A multi-base station cooperative data transmission system

Abstract

The present invention discloses a multi-base station collaborative data transmission system, which relates to the field of communication technology. It constructs a multi-base station digital twin model, uses the multi-base station digital twin model to obtain the signal transmission quality allowable confidence interval, and then screens out a candidate base station set based on the signal transmission quality allowable confidence interval; uses user trajectory parameters to predict the user's future location coordinates, and then outputs a user trajectory prediction map; uses the multi-base station digital twin model to output the time distribution characteristics and spatial distribution characteristics of the candidate base station set, and outputs the time-space channel quality map of the candidate base station set; based on the user trajectory prediction map and the time-space channel quality map of the candidate base station set, the candidate base station set is screened, and the comprehensive score of the screened base stations is obtained, and then a list of collaborative base stations and a switching strategy are output. Intelligent switching and efficient collaboration based on digital twin modeling and dynamic prediction are realized, significantly improving the reliability of the communication network and user experience.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a multi-base station collaborative data transmission system. Background Art

[0002] With the continuous development of mobile communication technology, users have increasingly higher requirements for data transmission stability, speed, and quality. Multi-base station collaborative data transmission has become an important means to improve system performance. More advanced technologies and methods are needed to achieve effective collaboration and optimized resource allocation among base stations.

[0003] Existing technologies lack accurate assessment and comprehensive consideration of signal transmission quality when selecting base stations. At the same time, due to the inability to accurately predict user behavior, the system finds it difficult to make advance preparations for resource allocation and switching. Furthermore, existing technologies lack comprehensive analysis of the spatiotemporal characteristics of channels, and collaboration and switching between base stations are not flexible and intelligent enough.

[0004] Therefore, to address the above issues, a multi-base station collaborative data transmission system is urgently needed. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a multi-base station collaborative data transmission system, which solves the problems of switching delay, unstable signal quality and uneven resource allocation caused by dynamic environmental changes in traditional multi-base station collaboration.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-base station collaborative data transmission system, including a digital twin modeling module, a dynamic confidence interval design module, a user behavior prediction module, a channel prediction and optimization module, and an intelligent switching and collaboration module, wherein: the digital twin modeling module is used to obtain the geographic coordinates, transmission power, antenna parameters and environmental topology data of the base station to construct a multi-base station digital twin model; the dynamic confidence interval design module is used to use the output data of the multi-base station digital twin model to obtain the allowable confidence interval of the signal transmission quality, and then screen out the candidate base station set through the allowable confidence interval of the signal transmission quality; the user behavior prediction module is used to obtain user trajectory parameters, use the user trajectory parameters to predict the user's future location coordinates, and then output the user trajectory prediction map; the channel prediction and optimization module is used to use the multi-base station digital twin model to output the time distribution characteristics and spatial distribution characteristics of the candidate base station set, and output the spatiotemporal channel quality map of the candidate base station set; the intelligent switching and collaboration module is used to screen the candidate base station set based on the user trajectory prediction map and the spatiotemporal channel quality map of the candidate base station set, and obtain the comprehensive score of the screened base stations, and then output the collaborative base station list and switching strategy.

[0007] Furthermore, the signal transmission quality allowable confidence interval is specifically a comprehensive evaluation confidence interval of transmission delay, path loss and channel quality.

[0008] Furthermore, the dynamic confidence interval design module is specifically analyzed as follows: using a multi-base station digital twin model to extract the transmission delay, path loss and channel quality related data of each base station, and preprocessing the relevant data, and then determining the quality evaluation value of each base station through transmission delay, path loss and channel quality; based on the sample data of the quality evaluation value, identifying the allowable confidence interval of the signal transmission quality, and then screening out base stations whose quality evaluation values ​​are within the allowable confidence interval of the signal transmission quality to form a set of candidate base stations.

[0009] Furthermore, the user behavior prediction module is specifically analyzed as follows: the user trajectory parameters include the base station information and movement parameters previously connected by the user, the base station information previously connected by the user includes the geographical coordinates of the base station and the connection time, and the movement parameters include the movement speed and movement direction; the historical user trajectory parameters are input into the recurrent neural network, the recurrent neural network is trained, and the change pattern of the trajectory is identified, and then the current user trajectory parameters are input into the trained recurrent neural network, and the position coordinates of the user at each future time point are output; the predicted position coordinates of the user at each future time point are integrated with the corresponding time point to form complete trajectory prediction data, and then the trajectory prediction data is displayed and updated in the form of a graph, and the user trajectory prediction graph is output.

