A Wireless Ad Hoc Network Transmission Management Method for Earthquake Early Warning Information

Abstract

The present invention relates to the technical field of self-organizing network transmission management, and discloses a wireless self-organizing network transmission management method for earthquake early warning information, comprising the following steps: S101, generating a comprehensive failure risk prediction value through a multi-source data fusion algorithm according to seismic wave data, geological structure data, historical earthquake data and meteorological data; S102, constructing a hierarchical failure probability model based on the comprehensive failure risk prediction value, and generating a node failure risk probability map; and allocating weights to each layer according to the earthquake stage; S103, constructing a network topology, calculating the survival time of network nodes, and calculating the weighted comprehensive failure probability of each network node, and dynamically updating the network topology; S104, fusing to obtain node stable utilization characteristics according to the weighted comprehensive failure probability and survival time of each network node, and then performing staged resource scheduling. The present invention realizes the accurate prediction of the failure risk of network nodes and staged resource scheduling in the whole process of earthquake disasters.

Description

Technical Field

[0001] The present invention belongs to the technical field of ad hoc network transmission management, and particularly relates to a wireless ad hoc network transmission management method for earthquake early warning information. Background Art

[0002] Currently, earthquake early warning systems generally rely on wired networks or fixed communication facilities for information transmission. However, in the epicenter area, basic communication facilities are extremely vulnerable to being damaged by earthquakes and becoming ineffective, resulting in the inability to issue information in a timely manner. To enhance the emergency response ability of the early warning system, wireless ad hoc networks have been widely used in communication guarantee under disaster environments due to their characteristics such as decentralization, dynamic reconfiguration, and flexible deployment.

[0003] However, traditional ad hoc network transmission schemes usually do not fully consider the risk of continuous failure of network nodes caused by the earthquake itself, as well as the dynamic changes in network topology caused by the propagation of seismic waves. In addition, existing schemes lack targeted resource scheduling strategies at all stages of earthquake disasters, resulting in difficulties in ensuring network reliability and communication efficiency. Summary of the Invention

[0004] The present invention provides a wireless ad hoc network transmission management method for earthquake early warning information, which solves the technical problems in the related art that there is a lack of effective countermeasures for continuous failure of network nodes, dynamic changes in topology, and phased resource scheduling, and it is difficult to ensure the reliability and timeliness of information transmission throughout the disaster cycle.

[0005] The present invention provides a wireless ad hoc network transmission management method for earthquake early warning information, including the following steps:

[0006] S101, according to the real-time received seismic wave data, geological structure data, historical earthquake data, and meteorological data, generate a comprehensive failure risk prediction value for each network node through a multi-source data fusion algorithm, and mark the network nodes whose comprehensive failure risk prediction value exceeds the first preset threshold as high-risk nodes;

[0007] S102, based on the comprehensive failure risk prediction value of the high-risk nodes, construct a hierarchical failure probability model and generate a node failure risk probability map;

[0008] The hierarchical failure probability model is used to decompose the comprehensive failure risk prediction value of the high-risk nodes into a physical layer failure probability, a link layer failure probability, and an energy layer failure probability, and allocate weights to each layer according to the earthquake stage using a first weight allocation strategy;

[0009] Among them, the earthquake stage includes: pre-earthquake stage, epicenter stage, and post-earthquake stage. In the first weight allocation strategy, the link layer is dominant in the pre-earthquake stage, the physical layer is dominant in the epicenter stage, and the energy layer is dominant in the post-earthquake stage;

[0010] S103. Construct the network topology based on the node failure risk probability graph. According to the seismic wave propagation model, calculate the survival time of network nodes, and calculate the weighted comprehensive failure probability of each network node according to the first weight allocation strategy in the current earthquake stage, and dynamically update the network topology;

[0011] S104. Based on the weighted comprehensive failure probability and survival time of each network node, fuse to obtain the node stable utilization characteristics, and then perform phased resource scheduling.

[0012] Furthermore, the seismic wave data includes: the epicenter location, the epicenter depth, the seismic wave velocity, the P-wave arrival time, the S-wave arrival time, and the amplitude; the geological structure data includes: the soil type, the shear wave velocity, the fault distance, and the epicenter altitude; the historical earthquake data includes: the historical epicenter location and the historical magnitude; the meteorological data includes: the rainfall intensity, the light intensity, the wind speed, the wind direction, and the meteorological warning level.

