Ad hoc network-based dangerous information early warning method and system, mobile terminal and medium

Abstract

The application relates to the field of communication technology, in particular to a dangerous information early warning method and system based on a self-organizing network, a mobile terminal and a medium, the method is applied to a first mobile terminal, the first mobile terminal and a second mobile terminal have both started a NAN self-organizing network function to complete team networking, the first mobile terminal obtains a dangerous type by detecting and analyzing a dangerous instruction, acquires the dangerous type and environmental data to identify a dangerous level, determines dangerous alert area data according to the dangerous type and the level, broadcasts the data to the second mobile terminal, and makes the second mobile terminal perform early warning. With the aid of the self-organizing network technology, the dangerous information can be timely and accurately shared, the team members can know the dangerous position and the influence scope in advance, and the outdoor activity risk can be effectively reduced.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method, system, mobile terminal and medium for early warning of dangerous information based on ad hoc networks. Background Technology

[0002] In outdoor team activities such as mountaineering, exploration, and wilderness surveys, team members face various potential dangers, such as loose rocks, honeycomb formations, and quicksand. These dangers not only pose a direct threat to the team member who discovers the danger but may also endanger team members behind them, leading to group safety risks.

[0003] Traditionally, team members who first spot danger mainly transmit warning information through shouting and gestures. However, complex environments such as mountainous areas and forests can easily obstruct sound transmission and block vision, causing danger information to be unable to be transmitted to team members behind in a timely and accurate manner, thus missing the opportunity to avoid danger. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this application provides a method, system, mobile terminal, and medium for early warning of dangerous information based on ad hoc networks. Through ad hoc network technology, dangerous information can be shared with other teammates in a timely and accurate manner.

[0005] The first aspect of this application provides a danger information early warning method based on a self-organizing network, applied to a first mobile terminal, wherein both the first mobile terminal and a second mobile terminal have activated the NAN self-organizing network function to complete team networking, the method comprising:

[0006] When a dangerous command is detected, the dangerous command is parsed to obtain the danger type;

[0007] The environmental data of the current environment of the first mobile terminal is obtained, and the hazard level is identified based on the hazard type and the environmental data;

[0008] Determine the hazard warning area data based on the hazard type and the hazard level;

[0009] The data of the danger warning area is broadcast to the second mobile terminal, so that the second mobile terminal can issue an early warning based on the data of the danger warning area.

[0010] Optionally, determining the hazard warning area data based on the hazard type and the hazard level includes:

[0011] The basic safety distance is determined based on the type and level of hazard.

[0012] Obtain the current ambient brightness and determine the actual response time based on the current ambient brightness;

[0013] Determine the distance correction factor based on the actual response time;

[0014] The basic safety distance is corrected based on the distance correction factor to obtain the warning distance;

[0015] The danger warning area data is generated with the location coordinates of the first mobile terminal as the center and the warning distance as the radius.

[0016] Optionally, broadcasting the danger warning area data to the second mobile terminal includes:

[0017] Send a broadcast start message to multiple second mobile terminals and receive broadcast feedback information from each second mobile terminal;

[0018] Based on the broadcast feedback information, select multiple target second mobile terminals from the plurality of second mobile terminals;

[0019] The data of the danger warning area is broadcast to the multiple target second mobile terminals, so that each target second mobile terminal acts as a first-level propagation node and selects a second-level propagation node to form a multi-level tree-like broadcast network to broadcast the data of the danger warning area.

[0020] Optionally, selecting multiple target second mobile terminals from the plurality of second mobile terminals based on the broadcast feedback information includes:

[0021] The signal strength, remaining battery power, and distance between the second mobile terminal and the first mobile terminal are obtained from the broadcast feedback information.

[0022] A terminal hit score is calculated based on the signal strength, the remaining battery power, and the distance.

[0023] Based on the terminal hit score, multiple target second mobile terminals are selected from the plurality of second mobile terminals.

[0024] Optionally, identifying the hazard level based on the hazard type and the environmental data includes:

[0025] The hazard type and the environmental data are input into a preset hazard level assessment model, and the hazard score output by the hazard level assessment model is obtained;

[0026] The hazard level is determined based on the hazard rating result and the preset rating threshold segmentation interval; the preset rating threshold segmentation interval includes multiple rating threshold intervals, and each rating threshold interval corresponds to a hazard level.

[0027] Optionally, the hazard level assessment model includes an input layer, a hidden layer, and an output layer;

[0028] The input layer is used to receive the hazard type and the environmental data;

[0029] The hidden layer includes a first hidden layer and a second hidden layer. The first hidden layer is used to extract feature information of the hazard type and the environmental data. The number of nodes in the first hidden layer is 1.5-2 times the number of nodes in the input layer.

[0030] The second hidden layer is used to integrate the feature information extracted by the first hidden layer, and the number of nodes in the second hidden layer is less than the number of nodes in the first hidden layer;

[0031] The output layer is used to output the hazard rating result, and the number of nodes in the output layer is 1.

[0032] Optionally, the number of input layer nodes is determined based on the hazard type and the environmental data.

[0033] The second aspect of this application is a danger information early warning system based on self-organizing network, the danger information early warning system based on self-organizing network includes: a first mobile terminal and a second mobile terminal, both of which have activated the NAN self-organizing network function to complete team networking.