[0010] Furthermore, the channel prediction and optimization module is specifically analyzed as follows: using a multi-base station digital twin model to obtain the time distribution characteristic parameters and spatial distribution characteristic parameters of the candidate base station set, and then using the time distribution characteristic parameters and spatial distribution characteristic parameters to respectively identify the time distribution characteristic values ​​and spatial distribution characteristic values ​​of each candidate base station; combining the time distribution characteristic values ​​and spatial distribution characteristic values ​​of each candidate base station to determine the space-time channel quality evaluation value of each candidate base station, and then generating a space-time channel quality map based on the location and space-time channel quality evaluation value of the candidate base station. The space-time channel quality map is marked with high-quality base station areas and low-quality base station areas. The high-quality base station area is specifically a base station area whose space-time channel quality evaluation value is greater than or equal to the space-time channel quality threshold, and the low-quality base station area is specifically a base station area whose space-time channel quality evaluation value is lower than the space-time channel quality threshold.

[0011] Furthermore, the temporal distribution characteristic value is specifically obtained by calculation using a STARMA model, and the spatial distribution characteristic value is specifically obtained by calculation using a Kriging interpolation method.

[0012] Furthermore, the intelligent switching and collaboration module is specifically analyzed as follows: superimposing the future position coordinate information in the user trajectory prediction map with the space-time channel quality map, marking the base stations in the superimposed area; based on the positional relationship between the user's future position and each marked base station, and the distribution of high-quality base station areas and low-quality base station areas in the space-time channel quality map, screening out base stations with a distance from the user's future position coordinates less than a distance threshold and a space-time channel quality assessment result of a high-quality base station area as a preferred base station; for each preferred base station, identifying the load condition of the base station, and then combining the distance between the base station and the user's future position and the space-time channel quality assessment value of the base station to determine the comprehensive score of each preferred base station, and selecting and forming a collaborative base station list based on the comprehensive score; real-time monitoring of the user's mobility status and the signal quality of the currently connected base station, and then determining whether to trigger the switching of the base station connection based on the user's mobility status and the signal quality of the currently connected base station.

[0013] The present invention has the following beneficial effects:

[0014] This multi-base station collaborative data transmission system builds a model by acquiring data from multiple base stations and the environment, reflecting the actual situation of the multi-base station system and providing an accurate basis for subsequent analysis; uses the output data of the digital twin model to determine the allowable confidence interval of signal transmission quality, thereby screening the candidate base station set, which can improve the accuracy and effectiveness of base station selection and ensure signal transmission quality; predicts future position coordinates by analyzing user trajectory parameters and outputs a map, which helps to plan data transmission in advance and improve the system's adaptability to user movement; outputs a spatiotemporal channel quality map based on the spatiotemporal and spatial distribution characteristics of the candidate base station set, intuitively displays the channel quality distribution, and facilitates the system to make reasonable decisions; screens and scores based on user trajectory predictions and channel quality maps, outputs a list of collaborative base stations and a switching strategy, realizes intelligent collaboration and switching between base stations, and improves the stability and efficiency of data transmission.

[0015] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a structural diagram of a multi-base station collaborative data transmission system according to the present invention.

[0017] Figure 2 This is a method flow chart of a multi-base station collaborative data transmission system according to the present invention. DETAILED DESCRIPTION

[0018] The embodiment of the present application realizes intelligent switching and efficient collaboration based on digital twin modeling and dynamic prediction through a multi-base station collaborative data transmission system, significantly improving the reliability of the communication network and user experience.

[0019] The overall idea of ​​the embodiment of the present application is: comprehensively acquire base station and environment-related data, build a multi-base station digital twin model, and provide accurate virtual model support for the system; based on the output data of the digital twin model, determine the allowable confidence interval of signal transmission quality, screen out a set of candidate base stations, and preliminarily narrow the range of base station selection; predict the user's future location by analyzing user trajectory parameters, and provide a basis for the system to know the user's mobility trend in advance; use the digital twin model to analyze the spatiotemporal and spatial distribution characteristics of the candidate base station set, output a spatiotemporal channel quality map, and intuitively present the channel quality situation; comprehensively integrate the user trajectory prediction map and the spatiotemporal channel quality map, further screen and score the candidate base station set, determine the list of collaborative base stations and the switching strategy, and realize intelligent collaborative data transmission of multiple base stations.

