Special zero-carbon intelligent scheduling management system for base station

Abstract

The invention relates to the technical field of base station scheduling management, in particular to a special zero-carbon intelligent scheduling management system for a base station, which comprises the following steps of: analyzing a base station load risk caused by frequent switching of vehicle-mounted flow connection based on operation data information and energy environment data in the operation process of the base station; in combination with the scheduling configuration information and the real-time traffic data, analyzing the connection competition risk of the base station during traffic jam; constructing a base station operation risk quantitative model based on a base station load risk analysis result caused by frequent switching of vehicle-mounted flow connection and a connection competition risk analysis result of the base station during traffic jam, and evaluating the operation risk in the operation process of each base station in the traffic network; according to the operation risk assessment result in the operation process of each base station in the traffic network, the scheduling management scheme of the base station is adjusted, so that early warning of potential overload risks is realized, and the operation reliability of the base station is improved.

Description

Technical Field

[0001] This invention relates to the field of base station scheduling and management technology, and in particular to a zero-carbon intelligent scheduling and management system for base stations. Background Technology

[0002] As a critical infrastructure supporting modern digital society and ensuring smooth mobile communications, the stable operation of communication base stations directly impacts regional economic development efficiency and the reliability of public communication services. During continuous operation, base stations not only face increasing data traffic pressure, leading to continuously rising energy consumption and severely impacting operating costs and environmental sustainability, but also experience drastic fluctuations in base station load due to dynamic changes in traffic flow. These fluctuations range from increased signaling pressure in the initial stage to intensified resource competition in the middle stage, and finally to a decline in service quality in the later stage. Failure to promptly warn and manage these operational risks can trigger communication incidents such as network congestion and connection interruptions, affecting user experience and hindering the development of key applications such as smart cities and connected vehicles. Traditional base station operation and maintenance management relies heavily on static thresholds and manual policy adjustments, resulting in delayed responses and difficulty in accurately adapting scheduling decisions to complex traffic environments and dynamic service loads. However, using big data analytics for operation and maintenance management leverages its powerful data processing capabilities to analyze base station operating status in real time, including indicators that are difficult to analyze manually in real time, such as load fluctuations and energy consumption changes. This overcomes the limitations of experience-based decision-making and improves network management efficiency.

[0003] However, existing technologies for base station operation and maintenance management do not deeply correlate and coordinate the signaling load risk of base stations with the risk of vehicle connection competition caused by traffic congestion. For example, when the base station load increases due to frequent handovers, it is difficult to accurately assess the comprehensive operational risk and potential service interruption probability of the base station without considering the pressure of rapidly increasing connection requests due to congestion on nearby roads, leading to one-sided scheduling decisions. At the same time, existing technologies also lack precise quantification of the dynamic relationship between base station energy consumption and service load, making it difficult for scheduling strategies to achieve refined energy saving while ensuring service quality, and failing to provide a comprehensive and optimized decision-making basis for intelligent scheduling under the zero-carbon goal.

[0004] To address these issues, this application presents a zero-carbon intelligent dispatch management system specifically designed for base stations. Summary of the Invention

[0005] The purpose of this invention is to provide a zero-carbon intelligent scheduling and management system for base stations. By acquiring base station operation, scheduling configuration, energy environment and real-time traffic data, it analyzes the load risk caused by handover and the connection competition risk caused by congestion, constructs an operational risk quantification model for evaluation, and dynamically adjusts the base station scheduling scheme based on the evaluation results to achieve zero-carbon intelligent scheduling and management of base stations.

[0006] This invention is implemented as follows: In a first aspect, the present invention provides a base station-specific zero-carbon intelligent dispatch management system, including a multi-source information acquisition module, a connection handover detection module, a congestion impact detection module, a base station operation evaluation module, and a dispatch adjustment module; Among them, the multi-source information acquisition module is used to acquire operational data information, scheduling configuration information, and energy and environmental data during the operation of the base station, and at the same time acquire real-time traffic data of the transportation network where the base station is located. The connection handover detection module is used to analyze the base station load risk caused by frequent handover of vehicle traffic connections based on operational data and energy and environmental data during base station operation. The congestion impact detection module is used to analyze the connection contention risk of base stations during traffic congestion by combining scheduling configuration information and real-time traffic data. The base station operation assessment module is used to construct a quantitative model of base station operation risk based on the risk analysis results of base station load caused by frequent switching of vehicle traffic connections and the risk analysis results of connection competition of base stations during traffic congestion, and to assess the operation risk of each base station in the traffic network during operation. The scheduling and adjustment module is used to adjust the scheduling and management scheme of base stations based on the operational risk assessment results of each base station in the transportation network.

[0007] As a preferred embodiment of the present invention, based on operational data and energy and environmental data during base station operation, an analysis is performed on the base station load risk caused by frequent switching of vehicle traffic connections, including the following specific steps: S21. Extract the handover event time sequence and performance monitoring data of each base station in the traffic network during the current monitoring period from the operational data information during the base station operation process; at the same time, extract the real-time power consumption data of each base station during the current monitoring period from the energy and environmental data; and perform time-series alignment of the handover event time sequence, performance monitoring data and real-time power consumption data of each base station during the current monitoring period. S22. Set a fixed-duration sliding time window for the current monitoring period, and scan the various types of data that have been time-aligned with a preset step size; based on the handover event time sequence of each base station in the current monitoring period, calculate the ratio of the total number of handover events occurring in the window to the window duration in each time window to obtain the handover frequency of each base station per unit time in each time window; divide the handover frequency of each base station per unit time by the average of the handover frequencies of all base stations per unit time to obtain the signaling relative pressure status of each base station in each time window; S23. Using the sliding standard deviation algorithm, extract the CPU utilization sequence of each base station from the performance monitoring data of each base station in the current monitoring period; calculate the standard deviation of the CPU utilization sequence of each base station in each time window, and calculate the Gini coefficient of the CPU utilization sequence of each base station based on the Lorenz curve principle; weight the standard deviation and Gini coefficient of the CPU utilization sequence of each base station according to the preset weights to generate the load fluctuation entropy value of each base station in each time window; divide the load fluctuation entropy value of each base station in each time window by the average load fluctuation entropy value of all base stations to obtain the relative load fluctuation state of each base station in each time window; S24. Based on the operational data and energy and environmental data during the operation of the base station, construct a base station energy consumption analysis model to quantify the energy consumption increment related to handover of each base station in each time window. S25. Perform minimum-maximum standardization on the relative signaling pressure state, relative load fluctuation state, and handover-related energy consumption increment of each base station in each time window. Then, perform weighted summation on the relative signaling pressure state, relative load fluctuation state, and handover-related energy consumption increment of each base station in each time window to obtain the base station load risk caused by frequent switching of vehicle traffic connections during the operation of each base station in each time window. S26. Import the base station load risk caused by frequent switching of vehicle traffic connections during the operation of each base station in each time window into the pre-built base station load risk time series prediction model, and output the base station load risk caused by frequent switching of vehicle traffic connections during the operation of each base station in the next time window.

