Intelligent power control method and system, storage medium and electronic equipment

Abstract

The invention discloses an intelligent power control method and system, a storage medium and electronic equipment, and relates to the technical field of wireless communication. The method comprises the following steps: dividing a target communication area into a plurality of sub-areas, and obtaining environment characteristics corresponding to the sub-areas; determining the basic transmitting power of the corresponding sub-region based on each environment feature; obtaining user behavior data of each sub-region, and determining a signal demand coefficient of the corresponding sub-region based on each user behavior data; adjusting corresponding basic transmitting power according to the signal demand coefficient of each sub-region to obtain target transmitting power corresponding to each sub-region; and controlling each sub-region to perform power adjustment according to the target transmitting power. By implementing the technical scheme provided by the invention, the better transmitting power can be accurately determined, so that the accuracy of the signal coverage range is improved.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to an intelligent power control method, system, storage medium and electronic device. Background Art

[0002] With the rapid development and popularization of wireless communication technology, how to effectively control the transmission power of signals to improve communication quality, protect user privacy and reduce signal interference has become an increasingly important technical challenge. Especially in complex environments such as cities, signal leakage may not only cause communication interference, but also lead to user privacy leakage.

[0003] In traditional wireless communication networks, the transmission power of base stations is usually configured according to fixed algorithms or preset standards, such as simply considering the environment of the communication area. However, in complex urban environments, there are high-density buildings and users. Changes in these factors will affect signal coverage. Traditional methods often use static configurations and cannot accurately determine the optimal transmission power, resulting in inaccurate signal coverage. Summary of the invention

[0004] The present application provides an intelligent power control method, system, storage medium and electronic device, which can accurately determine the optimal transmission power and improve the accuracy of signal coverage.

[0005] In a first aspect, the present application provides an intelligent power control method, the method comprising:

[0006] Divide the target communication area into multiple sub-areas, and obtain environmental characteristics corresponding to each of the sub-areas;

[0007] Determine the basic transmission power of the corresponding sub-area based on each of the environmental characteristics;

[0008] Acquire user behavior data of each of the sub-areas, and determine a signal demand coefficient of the corresponding sub-area based on each of the user behavior data;

[0009] Adjust the corresponding basic transmit power according to the signal demand coefficient of each sub-area to obtain the target transmit power corresponding to each sub-area;

[0010] Power adjustment is performed on each of the sub-areas according to the target transmit power control.

[0011] By adopting the above technical solution, by dividing the sub-areas and obtaining environmental characteristics, differentiated basic transmission powers can be formulated for different areas to avoid the blind spots of simply presetting the power based on the environment. At the same time, user behavior data is monitored in real time, and the signal demand coefficient of the corresponding sub-area is determined based on the user behavior data. Then, the basic transmission power of each sub-area is dynamically adjusted according to the signal demand coefficient. This can effectively respond to changes in user distribution and behavior patterns, and ultimately accurately determine the optimal target transmission power corresponding to each sub-area, thereby realizing refined and intelligent control of the wireless signal coverage range, which can significantly improve the accuracy of power control and the accuracy of signal coverage, while avoiding interference and safety hazards caused by excessive signal leakage.

[0012] Optionally, the determining the basic transmit power of the corresponding sub-area based on each of the environmental features includes:

[0013] Extracting building density and average building height from each of the environmental features;

[0014] Substituting the building density and the average building height into a preset power calculation formula to obtain a basic transmission power of each sub-area;

[0015] The power calculation formula is: P = P 0 +α·D+β·H+γ·I;

[0016] Among them, P is the basic transmission power, P 0 is the reference power, D is the building density, H is the average building height, I is the interference factor, and α, β, and γ are the corresponding weight coefficients.

[0017] By adopting the above technical solution, by extracting key parameters such as building density and average height, and establishing a power calculation formula that takes into account various environmental factors, it is possible to quantify the environmental characteristics of different regions. Compared with the single reliance on experience to give basic power, this method achieves accurate calculation and fitting of basic power, overcomes the limitation of existing technology that is poorly adaptable to complex environments, and can significantly improve the intelligence and accuracy of basic transmission power determination.

[0018] Optionally, determining the signal demand coefficient of the corresponding sub-area based on each of the user behavior data includes:

[0019] Preprocessing the user behavior data, and extracting the preprocessed data to obtain key data;

[0020] Classifying each of the key data, and determining a user behavior pattern based on each classification result;

[0021] A signal demand coefficient of a corresponding sub-area is determined based on each of the user behavior patterns.

[0022] By adopting the above technical solution, the key features are refined by pre-processing the original user data, and the classification algorithm is used to identify different user behavior patterns. Finally, the signal demand coefficients corresponding to different patterns can be dynamically matched. Compared with the traditional method of directly presetting the signal demand coefficient based on experience, this method realizes the accurate perception and mapping of individual user behavior, and can truly reflect the real-time network signal demand changes of users in each sub-area.

[0023] Optionally, the determining the signal demand coefficient of the corresponding sub-area based on each of the user behavior patterns includes:

[0024] Obtain a pre-trained signal demand model corresponding to each of the user behavior patterns;

[0025] The signal requirement coefficient of each of the sub-areas is determined according to the signal requirement model and the user behavior pattern.

