A method, device and medium for adaptive control of environmental parameters of a cable branch box

Abstract

This invention discloses an adaptive control method, device, and medium for environmental parameters of cable branch boxes, relating to the field of intelligent power optimization technology. The method includes: collecting environmental data from the cable branch box and performing noise reduction, filtering, and standardization on the environmental data to generate an environmental parameter dataset; predicting cable environmental fluctuations using a long short-term memory network to obtain fluctuation prediction data; optimizing the fluctuation trend of the fluctuation prediction data using a multi-level time series analysis method to generate a cable environmental fluctuation trend; managing the energy of the cable branch box based on the control execution report to obtain an energy management plan; and combining the energy management plan, cable environmental parameters, and cable load conditions to generate an energy dispatch plan. This invention achieves stable operation of the cable branch box and efficient management of energy consumption.

Description

Technical Field

[0001] This invention relates to the field of intelligent power optimization technology, and in particular to a method, device and medium for adaptive control of environmental parameters of cable branch boxes. Background Technology

[0002] With the increasing demand for electricity and the growing complexity of power grids, the operational stability and power management efficiency of cable branch boxes are of paramount importance. With the development of artificial intelligence and deep learning technologies, more and more research is attempting to apply advanced data analysis and machine learning methods to the environmental monitoring of cable branch boxes. In particular, deep learning models, such as Long Short-Term Memory (LSTM) networks, have demonstrated powerful capabilities in processing time-series data and predicting system states, providing more accurate predictions of environmental fluctuations than traditional methods.

[0003] Most existing monitoring and control systems rely on a single environmental parameter input and a simplified prediction model, neglecting the complexity of environmental fluctuations and the mutual influence between multi-dimensional data. For example, although parameters such as temperature and current can be monitored in real time, the fluctuation characteristics of these parameters often cannot be effectively predicted by a simple linear model in complex environments, making it difficult for the system to make accurate responses in complex dynamic environments. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an adaptive control method for environmental parameters of cable branch boxes, which solves the problem of not being able to effectively capture multi-dimensional environmental fluctuation characteristics and make accurate predictions.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides an adaptive control method for environmental parameters of a cable branch box, comprising: collecting environmental data of the cable branch box and performing noise reduction, filtering, and standardization processing on the environmental data to generate an environmental parameter dataset; predicting cable environmental fluctuations in the environmental parameter dataset using a long short-term memory network to obtain fluctuation prediction data, and optimizing the fluctuation trend of the fluctuation prediction data using a multi-level time series analysis method to generate a cable environmental fluctuation trend; combining the cable environmental fluctuation trend with real-time cable environmental changes, and analyzing future cable environmental fluctuations using a regression analysis method to generate cable environmental change values; formulating an environmental response control strategy based on the cable environmental change values, and monitoring the operating status of the cable branch box in real time to control the cable branch box and generate a control execution report; performing energy management on the cable branch box based on the control execution report, obtaining an energy management scheme, and combining the energy management scheme, cable environmental parameters, and cable load conditions to generate an energy dispatch scheme.

[0008] As a preferred embodiment of the adaptive control method for environmental parameters of cable branch boxes according to the present invention, the steps of collecting environmental data of the cable branch boxes and performing noise reduction, filtering, and standardization processing on the environmental data to generate an environmental parameter dataset are as follows.

[0009] The environmental data of the cable branch box is monitored in real time by a high-precision sensor array to generate the original environmental dataset;

[0010] The original environmental dataset is denoised to obtain a denoised environmental dataset. The denoised environmental dataset is then smoothed, mean-normalized, and standardized to generate an environmental parameter dataset.

[0011] As a preferred embodiment of the adaptive control method for environmental parameters of cable branch boxes described in this invention, the steps include: predicting cable environmental fluctuations using a long short-term memory network on the environmental parameter dataset to obtain fluctuation prediction data, and optimizing the fluctuation trend of the fluctuation prediction data using a multi-level time series analysis method to generate a cable environmental fluctuation trend. The specific steps are as follows.

[0012] The environmental parameter dataset is input into a long short-term memory network, and the dataset is divided into multiple time steps using a time window to generate fluctuation prediction data.

[0013] By using a multi-level time series analysis method, short-term fluctuation data in the fluctuation prediction data is captured to generate optimized short-term fluctuations.

[0014] The long-term fluctuation data of the fluctuation prediction data is smoothed and fitted with trend lines to generate an optimized long-term trend.

[0015] By combining the optimization of short-term fluctuations with the optimization of long-term trends, the predicted values ​​of cable environmental fluctuations are obtained. The rapid changes in short-term fluctuations are then used to adjust the predicted values ​​of cable environmental fluctuations, thereby generating cable environmental fluctuation trends.