[0008] As a preferred embodiment of the present invention, a base station energy consumption analysis model is constructed to quantify the handover-related energy consumption increment of each base station within each time window, specifically including the following steps: S241. Obtain operational data information and energy and environmental data during the operation of the base station. Take the unit time switching frequency, average user session duration, number of active users at the start of the time window in the current monitoring period of the operational data information, and the absolute increment of real-time power consumption in the current monitoring period of the energy and environmental data as the regression analysis dataset, and divide the regression analysis dataset into regression analysis training set and regression analysis validation set. S242. Construct a gradient boosting decision tree regression model. Use the unit time switching frequency, average user session duration, and number of active users at the start of the window in each time window of the regression analysis training set as input features of the gradient boosting decision tree regression model. Use the absolute increment of real-time power consumption in each time window of the regression analysis training set as the output target of the gradient boosting decision tree regression model. Train the gradient boosting decision tree regression model to obtain the initial regression analysis model. S243. The initial regression analysis model is validated using the regression analysis validation set, and the initial regression analysis model with an accuracy greater than or equal to the preset model is output as the base station energy consumption analysis model. S244. Based on steps S24-S26, obtain the base station energy consumption analysis model for each base station; use the Shapley value attribution analysis method to quantify the contribution of each input feature in the base station energy consumption analysis model to the absolute increment of real-time power consumption, take the contribution of the input feature as the handover frequency per unit time as the handover-related weight of each base station, and multiply the handover-related weight of each base station with the absolute increment of real-time power consumption of each base station in each time window to obtain the handover-related energy consumption increment of each base station in each time window.

[0009] As a preferred embodiment of the present invention, the connection contention risk of base stations during traffic congestion is analyzed by combining scheduling configuration information and real-time traffic data, including the following specific steps: S31. Obtain the geographical information of the coverage area of ​​each base station from the scheduling configuration information during the operation of the base station; obtain the geographical information and road segment level of each road segment in the traffic network where each base station is located, as well as the average traffic flow data of each road segment from the real-time traffic data, as a spatiotemporal correlation dataset. S32. Spatial overlay analysis is performed on the geographical information of the coverage area of ​​each base station in the spatiotemporal correlation dataset and the geographical information of each road segment in the traffic network to identify all traffic segments within the coverage area of ​​each base station; according to the road segment level, weights are assigned to each level of road segment, and the weights of all road segments within the coverage area are normalized to obtain the normalized weight coefficient of each road segment relative to the corresponding serving base station; wherein, the road segment level includes primary road segments and secondary road segments; based on the normalized weight coefficients, the real-time average traffic flow data of all road segments within the coverage area of ​​each base station in each time window are weighted and fused to calculate the average traffic flow density within the coverage area of ​​each base station in each time window. S33. Based on the duration of the time window within the current monitoring period, divide the historical monitoring period into historical time windows, and obtain the number of users, average traffic density, time segment characteristics, weekday type characteristics, and holiday markers of each base station in each historical time window within the same historical monitoring period; use the number of users, average traffic density, time segment characteristics, weekday type characteristics, and holiday markers of each base station in each historical time window as the user prediction dataset, and divide the user prediction dataset into a user prediction training set and a user prediction validation set.

[0010] As a preferred embodiment of the present invention, the analysis of the connection contention risk of base stations during traffic congestion, combining scheduling configuration information and real-time traffic data, also includes the following specific steps: S34. Construct a Long Short-Term Memory (LSTM) network model. Based on the user prediction training set, use the number of users, average traffic density, time segment features, weekday type features, and holiday markers of each base station in each historical time window as input features of the LTM network model. Use the number of users of each base station in the next historical time window in the user prediction training set as the output target of the LTM network. Train the LTM network model to obtain an initial user number prediction model. Validate the initial user number prediction model through the user prediction validation set. Output an initial user number prediction model with an accuracy greater than or equal to the preset second model as the user number prediction model. S35. Input the average traffic density, time segment characteristics, weekday type characteristics and holiday signs of each base station in each time window within the current monitoring period into the user number prediction model, and output the predicted number of users of each base station in the next time window; obtain the maximum connected user capacity of each base station from the scheduling configuration information, and use the ratio of the predicted number of users of each base station in the next time window to the maximum connected user capacity as the connection competition intensity of each base station in the next time window. S36. Divide the connection competition intensity of each base station in the next time window by the average connection competition intensity of all base stations in the next time window to obtain the connection competition risk of each base station in the next time window.

[0011] As a preferred embodiment of the present invention, a base station operation risk quantification model is constructed to assess the operation risks of each base station in the transportation network during operation, including the following specific steps: S41. Extract the base station load risk and connection contention risk caused by frequent switching of vehicle traffic connections during the operation of each base station in the next time window; S42. The base station load risk and connection competition risk caused by frequent switching of vehicle traffic connections during the operation of each base station in the next time window are weighted and summed to obtain the operational risk of each base station in the transportation network during the operation of each base station in the next time window.

[0012] As a preferred embodiment of the present invention, the scheduling and management scheme of the base stations is adjusted based on the operational risk assessment results of each base station in the transportation network, including the following specific contents: The system acquires the operational risks of all base stations within the same coverage area in the transportation network during the next time window, and prioritizes scheduling the base station with the lowest operational risk to receive new connection requests within its coverage area.

