Intelligent dynamic planning method and system for emergency communication network

Abstract

The invention discloses an intelligent dynamic planning method and system for an emergency communication network, and relates to the technical field of network communication. The method comprises the steps of firstly constructing an emergency communication network, then collecting link basic data, constructing a calculation model of a load update value and a load identification value according to the basic data, calculating a load statistical value, calculating a link evaluation result based on the load update value, the load statistical value and the load identification value, and performing corresponding dynamic planning. The system is used for implementing the method. According to the method, the link state is monitored in real time, and a high-real-time evaluation mechanism based on the load update value, the load statistical value and the load identification value is constructed, so that the real load condition of the network is comprehensively reflected in real time.

Description

Technical Field

[0001] The present invention relates to the field of network communication technology, and in particular to an intelligent dynamic planning method and system for an emergency communication network. Background Art

[0002] Natural disasters are characterized by suddenness in time and urgency of disaster relief, randomness in location and unpredictability of developments. Therefore, it is necessary to build a flexible, efficient and agile emergency communication system to ensure the smooth implementation of subsequent comprehensive rescue work.

[0003] At present, although the existing emergency communication network has also adopted the intelligent dynamic programming method to a certain extent to improve the robustness and response speed of the network, there are still some technical defects in practical application. The existing intelligent dynamic programming method usually adjusts the network configuration through the analysis of historical data and real-time data, but its main defects are:

[0004] Although existing methods can make a certain degree of dynamic adjustment according to the network status, the lack of a high-real-time evaluation mechanism leads to a lag in the adjustment process and an inability to respond to network traffic bursts or failures in a timely manner. Existing dynamic planning methods often rely on relatively rough indicators when evaluating network status, which cannot fully and accurately reflect the actual load of the network, thus affecting the accuracy of dynamic planning. Due to the inability to accurately predict and evaluate network load, resource allocation is not reasonable, especially in emergency communication scenarios, where resource waste is more serious. Summary of the invention

[0005] In view of this, the present invention proposes an intelligent dynamic planning method and system for emergency communication networks. The present invention monitors the link status in real time and constructs a high real-time evaluation mechanism based on load update value, load statistical value and load identification value, which fully and real-timely reflects the real load situation of the network.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] An intelligent dynamic planning method for an emergency communication network comprises the following steps:

[0008] Step 1: Build an emergency communication network;

[0009] Step 2: Use network equipment to monitor the link status of the emergency communication network in real time and collect basic data of the link, including throughput, packet loss rate and latency;

[0010] Step 3: construct a training sample set. Each training sample in the training sample set includes a current basic data X(t) and a historical basic data Y(t). X(t) is a 3x1 column vector, including the throughput, packet loss rate and delay of the corresponding link at time t. Y(t) is a 3x1 column vector, including the average values ​​of the throughput, packet loss rate and delay of the corresponding link in the duration Time before time t.

[0011] Step 4: construct a calculation model for the load update value and the load identification value according to the deep learning method; the input of the calculation model is the basic data X(t) of the link at time t and the corresponding historical basic data Y(t), and the output of the calculation model is the load update value and the load identification value of the link at time t;

[0012] Step 5: record the sum of the three elements in the historical basic data Y(t) as the load statistics of the link at time t, combine the load update value and load identification value of the link at time t, and calculate the evaluation result L of the link at time t t ;

[0013] Step 6: The evaluation result L of the link at time t t Compare with the preset network normal state limit value T and network warning state limit value F, where T is less than F; perform corresponding dynamic planning based on the comparison result to complete the intelligent dynamic planning of the emergency communication network.