[0016] As a preferred embodiment of the adaptive control method for environmental parameters of cable branch boxes described in this invention, the specific steps of combining the cable environment fluctuation trend with real-time cable environment changes and analyzing future cable environment fluctuations through regression analysis to generate cable environment change values ​​are as follows.

[0017] By combining the trend of cable environment fluctuations with real-time cable environment changes, and performing time alignment and data merging, a joint dataset is generated.

[0018] Multiple environmental variables were selected as independent variables from the joint dataset using regression analysis, and the contribution of the independent variables to future cable environmental fluctuations was calculated to generate cable environmental change values.

[0019] As a preferred embodiment of the adaptive control method for environmental parameters of cable branch boxes according to the present invention, the steps of formulating an environmental response control strategy based on changes in cable environmental values, monitoring the operating status of the cable branch boxes in real time for cable branch box control, and generating a control execution report are as follows.

[0020] Based on the changes in cable environment, the environmental changes are analyzed in real time through an environmental adaptation and control mechanism, and an environmental response and control strategy is formulated in combination with the preset safety range and change trend.

[0021] Adjust the operating parameters of the cable branch box according to the environmental response control strategy and generate an operation adjustment record;

[0022] Based on the operation adjustment records, the operating parameters of the cable branch box are monitored in real time by monitoring sensors, compared with the preset safety range, the control process and control effect are recorded, and a control execution report is generated.

[0023] As a preferred embodiment of the adaptive control method for environmental parameters of cable branch boxes according to the present invention, the step of monitoring the operating parameters of the cable branch box in real time through monitoring sensors based on operation adjustment records, comparing them with preset safety ranges, recording the control process and control effect, and generating a control execution report, includes the following specific steps.

[0024] Based on the operation adjustment records, the temperature, current and humidity parameters of the cable branch box are monitored in real time by monitoring sensors to generate a real-time monitoring dataset;

[0025] The real-time monitoring dataset is compared with the preset safety range. When the real-time monitoring data exceeds the preset safety range, a control command is obtained, and the operating parameters of the cable branch box are adjusted according to the control command to generate a control operation log.

[0026] Based on the control operation log, the control operation process is recorded in real time, and the operation parameters, control measures and response status before and after control are saved, and a control execution report is generated.

[0027] As a preferred embodiment of the adaptive control method for environmental parameters of cable branch boxes according to the present invention, the steps of performing energy management on the cable branch boxes based on the control execution report, obtaining an energy management plan, and combining the energy management plan, cable environmental parameters, and cable load conditions to generate an energy dispatching plan are as follows.

[0028] Based on the regulation and control implementation report, formulate energy dispatch plans, optimize the daily use of power resources, and generate energy management solutions;

[0029] The energy management solution is combined with real-time monitoring of cable environmental parameters and cable load conditions, and power allocation is optimized to generate an optimized energy management solution.

[0030] Analyze the changes in power consumption, load demand, and environmental parameters of the optimized energy management scheme, evaluate the effectiveness of the optimized energy management scheme, adjust fan speed and load allocation, and generate an energy dispatch scheme.

[0031] As a preferred embodiment of the adaptive control method for environmental parameters of cable branch boxes described in this invention, the steps of analyzing and optimizing the changes in power consumption, load demand, and environmental parameters of the optimized energy management scheme, evaluating the effectiveness of the optimized energy management scheme, adjusting fan speed and load allocation, and generating an energy dispatch scheme are as follows.

[0032] By comparing actual and predicted power consumption data with power demand and environmental parameters before and after optimization, the effectiveness of the optimized energy management scheme is evaluated, and power consumption error assessment data is generated.

[0033] Based on the power consumption error assessment data, the balance between power consumption and load demand is calculated, and the energy management scheme is adjusted and optimized to obtain an energy dispatch scheme.

[0034] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the adaptive control method for environmental parameters of cable branch boxes as described in the first aspect of the present invention.

[0035] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the adaptive control method for environmental parameters of cable branch boxes as described in the first aspect of the present invention.

[0036] The beneficial effects of this invention are as follows: Environmental data from cable branch boxes is collected in real time using high-precision sensors, and then denoised, filtered, and standardized to generate an environmental parameter dataset; long short-term memory networks are used to predict environmental data fluctuations, and multi-level time-series analysis is used to optimize short-term fluctuations and long-term trends to generate cable environmental fluctuation trends; combined with real-time environmental changes and fluctuation trends, regression analysis is used to generate cable environmental change values, thereby formulating response control strategies and adjusting the operating parameters of the cable branch boxes in real time; energy management is performed based on control execution reports to optimize the use of power resources, generate precise energy dispatching schemes, and achieve stable operation of the cable branch boxes and efficient management of energy consumption. Attached Figure Description

[0037] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 A flowchart for an adaptive control method of environmental parameters for cable branch boxes.

[0039] Figure 2 This is a flowchart of the data preprocessing process.

[0040] Figure 3 This is a flowchart for fluctuation prediction and analysis.

