnormal current, leading to equipment damage or safety accidents, and thus causing economic losses and safety hazards. Summary of the invention

[0005] The present application provides an electrical box management method based on the Internet of Things, which solves the shortcomings of traditional electrical box management methods in terms of real-time and accuracy. Traditional methods rely on intermittent data collection and subsequent analysis, lack effective real-time monitoring and rapid response mechanisms, resulting in delays in fault prediction and processing, increasing the downtime of the electrical box, affecting the overall operating efficiency and safety, and failing to promptly identify electrical box overheating or abnormal current, resulting in equipment damage or safety accidents, and further causing economic losses and safety hazards.

[0006] In view of the above problems, the present application provides an electrical box management method based on the Internet of Things.

[0007] The present application provides an electrical box management method based on the Internet of Things, wherein the method comprises the following steps:

[0008] S1: Based on the deployment of monitoring equipment inside and outside the electrical box, power consumption, voltage change, and temperature fluctuation parameters are collected, and the time series changes of parameters are analyzed to identify operating trends and abnormal patterns, and generate real-time monitoring records;

[0009] S2: Based on the real-time monitoring records, the moving window method is used to calculate the mean value, standard deviation, and coefficient of variation of each point, and an abnormal trend is determined. If an abnormal trend exists, an abnormal trend mark is generated;

[0010] S3: Based on the abnormal trend identification, analyze the cause of the fault, formulate a strategy for adjusting the heat dissipation state and load distribution of the electrical box, reduce the impact of potential faults, and generate a response strategy guide;

[0011] S4: After the adjustment plan is implemented based on the response strategy guide, the stability and efficiency of the electrical network are evaluated, and the changes in network operation parameters are checked using historical data comparison and analysis methods to generate performance adjustment evaluation results;

[0012] S5: Based on the performance adjustment evaluation results, evaluate the connection density and flow distribution of electrical network nodes, identify overloaded and weakly connected nodes, formulate measures to increase backup connection paths and configure network loads, and generate a comprehensive network performance optimization plan;

[0013] S6: Based on the comprehensive network performance optimization plan, adjustment measures such as adding connection paths and adjusting communication protocol parameters are executed, the effects are verified through continuous monitoring means, and a network configuration update record is obtained.

[0014] Preferably, the real-time monitoring record includes power consumption value, voltage change rate and temperature fluctuation amplitude parameters, the abnormal trend identifier is specifically the marked time point, abnormal parameter name and corresponding numerical value, the response strategy guide includes strategies for adjusting fan speed, adjusting electrical load distribution ratio and increasing the number of radiators, the performance adjustment evaluation results include power consumption reduction percentage, voltage stability improvement index and time evaluation index within the temperature control range, the comprehensive network performance optimization plan includes the increased number of backup connection paths, the adjusted network traffic distribution ratio and the newly configured load balancing algorithm, and the network configuration update record includes the wiring diagram of the newly added connection path, the updated communication protocol parameter value and the data comparison chart verified by continuous monitoring.

[0015] Preferably, based on the real-time monitoring records, the moving window method is used to calculate the mean value, standard deviation, and coefficient of variation of each point, and the abnormal trend is determined. If there is an abnormal trend, the specific steps of generating an abnormal trend mark are as follows:

[0016] S201: when processing the real-time monitoring records, the data points are arranged in time series, a fixed window size is used to slide through the data set, an average value of the data points is calculated within the window, and a time series average value set is generated;

[0017] S202: Based on the time series average value set, using time series analysis technology, applying a moving window with a fixed span to traverse the data set, calculating the standard deviation and coefficient of variation of the data points in the window, and generating a volatility measurement set;

[0018] S203: Using the volatility measurement set, setting a threshold judgment condition, marking an abnormality when the standard deviation and the coefficient of variation exceed a predetermined threshold, and generating an abnormal trend mark.

[0019] Preferably, the time series analysis technique is performed according to the formula

[0020]

[0021] and

[0022]

[0023] in, is the standard deviation, is the sample size, For multiple data points, is the average value, is the coefficient of variation, is the weight coefficient of the data point, is the adjustment factor, is the smoothing constant, Adjust the constant for the mean.

[0024] Preferably, based on the abnormal trend identification, analyzing the cause of the fault, formulating a strategy for adjusting the heat dissipation state and load distribution of the electrical box, reducing the impact of potential faults, and generating a response strategy guide are as follows:

[0025] S301: Based on the abnormal trend identification, review the operation log of the electrical network item by item, including voltage fluctuation and load abnormality records, identify the time period and environmental conditions where the fault occurred, explore the cause of the fault, and construct a fault cause analysis result;

[0026] S302: using the fault cause analysis result, checking the heat dissipation operation status of the electrical box and the load distribution status of the electrical network, identifying the key problem points that cause poor heat dissipation and load imbalance, and generating key problem positioning and strengthening targets;

[0027] S303: Based on the key problem positioning and strengthening objectives, an optimization plan is designed, including adjusting the heat dissipation configuration and replanning the electrical network load, optimizing the heat dissipation performance and load balance, intervening in the identified problems, and obtaining a response strategy guide.