[0014] Furthermore, the specific method of constructing the calculation model of the load update value and the load identification value according to the deep learning method in step 4 is as follows:

[0015] Step 401, for input samples X(t) and Y(t), calculate the corresponding load update value

[0016]

[0017] Among them, B m is the maximum throughput of the link, TR() represents the trace of the calculation matrix, W is the first updated weight matrix, W is a 3x3 matrix; V is the second updated weight matrix, V is a 3x3 matrix; the superscript T represents transpose;

[0018] Step 402: Calculate the corresponding load identification value for the input sample X(t)

[0019]

[0020] Wherein, q is the first recognition weight vector, and q is a 3x1 column vector; represents a nonlinear transformation function; p is the second recognition weight vector, p is a 3x1 column vector; b is an adjustment term, b is a constant;

[0021] Step 403, calculate the corresponding loss value

[0022]

[0023] in, represents the true value of the updated load value of the link at time t, The value of is the sum of the three elements in the column vector of X(t)-X(t-1); represents the true value of the load identification value of the link at time t, The value of is the link status value at time t that is artificially marked according to the link normal, overload, or underload status;

[0024] Step 404, based on the loss value Use the gradient descent method for back propagation to update the first update weight matrix, the second update weight matrix, the first recognition weight vector, and the second recognition weight vector until the loss value Convergence; saving the first update weight matrix, the second update weight matrix, the first identification weight vector and the second identification weight vector parameters; completing the construction of the calculation model of the load update value and the load identification value.

[0025] Furthermore, in step 5, the evaluation result L of the link at time t is calculated. t The specific method is as follows:

[0026]

[0027] in, is the load statistic value of the link at time t, μ1, μ2, and μ3 represent the adjustment coefficients of the load update value, the load statistic value, and the load identification value, respectively, and μ1, μ2, and μ3 are all positive numbers, and μ1+μ2+μ3=1.

[0028] Furthermore, the specific method of performing corresponding dynamic programming according to the comparison result in step 6 is as follows:

[0029] If L t <T, it is a normal state, and the corresponding dynamic programming is:

[0030] Resource optimization: Keep the current resource configuration unchanged and continue to monitor the link status of the emergency communication network;

[0031] Preventive measures: Maintain existing preventive measures and continue to regularly check the link status of the emergency communication network;

[0032] If T≤L tIf F, then it is a warning state, and the corresponding dynamic programming is as follows:

[0033] Resource adjustment: Dynamically adjust bandwidth allocation, optimize traffic distribution, and avoid overload;

[0034] Add redundancy: Add redundant connections to critical nodes to improve the robustness of the emergency communication network;

[0035] Preventive measures: Strengthen monitoring, detect and handle potential fault points in a timely manner;

[0036] If F ≤ L t , then it is an abnormal state, and the corresponding dynamic programming is as follows:

[0037] Emergency adjustment: Immediately adjust the network configuration, such as adding temporary base stations, adjusting frequency allocation, and optimizing route selection;

[0038] Resource reallocation: Reasonably allocate bandwidth and other resources to avoid overload of some nodes;

[0039] Fault recovery: Quickly locate the fault point and take measures to restore the emergency communication network service;

[0040] Emergency response: Set different quality of service (QoS) standards according to the emergency level and service type to ensure priority for critical communications.

[0041] An intelligent dynamic programming system for an emergency communication network, which is used to execute an intelligent dynamic programming method for an emergency communication network described in any one of the above, specifically including a construction module, a collection module, an evaluation module, and a dynamic programming module;

[0042] The construction module is used to construct an emergency communication network;

[0043] The collection module is connected to the construction module and is used to use network devices to monitor the link status of the emergency communication network in real time and collect the basic data of the links;

[0044] The evaluation module is connected to the collection module. The evaluation module specifically includes a training layer and an application layer. The training layer is used to construct a calculation model for the load update value and the load identification value according to the basic data of the collected links, and the application layer is used to calculate the evaluation result L of the link according to the basic data of the collected links and the calculation model of the load update value and the load identification value t ;

[0045] The dynamic programming module is connected to the evaluation module and is used to perform corresponding dynamic programming according to the evaluation result L of the link t .

[0046] Due to the adoption of the above technical solutions, the beneficial effects of the present invention compared with the prior art are as follows:

[0047] 1. The present invention uses a data-driven approach to obtain evaluation results, ensuring that the network can respond quickly when faced with sudden traffic or failures, thereby improving the overall robustness and flexibility of the network.

