Base station energy consumption control method, device and equipment for high-speed railway private network scene and medium

Abstract

The invention provides a base station energy consumption control method and device for a high-speed railway private network scene, equipment and a medium. The method comprises the following steps: carrying out performance detection on a base station energy consumption control module to obtain a performance detection result; calibrating a first control parameter of the base station energy consumption control module based on the performance detection result to obtain a second control parameter; executing energy consumption control based on the second control parameter, and obtaining real-time base station energy consumption data and energy consumption influence factors collected at the current moment t; analyzing the energy consumption influence factors based on a first energy consumption prediction model, and determining theoretical energy consumption data at the current moment t; constructing an objective function based on the deviation between the theoretical energy consumption data at the current moment t and the real-time base station energy consumption data at the current moment t, and updating the second control parameter based on the objective function to obtain a third control parameter; and continuing to execute energy consumption control based on the third control parameter. According to the technical scheme disclosed by the invention, the control parameters are accurately regulated and controlled, and the energy consumption is reduced.

Description

Technical Field

[0001] This disclosure relates to the field of mobile communication technology, and in particular to a base station energy consumption control method, apparatus, equipment and medium for a high-speed rail private network scenario. Background Technology

[0002] With the rapid development of rail transit and the continuous coverage of mobile communication networks along high-speed rail lines, base stations, as key communication facilities, operate under high load for extended periods. Currently, base station energy management has become an important component of green communication construction, closely related to stable network operation and efficient resource utilization.

[0003] Traditional energy consumption control methods for high-speed rail dedicated network base stations mostly rely on fixed parameter settings or simple adjustment strategies based on limited experience. In actual operation, train operating density varies significantly at different times (such as morning and evening peak hours and holidays), and the number of users, network service types, and traffic volume are also dynamically fluctuating. These factors are intertwined, leading to frequent and drastic fluctuations in base station load.

[0004] Existing control methods cannot accurately regulate energy consumption based on influencing factors. For example, during off-peak hours, base stations still operate according to peak-hour parameters, resulting in significant energy waste; while during peak hours, untimely parameter adjustments may lead to excessive energy consumption, and even problems such as equipment overheating and performance degradation. Summary of the Invention

[0005] This disclosure provides a base station energy consumption control method, device, equipment, and medium for a high-speed rail private network scenario, so as to achieve precise regulation of the control parameters of the base station energy consumption control module and save base station energy consumption.

[0006] According to one aspect of this disclosure, a method for controlling base station energy consumption in a high-speed rail private network scenario is provided, comprising: The performance of the base station power consumption control module was tested, and the performance test results were obtained. Based on the performance test results, the first control parameter of the base station energy consumption control module is calibrated to obtain the second control parameter; Energy consumption control is performed based on the second control parameters of the base station energy consumption control module to obtain real-time base station energy consumption data and energy consumption influencing factors collected at the current time t; Based on the first energy consumption prediction model, the energy consumption influencing factors are analyzed to determine the theoretical energy consumption data at the current time t; A target function is constructed based on the deviation between the theoretical energy consumption data at the current time t and the real-time base station energy consumption data at the current time t. The second control parameter of the base station energy consumption control module is updated based on the target function to obtain the third control parameter. Energy consumption control continues to be performed based on the third control parameter of the base station energy consumption control module.

[0007] According to another aspect of this disclosure, a base station energy consumption control device for a high-speed rail private network scenario is provided, the device comprising: The performance testing module is used to perform performance testing on the base station energy consumption control module and obtain the performance testing results. The second control parameter acquisition module is used to calibrate the first control parameter of the base station energy consumption control module based on the performance detection result to obtain the second control parameter; The second control parameter control module is used to perform energy consumption control based on the second control parameters of the base station energy consumption control module, and to obtain real-time base station energy consumption data and energy consumption influencing factors collected at the current time t. The energy consumption influencing factor analysis module is used to analyze the energy consumption influencing factors based on the first energy consumption prediction model and determine the theoretical energy consumption data at the current time t. The third control parameter acquisition module is used to construct an objective function based on the deviation between the theoretical energy consumption data at the current time t and the real-time base station energy consumption data at the current time t, and update the second control parameter of the base station energy consumption control module based on the objective function to obtain the third control parameter. The third control parameter control module is used to continue performing energy consumption control based on the third control parameters of the base station energy consumption control module.

[0008] According to another aspect of this disclosure, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the base station energy consumption control method for high-speed rail private network scenarios according to any embodiment of this disclosure.

[0009] According to another aspect of this disclosure, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being used to cause a processor to execute and implement the base station energy consumption control method for a high-speed rail private network scenario as described in any embodiment of this disclosure.

[0010] According to another aspect of this disclosure, a computer program product is provided, which, when executed by a processor, implements a base station energy consumption control method for a high-speed rail private network scenario as described in any of the embodiments of this disclosure.

[0011] This embodiment of the disclosure performs performance testing on the base station energy consumption control module to obtain performance test results; based on the performance test results, it calibrates the first control parameter of the base station energy consumption control module to obtain a second control parameter; based on the second control parameter of the base station energy consumption control module, it performs energy consumption control, acquiring real-time base station energy consumption data and energy consumption influencing factors collected at the current time t; based on a first energy consumption prediction model, it analyzes the energy consumption influencing factors to determine the theoretical energy consumption data at the current time t; based on the deviation between the theoretical energy consumption data at the current time t and the real-time base station energy consumption data at the current time t, it constructs an objective function, updates the second control parameter of the base station energy consumption control module based on the objective function, and obtains a third control parameter; based on the third control parameter of the base station energy consumption control module, it continues to perform energy consumption control. By testing, calibrating, and updating the base station energy consumption control module to obtain the third control parameter, the problem of resource waste caused by untimely adjustment of control parameters is solved, achieving precise regulation of the control parameters of the base station energy consumption control module and saving base station energy consumption.