[0034] The first mobile terminal is configured to, upon detecting a dangerous command, parse the dangerous command to obtain a dangerous type; acquire environmental data of the current environment in which the first mobile terminal is located, and identify a dangerous level based on the dangerous type and the environmental data; determine dangerous warning area data based on the dangerous type and the dangerous level; and broadcast the dangerous warning area data to the second mobile terminal.

[0035] The second mobile terminal is used to issue an early warning based on the data of the danger warning area.

[0036] A third aspect of this application provides a mobile terminal, the mobile terminal including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement all or part of the steps of the aforementioned danger information early warning method based on ad hoc networks.

[0037] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements all or part of the steps of the aforementioned danger information early warning method based on ad hoc networks.

[0038] In summary, the danger information early warning method system, mobile terminal, and medium based on ad hoc networks provided in this application have at least one of the following beneficial effects:

[0039] Both the first and second mobile terminals activate the NAN self-organizing network function to complete the team networking. The NAN self-organizing network technology enables direct communication between terminals without relying on traditional communication infrastructure. In complex outdoor environments, even without regular network signals, the mobile terminals of team members can connect to each other through the self-organizing network, ensuring that dangerous information can be transmitted in a timely manner within the team. This overcomes the problems of limited communication range and susceptibility to signal interference of traditional communication terminals (such as walkie-talkies).

[0040] When the first user (the first user to discover the danger) detects the danger command, the command is quickly parsed to obtain the danger type, and then the danger type and environmental data are obtained to identify the danger level. Based on the danger type and level, the danger warning area data is determined and broadcast to the second mobile terminal. The whole process is fast and efficient, and can transmit danger information to the rear team members in a timely manner, avoiding the danger caused by information transmission delays in traditional methods.

[0041] The data for determining the danger warning area based on the type of danger and the identified danger level takes into account the characteristics and severity of the danger itself. It can accurately define the scope of the danger's impact, rather than just informing the approximate location of the danger. This allows the rear team members to accurately understand the specific scope of the danger's impact, solving the problem that the lack of accurate location marking methods in traditional methods makes it difficult for the rear team members to accurately judge the location of the danger. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the architecture of a danger information early warning system based on a self-organizing network, as shown in an embodiment of this application;

[0043] Figure 2 This is a flowchart illustrating a danger information early warning method based on an embodiment of this application;

[0044] Figure 3 This is a schematic diagram of the functional modules of a danger information early warning device based on a self-organizing network, as shown in an embodiment of this application.

[0045] Figure 4 This is a schematic diagram of the structure of a mobile terminal shown in an embodiment of this application. Detailed Implementation

[0046] The present application will be further described below with reference to the accompanying drawings and embodiments.

[0047] The following will clearly and completely describe the concept, specific structure, and resulting technical effects of this application in conjunction with embodiments and accompanying drawings, so as to fully understand the purpose, features, and effects of this application. Obviously, the described embodiments are only a part of the embodiments of this application, not all of them. Other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are all within the scope of protection of this application. Furthermore, all connections / linkages involved in the patent do not simply refer to direct contact between components, but rather to the ability to form a better connection structure by adding or reducing connecting accessories according to specific implementation conditions. The various technical features in this application can be combined interactively without contradicting each other.

[0048] Example 1

[0049] Reference Figure 1 The diagram shown is an architectural schematic of a hazard information early warning system based on a self-organizing network, according to an embodiment of this application. The system includes multiple mobile terminals, such as a first mobile terminal for a first user and a second mobile terminal for a second user. The first user and the second user belong to the same outdoor team. The first user and the second user are relative; the first user can be any user within the same outdoor team. The first mobile terminal and the second mobile terminal are communicatively connected to perform multi-level, tree-like information broadcasting.

[0050] Each mobile terminal may include, but is not limited to: a Neighbor Awareness Networking (NAN) self-organizing network module and a dual-mode communication module.

[0051] The NAN self-organizing network module, as the core of the system's networking, is responsible for initiating the NAN self-organizing network function, parsing the team key to complete node access, and collecting basic networking parameters of mobile terminals (such as node identification and access time). It is also responsible for receiving synchronization beacon frames from core terminals and forwarding status feedback information from its own terminals, ensuring the connectivity and data exchange efficiency of the team network. It enables direct communication between terminals without relying on public base stations. NAN is a short-range self-organizing network technology based on the IEEE 802.11 standard. It does not rely on public base stations, routers, or other infrastructure. Mobile terminals can automatically build a network by detecting signals from neighboring nodes (such as other mobile terminals), enabling direct communication, synchronization, and data exchange between nodes.

[0052] The dual-mode communication module comprises a Bluetooth Mesh unit and a LoRa unit, providing dual communication modes for the system. The Bluetooth Mesh unit handles short-range (≤50 meters) high-bandwidth communication, detecting the signal strength of adjacent terminals in real time; the LoRa unit handles long-range (≤500 meters) interference-resistant communication, adapting to dispersed team scenarios. The dual-mode communication module automatically switches communication modes based on signal strength and sends a mode switching notification to the core terminal after switching, ensuring that the core terminal updates the team communication topology in real time and broadcasts it to all mobile terminals. For example, the dual-mode communication module uses the Bluetooth Mesh unit to detect the signal strength (RSSI) of adjacent terminals in real time and compares the detected signal strength with a preset signal strength threshold (e.g., -60dBm). When the detected signal strength is greater than the preset threshold, the Bluetooth Mesh communication mode is activated for short-range high-bandwidth data transmission; when the detected signal strength is less than the preset threshold, it automatically switches to LoRa communication mode for long-range interference-resistant data transmission. This enables adaptive matching of distance and communication mode.