[0020] See also Figure 1 、 Figure 2 , an embodiment of the present invention provides a technical solution: a multi-base station collaborative data transmission system, including a digital twin modeling module, a dynamic confidence interval design module, a user behavior prediction module, a channel prediction and optimization module, and an intelligent switching and collaboration module, wherein: the digital twin modeling module is used to obtain the geographic coordinates, transmission power, antenna parameters and environmental topology data of the base station to construct a multi-base station digital twin model; the dynamic confidence interval design module is used to use the output data of the multi-base station digital twin model to obtain the allowable confidence interval of the signal transmission quality, and then screen out a set of candidate base stations through the allowable confidence interval of the signal transmission quality; the user behavior prediction module is used to obtain user trajectory parameters, use the user trajectory parameters to predict the user's future location coordinates, and then output a user trajectory prediction map; the channel prediction and optimization module is used to use the multi-base station digital twin model to output the time distribution characteristics and spatial distribution characteristics of the candidate base station set, and output the spatiotemporal channel quality map of the candidate base station set; the intelligent switching and collaboration module is used to screen the candidate base station set based on the user trajectory prediction map and the spatiotemporal channel quality map of the candidate base station set, and obtain the comprehensive score of the screened base stations, and then output a collaborative base station list and a switching strategy.

[0021] Specifically, the signal transmission quality allowable confidence interval is specifically a comprehensive evaluation confidence interval of transmission delay, path loss and channel quality.

[0022] The specific analysis of the dynamic confidence interval design module is as follows: using the multi-base station digital twin model to extract the transmission delay, path loss and channel quality related data of each base station, and preprocessing the relevant data, and then determining the quality assessment value of each base station through transmission delay, path loss and channel quality; based on the sample data of the quality assessment value, identifying the allowable confidence interval of the signal transmission quality, and then screening out the base stations whose quality assessment values ​​are within the allowable confidence interval of the signal transmission quality to form a set of candidate base stations.

[0023] In this implementation plan, the specific steps for constructing a multi-base station digital twin model are as follows: obtaining relevant information such as the geographic coordinates of the base stations, transmission power, antenna parameters, and environmental topology data; determining the architecture of the digital twin model based on the characteristics of the collected data and system requirements, such as selecting a suitable mathematical model or algorithm to describe the behavior and relationships of the base stations; using the collected data such as the geographic coordinates, transmission power, and antenna parameters as the initial parameters of the model, and substituting them into the model for initialization settings; training the model using actual operating data, and by adjusting the parameters and structure of the model, enabling the model to simulate the actual operation of the multi-base station system and continuously optimize the performance and accuracy of the model; and using a verification data set to verify and evaluate the trained model to ensure that the model can accurately reflect the true characteristics of the multi-base station system and meet the design requirements.

[0024] The geographic coordinates of a base station are obtained through the base station's construction planning documents, Geographic Information System (GIS) data, or Global Positioning System (GPS). Operators record the base station's precise location information when building a base station, and this information is obtained from relevant databases or documents. Transmit power is obtained from the base station's equipment configuration file or management system. The base station's transmit power is clearly specified in the device settings and can be obtained by interacting with the base station device or accessing relevant management interfaces. Antenna parameters, including antenna gain, radiation pattern, and polarization, are provided by the antenna manufacturer and recorded in the antenna's technical specification documents. During base station construction and maintenance, relevant technicians will have access to this antenna parameter information, which can also be obtained from the operator's equipment management system. Environmental topology data describes the environmental characteristics surrounding the base station, such as buildings, topography, and landforms. It is obtained through Geographic Information System (GIS) data, urban planning data, or specialized environmental surveys. For example, remote sensing technology, map data, and field surveys are used to obtain environmental information around the base station and construct an environmental topology model.

[0025] Assume that the data output by the multi-base station digital twin model is: Transmission delay set: ,in Representative The transmission delay of each base station, is the total number of base stations; path loss set: ,in Representative Path loss of each base station; channel quality set: ,in Representative The channel quality of each base station.

[0026] Preprocessing of relevant data is specifically to remove outliers in the data, for example, for transmission delay ,like or (in is the mean transmission delay, is the standard deviation of the transmission delay), then the value is considered an outlier and removed; the minimum-maximum normalization method is used to normalize the transmission delay, path loss and channel quality data to the [0,1] interval. Taking the transmission delay as an example: , represents the normalized transmission delay, represents the minimum transmission delay, Indicates the maximum transmission delay. Similarly, for path loss and channel quality Perform normalization and obtain and , represents the normalized path loss, Indicates the channel quality after normalization.

[0027] Assign weights to transmission delay, path loss and channel quality respectively 、 and , and satisfy Calculate the Comprehensive value of signal transmission quality of base stations , ,in Because the smaller the transmission delay, the better, so it is negated to ensure consistency with the monotonicity of other indicators.

[0028] Assuming comprehensive values Obey the normal distribution and calculate the sample mean and the sample standard deviation ; For a given confidence level (e.g. 95%), calculate the confidence interval based on the properties of the normal distribution: ;in, is the quantile of the standard normal distribution, for a 95% confidence level, .