[0028] Preferably, after the adjustment plan based on the response strategy guide is implemented, the stability and efficiency of the electrical network are evaluated, and the changes in network operation parameters are checked by using a historical data comparison and analysis method, and the specific steps of generating performance adjustment evaluation results are as follows:

[0029] S401: Based on the response strategy guide, by collecting the electrical network operation data before and after the implementation of the adjustment plan, including voltage value, current load and fluctuation, collecting and analyzing the integrity and reliability of data support, and constructing a detailed parameter data set;

[0030] S402: using the refined parameter data set, adopting a difference comparison analysis technology to analyze changes in voltage stability and load balance indicators, identifying adjustment points of key parameters by comparing data differences before and after adjustment, and generating key parameter correction analysis results;

[0031] S403: Based on the key parameter correction analysis results, the effectiveness of the electrical network stability and efficiency adjustment is quantitatively analyzed through a quantitative evaluation method, and the effects of the adjustment measures are evaluated by comparing the performance indicators before and after the adjustment to obtain a performance adjustment evaluation result.

[0032] Preferably, the difference comparison analysis technique is performed according to the formula

[0033]

[0034] and

[0035]

[0036] in, Adjusted voltage fluctuation rate, The adjusted load imbalance ratio, is the maximum voltage during the observation period, is the minimum voltage, is the average voltage, is the maximum current load during the observation period, is the minimum current load, is the average current load, and are the weight coefficients of voltage and current respectively, is the ambient temperature, For humidity, and is the temperature influence coefficient, and is the humidity influence coefficient.

[0037] Preferably, based on the performance adjustment evaluation results, the specific steps of evaluating the connection density and flow distribution of electrical network nodes, identifying overloaded and weakly connected nodes, formulating measures to increase backup connection paths and configure network loads, and generating a comprehensive network performance optimization plan are as follows:

[0038] S501: By analyzing the performance adjustment evaluation results, evaluate the traffic usage and connection stability of multiple nodes in the electrical network, identify nodes with excessively high traffic concentration and insufficient connection strength, determine the problem nodes through quantitative methods based on traffic and connection data, and construct traffic and connection strength analysis results;

[0039] S502: According to the traffic and connection strength analysis results, identify nodes that need to be optimized, plan adjustment measures for the nodes, including adding backup paths and adjusting existing network loads, formulate targeted optimization strategies by locating problem points, and generate a node optimization strategy list;

[0040] S503: Relying on the node optimization strategy list, a network structure adjustment plan is comprehensively formulated, referring to the dual needs of network traffic distribution and connection reliability, and through adjustment measures, network performance and stability are optimized to obtain a comprehensive network performance optimization plan.

[0041] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0042] By deploying monitoring equipment inside and outside the electrical box to collect key parameters and using the moving window method for data analysis, high-precision identification of electrical operation trends and abnormal patterns can be achieved. The method's judgment logic enhances fault prediction capabilities, and by generating real-time monitoring records and abnormal trend identifiers, it effectively guides fault cause analysis and the formulation of response strategies. The stability and efficiency of the electrical network are evaluated using historical data comparison and analysis methods, and the method's adaptive adjustment capabilities are improved through the implementation of a comprehensive network performance optimization plan. This not only improves the reliability and safety of the method, but also enhances the comprehensive management of network performance, ensuring that the electrical network maintains efficient and stable operation in a constantly changing environment.

[0043] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The present invention proposes an overall flow chart of an electrical box management method based on the Internet of Things;

[0045] Figure 2 The present invention proposes a specific flow chart of S1 of an electrical box management method based on the Internet of Things;

[0046] Figure 3 The present invention proposes a specific flow chart of S2 of an electrical box management method based on the Internet of Things;

[0047] Figure 4 The present invention proposes a specific flow chart of S3 of an electrical box management method based on the Internet of Things;

[0048] Figure 5 The present invention proposes a specific flow chart of S4 of an electrical box management method based on the Internet of Things;

[0049] Figure 6 The present invention proposes a specific flow chart of S5 of an electrical box management method based on the Internet of Things;

[0050] Figure 7 The present invention proposes a S6 specific flow chart of an electrical box management method based on the Internet of Things. DETAILED DESCRIPTION

[0051] The present application provides an electrical box management method based on the Internet of Things.