[0053] All mobile terminals activate the NAN self-organizing network module, enter the team key to complete secure access, and form a decentralized, peer-to-peer team network.

[0054] When a user discovers a hazard (such as a loose boulder field, a honeycomb, or quicksand), they need to promptly and accurately inform the support team. Specifically, the user's mobile terminal becomes the Initial Broadcast Device (IBD). The IBD initiates the hazard broadcast process, becoming the first Core Device (CD). It then selects other CDs to form a multi-level tree-structured broadcast network for broadcasting and updating hazard messages. The core device operates through three interactions. First, an existing CD, acting as the parent node, sends a broadcast start message to all its one-hop neighbors. Upon receiving the broadcast start message, the terminal collects its own information (such as signal strength, remaining battery power, and location) and sends it back to its parent CD. Considering that simultaneous data packets from different neighbors can cause severe channel congestion, the terminal determines a waiting time based on the similarity between their locations (e.g., the closer the terminals are, the shorter the waiting time), and then sends a data packet containing its own information after the waiting time. Finally, the existing CD sends both the notification message and the hazard message to the newly selected CD. Terminals not selected as CDs become Ordinary Devices (ODs). Through three interactive processes, a higher-level CD selects one or more lower-level CDs, establishing a multi-level tree-like broadcast network with the IBD as the root node. When a lower-level CD begins a new selection process, the lower-level CD becomes the new higher-level CD, and all CDs are selected iteratively.

[0055] Example 2

[0056] Reference Figure 2 The diagram shown is a flowchart illustrating a danger information warning method based on a self-organizing network according to an embodiment of this application. The danger information warning method based on a self-organizing network is applied to a first mobile terminal. Both the first mobile terminal and the second mobile terminal have activated the NAN self-organizing network function to complete team networking. The method includes the following steps.

[0057] S21, When a dangerous command is detected, the dangerous command is parsed to obtain the danger type.

[0058] When the first member of the team (referred to as the first user) discovers a hazard (such as a loose boulder area, honeycomb, or quicksand), the first user can trigger a hazard command by pressing and holding the preset "hazard trigger" button on the first mobile terminal. When the first mobile terminal hears the hazard command, it obtains its own location coordinates through the built-in Global Positioning System (GPS) module and marks these coordinates as the first location coordinates, with the location corresponding to the first location coordinates as the hazard point.

[0059] Simultaneously, upon receiving a hazard command triggered by the first user, the first mobile terminal displays a pre-defined hazard type selection interface. This interface presents various common outdoor hazard types in a categorized list, including quicksand, landslides, loose rock areas, honeycomb, venomous snakes, steep slopes, and swamps. Each hazard type option corresponds to an icon and a brief description for easy user identification. The hazard type selection interface supports single-finger tap selection and also includes a custom hazard supplement entry to accommodate special risk scenarios. After the user selects a specific hazard type, the first mobile terminal automatically associates it with basic warning information for that type of hazard, laying the foundation for accurate subsequent hazard information delivery. The operation is simple and efficient, adapting to the rapid response needs in complex outdoor environments.

[0060] A hazard command is not simply a trigger signal, but rather structured information containing user operation data. Its data packet carries identification parameters (such as numerical codes or character labels) corresponding to the hazard type. The system uses a preset parsing algorithm to unpack the data packet, extract data, and verify its format. The extracted identification parameters are then matched against a built-in hazard type mapping library. This library pre-stores the associations between multiple preset hazard types (such as quicksand, landslides, and honeycomb) and their corresponding identification parameters. After matching and verification, the system can clearly parse out the specific hazard type corresponding to this hazard command, providing core data support for the accurate transmission of hazard information and the generation of warning content in subsequent stages.

[0061] In some embodiments, the first mobile terminal also provides a custom input window, allowing the first user to provide a detailed textual description of the hazard type. Based on the custom description input by the first user, the hazard type of the hazard point is initially determined, such as "risk of falling into quicksand, with a large quicksand area".

[0062] S22, obtain environmental data of the current environment of the first mobile terminal, and identify the hazard level based on the hazard type and the environmental data.

[0063] The first mobile terminal can access built-in environmental sensors to obtain environmental data about its current environment. These sensors may include a camera, temperature sensor, and humidity sensor. For example, the camera can capture images of the first location coordinates and the surrounding area, and image recognition technology can be used to analyze for unusual terrain features, obstacles, etc. Temperature and humidity sensors can be used to obtain environmental temperature and humidity information to determine if they match the environmental conditions for common hazards. Combining historical hazard data stored in the first mobile terminal with real-time meteorological and geological data obtained from an external server, the hazard type can be further analyzed. For instance, if historical data shows that landslides have occurred multiple times in the area during a specific season, and real-time meteorological data shows recent heavy rainfall, the likelihood of a landslide as the current hazard type increases significantly.

