Emergency communication network switching method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of communication, and provides an emergency communication network switching method and device, electronic equipment and a storage medium, and the method comprises the steps: constructing a corresponding heterogeneous network environment in a current emergency scene; acquiring a network connection state of the terminal equipment in the current network, and predicting future signal receiving strength based on the network connection state; and after the vertical switching event is triggered based on the future signal receiving strength, controlling the terminal equipment to execute a corresponding network switching decision, so that the terminal equipment selects an optimal target network for switching. By predicting the future signal receiving strength, determining the vertical switching time in advance and selecting the optimal target network, the communication continuity and stability are ensured, the problems of long switching delay and high network selection subjectivity in the prior art can be effectively solved, the overall performance of emergency communication is improved, the method adapts to a complex communication environment, and the communication efficiency is improved. And therefore, the rescue work can be efficiently carried out.

Description

Technical Field

[0001] The present application relates to the field of communication technologies, and in particular, to an emergency communication network switching method, apparatus, electronic device, and storage medium. Background Art

[0002] In the field of emergency communication, ensuring information transmission in emergency situations such as disaster relief is of crucial importance. Against the backdrop of frequent natural disasters such as earthquakes, landslides, heavy rain and snowstorms, and wind disasters, power infrastructure is extremely vulnerable to damage, which in turn leads to the interruption of power supply to communication base stations and the inability to provide normal communication services. In such a situation, communication between individual soldiers at the disaster site and between the site and the rear will be severely affected, and information is difficult to be transmitted in a timely and smooth manner, greatly hindering the efficient development of disaster relief work. In addition, in the affected area, the coverage of the communication network is extremely complex. Some communication base stations stop serving, making it impossible for the public network to fully cover the entire area, showing an irregular state of partial availability and partial unavailability. This characteristic of uneven and unpredictable network coverage poses a great challenge to communication guarantee work.

[0003] In order to adapt to such a complex communication environment and meet the requirements of continuity and reliability of emergency communication, building a hybrid emergency communication architecture has become a necessary choice. This architecture aims to achieve communication coverage in areas where the public network is unavailable by means of self-organizing network technology, while in areas where the public network is available, the existing public network communication resources are fully utilized for vertical handover of heterogeneous networks.

[0004] However, in the actual process of vertical handover of heterogeneous networks, the existing technologies have the following defects. On the one hand, when the terminal switches between different radio access technologies, the handover delay from triggering the handover to completing the handover is relatively long, seriously affecting the continuity of communication quality, which may lead to the delayed transmission or even loss of key information, thereby affecting the timeliness of rescue decision-making and actions. On the other hand, when selecting from multiple accessible networks, the existing method for determining the decision weight of network parameters is highly subjective, resulting in the accessed network not being the optimal choice, and may not be able to meet the communication requirements in specific emergency scenarios, such as requirements for high bandwidth, low latency, and high stability, thus reducing the performance and efficiency of the entire emergency communication system.

[0005] The foregoing description is for the purpose of providing general background information and does not necessarily constitute prior art. Summary of the Invention

[0006] In view of the above technical problems, the present application provides an emergency communication network switching method, apparatus, electronic device, and storage medium, which solve the problems of long delay during network switching and non-compliance with requirements in selecting the accessed network in the prior art, resulting in poor emergency communication quality.

[0007] To solve the above technical problems, the present application provides an emergency communication network switching method, including the following steps:

[0008] Construct a heterogeneous network environment corresponding to the current emergency scenario;

[0009] Obtain the network connection status of the terminal device in the current network, and predict the future signal reception strength based on the network connection status;

[0010] After triggering a vertical handover event based on the future signal reception strength, control the terminal device to execute the corresponding network switching decision, so that the terminal device selects the optimal target network for switching.

[0011] Further, in some embodiments of the present application, the construction of the heterogeneous network environment in the current emergency scenario includes:

[0012] Deploy multiple ad-hoc network nodes in the area where the public network is unavailable and then connect them;

[0013] Set multiple public network access points in the area where the public network can cover;

[0014] Based on the multiple ad-hoc network nodes and the multiple public network access points, construct a heterogeneous network environment corresponding to the current emergency scenario;

[0015] Manage the ad-hoc network nodes and the public network access points through a network management device.

[0016] Further, in some embodiments of the present application, the obtaining of the network connection status of the terminal device in the current network and the prediction of the future signal reception strength based on the network connection status include:

[0017] Obtain the real-time network connection status of the terminal device in the current network, where the real-time network connection status includes at least one of real-time signal reception strength, real-time signal-to-interference-plus-noise ratio, real-time bandwidth, real-time packet loss rate, real-time delay, and real-time jitter;

[0018] Adopt a grey prediction algorithm to predict the signal reception strength prediction value at a future moment based on the one-dimensional sequence corresponding to the real-time signal reception strength;

[0019] Determine the vertical handover time based on the signal reception strength prediction value.

[0020] Further, in some embodiments of the present application, the adopting of the grey prediction algorithm to predict the signal reception strength prediction value at a future moment based on the one-dimensional sequence corresponding to the real-time signal reception strength includes:

[0021] Based on the monitored real-time signal reception strength, convert it into a corresponding one-dimensional sequence of real-time signal reception strength;

[0022] Accumulate the one-dimensional sequence of the real-time signal reception strength to obtain a corresponding cumulative sequence of the real-time signal reception strength;

[0023] Construct a prediction model based on the cumulative sequence of the real-time signal reception strength, and determine the model coefficients through parameter fitting;

[0024] Calculate the predicted value of the signal reception strength at a future moment based on the prediction model.

[0025] Further, in some embodiments of the present application, controlling the terminal device to execute a corresponding network switching decision so that the terminal device selects an optimal target network for switching includes:

[0026] Obtain multiple decision parameters of the accessible networks, where the decision parameters include communication quality parameters and performance limit parameters;

[0027] Determine the comprehensive weight of the decision parameters by combining subjective weights and objective weights;

[0028] Perform multi-criteria scoring on the accessible networks based on the comprehensive weight, and select the network with the highest score as the optimal switching target;

[0029] Control the terminal device to execute a vertical switching operation from the current network to the optimal switching target.

[0030] Further, in some embodiments of the present application, the method for determining the subjective weight includes:

[0031] Construct a decision parameter importance discrimination matrix based on triangular fuzzy numbers to quantify the relative importance interval of the decision parameters;

[0032] Perform defuzzification processing on the decision parameter importance discrimination matrix to generate the subjective weights corresponding to the decision parameters.

[0033] Further, in some embodiments of the present application, the method for determining the objective weight includes:

[0034] Normalize the decision parameters of the accessible networks to generate a standardized decision matrix;

[0035] Calculate the information entropy of each decision parameter based on the standardized decision matrix;

[0036] Determine the objective weights corresponding to the decision parameters based on the degree of dispersion of the information entropy.

[0037] Correspondingly, the present application provides an emergency communication network switching device, including:

[0038] A building module for building a corresponding heterogeneous network environment in the current emergency scenario;

[0039] A prediction module for obtaining the network connection status of a terminal device in the current network and predicting the future signal reception strength based on the network connection status;

[0040] A switching module for controlling the terminal device to execute a corresponding network switching decision after triggering a vertical handover event based on the future signal reception strength, so that the terminal device selects an optimal target network for handover.

[0041] This application also provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. Wherein, when the processor executes the computer program, the steps of the emergency communication network switching method described above are implemented.

[0042] This application also provides a storage medium storing a computer program that can be loaded and executed by a processor to execute the emergency communication network switching method described above.

[0043] Implementing the embodiments of this application has the following beneficial effects:

[0044] As described above, an emergency communication network switching method, device, electronic device, and storage medium provided by this application. The method includes: First, build a corresponding heterogeneous network environment in the current emergency scenario; then, obtain the network connection status of the terminal device in the current network and predict the future signal reception strength based on the network connection status; after triggering a vertical handover event based on the future signal reception strength, control the terminal device to execute a corresponding network switching decision so that the terminal device selects an optimal target network for handover. The emergency communication network switching solution provided by this application enables the terminal device to monitor network parameters in real time, uses the grey prediction algorithm to predict the future value of the signal reception strength, determines the vertical handover time in advance, and reduces the handover delay; at the same time, combines the subjective weight determined by the fuzzy analytic hierarchy process and the objective weight determined by the entropy weight method to select the optimal target network, ensuring the continuity and stability of communication, so as to effectively solve the problems of long handover delay and strong subjectivity in network selection existing in the existing handover algorithms in the emergency communication scenario, improve the overall performance of communication, adapt to complex communication environments, and thus ensure the efficient development of rescue work. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings here are incorporated into and form a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application. To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, other accompanying drawings can also be obtained based on these drawings without creative efforts.