[0026] By adopting the above technical solution, by obtaining the signal demand model pre-trained for each behavior pattern, matching the corresponding pattern model according to the real-time identified user behavior, the demand coefficient of the pattern can be quickly determined, and a learnable mapping relationship between the behavior pattern and the demand quantity is realized. When a new unknown pattern appears, as long as its signal demand model is obtained, its signal demand coefficient can be quickly and accurately determined.

[0027] Optionally, adjusting the corresponding basic transmit power according to the signal demand coefficient of each sub-area to obtain the target transmit power corresponding to each sub-area includes:

[0028] Substituting the signal demand coefficient of each sub-area and the basic transmit power into a preset power adjustment formula to obtain a target transmit power corresponding to each sub-area;

[0029] The power adjustment formula is: P adj =P base ×(1+δ×ε);

[0030] Among them, P adj is the adjusted target transmit power, P base is the basic transmission power, δ is the signal demand coefficient, and ε is the adjustment coefficient.

[0031] By adopting the above technical solution, an adjustment formula combining basic power and signal demand coefficient was established. By substituting the basic power and signal demand coefficient determined in real time, the target transmission power can be accurately calculated, realizing the dynamic mapping and balance between basic power and user demand changes. When the signal demand increases, the target power will be higher than the basic power to improve the signal strength; when the signal demand decreases, the target power will be lower than the basic power to reduce energy consumption, which significantly improves the dynamic accuracy of the target transmission power determination.

[0032] Optionally, the controlling each of the sub-areas to adjust according to a corresponding target transmit power includes:

[0033] Obtaining historical feedback data and current transmit power corresponding to each of the sub-areas;

[0034] Calculating an adjustment value of the transmit power based on the current transmit power and the target transmit power;

[0035] Based on the historical feedback data and the adjustment value, each of the sub-areas is controlled to perform power adjustment.

[0036] By adopting the above technical solution, by obtaining historical feedback and current power data, calculating the precise adjustment amount to the target power, and using the feedback data for closed-loop control, accurate tracking of the power of each sub-area can be achieved. Compared with simple open-loop control based on target power, this closed-loop control method can correct control deviations in real time to ensure that the target power is ultimately achieved, significantly improving the accuracy and reliability of control.

[0037] Optionally, after controlling each of the sub-areas to adjust according to the corresponding target transmit power, the method further includes:

[0038] Acquire real-time signal coverage data and service quality data of each of the sub-areas;

[0039] Calculating a performance indicator of each of the sub-areas based on the real-time signal coverage data and the service quality data;

[0040] Comparing the performance indicator with a standard threshold to obtain a comparison result;

[0041] A non-compliant area is determined based on the comparison result, and an optimization strategy corresponding to the non-compliant area is generated.

[0042] By adopting the above technical solution, by obtaining real-time performance data, calculating performance indicators and comparing them with standard thresholds, the current power control parameters can be evaluated. For sub-areas that do not meet the standards, corresponding optimization strategies can be generated as new control inputs to complete the closed loop. Continuous detection and evaluation of control effects are achieved, and parameter optimization can be automatically completed, significantly improving the system's adaptability and reliability.

[0043] In a second aspect of the present application, an intelligent power control system is provided, the system comprising:

[0044] An area division module is used to divide the target communication area into multiple sub-areas and obtain the environmental characteristics corresponding to each of the sub-areas;

[0045] A basic power calculation module, used to determine the basic transmission power of the corresponding sub-area based on each of the environmental characteristics;

[0046] A demand coefficient determination module, used to obtain user behavior data of each sub-area, and determine the signal demand coefficient of the corresponding sub-area based on each user behavior data;

[0047] A target power calculation module, used to adjust the corresponding basic transmit power according to the signal demand coefficient of each sub-area to obtain the target transmit power corresponding to each sub-area;

[0048] The power adjustment module is used to control each of the sub-areas to adjust according to the corresponding target transmission power.

[0049] In a third aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the above method steps.

[0050] In a fourth aspect of the present application, an electronic device is provided, comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0051] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0052] By dividing the sub-areas and obtaining environmental characteristics, the present application can formulate differentiated basic transmission powers for different areas, avoiding the blind spots of simply presetting power based on the environment. At the same time, it monitors user behavior data in real time, and determines the signal demand coefficient of the corresponding sub-area based on the user behavior data. Then, the basic transmission power of each sub-area is dynamically adjusted according to the signal demand coefficient. It can effectively respond to changes in user distribution and behavior patterns, and ultimately accurately determine the optimal target transmission power corresponding to each sub-area, realizing refined and intelligent control of the wireless signal coverage range, which can significantly improve the accuracy of power control and the accuracy of signal coverage, while avoiding interference and safety hazards caused by excessive signal leakage. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flowchart of an intelligent power control method provided by an embodiment of the present application;

[0054] Figure 2 It is a module schematic diagram of an intelligent power control system provided by an embodiment of the present application;

[0055] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0056] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0057] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0058] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0059] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0060] The following will provide a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0061] Please refer to Figure 1 , a flow chart of an intelligent power control method is proposed, the method can be implemented by a computer program, can be implemented by a single-chip microcomputer, and can also be run on an intelligent power control system, the computer program can be integrated in a computer device, and can also be run as an independent tool application, specifically, the method includes steps 10 to 50, the above steps are as follows:

[0062] Step 10: Divide the target communication area into multiple sub-areas, and obtain the environmental characteristics corresponding to each sub-area.