[0052] Application Overview

[0053] The traditional electrical box management method in the prior art is insufficient in real-time and accuracy. The traditional method relies on intermittent data collection and subsequent analysis, lacks effective real-time monitoring and rapid response mechanisms, resulting in delays in fault prediction and processing, increasing the downtime of the electrical box, affecting the overall operating efficiency and safety, and failing to promptly identify electrical box overheating or abnormal current, resulting in equipment damage or safety accidents, and further causing economic losses and safety hazards.

[0054] In response to the above technical problems, the overall idea of ​​the technical solution provided by this application is as follows:

[0055] like Figure 1 As shown, the present application provides an electrical box management method based on the Internet of Things, wherein the method comprises the following steps:

[0056] S1: The monitoring equipment is deployed inside and outside the electrical box to collect power consumption, voltage changes, and temperature fluctuation parameters in real time. By analyzing the time series changes of the parameters, the operating trends and abnormal patterns are identified to obtain real-time monitoring records;

[0057] S2: Arrange the data points in the real-time monitoring records in chronological order, use the moving window method to calculate the statistical characteristics of each data point, including the mean, standard deviation, and coefficient of variation, and determine whether there is an abnormal trend based on the statistical characteristics. If it is determined that there is an abnormal trend, generate an abnormal trend mark;

[0058] S3: Based on the abnormal trend identification, analyze the potential causes of the fault and formulate a response strategy, including adjusting the heat dissipation working state inside the electrical box and adjusting the load distribution, reducing or avoiding the potential fault impact, and generating a response strategy guide;

[0059] S4: After the adjustment plan in the response strategy guide is implemented, the stability and efficiency of the electrical network are re-evaluated. By using historical data comparison and analysis methods, the changes in network operating parameters before and after the adjustment, including voltage stability and load balance, are checked to verify the effectiveness of the adjustment measures and generate performance adjustment evaluation results;

[0060] S5: Based on the performance adjustment evaluation results, evaluate the connection density and traffic distribution of multiple nodes in the electrical network, identify nodes with traffic overload or weak connections, and formulate adjustment measures for the nodes, including adding backup connection paths, reconfiguring network loads, optimizing the traffic distribution of the entire network and improving the reliability of connections, optimizing traffic distribution and improving connection reliability, and generating a comprehensive network performance optimization plan;

[0061] S6: Based on the comprehensive network performance optimization plan, the adjustment measures in the plan are implemented, including adding connection paths through physical or software means, adjusting communication protocol parameters between nodes, and forming a new electrical network configuration. After the configuration is implemented, continuous monitoring methods are used to verify the improvement effect and obtain the network configuration update record.

[0062] Real-time monitoring records include power consumption values, voltage change rate and temperature fluctuation amplitude parameters. Abnormal trend identification is specifically the marked time point, abnormal parameter name and corresponding value. Response strategy guidelines include strategies for adjusting fan speed, adjusting electrical load distribution ratio and increasing the number of radiators. Performance adjustment evaluation results include power consumption reduction percentage, voltage stability improvement index and time evaluation index within the temperature control range. The comprehensive network performance optimization plan includes the increased number of backup connection paths, the adjusted network traffic distribution ratio and the newly configured load balancing algorithm. The network configuration update record includes the wiring diagram of the newly added connection path, the updated communication protocol parameter value and the data comparison chart verified by continuous monitoring.

[0063] Specifically, if Figure 2 As shown, based on the deployment of monitoring equipment inside and outside the electrical box, power consumption, voltage change, and temperature fluctuation parameters are collected, and the time series changes of parameters are analyzed to identify operating trends and abnormal patterns, and generate real-time monitoring records;

[0064] S101: Based on the deployment of monitoring equipment inside and outside the electrical box, the data of power consumption, voltage changes, and temperature fluctuations are captured, and the data is automatically recorded to build a preliminary data set. The execution process is as follows;

[0065] Sub-step S101 is based on the deployment of monitoring equipment inside and outside the electrical box. It adopts a time series data collection method and uses the Python programming language with the Pandas library to automatically record data on power consumption, voltage changes, and temperature fluctuations. The data collection frequency parameter is configured to be once per minute. The Pandas DataFrame structure is used to store timestamps and corresponding power, voltage, and temperature readings. The collected data is written to a local or cloud database in real time to generate a preliminary data set.