[0046] Figure 1 is a schematic diagram of an application scenario of the emergency communication network switching method provided by an embodiment of this application;

[0047] Figure 2 is a schematic flowchart of the emergency communication network switching method provided by an embodiment of this application;

[0048] Figure 3 is a schematic structural diagram of the emergency communication network switching device provided by an embodiment of this application;

[0049] Figure 4 is a schematic structural diagram of the electronic device provided by an embodiment of this application.

[0050] The realization of the objectives, functional features, and advantages of this application will be further described with reference to the embodiments and the accompanying drawings. Through the above accompanying drawings, the specific embodiments of this application have been shown, and there will be more detailed descriptions hereinafter. These accompanying drawings and the textual descriptions are not intended to limit the scope of the concept of this application in any way, but to illustrate the concept of this application to those skilled in the art by referring to specific embodiments. Specific Embodiments

[0051] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different accompanying drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. On the contrary, they are merely examples of devices and methods consistent with some aspects of this application as detailed in the appended claims.

[0052] It should be noted that in this article, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including such element. In addition, components, features, and elements with the same name in different embodiments of this application may have the same meaning or different meanings, and their specific meanings need to be determined based on their explanations in the specific embodiments or further in combination with the context in the specific embodiments.

[0053] It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0054] In subsequent descriptions, the use of suffixes such as "module", "component" or "unit" to represent elements is only for the convenience of explaining this application, and it has no specific meaning in itself. Therefore, "module", "component" or "unit" can be used interchangeably.

[0055] When power infrastructure is damaged due to geological disasters such as earthquakes and landslides, and meteorological disasters such as heavy rain, snowstorms, and wind disasters, communication base stations at the disaster site cannot provide communication services due to power outages, and information is difficult to be transmitted in a timely manner among individual soldiers at the scene and between the scene and the rear, which is not conducive to carrying out disaster relief work. At the disaster site, the outage of some base stations results in the inability of the public network to cover the entire area, showing a state where the public network is locally available and locally unavailable, and this distribution is neither uniform nor predictable. In such a complex communication environment, to ensure the efficient development of disaster relief operations, a hybrid emergency communication architecture needs to be constructed, that is, communication coverage is achieved through self-organizing network technology in areas where the public network is unavailable, and vertical handover of heterogeneous networks is performed in areas where the public network is available, making full use of existing public network communication resources to meet the requirements of continuity and reliability of emergency communication.

[0056] However, when the prior art performs vertical handover of heterogeneous networks, the terminal needs to switch between different radio access technologies, and the handover delay from triggering the handover to completing the handover is relatively long, affecting the continuity of communication quality. In addition, when selecting among multiple accessible networks, there is a great deal of subjectivity in the process of determining the decision weights of network parameters, and the influence of network parameters in the actual environment on the weights is not considered, resulting in the access to a non-optimal network and being unable to meet the communication requirements in specific emergency scenarios, such as requirements for high bandwidth, low latency, and high stability, thereby reducing the performance and efficiency of the entire emergency communication system.

[0057] To solve the above technical problems, the present application provides an emergency communication network switching method, apparatus, electronic device, and storage medium.

[0058] Among them, the emergency communication network switching device can be specifically integrated in an electronic device, and the electronic device can be a smart phone, a tablet computer, a notebook computer, or a desktop computer, but is not limited thereto. The electronic device can be directly or indirectly connected to the server through wired or wireless communication means. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, as well as big data and artificial intelligence platforms. The present application does not make any restrictions here.

[0059] Please refer to Figure 1 , Figure 1 which is an application environment diagram of the emergency communication network switching method in an embodiment. Referring to Figure 1 , the emergency communication network switching method can be applied to an emergency communication network switching system. Among them, the emergency communication network switching system can include a terminal 110 and a server 120. The terminal 110 and the server 120 are connected through a network. The terminal 110 can be specifically a desktop terminal or a mobile terminal, and the mobile terminal can be specifically at least one of a mobile phone, a tablet computer, a notebook computer, etc. The server 120 can be implemented by an independent server or a server cluster composed of multiple servers. The terminal 110 is used to construct a heterogeneous network environment corresponding to the current emergency scenario; obtain the network connection status of the terminal device in the current network, and predict the future signal reception strength based on the network connection status; after triggering a vertical handover event based on the future signal reception strength, control the terminal device to execute the corresponding network switching decision, so that the terminal device selects the optimal target network for switching.

[0060] The following will be described in detail respectively. It should be noted that the description order of the following embodiments does not limit the priority order of the embodiments.

[0061] The present application provides an emergency communication network switching method, including: constructing a heterogeneous network environment corresponding to the current emergency scenario; obtaining the network connection status of the terminal device in the current network, and predicting the future signal reception strength based on the network connection status; after triggering a vertical handover event based on the future signal reception strength, controlling the terminal device to execute the corresponding network switching decision, so that the terminal device selects the optimal target network for switching.

[0062] Please refer to Figure 2 , Figure 2It is a schematic flowchart of the emergency communication network switching method provided by an embodiment of the present application. The emergency communication network switching method provided by this embodiment can be applied to an emergency communication network architecture, and specifically may include the following steps:

[0063] S1. Construct a heterogeneous network environment corresponding to the current emergency scenario;

[0064] Specifically, for step S1, in an emergency scenario, such as natural disasters like earthquakes and landslides that damage communication infrastructure, some public network base stations in certain areas are paralyzed, forming irregular public network blind spots. To ensure smooth communication for rescue teams in the disaster area, it is necessary to construct a heterogeneous network environment that includes a self-organizing network and a public network. Specifically, in areas where the public network is unavailable, multiple self-organizing network nodes are quickly deployed, such as emergency communication vehicles, portable communication base stations, etc. These nodes are interconnected through wireless self-organizing network technology to form a temporary self-organizing network, ensuring communication can also be achieved in areas without public network coverage. In areas where the public network is available, multiple public network access points are determined, such as undamaged base stations or temporarily erected communication base stations. These access points and self-organizing network nodes together constitute a heterogeneous network environment, providing multiple network access options for terminal devices.

[0065] In a specific embodiment, the deployment locations and quantities of self-organizing network nodes can be reasonably planned according to the terrain, area, and distribution of rescue teams in the disaster area to achieve the best coverage effect. For example, the number of nodes can be appropriately increased in mountainous areas to improve the signal coverage range and stability. Additionally, cooperation with communication operators can be carried out to obtain relevant information about public network access points, including signal strength, bandwidth, coverage range, etc., and optimize the configuration of these access points to improve their communication capabilities in the emergency scenario.

[0066] Through the construction of a heterogeneous network environment in this embodiment, the combination of a self-organizing network and a public network is realized, achieving comprehensive coverage of the disaster area and ensuring that rescue teams can obtain communication services in different areas; making full use of existing public network resources and self-organizing network technology, avoiding the limitations of a single network, improving the overall utilization rate of communication resources, and being able to better adapt to the complex communication environment in the emergency scenario, providing reliable communication guarantees for rescue work.

[0067] S2. Obtain the network connection status of the terminal device in the current network and predict the future signal reception strength based on the network connection status;

[0068] Specifically, for step S2, the terminal device (such as the mobile phone or walkie-talkie of the rescue team member) is installed with a software client that supports vertical handover in heterogeneous networks. This software client can monitor the current network connection status in real time, including parameters such as received signal strength (RSS), signal-to-interference-plus-noise ratio (SINR), bandwidth (B), packet loss rate (PLS), delay (D), and jitter (J). Taking the received signal strength (RSS) as an example, the terminal device periodically collects the RSS value of the current network through a built-in signal strength sensor and forms a one-dimensional sequence. Then, the grey prediction algorithm (GPA) is used to process this sequence to predict the received signal strength (PRSS) at future time points.