[0063] The target communication area refers to the entire wireless communication coverage area for intelligent power control, such as a city or a campus. The sub-area is a subdivided area after the target communication area is divided, and is divided according to different environmental characteristics, such as residential areas, business areas, parks and other sub-areas.

[0064] Environmental characteristics refer to various environmental parameters that affect the propagation and coverage of wireless signals, mainly including but not limited to: building density and average building height, topography, other equipment that may interfere with the signal, and functional area division of different sub-areas.

[0065] As a preferred embodiment, the target communication area of ​​the embodiment of the present application can be an urban area including multiple functional areas such as commercial areas, residential areas, parks and green spaces. In order to achieve accurate control and coverage of wireless signals in the target communication area, it is necessary to divide it into multiple sub-areas. The division of sub-areas can better adapt to environmental changes at different locations within the area, realize differentiated control of each sub-area, and thus improve the overall control effect.

[0066] Specifically, obtain a high-definition electronic map of the target communication area, which contains detailed information such as building distribution, road traffic, topography, etc. On the map, first preliminarily divide sub-areas such as residential areas, commercial areas, and public green spaces based on functional divisions. Sub-areas can also be subdivided based on road networks and topography to ensure that the environmental characteristics in each sub-area are relatively consistent. For example, residential areas can be divided into different communities, and parks can be divided into parks. The basis for division is not limited to factors such as building density, topography, and population distribution. Divide the target area into multiple sub-areas, such as sub-area A (city center business district), sub-area B (residential area), sub-area C (industrial area), and sub-area D (suburbs). Then, the environmental characteristics of each sub-area are collected and analyzed. Data can be collected by taking actual aerial photos of each sub-area through drones, or by obtaining relevant information of each sub-area based on electronic maps. Comprehensive consideration is mainly given to aspects such as building distribution density, building type, topography, and existing interference sources. For example, the central business district of a city: high-rise buildings are everywhere, and signal propagation is severely obstructed; residential areas: the building density is moderate, and signal propagation is relatively smooth; industrial areas: there are a large number of metal structures and equipment, which may interfere with the signal; suburban areas: there are few buildings, and the signal propagation range is wide.

[0067] Step 20: Determine the basic transmission power of the corresponding sub-area based on each environmental feature.

[0068] In the embodiment of the present application, the basic transmission power refers to the wireless signal transmission power of each sub-area calculated based on the environmental characteristics of the sub-area. It is the initial power value calculated based on the environmental characteristics without considering user behavior.

[0069] Since the environmental characteristics of different sub-areas vary greatly, such as building density, average building height, etc., this will have a great impact on the propagation path and attenuation characteristics of wireless signals. In order to ensure good signal coverage quality in each sub-area, a reasonable basic transmission power needs to be independently determined based on the environmental characteristics of each sub-area.

[0070] Specifically, characteristic data such as building density, building type and average height are extracted from the environmental characteristics of each sub-area. Secondly, an empirical model of environmental characteristics and signal propagation attenuation can be established based on the impact of different combinations of environmental characteristics on wireless signal propagation. Then the environmental characteristic data of each sub-area is substituted into the empirical model to calculate the theoretical propagation loss of the wireless signal in the sub-area. Finally, the basic transmission power value of the propagation loss is calculated based on the propagation loss. For example, for the central business district sub-area, due to the dense buildings and high average building height, according to the model calculation, its basic transmission power needs to be set to 20dBm; for the southern park green space, there is no building obstruction, so the basic power can be set to 15dBm.

[0071] Based on the above embodiment, as an optional embodiment, the step of determining the basic transmit power of the corresponding sub-area based on each environmental feature may further include the following steps:

[0072] Step 201: extracting the building density and average building height in each environmental feature.

[0073] Specifically, high-density buildings and taller buildings have a blocking and attenuation effect on wireless signal propagation, which will directly affect the wireless signal coverage effect of the sub-area. Therefore, extracting these two characteristic parameters can better establish the relationship between environmental characteristics and wireless signal propagation models, thereby determining a more accurate basic transmission power. Analyze the environmental characteristic data of each sub-area, first calculate the total area of ​​the buildings in the sub-area; then calculate the total area of ​​the area; finally, divide the total building area by the total area to obtain the building density, and collect the floor height data of the buildings in the sub-area, and calculate the average value to obtain the average height of the building.

[0074] Step 202: Substitute the building density and the average building height into a preset power calculation formula to obtain the basic transmission power of each sub-area.

[0075] Specifically, the building density and the average building height are substituted into a preset power calculation formula, and the basic transmission power of each sub-area can be calculated.

[0076] The power calculation formula is: P = P 0 +α·D+β·H+γ·I;

[0077] Among them, P is the basic transmission power, P 0 is the reference power, D is the building density, H is the average building height, I is the interference factor, and α, β, and γ are the corresponding weight coefficients.

[0078] We can first use machine learning methods to train the coefficients α, β, γ and the reference power P0 based on a large amount of historical data, and then substitute the data of the building density D and the average height H of each sub-area into the formula for calculation to obtain the basic transmission power P of the sub-area.