[0012] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this disclosure and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart of a base station energy consumption control method for a high-speed rail private network scenario according to an embodiment of this disclosure; Figure 2 This is a flowchart of a base station energy consumption control method for a high-speed rail private network scenario according to an embodiment of this disclosure; Figure 3 This is a schematic diagram of the base station energy consumption control process in the high-speed rail private network scenario according to an embodiment of this disclosure; Figure 4 This is a schematic diagram of the structure of a base station energy consumption control device according to an embodiment of this disclosure; Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this disclosure. Detailed Implementation

[0015] To enable those skilled in the art to better understand the present disclosure, the technical solutions of the present disclosure will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present disclosure, and not all embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present disclosure.

[0016] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0017] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0018] Figure 1 This flowchart illustrates a base station energy consumption control method for a high-speed rail private network scenario, provided by an embodiment of this disclosure. This embodiment is applicable to high-speed rail private network scenarios, where base station energy consumption is controlled by adjusting the control parameters of the base station energy consumption control module. This method can be executed by a base station energy consumption control device for a high-speed rail private network scenario, as described in this disclosure. This device can be implemented using software and / or hardware, and can be integrated into electronic devices such as computer equipment, servers, mobile terminals, or processors. Figure 1 As shown, the method specifically includes the following steps: S110 performs performance testing on the base station energy consumption control module and obtains the performance test results.

[0019] In this embodiment, the base station energy consumption control module can be specifically understood as a module installed inside the high-speed rail dedicated network base station, used to control the base station's energy consumption according to control parameters. The base station energy consumption control module can adjust the base station's transmit power, carrier quantity, channel sleep state, and power supply operating mode, thereby achieving precise control of base station energy consumption. The performance detection result can be specifically understood as a result reflecting the current performance of the base station energy consumption control module. The performance detection result can reflect the degree of attenuation of the current controlled performance relative to the standard performance. By obtaining the performance detection result, it is possible to accurately determine whether the module is working properly, promptly identify performance degradation, and improve the reliability and stability of energy consumption control.

[0020] Specifically, by performing performance testing on the base station energy consumption control module and obtaining the test results, we can understand the status of the base station energy consumption control module, provide an accurate basis for subsequent adjustment of the control parameters of the base station energy consumption control module, and ensure that the base station energy consumption control is continuous, stable, efficient and reliable.

[0021] S120, based on the performance test results, calibrate the first control parameter of the base station energy consumption control module to obtain the second control parameter.

[0022] In this embodiment, the first control parameter can be specifically understood as the original control parameter used by the base station energy consumption control module during performance testing. The first control parameter includes, but is not limited to, parameters such as transmit power level, number of carriers enabled, channel sleep sequence, and power supply operating mode. The first control parameter is a core configuration parameter ensuring the base station can perform energy consumption regulation normally and maintain stable basic operation. The second control parameter can be specifically understood as the parameter obtained after calibrating the first control parameter based on the performance testing results. The second control parameter is more closely aligned with the actual operating state of the current base station and can effectively improve the accuracy and stability of energy consumption control.

[0023] Specifically, based on the performance test results of the base station energy consumption control module, the first control parameter of the module is calibrated to obtain the second control parameter. Calibrating the first control parameter corrects it, ensuring it matches the current operating state of the base station, thus improving the accuracy and reliability of energy consumption control and guaranteeing the long-term stable, efficient, and energy-saving operation of the high-speed rail dedicated network base station.

[0024] Optionally, the first control parameter of the base station energy consumption control module is calibrated based on the performance test results to obtain the second control parameter, including: determining the parameter adjustment value based on the performance test results using the particle swarm algorithm; adjusting the first control parameter based on the parameter adjustment value to obtain the second control parameter.

[0025] In this embodiment, the parameter adjustment value can be specifically understood as a value calculated based on the particle swarm optimization algorithm. This parameter adjustment value can specifically compensate for the deviation between the first control parameter and the ideal control parameter, providing a quantitative basis for subsequent control parameter calibration, and making the adjusted second control parameter more in line with the real-time operation requirements of the base station.

[0026] Specifically, based on the performance testing results, the particle swarm optimization algorithm is used to determine the parameter adjustment values. The specific calculation formula for the parameter adjustment data is shown below: in, Adjust the value of the parameter; is the adjustment coefficient; P is the performance test result of the base station energy consumption control module, that is, the degree of performance degradation of the base station energy consumption control module; This is the optimization compensation term calculated by the particle swarm optimization algorithm. The first control parameter is adjusted based on the parameter adjustment values ​​calculated by the particle swarm optimization algorithm to obtain the second control parameter. The specific adjustment formula is shown below: in, This is the second control parameter; The first control parameter is determined by calculating the parameter adjustment value using a particle swarm optimization algorithm combined with performance testing results. Based on this value, the first control parameter is quantitatively corrected, enabling precise calibration of the control parameter. This makes the second control parameter more closely match the operating status of the high-speed rail dedicated network base station, improving energy consumption control efficiency while ensuring communication quality, and enhancing the system's stability and self-optimization capabilities.

[0027] S130, based on the second control parameters of the base station energy consumption control module, perform energy consumption control and obtain real-time base station energy consumption data and energy consumption influencing factors collected at the current time t.

[0028] In this embodiment, real-time base station energy consumption data can be specifically understood as the actual energy consumption data generated by the base station during actual operation based on the second control parameters at the current time t. Real-time base station energy consumption data can accurately reflect the actual energy consumption level of the base station under the current control parameters. Energy consumption influencing factors can be specifically understood as the factors that affect the energy consumption of the base station at the current time t. These energy consumption influencing factors can comprehensively characterize the actual operating environment of the base station in the high-speed rail private network scenario, providing a complete and accurate input basis for subsequent energy consumption prediction, and helping to improve the reliability and adaptability of the energy consumption control model.

[0029] Specifically, energy consumption control is performed using the second control parameter of the base station energy consumption control module. Real-time base station energy consumption data and energy consumption influencing factors collected at the current time t are obtained, which can provide real and reliable measured data support for subsequent control parameter iterations and continuously improve the accuracy and adaptability of high-speed rail private network base station energy consumption control.