[0013] Secondly, this invention provides a base station-specific zero-carbon intelligent scheduling and management method, comprising the following specific steps: It acquires operational data, scheduling and configuration information, and energy and environmental data during the operation of the base station, and also acquires real-time traffic data of the transportation network where the base station is located. Based on operational data and energy and environmental data during base station operation, the risk of base station load caused by frequent switching of vehicle traffic connections is analyzed. By combining scheduling configuration information and real-time traffic data, the connection contention risk of base stations during traffic congestion is analyzed. Based on the risk analysis results of base station load caused by frequent switching of vehicle traffic connections and the risk analysis results of base station connection competition during traffic congestion, a quantitative model of base station operation risk is constructed to assess the operation risk of each base station in the traffic network. Based on the operational risk assessment results of each base station in the transportation network, the scheduling and management plan for the base stations is adjusted.

[0014] Thirdly, the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a base station-specific zero-carbon intelligent scheduling and management method by calling the computer program stored in the memory.

[0015] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. This invention analyzes the base station load risk caused by frequent switching of vehicle traffic connections, realizes early warning of potential base station overload risk, and improves the reliability of base station operation. 2. This invention combines scheduling configuration with real-time traffic data analysis to identify connection competition risks, dynamically linking traffic conditions with base station connection capacity. This enables accurate prediction of network congestion risks, ensuring service quality and user experience under high business loads. 3. This invention achieves adaptive optimization of network resource allocation by constructing a base station operation risk quantification model and dynamically adjusting the scheduling scheme based on the risk assessment results. While ensuring service performance, it effectively reduces overall energy consumption and strongly supports the goal of zero-carbon operation of communication networks. Attached Figure Description

[0016] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the overall process of a base station-specific zero-carbon intelligent scheduling and management method according to the present invention; Figure 2 This is a schematic diagram of the structure of a base station-specific zero-carbon intelligent dispatch management system according to the present invention; Figure 3 The flowchart below shows the analysis of step S2 in the base station-specific zero-carbon intelligent scheduling and management method of the present invention. Figure 4 This is an analysis flowchart of step S3 of a base station-specific zero-carbon intelligent scheduling and management method according to the present invention. Detailed Implementation

[0017] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0018] Example 1 like Figure 1 As shown, this embodiment provides a base station-specific zero-carbon intelligent scheduling and management method, including the following specific steps: S1. Obtain operational data, scheduling configuration information, and energy and environmental data during the operation of the base station, and at the same time obtain real-time traffic data of the transportation network where the base station is located; S2. Based on operational data and energy and environmental data during base station operation, analyze the base station load risk caused by frequent switching of vehicle traffic connections; S3. Combine scheduling configuration information and real-time traffic data to analyze the connection contention risk of base stations during traffic congestion; S4. Based on the risk analysis results of base station load caused by frequent switching of vehicle traffic connections and the risk analysis results of base station connection competition during traffic congestion, a quantitative model of base station operation risk is constructed to assess the operation risk of each base station in the traffic network. S5. Based on the operational risk assessment results of each base station in the transportation network, adjust the scheduling and management plan for the base stations.

[0019] In this embodiment, as Figure 3As shown, step S2 analyzes the base station load risk caused by frequent switching of vehicle traffic connections based on operational data and energy and environmental data during base station operation. This includes the following specific steps: S21. Extract the handover event time sequence and performance monitoring data of each base station in the traffic network during the current monitoring period from the operational data information during the base station operation process; at the same time, extract the real-time power consumption data of each base station during the current monitoring period from the energy and environmental data; and perform time-series alignment of the handover event time sequence, performance monitoring data and real-time power consumption data of each base station during the current monitoring period. In this embodiment, the handover event time sequence refers to the sequence of time points during which a user equipment (UE) switches from one base station coverage area to another during movement, as recorded by the base station. This sequence is collected in real-time by the base station's radio resource management module and includes the handover request time, handover completion time, handover type, and related user identifiers. Performance monitoring data includes the base station's CPU utilization rate, which is periodically sampled through the base station's performance management system. The sampling frequency is typically set to once per second or dynamically adjusted according to network load to ensure data real-time performance and accuracy. In this embodiment, real-time power consumption data originates from the base station's energy monitoring system, collected through smart meters or embedded sensors. This data records the base station's power consumption at different time points, typically in kilowatt-hours, and is used to analyze the base station's energy efficiency and usage patterns. Different data sources may have different timestamp precision or collection intervals. Time alignment ensures that all data is aligned on the same timeline, enabling subsequent analysis to accurately correlate the relationships between different variables. Specifically, the time alignment process includes standardizing all data timestamps using a time synchronization protocol, and then unifying the data to the same sampling frequency through interpolation methods, such as aligning all data to one data point per second. This embodiment provides a high-quality and consistent data foundation for subsequent sliding window analysis and risk quantification, ensuring that the analysis results can truly reflect the operating status of the base station. At the same time, through alignment processing, the dynamic correlation between vehicle traffic switching events and base station load can be captured more accurately, providing data support for intelligent scheduling.

[0020] S22. A fixed-length sliding time window is set for the current monitoring period, and the various types of data that have undergone time-series alignment are scanned and processed with a preset step size. Based on the handover event time sequence of each base station in the current monitoring period, the ratio of the total number of handover events occurring in each time window to the window duration is calculated to obtain the handover frequency of each base station per unit time in each time window. The handover frequency of each base station per unit time is divided by the average handover frequency of all base stations per unit time to obtain the relative signaling pressure status of each base station in each time window. It should be noted that the duration of the sliding time window is dynamically set according to the service characteristics and network environment of the base station. For example, in this embodiment, the duration of the sliding time window is set to 10 minutes to adapt to the rapid changes in vehicle traffic. The preset step size determines the interval of window movement, such as moving once every 1 minute. This can achieve continuous coverage of the monitoring period and avoid missing key events. Furthermore, this embodiment quantifies the handover frequency of each base station per unit time in each time window, which reflects the intensity of the base station's processing of handover events per unit time and is an important indicator for measuring the signaling load of the base station. The calculation method for the unit-time handover frequency is simple and direct, yet it effectively captures signaling pressure fluctuations at base stations. For example, in high-traffic areas, frequent vehicle handovers can cause this value to rise sharply, indicating potential load risks. Furthermore, this embodiment analyzes the relative signaling pressure status of each base station within each time window, eliminating the impact of overall network fluctuations and highlighting the relative load of individual base stations within the entire network. For instance, if the unit-time handover frequency of a base station is significantly higher than the average, its relative signaling pressure status value will be greater than 1, indicating that the base station is experiencing an abnormally high signaling burden and may require priority scheduling. This embodiment, through relativization, makes risk analysis more comparable, enabling the identification of abnormal base stations relative to the overall network without being affected by global traffic changes. Simultaneously, through sliding window scanning, this embodiment can track the changing trend of signaling pressure in real time, providing a time-series data basis for predicting future risks. It also lays the foundation for subsequent load fluctuation and energy consumption analysis, enhancing the scheduling system's adaptability to dynamic network environments.