[0079] Exemplarily, for sub-area A, the building density in the area is determined to be 0.8, the average building height is 50 meters, the interference factor is 0.5, α, β, and γ are 0.3, 0.2, and 0.1, respectively. Substituting these data into the power calculation formula, P = 20 + 0.3·0.8 + 0.2·50 + 0.1·0.5 = 20 + 0.24 + 10 + 0.05 = 30.29≈30dBm, and the basic transmission power corresponding to sub-area A can be obtained.

[0080] Step 30: Obtain user behavior data of each sub-area, and determine the signal demand coefficient of the corresponding sub-area based on each user behavior data.

[0081] User behavior data in this embodiment of the application refers to data reflecting user behavior patterns collected by various sensors of the mobile terminal, including user movement speed, position change, movement type, etc. These data can indicate changes in user demand for wireless signals.

[0082] The signal requirement coefficient in the embodiment of the present application refers to a quantitative parameter of the wireless signal requirement strength under different user behavior modes. It comprehensively reflects the requirement level of a certain user behavior mode for indicators such as signal strength, coverage, and real-time performance, and its value range is between 0 and 1.

[0083] Specifically, sensors in user terminals, such as GPS, accelerometers, gyroscopes, etc., can be used to collect user behavior data such as speed, location, and movement type. For example, accelerometers can be used to detect the user's movement state, such as stillness, walking, running, etc.; accelerometers can be used to detect the user's movement state, such as stillness, walking, running, etc.; gyroscopes can be used to detect changes in the user's posture and assist in judging complex behavior patterns. Then the large amount of user behavior data collected is differentiated and summarized according to the sub-area in which it is located to form an overall user behavior data set for each sub-area. Again, using machine learning algorithms, such as KMeans clustering, hidden Markov models, etc., the user behavior pattern is extracted from the data set of each sub-area to identify the main behavior patterns of users in the area. For example, the behavior pattern may include but is not limited to stillness, walking, running, and driving. Finally, according to the analysis of the network signal requirements of different behavior patterns, the signal demand coefficient corresponding to each behavior pattern is determined to form the signal demand coefficient of each sub-area.

[0084] Based on the above embodiment, as an optional embodiment, the step of determining the signal demand coefficient of the corresponding sub-area based on each user behavior data may further include the following steps:

[0085] Step 301: pre-process the behavior data of each user, and extract the pre-processed data to obtain key data.

[0086] Specifically, after obtaining the original user behavior data of each sub-area, these data may be missing, abnormal, and redundant. Directly using them for pattern recognition will affect the effect. Therefore, it is necessary to preprocess the original data and then extract key data as features for user behavior analysis. To preprocess the user behavior data, first, use interpolation, smoothing and other methods to repair and filter incomplete or abnormal data, complete the data, and then use principal component analysis and other methods to eliminate redundancy in the data, reduce dimensionality and improve efficiency. Finally, use standardization / normalization and other methods to obtain preprocessed standard user behavior data.

[0087] Since there are still a lot of redundant and irrelevant feature parameters in the preprocessed data, directly using this data for user behavior pattern recognition will lead to a waste of computing resources and low recognition efficiency. Therefore, it is necessary to extract key data from the preprocessed data set and select only those key data that are closely related to user behavior pattern recognition. Key data can include but are not limited to the mean and variance of acceleration, the rate of position change, etc.

[0088] Step 302: Classify each key data, and determine the user behavior pattern based on each classification result.

[0089] Specifically, the extracted features are classified using machine learning algorithms (such as support vector machines (SVMs), decision trees, etc.) to identify the specific behavior patterns of users. The behavior patterns of the embodiments of the present application include: stationary: acceleration and position changes are minimal; walking: acceleration changes periodically, and position changes slowly; running: acceleration changes greatly, and position changes quickly; driving: position changes rapidly, and acceleration changes complexly.

[0090] Step 303: Determine the signal demand coefficient of the corresponding sub-area based on each user behavior pattern.

[0091] Traditional regionalized power control methods often only consider environmental characteristics, while ignoring the impact of user behavior on signal requirements. However, different user behavior patterns can lead to significant differences in signal requirements. For example, a stationary user may only need a lower signal strength, while a user moving at high speeds requires a stronger signal to ensure communication quality. By associating user behavior patterns with sub-regions, the signal requirements of the entire region can be more accurately assessed, thereby achieving more refined power control.

[0092] Specifically, the signal demand model corresponding to each user behavior pattern is obtained. These models can be obtained through pre-data analysis and machine learning methods. For example, algorithms such as support vector regression (SVR) or random forest can be used to train signal demand prediction models under different behavior patterns based on a large amount of historical data. These models take the user's behavior characteristics (such as movement speed, activity intensity, etc.) as input and output the corresponding signal demand coefficient.

[0093] Based on the above embodiment, as an optional embodiment, the step of determining the signal demand coefficient of the corresponding sub-area based on each user behavior pattern may further include the following steps:

[0094] Step 3031: Obtain the pre-trained signal demand model corresponding to each user behavior pattern.