[0030] Optionally, the energy consumption influencing factors include: the trend of user number changes, network service types and traffic data, train operation plans and actual operating status.

[0031] In this embodiment, the user number change trend can be specifically understood as the change pattern and fluctuation characteristics of the number of users accessing the base station within the coverage area of ​​the high-speed rail dedicated network. The user number change trend reflects the density of passenger communication needs within the high-speed rail carriages and is one of the key factors affecting the base station's service load and energy consumption. The user number change trend can be predicted through signaling analysis and user behavior modeling. Network service types and traffic data can be specifically understood as the traffic data of various network service types carried by the high-speed rail dedicated network base station. Network service types and traffic data can intuitively reflect the service load intensity and communication pressure of the base station and are one of the important factors directly affecting the base station's energy consumption level. Network service types and traffic data can be identified based on a deep learning-based traffic classification algorithm. Train operation plans and actual operating status can be specifically understood as the train operation plan, including the preset timetable, travel position, travel speed, departure and arrival times, and the real-time operating status of the train at the current moment. Train operation plan and actual operating status information can intuitively reflect the service coverage requirements and load change patterns of the high-speed rail dedicated network base station and are key scenario factors affecting the fluctuation of base station energy consumption. Among them, train operation plans and actual operation status can be obtained in real time through interaction with the railway dispatching system.

[0032] Optionally, after obtaining the factors influencing energy consumption, data preprocessing can be performed on these factors. The Isolation Forest algorithm can be used to detect and remove outliers from the energy consumption factors. This algorithm, by constructing a binary tree model, can quickly identify outliers in the data. The processed energy consumption factors are then normalized using a standardization method, with the specific formula as follows: in, The mean, Standard deviation, This is the normalized data on energy consumption influencing factors. This is the original data on factors affecting energy consumption.

[0033] S140, based on the first energy consumption prediction model, analyzes the factors affecting energy consumption and determines the theoretical energy consumption data at the current time t.

[0034] In this embodiment, the first energy consumption prediction model can be specifically understood as a model that predicts energy consumption based on energy consumption influencing factors. This model takes energy consumption influencing factors as input and outputs the theoretical energy consumption value corresponding to the current time t, providing a benchmark for subsequent energy consumption deviation calculation and control parameter optimization. For example, the first energy consumption prediction model can be a deep BP (Back Propagation) neural network model. The theoretical energy consumption data can be specifically understood as data inferred from the first energy consumption prediction model. The theoretical energy consumption data is used to compare with real-time base station energy consumption data, thereby reflecting the deviation between actual energy consumption and theoretically predicted energy consumption.

[0035] Specifically, based on the analysis of energy consumption influencing factors using the first energy consumption prediction model, the theoretical energy consumption data for the current time t is determined. For example, if the first energy consumption prediction model is a deep BP neural network model, that is, based on the traditional BP network, residual connections and attention mechanisms are introduced to improve training efficiency and prediction accuracy. Let the weight matrix from the input layer to the hidden layer be... The weight matrix from the hidden layer to the output layer is: The activation function of the hidden layer is The activation function of the output layer is The output of the deep BP neural network model is: in, This is theoretical energy consumption data. For the kth energy consumption influencing factor, It is a set of factors affecting energy consumption. This is a feature weighting term calculated for the attention mechanism. By analyzing the factors affecting energy consumption through the first energy consumption prediction model, it can accurately output theoretical energy consumption data that fits the dynamic operating conditions of the high-speed rail network, providing a reliable benchmark for subsequent energy consumption deviation judgment and control parameter optimization.

[0036] Optionally, the method further includes: determining the deviation between the theoretical energy consumption data at the current time t and the real-time base station energy consumption data at the current time t; if the deviation does not exceed the deviation threshold, continuing to perform energy consumption control based on the second control parameters of the base station energy consumption control module; and if the deviation exceeds the deviation threshold, performing a step of updating the second control parameters of the base station energy consumption control module.

[0037] In this embodiment, the deviation threshold can be specifically understood as a critical value used to determine whether the difference between theoretical energy consumption data and real-time base station energy consumption data is within a reasonable range. The deviation threshold can ensure the accuracy of energy consumption control and improve the overall stability and operating efficiency of the base station energy consumption control system.

[0038] Specifically, the deviation between the theoretical energy consumption data at the current time t and the real-time base station energy consumption data at the current time t is calculated. The formula for this deviation is as follows: in, This provides real-time base station energy consumption data. When the deviation is less than or equal to the deviation threshold, energy consumption control continues based on the second control parameters of the base station energy consumption control module; when the deviation exceeds the deviation threshold, the second control parameters of the base station energy consumption control module are updated. By calculating the deviation between the theoretical energy consumption data and the real-time base station energy consumption data in real time and comparing it with the preset deviation threshold, the accuracy of base station energy consumption control can be guaranteed, while avoiding system oscillations and additional computational overhead caused by frequent updates of control parameters, thus improving the stability and operating efficiency of the base station energy consumption control system.

[0039] S150: Construct an objective function based on the deviation between the theoretical energy consumption data at the current time t and the real-time base station energy consumption data at the current time t. Update the second control parameter of the base station energy consumption control module based on the objective function to obtain the third control parameter.

[0040] In this embodiment, the objective function can be understood as a function constructed with the error between theoretical energy consumption data and real-time base station energy consumption data as its core. This objective function is used to quantitatively evaluate the deviation between actual and ideal energy consumption under the current second control parameter, achieving parameter iteration by minimizing the energy consumption deviation. This objective function provides a clear optimization direction and convergence criterion for control parameter updates, improving energy consumption control accuracy. The third control parameter can be understood as the control parameter obtained after iteratively updating the second control parameter. It integrates the deviation correction between the theoretical energy consumption prediction value and the real-time energy consumption data, and is more closely aligned with the actual operating state of the base station at the current moment compared to the second control parameter. Using the third control parameter can further improve the accuracy and stability of energy consumption control, providing a better strategy basis for subsequent more efficient energy consumption control execution.