[0069] In a specific embodiment, in addition to monitoring the received signal strength, other network parameters such as signal-to-interference-plus-noise ratio and bandwidth are also monitored simultaneously to provide a more comprehensive basis for subsequent network handover decisions. According to the actual application scenario and data characteristics, the grey prediction algorithm is optimized to improve the accuracy and reliability of the prediction. For example, other prediction algorithms are introduced and combined with the grey prediction algorithm for hybrid prediction. In addition, the terminal device processes and updates the monitored network parameter data in real time to ensure the timeliness and accuracy of the prediction results.

[0070] In this embodiment, by predicting the future received signal strength, the terminal device can understand the change trend of the network signal in advance, and timely discover possible network problems; based on the prediction results, the vertical handover time is determined in advance to avoid communication interruption caused by weak signals, effectively reducing the handover delay and improving the communication continuity; the network handover is performed in advance to ensure the stability of the signal during the communication process, and reduce problems such as voice call interruption and data transmission errors caused by signal fluctuations, thereby improving the overall quality of the communication.

[0071] S3. After triggering the vertical handover event based on the future received signal strength, control the terminal device to execute the corresponding network handover decision so that the terminal device selects the optimal target network for handover;

[0072] Specifically, for step S3, when the predicted future received signal strength (PRSS) is lower than the RSS threshold of the current network, the vertical handover event is triggered. At this time, the terminal device needs to switch from the current network to other available networks to ensure the continuity and stability of the communication. To select the optimal target network, the terminal device will collect the relevant parameter values of other currently accessible networks, such as signal-to-interference-plus-noise ratio (SINR), bandwidth (B), received signal strength (RSS), packet loss rate (PLS), delay (D), and jitter (J). Then, combining the subjective weight determined by the fuzzy analytic hierarchy process (FAHP) and the objective weight determined by the entropy weight method (EWM), the selection of the optimal handover network is made.

[0073] In a specific embodiment, the terminal device can obtain the parameter values of the accessible networks in real time and dynamically update these parameters according to the changes in the network environment to ensure the accuracy of network switching decisions. In addition to FAHP and EWM, other multi-criteria decision-making methods such as TOPSIS, AHP, etc. can also be combined to further improve the scientificity and rationality of network selection. When performing network switching, optimize the switching process, reduce the communication interruption time during the switching process, and improve the efficiency and reliability of the switching.

[0074] In this embodiment, by timely triggering the vertical handover event and selecting the optimal target network, the terminal device can seamlessly switch between different networks, avoid communication interruption, and ensure the smooth progress of rescue work; selecting the optimal target network for handover reduces the dependence on a single network, enhances the stability of communication, and adapts to complex communication environments; comprehensively considering multiple network parameters and selecting the network most suitable for the current communication requirements optimize communication performance and meet the requirements for clear voice calls, high data transmission rates, etc. in emergency communication.

[0075] Further, in some embodiments, step S1, "constructing a heterogeneous network environment in the current emergency scenario", may specifically include:

[0076] S11. Deploy multiple ad hoc network nodes in the area where the public network is unavailable and then connect them;

[0077] Specifically, in an emergency scenario, such as natural disasters like earthquakes and landslides that damage communication infrastructure and make the public network unavailable, the communication coverage in the area where the public network is unavailable needs to be achieved by relying on ad hoc network nodes. These ad hoc network nodes can be devices such as emergency communication vehicles and portable communication base stations. When deploying, reasonably determine the number and location of ad hoc network nodes according to the terrain, area of the disaster area, and the distribution of rescue teams. For example, in mountainous areas or large-scale disaster-stricken areas, appropriately increase the number of nodes to ensure signal coverage. After deployment, through wireless ad hoc network technologies such as Mesh networks and Ad hoc networks, connect these nodes to form a temporary ad hoc network. This network can realize data transmission and routing functions between nodes, ensuring communication can also be carried out in areas without public network coverage. In addition, through methods and technologies for quickly deploying ad hoc network nodes, such as using pre-configured communication parameters and automatic networking protocols, the nodes can quickly form a network after arriving at the disaster area, reducing the deployment time, optimizing the hardware and software of the ad hoc network nodes, and improving their communication performance and stability in complex environments. For example, enhancing the anti-interference ability of the nodes and extending the battery life. Consider deploying redundant ad hoc network nodes at key positions to improve the reliability and fault tolerance of the network. When a certain node fails, other nodes can automatically take over its communication tasks to ensure the uninterrupted operation of the network.

[0078] In this embodiment, by deploying self-organizing network nodes, the communication gap in areas where the public network is unavailable is filled, ensuring that rescue teams can also communicate effectively in these areas; the self-organizing network nodes can flexibly adjust the deployment location and quantity according to the actual situation in the disaster area to adapt to different emergency scenarios and communication requirements.

[0079] S12. Set up multiple public network access points in areas covered by the public network;

[0080] Specifically, in areas covered by the public network, set up multiple public network access points, such as undamaged base stations or temporarily built communication base stations. These access points serve as the entry points for terminal devices to access the public network and jointly form a heterogeneous network environment with the self-organizing network nodes. When setting up, it is necessary to cooperate with communication operators to obtain relevant information about the public network access points, including signal strength, bandwidth, coverage range, etc. At the same time, optimize the configuration of these access points to improve their communication capabilities in emergency scenarios. For example, adjust parameters such as the transmission power and frequency band of the access points to adapt to the communication requirements in the disaster area. In addition, according to the changes in network load and communication requirements, dynamically adjust the parameters of the public network access points, such as transmission power and frequency band, to optimize communication performance. Establish a backup mechanism for the public network access points so that when a certain access point fails or is overloaded, it can automatically switch to the backup access point to ensure the continuity of communication.

[0081] In this embodiment, in areas covered by the public network, by setting up multiple access points, the existing public network communication resources are fully utilized to improve the efficiency and quality of communication; optimizing the configuration of the public network access points improves their communication capabilities in emergency scenarios and enhances the stability of communication.

[0082] S13. Based on multiple self-organizing network nodes and multiple public network access points, construct a heterogeneous network environment for the current emergency scenario;

[0083] Specifically, integrate the deployed self-organizing network nodes and public network access points to form a heterogeneous network environment that includes a self-organizing network and a public network. In this environment, terminal devices can switch between the self-organizing network and the public network according to their own communication requirements and network status, and select the optimal network for communication. For example, in areas where the signal of the self-organizing network is strong and the public network is unavailable, terminal devices preferentially choose the self-organizing network for communication; in areas where the signal of the public network is strong and available, terminal devices switch to the public network for communication, making full use of the communication resources and performance advantages of the public network. In addition, through the seamless handover technology between the self-organizing network and the public network, reduce the communication interruption time during the handover process and improve the user experience. Through the network management device, achieve load balancing between the self-organizing network and the public network, reasonably allocate communication traffic, and avoid network congestion.

[0084] In a heterogeneous network environment, the terminal device of this embodiment can select the most suitable network for communication according to the actual situation, improving the flexibility and adaptability of communication; through load balancing and collaborative optimization, the communication resources of the ad hoc network and the public network are fully utilized, improving the resource utilization rate.

[0085] S14. Manage the ad hoc network nodes and public network access points through a network management device;

[0086] Specifically, in the network management device, the ad hoc network nodes and public network access points are uniformly managed and coordinated. The network management device can be a dedicated network management system or a server with management functions. Through this device, the operating status of the network can be monitored in real time, including information such as the working conditions, communication traffic, and load of the ad hoc network nodes and public network access points. Based on this information, reasonable allocation and optimization adjustment of network resources are carried out. For example, when a certain ad hoc network node has a high load, part of the communication traffic can be guided to other nodes or public network access points; when a public network access point fails, the configuration of the ad hoc network nodes can be adjusted in time to ensure the continuity of communication. In addition, artificial intelligence and machine learning technologies can be introduced to achieve intelligent network management. For example, through data analysis and prediction models, the network configuration is automatically adjusted to optimize communication performance. Strengthen the security protection of the network management device to prevent illegal access and malicious operations, ensuring the reliability and security of network management. Support the remote network management function, enabling managers to monitor and manage the network in real time from a place far away from the disaster area, improving the efficiency and convenience of management.