[0095] Specifically, a large amount of historical data needs to be collected first, including user behavior data and corresponding actual signal demand data. User behavior data may include features such as movement speed, activity intensity, and location change frequency. These data can be obtained through various sensors on user devices (such as accelerometers, GPS, etc.), and actual signal demand data can be reflected through network performance indicators (such as signal-to-noise ratio, throughput, etc.). The collected data is preprocessed and feature engineered. The preprocessing includes steps such as data cleaning and outlier processing to ensure data quality. Feature engineering is to extract meaningful features from the raw data, such as calculating the mean and variance of the movement speed and the peak frequency of the activity intensity, in order to convert the raw data into a more representative and predictive feature set. Then, a suitable machine learning algorithm is selected to train the signal demand model. Considering that there may be a nonlinear relationship between user behavior and signal demand, algorithms such as random forest, support vector regression (SVR), or deep neural network can be selected. For example, a random forest algorithm can be used to train a model that can predict signal demand with the extracted user behavior features as input and the actual signal demand as output. Finally, the trained model is deployed in the actual power control system. For each identified user behavior mode (such as stationary, walking, running, driving, etc.), there is a corresponding pre-trained signal demand model.

[0096] Step 3032: Determine the signal demand coefficient of each sub-area according to the signal demand model and the user behavior pattern.

[0097] Specifically, when the system needs to evaluate the signal demand of a certain user or a certain area, it only needs to input the current user behavior pattern into the corresponding signal demand model to obtain the signal demand coefficient of each sub-area.

[0098] For example, the accelerometer on the user device detects that the mean and variance of the user's acceleration are 0.2 m / s2 and 0.05m / s 2 , GPS data shows that the user's position change rate is 5km / h, and gyroscope data shows that the user's posture changes slightly. The collected user behavior data is filtered to remove noise, and the acceleration mean, variance and position change rate are extracted as key data. Through the decision tree algorithm classification, the user's current behavior mode is identified as "walking". According to the signal demand model of the "walking" mode, the signal demand is determined to be medium, and the signal demand coefficient is δ=0.7.

[0099] Step 40: Adjust the corresponding basic transmit power according to the signal demand coefficient of each sub-area to obtain the target transmit power corresponding to each sub-area.

[0100] Specifically, the signal demand coefficient and the basic transmit power of each sub-area are substituted into a preset power adjustment formula to obtain the target transmit power corresponding to each sub-area.

[0101] The power adjustment formula is: P adj =P base ×(1+δ×ε);

[0102] Among them, P adj is the adjusted target transmit power, P base is the basic transmission power, δ is the signal demand coefficient, and ε is the adjustment coefficient.

[0103] First, we need to determine the values ​​of each parameter. base (Basic transmit power) is pre-set according to environmental characteristics and reflects the basic signal coverage requirements of the sub-area. δ (Signal requirement coefficient) is obtained through the previous user behavior analysis and signal requirement model, reflecting the degree of signal strength required by the current user behavior pattern. ε (Adjustment coefficient) is an adjustable parameter used to control the amplitude of power adjustment, which can be flexibly set according to actual network performance and energy consumption targets.

[0104] For example, assuming that the basic transmission power P of a certain sub-area is base is 20dBm, the signal demand coefficient δ obtained through user behavior analysis is 0.8, and the system preset adjustment coefficient ε is 0.5, then substituting it into the power adjustment formula, we get: P adj =20×(1+0.8×0.5)=20×1.4=28dBm.

[0105] Traditional power control methods often fail to effectively integrate environmental factors and user needs, resulting in inaccurate power adjustment. By introducing the power adjustment formula, the two key factors of environmental characteristics and user behavior can be organically combined to achieve more intelligent and dynamic power control.

[0106] Step 50: Control each sub-area to adjust according to the corresponding target transmission power.

[0107] Specifically, the historical feedback data and current transmit power corresponding to each sub-area are obtained. The historical feedback data may include indicators such as signal strength, user satisfaction, and energy consumption in the past period of time. These data will help the system understand the effect of past power adjustments and provide a reference for this adjustment. The current transmit power is the starting point for adjustment.

[0108] Based on the current transmit power and the target transmit power, the transmit power adjustment value is calculated. For example, if the current transmit power of a sub-area is 20dBm, and the calculated target transmit power is 28dBm, the adjustment value is 8dBm. In order to avoid network instability caused by sudden power changes, a gradual adjustment strategy can be adopted. For example, the 8dBm adjustment is divided into multiple small steps, 2dBm is adjusted each time, and the total adjustment is completed in 4 times. Based on historical feedback data and the calculated adjustment value, the system begins to control each sub-area to make actual power adjustments. During this process, the system will continuously monitor network performance indicators, such as signal strength, throughput, number of user connections, etc. If some indicators are found to be abnormal during the adjustment process, the system can adjust the strategy in time or even suspend the adjustment process.

[0109] Based on the above embodiment, as an optional embodiment, the step of controlling each sub-area to adjust according to the corresponding target transmit power may further include the following steps:

[0110] Step 501: Obtain historical feedback data and current transmit power corresponding to each sub-area.