[0064] In an optional embodiment, identifying the hazard level based on the hazard type and the environmental data includes:

[0065] The hazard type and the environmental data are input into a preset hazard level assessment model, and the hazard score output by the hazard level assessment model is obtained;

[0066] The hazard level is determined based on the hazard score results and the preset score threshold segmentation range.

[0067] The preset scoring threshold segmentation interval includes multiple scoring threshold intervals, and each scoring threshold interval corresponds to a risk level.

[0068] The system takes hazard type and environmental data as input parameters and inputs them into a preset hazard level assessment model. The hazard level assessment model outputs the corresponding hazard score. Subsequently, the system retrieves preset scoring threshold intervals, which are divided into multiple continuous scoring threshold intervals, each uniquely corresponding to a hazard level (e.g., high, medium, low). By comparing the hazard score with each scoring threshold interval, and matching it to the corresponding interval, the final hazard level can be determined, providing a quantitative basis for subsequent graded early warning and response.

[0069] The first mobile terminal pre-constructed a hazard level assessment model based on a multilayer feedforward neural network (MFNN), which was built upon a large amount of historical hazard event data. This historical hazard event data covered event records for different hazard types (such as quicksand, honeycomb, landslides, and venomous snake sightings). Each record included the hazard level, hazard-related parameters (such as quicksand area and groundwater level), and environmental data (such as temperature and humidity). For example, for a quicksand hazard event, the data recorded the area of ​​quicksand, groundwater level, surrounding temperature and humidity, and a level one hazard rating. Next, the collected data was cleaned to remove duplicates, errors, and incomplete data. Then, the data was standardized, converting parameters with different dimensions to a unified dimension, for example, scaling the values ​​of each parameter to the [0, 1] range to avoid some parameters having an excessively large numerical range that could significantly impact model training. Furthermore, the hazard level assessment model introduced a dynamic weighting mechanism. Based on different hazard types, the model automatically adjusted the weights of the parameters in each dimension. For example, for quicksand hazards, the area of ​​quicksand has a higher weight than the groundwater level; while for venomous snake hazards, vegetation density has a higher weight than environmental temperature and humidity. The dynamic adjustment of these weights is based on in-depth analysis of the influencing factors of various hazardous events and extensive experimental verification. Furthermore, since the output of the hazard level assessment is a continuous score, the loss function used in the hazard level assessment model in this application is the mean squared error loss function, which can effectively measure the difference between the model's predicted score and the actual hazard level score. The model parameters are optimized by minimizing this difference.

[0070] The hazard level assessment model includes an input layer, a hidden layer, and an output layer. The input layer receives the hazard type and the environmental data; the hidden layer includes a first hidden layer and a second hidden layer, wherein the first hidden layer extracts feature information from the hazard type and the environmental data; the second hidden layer integrates the feature information extracted by the first hidden layer; and the output layer outputs the hazard score result, with one node.

[0071] The number of input layer nodes is determined based on the hazard type and the environmental data. Assuming this embodiment considers five hazard levels (quicksand, honeycomb, landslide, venomous snakes, etc.) and eight environmental factors (temperature, humidity, groundwater level (for specific hazards like quicksand), vegetation density (for venomous snakes), etc.), then the number of input layer nodes is 5 + 8 = 13 nodes. Each node corresponds to a specific parameter, and hazard type-related data is accurately input into the corresponding node in parameter order. To achieve a balance between model complexity and computational efficiency, after multiple experiments and comparative analyses, this embodiment sets up a two-layer hidden model. For the first hidden layer, the number of nodes is determined by considering both the number of input layer nodes and the complexity of the data. Based on experience and experimental verification, setting the number of nodes in the first hidden layer to approximately 1.5-2 times the number of input layer nodes is appropriate.

[0072] In this embodiment, the input layer has 13 nodes. After experimental testing, the number of nodes in the first hidden layer was set to 20. This number of nodes allows for sufficient learning of the feature information in the input parameters without making the model overly complex and computationally intensive. The second hidden layer further abstracts and integrates the features extracted by the first hidden layer. The number of nodes in the second hidden layer is generally less than that in the first hidden layer. After experimental adjustment, it was set to 12 nodes, which can gradually compress information, improve the model's generalization ability, and avoid overfitting. In the hidden layer, the ReLU (Rectified LinearUnit) activation function is used. The ReLU function is expressed as f(x) = max(0, x), which has advantages such as simple computation and effective mitigation of the gradient vanishing problem, accelerating the training process of the neural network and enabling the model to converge faster. The preprocessed data is divided into training and test sets according to a certain ratio, for example, 0% of the data is used as the training set and 20% of the data is used as the test set. The neural network model is trained using a training set. Backpropagation and optimizers (such as stochastic gradient descent (SGD) or the Adam optimizer) are employed to adjust the model's connection weights and biases to minimize the mean squared error (MSE). During training, the model parameters are continuously optimized through multiple iterations, gradually bringing the model's predictions closer to the actual hazard levels. The trained model is then validated using a test set. Evaluation metrics such as the mean squared error on the test set are calculated to assess the model's performance until satisfactory results are achieved.

