Cellular network method and system

Abstract

The present invention discloses a cellular network method and system, which comprises: inputting dynamic environment perception information to be predicted into an optimal cellular adaptation prospect prediction model to obtain a cellular adaptation prospect prediction result; a protocol stack control intermediate layer dynamically allocates operating resources of each protocol stack based on the cellular adaptation prospect prediction result, and when the protocol stack control intermediate layer decides to switch from a first protocol stack to a second protocol stack operation based on the cellular adaptation prospect prediction result, the key network parameters of the currently connected cell are passed to the second protocol stack, so that the second protocol stack adjusts the communication strategy based on the key network parameters, which helps to solve the problems of high network switching delay, poor stability, high power consumption and inability to give full play to the advantages of each RAT due to the independent operation of the protocol stack, single switching decision and lack of dynamic optimization of resource allocation in a UE device in a multi-RAT cellular network environment.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a cellular network method and system. Background Art

[0002] With the rapid development of mobile Internet, smart terminal devices such as smartphones and tablets have been deeply integrated into people's daily lives. People are increasingly in need of high-quality, uninterrupted cellular network services anytime and anywhere. Cellular network technology has evolved over many generations, from the early GSM to the widely used LTE and 5G today, and the coexistence of multiple heterogeneous networks has gradually formed. Different radio access technologies (RATs) each have their own unique advantages and applicable scenarios, aiming to meet diverse user needs.

[0003] However, this complex multi-RAT network environment has brought many challenges to user equipment (UE). On the one hand, traditional UEs are often designed with relatively independent hardware modules and protocol stacks for different RATs, which not only makes the internal structure of the device bloated, significantly increases manufacturing costs and power consumption, but also occupies a lot of valuable physical space, hindering the development of devices towards thinness and portability. For example, in the early days, in order to support CDMA and GSM dual-mode networks, two almost independent RF front-end and baseband processing units were required inside the mobile phone, which undoubtedly increased the burden of product design.

[0004] On the other hand, at the software level, the independent operation of each protocol stack leads to a lack of efficient coordination mechanism when the UE needs to switch between different RATs. In the process of cell reselection, the conventional practice is usually to simply judge based on signal strength, which can easily cause problems. Due to the mobility of UE, the wireless signal environment changes rapidly. Relying solely on a single signal strength indicator may cause the UE to blindly reconnect to the original cell after switching the protocol stack, even if the signal of the cell has been sharply attenuated during the switching period, causing a significant increase in the delay in re-establishing the service, seriously affecting the application experience with high real-time requirements, such as online video freezes, voice call interruptions, etc.

[0005] At the same time, frequent and comprehensive scanning of each protocol stack to determine the best access cell will consume a lot of battery power, further shortening the UE's battery life, which runs counter to users' expectations for long-lasting devices. In addition, the network load conditions corresponding to different RATs vary significantly, and the lack of forward-looking resource allocation strategies means that during high-load periods, the UE may not be able to reasonably allocate limited operating resources to more advantageous protocol stacks, resulting in a decline in overall network performance and an inability to fully tap the potential of each RAT.

[0006] In summary, in order to overcome the many drawbacks of existing UEs in a multi-RAT cellular network environment, an innovative cellular network method is urgently needed that can not only accurately predict the network adaptation prospects, but also dynamically and intelligently manage the protocol stack and resource allocation to achieve efficient, stable and low-power cellular network communication to meet users' growing mobile network needs. Summary of the invention

[0007] In view of this, the present invention proposes a cellular network method and system, which realizes accurate prediction of network adaptation prospects and dynamic allocation of protocol stack resources by constructing a cellular adaptation prospect prediction model and combining it with the protocol stack management and control middle layer, effectively reducing protocol switching delay, improving network stability, and reducing power consumption, thereby bringing users high-quality, uninterrupted cellular network experience.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A cellular network method, comprising:

[0010] Constructing a cellular adaptation prospect prediction model, inputting dynamic environment perception information into the cellular adaptation prospect prediction model for training, so as to obtain an optimal cellular adaptation prospect prediction model;

[0011] Inputting the dynamic environment perception information to be predicted into the optimal cellular adaptation prospect prediction model to obtain the cellular adaptation prospect prediction result;