[0048] 2. The present invention can comprehensively evaluate the utilization of network resources, reasonably allocate bandwidth and other resources, and avoid resource waste by calculating load statistics. Especially in emergency communication scenarios, it can maximize resource utilization efficiency and ensure the completion of key communication tasks.

[0049] 3. The present invention can timely discover potential network failure points through the calculation of load identification values, and take preventive measures to avoid service quality degradation. Once a failure occurs, the system can quickly locate and restore, reducing downtime and ensuring communication continuity.

[0050] 4. The present invention can timely monitor the changes in network status, balance the load distribution in the network, and ensure that the load of each link is kept within a reasonable range by calculating the load update value. This can effectively avoid the situation where some links are overloaded while other link resources are idle, thereby improving the service quality of the overall network. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The present invention is an overall flow chart of an emergency communication network intelligent dynamic planning method in an embodiment of the present invention.

[0052] Figure 2 The present invention is a schematic diagram of the structure of an intelligent dynamic planning system for an emergency communication network in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0054] An intelligent dynamic planning method for emergency communication network, such as Figure 1 As shown, the following steps are included:

[0055] Step 1: Build an emergency communication network;

[0056] Specifically, it gathers and integrates the network resources, operation status, alarm information and other data of each subsystem of the entire network, and based on the real three-dimensional geographic information system, it truly presents the communication equipment status, communication quality, communication status, alarm information, etc. in the entire network, providing comprehensive, efficient and reliable operation and maintenance support for network security personnel;

[0057] Step 2: Use network equipment to monitor the link status of the emergency communication network in real time and collect basic data of the link, including throughput, packet loss rate and latency;

[0058] Step 3: construct a training sample set. Each training sample in the training sample set includes a current basic data X(t) and a historical basic data Y(t). X(t) is a 3x1 column vector, including the throughput, packet loss rate and delay of the corresponding link at time t. Y(t) is a 3x1 column vector, including the average values ​​of the throughput, packet loss rate and delay of the corresponding link in the duration Time before time t.

[0059] Step 4: construct a calculation model for the load update value and the load identification value according to the deep learning method; the input of the calculation model is the basic data X(t) of the link at time t and the corresponding historical basic data Y(t), and the output of the calculation model is the load update value and the load identification value of the link at time t;

[0060] Step 5: record the sum of the three elements in the historical basic data Y(t) as the load statistics of the link at time t, combine the load update value and load identification value of the link at time t, and calculate the evaluation result L of the link at time t t ;

[0061] Specifically, the evaluation result refers to the load status of the link, which comprehensively reflects the real-time load of the link, the long-term load trend, and the existence of potential problems. The evaluation results of the busy and idle degree will be used to guide the next step of dynamic planning measures.

[0062] This step provides a key decision-making basis for subsequent dynamic planning by evaluating the busyness of the link. Real-time monitoring and evaluation enable the system to detect changes in the busyness of the link in a timely manner. This is particularly important for emergency communication networks, because network bursts or failures may occur at any time. Through real-time evaluation, preventive measures can be taken in advance before the link becomes overloaded to avoid service quality degradation.

[0063] Step 6: The evaluation result L of the link at time t t Compare with the preset network normal state limit value T and network warning state limit value F, where T is less than F; perform corresponding dynamic planning based on the comparison result to complete the intelligent dynamic planning of the emergency communication network.