[0066] S102: Based on the preliminary data set, a sliding window average is performed on the collected data to smooth short-term fluctuations, and the result data is aggregated hourly to reduce the impact of noise and optimize the analyzability of the data. The execution process of obtaining the aggregated data set is as follows;

[0067] Based on the preliminary data set, sub-step S102 adopts the moving average algorithm and uses Python language and NumPy library to perform sliding window averaging. First, the sliding window size parameter is set to 5 minutes, and the moving average is applied to each data of power consumption, voltage change, and temperature fluctuation. The short-term fluctuations are smoothed by calculating the average value of the data every five minutes. Then, the resample method of Pandas is used to aggregate the result data by hour, and the aggregation function is set to average to reduce the noise impact of the data and generate an aggregated data set.

[0068] S103: Using the aggregated data set, perform segmented statistical analysis on power, voltage and temperature, identify the thresholds of the normal range and abnormal range of the data, classify and mark the data according to the thresholds, distinguish between normal and abnormal states, and construct the execution process of real-time monitoring records as follows;

[0069] Sub-step S103 utilizes the aggregated data set and adopts statistical analysis methods, using Python language and SciPy library to perform segmented statistical analysis of power, voltage and temperature. First, the thresholds of normal range and abnormal range are defined for each parameter, the normal range of power consumption is set to 0 to 5000 watts, the normal range of voltage change is set to 220 to 240 volts, and the normal range of temperature is set to -10 to 50 degrees Celsius. Then, each record in the aggregated data set is classified and marked according to the set threshold. If the record exceeds the normal range, it is marked as abnormal, otherwise it is marked as normal, and real-time monitoring records are constructed.

[0070] Specifically, if Figure 3As shown, based on real-time monitoring records, the moving window method is used to calculate the mean value, standard deviation, and coefficient of variation of each point to determine abnormal trends. If any, an abnormal trend mark is generated;

[0071] S201: When processing real-time monitoring records, the data points are arranged in time series, a fixed window size is used to slide through the data set, the average value of the data points is calculated within the window, and the execution flow of generating a time series average value set is as follows;

[0072] When processing real-time monitoring records, sub-step S201 arranges the data points in time series, uses the Python programming language combined with the Pandas library to slide through the data set using a fixed window size, sets the window size parameter to 10 minutes, and for each data point in the window, executes the Pandas rolling method to calculate the average value of the data points in the window. This operation is performed by specifying the window parameter as 10 and selecting mean as the aggregation function, traversing the entire data set, calculating the average value in each window, and generating a time-series average value set.

[0073] S202: Based on the time series average value set, the time series analysis technology is used to traverse the data set using a moving window with a fixed span, and the standard deviation and coefficient of variation of the data points in the window are calculated to generate a volatility measurement set. The execution process is as follows;

[0074] In sub-step S202, based on the time-series average value set, the rolling function in Python language and Pandas library is used to traverse the data set with a fixed-span moving window. The size of the moving window is set to 15 minutes. In each window, in addition to calculating the average value of the data point, the standard deviation and coefficient of variation are also calculated. The standard deviation is calculated by calling the rolling.std method. The coefficient of variation is calculated by first calling rolling.std to obtain the standard deviation, and then calling rolling.mean to obtain the mean. The standard deviation is divided by the mean to obtain the coefficient of variation, and a set of volatility metrics is generated.

[0075] Time series analysis techniques follow the formula

[0076]

[0077] and

[0078]

[0079] in, is the standard deviation, is the sample size, For multiple data points, is the average value, is the coefficient of variation, is the weight coefficient of the data point, is the adjustment factor, is the smoothing constant, Adjust the constant for the mean.

[0080] The execution process is as follows;

[0081] For each data point , apply the weight coefficient , the weight coefficient is dynamically adjusted based on the timestamp or related properties of the data point to reflect its importance in the overall data set, introducing the adjustment coefficient , used to fine-tune the impact of each data point based on the specific characteristics of the data set, adding a smoothing constant , aims to reduce the excessive impact of extreme values ​​on the standard deviation, enhance the robustness of the model, and adjust the constant by the mean value , fine-tune the denominator of the coefficient of variation to match special cases or outliers in the data set. The steps combine the original standard deviation and coefficient of variation calculation process to improve the accuracy and adaptability of the calculation by introducing new parameters. To determine the weight coefficient The value of can be adjusted based on the frequency of occurrence of data points in the time window, that is, within a given time window, the more frequently a data point appears, the higher its weight is, and the adjustment coefficient and mean adjustment constant The smoothing constant can be determined by preliminary analysis of the data and reflects a specific deviation or trend in the data set. The value of can be optimized through cross-validation to ensure the generalization ability of the model on different data sets.