[0012] Building a protocol stack control intermediate layer on the first protocol stack and the second protocol stack in the UE device, controlling the UE device to operate based on the first cellular communication protocol through the first protocol stack, and controlling the UE device to operate based on the second cellular communication protocol through the second protocol stack;

[0013] The protocol stack control intermediate layer dynamically allocates operating resources to each protocol stack based on the cellular adaptation prospect prediction result. When the protocol stack control intermediate layer decides to switch from the first protocol stack to the second protocol stack operation based on the cellular adaptation prospect prediction result, the key network parameters of the currently connected cell are passed to the second protocol stack, so that the second protocol stack adjusts the communication strategy based on the key network parameters.

[0014] On the basis of the above technical solution, the present invention can also be improved as follows:

[0015] Optionally, the cellular adaptation prospect prediction model includes a data processing layer, a spatial block, a temporal block, a fusion block, a prediction layer and an output layer;

[0016] The collected dynamic environment perception information is processed through the data processing layer, the spatial features of the dynamic environment perception information are extracted through the spatial block, the temporal features of the dynamic environment perception information are extracted through the temporal block, the temporal features and the spatial features are fused through the fusion layer to obtain fused features, the fused features are predicted through the prediction layer, and the cellular adaptation prospect prediction results are output through the output layer.

[0017] Optionally, the performing data processing on the collected dynamic environment perception information through the data processing layer includes:

[0018] The collected dynamic environment perception information is parsed according to the type to obtain several sub-information;

[0019] Perform noise interference removal processing on a plurality of sub-information;

[0020] The plurality of sub-information after the noise interference removal process is coded and converted.

[0021] Optionally, extracting spatial features of dynamic environment perception information through spatial blocks includes:

[0022] The spatial features are calculated by formula (1);

[0023]

[0024] In the formula, Feature ij is the spatial feature, X is the input matrix, k is the convolution kernel size, w mn Spatial weight, i is the row index of the input matrix, j is the column index of the input matrix, b is the bias term, α is the activation function, m is the row of the convolution kernel from 0 to k-1, and n is the column of the convolution kernel from 0 to k-1.

[0025] Optionally, extracting the time features of the dynamic environment perception information through the time block includes:

[0026] The time characteristics are calculated by formula (2);

[0027]

[0028] In the formula, Feature t is the time feature, x t is the dynamic environment perception information vector collected at t, n is the size of the time block, w i is the element in the weight vector, b is the bias term, y t is the intermediate calculation variable, and σ is the activation function.

[0029] Optionally, fusing the temporal feature and the spatial feature through a fusion layer to obtain a fusion feature includes:

[0030] Calculate the fusion features through formula (3);

[0031] Feature fusion =σ(α·Feature ij +β·Feature t +b fusion ) formula (3);

[0032] In the formula, Feature fusion is the fusion feature, α is the spatial fusion weight, β is the temporal fusion weight, b fusion is the fusion bias.

[0033] Optionally, the protocol stack control intermediate layer dynamically allocates operating resources of each protocol stack based on the cellular adaptation prospect prediction result, including:

[0034] Allocating allocatable resource units to the first protocol stack and the second protocol stack based on a priority ratio;

[0035] Re-evaluate the cellular adaptation prospect prediction results at predetermined intervals and adaptively adjust resource allocation according to the new results;

[0036] When a certain performance indicator prediction value of the first protocol stack or the second protocol stack exceeds a corresponding resource allocation threshold, corresponding resource increase allocation is performed on the first protocol stack or the second protocol stack.

[0037] A cellular network system, comprising:

[0038] A model training module is used to construct a cellular adaptation prospect prediction model, input dynamic environment perception information into the cellular adaptation prospect prediction model for training, so as to obtain an optimal cellular adaptation prospect prediction model;

[0039] A prediction module, used for inputting the dynamic environment perception information to be predicted into the optimal cellular adaptation prospect prediction model to obtain a cellular adaptation prospect prediction result;

[0040] A protocol stack control intermediate layer, which is arranged on the first protocol stack and the second protocol stack in the UE device, controls the UE device to operate based on the first cellular communication protocol through the first protocol stack, and controls the UE device to operate based on the second cellular communication protocol through the second protocol stack;

[0041] The protocol stack control intermediate layer dynamically allocates operating resources to each protocol stack based on the cellular adaptation prospect prediction result. When the protocol stack control intermediate layer decides to switch from the first protocol stack to the second protocol stack operation based on the cellular adaptation prospect prediction result, the key network parameters of the currently connected cell are passed to the second protocol stack, so that the second protocol stack adjusts the communication strategy based on the key network parameters.