[0041] Specifically, an objective function is constructed based on the deviation between the theoretical energy consumption data at current time t and the real-time base station energy consumption data at current time t. An adaptive moment estimation algorithm can be used to dynamically optimize the second control parameter. This algorithm combines the advantages of adaptive learning rate adjustment and momentum gradient descent, enabling rapid convergence in complex parameter spaces. The objective function is: in, The sample size is given. The second control parameter of the base station energy consumption control module is updated based on the objective function to obtain the third control parameter. By constructing an objective function to optimize the second control parameter, deviations can be quickly and accurately corrected, resulting in a third control parameter that better reflects the real-time operating status of the base station. This significantly improves the adaptive capability and control accuracy of base station energy consumption control in high-speed rail private network scenarios.

[0042] Optionally, the second control parameters of the base station energy consumption control module are updated based on the objective function to obtain the third control parameters, including: determining the first-order moment estimation data and the second-order moment estimation data of the control parameters based on the objective function; determining the first-order deviation correction coefficient based on the first-order moment estimation data; determining the second-order deviation correction coefficient based on the second-order moment estimation data; and updating the second control parameters based on the first-order deviation correction coefficient and the second-order deviation correction coefficient to obtain the third control parameters.

[0043] In this embodiment, the first-order moment estimation data can be specifically understood as data obtained by estimating the mean of the gradient of the objective function. The first-order moment estimation data reflects the expected trend of gradient change, maintaining the stability of the update direction during parameter updates, avoiding gradient oscillations, and improving the smoothness and convergence speed of the control parameter iteration process. The second-order moment estimation data can be specifically understood as data obtained by estimating the mean of the squared value of the gradient of the objective function. The second-order moment estimation data reflects the dispersion and fluctuation range of the gradient, adaptively capturing the differences in gradient changes under different control parameter dimensions, providing a core basis for subsequent adaptive adjustment of the learning rate. The first-order bias correction coefficient can be specifically understood as a number obtained after correcting the bias in the first-order moment estimation data. The first-order bias correction coefficient makes the gradient trend estimation more accurate, ensuring the stability of the parameter update direction. The second-order bias correction coefficient can be specifically understood as a number obtained after correcting the bias in the second-order moment estimation data. The second-order bias correction coefficient makes the gradient fluctuation estimation more reliable, improving the rationality of the adaptive learning rate and the accuracy of parameter updates.

[0044] Specifically, the adaptive moment estimation algorithm can be used to update the control parameters. The first-order and second-order moment estimates of the control parameters are calculated based on the objective function, as shown in the following formula: in, This is the first-order moment estimation data; This is the second-order moment estimation data; The first-order moment exponential decay rate; The second-order moment exponential decay rate; Let be the gradient of the objective function with respect to the second control parameter; n is the current iteration number. The first-order deviation correction coefficient is determined based on the first-order moment estimation data, and the second-order deviation correction coefficient is determined based on the second-order moment estimation data, as shown in the following formulas: in, This is the first-order deviation correction coefficient; This represents the second-order deviation correction coefficient. The second control parameter is updated based on the first-order and second-order deviation correction coefficients to obtain the third control parameter, as shown in the following formula: in, For learning rate, To prevent small constants with zero denominators, an adaptive moment estimation algorithm is used to update the second control parameter. This algorithm can quickly and stably converge to the optimal solution, effectively improving the response speed and control accuracy of base station energy consumption control.

[0045] It should be noted that an update threshold can be preset. When the absolute value of the deviation between the theoretical energy consumption data and the real-time base station energy consumption data is less than or equal to the preset update threshold, the iterative optimization of the adaptive moment estimation algorithm is stopped, and the control parameter of the current iteration is output as the third control parameter. When the absolute value of the deviation between the theoretical energy consumption data and the real-time base station energy consumption data is greater than the preset update threshold, the second control parameter is iteratively updated based on the adaptive moment estimation algorithm until the deviation between the theoretical energy consumption data and the real-time base station energy consumption data is less than or equal to the update threshold. The final output control parameter is used as the third control parameter.

[0046] S160, energy consumption control continues to be performed based on the third control parameters of the base station energy consumption control module.

[0047] Specifically, a third control parameter is configured into the base station energy consumption control module, which then continues to regulate the base station based on this parameter. This ensures that the deviation between the base station's real-time energy consumption data and theoretical energy consumption data remains within a reasonable range, achieving stable and precise control of the base station's energy consumption.

[0048] The technical solution of this embodiment involves: performing performance testing on the base station energy consumption control module to obtain performance test results; calibrating the first control parameter of the base station energy consumption control module based on the performance test results to obtain a second control parameter; performing energy consumption control based on the second control parameter of the base station energy consumption control module, acquiring real-time base station energy consumption data and energy consumption influencing factors collected at the current time t; analyzing the energy consumption influencing factors based on a first energy consumption prediction model to determine the theoretical energy consumption data at the current time t; constructing an objective function based on the deviation between the theoretical energy consumption data at the current time t and the real-time base station energy consumption data at the current time t; updating the second control parameter of the base station energy consumption control module based on the objective function to obtain a third control parameter; and continuing to perform energy consumption control based on the third control parameter of the base station energy consumption control module. By testing, calibrating, and updating the base station energy consumption control module to obtain the third control parameter, the problem of resource waste caused by untimely adjustment of control parameters is solved, achieving precise regulation of the control parameters of the base station energy consumption control module and saving base station energy consumption.

[0049] Figure 2 This is a flowchart illustrating a base station energy consumption control method for a high-speed rail private network scenario provided in this embodiment. This embodiment discloses the process of performing performance testing on the base station energy consumption control module and obtaining the performance testing results, such as... Figure 2 As shown, the method specifically includes the following steps: S210, acquire multiple test conditions, which include different combinations of meteorological data, network service data, and train operation data.