[0073] Once the relevant hazard data for the first location coordinates (i.e., the hazard type and environmental data for the first location coordinates) are obtained, the hazard type and environmental data are input into the preset hazard level assessment model. Taking quicksand hazard as an example, assuming the quicksand area weight is 0.6 and the quantified value is 7 (based on area level classification); the groundwater level weight is 0.4 and the quantified value is 9 (at a high level). The model calculation formula is: Hazard score = (Quicksand area quantified value × Quicksand area weight) + (Groundwater level quantified value × Groundwater level weight), that is, (8 × 0.6) + (9 × 0.4) = 4.2 + 3.6 = 7.8.

[0074] Based on the hazard score output by the assessment model, the hazard level is determined into three levels: low, medium, and high, according to a preset classification standard. For example, if a score of 0-5 indicates low hazard, 5-8 indicates medium hazard, and 8-10 indicates high hazard, then the quicksand hazard calculated above results in a score of 7.8, which is classified as medium hazard. Simultaneously, the model will provide detailed level explanations and recommended actions based on the score results, such as advising team members to remain vigilant and slowly evacuate to a safe distance under the medium hazard level.

[0075] S23, determine the danger warning area data based on the danger type and the danger level.

[0076] Once the hazard type and hazard level of the first location coordinates are determined, the first mobile terminal can calculate the warning distance corresponding to the hazard point based on the hazard type and hazard level, and determine the hazard warning area data based on the warning distance.

[0077] In an optional embodiment, determining the hazard warning area data based on the hazard type and the hazard level includes:

[0078] The basic safety distance is determined based on the type and level of hazard.

[0079] Obtain the current ambient brightness and determine the actual response time based on the current ambient brightness;

[0080] Determine the distance correction factor based on the actual response time;

[0081] The basic safety distance is corrected based on the distance correction factor to obtain the warning distance;

[0082] The danger warning area data is generated with the location coordinates of the first mobile terminal as the center and the warning distance as the radius.

[0083] Different types of hazards (such as quicksand and landslides) have different spread ranges and hazard radii. The hazard level directly reflects the severity of the risk. Combining the two to determine the basic safety distance can ensure that the distance is appropriate to the characteristics of the hazard itself, avoid uniform distances that are too close or too far, and provide a reasonable benchmark for the delineation of warning areas.

[0084] In some embodiments, the first mobile terminal can calculate the basic safety distance using a lookup table method based on the type of hazard (such as quicksand, honeycomb, landslide, venomous snakes, etc.) and the hazard level (such as level one to three). For example, the basic safety distance may be 100 meters for level one quicksand; 200 meters for level two landslide, and so on.

[0085] Simultaneously, the ambient brightness value (unit: lux) is acquired in real time via the light sensor or camera of the first mobile terminal. Ambient brightness affects the speed at which team members behind can detect warnings. Visual recognition is slow and response time is long in low-light environments, while response is fast in bright environments. Based on this, the actual response time is determined, and the distance adjustment is made to match the actual perception conditions, ensuring that team members have sufficient time to avoid danger.

[0086] For low-brightness scenarios, such as at night or in tunnels, where ambient brightness is low, visual perception is limited, and reaction time is prolonged, the actual response time can be set to twice the standard response time, for example, if the standard response time is 2 seconds, the actual response time is 4 seconds. For medium-brightness scenarios, such as on cloudy days or indoors, where ambient brightness is moderate, the actual response time can be set to be the same as the standard response time, for example, both are 2 seconds. For high-brightness scenarios, such as on sunny outdoor days, where ambient brightness is high and visual perception is clear, the actual response time can be set to half the standard response time, for example, if the standard response time is 2 seconds, the actual response time is 1 second.

[0087] The difference in actual response time needs to be compensated for by distance correction. A longer response time requires an increased distance correction factor to widen the distance, and vice versa. The distance correction factor transforms the time-dimensional risk avoidance requirements into a basis for spatial distance adjustment, improving distance accuracy. The distance correction factor is a dynamic coefficient used to quantify the impact of ambient brightness on safe distance. Based on the standard response time, it is adjusted to adapt the basic safe distance by combining the actual response time corresponding to the ambient brightness. When the actual response time is longer than the standard response time, the distance correction factor is greater than 1, increasing the safe distance; when the actual response time is shorter than the standard response time, the distance correction factor is less than 1, decreasing the safe distance, ultimately ensuring that the safe distance matches the team members' actual risk avoidance and response capabilities in the current environment.

[0088] First, calculate the ratio of the actual effective response time to the standard response time. Then, subtract 1 from this ratio to obtain the deviation coefficient. Next, weight the deviation coefficient using a preset weighting coefficient. Finally, add the weighted result to 1 to obtain the distance correction factor. The preset weighting coefficient is used to control the impact of the deviation between the actual response time and the default value on the correction factor. For example, if the standard response time is 2 seconds, and the actual response time is 1 second in a high-brightness scene, with a preset weighting coefficient of 0.5, then the distance correction factor is 0.75.

[0089] The basic safety distance does not take into account differences in environmental perception. By adjusting the basic safety distance using a distance correction factor, both the inherent characteristics of the hazard and environmental influences can be considered, resulting in a warning distance that better reflects the actual scenario and avoids untimely evacuation or distance redundancy due to environmental factors. For example, if the basic safety distance is 200 meters and the distance correction factor is 0.75, then the warning distance is 150 meters.