[0064] Furthermore, the specific method of constructing the calculation model of the load update value and the load identification value according to the deep learning method in step 4 is as follows:

[0065] Step 401, for input samples X(t) and Y(t), calculate the corresponding load update value

[0066] Among them, B mis the maximum throughput of the link, TR() represents the trace of the calculation matrix, W is the first updated weight matrix, W is a 3x3 matrix; V is the second updated weight matrix, V is a 3x3 matrix; the superscript T represents transpose;

[0067] Step 402: Calculate the corresponding load identification value for the input sample X(t)

[0068]

[0069] Wherein, q is the first recognition weight vector, and q is a 3x1 column vector; represents a nonlinear transformation function; p is the second recognition weight vector, p is a 3x1 column vector; b is an adjustment term, b is a constant;

[0070] Specifically, the nonlinear transformation function includes ReLU, Sigmoid function, etc. In this embodiment, the Sigmoid function is used. In this embodiment, b=0.5;

[0071] The load identification value is used to distinguish different states of the network, helping to identify whether the current network is in a normal state or whether there are potential problems. It provides a basis for dynamic planning and helps the system to schedule and optimize resources according to the current network state.

[0072] Step 403, calculate the corresponding loss value

[0073]

[0074] in, represents the true value of the updated load value of the link at time t, The value of is the sum of the three elements in the column vector of X(t)-X(t-1); represents the true value of the load identification value of the link at time t, The value of is the link status value at time t that is artificially marked according to the link normal, overload, or underload status;

[0075] Step 404, based on the loss value Use the gradient descent method for back propagation to update the first update weight matrix, the second update weight matrix, the first recognition weight vector, and the second recognition weight vector until the loss value Convergence; saving the first update weight matrix, the second update weight matrix, the first identification weight vector and the second identification weight vector parameters; completing the construction of the calculation model of the load update value and the load identification value.

[0076] Furthermore, in step 5, the evaluation result L of the link at time t is calculated. tThe specific method is as follows:

[0077]

[0078] in, is the load statistic value of the link at time t, μ1, μ2, and μ3 represent the adjustment coefficients of the load update value, load statistic value, and load identification value, respectively, and μ1, μ2, and μ3 are all positive numbers, and μ1+μ2+μ3=1;

[0079] In this embodiment, μ1=0.5, μ2=0.3, and μ3=0.2.

[0080] Furthermore, the specific method of performing corresponding dynamic programming according to the comparison result in step 6 is as follows:

[0081] If L t <T, it is a normal state, and the corresponding dynamic programming is:

[0082] Resource optimization: Keep the current resource configuration unchanged and continue to monitor the link status of the emergency communication network;

[0083] Preventive measures: Maintain existing preventive measures and continue to regularly check the link status of the emergency communication network;

[0084] If T≤L t <F, it is a warning state, and the corresponding dynamic programming is:

[0085] Resource adjustment: dynamically adjust bandwidth allocation, optimize traffic distribution, and avoid overload;

[0086] Increase redundancy: Add redundant connections to key nodes to improve the robustness of the emergency communication network;

[0087] Preventive measures: Strengthen monitoring to promptly identify and address potential failure points;

[0088] If F≤L t , then it is an abnormal state, and the corresponding dynamic programming is:

[0089] Emergency adjustment: Immediately adjust network configuration, such as adding temporary base stations, adjusting frequency allocation, and optimizing routing selection;

[0090] Resource reallocation: Rationally allocate bandwidth and other resources to avoid overloading certain nodes;

[0091] Fault recovery: quickly locate the fault point and take measures to restore emergency communication network services;

[0092] Emergency response: Set different QoS standards according to the degree of urgency and service type to ensure that critical communications are given priority.

[0093] Specifically,

[0094] 1. Add temporary base stations: When the evaluation results show that the network load is high or there is local overload, temporary base stations can be added to relieve pressure and improve coverage and service quality.

[0095] 2. Adjust frequency allocation: Dynamically adjust frequency allocation based on the evaluation results to ensure the rational use of spectrum resources, avoid co-frequency interference, and improve communication quality.

[0096] 3. Optimize route selection: Optimize route selection based on load statistics and identification values, avoid links with excessive loads, distribute traffic reasonably, and improve overall network efficiency.

[0097] 4. Add redundant connections: For key nodes, add redundant connections to improve the robustness and fault tolerance of the network and ensure that the network can still maintain connectivity in the event of partial link failure.