[0041] Figure 4 A flowchart for control, execution, and recording. Detailed Implementation

[0042] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0043] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0044] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0045] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides an adaptive control method for environmental parameters of a cable branch box, comprising the following steps:

[0046] S1. Collect environmental data from the cable branch box, and perform noise reduction, filtering, and standardization on the environmental data to generate an environmental parameter dataset.

[0047] S1.1. Real-time monitoring of environmental data of cable branch boxes is performed using a high-precision sensor array to generate raw environmental datasets.

[0048] It should be noted that multiple high-precision sensor arrays are deployed at different locations within the cable distribution box to ensure comprehensive coverage of environmental changes within the box. Each sensor will collect multiple environmental parameters in real time, including temperature, humidity, current, voltage, and air pressure, according to its specific function. The sensors continuously record environmental data through high-frequency data acquisition and transmission. The real-time collected data undergoes preliminary signal conversion by the sensor processing unit. During data processing, the real-time environmental data is stored as a raw environmental dataset.

[0049] S1.2 Denoise the original environmental dataset to obtain the denoised environmental dataset, and smooth, mean, and standard deviation the denoised environmental dataset to generate an environmental parameter dataset.

[0050] It should be noted that the cable branch box environmental dataset is processed using denoising algorithms to remove noise introduced by factors such as sensor errors and environmental interference. Commonly used denoising methods include median filtering and Kalman filtering, which can effectively eliminate high-frequency noise while preserving the core information of the signal. After denoising, the cable branch box environmental dataset will have smoother and more stable values. The denoised environmental dataset is then smoothed using a moving average method to smooth out sharp fluctuations, further improving data continuity and consistency. The mean of each data point in the denoised environmental dataset is reduced to eliminate bias and ensure data centering. Standardization is then performed by dividing each data point by the standard deviation of the dataset to ensure data standardization and a uniform scale for easier subsequent analysis. After smoothing, mean normalization, and standardization, an environmental parameter dataset is generated.

[0051] S2. Predict cable environmental fluctuations using a long short-term memory network on the environmental parameter dataset, obtain fluctuation prediction data, and optimize the fluctuation trend of the fluctuation prediction data using a multi-level time series analysis method to generate cable environmental fluctuation trends.

[0052] It should be noted that existing methods predict cable environmental fluctuations using traditional statistical methods or simple time series analysis. Common practices include using models such as linear regression and ARIMA to process environmental parameter data and predict cable environmental fluctuations. However, these methods have limited ability to model complex nonlinear relationships and long-term dependencies, and they struggle to capture deep-seated patterns and long-term trends in cable environmental changes.

[0053] Our invention employs a Long Short-Term Memory (LSTM) network to process cable environmental parameter data, effectively capturing long-term dependencies and complex nonlinear changes within the data. Multi-level time series analysis is used to further optimize the fluctuation prediction data, accurately identifying environmental fluctuation trends. This method can predict cable environmental fluctuation trends more accurately than traditional methods, providing more precise data support for subsequent control strategies.

[0054] S2.1 Input the environmental parameter dataset into the Long Short-Term Memory network, and divide the environmental parameter dataset into multiple time steps through a time window to generate fluctuation prediction data.

[0055] It should be noted that the environmental parameter dataset undergoes preprocessing to ensure data integrity and consistency. By setting time windows, the environmental parameter dataset is divided into multiple time steps in chronological order. Each time step contains environmental parameter data within a fixed time range, ensuring that the data at each time step reflects changes in the cable distribution box environment. The Long Short-Term Memory (LSTM) network processes the environmental parameter data at each time step and captures long-term and short-term dependencies through its internal memory units to better predict future fluctuation trends and generate fluctuation prediction data.

[0056] It should also be noted that the training process of the Long Short-Term Memory (LSTM) network involves inputting an environmental parameter dataset and dividing the data into multiple time steps. The data at each time step is processed by the LTM network, and the memory state is updated through the gating mechanism of the memory units. During training, the LTM network calculates the error between the predicted output and the actual target value and adjusts the network weights using the backpropagation algorithm. Through multiple iterative optimizations, the LTM network gradually captures the temporal dependencies in the data, ultimately achieving accurate predictions of future fluctuation trends.

[0057] S2.2. Using a multi-level time series analysis method, short-term fluctuation data in the fluctuation prediction data is captured to generate optimized short-term fluctuations.