[0029] Transmission delay refers to the time delay experienced by a signal from the sender to the receiver. It includes the signal's propagation time in the transmission medium, the time it takes for the base station to process the signal, and delays caused by factors such as network congestion. Transmission delay is obtained by recording the signal's transmission time and reception time at the sender and receiver, respectively, and calculating the difference between the two. In actual systems, transmission delay is measured using network testing tools or by adding timestamps to the communication protocol.

[0030] Path loss refers to the attenuation of signal strength during transmission due to factors such as propagation distance, signal reflection, scattering, and diffraction. It reflects the energy loss of the signal along the transmission path. Path loss is calculated by measuring the transmit power at the transmitter and the signal power received at the receiver, based on the difference between the two. In actual measurements, measurements are performed at different locations using equipment such as a signal strength tester, or using theoretical models to calculate path loss based on parameters such as the distance between the base station and the receiving point and environmental characteristics.

[0031] Channel quality is used to describe the quality of signals transmitted through a communication channel. It comprehensively considers factors such as the signal's bit error rate, signal-to-noise ratio, and fading. Channel quality directly affects the reliability and efficiency of data transmission. Channel quality is evaluated by analyzing and processing the received signal at the receiving end. For example, channel quality can be evaluated by measuring the signal-to-noise ratio (SNR) and bit error rate (BER) of the signal, or by using a channel estimation algorithm to obtain relevant channel parameters.

[0032] By comprehensively considering transmission delay, path loss and channel quality, the quality assessment value of the base station is determined, and the signal transmission quality of the base station is measured, avoiding the one-sidedness of single indicator evaluation; based on the signal transmission quality, the confidence interval is allowed to screen the set of candidate base stations, excluding base stations that do not meet the requirements, improving the efficiency and accuracy of base station selection, and providing a high-quality base station selection range for subsequent data transmission.

[0033] Specifically, the user behavior prediction module is analyzed as follows: user trajectory parameters include the base station information and movement parameters of the user's previous connection. The base station information of the user's previous connection includes the geographical coordinates of the base station and the connection time. The movement parameters include the movement speed and movement direction. The historical user trajectory parameters are input into the recurrent neural network, and the recurrent neural network is trained to identify the change pattern of the trajectory. The current user trajectory parameters are then input into the trained recurrent neural network to output the user's position coordinates at each future time point. The predicted position coordinates of the user at each future time point are integrated with the corresponding time point to form complete trajectory prediction data. The trajectory prediction data is then displayed and updated in the form of a graph, and the user trajectory prediction graph is output.

[0034] In this embodiment, the base station geographic coordinates and connection time are obtained from the base station management system or related database of the communication network. When a user connects to a base station, the base station will record the time of the user's connection and its own geographic coordinate information. These data can be read and used by the system.

[0035] The movement speed and direction are calculated by measuring the motion state of mobile devices such as mobile phones using built-in sensors, such as accelerometers and gyroscopes. The movement speed and direction can also be estimated based on the changes in the position of the base stations connected by the user at different times. For example, the average movement speed and direction can be obtained by calculating the distance and direction between two base stations connected at adjacent time points and combining the time interval.

[0036] The specific steps for identifying trajectory change patterns are as follows: preprocessing the collected historical trajectory parameters, including data cleaning, normalization and other operations, to ensure the quality and consistency of the data and facilitate the processing of the neural network. For example, normalizing data such as mobile speed and base station geographic coordinates, mapping them to a specific numerical range, to prevent certain features from affecting the training effect of the model due to excessively large or small values; initializing the recurrent neural network, setting the network structure and parameters, including the number of neurons, number of layers, activation function, etc. For example, selecting activation functions such as ReLU or Sigmoid function to introduce nonlinear characteristics and improve the expressive power of the model, at the same time, randomly initializing the weights and biases of the model to provide initial values ​​for subsequent training; inputting the preprocessed historical trajectory parameters into the recurrent neural network, and performing forward propagation calculations according to the network structure and the set algorithm. At each time step, the network will calculate a new hidden state based on the current input and the previous hidden state, and gradually pass it forward to finally obtain the output result, that is, the predicted value of the user's future location coordinates; comparing the predicted result with the actual user location coordinates For comparison, the difference between the predicted value and the true value is measured by defining an appropriate loss function. Common loss functions include mean squared error (MSE) and mean absolute error (MAE). Based on the calculation result of the loss function, the gradient of each parameter with respect to the loss function is calculated through the backpropagation algorithm. The weights and biases of the model are updated using optimization algorithms such as stochastic gradient descent, Adagrad, and Adadelta, so that the loss function gradually decreases. In each iteration, the parameters are adjusted in the opposite direction of the gradient to gradually optimize the performance of the model so that it can better fit the changing patterns in the historical trajectory data. During the training process, the model is regularly evaluated using the validation dataset to observe the changing trend of the loss function and the accuracy of the prediction results. If the loss function no longer decreases significantly or reaches the preset stopping conditions such as the number of iterations and accuracy, the model training is considered complete. Otherwise, iterative training continues, and the weights and biases of the model are continuously adjusted until the stopping conditions are met. Through continuous iterative optimization, the model gradually learns the changing patterns of user trajectories and makes predictions for new input data.

