Water area drowning prevention early warning method and system

Abstract

The invention relates to the technical field of water area early warning, in particular to a water area drowning prevention early warning method and system, and the method comprises the steps: determining a first positioning data set corresponding to a plurality of grid early warning regions at the periphery of a risk water area according to a panoramic positioning monitoring module disposed at the periphery of the water area in advance; according to a plurality of electronic fence partitions and the first positioning data set, generating electronic fence space mapping data corresponding to each electronic fence partition; and obtaining position track data of a student in the periphery of the risky water area in a first preset time period according to a wearable device, and determining an approaching rate of the student in the first preset time period according to the position track data and the electronic fence space mapping data. According to the invention, by obtaining the trajectory data of the student wearable device and combining the trajectory data with the environmental data, the rate of approaching the water area and the safety distance change of the student are dynamically calculated, so that early warning can be adjusted not only based on historical data, but also according to real-time conditions, and is closer to the actual danger occurrence moment.

Description

Technical Field

[0001] This invention relates to the field of water area early warning technology, specifically to a method and system for preventing drowning in water areas. Background Technology

[0002] A body of water refers to a region of water that exists naturally or artificially, either permanently or seasonally. It includes various types of water bodies, such as lakes, rivers, oceans, ponds, wetlands, and reservoirs. A characteristic of a body of water is that its water level exists year-round or temporarily.

[0003] Currently, students are prone to drowning and other safety accidents when approaching water bodies, especially in the absence of safety facilities or warning signs. In particular, students near schools are more likely to accidentally enter water bodies due to a lack of proper awareness of the dangers of water, resulting in accidental injuries.

[0004] In addition, traditional water safety protection often relies on manual patrols or fixed monitoring facilities, which cannot achieve comprehensive real-time monitoring and intelligent early warning response. These systems are usually slow to respond to environmental changes and cannot dynamically adjust early warning measures according to students' activity trajectories and the external environment, resulting in slow response to emergencies and potential monitoring blind spots. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for early warning of drowning in water areas, comprising: Based on the panoramic positioning monitoring modules pre-deployed around the water area, the first positioning dataset corresponding to several grid early warning areas around the risky water area is determined; Based on multiple electronic fence zones and the first positioning dataset, electronic fence spatial mapping data corresponding to each electronic fence zone is generated; wherein, the electronic fence zone uses the length of the corresponding grid warning area along the water boundary as the zone width to form a closed protective area around the risky water area; The location trajectory data of students within the vicinity of the risky water area is obtained by the wearable device within a first preset time period. Based on the location trajectory data and the electronic fence spatial mapping data, the approach rate of the students within the first preset time period is determined. The student's first distance change within a second preset time period is predicted based on the approach rate, wherein the second preset time period is after the first preset time period; Obtain environmental data of the area surrounding the risky waters during the second preset time period; determine environmental adjustment parameters based on the environmental data; adjust the first distance change based on the environmental adjustment parameters to obtain the second distance change of the student during the second preset time period. The risk coefficient of the area surrounding the risky water area is obtained, and the second distance change is adjusted according to the risk coefficient to obtain the target distance change of the student within the second preset time period; when the target distance change of the student is less than the preset safe distance, an early warning message is generated and sent to the monitoring terminal.

[0006] Preferably, the panoramic positioning monitoring module consists of multiple BeiDou positioning base stations; the number of BeiDou positioning base stations is at least two, and they are evenly distributed along the perimeter of the water area to collect positioning data from various locations around the water area to obtain panoramic positioning data; wherein, the panoramic positioning data is used to characterize the satellite positioning signals around the water area.

[0007] Preferably, before determining the first location dataset corresponding to several grid warning areas around the risky water area based on the panoramic positioning monitoring module pre-deployed around the water area, the method further includes: The panoramic positioning monitoring module is used to determine multiple surrounding areas corresponding to the water area specifications, as well as the grid size corresponding to each of the surrounding areas; wherein each of the surrounding areas corresponds to the surrounding area of ​​each water area; the water area includes at least one of shallow water, steep slope, deep water edge, and dock; Based on the grid size, the corresponding surrounding area is divided into grids to obtain several grid warning areas.