[0090] After calculating the final warning distance, the first mobile terminal uses Geographic Information System (GIS) technology to generate hazard warning area data, centered on the first location coordinates. This precise location of the core hazard and the delineation of areas according to the warning distance clearly defines the safe and dangerous zones, allowing rear team members to intuitively understand the avoidance boundaries and improving the intuitiveness and effectiveness of hazard warnings.

[0091] The terminal inputs the geographic coordinates (latitude and longitude) of the first location and the warning distance into the GIS system. Based on this information, the GIS system draws a circular area on the electronic map with the first location coordinates as the center and the warning distance as the radius (in some special cases, such as complex terrain or a significant deviation in the direction of danger spread, an irregular polygonal area may also be generated). This provides the outdoor team with accurate and effective safety warnings, maximizing the safety of team members. Simultaneously, the terminal annotates the generated danger warning area data, including the type of danger, danger level, and warning distance, and integrates this information with the electronic map so that team members can intuitively understand the specific details of the danger warning area data.

[0092] In other embodiments, after the first mobile terminal obtains the hazard type, it can broadcast the hazard type and the first location coordinates to the second mobile terminal. When the second mobile terminal receives the hazard type, it can determine the corresponding hazard type and hazard level, and generate corresponding hazard warning area data based on the hazard type and hazard level of the first location coordinates.

[0093] S24, broadcast the danger warning area data to the second mobile terminal, so that the second mobile terminal can issue an early warning based on the danger warning area data.

[0094] In some embodiments, after generating hazard warning area data corresponding to the first location coordinates, the first mobile terminal can broadcast the hazard warning area data to the second mobile terminals of the second users (user B, user C, user D, and user E) in the team. The hazard warning area data includes the hazard type, hazard level, first location coordinate information, and warning distance.

[0095] In an optional implementation, broadcasting the danger warning area data to the second mobile terminal includes:

[0096] Send a broadcast start message to multiple second mobile terminals and receive broadcast feedback information from each second mobile terminal;

[0097] Based on the broadcast feedback information, select multiple target second mobile terminals from the plurality of second mobile terminals;

[0098] In the complex communication environment of outdoor team activities, the broadcast coverage of a single terminal is limited and the signal penetration capability is weak. In order to achieve full-area and efficient transmission of data in dangerous warning areas, this implementation method can adopt a multi-level tree-like diffusion broadcast method.

[0099] The data of the danger warning area is broadcast to the multiple target second mobile terminals, so that each target second mobile terminal acts as a first-level propagation node and selects a second-level propagation node to form a multi-level tree-like broadcast network to broadcast the data of the danger warning area.

[0100] In some embodiments, after the first mobile terminal determines the danger alert area data, it enters the broadcast phase. First, the first mobile terminal (the danger detection terminal) sends a notification signal to all associated second mobile terminals (other team member terminals) within the team to initiate broadcasting, aiming to check the communication status and receiving willingness of the terminals; simultaneously, it receives feedback information from each second mobile terminal. This feedback information includes whether the terminal is online, signal strength, remaining battery power, and distance from the first mobile terminal, providing a basis for subsequent screening of valid receiving terminals.

[0101] Based on the feedback information obtained in the previous step, terminals that are offline, have weak signals, or cannot receive data are eliminated, and terminals with good communication status are selected as the target second mobile terminals. This avoids repeatedly sending data to invalid terminals, reduces the waste of communication resources, and ensures that data in dangerous warning areas can be preferentially transmitted to terminals with receiving capabilities, thereby improving broadcast efficiency and reliability.

[0102] The first mobile terminal first sends the data of the danger warning area to the selected target second mobile terminals, which become the first-level propagation nodes; then each first-level propagation node selects the next level of valid receiving terminals as the second-level propagation nodes based on the feedback information of the surrounding terminals, and so on to form a multi-level tree structure.

[0103] The above-mentioned optional embodiments can overcome the coverage limitations of single-node broadcasting, and are especially suitable for outdoor scenarios with weak signals and dispersed terminals, such as mountainous areas and forests, to achieve full-area and rapid coverage of dangerous information.

[0104] In an optional embodiment, selecting a plurality of target second mobile terminals from the plurality of second mobile terminals based on the broadcast feedback information includes:

[0105] The signal strength, remaining battery power, and distance between the second mobile terminal and the first mobile terminal are obtained from the broadcast feedback information.

[0106] A terminal hit score is calculated based on the signal strength, the remaining battery power, and the distance.

[0107] Based on the terminal hit score, multiple target second mobile terminals are selected from the plurality of second mobile terminals.

[0108] After receiving feedback information, the first mobile terminal obtains key data such as the signal strength, remaining battery power, and distance to each second mobile terminal. Signal strength reflects the quality of the communication signal between the first and second mobile terminals; higher signal strength indicates stronger stability and reliability of communication, and a lower probability of errors or interruptions during data transmission. Remaining battery power indicates the current battery level of the second mobile terminal; more remaining battery power means the terminal can continue operating for a longer period, making it more likely to reliably complete tasks when acting as a data forwarding node, avoiding sudden shutdowns due to insufficient power and interrupted information transmission. Distance refers to the spatial distance between the second and first mobile terminals; closer distances generally result in less signal loss and higher data transmission efficiency and accuracy; moreover, closer terminals may facilitate collaboration and communication in team activities. Based on a pre-set algorithm, a terminal hit score for each second mobile terminal is calculated with weights of 40% for signal strength, 30% for remaining battery power, and 30% for distance.