[0013] Furthermore, the steps of the multi-source data fusion algorithm include:

[0014] S201. Map the received seismic wave data, geological structure data, historical earthquake data, and meteorological data to the range of 0 to 1;

[0015] S202. Assign weights to the mapped data respectively;

[0016] S203. Combine the propagation time difference between the seismic wave and the node with the preset meteorological interference coefficient to generate the time-domain attenuation coefficient;

[0017] S204. Multiply the weighted average result of the mapped data by the time-domain attenuation coefficient of the corresponding network node to obtain the comprehensive failure risk prediction value of each network node.

[0018] Furthermore, the physical layer failure probability is obtained by fusing the distance between the network node and the epicenter, the shear wave velocity, and the building seismic resistance level; the link layer failure probability is obtained by fusing the rainfall intensity and the lightning frequency; the energy layer failure probability is obtained by fusing the remaining power of the network node and the light intensity.

[0019] Furthermore, the earthquake stage is judged and triggered by the following conditions:

[0020] Pre-earthquake stage: The network node has not received the P-wave, but the epicenter location has been determined;

[0021] Epicenter stage: The network node receives the P-wave;

[0022] Post-earthquake stage: The seismic wave is more than the first preset distance away from the network node.

[0023] Furthermore, the seismic buffer time of the network node is obtained by combining the ratio of the distance from the network node to the earthquake source location to the historical maximum damage distance and the earthquake resistance level of the building. The survival time of the network node is obtained by combining the ratio of the earthquake source depth to the S wave speed, the buffer time and the ratio of the remaining power of the network node to the energy consumption rate.

[0024] Furthermore, the network topology represents the connection relationship and transmission path between network nodes, including: an available node list, a main path planning and a backup path planning, and the dynamic update of the network topology includes:

[0025] Node removal rule: If the survival time of a network node is less than a first preset time or the weighted comprehensive failure probability exceeds a second preset threshold, the network node is removed from the network topology;

[0026] Main path selection rule: give priority to selecting network nodes whose survival time is greater than the second preset time and whose weighted comprehensive failure probability is less than the third preset threshold;

[0027] Backup path generation rule: generate no less than two backup paths for each main path node, and the horizontal distance between the network node of the backup path and the main node is greater than the second preset distance.

[0028] Furthermore, the node stable utilization feature is obtained by the ratio of the weighted comprehensive failure probability of the network nodes to the survival time of the network nodes in the current earthquake stage, wherein the calculation formula of the node stable utilization feature is: ,NSUI represents the node stable utilization feature, represents the weighted comprehensive failure probability of network nodes, Indicates the survival time of the network node. Represents a minimum positive value.

[0029] Furthermore, the phased resource scheduling includes:

[0030] In the pre-earthquake phase, communication resources are pre-configured based on the weighted comprehensive failure probability of network nodes;

[0031] In the epicenter phase, according to the main path selection rule, the network nodes of the main path are sorted based on the node stability utilization characteristics, and communication resources are preferentially allocated to the network nodes whose node stability utilization characteristics are lower than the fourth preset threshold;

[0032] In the post-earthquake stage, network nodes with a remaining power level higher than a fifth preset threshold are regarded as stable recovery nodes, and communication resources are allocated to them.

[0033] The beneficial effects of the present invention are as follows: By integrating multi-source data such as seismic waves, geological structures, historical earthquakes, and meteorology, the present invention constructs a hierarchical failure probability model, dynamically adjusts the weights of each layer in combination with the earthquake stage, and realizes accurate prediction of the failure risk of network nodes; introduces the node survival time and node stable utilization characteristics to improve the routing stability in the epicenter stage; adopts differential resource scheduling strategies before, during, and after the earthquake respectively to enhance the adaptability and response ability of the present invention in the whole process of earthquake disasters. The present invention can significantly improve the transmission reliability, continuity, and recovery efficiency of earthquake early warning information in complex disaster environments, and has good engineering application value and disaster resistance guarantee ability. Brief Description of the Drawings

[0034] Figure 1 is a flowchart of a method for wireless ad-hoc network transmission management of earthquake early warning information according to the present invention. Detailed Embodiments

[0035] Now, the subject matter described herein will be discussed with reference to exemplary embodiments. It should be understood that discussing these embodiments is only to enable those skilled in the art to better understand and thus implement the subject matter described herein. Without departing from the scope of protection of the content of this specification, changes can be made to the functions and arrangements of the elements discussed. Each example can omit, substitute, or add various processes or components as needed. Additionally, the features described relative to some examples can also be combined in other examples.