[0058] It should be noted that the fluctuation prediction data is decomposed, and the short-term fluctuation components are extracted, determined based on the characteristics of cable environmental fluctuations and the expected short-term fluctuation cycle. The size of the time window typically depends on the frequency of environmental data acquisition and the rapid change characteristics of the fluctuations. The time window should be set to capture the period of rapid fluctuations while avoiding excessively long windows that might smooth out important short-term changes. Through experiments or historical data analysis, an appropriate window length can be selected to effectively reflect the rapid changes of short-term fluctuations within each window without losing information about the overall fluctuation trend. The fluctuation prediction data is divided according to short-term cycles to identify rapidly changing short-term fluctuations. When capturing short-term fluctuations, time series analysis methods, such as wavelet transform or Fourier transform, are used to separate the high-frequency and low-frequency components in the fluctuation prediction data. For the short-term fluctuation data portion, multi-level analysis methods are used to further refine the identification of the fluctuation amplitude and trend at each time step. Through different levels of frequency filtering and trend correction, the short-term fluctuation data is optimized.

[0059] It should also be noted that multilevel analysis is a method for processing complex time-series data. By analyzing the data at different time scales or frequency levels, it helps extract information at different levels within the data. Through multilevel analysis, useful local information and global trends can be effectively extracted from complex data, helping to more accurately understand the patterns in the data, and it is particularly suitable for the analysis of time-series data with multiple changing characteristics.

[0060] S2.3 Smooth the long-term fluctuation data of the fluctuation prediction data and fit the trend line to generate an optimized long-term trend.

[0061] It should be noted that the long-term fluctuation component is extracted from the fluctuation prediction data. By removing short-term fluctuations and noise, the long-term fluctuation data accurately reflects the overall changing trend of the cable branch box environment. A smoothing process is used to smooth the long-term fluctuation data to eliminate potential local fluctuations and outliers, making the data more stable and facilitating subsequent trend analysis. After smoothing, a trend line is fitted to the processed long-term fluctuation data. Commonly used trend line fitting methods include linear regression, multinomial regression, or exponential fitting. The fitting method is selected to generate the trend line based on the characteristics of the long-term fluctuation data. The key to this step is to calculate the overall trend of the long-term fluctuation data through fitting, ensuring that the fitting result truly reflects the long-term trend of environmental changes and generating an optimized long-term trend.

[0062] S2.4 Combine the optimization of short-term fluctuations with the optimization of long-term trends to obtain the predicted value of cable environmental fluctuations, and use the rapid change characteristics of short-term fluctuations to adjust the predicted value of cable environmental fluctuations to generate the cable environmental fluctuation trend.

[0063] It should be noted that the rapidly changing components of short-term fluctuation data are combined with the stable components of long-term trend data to ensure that the predicted cable environmental fluctuations simultaneously reflect both short-term rapid fluctuations and long-term stable trends. The correction amount for the predicted cable environmental fluctuations is calculated based on the amplitude and rate of change of short-term fluctuations, and this correction is added to the predicted values. This adjustment process ensures that the predicted values ​​better capture the rapidly changing fluctuation characteristics of the cable branch box environment. By combining and optimizing short-term fluctuations and long-term trends, a cable environmental fluctuation trend is generated.

[0064] It should also be noted that the cable environment fluctuation trend refers to the changing patterns and trends of various parameters (such as temperature, current, and humidity) in the environment where the cable branch box is located. These parameters may exhibit periodic, sudden, or long-term changes over time, reflecting the dynamic characteristics of the cable operating environment. By analyzing these fluctuations, patterns of environmental change can be identified, and future trends can be predicted, thus providing accurate data support for equipment control. For example, suppose that during the high temperatures of summer, the temperature inside the cable branch box will gradually increase, and the current load may also increase accordingly. This trend of temperature and current change is a short-term fluctuation. In the long term, however, with seasonal changes, the temperature fluctuation pattern can be modeled to form a long-term fluctuation trend, providing a reference for energy dispatching and safety control of the cable branch box.

[0065] ;

[0066] in, This is the correction amount for the predicted value of cable environmental fluctuations due to short-term fluctuations. The correction factor (a constant) represents the weight of the influence of short-term fluctuation forecast on the cable environmental fluctuation forecast. It is obtained by statistical analysis of the correlation between short-term fluctuation forecast and actual environmental change amplitude in historical environmental parameter datasets and calibrated based on the minimum prediction error criterion. It is used to control the degree of influence of short-term fluctuation on the forecast value. For time step Short-term fluctuation forecast values ​​at any given time; For time step Long-term trend forecast at any given time.

[0067] S3. Combine the trend of cable environment fluctuations with real-time cable environment changes, and analyze future cable environment fluctuations through regression analysis to generate cable environment change values.

[0068] S3.1 Combine the cable environment fluctuation trend with the real-time cable environment changes, and perform time alignment and data merging to generate a joint dataset.

[0069] It should be noted that the cable environment fluctuation trend data and real-time cable environment change data are matched by timestamp to ensure that the environmental fluctuation trend at each point in time corresponds to the real-time environmental changes. This process includes time alignment of the real-time cable environment change data to ensure that its time series is consistent with the time series of the cable environment fluctuation trend data. If the time steps of the two are not completely consistent, interpolation is required to adjust the real-time cable environment change data to the time step corresponding to the cable environment fluctuation trend data. The cable environment fluctuation trend data and the adjusted real-time cable environment change data are then merged to generate a joint dataset.