[0042] An electronic device comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the method when executing the computer program.

[0043] A non-transitory computer-readable storage medium stores a computer program, which implements the steps of the method when executed by a processor.

[0044] The present invention has the following advantages:

[0045] The cellular network method in the present invention allows each crawling node to perform its duties with precise task allocation, avoiding idle resources or competition. For example, LLM construction that integrates multi-domain knowledge, different crawling nodes specialize in academic, news, social and other data sources, greatly improving the overall startup efficiency, shortening the initial time of data acquisition, and accelerating the model iteration cycle.

[0046] The cellular network method of the present invention, by constructing and applying a cellular adaptation prospect prediction model, accurately collects and analyzes dynamic environmental perception information, can gain insight into the network situation in advance, accurately predict the signal strength trend of each cellular cell, the network load level and the adaptation performance of different wireless access technologies, and provide a highly reliable basis for subsequent decision-making; secondly, a protocol stack control intermediate layer is innovatively set in the UE device to break the deadlock of independent operation of the traditional protocol stack, dynamically allocate operating resources according to the prediction results, and ensure that resources are tilted towards the protocol stack with the best adaptability and urgent performance requirements, greatly improve resource utilization efficiency, avoid resource waste, and optimize overall network performance; thirdly, when it comes to protocol stack switching, the key network parameters of the current connected cell are transmitted in a timely manner to help the newly enabled protocol stack quickly adjust the communication strategy accordingly, greatly shorten the switching time, reduce the risk of communication interruption, and allow the UE device to always maintain a stable, efficient and smooth communication connection in a complex and changeable cellular network environment, thereby comprehensively improving the user's network usage experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] For the purpose of illustration and not limitation, the present invention is now described in conjunction with the embodiments of the present invention and the accompanying drawings, in which:

[0048] Figure 1 is a schematic diagram of a flow chart of a cellular network method in an embodiment of the present invention;

[0049] Figure 2 is a schematic diagram of the main components of a cellular network system in an embodiment of the present invention;

[0050] Figure 3 This is a schematic diagram of the physical structure of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution in the implementation of the present invention will be clearly and completely described below in conjunction with the drawings in the implementation of the present invention. Obviously, the described implementation is only a part of the implementation of the present invention, not all of the implementations. Based on the implementation of the present invention, all other implementations obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0052] It should be noted that the terms "first", "second", etc. in the specification of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so as to describe the embodiments of the present invention described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0053] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0054] Figure 1 is a flow chart of a cellular network method in an embodiment of the present invention, such as Figure 1 As shown, the cellular network method provided by the embodiment of the present invention includes the following steps S101 to S104.

[0055] S101, constructing a cellular adaptation prospect prediction model, inputting dynamic environment perception information into the cellular adaptation prospect prediction model for training, so as to obtain an optimal cellular adaptation prospect prediction model.

[0056] Specifically, firstly, the multi-type sensors built into the UE device are used to comprehensively collect dynamic environmental perception information, including the real-time geographic location information of the UE device, accurate to longitude and latitude, its moving speed and direction, the speed unit is meters per second, the direction is measured clockwise with due north as 0 degrees, and the surrounding wireless signal environment, including the signal strength of each base station in dBm, frequency band, Wi-Fi hotspot signal strength, etc. Then, the collected information is pre-processed, and key features are extracted through feature extraction algorithms, such as regional type features in geographic location, signal stability features in wireless signals, etc.; data cleaning methods are used to remove outliers caused by sensor failure and electromagnetic interference; and normalization methods are used to unify the scale of features of different magnitudes. The constructed model includes a data processing layer, a neural network core layer and an output layer. The data processing layer performs the above pre-processing, and the neural network core layer uses convolutional neural networks to mine spatial correlations, recurrent neural networks to capture time series features, and fully connected neural networks to fuse multiple features to learn complex relationships. After multiple rounds of iterative training, the optimal model is selected using the principle of minimizing the loss function.