[0050] In this embodiment, the test conditions can be specifically understood as data combinations used to simulate different operating scenarios of high-speed rail dedicated network base stations. Multiple test conditions include different combinations of meteorological data, network service data, and train operation data. Each set of test conditions corresponds to a specific actual operating condition of the base station. By setting multiple test conditions with different data combinations, the operating scenarios of high-speed rail dedicated network base stations under different meteorological environments, different service loads, and different train operating states can be comprehensively covered. Meteorological data includes, but is not limited to, temperature, humidity, wind speed, and air pressure. Network service data includes, but is not limited to, user concurrency, data traffic, and signal strength. Train operation data includes, but is not limited to, obtaining train operation density, operating speed, and carriage occupancy rate. Meteorological data is used to characterize the external environmental state of the base station, network service data is used to characterize the current communication load and service requirements of the base station, and train operation data is used to characterize the operating characteristics of the high-speed rail line.

[0051] Specifically, meteorological data, network service data, and train operation data with different values ​​are acquired and combined to obtain multiple sets of distinct test conditions. By constructing multiple sets of different data combinations to obtain multiple test conditions, it is possible to fully cover various typical operating scenarios of high-speed rail dedicated network base stations in sunny weather, rainy weather, high temperature, low temperature, peak service, off-peak service, high-density train traffic, and low-density train traffic, providing comprehensive and realistic scenario support for the subsequent training and verification of base station energy consumption models and the debugging of control parameters.

[0052] It should be noted that meteorological data, network service data, and train operation data can be obtained by deploying a multimodal sensor array at the base station. Specifically, meteorological data is obtained through meteorological sensors, network service data is obtained through service sensors, and train operation data is obtained through train parameter sensors.

[0053] S220, based on the second energy consumption prediction model, determines the expected energy consumption value of the base station energy consumption control module under each test condition for different control parameters.

[0054] In this embodiment, the second energy consumption prediction model can be specifically understood as a model used to obtain the initial energy consumption value before it passes through the base station energy consumption control module, based on different test conditions. The second energy consumption prediction model can be a long short-term memory network model. The expected energy consumption value can be specifically understood as the energy consumption value obtained after the base station energy consumption control module adjusts the initial energy consumption value using corresponding control parameters. The expected energy consumption value is used to characterize the energy consumption level of the base station under the current test conditions and control parameters.

[0055] Specifically, the initial energy consumption value is obtained by the second energy consumption prediction model based on the current test conditions. The initial energy consumption value is input into the base station energy consumption control module using different control parameters to obtain the corresponding energy-saving value. The expected energy consumption values ​​for different control parameters under each test condition were calculated. The calculation formula is as follows: By obtaining initial energy consumption values ​​based on test conditions and then combining them with different control parameters to determine corresponding energy-saving and expected energy consumption values, the energy-saving effect of different control parameters can be quantitatively evaluated in various scenarios. This provides objective and accurate data support for subsequent selection, comparison, and optimization of control parameters, effectively improving the reliability and scenario adaptability of base station energy consumption control strategies. For example, the second energy consumption prediction model can be a long short-term memory network model, establishing a mapping relationship between test conditions and initial energy consumption values. Let the sequence of input test conditions be... The hidden layer state is The memory cell state is The calculation process of the Long Short-Term Memory (LSTM) network model is as follows: Input Gate: Forgotten Gate: Output gate: Memory unit update: Hidden layer output: in, It is the Sigmoid activation function. This indicates element-wise multiplication. This is the weight matrix. This is the bias vector.

[0056] S230 reads the standard energy consumption value corresponding to the combination of each energy consumption influencing factor and each control parameter from the database.

[0057] In this embodiment of the disclosure, the database can be specifically understood as a database used to store the standard energy consumption values ​​corresponding to each combination of energy consumption influencing factors and each control parameter. The standard energy consumption values ​​stored in this database can serve as a benchmark for comparison and verification with expected energy consumption values, providing a reference for the selection and optimization of control parameters. Specifically, the standard energy consumption value can be understood as a benchmark energy consumption value that the base station pre-calibrates to meet the requirements of normal operation and energy saving under the corresponding combination of energy consumption influencing factors and control parameters, used as a reference standard for evaluating whether the expected energy consumption value is reasonable and whether the control parameters are optimized.

[0058] Specifically, the baseline energy consumption values ​​of high-speed rail dedicated network base stations are obtained under all combinations of energy consumption influencing factors and different control parameters. The mapping relationship between each combination of energy consumption influencing factors and control parameters and its corresponding standard energy consumption value is structured and stored in a database. This database can be constructed using a deep belief network. Establishing this database provides a reliable basis for subsequent determination of detection results.

[0059] S240 determines the performance test results based on the standard energy consumption value and expected energy consumption value corresponding to the combination of various energy consumption influencing factors and control parameters.

[0060] Specifically, for multiple combinations of different energy consumption influencing factors and control parameters, the corresponding standard energy consumption value and expected energy consumption value are retrieved one by one for comparison. The control effect and performance stability of the base station energy consumption control module under different operating conditions are calculated, forming a performance test result that can comprehensively reflect the module's operating status.

[0061] Optionally, the performance test result is determined based on the standard energy consumption value and the expected energy consumption value corresponding to the combination of multiple energy consumption influencing factors and the control parameters, including: determining the average energy consumption value of the standard energy consumption value corresponding to the combination of multiple energy consumption influencing factors and the control parameters; determining the cumulative energy consumption deviation based on the difference between the standard energy consumption value and the expected energy consumption value corresponding to the combination of multiple energy consumption influencing factors and the control parameters; and determining the attenuation degree of the base station energy consumption control module based on the cumulative energy consumption deviation and the average energy consumption value, as the performance test result.

[0062] In this embodiment, the average energy consumption of the standard energy consumption value can be understood as the value obtained by averaging the standard energy consumption values ​​under various combinations of energy consumption influencing factors and control parameters. The average energy consumption of the standard energy consumption value serves as a unified benchmark to reflect the overall level of standard energy consumption of the base station, improving the objectivity and stability of performance testing results. The cumulative energy consumption deviation can be understood as the sum of the differences between the standard energy consumption value and the expected energy consumption value under various combinations of energy consumption influencing factors and control parameters. The cumulative energy consumption deviation provides direct data for the subsequent quantitative assessment of the attenuation level.