[0111] Specifically, for historical feedback data, various network performance indicators in the past period (for example, the last 24 hours or 7 days) can be collected, including but not limited to signal strength (RSSI), signal-to-noise ratio (SNR), throughput, number of user connections, drop rate, etc. These data should be marked and stored according to timestamps and sub-areas for subsequent analysis. For example, a time series database such as InfluxDB can be used to store these high-frequency updated data. For the acquisition of the current transmission power, it is necessary to read the power settings of the base stations or access points in each sub-area in real time. This can be achieved through a network management system (NMS) or a dedicated power monitoring module. In order to ensure the accuracy of the data, the system can adopt a redundant design, that is, to obtain power data through multiple channels and perform cross-validation.

[0112] Step 502: Based on the current transmit power and the target transmit power, an adjustment value of the transmit power is calculated.

[0113] Specifically, the system compares the target transmit power with the current transmit power and calculates the difference between them. This difference is the required transmit power adjustment value. The calculation process can be expressed as: adjustment value = target transmit power - current transmit power. If the obtained adjustment value is positive, it means that the transmit power needs to be increased; if it is negative, it means that the transmit power needs to be reduced; if it is zero, it means that the current transmit power has reached the target value and no adjustment is required.

[0114] Step 503: Based on the historical feedback data and the adjustment value, control each sub-area to adjust the power.

[0115] Specifically, when performing power adjustment, the embodiment of the present application will consider both the current real-time feedback data and the historical feedback data. Real-time data reflects the immediate state of the current network environment, while historical data provides trends and patterns over a longer time scale. The system uses an improved PSO algorithm to integrate these two types of data into a fitness function. Here, the fitness function is defined as a balance indicator between signal coverage and signal leakage, which comprehensively considers two key factors: communication quality and interference control.

[0116] The particle position in the PSO algorithm represents the adjustment value of the emission power of each sub-region. Through iterative optimization, the algorithm continuously updates the speed and position of the particles. The speed update formula takes into account the inertia weight, individual optimal position and global optimal position, which enables the algorithm to strike a balance between exploring new solution spaces and using known optimal solutions. The position update formula adjusts the particle position according to the updated speed, that is, the power adjustment value.

[0117] In each iteration, the system will evaluate the current power adjustment plan. If the new plan can minimize signal leakage while ensuring effective signal coverage and meet other constraints (such as total power consumption limit), then this plan will be adopted. The system will apply this new adjustment plan to the transmitting equipment in each sub-area to achieve actual power adjustment.

[0118] Exemplarily, the embodiment of the present application uses a particle swarm optimization algorithm (PSO), which is suitable for multi-parameter optimization problems and can quickly find the optimal power adjustment solution. According to the characteristics of this system, the PSO algorithm is appropriately adjusted: fitness function: defined as a balance indicator between signal coverage and signal leakage; particle position: represents the adjustment value of the target transmission power;

[0119] Speed ​​update formula:

[0120] v i (t+1)=w·v i (t)+c 1 ·r 1 ·(p best，i -xi (t))+c 2 ·r 2 ·(g best -x i (t));

[0121] In the formula, v i (t) is the velocity of the ith particle at time t, w is the inertia weight, c 1 、c 2 is the learning factor, r 1 、r 2 is a random number, p best，i is the historical optimal position of the ith particle, g best is the global optimal position.

[0122] Position update formula: x i (t+1)=x i (t)+v i (t+1)

[0123] Where: x i (t) is the position of the i-th particle at time t.

[0124] Based on the above embodiment, as an optional embodiment, after the step of controlling each sub-area to adjust according to the corresponding target transmit power, the following steps may be further included:

[0125] Step 601: Acquire real-time signal coverage data and service quality data of each sub-area.

[0126] Specifically, real-time data is collected through a sensor network deployed in each sub-area. These sensors include but are not limited to signal strength detectors, interference analyzers, and service quality monitoring devices. The signal strength detector is used to measure the signal receiving strength (RSSI) at each location, the interference analyzer is used to evaluate the degree of signal interference, and the service quality monitoring device is used to collect key performance indicators such as data transmission rate, delay, and packet loss rate. In order to ensure the comprehensiveness and accuracy of the data, the system uses multi-point sampling and dynamic scanning technology. Multi-point sampling means deploying multiple data collection points in each sub-area to cover different geographical locations and environmental conditions. Dynamic scanning technology allows the system to adjust the frequency and scope of data collection as needed, and conduct more intensive data collection in specific areas or time periods. The collected raw data is transmitted to the central processing unit in real time through a secure communication link. Here, the data is first pre-processed, including denoising, outlier detection, and data normalization. Subsequently, the system uses advanced data fusion algorithms to integrate data from different sources to form a comprehensive signal coverage map and service quality evaluation report for each sub-area.

[0127] Step 602: Calculate the performance index of each sub-area based on the real-time signal coverage data and the service quality data.

[0128] Specifically, the system uses a series of predefined algorithms to calculate the performance indicators of each sub-area. These indicators mainly include but are not limited to: signal coverage, signal quality index, network capacity utilization, user satisfaction index, etc. Signal coverage is calculated by analyzing signal strength data, reflecting the scope of effective signal coverage in the sub-area. The signal quality index takes into account factors such as signal-to-noise ratio and interference level, reflecting the clarity and stability of the signal. Network capacity utilization is calculated based on data throughput and network load conditions, indicating the efficiency of network resource utilization. The user satisfaction index evaluates user experience by comprehensively analyzing service quality data (such as delay, packet loss rate, etc.). When calculating these indicators, the system adopts a weighted average method. Different indicators are assigned different weights according to their importance, and these weights can be dynamically adjusted according to network operation goals. For example, in high-density user areas, network capacity utilization may be more important; while in suburban areas, signal coverage may be more important.