[0008] Preferably, based on multiple electronic fence partitions and the first positioning dataset, electronic fence spatial mapping data corresponding to each of the electronic fence partitions is generated, including: Determine the boundary range of the electronic fence zone, construct the electronic fence spatial framework based on the boundary range, and divide the electronic fence spatial framework into zones to obtain each electronic fence zone; A spatial index is labeled for each of the electronic fence zones, and the zone number corresponding to each of the electronic fence zones is determined according to the spatial index to obtain an initial location association list; For each location point in the first location dataset, a spatial location range is created, and the coordinate boundaries of the spatial location range are determined. Based on the coordinate boundaries of the spatial location range, the electronic fence partition number where the coordinate boundaries are located is determined. The initial location association list of the corresponding electronic fence partition is updated to obtain an updated location association list. The updating of the initial location association list of the corresponding electronic fence partition to obtain the updated location association list includes: Obtain the coordinates of the positioning point in the water coordinate system, and determine the coordinate boundaries of the spatial positioning range based on the coordinates. Calculate the numbering range of the electronic fence zone corresponding to each boundary point in the direction of the water boundary and the vertical boundary based on the coordinate boundary of the spatial positioning range. Calculate the number of the electronic fence partition corresponding to each boundary point according to the number range, update the initial location association list of the electronic fence partition with the corresponding number, store the location point coordinates and timestamp data associated with the electronic fence partition, and obtain the updated location association list. Based on the partition attributes of each electronic fence partition, the updated location association list, and the characteristics of the grid warning area, the electronic fence spatial mapping data corresponding to each electronic fence partition is determined.

[0009] Preferably, determining the student's approach rate within the first preset time period based on the location trajectory data and the electronic fence spatial mapping data includes: Based on the water boundaries of each electronic fence zone, the electronic fence zone number to which each trajectory point in the location trajectory data belongs is determined by a spatial point-surface matching algorithm. Calculate the straight-line distance from each trajectory point to the nearest water boundary, wherein the straight-line distance is obtained by calculating the Euclidean distance between the coordinates of the trajectory point and the boundary coordinates of the water boundary; Add a timestamp, the electronic fence zone number to which it belongs, and the straight-line distance to the water boundary to each trajectory point, and arrange them in chronological order to obtain a sequence of trajectory points with zone identifiers; Extract position change feature vectors from the trajectory point sequence with partition identifiers, wherein the position change feature vectors include the trajectory point movement direction angle, the time interval between adjacent trajectory points, and the distance change across partition boundaries; The movement direction angle in the position change feature vector is compared with the preset water area pointing angle, and the direction consistency coefficient is calculated. When the direction consistency coefficient is greater than the preset direction threshold, it is determined to be a valid approach movement. For the effective approach movement trajectory point pair, calculate the change in straight-line distance between adjacent trajectory points and compare it with the corresponding time interval to obtain the instantaneous approach rate; The preset water distance attenuation coefficient is multiplied by the instantaneous approach rate to obtain the corrected instantaneous approach rate sequence, wherein the water distance attenuation coefficient increases as the distance to the water boundary decreases; Calculate the weighted average of all the corrected instantaneous approach rates within the first preset time period to obtain the average approach rate of the student within the first preset time period, wherein the weighting weight is inversely proportional to the distance from the corresponding trajectory point to the water boundary.

[0010] Preferably, extracting position change feature vectors from the trajectory point sequence with partition identifiers includes: Calculate the coordinate difference between two adjacent trajectory points, and calculate the movement direction angle of the trajectory point using the arctangent function, wherein the movement direction angle is based on the direction pointing to the water boundary as 0 degrees. Calculate the timestamp differences between adjacent trajectory points, generate a time interval sequence, and calculate the mean and variance of the time intervals; Identify the pairs of trajectory points that cross the boundaries of the electronic fence zones in the trajectory point sequence, and calculate the distance change of the trajectory point pairs on both sides of the electronic fence zone boundaries; The position change feature vector is obtained by combining the statistical parameters of the movement direction angle, the time interval sequence, and the cross-regional distance change.

[0011] Preferably, the environmental data includes weather conditions and time-of-day characteristics; Based on the environmental data, environmental regulation parameters are generated, including: Based on the weather conditions, weather impact parameters are generated, wherein the weather impact parameters are positively correlated with the severity of severe weather, and the weather impact parameters are the ratio of the actual weather risk value to the preset weather safety threshold. Based on the time period characteristics, a time period impact parameter is generated, wherein the time period impact parameter is positively correlated with the degree of correlation of dangerous time periods, and the time period impact parameter is the ratio of the actual time period risk value to the preset time period safety threshold; Environmental regulation parameters are generated based on the weather impact parameters and the time period impact parameters; wherein, generating environmental regulation parameters based on the weather impact parameters and the time period impact parameters includes: Determine the first weight value corresponding to the weather impact parameter; determine the second weight value corresponding to the time period impact parameter; The weather impact parameters are weighted together with the first weight value to obtain the first weighting coefficient; The time period influence parameter is weighted by the second weight value to obtain the second weighting coefficient; The first weighting coefficient and the second weighting coefficient are fused to generate environmental adjustment parameters.