[0037] By considering the base station information and movement parameters previously connected by the user, a recurrent neural network can be used to learn the changing patterns of the user's trajectory, thereby predicting the user's future location coordinates, providing a reliable basis for subsequent base station switching and collaboration; integrating the predicted location coordinates with time information and displaying the updates in the form of a graph can reflect the user's dynamic trajectory in real time, facilitating the system to make timely adjustments and decisions, thereby improving the system's real-time and adaptability.

[0038] Specifically, the channel prediction and optimization module is analyzed as follows: using the multi-base station digital twin model to obtain the time distribution characteristic parameters and spatial distribution characteristic parameters of the candidate base station set, and then using the time distribution characteristic parameters and spatial distribution characteristic parameters to respectively identify the time distribution characteristic values ​​and spatial distribution characteristic values ​​of each candidate base station; combining the time distribution characteristic values ​​and spatial distribution characteristic values ​​of each candidate base station, determine the space-time channel quality evaluation value of each candidate base station, and then generate a space-time channel quality map based on the location and space-time channel quality evaluation value of the candidate base station. The space-time channel quality map is marked with high-quality base station areas and low-quality base station areas. The high-quality base station area is specifically the base station area whose space-time channel quality evaluation value is greater than or equal to the space-time channel quality threshold, and the low-quality base station area is specifically the base station area whose space-time channel quality evaluation value is lower than the space-time channel quality threshold.

[0039] In this embodiment, the temporal distribution characteristic value is specifically obtained by calculation using the STARMA model, and the spatial distribution characteristic value is specifically obtained by calculation using the Kriging interpolation method.

[0040] For the time distribution characteristic value: use the STARMA model to calculate, assuming that the observed spatiotemporal series data is ,in Indicates spatial location, represents time, the general form of the STARMA model is expressed as: ,in and They are respectively about the time lag operator and spatial lag operators The polynomial of It is a white noise sequence. By fitting and estimating historical spatiotemporal data, the model parameters are obtained, and then the model is used to predict the future time distribution characteristic values. The specific calculation process involves estimating and solving the model parameters, using methods such as maximum likelihood estimation.

[0041] For the spatial distribution characteristic values: Use Kriging interpolation method to calculate and collect the spatial distribution characteristic parameter values ​​(such as signal strength, propagation loss) of known location points. Let these known points be , the corresponding parameter value is For the position point to be estimated , its spatial distribution characteristic value It can be calculated using the Kriging interpolation formula: ,in is the weight coefficient obtained by solving the Kriging equations, Indicates the number of the spatial distribution characteristic parameter value of a known location point, Represents the total number of spatial distribution characteristics of known location points. The set of equations takes into account the spatial correlation between known points and the distance relationship between the estimated point and the known points.

[0042] The time distribution characteristic parameters include parameters related to the time dimension, such as the change in base station signal strength at different time points, the change in signal propagation delay over time, and the time correlation of channel fading. These parameters reflect the dynamic change characteristics of channel quality in time and space.

[0043] Spatial distribution characteristic parameters include the distance between the base station and the user, the propagation loss of the signal in space, the difference in signal strength at different locations, the spatial correlation of the signal, and other parameters that are only related to the spatial position, which describe the spatial distribution characteristics of the channel quality.

[0044] The base station digital twin model combines the actual signal monitoring data to obtain too many time distribution characteristic parameters. The digital twin model simulates the propagation of base station signals under different time and space conditions. At the same time, it combines the real-time signal data collected by sensors or monitoring equipment actually deployed in the network to calibrate and optimize the model, thereby obtaining accurate time distribution characteristic parameters.

[0045] The spatial distribution characteristic parameters can be directly obtained by measuring the distance between the base station and the user equipment and the signal strength. The digital twin model can also be used to simulate the propagation of the signal in space, considering the impact of environmental factors such as terrain and buildings on signal propagation, so as to obtain the spatial distribution characteristic parameters.

[0046] The specific steps for generating a space-time channel quality map based on the location and space-time channel quality evaluation values ​​of candidate base stations are as follows: mark the location information of each candidate base station in geographic space, compare the calculated space-time channel quality evaluation value with the pre-set space-time channel quality threshold, determine the area to which each base station belongs (high-quality base station area or low-quality base station area), and use different colors or marks to distinguish high-quality base station areas from low-quality base station areas to form a space-time channel quality map.