[0021] S23. Using the sliding standard deviation algorithm, the CPU utilization sequence of each base station is extracted from the performance monitoring data of each base station within the current monitoring period. The standard deviation of the CPU utilization sequence of each base station within each time window is calculated. At the same time, the Gini coefficient of the CPU utilization sequence of each base station is calculated based on the Lorenz curve principle. The standard deviation and Gini coefficient of the CPU utilization sequence of each base station are weighted and summed according to preset weights to generate the load fluctuation entropy value of each base station within each time window. The load fluctuation entropy value of each base station within each time window is divided by the average load fluctuation entropy value of all base stations to obtain the relative load fluctuation state of each base station within each time window. It should be noted that the CPU utilization sequence is a key indicator extracted from the performance monitoring data. It is collected by the base station monitoring system at fixed intervals (the default fixed interval is 1 second in this embodiment) and reflects the load status of the base station's processing capacity. The sliding standard deviation algorithm is used to calculate the degree of fluctuation of the utilization sequence within each time window. The larger the standard deviation, the more unstable the load, which may lead to resource contention due to frequent switching events. The Gini coefficient is calculated using inequality measurement methods from economics. It assesses the uniformity of utilization rate distribution by constructing a Lorenz curve. Specifically, the ratio of the area under the cumulative distribution curve to the absolute flat line after sorting utilization rate values ​​is used as the Gini coefficient. A higher Gini coefficient indicates a more uneven load distribution; for example, processor utilization peaks at certain times while remaining low at others. This imbalance may exacerbate base station risks. The load fluctuation entropy value is generated by weighted summation of the standard deviation and the Gini coefficient. Preset weights are typically set based on historical data or expert experience. For example, in this embodiment, the standard deviation weight is set to 0.6, and the Gini coefficient weight is set to 0.4 to balance the impact of fluctuation amplitude and distribution uniformity. A higher load fluctuation entropy value indicates more severe and uneven load fluctuations, suggesting unstable base station operation. Furthermore, this embodiment comprehensively evaluates the load fluctuation characteristics of base stations through composite indicators, considering not only fluctuation amplitude but also the unevenness of temporal distribution. This allows for a more comprehensive capture of potential risks brought about by vehicle traffic switching. For example, during traffic congestion, base station load may spike instantaneously, and the entropy value can promptly reflect this sudden change. Furthermore, this embodiment can also dynamically adjust the weights through a machine learning model to adapt to different network scenarios and improve the accuracy of risk assessment.

[0022] S24. Based on the operational data and energy and environmental data during the operation of the base station, construct a base station energy consumption analysis model to quantify the energy consumption increment related to handover of each base station in each time window. In this embodiment, a base station energy consumption analysis model is constructed to quantify the handover-related energy consumption increment of each base station within each time window. This includes the following steps: S241. Obtain operational data information and energy and environmental data during the operation of the base station. Take the unit time switching frequency, average user session duration, number of active users at the start of the time window in the current monitoring period of the operational data information, and the absolute increment of real-time power consumption in the current monitoring period of the energy and environmental data as the regression analysis dataset, and divide the regression analysis dataset into regression analysis training set and regression analysis validation set. S242. Construct a gradient boosting decision tree regression model. Use the unit time switching frequency, average user session duration, and number of active users at the start of the window in each time window of the regression analysis training set as input features of the gradient boosting decision tree regression model. Use the absolute increment of real-time power consumption in each time window of the regression analysis training set as the output target of the gradient boosting decision tree regression model. Train the gradient boosting decision tree regression model to obtain the initial regression analysis model. S243. The initial regression analysis model is validated using the regression analysis validation set, and the initial regression analysis model with an accuracy greater than or equal to the preset model is output as the base station energy consumption analysis model. S244. Based on steps S24-S26, obtain the base station energy consumption analysis model for each base station; use the Shapley value attribution analysis method to quantify the contribution of each input feature in the base station energy consumption analysis model to the absolute increment of real-time power consumption, take the contribution of the input feature as the handover frequency per unit time as the handover-related weight of each base station, and multiply the handover-related weight of each base station with the absolute increment of real-time power consumption of each base station in each time window to obtain the handover-related energy consumption increment of each base station in each time window.

[0023] Specifically, in this embodiment, the unit-time switching frequency is calculated from step S22, reflecting the signaling load intensity; the average user session duration is extracted from the base station connection log, representing the average user connection duration, calculated by statistically analyzing the difference between session start and end times; the number of active users refers to the number of users in a connected state at the start of the time window, obtained in real time from the base station user management module; the absolute increment of real-time power consumption is obtained by comparing the power consumption values ​​of adjacent time windows, representing the change in energy consumption. These data collectively constitute the regression analysis dataset, which is divided into a regression analysis training set and a regression analysis validation set. In this embodiment, the default division ratio is 7:3 to ensure the independence of model training and evaluation. In this embodiment, the gradient boosting decision tree regression model is an ensemble learning algorithm that iteratively trains multiple decision trees to fit complex nonlinear relationships. The unit-time switching frequency, average user session duration, and number of active users at the start of the window within each time window in the regression analysis training set are used as input features, and the absolute increment of real-time power consumption within each time window in the regression analysis training set is used as the output target to train the model. In this embodiment, the model training process includes setting hyperparameters such as the number of trees and the learning rate, and optimizing performance through cross-validation to obtain an initial regression analysis model. Then, the initial regression analysis model is validated using a regression analysis validation set. Accuracy is evaluated using metrics such as mean squared error or R-squared value. Models with an accuracy greater than or equal to a preset model accuracy (90% by default in this embodiment) are used as the base station energy consumption analysis model. Finally, the Shapley value attribution analysis method is used to quantify the contribution of each input feature in the base station energy consumption analysis model to the absolute increment of real-time power consumption. Specifically, in this embodiment, the Shapley value is based on cooperative game theory, calculating the average marginal contribution through permutation and combination features. The contribution of the input feature, the handover frequency per unit time, is used as the handover-related weight for each base station. Then, the handover-related weight for each base station is multiplied by the absolute increment of real-time power consumption of each base station within each time window to obtain the handover-related energy consumption increment for each base station within each time window. Based on the above, this embodiment accurately quantifies the impact of handover events on energy consumption through a machine learning model, avoiding the bias that may arise from simple correlation analysis.