[0087] Through the network management device in this embodiment, the operating status of the network can be quickly obtained, adjusted and optimized in time, improving the efficiency of network management; real-time monitoring and adjustment of the network configuration can timely discover and solve network problems, enhancing the reliability and stability of the network; strengthening the security protection of network management ensures the secure operation of the communication network, preventing information leakage and being tampered with.

[0088] Further, in some embodiments, step S2, "Obtain the network connection status of the terminal device in the current network and predict the future signal reception strength based on the network connection status", may specifically include:

[0089] S21. Obtain the real-time network connection status of the terminal device in the current network, and the real-time network connection status includes at least one of real-time signal reception strength, real-time signal-to-interference-plus-noise ratio, real-time bandwidth, real-time packet loss rate, real-time delay, and real-time jitter;

[0090] Specifically, when the terminal device operates in the current network, it collects key parameters reflecting network performance in real time through a built-in monitoring module or software client. These parameters include, but are not limited to, real-time signal reception strength (RSS), real-time signal-to-interference-plus-noise ratio (SINR), real-time bandwidth (B), real-time packet loss rate (PLS), real-time delay (D), and real-time jitter (J). For example, the real-time signal reception strength is obtained by periodically measuring through the signal strength sensor of the device, the real-time signal-to-interference-plus-noise ratio is calculated by analyzing the ratio of the signal to the noise, the real-time bandwidth is determined by the data transmission rate of the current network, the real-time packet loss rate is obtained by statistically calculating the ratio of the number of lost data packets to the total number of transmitted data packets within a specific time period, and the real-time delay and real-time jitter are determined by measuring the transmission time of the data packet from the sender to the receiver and its fluctuation range respectively. These parameters can comprehensively reflect the communication quality and service level of the network, providing basic data support for subsequent network handover decisions. Multiple network parameters can also be monitored simultaneously to reduce resource consumption and interference to communication during the monitoring process. For example, parallel processing technology and optimized monitoring algorithms are adopted to improve the monitoring efficiency. By improving the monitoring device and algorithm, the accuracy of each parameter monitoring is improved. Such as using a signal sensor with higher sensitivity and optimizing the statistical methods for packet loss rate and delay. A fast and reliable mechanism is established to transmit the monitored real-time network parameter data to the processing module of the terminal device and update the stored data in a timely manner to ensure the timeliness and accuracy of the data.

[0091] By obtaining multi-dimensional network connection status parameters in this embodiment, it is possible to more comprehensively and accurately understand the communication quality and service level of the current network, providing sufficient basis for subsequent network handover decisions; real-time monitoring of the changes in network parameters can promptly detect abnormal situations in the network, such as a sudden weakening of the signal or a sharp increase in the packet loss rate, so as to quickly take corresponding measures; the comprehensive monitoring of multiple parameters can better reflect the real situation of the network than a single parameter, improving the accuracy and reliability of the communication quality assessment.

[0092] S22. Use the grey prediction algorithm to predict the predicted value of the signal reception strength at a future moment based on the one-dimensional sequence corresponding to the real-time signal reception strength;

[0093] Specifically, the obtained real-time received signal strength (RSS) data sequence is used as the input, and the grey prediction algorithm (GPA) is applied to predict the RSS value at future times. The specific steps include: First, the RSS sequence is cumulatively processed to obtain a cumulative sequence to reduce the volatility and randomness of the data; Then, a first-order ordinary differential equation prediction model based on the cumulative sequence is constructed, and the undetermined coefficients in the model are determined through parameter fitting; Finally, the predicted RSS value at future times is calculated using the obtained prediction model. For example, assuming the currently obtained RSS sequence is [-50, -52, -55, -53, -54] dBm, after accumulation, model construction, and solution, the predicted RSS value at the 3rd future time step can be predicted to be -56 dBm. This predicted value can reflect the change trend of the received signal strength in advance, providing a basis for determining the optimal network handover time. In addition, the grey prediction algorithm is optimized according to the data characteristics in different scenarios, such as introducing correction terms, adjusting model parameters, etc., to improve the accuracy and adaptability of the prediction. The grey prediction algorithm is combined with other prediction methods (such as neural networks, time series analysis, etc.) to form a hybrid prediction model, making full use of the advantages of each algorithm to further improve the prediction performance. According to the changes in real-time monitoring data, the parameters or structure of the prediction model are dynamically adjusted to enable the model to better adapt to the dynamic changes in the network environment.

[0094] In this embodiment, by predicting the received signal strength at future times, the trend of signal enhancement or weakening can be understood in advance, providing forward-looking support for network handover decisions; predicting signal changes in advance allows timely measures to be taken before the signal weakens, such as adjusting the antenna direction, increasing the transmission power, etc., to reduce the impact of signal fluctuations on communication quality; optimizing the grey prediction algorithm and combining other algorithms can improve the accuracy and reliability of the prediction results, better guiding network handover decisions.

[0095] S23. Determine the vertical handover time based on the predicted value of the received signal strength;

[0096] Specifically, according to the predicted value of the received signal strength (RSS) at future times obtained by prediction, it is compared with a preset RSS threshold to determine whether to trigger a vertical handover event and the optimal timing of the handover. For example, if the RSS threshold of the current network is set to -60 dBm, and the predicted RSS value at a certain future time is lower than this threshold, it is determined that the signal of the current network is about to become unstable, and a vertical handover needs to be triggered before this time to switch the terminal device to other available networks to ensure the continuity and stability of communication. This process avoids communication interruption or quality degradation caused by too weak signals by setting reasonable RSS thresholds and handover strategies.

[0097] In a specific embodiment, the RSS threshold can be dynamically adjusted according to different network environments and communication requirements to optimize the handover decision. For example, the threshold can be appropriately increased in a high-interference environment and appropriately decreased in a stable signal environment. In addition to the RSS predicted value, the changing trends of other network parameters (such as signal-to-interference-plus-noise ratio, bandwidth, etc.) are comprehensively considered to formulate a more comprehensive handover decision strategy.

[0098] In this embodiment, by determining the vertical handover time in advance and completing the network handover before the signal deteriorates, communication interruption is avoided, and the continuity of communication is ensured; the reasonable selection of the handover timing and the comprehensive consideration of parameters improve the stability and reliability of communication and reduce the communication fluctuations caused by network handover; the dynamic adjustment of the RSS threshold and the combination with other parameters make the handover decision more scientific and reasonable, improving the accuracy and effectiveness of the handover.

[0099] Further, in some embodiments, step S22, "using the grey prediction algorithm to predict the signal reception strength prediction value at a future moment based on the one-dimensional sequence corresponding to the real-time signal reception strength", may specifically include:

[0100] S221. Based on the monitored real-time signal reception strength, convert it into a corresponding one-dimensional sequence of real-time signal reception strength;

[0101] Specifically, the real-time signal reception strength (RSS) is a key indicator for measuring the strength of the network signal, usually in decibels milliwatt (dBm). The terminal device periodically collects the RSS value of the current network through a built-in signal strength sensor and arranges these discrete RSS data points in chronological order to form a one-dimensional sequence. For example, the collected RSS values may be: RSS = {-50, -52, -55, -53, -54} (unit: dBm). This one-dimensional sequence is the basic data for subsequent grey prediction algorithm processing, which intuitively reflects the changing trend of the network signal strength over time.

[0102] In a specific embodiment, before converting the RSS value into a one-dimensional sequence, the collected raw data can be preprocessed, such as filtering to remove noise interference, smoothing to reduce data fluctuations, etc., to improve the quality and stability of the data. Design an efficient data storage structure to store and manage the collected RSS sequence for convenient subsequent reading and processing. For example, use a circular buffer or a database table structure, etc. Ensure the integrity of the collected RSS data. For missing or abnormal data points, interpolation or resampling methods are used for supplementation and correction to avoid affecting the subsequent prediction accuracy.

[0103] In this embodiment, by converting the real-time signal reception strength into a one-dimensional sequence, an ordered and standardized data basis is provided for subsequent gray prediction algorithm processing, facilitating the unified processing and analysis of the algorithm. Through the one-dimensional sequence, the change of network signal strength over time can be intuitively displayed, which helps to discover the fluctuations and change rules of the signal in advance. The data sequence after preprocessing and integrity guarantee has higher quality, can better support the operation of the prediction algorithm, and improve the reliability of the prediction results.