[0047] The method for obtaining the space-time channel quality threshold is as follows: based on previous communication system operation experience, experimental data in similar scenarios or industry standards, an initial space-time channel quality threshold is set. For example, in certain specific communication environments, based on long-term testing and evaluation, it is found that when the channel quality evaluation value reaches a certain range, the system can ensure good communication quality and stability, so the boundary value of this range is used as the initial threshold; a theoretical threshold can also be obtained through mathematical calculation based on communication theory and channel model. For example, according to relevant theories such as Shannon's formula, combined with parameters such as the system's bandwidth and noise level, a channel quality threshold that can meet certain data transmission rate and bit error rate requirements can be calculated; an adaptive method can also be used to adjust the channel quality based on the current system conditions. The system can dynamically adjust the spatiotemporal channel quality threshold based on the operating status and performance indicators of the network. For example, the system can monitor indicators such as the success rate, delay, and packet loss rate of data transmission in real time. When these indicators show abnormal changes, the threshold is adjusted accordingly to optimize system performance. The specific adjustment algorithm is based on feedback control theory and is adjusted according to the deviation between the performance indicator and the target value. Machine learning algorithms, such as clustering algorithms and decision tree algorithms, can also be used to analyze and process large amounts of channel quality data, automatically learn and determine appropriate thresholds. For example, a clustering algorithm can be used to divide channel quality data into different categories, and then the threshold is determined based on the boundaries between the categories, or a decision tree algorithm can be used to divide the channel quality levels according to different characteristics and conditions to determine the corresponding threshold.

[0048] By comprehensively considering the temporal and spatial distribution characteristics of the candidate base station set, the channel quality of each candidate base station can be evaluated, providing users with more accurate channel status information; the generation of a spatiotemporal channel quality map helps the system to reasonably allocate resources based on the channel quality of the base station, assigning data transmission tasks to high-quality base station areas, and improving the overall performance of the system and data transmission efficiency; taking into account the temporal distribution characteristics enables the system to adapt to dynamic changes in the network environment, such as the impact of factors such as user mobility and signal interference on channel quality, thereby improving the robustness and adaptability of the system.

[0049] Specifically, the intelligent switching and collaboration module is specifically analyzed as follows: superimposing the future position coordinate information in the user trajectory prediction map with the space-time channel quality map, marking the base stations in the superimposed area; based on the positional relationship between the user's future position and each marked base station, and the distribution of high-quality base station areas and low-quality base station areas in the space-time channel quality map, screen out base stations whose distance from the user's future position coordinates is less than the distance threshold and whose space-time channel quality assessment results are high-quality base station areas as preferred base stations; for each preferred base station, identify the load situation of the base station, and then determine the comprehensive score of each preferred base station based on the distance between the base station and the user's future position and the space-time channel quality assessment value of the base station, and select and form a list of collaborative base stations based on the comprehensive score; monitor the user's mobility status and the signal quality of the currently connected base station in real time, and then determine whether to trigger the switching of the base station connection based on the user's mobility status and the signal quality of the currently connected base station.

[0050] In this embodiment, the specific steps of superimposing the user trajectory prediction map with the spatiotemporal channel quality map and marking the base stations are as follows: ensuring that the user trajectory prediction map contains future position coordinate information, the spatiotemporal channel quality map contains base station location and channel quality assessment information, and that the two have a unified geographic coordinate system; traversing each future position coordinate in the user trajectory prediction map, checking whether the coordinate falls within the coverage area of ​​a base station in the spatiotemporal channel quality map, where the coverage area can be determined based on information such as the signal propagation range and geographic coordinates of the base station; if the future position coordinate falls within the coverage area of ​​a base station, marking the base station as a base station in the superimposed area, and highlighting these base stations in the map with a specific color, symbol, or logo.

[0051] The distance threshold is set as follows: based on previous communication network operation experience and test data, a fixed distance threshold is set. For example, in densely populated urban areas, the distance threshold can be set to several hundred meters, and in suburban or rural areas, the distance threshold can be appropriately increased. The distance threshold can also be dynamically adjusted based on factors such as the user's movement speed and the type of communication service. For example, for high-speed mobile users, the distance threshold can be appropriately increased to prepare for base station switching in advance. For communication services with high real-time requirements, the distance threshold can be reduced to ensure signal quality. The distance threshold can also be adjusted by continuously monitoring network performance indicators, such as communication success rate and packet loss rate, to optimize network performance. Machine learning algorithms can be used to automatically optimize the distance threshold based on historical data and real-time monitoring results.