[0129] Step 603: Compare the performance indicator with the standard threshold to obtain a comparison result.

[0130] Specifically, first, standard thresholds for various performance indicators need to be set. These thresholds may come from industry standards, service quality commitments of operators, or best practices based on historical data analysis. For example, the signal coverage may be required to reach more than 95%, the signal quality index needs to be maintained above 8.0 (assuming the full score is 10), the network capacity utilization should be controlled below 70% to retain sufficient redundancy, and the user satisfaction index may be required to reach more than 4.5 (assuming the full score is 5). These thresholds can be dynamically adjusted according to different network environments and operational requirements. Then, the performance indicators of each sub-area calculated in the previous step are compared with these standard thresholds one by one. The comparison process uses a fuzzy logic algorithm, which enables the system to handle situations close to the threshold boundary more flexibly. For example, if a certain indicator is only slightly lower than the threshold, the system may give it a lighter weight; if it is significantly lower than the threshold, it will be given a higher weight. This method avoids the decision bias that may be caused by simple binary judgment and obtains the comparison result.

[0131] Step 604: Determine the non-compliant area based on the comparison result, and generate an optimization strategy corresponding to the non-compliant area.

[0132] Specifically, based on the comparison results, the system can quickly identify the sub-areas that do not meet the standards. Non-compliance may be manifested as a single indicator not meeting the standard, or it may be the result of a comprehensive evaluation of multiple indicators. The system will classify these non-compliance areas and divide them into different levels such as urgent optimization, need for attention, and slight deviation according to the degree of deviation from the threshold and the size of the impact range. After determining the non-compliance area, a corresponding optimization strategy is generated for each non-compliance area. This process utilizes machine learning algorithms, especially reinforcement learning technology. It can be based on historical optimization experience and current network status to recommend the most likely effective optimization solution for each non-compliance area. These optimization strategies may include but are not limited to: adjusting transmit power, reconfiguring antenna parameters, enabling additional frequency band resources, adjusting load balancing strategies, etc. The generation process of the optimization strategy takes into account multiple factors, including the type and degree of non-compliance indicators, network conditions in surrounding areas, available hardware resources, and potential optimization costs. The system will generate multiple possible optimization solutions for each non-compliance area, score and rank these solutions, and finally recommend the best optimization strategy.

[0133] See also Figure 2 , is a module diagram of an intelligent power control system provided in an embodiment of the present application, the intelligent power control system may include: a region division module, a basic power calculation module, a demand coefficient determination module, a target power calculation module and a power adjustment module, wherein:

[0134] An area division module is used to divide the target communication area into multiple sub-areas and obtain the environmental characteristics corresponding to each of the sub-areas;

[0135] A basic power calculation module, used to determine the basic transmission power of the corresponding sub-area based on each of the environmental characteristics;

[0136] A demand coefficient determination module, used to obtain user behavior data of each sub-area, and determine the signal demand coefficient of the corresponding sub-area based on each user behavior data;

[0137] A target power calculation module, used to adjust the corresponding basic transmit power according to the signal demand coefficient of each sub-area to obtain the target transmit power corresponding to each sub-area;

[0138] The power adjustment module is used to control each of the sub-areas to adjust according to the corresponding target transmission power.

[0139] Optionally, the basic power calculation module is further used to extract the building density and average building height in each of the environmental features;

[0140] Substituting the building density and the average building height into a preset power calculation formula to obtain a basic transmission power of each sub-area;

[0141] The power calculation formula is: P = P 0 +α·D+β·H+γ·I;

[0142] Among them, P is the basic transmission power, P 0 is the reference power, D is the building density, H is the average building height, I is the interference factor, and α, β, and γ are the corresponding weight coefficients.

[0143] Optionally, the demand coefficient determination module is further used to perform data preprocessing on each of the user behavior data, and perform data extraction on the preprocessed data to obtain each key data;

[0144] Classifying each of the key data, and determining a user behavior pattern based on each classification result;

[0145] A signal demand coefficient of a corresponding sub-area is determined based on each of the user behavior patterns.

[0146] Optionally, the demand coefficient determination module is further used to obtain a pre-trained signal demand model corresponding to each of the user behavior patterns;

[0147] The signal requirement coefficient of each of the sub-areas is determined according to the signal requirement model and the user behavior pattern.

[0148] Optionally, the target power calculation module is further used to substitute the signal demand coefficient of each sub-area and the basic transmit power into a preset power adjustment formula to obtain the target transmit power corresponding to each sub-area;

[0149] The power adjustment formula is: P adj =P base ×(1+δ×ε);

[0150] Among them, P adj is the adjusted target transmit power, P base is the basic transmission power, δ is the signal demand coefficient, and ε is the adjustment coefficient.

[0151] Optionally, the power adjustment module is further used to obtain historical feedback data and current transmit power corresponding to each of the sub-areas;

[0152] Calculating an adjustment value of the transmit power based on the current transmit power and the target transmit power;

[0153] Based on the historical feedback data and the adjustment value, each of the sub-areas is controlled to perform power adjustment.