[0057] The cellular adaptation prospect prediction model includes a data processing layer, a space block, a time block, a fusion block, a prediction layer and an output layer;

[0058] The collected dynamic environment perception information is processed through the data processing layer, the spatial features of the dynamic environment perception information are extracted through the spatial block, the temporal features of the dynamic environment perception information are extracted through the temporal block, the temporal features and the spatial features are fused through the fusion layer to obtain fused features, the fused features are predicted through the prediction layer, and the cellular adaptation prospect prediction results are output through the output layer.

[0059] The data processing layer performs data processing on the collected dynamic environment perception information, including:

[0060] The collected dynamic environment perception information is parsed according to the type to obtain several sub-information;

[0061] Perform noise interference removal processing on a plurality of sub-information;

[0062] The plurality of sub-information after the noise interference removal process is coded and converted.

[0063] The extracting of spatial features of dynamic environment perception information through spatial blocks includes:

[0064] The spatial features are calculated by formula (1);

[0065]

[0066] In the formula, Feature ij is the spatial feature, X is the input matrix, k is the convolution kernel size, wmn Spatial weight, i is the row index of the input matrix, j is the column index of the input matrix, b is the bias term, α is the activation function, m is the row of the convolution kernel from 0 to k-1, and n is the column of the convolution kernel from 0 to k-1.

[0067] The step of extracting the time characteristics of the dynamic environment perception information through the time block includes:

[0068] The time characteristics are calculated by formula (2);

[0069]

[0070] In the formula, Feature t is the time feature, x t is the dynamic environment perception information vector collected at t, n is the size of the time block, w i is the element in the weight vector, b is the bias term, y t is the intermediate calculation variable, and σ is the activation function.

[0071] The step of fusing the temporal feature and the spatial feature through a fusion layer to obtain a fusion feature includes:

[0072] Calculate the fusion features through formula (3);

[0073] Feature fusion =σ(α·Feature ij +β·Feature t +b fusion ) formula (3);

[0074] In the formula, Feature fusion is the fusion feature, α is the spatial fusion weight, β is the temporal fusion weight, b fusion is the fusion bias.

[0075] S102, inputting the dynamic environment perception information to be predicted into the optimal cellular adaptation prospect prediction model to obtain a cellular adaptation prospect prediction result.

[0076] Specifically, when there is a need for real-time communication, the UE device instantly collects the current dynamic environment perception information to be predicted, and also undergoes preliminary processing as in S101 to ensure that the input information format is standardized and the data is reliable. It is fed into the optimal cellular adaptation prospect prediction model. The model quickly outputs the cellular adaptation prospect prediction results based on the rules learned in the early stage, including the prediction of each cellular cell in the future, such as the 5-minute signal strength change curve, the estimated network load, divided into high, medium and low load probabilities, and the applicability probability of different wireless access technologies in the current environment, as well as the data transmission rate, delay and other performance indicators that can be achieved if the technology is adopted, providing accurate guidance for subsequent decisions.

[0077] S103, constructing a protocol stack control intermediate layer on the first protocol stack and the second protocol stack in the UE device, controlling the UE device to operate based on the first cellular communication protocol through the first protocol stack, and controlling the UE device to operate based on the second cellular communication protocol through the second protocol stack.

[0078] Specifically, in the software architecture level design of the UE device, a protocol stack control middle layer is introduced above the physical layer and below the application layer, which interacts closely with the first protocol stack and the second protocol stack. The first protocol stack adapts to the first cellular communication protocols such as LTE, which are relatively mature and widely covered but slightly insufficient in high data rate scenarios. It is responsible for handling the related signaling interaction, data encapsulation and transmission processes to ensure stable communication of the UE device under the protocol; the second protocol stack adapts to the second cellular communication protocols such as 5G, which have the advantages of high bandwidth and low latency but may have limited coverage in the initial stage, and also undertakes its exclusive communication process control. As the overall coordinator, the protocol stack control middle layer monitors the operating status of the two protocol stacks, collects feedback information, and lays a solid foundation for subsequent dynamic management.