[0104] S222. Perform an accumulation process on the one-dimensional sequence of the real-time signal reception strength to obtain the corresponding accumulated sequence of the real-time signal reception strength;

[0105] Specifically, in order to reduce the volatility and randomness of the data and improve the regularity and predictability of the data, an accumulation process is performed on the RSS one-dimensional sequence. Specifically, each element of the accumulated sequence is the sum of the corresponding position and all previous elements in the original RSS sequence. The generation of the accumulated sequence is a key step in the gray prediction algorithm, which converts the original data into a more regular form and lays a foundation for subsequent model construction and parameter fitting.

[0106] In addition, according to the actual data characteristics and prediction requirements, the accumulation method can be optimized and adjusted. For example, weighted accumulation can be used to assign a greater weight to recent data to highlight its impact on prediction; further smoothing processing can be performed on the accumulated sequence, such as moving average, etc., to reduce the mutations and fluctuations that may occur during the accumulation process and improve the smoothness and stability of the sequence. In addition to the accumulation process, other data transformation methods, such as difference, logarithmic transformation, etc., can also be studied, and the most suitable data processing method can be selected according to different data characteristics and prediction goals.

[0107] The sequence after the accumulation process in this embodiment is more regular than the original sequence, and the change trend between data points is more obvious, which is beneficial to improving the fitting effect and prediction accuracy of the prediction model. Through accumulation, the random fluctuations and noise interference in the original data are weakened, making the data smoother and improving the stability and predictability of the data.

[0108] S223. Construct a prediction model based on the accumulated sequence of the real-time signal reception strength, and determine the model coefficients through parameter fitting;

[0109] Specifically, based on the cumulative sequence, a first-order ordinary differential equation of the grey prediction model is constructed. By using the method of parameter fitting and mathematical means such as the least squares method, the error between the value calculated by the model and the actual cumulative sequence is minimized, and the specific values of coefficients a and b are solved, thus obtaining a complete prediction model. In addition, according to the real-time updated cumulative sequence, the model parameters a and b are dynamically adjusted to adapt to the changes in the network environment, improving the real-time performance and prediction accuracy of the model. The error between the prediction result and the actual value is analyzed, the reasons for the error are studied, and corresponding correction measures are taken, such as introducing a correction coefficient, adjusting the model structure, etc., to reduce the error and improve the prediction accuracy.

[0110] The prediction model constructed in this embodiment through the model coefficients determined by parameter fitting can accurately describe the change law of the cumulative sequence, providing a reliable mathematical tool for subsequent signal reception strength prediction; parameter fitting based on actual data makes the model more in line with the actual situation, and the prediction result is more scientific and reasonable.

[0111] S224. Calculate the predicted value of the signal reception strength at a future moment based on the prediction model;

[0112] Specifically, using the already constructed prediction model, the predicted value of the signal reception strength at a future moment can be calculated. Specifically, by solving the above differential equation, the value of the cumulative sequence at any future moment can be predicted. Then, by calculating the difference between the cumulative sequences at adjacent moments, the predicted value of the signal reception strength at a future moment is obtained. For example, if it is necessary to predict the RSS value at the 3rd future time step, then calculate PRSS 3 = S(3) - S(2). This predicted value can reflect the change trend of the signal reception strength in advance, providing a basis for determining the optimal network switching time. In addition, according to the actual application scenario and requirements, the prediction step can be optimized, that is, how many future time steps of the signal reception strength are predicted. For example, in a network environment with rapid changes, the prediction step is appropriately reduced to improve the prediction accuracy and timeliness. The credibility of the prediction result is evaluated, and by calculating indicators such as the confidence interval and error range, the reliability of the predicted value is judged, providing more comprehensive information for the switching decision. Combine multiple different prediction models, such as the grey prediction model, neural network model, etc., for fusion prediction, and take the average value or weighted average value of the prediction results of each model as the final predicted value to improve the prediction accuracy and stability.

[0113] In this embodiment, by calculating the predicted value of the signal reception strength at a future time, the trend of signal enhancement or weakening can be understood in advance, providing forward-looking support for network handover decisions and avoiding communication interruptions; improving the accuracy of handover decisions: determining the vertical handover time based on the predicted value is more accurate than the traditional handover method based on the current signal strength, can better adapt to the dynamic changes of the signal, and improve the success rate of handover decisions; accurate prediction of signal reception strength enables the terminal device to switch to a better network in time before the signal deteriorates, reducing the degradation of communication quality caused by signal fluctuations and enhancing the stability of communication.

[0114] In a specific embodiment, when the public network is available, the terminal device can switch between the ad hoc network and the public network according to the RSS. Based on the current one-dimensional RSS sequence of the terminal device, the GPA is used to obtain the future predicted signal reception strength PRSS, so as to determine the appropriate handover time in advance to reduce the handover delay and maintain the continuity of communication quality. Denote the current RSS sequence as X (0) :

[0115]

[0116] Calculate X (0) 's cumulative sequence X (1) , and the calculation method of the sequence elements is as follows:

[0117]

[0118] Use the following ordinary differential equation to approximate and fit X (1) :

[0119]

[0120] In the formula, a and b are undetermined coefficients, and their calculation methods are as follows:

[0121]

[0122] Solve this equation to get:

[0123]

[0124] From this, it can be obtained that:

[0125]

[0126] The GPA is based on the current RSS sequence and recursively predicts the PRSS backward. The PRSS for the selected prediction steps is used as the basis for whether to perform a network handover. If the PRSS is lower than the RSS threshold of the current network where the terminal is located, a network handover is performed.

[0127] Further, in some embodiments, "controlling the terminal device to execute the corresponding network switching decision so that the terminal device selects the optimal target network for switching" in step S5 may specifically include:

[0128] S51 obtains multiple decision parameters for access to the network, the decision parameters include communication quality parameters and performance limit parameters;

[0129] Specifically, when making a network switching decision, multiple decision parameters need to be considered comprehensively. These parameters can be divided into two categories: communication quality parameters and performance limitation parameters. Communication quality parameters mainly include signal-to-interference-and-noise ratio (SINR), received signal strength (RSS), etc. These parameters reflect the communication quality and service level of the network; performance limitation parameters include delay (D), jitter (J), packet loss rate (PLS), etc. These parameters limit the communication performance and application scope of the network. For example, in conversation applications, delay and jitter have a greater impact on communication quality, while in data transmission applications, packet loss rate and bandwidth are more important. The terminal device obtains the values ​​of these decision parameters of the currently accessible network through network scanning and signaling interaction. According to different application scenarios and communication requirements, the decision parameters can be flexibly selected and combined.

[0130] S52. Determine the comprehensive weight of the decision parameters by combining the subjective weight and the objective weight;

[0131] Specifically, the combination of subjective weights and objective weights is a key step in achieving scientific and reasonable network switching decisions. Subjective weights reflect the importance that decision makers (such as network operators and users) attach to different decision parameters, and are usually determined by methods such as the analytic hierarchy process (AHP). For example, by constructing a judgment matrix, the relative importance of parameters is compared pairwise to obtain subjective weights. Objective weights are determined based on objective indicators such as the statistical characteristics of actual data and information entropy, reflecting the variability and information content of the parameters themselves. For example, the objective weight of each decision parameter is calculated using the entropy weight method. The smaller the information entropy, the greater the objective weight, indicating that the parameter is more important for distinguishing the performance of different networks. The comprehensive weight is obtained by weighted fusion of subjective weights and objective weights, which takes into account both the subjective will of the decision maker and the objective characteristics of the data.

[0132] S53. Perform multi-criteria scoring on the accessible networks based on the comprehensive weights, and select the network with the highest score as the optimal switching target;

[0133] Specifically, after determining the comprehensive weights of the decision parameters, multi-criteria scoring is performed for each accessible network. Specifically, the decision parameter values of each network are multiplied by the corresponding comprehensive weights to obtain the weighted scores of the network on each parameter, and then these weighted scores are added up to obtain the total score of the network. For example, suppose there are two accessible networks A and B, the decision parameters include RSS, D, and J, and the comprehensive weights are w1, w2, and w3 respectively. The parameter values of network A are RSS_A, D_A, and J_A, and the parameter values of network B are RSS_B, D_B, and J_B. Then the total score of network A is w1×RSS_A + w2×D_A + w3×J_A, and the total score of network B is w1×RSS_B + w2×D_B + w3×J_B. Select the network with the highest total score (such as network A) as the optimal handover target to ensure that the network after handover can provide the best communication performance.