[0154] Optionally, the power adjustment module is further used to obtain real-time signal coverage data and service quality data of each sub-area;

[0155] Calculating a performance indicator of each of the sub-areas based on the real-time signal coverage data and the service quality data;

[0156] Comparing the performance indicator with a standard threshold to obtain a comparison result;

[0157] A non-compliant area is determined based on the comparison result, and an optimization strategy corresponding to the non-compliant area is generated.

[0158] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0159] An embodiment of the present application also provides a computer storage medium, which can store multiple instructions. The instructions are suitable for being loaded by a processor and executing an intelligent power control method of the above embodiment. The specific execution process can be found in the specific description of the above embodiment and will not be repeated here.

[0160] Please refer to Figure 3 The application also discloses an electronic device. Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0161] The communication bus 302 is used to realize the connection and communication between these components.

[0162] The user interface 303 may include a display screen (Display) and a camera (Camera). The optional user interface 303 may also include a standard wired interface and a wireless interface.

[0163] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0164] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0165] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of an intelligent power control method.

[0166] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call an application program storing an intelligent power control method in the memory 305, and when executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0167] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0168] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0169] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0170] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0171] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0172] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0173] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An intelligent power control method, characterized in that: The method comprises: Divide the target communication area into multiple sub-areas, and obtain environmental characteristics corresponding to each of the sub-areas; Determine the basic transmission power of the corresponding sub-area based on each of the environmental characteristics; Acquire user behavior data of each of the sub-areas, and determine a signal demand coefficient of the corresponding sub-area based on each of the user behavior data; Adjust the corresponding basic transmit power according to the signal demand coefficient of each sub-area to obtain the target transmit power corresponding to each sub-area; Each of the sub-areas is controlled to be adjusted according to the corresponding target transmit power.

2. The intelligent power control method according to claim 1, characterized in that: The determining the basic transmit power of the corresponding sub-area based on each of the environmental characteristics includes: Extracting building density and average building height from each of the environmental features; Substituting the building density and the average building height into a preset power calculation formula to obtain a basic transmission power of each sub-area; The power calculation formula is: P = P0 + α·D + β·H + γ·I; Among them, P is the basic transmission power, P0 is the reference power, D is the building density, H is the average building height, I is the interference factor, and α, β, and γ are the corresponding weight coefficients respectively.

3. The intelligent power control method according to claim 1, characterized in that: The determining the signal demand coefficient of the corresponding sub-area based on each of the user behavior data includes: Preprocessing the user behavior data, and extracting the preprocessed data to obtain key data; Classifying each of the key data, and determining a user behavior pattern based on each classification result; A signal demand coefficient of a corresponding sub-area is determined based on each of the user behavior patterns.

4. The intelligent power control method according to claim 1, characterized in that: The determining the signal demand coefficient of the corresponding sub-area based on each of the user behavior patterns includes: Obtain a pre-trained signal demand model corresponding to each of the user behavior patterns; The signal requirement coefficient of each of the sub-areas is determined according to the signal requirement model and the user behavior pattern.

5. The intelligent power control method according to claim 1, characterized in that: The adjusting the corresponding basic transmit power according to the signal demand coefficient of each sub-area to obtain the target transmit power corresponding to each sub-area includes: Substituting the signal demand coefficient of each sub-area and the basic transmit power into a preset power adjustment formula to obtain a target transmit power corresponding to each sub-area; The power adjustment formula is: P adj =P base ×(1+δ×ε); Among them, P adj is the adjusted target transmit power, P base is the basic transmission power, δ is the signal demand coefficient, and ε is the adjustment coefficient.

6. The intelligent power control method according to claim 1, characterized in that: The controlling each of the sub-areas to adjust according to the corresponding target transmit power includes: Obtaining historical feedback data and current transmit power corresponding to each of the sub-areas; Calculating an adjustment value of the transmit power based on the current transmit power and the target transmit power; Based on the historical feedback data and the adjustment value, each of the sub-areas is controlled to perform power adjustment.

7. The intelligent power control method according to claim 1, characterized in that: After controlling each of the sub-areas to adjust according to the corresponding target transmit power, the method further includes: Acquire real-time signal coverage data and service quality data of each of the sub-areas; Calculating a performance indicator of each of the sub-areas based on the real-time signal coverage data and the service quality data; Comparing the performance indicator with a standard threshold to obtain a comparison result; A non-compliant area is determined based on the comparison result, and an optimization strategy corresponding to the non-compliant area is generated.

8. An intelligent power control system, characterized in that: The system comprises: An area division module is used to divide the target communication area into multiple sub-areas and obtain the environmental characteristics corresponding to each of the sub-areas; A basic power calculation module, used to determine the basic transmission power of the corresponding sub-area based on each of the environmental characteristics; A demand coefficient determination module, used to obtain user behavior data of each sub-area, and determine the signal demand coefficient of the corresponding sub-area based on each user behavior data; A target power calculation module, used to adjust the corresponding basic transmit power according to the signal demand coefficient of each sub-area to obtain the target transmit power corresponding to each sub-area; The power adjustment module is used to control each of the sub-areas to adjust according to the corresponding target transmission power.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

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