[0012] Preferably, obtaining the regional hazard factor around the risky water area includes: Obtain hydrological characteristic data of the risky water area, and generate a natural hazard coefficient based on the hydrological characteristic data; Obtain monitoring data of surrounding facilities in the risky water area, and generate facility hazard coefficients based on the monitoring data. The monitoring data of surrounding facilities includes at least one of the following: integrity of guardrails, clarity of warning signs, and completeness of rescue equipment. The regional risk coefficient of the risky water area is generated by weighted summation of the natural risk coefficient and the facility risk coefficient.

[0013] Preferably, adjusting the first distance change based on the environmental adjustment parameters to obtain the second distance change of the student within the second preset time period includes: The first distance change is weighted with the environmental adjustment parameter to obtain the second distance change; The second distance change is adjusted based on the regional risk coefficient to obtain the target distance change of the student within the second preset time period, including: The target distance change is obtained by weighting the second distance change with the area hazard coefficient.

[0014] A water area drowning prevention early warning system, applicable to the aforementioned water area drowning prevention early warning method, includes: The positioning and monitoring unit is used to determine the first positioning dataset corresponding to several grid warning areas around the risky water area based on the panoramic positioning and monitoring modules pre-deployed around the water area. The spatial mapping unit is used to generate electronic fence spatial mapping data corresponding to each of the multiple electronic fence partitions and the first positioning dataset; wherein, the electronic fence partition uses the length of the corresponding grid warning area along the water boundary as the partition width to form a closed protection area around the risky water area. The rate calculation unit is used to obtain the location trajectory data of the student in the vicinity of the risky water area within a first preset time period based on the wearable device, and to determine the student's approach rate within the first preset time period based on the location trajectory data and the electronic fence spatial mapping data. A distance prediction unit is used to predict the first distance change of the student within a second preset time period based on the approach rate, wherein the second preset time period is after the first preset time period; An environmental weighting unit is used to acquire environmental data around the risky water area during the second preset time period, determine environmental adjustment parameters based on the environmental data, adjust the first distance change amount based on the environmental adjustment parameters, and obtain the second distance change amount of the student during the second preset time period. The water area early warning unit is used to obtain the regional danger coefficient around the risky water area, adjust the second distance change amount according to the regional danger coefficient, and obtain the target distance change amount of the student within the second preset time period; when the target distance change amount of the student is less than the preset safe distance, an early warning information is generated and sent to the monitoring terminal.

[0015] Compared with the prior art, the beneficial effects of the present invention are: (1) Through the collaborative work of the panoramic positioning monitoring module and multiple Beidou positioning base stations, this invention can accurately acquire positioning data around the water area in real time. This data provides strong technical support for predicting the trajectory and speed of students approaching the water area, making the early warning more timely and accurate. Furthermore, by acquiring the trajectory data of students' wearable devices and combining it with environmental data (such as weather and time period characteristics), the speed and safe distance of students approaching the water area are dynamically calculated, so that the early warning is not only based on historical data, but can also be adjusted according to the real-time situation, making it closer to the moment when the actual danger occurs. (2) By combining environmental data, this invention generates environmental adjustment parameters, which can effectively adjust the prediction of students approaching water areas and further improve the accuracy of early warning. Especially in severe weather or high-risk periods, the early warning system can react faster to prevent accidents. In addition, by relying on location and speed, it also combines the natural environment of the water area (such as hydrological characteristics) and the danger coefficient of surrounding facilities (such as guardrails, warning signs, etc.). The multi-dimensional risk assessment ensures a more comprehensive risk prediction and adjusts the early warning response according to the level of risk when necessary. (3) By precisely dividing the electronic fence into zones and grid areas, the present invention forms a closed protective area. The system can issue an alarm in time when students enter the boundary of the risky water area. The intelligent protective area design not only improves the coverage of monitoring, but also accurately judges the students' approach speed and location, thereby achieving automated safety protection. Attached Figure Description

[0016] Figure 1 This is a schematic flowchart of the overall method in one embodiment of the present invention; Figure 2 This is a schematic diagram of the overall system architecture in one embodiment of the present invention.

