Internet-of-things ad hoc network emergency communication method and system based on Beidou positioning

Abstract

The invention provides an Internet of Things ad hoc network emergency communication method and system based on Beidou positioning, and relates to the technical field of emergency communication. The method comprises the following steps: acquiring communication record data of a target area about a plurality of communication nodes and positioning data of each communication node, determining a plurality of grid areas, performing communication association analysis according to the communication record data and the positioning data, extracting communication characteristic parameters among the grid areas, and constructing a communication characteristic association map, determining and generating a plurality of candidate communication paths related to emergency communication requirements of a target node, obtaining reference mobile data of a plurality of communication nodes, carrying out communication interference analysis on the candidate communication paths, extracting node interference characteristics of a plurality of relay nodes, carrying out global interference analysis on each candidate communication path, and carrying out global interference analysis on each candidate communication path; global communication indexes of the candidate communication paths are obtained through calculation, and a communication path optimization strategy about emergency communication requirements is determined. According to the invention, timeliness and stability of key data transmission are guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of emergency communication technology, and in particular to a Beidou positioning-based IoT self-organizing network emergency communication method and system. Background Art

[0002] In complex emergency scenarios, communication nodes are often in a state of constant mobility and, due to environmental influences, cannot rely on fixed communication infrastructure. To effectively transmit information, it is often necessary to rely on an ad hoc IoT network built between a command vehicle and multiple mobile nodes, relaying and transmitting data through multiple hops. In such a dynamic network environment, selecting a reliable and efficient communication path is crucial for ensuring the timely transmission of mission-critical data. Beidou positioning technology enables the acquisition of real-time location information for different mobile nodes, providing strong data support for organizing and optimizing communication paths.

[0003] Due to various factors such as building obstructions, signal interference, and changes in node density, actual communication performance varies significantly across space. An important current research direction is to identify communication correlations between different regions based on existing communication behavior and positioning data, and to use this information to assist in path selection. Some technologies combine the connectivity characteristics between nodes with static location information for path assessment. However, in dynamic emergency environments, the accuracy and adaptability of communication path scheduling can be easily affected if factors such as node mobility trends, changes in surrounding network status, and potential contention and interference on links are not fully considered. Summary of the Invention

[0004] To solve the above technical problems, the present invention proposes an emergency communication method and system for an IoT self-organizing network based on Beidou positioning. By combining communication behavior and positioning information, it perceives the usage environment and potential interference risks of different communication areas, determines an efficient and stable communication path, and ensures the timeliness and stability of critical data transmission.

[0005] In a first aspect, the present invention provides an emergency communication method for an ad hoc IoT network based on Beidou positioning, comprising: Acquire communication record data between multiple communication nodes of the ad hoc network of things in the target area, as well as positioning data of each communication node, and divide the target area into grids to determine multiple grid areas; Perform communication correlation analysis on multiple grid areas based on communication record data and positioning data, extract communication feature parameters between any two grid areas, and construct a communication feature correlation map of the target area; After determining the emergency communication needs of the target node, multiple candidate communication paths related to the emergency communication needs are generated according to the communication feature association graph; Acquire reference mobility data of multiple communication nodes, perform communication interference analysis on each candidate communication path based on the reference mobility data, and generate node interference features for multiple relay nodes in the candidate communication path; Based on the node interference characteristics and communication characteristic association maps of the candidate communication paths, a global interference analysis is performed on each candidate communication path, and the global communication index of each candidate communication path is calculated. According to the global communication index, the communication path optimization strategy for emergency communication needs is determined.

[0006] Preferably, communication correlation analysis is performed on multiple grid areas based on the communication record data and positioning data, communication characteristic parameters between any two grid areas are extracted, and a communication characteristic correlation map of the target area is constructed, including: Divide the communication record data into regions according to the positioning data, and extract the local communication data between any two grid areas; Extracting multiple communication characteristic parameters between the two grid areas from local communication data between the two grid areas, including determining the number of successful communication connections and the total number of communication connections between the two grid areas, generating a connection stability parameter, calculating the average duration corresponding to a successful communication connection between the two grid areas, generating a connection duration parameter, performing a connection direction difference analysis on the number of successful communication connections between the two grid areas, calculating a direction influence parameter between the two grid areas, and extracting an entropy value feature of the communication signal strength between the two grid areas to obtain a connection fluctuation parameter; Determining a combination of neighboring regions of a plurality of grid regions based on the positioning data, generating a communication feature vector for each combination of neighboring regions based on communication feature parameters between the grid regions, aggregating communication features of the plurality of grid regions based on the communication feature vectors, including iteratively clustering the plurality of combinations of neighboring regions based on the communication feature vectors, subjecting the iterative clustering process to regional discrete constraints, generating a plurality of communication feature aggregation regions, and assigning communication feature labels to the plurality of communication feature aggregation regions; According to the communication characteristic parameters between the grid areas and the communication characteristic labels corresponding to each grid area, a communication characteristic association map about multiple communication nodes is generated.