[0134] S54. Control the terminal device to perform a vertical handover operation from the current network to the optimal handover target;

[0135] Specifically, after determining the optimal handover target, it is necessary to control the terminal device to switch from the current network to the target network. This process involves a series of signaling interactions and parameter configuration updates. Specifically, the terminal device first sends a handover request to the current network, indicating the intention to switch to the target network; then, the current network coordinates with the target network to allocate new resources and parameters for the terminal device; finally, the terminal device completes the handover operations at each layer such as the physical layer and the link layer according to the received handover instructions and new parameters, and establishes a connection with the target network. For example, when switching from an ad hoc network to a public network, the terminal device needs to obtain information such as the IP address and frequency point allocated by the public network, and re-establish the communication link. A handover failure fallback mechanism and redundant design are introduced to ensure the continuity of communication even in case of abnormal situations during the handover process. For example, a handover timeout retry mechanism is set.

[0136] In this embodiment, by obtaining multiple decision parameters, the performance of accessible networks can be comprehensively evaluated, avoiding handover decision errors caused by the one-sidedness of a single parameter; the combination of subjective and objective weights overcomes the limitations of a single weight, making the weight allocation of decision parameters more reasonable and scientific; the multi-criteria scoring method can comprehensively consider the advantages and disadvantages of each network, select the network most suitable for the current communication requirements, improve communication performance and user experience; through the optimized handover process and reliability guarantee measures, the terminal device can achieve seamless handover between different networks, reduce the communication interruption time, and improve the continuity of communication.

[0137] Further, in some embodiments, the method for determining the subjective weight includes:

[0138] Construct a decision parameter importance discrimination matrix based on triangular fuzzy numbers to quantify the relative importance interval of decision parameters;

[0139] Specifically, when determining the subjective weights of decision parameters, the fuzzy analytic hierarchy process (FAHP) is adopted. By constructing an importance discrimination matrix based on triangular fuzzy numbers, the relative importance among decision parameters is quantified. A triangular fuzzy number consists of three values, representing the lower limit, middle value, and upper limit of relative importance respectively. Its value range is an integer between 1 and 9, and the increasing order represents the increasing degree of importance. For example, if it is considered that the signal reception strength (RSS) is relatively more important than the delay (D), its triangular fuzzy number can be given as (3, 5, 7), indicating that its relative importance is between 3 and 7, and the middle value is 5. In this way, expert experience or subjective judgment is transformed into interval weights, forming a fuzzy importance discrimination matrix. In addition, according to different application scenarios and expert experience, the value range and shape of triangular fuzzy numbers are allowed to be flexibly defined. For example, in some scenarios, a wider or narrower value range can be adopted, or other types of fuzzy numbers (such as trapezoidal fuzzy numbers) can be used to more accurately express relative importance. When multiple experts participate in weight determination, the triangular fuzzy number opinions of different experts are fused to form a comprehensive importance discrimination matrix. For example, by taking the average value, median, or other aggregation functions to integrate multiple fuzzy numbers. With the changes in the network environment and user requirements, the triangular fuzzy numbers in the importance discrimination matrix are allowed to be dynamically adjusted to reflect the new relative importance relationship. For example, when the network is congested, the relative importance of the bandwidth parameter is appropriately increased.

[0140] In this embodiment, the use of triangular fuzzy numbers can more flexibly represent experts' subjective judgments on the relative importance of parameters, avoiding the limitations of single values in the traditional analytic hierarchy process, and improving the accuracy of determining subjective weights; by quantifying the relative importance interval, the uncertainty and fuzziness of judgment are considered, making the determination of subjective weights more in line with the actual situation and enhancing the reliability of weights; the ability to flexibly define fuzzy numbers and dynamically adjust the matrix enables the weight determination process to better adapt to different network environments and user requirements, improving the flexibility and adaptability of handover decisions.

[0141] Perform defuzzification processing on the decision parameter importance discrimination matrix to generate the subjective weights corresponding to each decision parameter;

[0142] Specifically, after constructing the importance discrimination matrix based on triangular fuzzy numbers, it is necessary to defuzzify it to convert the fuzzy relative importance interval into specific subjective weight values. The defuzzification process usually adopts the centroid method or other appropriate defuzzification techniques. For example, for the triangular fuzzy number of each decision parameter, calculate the value corresponding to its centroid as the subjective weight of this parameter. Suppose there is a triangular fuzzy number (3, 5, 7), and its defuzzified weight may be 5. In this way, the fuzzy importance discrimination matrix is converted into a specific subjective weight vector, providing a basis for subsequent comprehensive weight calculation. In addition, study and apply different defuzzification methods, such as the maximum membership degree method, the average value method, etc., and select the most suitable defuzzification technique according to the specific application scenario to improve the accuracy and rationality of the defuzzification result. Further optimize the subjective weights obtained by defuzzification, such as normalization, smoothing, etc., to ensure that the weight values meet specific constraints (such as the sum of weights is 1), and improve their stability and usability. During the defuzzification process, consider introducing some objective information (such as historical data, network performance indicators, etc.) to adjust and correct the subjective weights to make them more in line with the actual network situation.

[0143] The defuzzification process in this embodiment makes the determination of subjective weights more specific and operable, providing a clear numerical basis for subsequent comprehensive weight calculation and network handover decision-making; by selecting appropriate defuzzification methods and optimizing the results, it ensures that the subjective weights can accurately and reasonably reflect the relative importance of decision parameters, improving the scientific nature of handover decisions; the subjective weights after defuzzification and optimization are more in line with actual application requirements, can be directly applied to the calculation of comprehensive weights, and improve the practicality and effectiveness of network handover decisions.

[0144] Furthermore, in some embodiments, the method for determining the objective weights includes:

[0145] Normalize the decision parameters of the accessible networks to generate a standardized decision matrix;

[0146] Specifically, when determining the objective weights of decision parameters, it is first necessary to normalize the decision parameters of each accessible network. Since the dimensions and magnitudes of different decision parameters may vary, direct comparison and weight calculation can cause inconvenience and errors. The purpose of normalization is to map the parameter values to a unified interval, usually [0, 1]. Common normalization methods include min-max normalization, mean-standard deviation normalization, etc. For example, for the quality of service parameter of signal reception strength (RSS), max normalization is adopted. According to the characteristics of different decision parameters and application scenarios, appropriate normalization methods are selected. In addition to min-max normalization, other methods such as logarithmic normalization and exponential normalization can also be used to better reflect the relative relationship between parameters. As the network environment changes, the value range of decision parameters will also change. Therefore, it is necessary to establish a dynamic normalization adjustment mechanism to automatically adjust the normalization parameters according to real-time data to ensure the stability and accuracy of the normalization effect. Normalization enables decision parameters with different dimensions and magnitudes to be compared and analyzed on the same scale, improving the accuracy and comparability of weight calculation. Through the unified normalization interval, the decision parameter values of each network are more comparable, and the performance differences between networks can be more intuitively reflected.

[0147] Calculate the information entropy of each decision parameter based on the standardized decision matrix;

[0148] Specifically, calculate the average value of each decision parameter in all networks, calculate the entropy value of each decision parameter. The smaller the information entropy, the greater the uncertainty of the decision parameter, and the more important it is for distinguishing the performance of different networks. By calculating the information entropy, the uncertainty of each decision parameter can be quantified, providing a scientific basis for determining the objective weight.

[0149] Determine the objective weights corresponding to each decision parameter based on the degree of dispersion of the information entropy;

[0150] Specifically, calculate the degree of dispersion of the information entropy of each decision parameter. The greater the degree of dispersion of the information entropy, the smaller the uncertainty of the parameter, and the stronger its ability to distinguish network performance. Calculate the objective weights of each decision parameter. In this way, the degree of dispersion of the information entropy is converted into the objective weights of the decision parameters, making the weight allocation more reasonable and scientific. Determining the objective weight based on the degree of dispersion of the information entropy can reasonably allocate the weights of each decision parameter, highlight the parameters with strong network performance discrimination ability, and improve the accuracy of handover decisions.