[0024] S25. The signaling relative pressure state, load relative fluctuation state, and handover-related energy consumption increment of each base station in each time window are respectively subjected to minimum-maximum standardization. The standardized signaling relative pressure state, load relative fluctuation state, and handover-related energy consumption increment of each base station in each time window are then weighted and summed to obtain the base station load risk caused by frequent handover of vehicular traffic connections during the operation of each base station in each time window. It should be noted that the minimum-maximum standardization in this embodiment aims to transform them to the [0,1] interval, eliminate the influence of dimensions, and make the weighted sum more comparable. The standardization formula is the difference between the original value and the minimum value divided by the difference between the maximum value and the minimum value, where the minimum and maximum values ​​are calculated based on the data of all base stations in the current monitoring period to ensure that the standardized values ​​reflect the relative position. For example, in this embodiment, the original value of the signaling relative pressure state may be between 0.5 and 2, and after standardization, it becomes a value between 0 and 1. Other indicators are processed similarly. After standardization, a weighted sum is performed. The weights are set through regression analysis of historical data. For example, in this embodiment, the weight of the relative signaling pressure state is set to 0.4, the weight of the relative load fluctuation state is set to 0.3, and the weight of the handover-related energy consumption increment is set to 0.3, to reflect the importance of different factors to load risk. In this embodiment, a higher base station load risk indicates a more severe load problem faced by the base station due to vehicular traffic handover. In practical applications, this embodiment can also dynamically adjust the weights based on historical fault data or real-time network status. For example, the weight of energy consumption increment can be increased during periods of energy shortage to optimize the zero-carbon target.

[0025] S26. The base station load risk caused by frequent switching of vehicular traffic connections during the operation of each base station within each time window is imported into a pre-built base station load risk time series prediction model, and the output is the base station load risk caused by frequent switching of vehicular traffic connections during the operation of each base station in the next time window. It should be noted that the base station load risk time series prediction model is usually built based on time series analysis or machine learning methods, such as using an autoregressive integral moving average model or a long short-term memory network, using historical load risk data as input to predict future values. In this embodiment, firstly, the load risk time series data of each base station within the historical monitoring period is collected, including the risk value of each time window, to form a training dataset. Then, the data is preprocessed, such as removing outliers and smoothing, to improve model stability. During model training, historical risk sequences are used as features, and the risk value of the next time window is used as a label, and the prediction error is minimized through an iterative optimization algorithm. The load risk value of the current time window is input into the model, and the predicted value of the next time window is output, thereby identifying potentially high-risk base stations in advance; this realizes the proactive management of load risk, allowing the scheduling system to take intervention measures before problems occur, such as allocating resources in advance or adjusting traffic, reducing the probability of service interruption.

[0026] In this embodiment, as Figure 4 As shown, step S3 combines scheduling configuration information and real-time traffic data to analyze the connection contention risk of base stations during traffic congestion, including the following specific steps: S31. Obtain the geographical coverage information of each base station from the scheduling and configuration information during base station operation; obtain the geographical information and road segment level of each road segment in the traffic network where each base station is located, as well as the average traffic flow data of each road segment, from real-time traffic data as a spatiotemporal correlation dataset. Specifically, obtain the geographical coverage information of each base station from the scheduling and configuration information during base station operation. This information is usually stored in the form of polygon coordinates, describing the geographical area covered by the base station signal. Obtain the geographical information and road segment level of each road segment in the traffic network where each base station is located, as well as the average traffic flow data of each road segment, from real-time traffic data as a spatiotemporal correlation dataset. The geographical coverage information is exported through base station planning tools or geographic information systems, including latitude and longitude boundaries and coverage radius. The geographical information of road segments is obtained from traffic management departments, including the coordinates of the start and end points of the road segment and its length. The road segment level is divided into primary road segments and secondary road segments, based on road function and traffic flow. For example, highways are arterial roads, and urban roads are secondary arterial roads. The average traffic flow data is obtained from traffic sensors, representing the number of vehicles passing through the road segment per unit time. These data are integrated into a spatiotemporal correlation dataset to establish a mapping relationship between base station coverage and traffic conditions. Through spatial data fusion, this embodiment enables base station risk assessment to incorporate real-time traffic conditions, improving accuracy. For example, during traffic congestion, dense vehicle traffic may lead to a surge in base station connection requests; correlation analysis can provide early warnings.

[0027] S32. Spatial overlay analysis is performed on the geographical information of the coverage area of ​​each base station in the spatiotemporal correlation dataset and the geographical information of each road segment in the traffic network to identify all traffic segments within the coverage area of ​​each base station. Based on the road segment level, weights are assigned to each level of road segment, and the weights of all road segments within the coverage area are normalized to obtain the normalized weight coefficient of each road segment relative to its corresponding serving base station. The road segment level includes primary and secondary road segments. Based on the normalized weight coefficients, the real-time average traffic flow data of all road segments within the coverage area of ​​each base station in each time window are weighted and fused to calculate the average traffic flow density within the coverage area of ​​each base station in each time window. It should be noted that the spatial overlay analysis uses geographic information system algorithms, such as point-in-polygon or buffer analysis, to compare the road segment coordinates with the base station coverage polygon to determine which road segments are located within the coverage area. For example, for a base station, its coverage area may include multiple urban roads; overlay analysis can accurately list these road segments. Based on road segment levels, weights are assigned to road segments at each level. These weights are set based on the importance and traffic contribution of the road segment. For example, in this embodiment, the weight of primary road segments is 0.7 by default, and the weight of secondary road segments is 0.3 by default, to reflect the differences in the impact of different road segments on base station load. The weights of all road segments within the coverage area are normalized to obtain a normalized weight coefficient for each road segment relative to its corresponding serving base station. The normalized weight coefficient is calculated by dividing the weight of each road segment by the sum of all weights, ensuring comparability of the weight coefficients. Based on the normalized weight coefficients, the real-time average traffic flow data of all road segments within the coverage area of ​​each base station in each time window are weighted and fused to calculate the average traffic density within the coverage area of ​​each base station in each time window. A higher average traffic density after weighted fusion indicates more vehicles in the coverage area and a denser potential connection request density. This embodiment transforms dispersed traffic data into base station-level load indicators through spatial weighting and fusion processing, avoiding the bias that may result from simple summation. Simultaneously, normalization ensures fair comparison between different base stations. For example, in transportation hub areas, high traffic density may indicate a high risk of connection competition, and weighted fusion can more accurately capture this correlation.