[0007] Preferably, performing communication interference analysis on each candidate communication path according to the reference mobility data to generate node interference features for multiple relay nodes in the candidate communication path includes: For any candidate communication path, determine multiple relay nodes in the candidate communication path and the grid area to which each relay node belongs, and construct a communication interference area of ​​each relay node according to the grid area to which the relay node belongs; Based on the reference mobility data of the communication node, multiple interference nodes associated with each communication interference area are determined. According to the reference mobility data of the interference node and the communication characteristic parameters between the interference node and the relay node, the node interference characteristics of each relay node are extracted, including determining the distance parameter between each interference node and the relay node according to the reference mobility data of the interference node, fusing the communication characteristic parameters and distance parameters between the interference node and the relay node, and calculating the local communication interference index of each relay node.

[0008] Preferably, performing a global interference analysis on each candidate communication path and calculating a global communication index of each candidate communication path includes: The communication score of each relay node is determined according to the communication feature label, and multiple communication scores associated with the candidate communication path are corrected according to the local communication interference index to generate a local interference score for each relay node. The global communication index of each candidate communication path is calculated based on the multiple local interference scores.

[0009] Preferably, multiple adjacent area combinations are iteratively clustered according to the communication feature vectors, and regional discrete constraints are imposed on the iterative clustering process to generate multiple communication feature aggregation areas, including: The K-Means clustering algorithm is used to iteratively cluster multiple neighboring area combinations. For multiple stage clusters corresponding to multiple iterative processes, regional discrete analysis is performed on multiple individuals in the stage clusters, including determining the location information of each neighboring area combination according to the positioning data, determining the reference discrete distance of each individual based on the location information, updating the regional discrete constraints of each stage cluster group according to a preset discrete threshold, and iterative clustering based on the updated stage cluster group. Multiple neighboring area combinations are aggregated to generate multiple communication feature aggregation areas.

[0010] Preferably, for the communication feature association graph, multiple grid areas are nodes, grid area pairs with communication behaviors are edges, edge weights are represented by communication feature parameter vectors, and nodes include communication feature labels corresponding to the grid areas.

[0011] In a second aspect, the present invention provides an IoT self-organizing network emergency communication system based on Beidou positioning, which is used to implement the above-mentioned IoT self-organizing network emergency communication method based on Beidou positioning, including: The communication area analysis module is used to obtain communication record data between multiple communication nodes in the ad hoc network of the Internet of Things in the target area, as well as the positioning data of each communication node, and to divide the target area into grids to determine multiple grid areas; The communication feature correlation analysis module is used to perform communication correlation analysis on multiple grid areas based on communication record data and positioning data, extract the communication feature parameters between any two grid areas, and construct a communication feature correlation map for the target area; A candidate communication path generation module is used to generate multiple candidate communication paths for the emergency communication needs according to the communication feature association map after determining the emergency communication needs of the target node; a node interference analysis module, configured to obtain reference mobility data of a plurality of communication nodes, perform communication interference analysis on each candidate communication path based on the reference mobility data, and generate node interference features for a plurality of relay nodes in the candidate communication path; The communication path optimization module is used to perform global interference analysis on each candidate communication path based on the node interference characteristics and communication characteristic association map of the candidate communication path, calculate the global communication index of each candidate communication path, and determine the communication path optimization strategy for emergency communication needs based on the global communication index.

[0012] The present invention has the following beneficial effects: The present invention divides the target area into grids, combines communication record data with Beidou positioning data to construct a communication feature association map, identifies and mines different communication behavior characteristics between different grid areas, and introduces communication feature clustering and regional labeling mechanisms. It can identify communication bottleneck areas or advantageous areas caused by environmental interference such as building obstructions, providing a more targeted reference for path planning. It further combines the real-time reference movement data of the nodes to analyze the dynamic interference situation of the area where the path relay nodes are located, identifies the risk of communication competition caused by node aggregation or movement, and then constructs a more realistic path interference analysis mechanism. By fusing static map structure with dynamic interference information, it quantitatively evaluates the availability and stability of candidate paths, thereby providing guarantees for the timeliness and stability of key data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flowchart of an emergency communication method for an IoT ad hoc network based on Beidou positioning is provided in an embodiment of the present invention.