[0036] It should be noted that unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present invention should have the ordinary meaning understood by those of ordinary skill in the art to which the present invention pertains. The "first", "second", and similar terms used in one or more embodiments of the present invention do not denote any order, quantity, or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connected" or "coupled" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0037] As Figure 1 shown, a method for wireless ad-hoc network transmission management of earthquake early warning information includes the following steps:

[0038] S101. Based on the real-time received seismic wave data, geological structure data, historical earthquake data, and meteorological data, generate the comprehensive failure risk prediction value of each network node through a multi-source data fusion algorithm, and mark the network nodes whose comprehensive failure risk prediction value exceeds the first preset threshold as high-risk nodes;

[0039] S102. Based on the comprehensive failure risk prediction value of the high-risk nodes, construct a hierarchical failure probability model and generate a node failure risk probability map;

[0040] The hierarchical failure probability model is used to decompose the comprehensive failure risk prediction value of the high-risk nodes into physical layer failure probability, link layer failure probability, and energy layer failure probability, and allocate weights to each layer according to the first weight allocation strategy in the earthquake stage;

[0041] Among them, the earthquake stage includes: pre-earthquake stage, epicenter stage, and post-earthquake stage. In the first weight allocation strategy, the link layer is dominant in the pre-earthquake stage, the physical layer is dominant in the epicenter stage, and the energy layer is dominant in the post-earthquake stage;

[0042] S103. Construct a network topology based on the node failure risk probability map, calculate the survival time of the network nodes according to the seismic wave propagation model, and calculate the weighted comprehensive failure probability of each network node according to the first weight allocation strategy in the current earthquake stage, and dynamically update the network topology;

[0043] S104. According to the weighted comprehensive failure probability and survival time of each network node, fuse to obtain the node stable utilization characteristics, and then perform phased resource scheduling.

[0044] In an embodiment of the present invention, the network node refers to each device in the network, including: wireless communication devices, sensors, routers, and receiving points. The receiving points are used to receive the final warning information, such as rescue department or public receiving points.

[0045] In an embodiment of the present invention, the node failure risk probability map includes the geographical location, physical layer failure probability, link layer failure probability, and energy layer failure probability of each node.

[0046] In an embodiment of the present invention, the seismic wave data includes: epicenter location, focal depth, seismic wave velocity, P-wave arrival time, S-wave arrival time, and amplitude; the geological structure data includes: soil type, shear wave velocity, fault distance, and focal elevation; the historical earthquake data includes: historical epicenter location and historical magnitude; the meteorological data includes: rainfall intensity, light intensity, wind speed, wind direction, and meteorological warning level.

[0047] Among them, the seismic wave data is obtained by accessing the China Earthquake Networks Center, and the epicenter location is represented by longitude and latitude coordinates; the soil type and shear wave velocity are obtained by accessing the China Geological Survey, the fault distance and the epicenter altitude are obtained by accessing the Geographic Information System, and the meteorological data is obtained by accessing the data interface of the China Meteorological Administration.

[0048] In an embodiment of the present invention, the steps of the multi-source data fusion algorithm include:

[0049] S201, mapping the received seismic wave data, geological structure data, historical earthquake data, and meteorological data to the range of 0 to 1;

[0050] S202, respectively assigning weights to the mapped data, and the weights are obtained based on expert presetting;

[0051] S203, combining the propagation time difference between the seismic wave and the node with a preset meteorological interference coefficient to generate a time-domain attenuation coefficient. Specifically, the calculation formula of the time-domain attenuation coefficient is: , where represents the time-domain attenuation coefficient, which is used to reflect the risk dynamics of the network node over time, represents the seismic wave influence attenuation coefficient, represents the propagation time difference between the seismic wave and the node, represents the meteorological adjustment weight coefficient, represents the meteorological interference coefficient, which is obtained by comprehensively considering the real-time rainfall intensity, wind speed, and lightning frequency;

[0052] S204, multiplying the weighted average result of the mapped data by the time-domain attenuation coefficient of the corresponding network node to obtain the comprehensive failure risk prediction value of each network node.