[0070] S3.2. Select multiple environmental variables as independent variables from the joint dataset using regression analysis, calculate the contribution of the independent variables to future cable environmental fluctuations, and generate cable environmental change values.

[0071] It should be noted that, based on the trends and characteristics of real-time cable environmental fluctuations, environmental variables that may affect cable environmental fluctuations are identified. These environmental variables include temperature, humidity, current, and voltage. When selecting these variables, the correlation and degree of influence between each variable and cable environmental fluctuations must be considered. These selected environmental variables are extracted as independent variables from the joint dataset and compared with historical data on cable environmental fluctuations to identify their contribution to future cable environmental fluctuations. The influence weight of each independent variable on future cable environmental fluctuations is calculated using regression analysis to obtain the cable environmental change value.

[0072] S4. Develop environmental response control strategies based on changes in cable environment values, monitor the operating status of cable branch boxes in real time, control the cable branch boxes, and generate control execution reports.

[0073] S4.1 Based on the changes in the cable environment, analyze the environmental changes in real time through the environmental adaptation and control mechanism, and formulate environmental response and control strategies in combination with the preset safety range and change trend.

[0074] It should be noted that changes in the cable environment are compared with preset safety ranges to determine whether the current environmental changes in the cable branch box exceed these ranges. If they do, the environmental adaptation and control mechanism analyzes the trends of environmental changes, including the rate of change and fluctuation characteristics of multiple environmental parameters such as temperature, humidity, and current. This analysis process assesses the stability of the cable branch box under current environmental conditions by comparing historical environmental data and predicts possible future trends in environmental changes. Based on the analysis results of cable environmental changes and combined with environmental change trends, corresponding environmental response and control strategies are formulated. These strategies determine how to adjust the operating parameters of the cable branch box, such as temperature, current, and load, to ensure that the cable branch box remains within safe limits under different environmental conditions, avoiding equipment failures or damage caused by environmental fluctuations.

[0075] It should also be noted that the preset safety range is set based on the standard parameters, historical data, and operating conditions of the environment where the cable distribution box is located. These standard parameters include the normal fluctuation range of environmental parameters such as temperature, humidity, and current. By analyzing the performance of the cable distribution box under different environmental conditions, the safe operating range is determined.

[0076] S4.2 Adjust the operating parameters of the cable branch box according to the environmental response control strategy and generate an operation adjustment record.

[0077] It should be explained that the specific environmental changes of the cable branch box are identified by comparing the changes in the cable environment with the preset safety range. According to the environmental response control strategy, key parameters such as temperature, current, and humidity in the environment where the cable branch box is located are checked to see if they exceed the predetermined safety range. If they do, the strategy will specify the operational parameters to be adjusted, such as temperature settings and current limits. The operational parameters of the cable branch box are then adjusted according to the environmental response control strategy. For example, if the temperature is too high, it may be necessary to reduce the current or adjust the fan speed to ensure stable equipment operation. During operational adjustments, the appropriate adjustment range is calculated based on the actual operating status of the cable branch box and environmental changes, and the equipment parameters are adjusted accordingly. After each operational adjustment, the changes in operational parameters before and after the adjustment are recorded, generating an operational adjustment record.

[0078] S4.3 Based on the operation adjustment record, the temperature, current and humidity parameters of the cable branch box are monitored in real time by monitoring sensors to generate a real-time monitoring dataset.

[0079] It should be noted that the temperature, current, and humidity of the cable branch box are monitored in real time using sensors. Each sensor continuously measures various key parameters in the cable branch box environment according to its set acquisition frequency. The temperature sensor records the temperature changes inside the cable branch box in real time, the current sensor monitors the current load of the cable branch box in real time, and the humidity sensor records the real-time fluctuations of the ambient humidity. All monitoring data are collected chronologically and stored at each time point in the real-time monitoring dataset to ensure data integrity and continuity. During data acquisition, each sensor periodically transmits real-time monitoring data to the data processing unit to ensure data timeliness and accuracy. The data in the real-time monitoring dataset includes the environmental data of the cable branch box collected by each sensor, and these data are timestamped to ensure that each data point accurately corresponds to a specific acquisition time. Through the accumulation and analysis of these real-time monitoring data, a real-time monitoring dataset is generated.

[0080] S4.4. Compare the real-time monitoring dataset with the preset safety range. When the real-time monitoring data exceeds the preset safety range, obtain the control command and adjust the operating parameters of the cable branch box according to the control command, and generate the control operation log.