[0098] 5. Set QoS standards: Based on the evaluation results, set different quality of service (QoS) standards according to the urgency and service type of different businesses to ensure priority transmission of critical information.

[0099] An emergency communication network intelligent dynamic planning system, used to execute any of the above-mentioned emergency communication network intelligent dynamic planning methods, such as Figure 2 As shown, it specifically includes a construction module, a collection module, an evaluation module and a dynamic programming module;

[0100] The building module is used to build an emergency communication network;

[0101] The collection module is connected to the construction module and is used to monitor the link status of the emergency communication network in real time using network equipment and collect basic data of the link;

[0102] The evaluation module is connected to the collection module, and the evaluation module specifically includes a training layer and an application layer. The training layer is used to complete the construction of a calculation model of a load update value and a load identification value according to the basic data of the collection link, and the application layer is used to calculate the evaluation result L of the link according to the basic data of the collection link and the calculation model of the load update value and the load identification value. t ;

[0103] The dynamic planning module is connected to the evaluation module and is used to calculate the link evaluation result L t Carry out corresponding dynamic planning.

[0104] In summary, the present invention monitors the link status in real time and constructs a high real-time evaluation mechanism based on load update value, load statistical value and load identification value, which fully and real-timely reflects the real load situation of the network.

[0105] The electronic device provided by an intelligent dynamic planning system for an emergency communication network in this embodiment includes one or more processors and a memory.

[0106] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0107] The memory may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may run the program instructions to implement an emergency communication network intelligent dynamic planning method and / or other desired functions of any embodiment of the present application described above. Various contents such as initial external parameters, thresholds, etc. may also be stored in the computer-readable storage medium.

[0108] In one example, the electronic device may further include: an input device and an output device, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown). The input device may include, for example, a keyboard, a mouse, etc. The output device may output various information to the outside, including early warning information, braking force, etc. The output device may include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, etc.

[0109] In addition, the electronic device may further include any other appropriate components according to specific application scenarios.

[0110] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of an emergency communication network intelligent dynamic planning method provided in any embodiment of the present application.

[0111] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0112] In addition, an embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of an emergency communication network intelligent dynamic planning method provided in any embodiment of the present application.

[0113] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0114] It should be noted that the terms used in the present invention are only for describing specific embodiments, rather than limiting the scope of the present application. As shown in the present specification, unless the context clearly indicates an exception, the words "one", "a", "a kind of" and / or "the" do not specifically refer to the singular, but may also include the plural. The terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of more restrictions, the elements defined by the sentence "include one..." do not exclude the presence of other identical elements in the process, method or device including the elements.

[0115] It should also be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", etc. should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent dynamic planning method for emergency communication network, characterized in that: The following steps are involved: Step 1: Build an emergency communication network; Step 2: Use network equipment to monitor the link status of the emergency communication network in real time and collect basic data of the link, including throughput, packet loss rate and latency; Step 3: construct a training sample set. Each training sample in the training sample set includes a current basic data X(t) and a historical basic data Y(t). X(t) is a 3x1 column vector, including the throughput, packet loss rate and delay of the corresponding link at time t. Y(t) is a 3x1 column vector, including the average values ​​of the throughput, packet loss rate and delay of the corresponding link in the duration Time before time t. Step 4: construct a calculation model for the load update value and the load identification value according to the deep learning method; the input of the calculation model is the basic data X(t) of the link at time t and the corresponding historical basic data Y(t), and the output of the calculation model is the load update value and the load identification value of the link at time t; Step 5: record the sum of the three elements in the historical basic data Y(t) as the load statistics of the link at time t, combine the load update value and load identification value of the link at time t, and calculate the evaluation result L of the link at time t. t ; Step 6: The evaluation result L of the link at time t t Compare with the preset network normal state limit value T and network warning state limit value F, where T is less than F; perform corresponding dynamic planning based on the comparison result to complete the intelligent dynamic planning of the emergency communication network.