[0053] In an embodiment of the present invention, the physical layer failure probability is obtained by fusing the distance between the network node and the epicenter, the shear wave velocity, and the building seismic resistance level. The link layer failure probability is obtained by fusing the rainfall intensity and the lightning frequency. The energy layer failure probability is obtained by fusing the remaining power of the network node and the light intensity.

[0054] Specifically, the calculation formula of the physical layer failure probability is: , where represents the physical layer failure probability, represents the distance between the network node and the epicenter, represents the shear wave velocity, which is used to reflect the softness of the geology, and A represents the building seismic resistance level, , and respectively represent the first weight coefficient, the second weight coefficient, and the third weight coefficient;

[0055] The calculation formula for the link layer failure probability is: , where represents the link layer failure probability, represents the rainfall intensity, represents the lightning frequency, and respectively represent the fourth weight coefficient and the fifth weight coefficient;

[0056] The calculation formula for the energy layer failure probability is: , where represents the energy layer failure probability, represents the remaining power of the network node, represents the light intensity, is used to estimate the solar energy supply capacity, and respectively represent the sixth weight coefficient and the seventh weight coefficient.

[0057] By decomposing the comprehensive failure risk of high-risk nodes into three sub-levels: the physical layer, the link layer, and the energy layer, and dynamically adjusting the weights of each layer in combination with the earthquake stage, it can more accurately reflect the influence trend of different factors on the failure risk of network nodes, and realize the transformation from static perception to dynamic evolution prediction; this "layered + staged" risk modeling method improves the interpretability of the model and the accuracy of scheduling decisions, and significantly enhances the adaptive ability of the present invention especially in emergency disaster situations.

[0058] In a preferred embodiment of the present invention, the first weight allocation strategy includes:

[0059] Before the earthquake stage: the link layer weight is set to 50%, the physical layer weight is set to 30%, and the energy layer weight is set to 20%;

[0060] During the earthquake epicenter stage: the link layer weight is set to 30%, the physical layer weight is set to 60%, and the energy layer weight is set to 10%;

[0061] After the earthquake stage: the link layer weight is set to 30%, the physical layer weight is set to 10%, and the energy layer weight is set to 60%.

[0062] Among them, the first weight allocation strategy is dynamically adjusted according to real-time data:

[0063] Before the earthquake stage: if the real-time rainfall intensity ≥ 50 mm / h, the link layer weight is increased to 60%;

[0064] During the earthquake epicenter stage: if the aftershock frequency ≥ 3 times / hour, the physical layer weight is increased to 70%;

[0065] After the earthquake stage: if the average remaining power of the node ≤ 30%, the energy layer weight is increased to 75%.

[0066] In an embodiment of the present invention, the earthquake stage is determined and triggered by the following conditions:

[0067] Pre-earthquake stage: The network node has not received the P wave, but the epicenter location has been determined;

[0068] Epicenter stage: The network node receives the P wave;

[0069] Post-earthquake stage: The seismic wave is more than a first preset distance away from the network node.

[0070] In an embodiment of the present invention, by combining the ratio of the distance from the network node to the epicenter location with the historical maximum damage distance and the building seismic resistance level, the seismic resistance buffer time of the network node is obtained. By combining the ratio of the epicenter depth to the S-wave velocity, the buffer time, and the ratio of the remaining power of the network node to the energy consumption rate, the survival time of the network node is obtained.

[0071] Specifically, the calculation formula for the seismic resistance buffer time is: , where represents the seismic resistance buffer time, which is determined by the empirical model between the building seismic resistance level in the area where the network node is located and the earthquake magnitude, k represents the first correction coefficient, represents the historical maximum damage distance, that is, in historical earthquake events, the maximum horizontal distance from the damaged network node to the epicenter;

[0072] The calculation formula for the survival time of the network node is: , where represents the survival time of the network node, represents the energy consumption rate, D represents the epicenter depth, represents the S-wave velocity.

[0073] In an embodiment of the present invention, the network topology represents the connection relationship and transmission path between network nodes, including: a list of available nodes, main path planning, and backup path planning. The dynamic update of the network topology includes:

[0074] Node elimination rule: If the survival time of the network node is less than a first preset duration or the weighted comprehensive failure probability exceeds a second preset threshold, the network node is removed from the network topology;

[0075] Main path selection rule: Preferentially select network nodes with a survival time greater than a second preset duration and a weighted comprehensive failure probability less than a third preset threshold;

[0076] Backup path generation rule: Generate no less than two backup paths for each main path node, and the horizontal distance between the network nodes of the backup path and the main node is greater than a second preset distance.