[0028] S33. Based on the duration of the time window within the current monitoring period, historical time windows are divided for the same monitoring period. The number of users, average traffic density, time segment features, weekday type features, and holiday markers for each base station within each historical time window are obtained. These data are used as the user prediction dataset, which is then divided into a user prediction training set and a user prediction validation set. It should be noted that in this embodiment, the same historical monitoring period refers to the same time period as the current period, such as the same morning peak hours, to ensure data comparability. The time window division is consistent with the current monitoring period. The number of users is extracted from the base station user management log, representing the number of connected users. Time segment features refer to time periods within a day, such as morning and afternoon, used to capture traffic cycle patterns. Weekday type features are used to distinguish between weekdays and weekends. Holiday markers are used to indicate whether it is a statutory holiday; these features are obtained from calendar data. This data is used as the user prediction dataset and divided into a user prediction training set and a user prediction validation set. In this embodiment, the default division ratio is 7:3 to support model training and evaluation. Furthermore, this embodiment constructs a rich predictive feature set using historical data, which can capture the spatiotemporal patterns of user connections. For example, weekend traffic density may differ from weekday traffic density, thereby improving prediction accuracy. Simultaneously, data partitioning ensures the model's generalization ability and avoids overfitting.

[0029] In this embodiment, the analysis of connection contention risks of base stations during traffic congestion, combining scheduling configuration information and real-time traffic data, also includes the following specific steps: S34. Construct a Long Short-Term Memory (LSTM) network model. Based on the user prediction training set, use the number of users, average traffic density, time segment features, weekday type features, and holiday markers of each base station in each historical time window as input features of the LTM network model. Use the number of users of each base station in the next historical time window in the user prediction training set as the output target of the LTM network. Train the LTM network model to obtain an initial user number prediction model. Validate the initial user number prediction model using a user prediction validation set. The initial user number prediction model with an accuracy greater than or equal to the preset second model is taken as the user number prediction model. In this embodiment, the LTM network model is a recurrent neural network model, which is good at processing time-series data and captures long-term dependencies through a gating mechanism. In this embodiment, model construction includes defining the network structure, such as the number of hidden layers and neurons, setting training parameters such as the learning rate and the number of iterations, and then using the training set data to optimize the weights through the backpropagation algorithm to minimize the prediction error and obtain the initial user number prediction model. The initial user count prediction model is validated using a user prediction validation set. Accuracy is assessed using metrics such as mean absolute percentage error. Models whose output is greater than or equal to a preset second model accuracy (exemplarily, the preset second model accuracy is 85% by default in this embodiment) are used as the user count prediction model. Furthermore, user counts are directly related to base station connection load. This embodiment utilizes a deep learning model to accurately predict future user counts, thereby indirectly assessing connection contention risks. For example, during traffic congestion, the number of users may surge, and the model can predict this change in advance. Simultaneously, Long Short-Term Memory (LSTM) networks can handle nonlinear temporal patterns and adapt to complex network environments. This embodiment, through advanced prediction techniques, provides forward-looking insights for scheduling management and supports efficient allocation of base station resources under zero-carbon goals.

[0030] S35. Input the average traffic density, time segment characteristics, weekday type characteristics, and holiday markers of each base station in each time window within the current monitoring period into the user number prediction model, and output the predicted user number of each base station in the next time window; obtain the maximum connected user capacity of each base station from the scheduling configuration information, and use the ratio of the predicted user number of each base station in the next time window to the maximum connected user capacity as the connection competition intensity of each base station in the next time window; it should be noted that in this embodiment, the time segment characteristics, weekday type characteristics, and holiday markers are obtained from calendar data in real time. These features are preprocessed and then input into the trained user number prediction model. The model outputs the predicted user number, representing the number of devices that may be connected in the future time window. Obtain the maximum connected user capacity of each base station from the scheduling configuration information. The maximum connected user capacity of a base station is determined by the base station hardware specifications and stored in the configuration database. Use the ratio of the predicted user number of each base station in the next time window to the maximum connected user capacity as the connection competition intensity of each base station in the next time window. The higher the value, the more strained the connection resources are and the greater the competition risk. For example, if the predicted user number is close to the maximum capacity, the connection competition intensity value will be close to 1, indicating that connection failure or service quality degradation may occur. This embodiment transforms user predictions into intuitive competitive indicators, making it easier to identify high-risk base stations. At the same time, it takes into account base station hardware limitations based on capacity ratios, making the assessment more realistic.

[0031] S36. Divide the connection contention intensity of each base station in the next time window by the average connection contention intensity of all base stations in the next time window to obtain the connection contention risk of each base station in the next time window. In this embodiment, the connection contention intensity represents the connection pressure of a single base station, while the average connection contention intensity is calculated based on the values ​​of all base stations in the entire traffic network, reflecting the overall network contention level. The connection contention risk obtained after division is a relative value. If the connection contention risk is greater than 1, it indicates that the contention intensity of the base station is higher than the network average level, and the risk is high; if the connection contention risk is less than 1, it indicates that the risk is low. This embodiment eliminates global factors, such as the impact of increased overall traffic during holidays, through relativization, making the risk assessment more focused on the abnormal situation of individual base stations. For example, when the overall traffic is high, even if the absolute value of the contention intensity of a certain base station is high, it may not be prominent relative to the average, thereby avoiding false alarms.