[0151] In a specific embodiment, the essence of network switching is a multi-criteria decision-making problem. The decision-making parameters include signal-to-interference-plus-noise ratio (SINR), bandwidth (B), received signal strength (RSS), packet loss rate (PLS), delay (D), jitter (J), etc. According to different application scenarios and communication requirements, some of these parameters are selected for switching selection. Taking session-based applications as an example, three decision-making parameters, namely D, J, and RSS, are selected. Among them, RSS is the quality of service parameter Q server (the larger the better), and D and J are performance-limiting parameters Q lim (the smaller the better). Normalization methods are designed for these two types of parameters x respectively:

[0152]

[0153] Combining the subjective weights of FAHP and the objective weights of EWM, the best switching network for the terminal is selected. Compared with the traditional Analytic Hierarchy Process (AHP) which only has a definite comparison of importance levels, FAHP uses triangular fuzzy numbers as shown below to extend the result of the importance level comparison to an interval.

[0154]

[0155] In the formula, L, M, and H represent the lower limit, middle value, and upper limit relative to importance respectively, and their values are integers between 1 and 9, with the importance level increasing in order from small to large.

[0156] Construct the following importance discrimination matrix.

[0157]

[0158] In the formula, a ij =(L ij , M ij , H ij ) is a triple array representing an importance interval, and its addition is the addition of corresponding elements, that is:

[0159] a 11 +a 12 =(L 11 +L 12 , M 11 +M 12 , H 11 +H 12 ) (11)

[0160] Accordingly, the fuzzy weight triple arrays of each decision-making parameter can be obtained as:

[0161]

[0162] Defuzzify the triple array to obtain the subjective weight of each decision-making parameter as ω i =(ωLi +ω Mi +ω Hi ) / 3。

[0163] Then, the entropy weight method is used to determine the objective weight. Suppose there are n decision parameters for each accessible cluster network, and the m×n decision parameters of all m optional networks are composed into a decision matrix as follows:

[0164]

[0165] where c ij is the normalized value of the j-th parameter of the i-th network.

[0166] Then, the information entropy of a certain decision parameter j of the optional cluster is:

[0167]

[0168] Finally, the entropy weights of each attribute, that is, the objective weights are:

[0169]

[0170] The subjective fuzzy weight ω and the objective entropy weight ζ are weighted by the parameter α to obtain the comprehensive weight of the parameter as W = αω+(1 - α)ζ. Multiplying the weight vector by the decision matrix J can obtain the score sequence of each accessible network as , where the network with the highest score is the best handover choice.

[0171] To sum up, for the emergency communication network handover method provided in this embodiment, first, a heterogeneous network environment corresponding to the current emergency scenario is constructed; then, the network connection status of the terminal device in the current network is obtained, and the future signal reception strength is predicted based on the network connection status; after triggering a vertical handover event based on the future signal reception strength, the terminal device is controlled to execute the corresponding network handover decision so that the terminal device selects the optimal target network for handover. In this embodiment, the terminal device monitors network parameters in real time, uses the grey prediction algorithm to predict the future value of the signal reception strength, determines the vertical handover time in advance, and reduces the handover delay; at the same time, combining the subjective weight determined by the fuzzy analytic hierarchy process and the objective weight determined by the entropy weight method, the optimal target network is selected to ensure the continuity and stability of communication, so as to effectively solve the problems of long handover delay and strong subjectivity in network selection existing in the existing handover algorithms in the emergency communication scenario, improve the overall performance of communication, adapt to complex communication environments, and thus ensure the efficient development of rescue work.

[0172] To facilitate better implementation of an emergency communication network switching method according to an embodiment of the present application, an emergency communication network switching device is also provided in an embodiment of the present application. The meanings of the nouns are the same as those in the above-mentioned emergency communication network switching method, and the specific implementation details can be referred to the description in the method embodiment.

[0173] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of the emergency communication network switching device provided by an embodiment of the present application. The emergency communication network switching device may specifically include a construction module 201, a prediction module 202, and a switching module 203, which are specifically as follows:

[0174] The construction module 201 is configured to construct a heterogeneous network environment corresponding to the current emergency scenario;

[0175] The prediction module 202 is configured to obtain the network connection status of the terminal device in the current network and predict the future signal reception strength based on the network connection status;

[0176] The switching module 203 is configured to control the terminal device to execute a corresponding network switching decision after triggering a vertical switching event based on the future signal reception strength, so that the terminal device selects an optimal target network for switching.

[0177] Further, in some embodiments, the construction module 201 may specifically include:

[0178] The ad hoc network unit is configured to deploy and connect multiple ad hoc network nodes in an area where the public network is unavailable;

[0179] The public network unit is configured to set multiple public network access points in an area where the public network can be covered;

[0180] The construction unit is configured to construct a heterogeneous network environment corresponding to the current emergency scenario based on multiple ad hoc network nodes and multiple public network access points;

[0181] The management unit is configured to manage the ad hoc network nodes and the public network access points through a network management device.

[0182] Further, in some embodiments, the prediction module 202 may specifically include:

[0183] The monitoring unit is configured to obtain the real-time network connection status of the terminal device in the current network. The real-time network connection status includes at least one of real-time signal reception strength, real-time signal-to-interference-plus-noise ratio, real-time bandwidth, real-time packet loss rate, real-time delay, and real-time jitter;

[0184] The prediction unit is configured to predict the signal reception strength prediction value at a future moment based on a one-dimensional sequence corresponding to the real-time signal reception strength by using a grey prediction algorithm;

[0185] A determination unit, configured to determine a vertical handover time based on a predicted value of signal reception strength.

[0186] Further, in some embodiments, the prediction unit may specifically be configured to:

[0187] Based on the monitored real-time signal reception strength, convert it into a corresponding one-dimensional sequence of real-time signal reception strength;

[0188] Perform an accumulation process on the one-dimensional sequence of real-time signal reception strength to obtain a corresponding accumulation sequence of real-time signal reception strength;

[0189] Construct a prediction model based on the accumulation sequence of real-time signal reception strength, and determine the model coefficients through parameter fitting;

[0190] Calculate the predicted value of signal reception strength at a future moment based on the prediction model.

[0191] Further, in some embodiments, the handover module 203 may specifically include:

[0192] An acquisition unit, configured to acquire a plurality of decision parameters of an accessible network, where the decision parameters include communication quality parameters and performance limit parameters;

[0193] A weight unit, configured to determine the comprehensive weight of the decision parameters by combining subjective weights and objective weights;

[0194] A scoring unit, configured to perform multi-criteria scoring on the accessible network based on the comprehensive weight, and select the network with the highest score as the optimal handover target;

[0195] A handover unit, configured to control the terminal device to perform a vertical handover operation from the current network to the optimal handover target.

[0196] Further, in some embodiments, the method for determining the subjective weight includes:

[0197] Construct a decision parameter importance discrimination matrix based on triangular fuzzy numbers to quantify the relative importance interval of the decision parameters;

[0198] Perform defuzzification processing on the decision parameter importance discrimination matrix to generate the subjective weights corresponding to the decision parameters.

[0199] Further, in some embodiments, the method for determining the objective weight includes:

[0200] Perform normalization processing on the decision parameters of the accessible network to generate a standardized decision matrix;

[0201] Calculate the information entropy of each decision parameter based on the standardized decision matrix;

[0202] Determine the objective weights corresponding to each decision parameter based on the degree of dispersion of information entropy.

[0203] In summary, the emergency communication network switching device provided in this embodiment constructs a heterogeneous network environment corresponding to the current emergency scenario through the construction module 201; obtains the network connection status of the terminal device in the current network through the prediction module 202, and predicts the future signal reception strength based on the network connection status; after triggering a vertical handover event based on the future signal reception strength through the handover module 203, controls the terminal device to execute the corresponding network handover decision, so that the terminal device selects the optimal target network for handover. The emergency communication network switching device provided in this embodiment monitors network parameters in real time through the terminal device, uses the grey prediction algorithm to predict the future value of the signal reception strength, determines the vertical handover time in advance, and reduces the handover delay; at the same time, combines the subjective weight determined by the fuzzy analytic hierarchy process and the objective weight determined by the entropy weight method to select the optimal target network, ensuring the continuity and stability of communication, so as to effectively solve the problems of long handover delay and strong subjectivity in network selection existing in the existing handover algorithms in the emergency communication scenario, improve the overall performance of communication, adapt to complex communication environments, and thus ensure the efficient development of rescue work.