[0014] Figure 2 A schematic structural diagram of an IoT ad hoc emergency communication system based on Beidou positioning is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] See Figure 1 The embodiment of the present invention provides an emergency communication method for an ad hoc IoT network based on Beidou positioning, the method comprising: Step S10: Acquire communication record data between multiple communication nodes of the ad hoc network of things in the target area, as well as positioning data of each communication node, and divide the target area into grids to determine multiple grid areas.

[0017] In this embodiment, for a target area where emergency communication needs are met through an ad hoc network of the Internet of Things, communication record data between multiple communication nodes is obtained, which may include connection establishment information between nodes, number of successful communications, average delay, packet loss rate, etc., to reflect the communication behavior characteristics between nodes. Specifically, it may involve actual communication behavior, such as the necessary information transmission between different mobile nodes after collecting data during normal operation, or communication behavior such as a command vehicle sending tasks to different mobile nodes. It may also be a communication behavior involved in a conventional communication reachability judgment method, such as the process in which a node periodically broadcasts "Hello" or "Beacon" packets to the surrounding area under a broadcast sniffing mechanism. The communication record data may include connection establishment information between nodes, number of successful communications, average delay, packet loss rate, etc., to reflect the communication behavior characteristics between nodes. The positioning data involved in different communication nodes, such as mobile cars and drones, is the spatial position information obtained by the node based on the Beidou satellite system, with information in the form of time series such as precise coordinates and timestamps.

[0018] To conduct in-depth analysis of communication characteristics across different regions, the target region is spatially gridded. This division can be done using a regular, equidistant, two-dimensional grid, dividing the entire target region into several spatially non-overlapping grid regions. The location information of each node can be used to determine its grid region at different points in time, enabling subsequent regional-level communication relationship analysis.

[0019] Step S20: Perform communication correlation analysis on multiple grid areas based on the communication record data and positioning data, extract communication feature parameters between any two grid areas, and construct a communication feature correlation map for the target area.

[0020] In this embodiment, based on the above-mentioned communication record data and positioning data, a correlation analysis is performed on the communication behavior between different grid areas. For example, for the communication records between communication nodes falling within grid area A and area B, information such as communication success rate, connection frequency, and average duration is extracted to characterize the communication relationship between the two areas. Ultimately, the communication characteristic parameters between different grid areas are obtained. This is used to construct a communication characteristic correlation map for multiple communication nodes in the target area. This map is a weighted graph structure with grid areas as nodes and communication characteristics as edge weights. It is used to describe the communication capabilities and communication behavior patterns between each area, providing a structural basis for subsequent path planning. At the same time, the communication correlation between different areas can be further analyzed. According to the communication impact status, for example, the communication quality in a certain area is relatively poor, which may be due to building obstruction, etc., which affects the communication quality. By analyzing the communication characteristics between different grid areas, different grid areas can be further aggregated and divided, so as to better optimize link selection when multi-hop communication links need to be determined later.

[0021] As an exemplary implementation process, the communication correlation analysis of multiple grid areas is performed based on the communication record data and positioning data, the communication characteristic parameters between any two grid areas are extracted, and a communication characteristic correlation map of the target area is constructed, including: The communication record data is divided into regions according to the positioning data, local communication data between any two grid regions is extracted, and multiple communication feature parameters between the two grid regions are extracted from the local communication data between the two grid regions.

[0022] Specifically, communication log data specifically involves recorded information corresponding to communication behaviors between different communication nodes. The grid area to which a communication node belongs can be determined based on its positioning data. The communication behaviors between two communication nodes can be mapped to the communication between the two grid areas, thereby mapping different communication behaviors to the communication behaviors between two specific grid areas. By traversing all communication log data, local communication data between any two grid areas can be extracted. Based on this local communication data between the grid areas, multiple communication characteristic parameters between any two grid areas can be extracted.