[0032] In this embodiment, step S4 involves constructing a base station operation risk quantification model to assess the operation risks of each base station in the transportation network, including the following specific steps: S41. Extract the base station load risk and connection contention risk caused by frequent switching of vehicle traffic connections during the operation of each base station in the next time window; S42. The base station load risk and connection contention risk caused by frequent switching of vehicle traffic connections during the operation of each base station within the next time window are weighted and summed to obtain the operational risk of each base station in the transportation network during the next time window. In this embodiment, the weights are set through regression analysis of historical data. For example, based on historical data, the load risk weight can be set to 0.5, and the connection contention risk weight can be set to 0.5 to balance the impact of different risk sources, or adjusted according to real-time needs, such as increasing the load risk weight when energy is scarce. A higher operational risk indicates a greater overall operational risk for the base station, which may require immediate intervention.

[0033] In this embodiment, step S5 adjusts the scheduling and management scheme of the base stations based on the operational risk assessment results during the operation of each base station in the transportation network, including the following specific contents: This embodiment acquires the operational risks of all base stations within the same coverage area of ​​the transportation network during the next time window, and prioritizes scheduling the base station with the lowest operational risk to receive new connection requests within its coverage area. In this embodiment, the same coverage area refers to geographically overlapping base station coverage areas, determined through spatial analysis. Lower operational risks indicate more stable base station operation. During scheduling, this embodiment monitors new connection requests in real time and prioritizes allocating new user connections to the base station with the lowest risk based on operational risk ranking, thereby balancing the load and reducing overall risk. Based on the above, this embodiment achieves dynamic load balancing, avoids overload of high-risk base stations, improves network reliability and user experience, and extends equipment lifespan and reduces energy consumption by prioritizing the scheduling of low-risk base stations, supporting zero-carbon goals.

[0034] Example 2 like Figure 2 As shown, this embodiment provides a base station-specific zero-carbon intelligent dispatch management system, including a multi-source information acquisition module, a connection handover detection module, a congestion impact detection module, a base station operation evaluation module, and a dispatch adjustment module; Among them, the multi-source information acquisition module is used to acquire operational data information, scheduling configuration information, and energy and environmental data during the operation of the base station, and at the same time acquire real-time traffic data of the transportation network where the base station is located. The connection handover detection module is used to analyze the base station load risk caused by frequent handover of vehicle traffic connections based on operational data and energy and environmental data during base station operation. The congestion impact detection module is used to analyze the connection contention risk of base stations during traffic congestion by combining scheduling configuration information and real-time traffic data. The base station operation assessment module is used to construct a quantitative model of base station operation risk based on the risk analysis results of base station load caused by frequent switching of vehicle traffic connections and the risk analysis results of connection competition of base stations during traffic congestion, and to assess the operation risk of each base station in the traffic network during operation. The scheduling and adjustment module is used to adjust the scheduling and management scheme of base stations based on the operational risk assessment results of each base station in the transportation network.

[0035] The parameters and steps of each unit module in the base station-specific zero-carbon intelligent dispatch management system of the present invention described above can be referred to the parameters and steps in the embodiments of the base station-specific zero-carbon intelligent dispatch management method described above, and will not be repeated here.

[0036] Example 3 An electronic device according to an embodiment of the present invention includes a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a base station-specific zero-carbon intelligent scheduling and management method by calling the computer program stored in the memory. It should be noted that all computer programs for the base station-specific zero-carbon intelligent scheduling and management method are implemented using C language, and the multi-source information acquisition module, connection handover detection module, congestion impact detection module, base station operation evaluation module, and scheduling adjustment module are all controlled by a remote server.

[0037] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0038] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A zero-carbon intelligent dispatch and management system for base stations, characterized in that, It includes a multi-source information acquisition module, a connection handover detection module, a congestion impact detection module, a base station operation assessment module, and a scheduling adjustment module; Among them, the multi-source information acquisition module is used to acquire operational data information, scheduling configuration information, and energy and environmental data during the operation of the base station, and at the same time acquire real-time traffic data of the transportation network where the base station is located. The connection handover detection module is used to analyze the base station load risk caused by frequent handover of vehicle traffic connections based on operational data and energy and environmental data during base station operation. The congestion impact detection module is used to analyze the connection contention risk of base stations during traffic congestion by combining scheduling configuration information and real-time traffic data. The base station operation assessment module is used to construct a quantitative model of base station operation risk based on the risk analysis results of base station load caused by frequent switching of vehicle traffic connections and the risk analysis results of connection competition of base stations during traffic congestion, and to assess the operation risk of each base station in the traffic network during operation. The scheduling and adjustment module is used to adjust the scheduling and management scheme of base stations based on the operational risk assessment results of each base station in the transportation network.

2. The base station-specific zero-carbon intelligent dispatch management system according to claim 1, characterized in that, The analysis of base station load risks caused by frequent switching of vehicle traffic connections, based on operational data and energy and environmental data during base station operation, includes the following specific steps: S21. Extract the handover event time sequence and performance monitoring data of each base station in the traffic network during the current monitoring period from the operational data information during the base station operation process; at the same time, extract the real-time power consumption data of each base station during the current monitoring period from the energy and environmental data. The handover event timing sequence, performance monitoring data, and real-time power consumption data of each base station within the current monitoring period are time-aligned. S22. Set a fixed-duration sliding time window for the current monitoring cycle, and scan and process the various types of data that have been aligned in time sequence with a preset step size. Based on the handover event time sequence of each base station in the current monitoring period, the relative signaling pressure status of each base station in each time window is analyzed. S23. Using the sliding standard deviation algorithm, extract the CPU utilization sequence of each base station from the performance monitoring data of each base station in the current monitoring period; based on the CPU utilization sequence of each base station, analyze the relative load fluctuation of each base station in each time window. S24. Based on the operational data and energy and environmental data during the operation of the base station, construct a base station energy consumption analysis model to quantify the energy consumption increment related to handover of each base station in each time window. S25. Perform minimum-maximum standardization on the relative signaling pressure state, relative load fluctuation state, and handover-related energy consumption increment of each base station in each time window. Then, perform weighted summation on the relative signaling pressure state, relative load fluctuation state, and handover-related energy consumption increment of each base station in each time window to obtain the base station load risk caused by frequent switching of vehicle traffic connections during the operation of each base station in each time window. S26. Import the base station load risk caused by frequent switching of vehicle traffic connections during the operation of each base station in each time window into the pre-built base station load risk time series prediction model, and output the base station load risk caused by frequent switching of vehicle traffic connections during the operation of each base station in the next time window.