[0063] Specifically, the average energy consumption value corresponding to the standard energy consumption value of a combination of various energy consumption influencing factors and control parameters is determined. The cumulative energy consumption deviation is calculated based on the difference between the standard energy consumption value and the expected energy consumption value corresponding to the combination of various energy consumption influencing factors and control parameters. The attenuation degree of the base station energy consumption control module is calculated based on the calculated cumulative energy consumption deviation and the average energy consumption value, serving as the performance test result. The formula for calculating the attenuation degree P of the base station energy consumption control module is as follows: in, and The first Standard energy consumption value and expected energy consumption value under a combination of energy consumption influencing factors and control parameters. The average energy consumption value is the standard energy consumption value. This represents the cumulative energy consumption deviation. This formula can more comprehensively reflect the performance degradation of the energy consumption control module under multiple operating conditions.

[0064] It should be noted that a performance degradation threshold can be preset. When the degradation level P of the base station energy consumption control module exceeds the performance degradation threshold, the module update mechanism is triggered. The update strategy adopts transfer learning technology, which transfers the control parameters of the base station energy consumption control module trained in similar scenarios to the current base station energy consumption control module, and makes fine adjustments based on on-site data to quickly restore the performance of the base station energy consumption control module and reduce update time and resource consumption.

[0065] S250 calibrates the first control parameter of the base station energy consumption control module based on the performance test results to obtain the second control parameter.

[0066] It should be noted that the obtained second control parameter can be evaluated. The root mean square error (RMSE) is used to evaluate the effectiveness of the second control parameter. The RMSE is calculated using the expected energy consumption value output by the second energy consumption prediction model and the standard energy consumption value of the corresponding combination of energy consumption influencing factors and control parameters. This RMSE serves as the calibration effectiveness evaluation index, as shown in the formula below: in, The energy consumption value corresponding to the adjusted control parameters. This is the standard energy consumption value. The sample size is defined as follows. A preset calibration evaluation threshold is established. When the RMSE is greater than or equal to the calibration evaluation threshold, the performance test process is re-executed based on the current second control parameter to obtain the performance test results, and then the parameter adjustment value is obtained to determine the new second control parameter. This process is iteratively adjusted until the RMSE is less than the calibration evaluation threshold, at which point the second control parameter is determined. By setting the calibration evaluation threshold and performing iterative optimization, the required second control parameter can be quickly converged while ensuring the accuracy of energy consumption control, providing a more reliable parameter basis for subsequent real-time energy consumption control of the base station.

[0067] S260, based on the second control parameters of the base station energy consumption control module, performs energy consumption control and obtains real-time base station energy consumption data and energy consumption influencing factors collected at the current time t.

[0068] S270, based on the first energy consumption prediction model, analyzes the factors affecting energy consumption and determines the theoretical energy consumption data at the current time t.

[0069] S280: Construct an objective function based on the deviation between the theoretical energy consumption data at the current time t and the real-time base station energy consumption data at the current time t; update the second control parameter of the base station energy consumption control module based on the objective function to obtain the third control parameter.

[0070] S290, energy consumption control continues to be performed based on the third control parameter of the base station energy consumption control module.

[0071] The technical solution of this embodiment involves acquiring multiple test conditions, including different combinations of meteorological data, network service data, and train operation data; determining the expected energy consumption value of the base station energy consumption control module under each test condition based on a second energy consumption prediction model for different control parameters; reading the standard energy consumption value corresponding to each combination of energy consumption influencing factors and control parameters from the database; determining the performance test result based on the standard energy consumption value and expected energy consumption value corresponding to the combinations of multiple energy consumption influencing factors and control parameters; calibrating the first control parameter of the base station energy consumption control module based on the performance test result to obtain the second control parameter; performing energy consumption control based on the second control parameter of the base station energy consumption control module to acquire real-time base station energy consumption data and energy consumption influencing factors collected at the current time t; analyzing the energy consumption influencing factors based on the first energy consumption prediction model to determine the theoretical energy consumption data at the current time t; constructing an objective function based on the deviation between the theoretical energy consumption data at the current time t and the real-time base station energy consumption data at the current time t; and updating the second control parameter of the base station energy consumption control module based on the objective function to obtain the third control parameter. By establishing a database of each energy consumption influencing factor, each control parameter combination, and the corresponding standard energy consumption value, and then determining the performance test results of the base station energy consumption control module based on the standard energy consumption value and the expected energy consumption value, the control performance of the base station energy consumption control module under different weather, network service, and train operation conditions can be accurately quantified. This provides a reliable benchmark for subsequent control parameter calibration, ensures the accuracy of parameter calibration and updates, and thus improves the adaptability and stability of base station energy consumption control, adapting to the complex and ever-changing operating scenarios of high-speed rail private network base stations.

[0072] Based on the above embodiments, an optional example is provided, which can be used to perform performance testing on the base station energy consumption control module of a base station and set its control parameters.

[0073] like Figure 3 The diagram illustrates the process of base station energy consumption control in a high-speed rail private network scenario. The first step is test condition acquisition and initial energy consumption testing: multiple test conditions are acquired, and the expected energy consumption value for each test condition is determined based on a long short-term memory network model for different control parameters of the base station energy consumption control module. A database is constructed using a deep belief network, and the standard energy consumption value corresponding to each combination of energy consumption influencing factors and control parameters is retrieved from the database.

[0074] The second step is performance degradation assessment and module update decision-making. This involves determining the average energy consumption value corresponding to the standard energy consumption value for a combination of various energy consumption influencing factors and control parameters. The cumulative energy consumption deviation is determined based on the difference between the standard energy consumption value and the expected energy consumption value corresponding to the combination of various energy consumption influencing factors and control parameters. The degree of degradation of the base station energy consumption control module is calculated based on the cumulative energy consumption deviation and the average energy consumption. Finally, based on the performance degradation threshold, it is determined whether a module update mechanism needs to be triggered.