[0079] S104, the protocol stack control middle layer dynamically allocates the operating resources of each protocol stack based on the cellular adaptation prospect prediction result. When the protocol stack control middle layer decides to switch from the first protocol stack to the second protocol stack operation based on the cellular adaptation prospect prediction result, the key network parameters of the currently connected cell are passed to the second protocol stack, so that the second protocol stack adjusts the communication strategy based on the key network parameters.

[0080] Specifically, resource units are allocated to the first protocol stack and the second protocol stack based on a priority ratio; the cellular adaptation prospect prediction results are re-evaluated at predetermined intervals, and resource allocation is adaptively adjusted according to the new results; when the predicted value of a performance indicator of the first protocol stack or the second protocol stack exceeds the corresponding resource allocation threshold, corresponding resource increase allocation is performed on the first protocol stack or the second protocol stack.

[0081] After the protocol stack control middle layer obtains the cellular adaptation prospect prediction results, it quickly starts the resource allocation process. For computing resources, the CPU core time slice is used as the allocation unit, and the priority is allocated according to the protocol stack. The priority is comprehensively evaluated by signal strength stability, network load expectation, and wireless access technology performance; storage resources are allocated in fixed-size memory blocks to ensure sufficient storage for high-demand protocol stacks; network resources are allocated according to certain data transmission rate units. If the prediction results show that the current environment is more suitable for the second protocol stack, the switching mechanism is triggered. At this time, the protocol stack control middle layer will pass the key network parameters of the currently connected cell, such as base station identification, signal strength, frequency band, allocated resources and other information to the second protocol stack. After receiving the second protocol stack, it will optimize the signaling sending frequency and adjust the data transmission mode accordingly, quickly adapt to the new environment, achieve seamless switching, and ensure smooth communication.

[0082] Figure 2 FIG. 1 is a schematic diagram of the main components of the cellular network system in an embodiment of the present invention. Figure 2 As shown, the cellular network system 1 provided in the embodiment of the present invention includes a model training module 10, a prediction module 20 and a protocol stack management and control intermediate layer 30.

[0083] The model training module 10 is used to construct a cellular adaptation prospect prediction model, input dynamic environment perception information into the cellular adaptation prospect prediction model for training, so as to obtain an optimal cellular adaptation prospect prediction model;

[0084] The prediction module 20 is used to input the dynamic environment perception information to be predicted into the optimal cellular adaptation prospect prediction model to obtain the cellular adaptation prospect prediction result;

[0085] The protocol stack control intermediate layer 30 is arranged on the first protocol stack and the second protocol stack in the UE device, and controls the UE device to operate based on the first cellular communication protocol through the first protocol stack, and controls the UE device to operate based on the second cellular communication protocol through the second protocol stack;

[0086] The protocol stack control intermediate layer dynamically allocates operating resources to each protocol stack based on the cellular adaptation prospect prediction result. When the protocol stack control intermediate layer decides to switch from the first protocol stack to the second protocol stack operation based on the cellular adaptation prospect prediction result, the key network parameters of the currently connected cell are passed to the second protocol stack, so that the second protocol stack adjusts the communication strategy based on the key network parameters.

[0087] Figure 3 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention, such as Figure 3 As shown, the electronic device 40 includes: a processor 401 (processor), a memory 402 (memory) and a bus 403;

[0088] The processor 401 and the memory 402 communicate with each other via the bus 403;

[0089] The processor 401 is used to call the program instructions in the memory 402 to execute the methods provided by the above-mentioned method embodiments, so as to execute the methods provided by the implementation mode of the present invention.

[0090] This embodiment provides a non-transitory computer-readable storage medium, which stores computer instructions. The computer instructions enable a computer to execute the method provided by the embodiment of the present invention.

[0091] A person skilled in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various storage media that can store program codes.

[0092] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A cellular network method, characterized in that: include: Constructing a cellular adaptation prospect prediction model, inputting dynamic environment perception information into the cellular adaptation prospect prediction model for training, so as to obtain an optimal cellular adaptation prospect prediction model; Inputting the dynamic environment perception information to be predicted into the optimal cellular adaptation prospect prediction model to obtain the cellular adaptation prospect prediction result; Building a protocol stack control intermediate layer on the first protocol stack and the second protocol stack in the UE device, controlling the UE device to operate based on the first cellular communication protocol through the first protocol stack, and controlling the UE device to operate based on the second cellular communication protocol through the second protocol stack; The protocol stack control intermediate layer dynamically allocates operating resources to each protocol stack based on the cellular adaptation prospect prediction result. When the protocol stack control intermediate layer decides to switch from the first protocol stack to the second protocol stack operation based on the cellular adaptation prospect prediction result, the key network parameters of the currently connected cell are passed to the second protocol stack, so that the second protocol stack adjusts the communication strategy based on the key network parameters.