[0052] The load condition of a base station is identified in the following ways: the base station's own management system records and compiles statistics on the base station's load in real time, such as the number of currently connected users, data transmission rate, bandwidth occupancy, etc., and directly obtains these load data by communicating with the base station management system; traffic monitoring equipment can also be deployed in the network to monitor the data traffic between the base station and users in real time, and estimate the base station's load condition based on the traffic volume; or historical base station load data and related influencing factors (such as time, location, user behavior, etc.) are collected, and a load prediction model is established using a machine learning algorithm to predict the base station's load condition based on current environmental information and real-time data.

[0053] The specific steps for selecting and forming a collaborative base station list based on the comprehensive score are: sorting the preferred base stations from high to low according to the comprehensive score; and selecting several base stations with high rankings to form a collaborative base station list based on the system requirements and set rules.

[0054] The user's mobility status and the signal quality of the currently connected base station are monitored as follows: the user device (such as a mobile phone) monitors its own mobility status (such as acceleration, speed, direction, etc.) in real time and feeds this information back to the base station and the communication system. At the same time, the user device can also measure the signal strength and signal quality (such as signal-to-noise ratio, bit error rate, etc.) of the currently connected base station and upload them to the system; the base station evaluates the user device's mobility status and signal quality by monitoring the communication parameters between it and the user device, such as signal strength, transmission rate, packet loss rate, etc.

[0055] The specific steps for determining whether to trigger base station connection switching based on the user's mobility status and the signal quality of the currently connected base station are as follows: pre-set signal quality thresholds (such as signal strength thresholds, signal-to-noise ratio thresholds, etc.) and mobility status thresholds (such as mobility speed thresholds); obtain the user's mobility status and the signal quality of the currently connected base station in real time; if the signal quality of the currently connected base station is lower than the signal quality threshold, and the user's mobility speed exceeds the mobility status threshold, the base station switching is triggered; if the signal quality of the currently connected base station continues to be lower than the signal quality threshold within a preset time, even if the user's mobility speed does not exceed the threshold, the base station switching is triggered; if the user moves to a location farther away from the currently connected base station, and there is a base station with a higher comprehensive score in the collaborative base station list, the base station switching is triggered.

[0056] By combining user trajectory prediction and spatiotemporal channel quality maps, high-quality base stations suitable for the user's future location are screened out, ensuring that the user always maintains communication connection during movement, reducing signal interruption and interference, and improving communication stability and data transmission rate; the selected collaborative base stations comprehensively consider distance, channel quality and load conditions, which helps to allocate base station resources, avoid excessive load on some base stations, and improve resource utilization of the entire communication network; real-time monitoring of user mobility status and current base station signal quality, and based on this, determine whether to switch base stations, can realize intelligent and timely base station switching, avoid communication failures caused by signal quality degradation, and improve the user's communication experience.

[0057] In summary, this application has at least the following effects:

[0058] A virtual model is constructed based on the base station's geographic coordinates, transmission power, antenna parameters, and environmental topology data. This can simulate signal propagation characteristics in real time, create a "digital clone" for the base station, and predict signal coverage blind spots or interference hotspots in advance. The model output data is used to dynamically screen the set of candidate base stations to avoid misjudgments or missed judgments caused by fixed thresholds, adapt to the dynamic impact of complex environments on signals, and improve the robustness of switching decisions. User trajectory parameters are used to predict future locations and generate trajectory prediction maps to provide forward-looking guidance for base station switching. Combined with the time distribution characteristics of candidate base stations, a channel quality map is generated to quantify indicators such as signal strength and latency between different base stations and users, providing data support for base station selection, giving priority to base stations with the best channel quality, and improving user experience. Based on user trajectory predictions and channel quality maps, candidate base stations are scored in multiple dimensions, and a list of collaborative base stations and switching strategies are output to reduce switching interruption time and avoid video freezes or call interruptions. Through the digital twin model and dynamic confidence intervals, the system can perceive environmental changes in real time and quickly adjust base station collaboration strategies to avoid excessive occupation of high-load base stations.

[0059] Those skilled in the art will appreciate that embodiments of the present invention may be provided as systems. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] The present invention is described with reference to a block diagram of a system according to an embodiment of the present invention. It should be understood that the combination of each structure in the block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, such that the instructions, executed by the processor of the computer or other programmable data processing device, produce a device for implementing the functions specified in each structure in the block diagram.

[0061] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in each structure of the structural diagram.

[0062] These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in each structure in the structural diagram.