[0023] Taking any two grid areas as an example, a statistical analysis of local communication data between the two grid areas is performed to determine the number of successful and total communication connections between the two grid areas. These numbers are used as connection stability parameters to measure the reliability of the communication connection between the two grid areas. For all successfully established communication connections between the two grid areas, the duration of each connection is counted and averaged to obtain the average connection duration, which reflects the link's ability to maintain stability. The connection between the two areas is analyzed for directional bias. For example, is the number of successful connections from area A to area B significantly higher than in the opposite direction? The ratio of the number of successful connections in the two directions is calculated as the directional influence parameter between the grid areas, reflecting the asymmetric characteristics of the communication directions. Based on the signal strength data in the communication logs, the entropy of the corresponding signal strength change sequence during successful connections between the two areas is calculated as the connection fluctuation parameter, which measures the volatility of the communication link. A larger entropy value indicates more drastic changes in connection quality. These communication characteristic parameters reflect the stability, directionality, and volatility of communication behavior between different area pairs.

[0024] Based on the positioning data, a combination of neighboring areas of multiple grid areas is determined. Based on the communication characteristic parameters between the grid areas, a communication characteristic vector is jointly composed to describe the communication quality between area pairs, that is, different neighboring area combinations. Based on the communication characteristic vector, communication characteristics of the multiple grid areas are aggregated to generate multiple communication characteristic aggregation areas, and communication characteristic labels are assigned to the multiple communication characteristic aggregation areas.

[0025] Specifically, in complex emergency scenarios, such as natural disasters, geological accidents, or complex urban emergency response scenarios, there may be common communication obstacles or advantages. For example, due to the dense buildings in a certain area, the communication connection quality is generally weak and volatile, while in some open areas, the connection is stable and directional. There is even an underground passage area that only has one-way available links with poor continuity. Therefore, it is considered to aggregate multiple areas with the same communication characteristics. During the communication path planning process, optimization can be performed based on regional characteristics. For example, after determining the highly volatile aggregation area, it is possible to avoid selecting key relay nodes in this area to reduce the risk of communication interruption. During the path planning process, priority control analysis can be performed for different areas to select high-quality communication paths.

[0026] During the regional aggregation process, to enhance the structural rationality and spatial continuity of the regional aggregation process, neighboring region combinations of multiple grid areas are constructed based on positioning data. For example, two adjacent or diagonal grid areas are considered as a neighboring region combination, which serves as the basic unit of the aggregation process. Then, an iterative clustering operation is performed on all neighboring region combinations to identify a set of regions with similar communication characteristics. To avoid spatial structural fragmentation and ensure the consistency of clustering results in the physical area, a regional discrete constraint mechanism is introduced in the clustering process. For example, a K-Means clustering algorithm is used as the basis for cluster analysis of multiple neighboring region combinations. After each iteration, multiple phased clusters are generated, that is, temporary cluster groups formed by multiple cluster centers. Regional discrete analysis is performed on multiple individuals in the phased clusters. The location information of the neighboring region combination is determined based on the center point of the neighboring region combination. Then, based on the location information, the reference discrete distance corresponding to each individual in the phased cluster is statistically calculated. Specifically, the shortest distance between the individual and the remaining individuals can be calculated based on the location information of the neighboring region combination. Then, individuals whose reference discrete distance is greater than a preset discrete threshold are removed from the stage clusters. In this way, each stage cluster is updated. For the updated clusters, the cluster centers can be re-determined using the remaining individuals, and the next round of iteration is carried out, i.e., individuals are redistributed to obtain clusters, etc. In other words, in the conventional K-Means clustering algorithm, after completing the cluster center-based allocation of multiple neighboring region combinations, the above-mentioned regional discrete constraint analysis is performed to reduce the influence of discrete individuals on the new cluster centers determined in each iteration.

[0027] In this way, the clustering process is subjected to regional discrete constraints, and the clustering process is completed after the iterative termination condition is reached, for example, the change in the position of the cluster center is less than the set threshold. The specific implementation process of the K-Means clustering algorithm is a technical means well known to those skilled in the art and will not be repeated here. After the clustering is completed, multiple communication feature aggregation areas can be obtained. Each aggregation area is regarded as a type of communication unit with similar structure and similar communication behavior. A communication feature label can be assigned to it to indicate the communication characteristics of this type of area. For example, based on the communication feature parameters of different individuals in the aggregation area, label information such as high stability area, strong directionality area, and fluctuation area is determined to identify the overall communication characteristics of different aggregation areas.

[0028] Finally, based on the communication characteristic parameters between grid areas and the communication characteristic labels corresponding to each individual grid area, a communication characteristic association map of multiple communication nodes is generated.