[0081] It should be noted that each data point in the real-time monitoring dataset (such as temperature, current, humidity, etc.) is compared one by one with the preset safety range. When the real-time monitoring data exceeds the preset safety range, it indicates that the operating environment of the cable branch box has become abnormal, which may affect the safe operation of the equipment. In this case, control instructions are obtained through the environmental adaptation and control mechanism. These control instructions determine the adjusted operating parameters based on the specific changes in the cable environment. Control instructions may include adjusting parameters such as temperature, current, or humidity to restore stable operation of the equipment. According to the control instructions, the operating parameters of the cable branch box will be adjusted, such as reducing the temperature or changing the current load. All operation adjustment processes will be recorded and a control operation log will be generated.

[0082] S4.5. Based on the control operation log, record the control operation process in real time, and save the operation parameters, control measures and response status before and after control, and generate a control execution report.

[0083] It should be noted that, according to the control operation log, the control operation process is recorded in real time. Before each adjustment operation, the operating parameters before control are first recorded, including environmental parameters such as temperature, current, and humidity of the cable branch box, as well as the operating status of the cable branch box. The implemented control measures are recorded, specifically including which operating parameters were adjusted, such as temperature settings and current limits, and indicating the magnitude and direction of the adjustment. During the control process, the response of the cable branch box also needs to be monitored in real time, including the deviation between the actual response of the cable branch box and the expected result. All data and measures during the control operation process will be recorded in detail to ensure that each adjustment is traceable and to provide data support for subsequent analysis, generating a control execution report.

[0084] S5. Perform energy management on the cable branch boxes according to the control execution report, obtain the energy management plan, and combine the energy management plan, cable environmental parameters and cable load conditions to generate an energy dispatch plan.

[0085] It should be noted that existing methods manage energy in cable distribution boxes through simple rules. These methods rely primarily on basic monitoring of cable load data and environmental parameters, adjusting power consumption according to fixed patterns, lacking flexibility and real-time performance. Energy dispatch schemes are often set based on past experience or basic load forecasts, failing to adequately consider environmental changes and load fluctuations.

[0086] This invention dynamically generates energy management schemes by combining data from control execution reports. Based on this, it deeply integrates cable environmental parameters, load conditions, and the energy management scheme, adjusting power consumption in real time and enabling intelligent energy dispatching. This method can automatically optimize power allocation based on real-time data, responding more accurately to changes in the cable environment and load fluctuations, generating efficient energy dispatching schemes, improving energy utilization efficiency, and reducing energy consumption.

[0087] S5.1. Develop an energy dispatch plan based on the control and regulation implementation report, optimize the daily use of power resources, and generate an energy management solution.

[0088] It should be noted that the data in the control execution report should be analyzed, especially the operating status of the cable branch boxes, changes in power consumption during the control process, and adjusted environmental parameters. By analyzing this data, the power resource usage patterns and potential optimization opportunities can be identified. Feedback information from the control execution report should be included, such as changes in temperature, current, and humidity of the cable branch boxes before and after control, the magnitude and direction of operational parameter adjustments, the stability of the control response, and the assessment results of whether the operating status returned to the preset safe range after control. To optimize the daily use of power resources, the allocation efficiency of power resources and the actual demand for power consumption should be assessed to ensure that energy use meets equipment operating requirements while minimizing waste. By rationally adjusting the load balancing operating periods for power consumption, unnecessary energy consumption can be further reduced, leading to the generation of an energy management plan.

[0089] It should also be noted that the power resource usage pattern is obtained by analyzing the pattern of power consumption changes over time in the control and regulation execution report. The focus is on identifying the distribution characteristics of power load, peak-valley changes, and the correspondence with changes in environmental parameters during different operating periods. Based on this, by comparing the degree of matching between power consumption and actual load demand in each period, operating intervals with overload, no-load, or redundant power supply are identified. This clarifies the unreasonable links in the time-series allocation and load scheduling of power resources, and further determines the potential optimization space for energy saving and efficiency improvement through load redistribution, dynamic power adjustment, and other methods.

[0090] S5.2. Combine the energy management scheme with the real-time monitored cable environmental parameters and cable load conditions, and optimize power allocation to generate an optimized energy management scheme.

[0091] It should be noted that real-time monitored cable environmental parameter data (such as temperature, current, humidity, etc.) are integrated with cable load data (such as load changes, load demand, etc.) to generate a comprehensive dataset. This comprehensive dataset provides necessary background information for optimizing power allocation. By analyzing the relationship between cable environmental parameters and cable load conditions, the mutual influence between load demand and cable environmental conditions is identified. For example, certain environmental factors, such as increased temperature, may cause fluctuations in cable load, thereby affecting power demand. Next, based on this analysis, the power load allocation is adjusted to optimize the power allocation scheme, ensuring that power resources can be dynamically allocated according to actual demand, thereby achieving energy saving and load balancing. The adjusted power allocation scheme will be updated in real time according to changes in the cable environment and load demand, generating an optimized energy management scheme under the new cable environmental conditions.