2. According to claim 1, a method for intelligent dynamic planning of emergency communication network is characterized in that: The specific method of constructing the calculation model of the load update value and the load identification value according to the deep learning method in step 4 is as follows: Step 401, for input samples X(t) and Y(t), calculate the corresponding load update value Among them, B m is the maximum throughput of the link, TR() represents the trace of the calculation matrix, W is the first updated weight matrix, W is a 3x3 matrix; V is the second updated weight matrix, V is a 3x3 matrix; the superscript T represents transpose; Step 402: Calculate the corresponding load identification value for the input sample X(t) Wherein, q is the first recognition weight vector, and q is a 3x1 column vector; represents a nonlinear transformation function; p is the second recognition weight vector, p is a 3x1 column vector; b is an adjustment term, b is a constant; Step 403, calculate the corresponding loss value in, represents the true value of the updated load value of the link at time t, The value of is the sum of the three elements in the column vector of X(t)-X(t-1); represents the true value of the load identification value of the link at time t, The value of is the link status value at time t that is artificially marked according to the link normal, overload, or underload status; Step 404, based on the loss value Use the gradient descent method for back propagation to update the first update weight matrix, the second update weight matrix, the first recognition weight vector, and the second recognition weight vector until the loss value Convergence; saving the first update weight matrix, the second update weight matrix, the first identification weight vector and the second identification weight vector parameters; completing the construction of the calculation model of the load update value and the load identification value.

3. The method for intelligent dynamic planning of emergency communication network according to claim 2, characterized in that: In step 5, the evaluation result L of the link at time t is calculated t The specific method is as follows: in, is the load statistic value of the link at time t, μ1, μ2, and μ3 represent the adjustment coefficients of the load update value, the load statistic value, and the load identification value, respectively, and μ1, μ2, and μ3 are all positive numbers, and μ1+μ2+μ3=1.

4. The method for intelligent dynamic planning of emergency communication network according to claim 3, characterized in that: The specific method of performing corresponding dynamic programming according to the comparison results in step 6 is as follows: If L t < is less than T, it is in a normal state, and the corresponding dynamic programming is as follows: Resource optimization: Keep the current resource configuration unchanged and continue to monitor the link status of the emergency communication network; Preventive measures: Maintain existing preventive measures and continue to regularly check the link status of the emergency communication network; If T ≤ L t <F, then it is a warning state, and the corresponding dynamic programming is as follows: Resource adjustment: dynamically adjust bandwidth allocation, optimize traffic distribution, and avoid overload; Increase redundancy: Add redundant connections to key nodes to improve the robustness of the emergency communication network; Preventive measures: Strengthen monitoring to promptly identify and address potential failure points; If F≤L t , then it is an abnormal state, and the corresponding dynamic programming is: Emergency adjustment: Immediately adjust network configuration, such as adding temporary base stations, adjusting frequency allocation, and optimizing routing selection; Resource reallocation: Rationally allocate bandwidth and other resources to avoid overloading certain nodes; Fault recovery: quickly locate the fault point and take measures to restore emergency communication network services; Emergency response: Set different QoS standards according to the degree of urgency and service type to ensure that critical communications are given priority.

5. An intelligent dynamic planning system for emergency communication network, characterized in that: A method for intelligent dynamic planning of an emergency communication network used to execute any one of claims 1 to 4 above, specifically comprising a construction module, a collection module, an evaluation module and a dynamic planning module; The building module is used to build an emergency communication network; The collection module is connected to the construction module and is used to monitor the link status of the emergency communication network in real time using network equipment and collect basic data of the link; The evaluation module is connected to the collection module, and the evaluation module specifically includes a training layer and an application layer. The training layer is used to complete the construction of a calculation model of a load update value and a load identification value according to the basic data of the collection link, and the application layer is used to calculate the evaluation result L of the link according to the basic data of the collection link and the calculation model of the load update value and the load identification value. t ; The dynamic planning module is connected to the evaluation module and is used to calculate the link evaluation result L t Carry out corresponding dynamic planning.

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