[0109] The specific calculation method is as follows: Assume that the signal strength score of a second mobile terminal is S1 (the stronger the signal strength, the higher the score, which can be normalized according to the actual value range of the signal strength, for example, mapping the signal strength value to the range of 0-100), the remaining battery score is S2 (the more remaining battery, the higher the score, which is also normalized), and the distance score from the first mobile terminal is S3 (the closer the distance, the higher the score, which is also normalized). Then the terminal hit score of the second mobile terminal can be obtained by weighted calculation based on S1, S2, and S3.

[0110] Based on the calculated terminal hit score, the top K second mobile terminals with the highest scores are selected as the target second mobile terminals.

[0111] Because the mobile terminal is mobile, the second location coordinates will constantly change. The second mobile terminal reacquires the second location coordinates at regular time intervals (e.g., every 5 minutes) and repeats the distance calculation and early warning strategy execution steps described above to achieve dynamic early warning updates. If the user approaches or moves away from the danger warning area during movement, the terminal can promptly adjust the warning level and notification method to ensure that the user always receives accurate and timely danger warning information.

[0112] Example 3

[0113] Figure 3 This is a functional block diagram of the danger information early warning device based on self-organizing network provided in the embodiments of this application.

[0114] The self-organizing network-based hazard information early warning device 30 operates in the first mobile terminal. The first mobile terminal and multiple second mobile terminals have all activated the NAN self-organizing network function and completed team networking.

[0115] In some embodiments, the ad hoc network-based hazard information warning device 30 may include multiple functional modules composed of program code segments. The program code of each program segment in the ad hoc network-based hazard information warning device 30 may be stored in the memory of the mobile terminal and executed by at least one processor to perform (see details). Figure 2 (Description) A function for early warning of danger information based on self-organizing networks.

[0116] In this embodiment, the danger information early warning device 30 based on the self-organizing network can be divided into multiple functional modules according to its functions. The functional modules may include: a detection module 301, an identification module 302, a determination module 303, and an early warning module 304. As used in this application, a module refers to a series of computer-readable instruction segments that can be executed by at least one processor and perform a fixed function, stored in memory. In this embodiment, the functions of each module will be detailed in subsequent embodiments.

[0117] The detection module 301 is used to parse the dangerous command when it is detected to obtain the danger type;

[0118] The identification module 302 is used to acquire environmental data of the environment in which the first mobile terminal is currently located, and to identify the hazard level based on the hazard type and the environmental data;

[0119] The determining module 303 is used to determine the danger warning area data based on the danger type and the danger level;

[0120] The early warning module 304 is used to broadcast the danger warning area data to the second mobile terminal, so that the second mobile terminal can issue an early warning based on the danger warning area data.

[0121] It should be understood that the various variations and specific embodiments of the danger information warning method based on ad hoc networks provided in the above embodiments are also applicable to the danger information warning device based on ad hoc networks in this embodiment. Through the detailed description of the danger information warning method based on ad hoc networks described above, those skilled in the art can clearly understand the implementation process of the danger information warning device based on ad hoc networks in this embodiment. For the sake of brevity, it will not be described in detail here.

[0122] Example 4

[0123] This embodiment provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps described in the above embodiment of the danger information early warning method based on ad hoc networks.

[0124] Example 5

[0125] See Figure 4 The diagram shown is a structural schematic of a mobile terminal provided in an embodiment of this application. In a preferred embodiment of this application, the mobile terminal 4 includes: a memory 401, at least one processor 402, at least one communication bus 403, multiple sensors 404, and a display screen 405.

[0126] Those skilled in the art should understand that Figure 4 The structure of the mobile terminal shown does not constitute a limitation of the embodiments of this application. The mobile terminal 4 may also include more or fewer other hardware or software, or different component arrangements than shown.

[0127] In some embodiments, the memory 401 stores a computer program and an operating system. When executed by the at least one processor 402, the computer program implements all or part of the steps in the ad hoc network-based danger information early warning method described above. The memory 401 includes read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data. Further, the computer-readable storage medium may primarily include a program storage area and a data storage area, wherein the program storage area may store the operating system, at least one application program required for a function, etc.

[0128] In some embodiments, the at least one processor 402 is the control unit of the mobile terminal 4, connecting various components of the mobile terminal 4 via various interfaces and lines. It executes programs or modules stored in the memory 401 and calls data stored in the memory 401 to perform various functions and process data of the mobile terminal 4. For example, when the at least one processor 402 executes a computer program stored in the memory, it implements all or part of the steps of the danger information warning method based on ad hoc networks described in this application embodiment; or it implements all or part of the functions of a danger information warning device based on ad hoc networks. The at least one processor 402 may be composed of integrated circuits, such as a single-packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips.

[0129] In some embodiments, the at least one communication bus 403 is configured to enable communication between the memory 401 and the at least one processor 402, etc. Although not shown, the mobile terminal 4 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 402 via a power management device, thereby enabling functions such as charging, discharging, and power consumption management. The power supply may also include one or more DC or AC power supplies, a rechargeable power fault detection circuit, a power converter or inverter, a power status indicator, and any other components.