[0075] The third step is control parameter calibration. Based on the performance test results, the first control parameter of the base station energy consumption control module is calibrated to obtain the second control parameter. The fourth step is target data acquisition and energy consumption comparison. Energy consumption control is executed based on the second control parameter of the base station energy consumption control module, collecting real-time base station energy consumption data and energy consumption influencing factors at the current time t. The energy consumption influencing factors are analyzed using a deep BP neural network model to determine the theoretical energy consumption data at the current time t. The deviation between the theoretical energy consumption data at the current time t and the real-time base station energy consumption data at the current time t is determined. If the deviation does not exceed the deviation threshold, energy consumption control continues based on the second control parameter of the base station energy consumption control module. If the deviation exceeds the deviation threshold, the second control parameter of the base station energy consumption control module is updated.

[0076] The fifth step is control parameter optimization. An objective function is constructed based on the deviation between the theoretical energy consumption data and the real-time base station energy consumption data at current time t. This objective function is then used to update the second control parameters of the base station energy consumption control module, resulting in the third control parameter. When the absolute value of the deviation between the theoretical energy consumption data and the real-time base station energy consumption data is less than or equal to a preset update threshold, iterative optimization stops, and the control parameter of the current iteration is output as the third control parameter. When the absolute value of the deviation between the theoretical energy consumption data and the real-time base station energy consumption data is greater than the preset update threshold, iterative updates to the second control parameter based on the adaptive moment estimation algorithm continue until the deviation between the theoretical energy consumption data and the real-time base station energy consumption data is less than or equal to the update threshold. The final output control parameter is then used as the third control parameter. Energy consumption control continues based on the finally determined third control parameter of the base station energy consumption control module.

[0077] Figure 4 This is a schematic diagram of a base station energy consumption control device for a high-speed rail private network scenario provided in this embodiment. This embodiment is applicable to situations where base station energy consumption is controlled by adjusting the control parameters of the base station energy consumption control module in a high-speed rail private network scenario. The device can be implemented using software and / or hardware, and can be integrated into any device that provides base station energy consumption control functionality for high-speed rail private network scenarios, such as… Figure 4As shown, the device for base station energy consumption control in the high-speed rail private network scenario specifically includes: a performance detection module 410, a second control parameter acquisition module 420, a second control parameter control module 430, an energy consumption influencing factor analysis module 440, a third control parameter acquisition module 450, and a third control parameter control module 460.

[0078] The performance testing module 410 is used to perform performance testing on the base station energy consumption control module and obtain the performance testing results. The second control parameter acquisition module 420 is used to calibrate the first control parameter of the base station energy consumption control module based on the performance detection result to obtain the second control parameter; The second control parameter control module 430 is used to perform energy consumption control based on the second control parameters of the base station energy consumption control module, and to obtain real-time base station energy consumption data and energy consumption influencing factors collected at the current time t. The energy consumption influencing factor analysis module 440 is used to analyze the energy consumption influencing factors based on the first energy consumption prediction model and determine the theoretical energy consumption data at the current time t. The third control parameter acquisition module 450 is used to construct an objective function based on the deviation between the theoretical energy consumption data at the current time t and the real-time base station energy consumption data at the current time t, and update the second control parameter of the base station energy consumption control module based on the objective function to obtain the third control parameter. The third control parameter control module 460 is used to continue to perform energy consumption control based on the third control parameters of the base station energy consumption control module.

[0079] The technical solution of this embodiment involves: performing performance testing on the base station energy consumption control module to obtain performance test results; calibrating the first control parameter of the base station energy consumption control module based on the performance test results to obtain a second control parameter; performing energy consumption control based on the second control parameter of the base station energy consumption control module, acquiring real-time base station energy consumption data and energy consumption influencing factors collected at the current time t; analyzing the energy consumption influencing factors based on a first energy consumption prediction model to determine the theoretical energy consumption data at the current time t; constructing an objective function based on the deviation between the theoretical energy consumption data at the current time t and the real-time base station energy consumption data at the current time t; updating the second control parameter of the base station energy consumption control module based on the objective function to obtain a third control parameter; and continuing to perform energy consumption control based on the third control parameter of the base station energy consumption control module. By testing, calibrating, and updating the base station energy consumption control module to obtain the third control parameter, the problem of resource waste caused by untimely adjustment of control parameters is solved, achieving precise regulation of the control parameters of the base station energy consumption control module and saving base station energy consumption.

[0080] Optionally, based on the above embodiments, the performance testing module 410 is configured to: acquire multiple test conditions, wherein the multiple test conditions respectively include different combinations of meteorological data, network service data, and train operation data; determine the expected energy consumption value of the base station energy consumption control module for different control parameters under each test condition based on a second energy consumption prediction model; read the standard energy consumption value corresponding to each combination of energy consumption influencing factors and each combination of control parameters from the database; and determine the performance testing result based on the standard energy consumption value and expected energy consumption value corresponding to the combinations of multiple energy consumption influencing factors and control parameters.

[0081] Optionally, based on the above embodiments, the performance detection module 410 is further configured to: determine the average energy consumption value of the standard energy consumption value corresponding to the combination of various energy consumption influencing factors and the control parameters; determine the cumulative energy consumption deviation based on the difference between the standard energy consumption value and the expected energy consumption value corresponding to the combination of various energy consumption influencing factors and the control parameters; and determine the attenuation degree of the base station energy consumption control module based on the cumulative energy consumption deviation and the average energy consumption, as the performance detection result.

[0082] Optionally, based on the above embodiments, the second control parameter acquisition module 420 is used to: determine the parameter adjustment value based on the performance detection result using the particle swarm optimization algorithm; and adjust the first control parameter based on the parameter adjustment value to obtain the second control parameter.

[0083] Optionally, based on the above embodiments, the device further includes a deviation determination module, used to: determine the deviation between the theoretical energy consumption data at the current time t and the real-time base station energy consumption data at the current time t; if the deviation does not exceed the deviation threshold, continue to perform energy consumption control based on the second control parameters of the base station energy consumption control module; if the deviation exceeds the deviation threshold, perform the step of updating the second control parameters of the base station energy consumption control module.