3. The base station-specific zero-carbon intelligent dispatch management system according to claim 2, characterized in that, The construction of the base station energy consumption analysis model quantifies the handover-related energy consumption increment of each base station within each time window, specifically including the following steps: S241. Obtain operational data information and energy and environmental data during the operation of the base station. Take the unit time switching frequency, average user session duration, number of active users at the start of the time window in the current monitoring period of the operational data information, and the absolute increment of real-time power consumption in the current monitoring period of the energy and environmental data as the regression analysis dataset, and divide the regression analysis dataset into regression analysis training set and regression analysis validation set. S242. Construct a gradient boosting decision tree regression model. Use the unit time switching frequency, average user session duration, and number of active users at the start of the window in each time window of the regression analysis training set as input features of the gradient boosting decision tree regression model. Use the absolute increment of real-time power consumption in each time window of the regression analysis training set as the output target of the gradient boosting decision tree regression model. Train the gradient boosting decision tree regression model to obtain the initial regression analysis model. S243. The initial regression analysis model is validated using the regression analysis validation set, and the initial regression analysis model with an accuracy greater than or equal to the preset model is output as the base station energy consumption analysis model. S244. Based on steps S24-S26, obtain the base station energy consumption analysis model for each base station; use the Shapley value attribution analysis method to quantify the contribution of each input feature in the base station energy consumption analysis model to the absolute increment of real-time power consumption, take the contribution of the input feature as the handover frequency per unit time as the handover-related weight of each base station, and multiply the handover-related weight of each base station with the absolute increment of real-time power consumption of each base station in each time window to obtain the handover-related energy consumption increment of each base station in each time window.

4. The base station-specific zero-carbon intelligent dispatch management system according to claim 3, characterized in that, The method of combining scheduling configuration information and real-time traffic data to analyze the connection contention risk of base stations during traffic congestion includes the following specific steps: S31. Obtain the geographical coverage information of each base station from the scheduling configuration information during the base station operation process; Geographic information and road segment level of each road segment in the traffic network where each base station is located, as well as the average traffic flow data of each road segment, are obtained from real-time traffic data to form a spatiotemporal correlation dataset. S32. Spatial overlay analysis is performed on the geographical information of the coverage area of ​​each base station in the spatiotemporal correlation dataset and the geographical information of each road segment in the traffic network to identify all traffic segments within the coverage area of ​​each base station; the average traffic density within the coverage area of ​​each base station in each time window is quantified. S33. Based on the duration of the time window within the current monitoring period, divide the historical monitoring period into historical time windows, and obtain the number of users, average traffic density, time segment characteristics, weekday type characteristics, and holiday markers of each base station in each historical time window within the same historical monitoring period; use the number of users, average traffic density, time segment characteristics, weekday type characteristics, and holiday markers of each base station in each historical time window as the user prediction dataset, and divide the user prediction dataset into a user prediction training set and a user prediction validation set.

5. The base station-specific zero-carbon intelligent dispatch management system according to claim 4, characterized in that, The method of combining scheduling configuration information and real-time traffic data to analyze the connection contention risk of base stations during traffic congestion also includes the following specific steps: S34. Construct a Long Short-Term Memory (LSTM) network model. Based on the user prediction training set, use the number of users, average traffic density, time segment features, weekday type features, and holiday markers of each base station in each historical time window as input features of the LTM network model. Use the number of users of each base station in the next historical time window in the user prediction training set as the output target of the LTM network. Train the LTM network model to obtain an initial user number prediction model. Validate the initial user number prediction model through the user prediction validation set. Output an initial user number prediction model with an accuracy greater than or equal to the preset second model as the user number prediction model. S35. Input the average traffic density, time segment characteristics, weekday type characteristics and holiday signs of each base station in each time window within the current monitoring period into the user number prediction model, and output the predicted number of users of each base station in the next time window; obtain the maximum connected user capacity of each base station from the scheduling configuration information, and use the ratio of the predicted number of users of each base station in the next time window to the maximum connected user capacity as the connection competition intensity of each base station in the next time window. S36. Divide the connection competition intensity of each base station in the next time window by the average connection competition intensity of all base stations in the next time window to obtain the connection competition risk of each base station in the next time window.

6. The base station-specific zero-carbon intelligent dispatch management system according to claim 5, characterized in that, The construction of a base station operation risk quantification model to assess the operational risks of each base station in the transportation network includes the following specific steps: S41. Extract the base station load risk and connection contention risk caused by frequent switching of vehicle traffic connections during the operation of each base station in the next time window; S42. The base station load risk and connection competition risk caused by frequent switching of vehicle traffic connections during the operation of each base station in the next time window are weighted and summed to obtain the operational risk of each base station in the transportation network during the operation of each base station in the next time window.

7. A base station-specific zero-carbon intelligent dispatch management system according to claim 6, characterized in that, The adjustment of the base station scheduling and management scheme based on the operational risk assessment results of each base station in the transportation network includes the following specific contents: The system acquires the operational risks of all base stations within the same coverage area in the transportation network during the next time window, and prioritizes scheduling the base station with the lowest operational risk to receive new connection requests within its coverage area.

8. A base station-specific zero-carbon intelligent scheduling management method, implemented based on any one of claims 1-7, characterized in that, The specific steps include the following: It acquires operational data, scheduling and configuration information, and energy and environmental data during the operation of the base station, and also acquires real-time traffic data of the transportation network where the base station is located. Based on operational data and energy and environmental data during base station operation, the risk of base station load caused by frequent switching of vehicle traffic connections is analyzed. By combining scheduling configuration information and real-time traffic data, the connection contention risk of base stations during traffic congestion is analyzed. Based on the risk analysis results of base station load caused by frequent switching of vehicle traffic connections and the risk analysis results of base station connection competition during traffic congestion, a quantitative model of base station operation risk is constructed to assess the operation risk of each base station in the traffic network. Based on the operational risk assessment results of each base station in the transportation network, the scheduling and management plan for the base stations is adjusted.

9. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes a base station-specific zero-carbon intelligent scheduling and management method as described in any one of claims 8 by calling the computer program stored in the memory.

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