[0204] In addition, an embodiment of the present application also provides an electronic device, as Figure 4 shown, which shows a schematic structural diagram of the electronic device involved in the embodiment of the present application. Specifically: the electronic device may include a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, a power supply 303, an input unit 304 and other components. Those skilled in the art can understand that Figure 4 the structural diagram of the electronic device shown in does not constitute a limitation on the electronic device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Among them:

[0205] The processor 301 is the control center of the electronic device, connects various parts of the entire electronic device through various interfaces and lines, runs or executes software programs and / or modules stored in the memory 302, and calls data stored in the memory 302 to execute various functions of the electronic device and process data, thereby monitoring the electronic device as a whole. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modulation and demodulation processor, where the application processor mainly processes the operating system, user interface, application programs, etc., and the modulation and demodulation processor mainly processes wireless communication. It can be understood that the above modulation and demodulation processor may not be integrated into the processor 301.

[0206] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and the emergency communication network switching method by running the software programs and modules stored in the memory 302. The memory 302 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the electronic device. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices. Correspondingly, the memory 302 may further include a memory controller to provide the processor 301 with access to the memory 302.

[0207] The electronic device further includes a power supply 303 for supplying power to each component. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, so as to implement functions such as management of charging, discharging, and power consumption management through the power management system. The power supply 303 may further include any components such as one or more DC or AC power supplies, a recharge system, a power failure detection circuit, a power converter or inverter, and a power status indicator.

[0208] The electronic device may further include an input unit 304, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function controls.

[0209] Although not shown, the electronic device may further include a display unit, etc., which will not be elaborated here. Specifically, in this embodiment, the processor 301 in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 will run the application programs stored in the memory 302 to implement various functions as follows:

[0210] Construct a heterogeneous network environment corresponding to the current emergency scenario; obtain the network connection status of the terminal device in the current network, and predict the future signal reception strength based on the network connection status; after triggering a vertical handover event based on the future signal reception strength, control the terminal device to execute the corresponding network handover decision, so that the terminal device selects the optimal target network for handover.

[0211] For the specific implementation of each of the above operations, reference may be made to the previous embodiments, which will not be elaborated here.

[0212] In the embodiments of the present application, the terminal device monitors network parameters in real time, uses the grey prediction algorithm to predict the future value of the signal reception strength, determines the vertical handover time in advance, and reduces the handover delay. At the same time, by combining the subjective weight determined by the fuzzy analytic hierarchy process and the objective weight determined by the entropy weight method, the optimal target network is selected to ensure the continuity and stability of communication. Thus, in the emergency communication scenario, the problems of long handover delay and strong subjectivity in network selection existing in the existing handover algorithms can be effectively solved, the overall performance of communication can be improved, and the complex communication environment can be adapted, thereby ensuring the efficient development of rescue work.

[0213] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by controlling related hardware through instructions. The instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0214] Therefore, the embodiments of the present application provide a storage medium, which stores multiple instructions that can be loaded by a processor to execute the steps in any one of the emergency communication network handover methods provided by the embodiments of the present application. For example, the instructions can perform the following steps:

[0215] Construct a heterogeneous network environment corresponding to the current emergency scenario; obtain the network connection status of the terminal device in the current network, and predict the future signal reception strength based on the network connection status; after triggering a vertical handover event based on the future signal reception strength, control the terminal device to execute the corresponding network handover decision, so that the terminal device selects the optimal target network for handover.

[0216] For the specific implementation of each of the above operations, reference can be made to the previous embodiments and will not be elaborated here.

[0217] Among them, the storage medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), a magnetic disk or an optical disc, etc. Since the instructions stored in the storage medium can execute the steps in any one of the emergency communication network handover methods provided by the embodiments of the present application, the beneficial effects that can be achieved by any one of the emergency communication network handover methods provided by the embodiments of the present application can be realized. For details, reference can be made to the previous embodiments and will not be elaborated here.

[0218] The above has introduced in detail a method, device, electronic device and storage medium for switching an emergency communication network provided by the embodiments of the present application. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for switching an emergency communication network, characterized in that: The steps include: Build a heterogeneous network environment corresponding to the current emergency scenario; Obtaining a network connection status of the terminal device in the current network, and predicting future signal reception strength based on the network connection status; After a vertical switching event is triggered based on the future signal reception strength, the terminal device is controlled to execute a corresponding network switching decision so that the terminal device selects an optimal target network for switching.

2. The method for switching an emergency communication network according to claim 1, characterized in that: The construction of a heterogeneous network environment in the current emergency scenario includes: Deploy multiple ad hoc network nodes in areas where the public network is unavailable and connect them; Set up multiple public network access points in areas covered by the public network; Based on the multiple ad hoc network nodes and the multiple public network access points, a heterogeneous network environment in the current emergency scenario is constructed; The self-organizing network nodes and the public network access points are managed through a network management device.

3. The method for switching an emergency communication network according to claim 1, characterized in that: The obtaining of the network connection status of the terminal device in the current network and predicting the future signal reception strength based on the network connection status includes: Acquire a real-time network connection status of the terminal device in the current network, where the real-time network connection status includes at least one of real-time signal reception strength, real-time signal-to-interference-noise ratio, real-time bandwidth, real-time packet loss rate, real-time delay, and real-time jitter; Using a grey prediction algorithm to predict the signal reception strength prediction value at a future moment based on the one-dimensional sequence corresponding to the real-time signal reception strength; A vertical switching time is determined based on the signal reception strength prediction value.

4. The method for switching an emergency communication network according to claim 3, characterized in that: The method of using a grey prediction algorithm to predict a signal reception strength prediction value at a future time based on a one-dimensional sequence corresponding to the real-time signal reception strength includes: Based on the monitored real-time signal reception strength, converting into a corresponding one-dimensional sequence of real-time signal reception strength; Performing accumulation processing on the one-dimensional sequence of real-time signal reception strength to obtain a corresponding real-time signal reception strength accumulation sequence; Constructing a prediction model based on the real-time signal reception strength accumulation sequence, and determining the model coefficients by parameter fitting; The signal reception strength prediction value at a future time is calculated based on the prediction model.

5. The method for switching an emergency communication network according to claim 1, characterized in that: The controlling the terminal device to execute the corresponding network switching decision so that the terminal device selects the optimal target network for switching includes: Acquire a plurality of decision parameters of an accessible network, wherein the decision parameters include a communication quality parameter and a performance limitation parameter; Determine the comprehensive weight of the decision parameter by combining the subjective weight and the objective weight; Performing a multi-criteria scoring on the accessible networks based on the comprehensive weights, and selecting the network with the highest score as the optimal switching target; The terminal device is controlled to perform a vertical switching operation from the current network to the optimal switching target.

6. The method for switching an emergency communication network according to claim 5, characterized in that: The method for determining the subjective weight includes: Constructing a decision parameter importance discriminant matrix based on triangular fuzzy numbers to quantify the relative importance interval of the decision parameters; The decision parameter importance discriminant matrix is ​​defuzzified to generate subjective weights corresponding to the decision parameters.

7. The method for switching an emergency communication network according to claim 5, characterized in that: The objective weight is determined by: Normalizing the decision parameters of the accessible network to generate a standardized decision matrix; Calculate the information entropy of each of the decision parameters based on the standardized decision matrix; The objective weight corresponding to each of the decision parameters is determined based on the discrete degree of the information entropy.

8. An emergency communication network switching device, characterized in that: include: Construction module, used to build the corresponding heterogeneous network environment in the current emergency scenario; A prediction module, used to obtain the network connection status of the terminal device in the current network, and predict the future signal reception strength based on the network connection status; The switching module is used to control the terminal device to execute the corresponding network switching decision after a vertical switching event is triggered based on the future signal reception strength, so that the terminal device selects the optimal target network for switching.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the emergency communication network switching method as described in any one of claims 1 to 7 are implemented.

10. A storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the emergency communication network switching method as described in any one of claims 1 to 7.

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