[0130] In some embodiments, the plurality of sensors 404 include a temperature and humidity sensor and a camera sensor.

[0131] The mobile terminal 4 may also include a Bluetooth module, a Wi-Fi module, internal memory, a network interface, an input location, and a display screen, etc., which will not be described in detail here.

[0132] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0133] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0134] The above is a detailed description of the preferred embodiments of this application. However, the invention of this application is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for early warning of danger information based on ad hoc networks, characterized in that, Applied to a first mobile terminal, where both the first and second mobile terminals have activated the NAN self-organizing network function to complete team networking, the method includes: When a dangerous command is detected, the dangerous command is parsed to obtain the danger type; The environmental data of the current environment of the first mobile terminal is obtained, and the hazard level is identified based on the hazard type and the environmental data; A basic safety distance is determined based on the hazard type and hazard level; the current ambient brightness is obtained, and the actual response time is determined based on the current ambient brightness; the ratio of the actual effective response time to the standard response time is calculated, and then 1 is subtracted from the ratio to obtain a deviation coefficient. Subsequently, the deviation coefficient is weighted using a preset weighting coefficient, and finally the weighted result is added to 1 to obtain a distance correction factor; the basic safety distance is corrected based on the distance correction factor to obtain a warning distance; hazard warning area data is generated with the location coordinates of the first mobile terminal as the center and the warning distance as the radius. The data of the danger warning area is broadcast to the second mobile terminal, so that the second mobile terminal can issue an early warning based on the data of the danger warning area.

2. The danger information early warning method based on ad hoc networks according to claim 1, characterized in that, The step of broadcasting the danger warning area data to the second mobile terminal includes: Send a broadcast start message to multiple second mobile terminals and receive broadcast feedback information from each second mobile terminal; Based on the broadcast feedback information, select multiple target second mobile terminals from the plurality of second mobile terminals; The data of the danger warning area is broadcast to the multiple target second mobile terminals, so that each target second mobile terminal acts as a first-level propagation node and selects a second-level propagation node to form a multi-level tree-like broadcast network to broadcast the data of the danger warning area.

3. The danger information early warning method based on ad hoc networks according to claim 2, characterized in that, The step of selecting multiple target second mobile terminals from the plurality of second mobile terminals based on the broadcast feedback information includes: The signal strength, remaining battery power, and distance between the second mobile terminal and the first mobile terminal are obtained from the broadcast feedback information. A terminal hit score is calculated based on the signal strength, the remaining battery power, and the distance. Based on the terminal hit score, multiple target second mobile terminals are selected from the plurality of second mobile terminals.

4. The danger information early warning method based on ad hoc networks according to claim 1, characterized in that, The identification of hazard levels based on the hazard type and the environmental data includes: The hazard type and the environmental data are input into a preset hazard level assessment model, and the hazard score output by the hazard level assessment model is obtained; The hazard level is determined based on the hazard rating result and the preset rating threshold segmentation interval; the preset rating threshold segmentation interval includes multiple rating threshold intervals, and each rating threshold interval corresponds to a hazard level.

5. The danger information early warning method based on ad hoc networks according to claim 4, characterized in that, The hazard level assessment model includes an input layer, a hidden layer, and an output layer; The input layer is used to receive the hazard type and the environmental data; The hidden layer includes a first hidden layer and a second hidden layer. The first hidden layer is used to extract feature information of the hazard type and the environmental data. The number of nodes in the first hidden layer is 1.5-2 times the number of nodes in the input layer. The second hidden layer is used to integrate the feature information extracted by the first hidden layer, and the number of nodes in the second hidden layer is less than the number of nodes in the first hidden layer; The output layer is used to output the hazard rating result, and the number of nodes in the output layer is 1.

6. The danger information early warning method based on ad hoc networks according to claim 5, characterized in that, The number of input layer nodes is determined based on the hazard type and the environmental data.

7. A hazard information early warning system based on a self-organizing network, characterized in that, The danger information early warning system based on self-organizing network includes: a first mobile terminal and a second mobile terminal, both of which have activated the NAN self-organizing network function to complete team networking. The first mobile terminal, upon detecting a dangerous command, parses the dangerous command to obtain a dangerous type; acquires environmental data of the current environment of the first mobile terminal and identifies a dangerous level based on the dangerous type and the environmental data; determines a basic safe distance based on the dangerous type and the dangerous level; acquires the current ambient brightness and determines the actual response time based on the current ambient brightness; calculates the ratio of the actual effective response time to the standard response time, subtracts 1 from the ratio to obtain a deviation coefficient, then weights the deviation coefficient with a preset weighting coefficient, and finally adds the weighted result to 1 to obtain a distance correction factor; corrects the basic safe distance based on the distance correction factor to obtain a warning distance; generates dangerous warning area data with the location coordinates of the first mobile terminal as the center and the warning distance as the radius; and broadcasts the dangerous warning area data to the second mobile terminal. The second mobile terminal is used to issue an early warning based on the data of the danger warning area.

8. A mobile terminal, characterized in that, The mobile terminal includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements all or part of the steps of the danger information early warning method based on ad hoc networks according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by the processor, it implements all or part of the steps of the danger information early warning method based on ad hoc networks according to any one of claims 1 to 6.

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