2. The cellular network method according to claim 1, characterized in that: The cellular adaptation prospect prediction model includes a data processing layer, a space block, a time block, a fusion block, a prediction layer and an output layer; The collected dynamic environment perception information is processed through the data processing layer, the spatial features of the dynamic environment perception information are extracted through the spatial block, the temporal features of the dynamic environment perception information are extracted through the temporal block, the temporal features and the spatial features are fused through the fusion layer to obtain fused features, the fused features are predicted through the prediction layer, and the cellular adaptation prospect prediction results are output through the output layer.

3. The cellular network method according to claim 2, characterized in that: The data processing layer performs data processing on the collected dynamic environment perception information, including: The collected dynamic environment perception information is parsed according to the type to obtain several sub-information; Performing noise interference removal processing on a plurality of sub-information; The plurality of sub-information after the noise interference removal process is coded and converted.

4. The cellular network method according to claim 1, characterized in that: The extracting of spatial features of dynamic environment perception information through spatial blocks includes: The spatial features are calculated by formula (1); In the formula, Feature ij is the spatial feature, X is the input matrix, k is the convolution kernel size, w mn Spatial weight, i is the row index of the input matrix, j is the column index of the input matrix, b is the bias term, σ is the activation function, m is the row of the convolution kernel from 0 to k-1, and n is the column of the convolution kernel from 0 to k-1.

5. The cellular network method according to claim 2, characterized in that: The step of extracting the time characteristics of the dynamic environment perception information through the time block includes: The time characteristics are calculated by formula (2); In the formula, Feature t is the time feature, x t is the dynamic environment perception information vector collected at t, n is the size of the time block, w i is the element in the weight vector, b is the bias term, y t is the intermediate calculation variable, and σ is the activation function.

6. The cellular network method according to claim 5, characterized in that: The step of fusing the temporal feature and the spatial feature through a fusion layer to obtain a fusion feature includes: Calculate the fusion features through formula (3); Feature fusion = σ(α·Feature ij + β·Feature t + b fusion ) Equation (3); In the formula, Feature fusion is the fusion feature, α is the spatial fusion weight, β is the temporal fusion weight, b fusion is the fusion bias.

7. The cellular network method according to claim 1, characterized in that: The protocol stack control intermediate layer dynamically allocates operating resources of each protocol stack based on the cellular adaptation prospect prediction result, including: Allocating allocatable resource units to the first protocol stack and the second protocol stack based on a priority ratio; Re-evaluate the cellular adaptation prospect prediction results at predetermined intervals and adaptively adjust resource allocation according to the new results; When a certain performance indicator prediction value of the first protocol stack or the second protocol stack exceeds a corresponding resource allocation threshold, corresponding resource increase allocation is performed on the first protocol stack or the second protocol stack.

8. A cellular network system, characterized in that: include: A model training module is used to construct a cellular adaptation prospect prediction model, input dynamic environment perception information into the cellular adaptation prospect prediction model for training, so as to obtain an optimal cellular adaptation prospect prediction model; A prediction module, used for inputting the dynamic environment perception information to be predicted into the optimal cellular adaptation prospect prediction model to obtain a cellular adaptation prospect prediction result; A protocol stack control intermediate layer, which is arranged on the first protocol stack and the second protocol stack in the UE device, controls the UE device to operate based on the first cellular communication protocol through the first protocol stack, and controls the UE device to operate based on the second cellular communication protocol through the second protocol stack; The protocol stack control intermediate layer dynamically allocates operating resources to each protocol stack based on the cellular adaptation prospect prediction result. When the protocol stack control intermediate layer decides to switch from the first protocol stack to the second protocol stack operation based on the cellular adaptation prospect prediction result, the key network parameters of the currently connected cell are passed to the second protocol stack, so that the second protocol stack adjusts the communication strategy based on the key network parameters.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A non-transitory computer readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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