[0029] The graph uses grid areas as nodes and pairs of areas with communication behaviors between areas as edges. The edge weights are represented by communication characteristic parameter vectors. The nodes are accompanied by corresponding communication label attributes. It can serve as the basic structure for communication scheduling and path planning to better formulate refined and dynamically perceived emergency communication optimization strategies.

[0030] Step S30: After determining the emergency communication requirements of the target node, generate multiple candidate communication paths related to the emergency communication requirements based on the communication feature association graph.

[0031] In this embodiment, when a target node is detected to have an emergency communication need, such as uploading mission data to a command vehicle or sending information to a designated target node, a graph search algorithm is used based on the constructed communication feature association graph to generate multiple candidate communication paths that meet specific constraints, such as the minimum number of hops and regional stability. These paths are composed of multiple relay nodes covering different grid areas, resulting in multiple feasible and potentially advantageous candidate communication paths.

[0032] Step S40: Acquire reference mobility data of multiple communication nodes, perform communication interference analysis on each candidate communication path based on the reference mobility data, and generate node interference features for multiple relay nodes in the candidate communication path.

[0033] In this embodiment, we consider that different communication nodes may move in a chaotic manner due to complex factors such as terrain, building structure, or changing mission requirements. For example, they may cluster or linger in certain areas, affecting the distribution and interference patterns of communication resources. Although some areas have good overall communication characteristics in the communication feature correlation map, in actual operation, they may not perform as expected due to local node density.

[0034] In this case, the reference mobility data of multiple communication nodes in the short term is obtained, such as the information on the change of node movement path over time provided by Beidou positioning data, from which short-term estimated data of movement speed and direction can be further extracted. Based on this information, interference characteristics reflecting the potential communication interference conditions of different mobile nodes can be extracted.

[0035] As an exemplary implementation process, performing communication interference analysis on each candidate communication path based on reference mobility data to generate node interference features for multiple relay nodes in the candidate communication path includes: For any candidate communication path, multiple relay nodes in the candidate communication path and the grid area to which each relay node belongs are determined, and a communication interference area of ​​each relay node is constructed according to the grid area to which the relay node belongs.

[0036] Specifically, the communication interference area can be reasonably set according to the actual range in which the relay node can transmit information, such as the grid unit to which the relay node belongs and multiple grid units within a certain adjacent range around it, which is used to further analyze the interference that the communication nodes contained in the specific area may cause to the communication of the relay node.

[0037] Based on the reference mobility data of the communication node, multiple interference nodes associated with each communication interference area are determined, and according to the reference mobility data of the interference node and the communication characteristic parameters between the interference node and the relay node, the node interference characteristics of each relay node are extracted.

[0038] Specifically, the multiple interference nodes associated with the communication interference area are mobile nodes in the area that may cause communication interference. For the selection of interference nodes, the historical trajectory, speed, residence time and other information can be determined based on the reference mobile data of the communication node, and the nodes that will stay in the area for a period of time or enter the area from areas outside the communication interference area can be identified. For example, a simple assessment is made based on the movement speed and direction of the communication node to estimate its possible landing point in the short term.

[0039] For node interference characteristics, the distance parameter between each interfering node and the relay node is first determined based on the interfering node's reference mobility data. This distance parameter can be the current distance between the two nodes, or the distance between the two nodes based on an estimate of the interfering node's likely short-term location. The communication characteristic parameters and distance parameters between the interfering node and the relay node are then fused. For example, the distance parameter is used as a weight to perform a weighted correction on the communication characteristic parameters between the interfering node and the relay node. The weighted feature values ​​of each communication characteristic parameter between the interfering node and the relay node are then weighted and combined with the interference contribution of non-interfering nodes to the relay node using a pre-set fusion weight (e.g., a parameter determined by experts evaluating the importance of features from different dimensions). This is then normalized to obtain a local communication interference index for each relay node. A larger local communication interference index indicates greater interference. This method provides a fine-grained assessment of the interference situation of each relay node in a candidate communication path, avoiding the need for relay points in high-interference areas and improving the overall communication quality and transmission stability of the path.

[0040] Step S50: Perform a global interference analysis on each candidate communication path based on the node interference characteristics and communication characteristic association map of the candidate communication path, calculate the global communication index of each candidate communication path, and determine the communication path optimization strategy for emergency communication needs based on the global communication index.