[0082] S203: Using the volatility measurement set, setting the threshold judgment condition, marking anomalies when the standard deviation and coefficient of variation exceed the predetermined threshold, and generating the abnormal trend mark execution process is as follows;

[0083] Sub-step S203 uses a set of volatility metrics to set threshold judgment conditions, and uses the Python programming language and Pandas library to perform operations. First, the threshold of the standard deviation is preset to 1.5, and the threshold of the coefficient of variation is preset to 0.5. The apply function of Pandas is used to traverse each data point in the data set. For each point, check whether its standard deviation and coefficient of variation exceed the preset threshold. If any indicator exceeds the threshold, the marking function of Pandas is used to mark the data point as abnormal, and an abnormal trend identifier is generated.

[0084] Specifically, if Figure 4 As shown, based on the abnormal trend identification, the cause of the fault is analyzed, a strategy for adjusting the heat dissipation status and load distribution of the electrical box is formulated to reduce the impact of potential faults and generate a response strategy guide;

[0085] S301: Based on the abnormal trend identification, the operation log of the electrical network is reviewed item by item, including the voltage fluctuation and load abnormality records, the time period and environmental conditions of the fault occurrence are identified, the cause of the fault is mined, and the execution process of constructing the fault cause analysis result is as follows;

[0086] The S301 sub-step is based on abnormal trend identification, adopts time series analysis method, uses Python's Pandas library to screen data, sorts the voltage fluctuation and load abnormality records in the electrical network operation log in time series, uses the Spearman rank correlation coefficient calculation function in the SciPy library to evaluate the correlation between voltage fluctuation and load abnormality, screens the log data in chronological order, uses the Matplotlib library to draw a time series graph, identifies the time period when the fault occurred, uses the Numpy library to perform numerical analysis on the environmental condition data, identifies the environmental conditions associated with the fault time period, and generates fault cause analysis results.

[0087] S302: Using the fault cause analysis results, check the heat dissipation operation status of the electrical box and the load distribution status of the electrical network, identify the key problem points that cause poor heat dissipation and load imbalance, and generate the execution process of key problem location and strengthening goals as follows;

[0088] In sub-step S302, the fault cause analysis results are used, thermodynamic analysis is adopted, and fluid dynamics simulation is performed using OpenFOAM to simulate the air flow and heat exchange process inside the electrical box, identify areas with low heat dissipation efficiency, use Matlab to perform load analysis, and use the minimum cut algorithm in graph theory to determine the load unbalanced nodes in the electrical network. The topological structure of the electrical network is analyzed to identify the key nodes with uneven load distribution, and generate key problem positioning and strengthening goals.

[0089] S303: Based on the key problem positioning and strengthening goals, design optimization solutions, including adjusting the heat dissipation configuration and re-planning the electrical network load, optimizing the heat dissipation performance and load balance, and intervening in the identified problems to obtain the execution process of the response strategy guide as follows;

[0090] Sub-step S303 designs an optimization plan based on the key problem positioning and strengthening goals, adopts genetic algorithms, uses Python's DEAP library to optimize parameters, adjusts the cooling configuration parameters including fan speed and radiator position, uses network flow algorithms, and uses Python's NetworkX library to redistribute the electrical network load. By modifying the node connection weights, load balance is achieved, and the optimized cooling performance and load balance are evaluated. The adjustment effect is verified by simulation, and a response strategy guide is generated.

[0091] Specifically, if Figure 5 As shown, after the implementation of the adjustment plan based on the response strategy guide, the stability and efficiency of the electrical network are evaluated, and the historical data comparison and analysis method is used to check the changes in network operation parameters and generate performance adjustment evaluation results;

[0092] S401: Based on the response strategy guide, by collecting the electrical network operation data before and after the implementation of the adjustment plan, including voltage values, current loads and fluctuations, collecting and analyzing the integrity and reliability of data support, the execution process of constructing a detailed parameter data set is as follows;

[0093] Based on the response strategy guide, sub-step S401 collects the electrical network operation data before and after the implementation of the adjustment plan, uses the Python programming language combined with the Pandas library and the SQL database to collect and analyze the data, sets the voltage value, current load and fluctuation as the parameters of interest, first defines the SQL query statement to extract data from the specified electrical network data table, and determines the records before and after the implementation date of the specified screening adjustment plan in the query statement. Pandas' DataFrame is used for data loading and preliminary processing, including timestamp formatting and missing value processing. The describe method is used to perform statistical analysis on the voltage value, current load and fluctuation to ensure the integrity and reliability of the data and generate a refined parameter data set.

[0094] S402: Using the refined parameter data set, adopting the difference comparison analysis technology to analyze the changes of voltage stability and load balance indicators, by comparing the data differences before and after adjustment, identifying the adjustment points of key parameters, and generating the key parameter correction analysis results. The execution process is as follows;

[0095] In sub-step S402, the refined parameter data set is used to analyze the changes in voltage stability and load balance indicators using difference comparison analysis technology. The Python programming language and Pandas library are used to set up a comparison analysis process including loading the data set before and after adjustment, difference calculation, and identification of key parameters. For the loaded data set, the merge method of Pandas is first used to merge the data set before and after adjustment according to the timestamp, and then the subtract method is used to calculate the difference in voltage value and current load. The time point when the voltage stability and load balance indicators change significantly is identified through conditional screening, and the time point is marked as the adjustment point of the key parameter to generate the key parameter correction analysis results.