[0077] In one embodiment of the present invention, the node stability utilization feature is obtained by the ratio of the weighted comprehensive failure probability of the network node to the survival time of the network node in the current earthquake stage, wherein the calculation formula of the node stability utilization feature is: NSUI represents the node stable utilization characteristic. The smaller the characteristic value of the network node, the lower the failure risk, the higher the stability, and the higher the resource scheduling priority. represents the weighted comprehensive failure probability of network nodes, Represents the smallest positive value.

[0078] By introducing the node stability utilization feature, the failure probability and expected survival time of network nodes are comprehensively evaluated. This feature reflects the "risk density per unit survival time" and is used to give priority to network nodes with more stable communications and longer lifespans in the epicenter phase of scheduling. Compared with the traditional scheduling method that only focuses on failure rate or survival time, this method can accurately screen the optimal nodes when resources are tight and risks fluctuate violently, improve the stability of communication links, significantly reduce the risk of interruption, and effectively ensure the real-time transmission of early warning information.

[0079] In one embodiment of the present invention, the phased resource scheduling includes:

[0080] In the pre-earthquake stage, communication resources are pre-configured according to the weighted comprehensive failure probability of network nodes. Specifically, before the earthquake wave reaches the network node, the main goal of resource scheduling is to identify high-risk areas in advance and configure disaster recovery redundant paths. Therefore, based on the weighted comprehensive failure probability, communication resources are pre-configured to node areas with low failure probability. Among them, communication resources include but are not limited to: frequency bands, channels and bandwidths of wireless communication;

[0081] In the epicenter phase, according to the main path selection rule, the network nodes of the main path are sorted based on the node stability utilization characteristics, and communication resources are preferentially allocated to the network nodes whose node stability utilization characteristics are lower than the fourth preset threshold value, so as to improve the continuity and reliability of information transmission;

[0082] In the post-earthquake stage, network nodes with remaining power higher than the fifth preset threshold are regarded as stable recovery nodes, and communication resources are allocated to them, avoiding wasting resources on low-power network nodes in the post-earthquake stage, thereby improving the network's self-recovery efficiency and actual response capability.

[0083] In the network topology update phase, the main goal is to generate a set of available nodes, build the main path and backup path, and eliminate unstable network nodes by setting the failure probability threshold and survival time lower limit. This phase mainly provides structural guarantees for subsequent resource scheduling to ensure that the network nodes participating in resource scheduling have met the basic survival conditions and transmission availability.

[0084] In the resource scheduling stage at the epicenter, sorting and resource allocation are carried out based on the node stable utilization coefficient to ensure that limited communication resources are allocated to the nodes with the lowest current risk and the strongest sustainability.

[0085] The embodiments of the present invention have been described above, but the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative rather than restrictive. Under the inspiration of this embodiment, those of ordinary skill in the art can also make many forms, all of which fall within the protection scope of this embodiment.

Claims

1. A wireless ad-hoc network transmission management method for earthquake early warning information, characterized in that, It includes the following steps: S101: According to the real-time received seismic wave data, geological structure data, historical earthquake data and meteorological data, through a multi-source data fusion algorithm, generate the comprehensive failure risk prediction value of each network node, and mark the network nodes whose comprehensive failure risk prediction value exceeds the first preset threshold as high-risk nodes; S102: Based on the comprehensive failure risk prediction value of the high-risk nodes, construct a hierarchical failure probability model and generate a node failure risk probability map; The hierarchical failure probability model is used to decompose the comprehensive failure risk prediction value of the high-risk nodes into the physical layer failure probability, link layer failure probability and energy layer failure probability, and allocate the weights of each layer according to the first weight allocation strategy in the earthquake stage; Among them, the earthquake stage includes: pre-earthquake stage, epicenter stage and post-earthquake stage. In the first weight allocation strategy, the link layer is dominant in the pre-earthquake stage, the physical layer is dominant in the epicenter stage, and the energy layer is dominant in the post-earthquake stage; S103: Based on the node failure risk probability map, construct a network topology, calculate the survival time of the network nodes according to the seismic wave propagation model, and calculate the weighted comprehensive failure probability of each network node according to the first weight allocation strategy in the current earthquake stage, and dynamically update the network topology; S104: According to the weighted comprehensive failure probability and survival time of each network node, fuse to obtain the node stable utilization characteristics, and then perform phased resource scheduling.