[0063] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0064] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A multi-base station cooperative data transmission system, characterized in that: It includes a digital twin modeling module, a dynamic confidence interval design module, a user behavior prediction module, a channel prediction and optimization module, and an intelligent switching and collaboration module, among which: The digital twin modeling module is used to obtain base station geographic coordinates, transmission power, antenna parameters and environmental topology data to build a multi-base station digital twin model; The dynamic confidence interval design module is used to obtain the permissible confidence interval of signal transmission quality by using the output data of the multi-base station digital twin model, and then screen out the candidate base station set by the permissible confidence interval of signal transmission quality; The user behavior prediction module is used to obtain user trajectory parameters, use the user trajectory parameters to predict the user's future location coordinates, and then output a user trajectory prediction map; The channel prediction and optimization module is used to output the time distribution characteristics and spatial distribution characteristics of the candidate base station set using the multi-base station digital twin model, and output the spatiotemporal channel quality spectrum of the candidate base station set; The intelligent switching and collaboration module is used to screen the candidate base station set based on the user trajectory prediction map and the spatiotemporal channel quality map of the candidate base station set, obtain the comprehensive scores of the screened base stations, and then output a list of collaborative base stations and a switching strategy.

2. A multi-base station cooperative data transmission system according to claim 1, characterized in that: The signal transmission quality allowable confidence interval is specifically a comprehensive evaluation confidence interval of transmission delay, path loss and channel quality.

3. The multi-base station cooperative data transmission system according to claim 2, characterized in that: The dynamic confidence interval design module is specifically analyzed as follows: The multi-base station digital twin model is used to extract the transmission delay, path loss, and channel quality data of each base station, and the relevant data is preprocessed to determine the quality assessment value of each base station based on the transmission delay, path loss, and channel quality. Based on the sample data of the quality evaluation value, the signal transmission quality allowable confidence interval is identified, and then the base stations whose quality evaluation values ​​are within the signal transmission quality allowable confidence interval are screened out to form a set of candidate base stations.

4. The multi-base station cooperative data transmission system according to claim 1, wherein: The user behavior prediction module specifically analyzes that: the user trajectory parameters include the base station information and movement parameters of the user's previous connection, the base station information of the user's previous connection includes the geographical coordinates of the base station and the connection time, and the movement parameters include the movement speed and movement direction; The historical user trajectory parameters are input into the recurrent neural network, which is trained to identify the changing patterns of the trajectory. The current user trajectory parameters are then input into the trained recurrent neural network to output the user's location coordinates at each future time point. The predicted position coordinates of the user at each future time point are integrated with the corresponding time point to form complete trajectory prediction data, which is then displayed and updated in the form of a graph to output the user trajectory prediction graph.

5. The multi-base station cooperative data transmission system according to claim 1, wherein: The channel prediction and optimization module is specifically analyzed as follows: using the multi-base station digital twin model to obtain the time distribution characteristic parameters and spatial distribution characteristic parameters of the candidate base station set, and then using the time distribution characteristic parameters and spatial distribution characteristic parameters to respectively identify the time distribution characteristic value and spatial distribution characteristic value of each candidate base station; Combined with the time distribution characteristic values ​​and spatial distribution characteristic values ​​of each candidate base station, the space-time channel quality evaluation value of each candidate base station is determined, and then a space-time channel quality map is generated based on the position and space-time channel quality evaluation value of the candidate base station. The space-time channel quality map includes high-quality base station areas and low-quality base station areas. The high-quality base station area is specifically a base station area whose space-time channel quality evaluation value is greater than or equal to the space-time channel quality threshold, and the low-quality base station area is specifically a base station area whose space-time channel quality evaluation value is lower than the space-time channel quality threshold.

6. The multi-base station cooperative data transmission system according to claim 5, characterized in that: The temporal distribution characteristic value is specifically obtained by calculation using a STARMA model, and the spatial distribution characteristic value is specifically obtained by calculation using a Kriging interpolation method.

7. The multi-base station cooperative data transmission system according to claim 5, characterized in that: The intelligent switching and collaboration module is specifically analyzed as follows: superimposing the future position coordinate information in the user trajectory prediction map with the spatiotemporal channel quality map, and marking the base stations in the superimposed area; Based on the relationship between the user's future location and each marked base station, as well as the distribution of high-quality and low-quality base station areas in the spatiotemporal channel quality map, base stations whose distance to the user's future location coordinates is less than a distance threshold and whose spatiotemporal channel quality assessment results indicate a high-quality base station area are selected as preferred base stations. For each preferred base station, identify the base station load, and then determine the comprehensive score of each preferred base station based on the distance between the base station and the user's future location and the base station's spatiotemporal channel quality assessment value. Based on the comprehensive score, select and form a list of cooperative base stations; Monitor the user's mobility status and the signal quality of the currently connected base station in real time, and then determine whether to trigger the base station connection switch based on the user's mobility status and the signal quality of the currently connected base station.

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