[0096] The difference comparison analysis technique is based on the formula

[0097]

[0098] and

[0099]

[0100] in, Adjusted voltage fluctuation rate, The adjusted load imbalance ratio, is the maximum voltage during the observation period, is the minimum voltage, is the average voltage, is the maximum current load during the observation period, is the minimum current load, is the average current load, and are the weight coefficients of voltage and current respectively, is the ambient temperature, For humidity, and is the temperature influence coefficient, and is the humidity influence coefficient.

[0101] The execution process is as follows;

[0102] The maximum and minimum voltage and current load values, as well as the average value, during the observation period are calculated, which provides basic data for analysis. The ambient temperature and humidity parameters are introduced to refer to the impact on voltage stability and load balance. By adding the temperature influence coefficient and the humidity influence coefficient, the impact of environmental factors can be more accurately evaluated. and Used to adjust the relative importance of voltage and current loads. Taking into account the different sensitivities of differentiated electrical boxes to voltage stability and load balance, the weight coefficient can be determined through historical data analysis. For example, the weight value that best explains the change is determined through regression analysis. Combining parameters and coefficients, the improved voltage stability and load balance indicators can be calculated, providing more accurate data basis for system optimization and adjustment.

[0103] S403: Based on the key parameter correction analysis results, the effectiveness of the electrical network stability and efficiency adjustment is quantitatively analyzed through a quantitative evaluation method, and the effect of the adjustment measures is evaluated by comparing the performance indicators before and after the adjustment. The execution process of obtaining the performance adjustment evaluation results is as follows;

[0104] Based on the key parameter correction analysis results, sub-step S403 conducts a quantitative analysis of the effectiveness of the electrical network stability and efficiency adjustment through a quantitative evaluation method. The Python language is combined with the SciPy library and a custom evaluation function to set up an evaluation process including the selection of performance indicators, the comparison of performance indicators before and after adjustment, and effect evaluation. The selected performance indicators include the power supply stability index and energy efficiency ratio of the power grid. The t-test method in the SciPy library is used to perform a statistical comparison of the performance indicator data before and after the adjustment to determine the significance of the difference. According to the t-test results, the custom evaluation function is used to calculate the effect score, evaluate the effect of the adjustment measures, and obtain the performance adjustment evaluation results.

[0105] Specifically, if Figure 6 As shown, based on the performance adjustment evaluation results, the connection density and flow distribution of electrical network nodes are evaluated, overloaded and weakly connected nodes are identified, measures to increase backup connection paths and configure network loads are formulated, and a comprehensive network performance optimization plan is generated;

[0106] S501: By analyzing the performance adjustment evaluation results, the traffic usage and connection stability of multiple nodes in the electrical network are evaluated, and nodes with excessively high traffic concentration and insufficient connection strength are identified. Based on the traffic and connection data, the problem nodes are determined by quantitative methods, and the execution process of constructing the traffic and connection strength analysis results is as follows;

[0107] The S501 sub-step analyzes the performance adjustment evaluation results, adopts the network analysis algorithm, and uses Python's NetworkX library to perform quantitative analysis of multi-node traffic usage and connection stability, counts the node traffic in the electrical network, and uses the traffic density function to calculate the traffic concentration of each node. The connection strength evaluation algorithm is used to calculate the connection strength using the edge weights and number of connections between nodes. Through the comparative analysis of traffic density and connection strength, nodes with excessively high traffic concentration and insufficient connection strength are identified, and the clustering algorithm is used to classify the problem nodes to generate traffic and connection strength analysis results.

[0108] S502: Based on the traffic and connection strength analysis results, identify the nodes that need to be optimized, plan adjustment measures for the nodes, including adding backup paths and adjusting the existing network load, and formulate targeted optimization strategies by locating the problem points. The execution process of generating a node optimization strategy list is as follows;

[0109] In sub-step S502, based on the results of traffic and connection strength analysis, a graph theory optimization algorithm is used to optimize the node network using Python's SciPy library, identify the nodes that need to be optimized, calculate the feasibility of adding backup paths through the shortest path algorithm, and use the network load balancing algorithm to evaluate the distribution of the existing network load. Through the comprehensive application of the shortest path algorithm in graph theory and the network load balancing algorithm, the node optimization strategy is determined, including the addition of backup paths between nodes and the load redistribution plan, and a node optimization strategy list is generated.