2. The wireless ad-hoc network transmission management method for earthquake early warning information according to claim 1, characterized in that, The seismic wave data includes: earthquake source location, earthquake source depth, seismic wave velocity, P-wave arrival time, S-wave arrival time and amplitude; the geological structure data includes: soil type, shear wave velocity, fault distance and earthquake source altitude; the historical earthquake data includes: historical epicenter location and historical magnitude; the meteorological data includes: rainfall intensity, light intensity, wind speed, wind direction and meteorological warning level.

3. A wireless ad-hoc network transmission management method for earthquake early warning information according to claim 1, characterized in that The steps of the multi-source data fusion algorithm include: S201: Map the received seismic wave data, geological structure data, historical earthquake data and meteorological data to the range of 0 to 1; S202: Allocate weights to the mapped data respectively; S203: Combine the propagation time difference of the seismic wave from the node with the preset meteorological interference coefficient to generate a time-domain attenuation coefficient; S204: Multiply the weighted average result of the mapped data by the time-domain attenuation coefficient of the corresponding network node to obtain the comprehensive failure risk prediction value of each network node.

4. A wireless ad-hoc network transmission management method for earthquake early warning information according to claim 2, characterized in that The physical layer failure probability is obtained by fusing the distance between the network node and the earthquake source, shear wave velocity and building seismic resistance level; the link layer failure probability is obtained by fusing rainfall intensity and lightning frequency; the energy layer failure probability is obtained by fusing the remaining power of the network node and light intensity.

5. A wireless ad-hoc network transmission management method for earthquake early warning information according to claim 2, characterized in that, The earthquake stage is judged and triggered by the following conditions: Pre-earthquake stage: The network node has not received the P-wave, but the earthquake source location has been determined; Epicenter stage: The network node receives the P-wave; Post-earthquake stage: The seismic wave is more than the first preset distance away from the network node.

6. A wireless ad-hoc network transmission management method for earthquake early warning information according to claim 2, characterized in that The seismic buffer time of the network node is obtained by combining the ratio of the distance from the network node to the seismic source location to the historical maximum damage distance with the building seismic resistance level. The survival time of the network node is obtained by combining the ratio of the seismic source depth to the S-wave velocity, the buffer time, and the ratio of the remaining power of the network node to the energy consumption rate.

7. A wireless ad-hoc network transmission management method for earthquake early warning information according to claim 6, characterized in that, The network topology represents the connection relationship and transmission path between network nodes, including: a list of available nodes, main path planning, and alternate path planning. The dynamic update of the network topology includes: Node removal rule: If the survival time of the network node is less than the first preset duration or the weighted comprehensive failure probability exceeds the second preset threshold, the network node is removed from the network topology. Main path selection rule: Preferentially select network nodes with a survival time greater than the second preset duration and a weighted comprehensive failure probability less than the third preset threshold. Alternate path generation rule: Generate no less than two alternate paths for each main path node, and the horizontal distance between the network nodes of the alternate path and the main node is greater than the second preset distance.

8. A wireless ad-hoc network transmission management method for earthquake early warning information according to claim 1, characterized in that The node stability utilization feature is obtained by the ratio of the weighted comprehensive failure probability of the network nodes to the survival time of the network nodes in the current earthquake stage, wherein the calculation formula of the node stability utilization feature is: ,NSUI represents the node stable utilization feature, represents the weighted comprehensive failure probability of network nodes, Indicates the survival time of the network node. Represents the smallest positive value.

9. A wireless ad-hoc network transmission management method for earthquake early warning information according to claim 7, characterized in that The staged resource scheduling includes: In the pre-seismic stage, pre-configure communication resources according to the weighted comprehensive failure probability of the network nodes. In the seismic stage, according to the main path selection rule, sort the network nodes of the main path based on the node stable utilization characteristics, and preferentially allocate communication resources to the network nodes with node stable utilization characteristics lower than the fourth preset threshold. In the post-seismic stage, regard the network nodes with remaining power higher than the fifth preset threshold as stable recovery nodes and allocate communication resources to them.

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