[0084] Optionally, based on the above embodiments, the third control parameter acquisition module 450 is used to: determine the first-order moment estimation data and the second-order moment estimation data of the control parameter based on the objective function; determine the first-order deviation correction coefficient based on the first-order moment estimation data; determine the second-order deviation correction coefficient based on the second-order moment estimation data; and update the second control parameter based on the first-order deviation correction coefficient and the second-order deviation correction coefficient to obtain the third control parameter.

[0085] Optionally, based on the above embodiments, the energy consumption influencing factors include: the trend of user number changes, network service types and traffic data, train operation plans and actual operating status.

[0086] The above-described products can perform the methods provided in any embodiment of this disclosure, and have the corresponding functional modules and beneficial effects for performing the methods.

[0087] Figure 5 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0088] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0089] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0090] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the base station energy consumption control method in a high-speed rail private network scenario.

[0091] In some embodiments, the base station energy consumption control method for a high-speed rail dedicated network scenario can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the base station energy consumption control method for a high-speed rail dedicated network scenario described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the base station energy consumption control method for a high-speed rail dedicated network scenario by any other suitable means (e.g., by means of firmware).

[0092] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0093] Computer programs used to implement the methods of this disclosure may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0094] In the context of this disclosure, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0095] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0096] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0097] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0098] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this disclosure can be achieved, and this is not limited herein.

[0099] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the base station energy consumption control method for a high-speed rail private network scenario according to any embodiment of this disclosure.

[0100] In implementing a computer program product, computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0101] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for controlling base station energy consumption in a high-speed rail private network scenario, characterized in that, include: The performance of the base station power consumption control module was tested, and the performance test results were obtained. Based on the performance test results, the first control parameter of the base station energy consumption control module is calibrated to obtain the second control parameter; Energy consumption control is performed based on the second control parameters of the base station energy consumption control module to obtain real-time base station energy consumption data and energy consumption influencing factors collected at the current time t; Based on the first energy consumption prediction model, the energy consumption influencing factors are analyzed to determine the theoretical energy consumption data at the current time t; A target function is constructed based on the deviation between the theoretical energy consumption data at the current time t and the real-time base station energy consumption data at the current time t. The second control parameter of the base station energy consumption control module is updated based on the target function to obtain the third control parameter. Energy consumption control continues to be performed based on the third control parameter of the base station energy consumption control module.

2. The method according to claim 1, characterized in that, The performance of the base station power consumption control module was tested, and the performance test results were obtained, including: Multiple test conditions are acquired, which include different combinations of meteorological data, network service data, and train operation data. Based on the second energy consumption prediction model, the expected energy consumption value of the base station energy consumption control module under each test condition is determined for different control parameters. Read the standard energy consumption value corresponding to each combination of energy consumption influencing factors and control parameters from the database; The performance test results are determined based on the standard energy consumption value and the expected energy consumption value corresponding to the combination of various energy consumption influencing factors and the control parameters.

3. The method according to claim 2, characterized in that, The performance test results are determined based on the standard energy consumption value and expected energy consumption value corresponding to the combination of various energy consumption influencing factors and the control parameters, including: Determine the average energy consumption value of the standard energy consumption value corresponding to the combination of various energy consumption influencing factors and the control parameters; The cumulative energy consumption deviation is determined based on the difference between the standard energy consumption value and the expected energy consumption value corresponding to the combination of the various energy consumption influencing factors and the control parameters. The degree of attenuation of the base station energy consumption control module is determined based on the cumulative energy consumption deviation and the average energy consumption, and is used as the performance test result.

4. The method according to claim 1, characterized in that, Based on the performance test results, the first control parameter of the base station energy consumption control module is calibrated to obtain the second control parameter, including: Based on the performance test results, the particle swarm optimization algorithm is used to determine the parameter adjustment values. The first control parameter is adjusted based on the value of the parameter adjustment to obtain the second control parameter.

5. The method according to claim 1, characterized in that, The method further includes: Determine the deviation between the theoretical energy consumption data at the current time t and the real-time base station energy consumption data at the current time t; If the deviation does not exceed the deviation threshold, energy consumption control continues to be performed based on the second control parameters of the base station energy consumption control module; If the deviation exceeds the deviation threshold, the step of updating the second control parameter of the base station energy consumption control module is performed.

6. The method according to claim 1, characterized in that, The second control parameters of the base station energy consumption control module are updated based on the objective function to obtain the third control parameters, including: Based on the objective function, determine the first-order moment estimation data and the second-order moment estimation data of the control parameters; The first-order deviation correction coefficient is determined based on the first-order moment estimation data, and the second-order deviation correction coefficient is determined based on the second-order moment estimation data. The second control parameter is updated based on the first-order deviation correction coefficient and the second-order deviation correction coefficient to obtain the third control parameter.

7. The method according to claim 1, characterized in that, The factors affecting energy consumption include: the trend of user number changes, network service types and traffic data, train operation plans and actual operating status.

8. A base station energy consumption control device for a high-speed rail private network scenario, characterized in that, include: The performance testing module is used to perform performance testing on the base station energy consumption control module and obtain the performance testing results. The second control parameter acquisition module is used to calibrate the first control parameter of the base station energy consumption control module based on the performance detection result to obtain the second control parameter; The second control parameter control module is used to perform energy consumption control based on the second control parameters of the base station energy consumption control module, and to obtain real-time base station energy consumption data and energy consumption influencing factors collected at the current time t. The energy consumption influencing factor analysis module is used to analyze the energy consumption influencing factors based on the first energy consumption prediction model and determine the theoretical energy consumption data at the current time t. The third control parameter acquisition module is used to construct an objective function based on the deviation between the theoretical energy consumption data at the current time t and the real-time base station energy consumption data at the current time t, and update the second control parameter of the base station energy consumption control module based on the objective function to obtain the third control parameter. The third control parameter control module is used to continue performing energy consumption control based on the third control parameters of the base station energy consumption control module.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the base station energy consumption control method for the high-speed rail private network scenario according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which are used to cause a processor to execute the base station energy consumption control method for the high-speed rail private network scenario as described in any one of claims 1-7.

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