[0110] S503: Relying on the node optimization strategy list, a network structure adjustment plan is comprehensively formulated, referring to the dual requirements of network traffic distribution and connection reliability, and optimizing network performance and stability through adjustment measures. The execution process of the comprehensive network performance optimization plan is as follows;

[0111] The S503 sub-step relies on the node optimization strategy list, adopts the system dynamics model, and uses Matlab to comprehensively formulate the network structure adjustment plan. Referring to the analysis results of network traffic distribution and connection reliability, the impact of various adjustment measures on network performance and stability is tested through dynamic simulation. The effect of the adjustment measures is determined using feedback control theory. Through the simulation results of the system dynamics model, the optimal network structure adjustment plan is selected to optimize network performance and stability, and a comprehensive network performance optimization plan is generated.

[0112] Specifically, if Figure 7 As shown, based on the comprehensive network performance optimization plan, adjustment measures such as adding connection paths and adjusting communication protocol parameters are implemented, and the effects are verified through continuous monitoring means to obtain network configuration update records;

[0113] S601: According to the comprehensive network performance optimization plan, perform physical expansion of the network and update the software configuration, including adding new connection paths and optimizing the settings of communication protocols, strengthening network traffic management and connection stability, and obtaining the execution process of the network adjustment execution record as follows;

[0114] The S601 sub-step is based on the comprehensive network performance optimization plan, performs physical expansion of the network and software configuration updates, and uses network topology design tools and protocol analysis software to perform operations. First, a new connection path is added through the network topology design tool, and tool parameters are set including target nodes, connection types (wired or wireless), and expected bandwidth. Then, the protocol analysis software is used to optimize the settings of the communication protocol. The configuration parameters include selecting the protocol (such as TCP / IP or UDP), adjusting the timeout time and the number of retransmissions, strengthening the network's traffic management and connection stability, and generating network adjustment execution records.

[0115] S602: Based on the network adjustment execution record, deploy continuous monitoring equipment, track network performance in real time, including paying attention to the impact of the new configuration on traffic distribution and connection stability, verify the effectiveness of the adjustment measures, and generate the execution process of performance monitoring results as follows;

[0116] Sub-step S602 deploys continuous monitoring equipment based on the network adjustment execution record, uses network performance monitoring tools to track network performance in real time, sets monitoring parameters including traffic distribution monitoring, connection stability detection, and new configuration implementation impact analysis, uses the tool's real-time data capture and analysis capabilities, pays attention to the impact of new configurations on traffic distribution and connection stability, verifies the effectiveness of adjustment measures, and generates performance monitoring results.

[0117] S603: Based on the performance monitoring results, analyze the effect of the adjusted network configuration, evaluate the contribution of the newly added connection path and communication protocol adjustment to the network performance, analyze the network performance improvement and potential problems, and construct the execution process of the network configuration update record as follows;

[0118] Based on the performance monitoring results, sub-step S603 analyzes the effect of the adjusted network configuration, and uses data analysis and network simulation software to evaluate the contribution of the newly added connection path and communication protocol adjustment to network performance. First, use the data analysis software to load the performance monitoring results, set the analysis parameters including the traffic increase and decrease ratio, delay change and packet loss rate, and then use the network simulation software to simulate the network environment before and after the adjustment, compare the differences in performance indicators such as throughput and response time, analyze the network performance improvements and potential problems, and build a network configuration update record.

[0119] Although the present application has been described in conjunction with specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely exemplary illustrations of the present application as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include these modifications and variations.