[0041] In this embodiment, the node interference characteristics of each relay node in a candidate communication path are integrated with the communication characteristic association map to perform a global risk assessment for each candidate path. During the assessment process, the communication characteristic association map can be used to assign communication scores corresponding to different communication characteristic labels pre-set according to transmission requirements. For example, a score of 3 is assigned to highly stable areas, and a score of 2 is assigned to fluctuating areas. A higher score indicates a higher priority for the path. The specific communication score can be appropriately set based on actual transmission requirements. Furthermore, the multiple communication scores corresponding to the candidate communication paths are further considered based on the node interference characteristics. The communication scores are corrected using the calculated local communication interference index. The ratio between the communication score and the local communication interference index is used as the local interference score for each relay node. After summing up the multiple local interference scores, a global communication index for the candidate communication path is obtained. A higher global communication index indicates a higher recommendation. In this way, static communication interference in complex environments between different regions is integrated with dynamic communication interference caused by communication node movement to comprehensively evaluate the communication quality of different candidate communication paths. Based on the global communication index of each candidate path, the path with the highest global communication index can be selected as the transmission path for the current emergency communication task. In this way, a communication path optimization strategy can be formulated to improve the reliability and adaptability of task data transmission in a dynamic environment.

[0042] See Figure 2 Based on the same inventive concept, an embodiment of the present invention further provides an IoT self-organizing network emergency communication system based on Beidou positioning, the system comprising: The communication area analysis module is used to obtain communication record data between multiple communication nodes in the ad hoc network of the Internet of Things in the target area, as well as the positioning data of each communication node, and to divide the target area into grids to determine multiple grid areas; The communication feature correlation analysis module is used to perform communication correlation analysis on multiple grid areas based on communication record data and positioning data, extract the communication feature parameters between any two grid areas, and construct a communication feature correlation map for the target area; A candidate communication path generation module is used to generate multiple candidate communication paths for the emergency communication needs according to the communication feature association map after determining the emergency communication needs of the target node; a node interference analysis module, configured to obtain reference mobility data of a plurality of communication nodes, perform communication interference analysis on each candidate communication path based on the reference mobility data, and generate node interference features for a plurality of relay nodes in the candidate communication path; The communication path optimization module is used to perform global interference analysis on each candidate communication path based on the node interference characteristics and communication characteristic association map of the candidate communication path, calculate the global communication index of each candidate communication path, and determine the communication path optimization strategy for emergency communication needs based on the global communication index.

[0043] The foregoing description is merely a detailed description of the present invention, which is intended to enable those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art. Portions not described in detail in this specification are well known to those skilled in the art.

Claims

1. A method for emergency communication in an ad hoc IoT network based on Beidou positioning, characterized in that: include: Acquire communication record data between multiple communication nodes of the ad hoc network of things in the target area, as well as positioning data of each communication node, and divide the target area into grids to determine multiple grid areas; Perform communication correlation analysis on multiple grid areas based on communication record data and positioning data, extract communication feature parameters between any two grid areas, and construct a communication feature correlation map of the target area; After determining the emergency communication needs of the target node, multiple candidate communication paths related to the emergency communication needs are generated according to the communication feature association graph; Acquire reference mobility data of multiple communication nodes, perform communication interference analysis on each candidate communication path based on the reference mobility data, and generate node interference features for multiple relay nodes in the candidate communication path; Based on the node interference characteristics and communication characteristic association maps of the candidate communication paths, a global interference analysis is performed on each candidate communication path, and the global communication index of each candidate communication path is calculated. According to the global communication index, the communication path optimization strategy for emergency communication needs is determined.

2. The method for emergency communication in an ad hoc IoT network based on Beidou positioning according to claim 1, characterized in that: Based on the communication record data and positioning data, communication correlation analysis is performed on multiple grid areas, and the communication characteristic parameters between any two grid areas are extracted to construct a communication characteristic correlation map of the target area, including: Divide the communication record data into regions according to the positioning data, and extract the local communication data between any two grid areas; Extracting multiple communication characteristic parameters between the two grid areas from local communication data between the two grid areas, including determining the number of successful communication connections and the total number of communication connections between the two grid areas, generating a connection stability parameter, calculating the average duration corresponding to a successful communication connection between the two grid areas, generating a connection duration parameter, performing a connection direction difference analysis on the number of successful communication connections between the two grid areas, calculating a direction influence parameter between the two grid areas, and extracting an entropy value feature of the communication signal strength between the two grid areas to obtain a connection fluctuation parameter; Determining a combination of neighboring regions of a plurality of grid regions based on the positioning data, generating a communication feature vector for each combination of neighboring regions based on communication feature parameters between the grid regions, aggregating communication features of the plurality of grid regions based on the communication feature vectors, including iteratively clustering the plurality of combinations of neighboring regions based on the communication feature vectors, subjecting the iterative clustering process to regional discrete constraints, generating a plurality of communication feature aggregation regions, and assigning communication feature labels to the plurality of communication feature aggregation regions; According to the communication characteristic parameters between the grid areas and the communication characteristic labels corresponding to each grid area, a communication characteristic association map about multiple communication nodes is generated.