[0092] It should also be noted that the load demand is determined based on the operating load of each device in the cable distribution box, the power requirements of the connected loads, and real-time load fluctuations. By monitoring real-time cable load data, power consumption patterns and future load demands can be predicted, and appropriate load standards can be set based on this data to ensure that the cable distribution box can operate stably under different load conditions.

[0093] The setting of cable environmental conditions is based on standard environmental parameters such as temperature, humidity, and current. The range of environmental parameters is set according to the cable equipment. By comprehensively considering the tolerance range of the cable equipment, changes in the external environment, and the characteristics of the cable materials, the maximum and minimum values ​​of the environmental parameters are set. The purpose of setting these environmental conditions is to ensure the safe and stable operation of the equipment under various environmental conditions and to avoid negative impacts of environmental changes on cable performance.

[0094] S5.3 Compare the actual data and predicted power consumption data of power consumption, load demand and environmental parameters before and after optimization, evaluate the effectiveness of the optimized energy management scheme, and generate power consumption error assessment data.

[0095] It should be noted that actual data on power consumption, load demand, and environmental parameters before and after optimization should be collected. This data includes actual power consumption values, changes in load demand, and environmental parameters such as temperature, current, and humidity. Predicted power consumption data should also be collected, typically based on the model or prediction method used before optimization. By comparing the actual and predicted data item by item, the error value for each data point should be calculated, particularly the differences between power consumption, load demand, and environmental parameters. The error value can be obtained by calculating the absolute or relative difference between the actual and predicted values, thereby evaluating the effectiveness of the optimized energy management scheme. For power consumption errors, further analysis of the causes is needed, such as whether they are related to cable branch box overload, environmental fluctuations, or other factors, to generate power consumption error assessment data.

[0096] S5.4 Based on the power consumption error assessment data, calculate the balance between power consumption and load demand, adjust and optimize the energy management scheme, and obtain the energy dispatch scheme.

[0097] It should be noted that the actual power consumption is compared with the predicted power consumption to calculate the error value. This error value can be obtained by calculating the absolute or relative difference between the actual and predicted power consumption. The balance between power consumption and load demand in the cable branch box is established by comparing their changing trends to identify the correlation and calculate a correction coefficient between load demand and power consumption. If a mismatch or imbalance exists between power consumption and load demand, the energy management scheme is adjusted and optimized based on the error assessment data. During the adjustment process, the changing trends of power consumption and load demand are analyzed to optimize power consumption allocation, ensuring that power resources are dynamically allocated according to real-time load demand, avoiding waste or overload. The adjusted and optimized energy management scheme will further optimize energy dispatch and generate a new energy dispatch scheme.

[0098] This embodiment also provides a computer device applicable to the adaptive control method of environmental parameters of cable branch boxes, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the adaptive control method of environmental parameters of cable branch boxes as proposed in the above embodiment.

[0099] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0100] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the adaptive control method for environmental parameters of cable branch boxes as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0101] In summary, this invention achieves accurate prediction of cable environment fluctuations through long short-term memory networks and optimizes fluctuation trends through multi-level time series analysis, thereby improving prediction accuracy and stability and providing a reliable basis for real-time control of cable branch boxes. It also enables intelligent energy management based on control execution reports, optimizes power consumption by combining real-time environmental parameters and load conditions, activates energy dispatch functions, and achieves optimal allocation of power resources by adjusting fan speed and load distribution. This effectively improves the operating efficiency, safety, and energy efficiency of cable branch boxes and reduces energy waste.

[0102] It should be noted that 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for adaptive control of environmental parameters of a cable branch box, characterized in that: include, Collect environmental data from cable branch boxes, and perform noise reduction, filtering, and standardization on the environmental data to generate an environmental parameter dataset; Cable environmental fluctuation prediction is performed on environmental parameter datasets using a long short-term memory network to obtain fluctuation prediction data. Then, the fluctuation trend is optimized by multi-level time series analysis to generate cable environmental fluctuation trends. By combining the trend of cable environmental fluctuations with real-time cable environmental changes, and using regression analysis to analyze future cable environmental fluctuations, cable environmental change values ​​are generated. Based on changes in cable environment values, formulate environmental response control strategies, monitor the operating status of cable branch boxes in real time, control the cable branch boxes, and generate control execution reports; Based on the control and control implementation report, energy management is carried out on the cable branch boxes to obtain energy management plans. The energy management plans, cable environmental parameters, and cable load conditions are combined to generate an energy dispatch plan.

2. The adaptive control method for environmental parameters of a cable branch box as described in claim 1, characterized in that: The environmental data from the cable branch box is collected, and the data is then denoised, filtered, and standardized to generate an environmental parameter dataset. The specific steps are as follows: The environmental data of the cable branch box is monitored in real time by a high-precision sensor array to generate the original environmental dataset; The original environmental dataset is denoised to obtain a denoised environmental dataset. The denoised environmental dataset is then smoothed, mean-normalized, and standardized to generate an environmental parameter dataset.