Claims

1. An electrical box management method based on the Internet of Things, characterized in that: The method comprises the following steps: Based on the deployment of monitoring equipment inside and outside the electrical box, power consumption, voltage changes, and temperature fluctuation parameters are collected, and the time series changes of parameters are analyzed to identify operating trends and abnormal patterns, and generate real-time monitoring records; The real-time monitoring records include power consumption value, voltage change rate and temperature fluctuation amplitude parameters; Based on the real-time monitoring records, the moving window method is used to calculate the mean value, standard deviation, and coefficient of variation of each point, and an abnormal trend is determined. If an abnormal trend exists, an abnormal trend mark is generated; The abnormal trend identifier specifically includes the marked time point, the abnormal parameter name and the corresponding value; The specific steps of generating the abnormal trend mark are as follows: When processing the real-time monitoring records, the data points are arranged in time series, a fixed window size is used to slide through the data set, the average value of the data points is calculated within the window, and a time series average value set is generated; Based on the time series average value set, using time series analysis technology, applying a moving window with a fixed span to traverse the data set, calculating the standard deviation and coefficient of variation of the data points in the window, and generating a volatility measurement set; Using the volatility measurement set, setting threshold judgment conditions, marking anomalies when standard deviation and coefficient of variation exceed predetermined thresholds, and generating an abnormal trend mark; The time series analysis technique is described according to the formula ； and ； in, is the standard deviation, is the sample size, For multiple data points, is the average value, is the coefficient of variation, is the weight coefficient of the data point, is the adjustment factor, is the smoothing constant, Adjust the constant for the mean; Based on the abnormal trend identification, analyze the cause of the fault, formulate a strategy for adjusting the heat dissipation state and load distribution of the electrical box, reduce the impact of potential faults, and generate a response strategy guide; The coping strategy guide includes adjusting fan speed, adjusting electrical load distribution ratio and increasing the number of heat sinks; The specific steps for generating the response strategy guide are as follows: Based on the abnormal trend identification, the operation logs of the electrical network are reviewed item by item, including voltage fluctuation and load abnormality records, the time period and environmental conditions in which the fault occurred are identified, the cause of the fault is explored, and the fault cause analysis results are constructed; Using the fault cause analysis results, check the heat dissipation operation status of the electrical box and the load distribution status of the electrical network, identify the key problem points that cause poor heat dissipation and load imbalance, and generate key problem positioning and strengthening goals; Based on the key problem positioning and strengthening objectives, design optimization solutions, including adjusting the cooling configuration and re-planning the electrical network load, optimizing the cooling performance and load balance, intervening in the identified problems, and obtaining the response strategy guidelines; After the adjustment plan is implemented based on the response strategy guide, the stability and efficiency of the electrical network are evaluated, and the changes in network operating parameters are checked using historical data comparison and analysis methods to generate performance adjustment evaluation results; The performance adjustment evaluation results include a percentage of power consumption reduction, a voltage stability improvement index, and a time evaluation index within a temperature control range; The specific steps of generating the performance adjustment evaluation result are as follows: Based on the response strategy guidelines, by collecting the electrical network operation data before and after the implementation of the adjustment plan, including voltage values, current loads and fluctuations, collecting and analyzing the integrity and reliability of data support, and constructing a detailed parameter data set; Utilizing the refined parameter data set, adopting the difference comparison analysis technology to analyze the changes of voltage stability and load balance indicators, identifying the adjustment points of key parameters by comparing the data differences before and after adjustment, and generating key parameter correction analysis results; Based on the key parameter correction analysis results, the effectiveness of the electrical network stability and efficiency adjustment is quantitatively analyzed through a quantitative evaluation method, and the effect of the adjustment measures is evaluated by comparing the performance indicators before and after the adjustment to obtain the performance adjustment evaluation results; The difference comparison analysis technique is based on the formula ； and ； in, Adjusted voltage fluctuation rate, The adjusted load imbalance ratio, is the maximum voltage during the observation period, is the minimum voltage, is the average voltage, is the maximum current load during the observation period, is the minimum current load, is the average current load, and are the weight coefficients of voltage and current respectively, is the ambient temperature, For humidity, and is the temperature influence coefficient, and is the humidity influence coefficient; Based on the performance adjustment evaluation results, evaluate the connection density and flow distribution of electrical network nodes, identify overloaded and weakly connected nodes, formulate measures to increase backup connection paths and configure network loads, and generate a comprehensive network performance optimization plan; The comprehensive network performance optimization scheme includes an increased number of backup connection paths, an adjusted network traffic distribution ratio, and a newly configured load balancing algorithm; The specific steps of generating the comprehensive optimization plan for network performance are as follows: By analyzing the performance adjustment evaluation results, the traffic usage and connection stability of multiple nodes in the electrical network are evaluated, and nodes with excessively high traffic concentration and insufficient connection strength are identified. Based on the traffic and connection data, problem nodes are determined through quantitative methods, and traffic and connection strength analysis results are constructed; According to the traffic and connection strength analysis results, identify the nodes that need to be optimized, plan adjustment measures for the nodes, including adding backup paths and adjusting the existing network load, and formulate targeted optimization strategies by locating the problem points, and generate a node optimization strategy list; Relying on the node optimization strategy list, a comprehensive network structure adjustment plan is formulated, referring to the dual needs of network traffic distribution and connection reliability, and through adjustment measures, network performance and stability are optimized to obtain a comprehensive network performance optimization plan; Based on the comprehensive network performance optimization plan, adjustment measures such as adding connection paths and adjusting communication protocol parameters are executed, and the effects are verified by continuous monitoring means to obtain network configuration update records; The network configuration update record includes a wiring diagram of a newly added connection path, updated communication protocol parameter values, and a data comparison diagram of continuous monitoring verification.

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