3. The method for emergency communication of an ad hoc IoT network based on Beidou positioning according to claim 2, characterized in that: Perform communication interference analysis on each candidate communication path based on the reference mobility data, and generate node interference features for multiple relay nodes in the candidate communication path, including: For any candidate communication path, determine multiple relay nodes in the candidate communication path and the grid area to which each relay node belongs, and construct a communication interference area of ​​each relay node according to the grid area to which the relay node belongs; Based on the reference mobility data of the communication node, multiple interference nodes associated with each communication interference area are determined. According to the reference mobility data of the interference node and the communication characteristic parameters between the interference node and the relay node, the node interference characteristics of each relay node are extracted, including determining the distance parameter between each interference node and the relay node according to the reference mobility data of the interference node, fusing the communication characteristic parameters and distance parameters between the interference node and the relay node, and calculating the local communication interference index of each relay node.

4. The method for emergency communication in an ad hoc IoT network based on Beidou positioning according to claim 3, characterized in that: Perform global interference analysis on each candidate communication path and calculate the global communication index of each candidate communication path, including: The communication score of each relay node is determined according to the communication feature label, and multiple communication scores associated with the candidate communication path are corrected according to the local communication interference index to generate a local interference score for each relay node. The global communication index of each candidate communication path is calculated based on the multiple local interference scores.

5. The method for emergency communication in an ad hoc IoT network based on Beidou positioning according to claim 2, characterized in that: Iteratively cluster multiple adjacent regions based on the communication feature vectors, and impose regional discrete constraints on the iterative clustering process to generate multiple communication feature aggregation regions, including: The K-Means clustering algorithm is used to iteratively cluster multiple neighboring area combinations. For multiple stage clusters corresponding to multiple iterative processes, regional discrete analysis is performed on multiple individuals in the stage clusters, including determining the location information of each neighboring area combination according to the positioning data, determining the reference discrete distance of each individual based on the location information, updating the regional discrete constraints of each stage cluster group according to a preset discrete threshold, and iterative clustering based on the updated stage cluster group. Multiple neighboring area combinations are aggregated to generate multiple communication feature aggregation areas.

6. The method for emergency communication in an ad hoc IoT network based on Beidou positioning according to claim 2, characterized in that: For the communication feature association graph, multiple grid areas are nodes, grid area pairs with communication behaviors are edges, edge weights are represented by communication feature parameter vectors, and nodes include communication feature labels corresponding to grid areas.

7. A Beidou positioning-based IoT self-organizing network emergency communication system, characterized in that: The system is used to implement the Beidou positioning-based IoT self-organizing network emergency communication method according to any one of claims 1 to 6, comprising: The communication area analysis module is used to obtain communication record data between multiple communication nodes in the ad hoc network of the Internet of Things in the target area, as well as the positioning data of each communication node, and to divide the target area into grids to determine multiple grid areas; The communication feature correlation analysis module is used to perform communication correlation analysis on multiple grid areas based on communication record data and positioning data, extract the communication feature parameters between any two grid areas, and construct a communication feature correlation map for the target area; A candidate communication path generation module is used to generate multiple candidate communication paths for the emergency communication needs according to the communication feature association map after determining the emergency communication needs of the target node; a node interference analysis module, configured to obtain reference mobility data of a plurality of communication nodes, perform communication interference analysis on each candidate communication path based on the reference mobility data, and generate node interference features for a plurality of relay nodes in the candidate communication path; The communication path optimization module is used to perform global interference analysis on each candidate communication path based on the node interference characteristics and communication characteristic association map of the candidate communication path, calculate the global communication index of each candidate communication path, and determine the communication path optimization strategy for emergency communication needs based on the global communication index.

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