3. The adaptive control method for environmental parameters of a cable branch box as described in claim 2, characterized in that: The process involves using a Long Short-Term Memory (LSTM) network to predict cable environmental fluctuations from an environmental parameter dataset, obtaining fluctuation prediction data, and then optimizing the fluctuation trend of the prediction data using a multi-level time series analysis method to generate a cable environmental fluctuation trend. The specific steps are as follows: The environmental parameter dataset is input into a long short-term memory network, and the dataset is divided into multiple time steps using a time window to generate fluctuation prediction data. By using a multi-level time series analysis method, short-term fluctuation data in the fluctuation prediction data is captured to generate optimized short-term fluctuations. The long-term fluctuation data of the fluctuation prediction data is smoothed and fitted with trend lines to generate an optimized long-term trend. By combining the optimization of short-term fluctuations with the optimization of long-term trends, the predicted values ​​of cable environmental fluctuations are obtained. The rapid changes in short-term fluctuations are then used to adjust the predicted values ​​of cable environmental fluctuations, thereby generating cable environmental fluctuation trends.

4. The adaptive control method for environmental parameters of a cable branch box as described in claim 3, characterized in that: The process involves combining cable environment fluctuation trends with real-time cable environment changes, and using regression analysis to analyze future cable environment fluctuations and generate cable environment change values. The specific steps are as follows: By combining the trend of cable environment fluctuations with real-time cable environment changes, and performing time alignment and data merging, a joint dataset is generated. Multiple environmental variables were selected as independent variables from the joint dataset using regression analysis, and the contribution of the independent variables to future cable environmental fluctuations was calculated to generate cable environmental change values.

5. The adaptive control method for environmental parameters of a cable branch box as described in claim 4, characterized in that: The process involves formulating an environmental response control strategy based on changes in cable environmental values, monitoring the real-time operating status of the cable branch boxes for control, and generating a control execution report. The specific steps are as follows: Based on the changes in cable environment, the environmental changes are analyzed in real time through an environmental adaptation and control mechanism, and an environmental response and control strategy is formulated in combination with the preset safety range and change trend. Adjust the operating parameters of the cable branch box according to the environmental response control strategy and generate an operation adjustment record; Based on the operation adjustment records, the operating parameters of the cable branch box are monitored in real time by monitoring sensors, compared with the preset safety range, the control process and control effect are recorded, and a control execution report is generated.

6. The adaptive control method for environmental parameters of a cable branch box as described in claim 5, characterized in that: The process based on operation adjustment records involves real-time monitoring of the cable branch box's operating parameters using sensors, comparing these parameters to preset safety ranges, recording the adjustment process and its effects, and generating an adjustment execution report. The specific steps are as follows: Based on the operation adjustment records, the temperature, current and humidity parameters of the cable branch box are monitored in real time by monitoring sensors to generate a real-time monitoring dataset; The real-time monitoring dataset is compared with the preset safety range. When the real-time monitoring data exceeds the preset safety range, a control command is obtained, and the operating parameters of the cable branch box are adjusted according to the control command to generate a control operation log. Based on the control operation log, the control operation process is recorded in real time, and the operation parameters, control measures and response status before and after control are saved, and a control execution report is generated.

7. The adaptive control method for environmental parameters of a cable branch box as described in claim 5 or 6, characterized in that: The process involves energy management of the cable branch boxes based on the control execution report, obtaining an energy management plan, and combining the energy management plan, cable environmental parameters, and cable load conditions to generate an energy dispatch plan. The specific steps are as follows: Based on the regulation and control implementation report, formulate energy dispatch plans, optimize the daily use of power resources, and generate energy management solutions; The energy management solution is combined with real-time monitoring of cable environmental parameters and cable load conditions, and power allocation is optimized to generate an optimized energy management solution. Analyze the changes in power consumption, load demand, and environmental parameters of the optimized energy management scheme, evaluate the effectiveness of the optimized energy management scheme, adjust fan speed and load allocation, and generate an energy dispatch scheme.

8. The adaptive control method for environmental parameters of a cable branch box as described in claim 7, characterized in that: The analysis examines changes in power consumption, load demand, and environmental parameters after optimizing the energy management scheme. The effectiveness of the optimized scheme is evaluated, and fan speeds and load allocation are adjusted to generate an energy dispatch plan. The specific steps are as follows: By comparing actual and predicted power consumption data with power demand and environmental parameters before and after optimization, the effectiveness of the optimized energy management scheme is evaluated, and power consumption error assessment data is generated. Based on the power consumption error assessment data, the balance between power consumption and load demand is calculated, and the energy management scheme is adjusted and optimized to obtain an energy dispatch scheme.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the adaptive control method for environmental parameters of the cable branch box as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the adaptive control method for environmental parameters of the cable branch box as described in any one of claims 1 to 8.

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