[0017] In the diagram: 1. Positioning and monitoring unit; 2. Spatial mapping unit; 3. Rate calculation unit; 4. Distance prediction unit; 5. Environmental weighting unit; 6. Water area early warning unit. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1, please refer to Figure 1 This invention provides a technical solution: a method for early warning of drowning prevention in water areas, comprising: S1. Based on the panoramic positioning monitoring module pre-deployed around the water area, determine the first positioning dataset corresponding to several grid early warning areas around the risky water area; S2. Based on multiple electronic fence zones and the first positioning dataset, generate electronic fence spatial mapping data corresponding to each electronic fence zone; wherein, the electronic fence zone uses the length of the corresponding grid warning area along the water boundary as the zone width, forming a closed protection area around the risky water area; S3. Obtain the location trajectory data of students in the vicinity of the risky water area within the first preset time period based on the wearable device, and determine the approach rate of students within the first preset time period based on the location trajectory data and the electronic fence spatial mapping data. S4. Predict the student's first distance change within a second preset time period based on the approach rate, wherein the second preset time period is after the first preset time period; S5. Obtain environmental data of the area surrounding the risky waters within a second preset time period. Based on the environmental data, determine the environmental adjustment parameters. Adjust the first distance change based on the environmental adjustment parameters to obtain the second distance change of the student within the second preset time period. S6. Obtain the regional hazard coefficient around the risky water area, adjust the second distance change amount according to the regional hazard coefficient, and obtain the target distance change amount of the student in the second preset time period; when the target distance change amount of the student is less than the preset safe distance, generate and send the warning information to the monitoring terminal.

[0020] It should be noted that the system first deploys panoramic positioning monitoring modules around the water area to determine the boundaries of the risky water area; based on the monitored location data, the area around the water area is divided into several grid areas (these areas can be considered as warning areas); the system divides the area around the water area according to the electronic fence zoning; the width of each electronic fence area is determined according to the length of the corresponding grid warning area, thus forming a closed protective area; then, the system generates "spatial mapping data" of the electronic fence areas, that is, establishes the correspondence between these electronic fence areas and actual geographical locations; the devices worn by students collect their activity data (such as location information) around the water area; in the first preset time period (e.g., within 5 minutes), the system calculates the student's approach rate to the danger zone using this data; the approach rate is the speed at which the student moves towards the danger zone; based on the approach rate in the first preset time period, the system predicts the change in the student's distance from the danger zone in the next second preset time period (e.g., after another 5 minutes); assuming the student's approach speed in the first 5 minutes is 0.5 meters / second, then in the next 5 minutes, the system predicts that the student will move 2.5 meters closer to the danger zone (0.5 meters / second × 5 seconds = ...). (2.5 meters); The system will also acquire environmental data around the water area (such as wind speed, water flow, etc.), which may affect the student's movement speed or trajectory; Based on the environmental data, the system will calculate an "environmental adjustment parameter", which will be used to adjust the previously predicted distance change; The system will also assess the regional danger coefficient around the water area, which reflects the degree of danger in the area; Based on the regional danger coefficient, the system will adjust the student's target distance change again; If, after the above adjustments, the target distance change obtained by the system is less than the set safe distance (for example, 10 meters), it means that the student is at risk of entering the dangerous area, and the system will generate a warning message and send it to the student's guardian.

[0021] In an optional embodiment, the panoramic positioning monitoring module consists of multiple BeiDou positioning base stations; the number of BeiDou positioning base stations is at least two, and they are evenly distributed along the perimeter of the water area to collect positioning data from various locations around the water area to obtain panoramic positioning data; wherein, the panoramic positioning data is used to characterize the satellite positioning signals around the water area.

[0022] It should be noted that the system requires at least two BeiDou positioning base stations, which are evenly distributed along the perimeter of the water area to ensure effective coverage of every location around the water. Data from multiple base stations can improve the accuracy and reliability of positioning. The data collected by each base station will be aggregated to obtain a "panoramic positioning dataset". This dataset records the satellite positioning signal strength, coordinate information, etc. of various locations around the water area. Panoramic positioning data can characterize the satellite signal coverage around the water area, providing basic data for subsequent risk area delineation, electronic fence generation, and student location monitoring.

[0023] In an optional embodiment, before determining the first location dataset corresponding to several grid warning areas around the risky water area based on a panoramic positioning monitoring module pre-deployed around the water area, the method further includes: The panoramic positioning monitoring module is assigned multiple surrounding areas corresponding to the water area specifications, as well as the grid size corresponding to each surrounding area. Each surrounding area corresponds to the surrounding area of ​​a specific water area. The water area includes at least one of the following: shoals, steep slopes, deep water edges, and docks. Based on the size of each grid, the corresponding surrounding areas are divided into grids to obtain several grid warning areas.

[0024] It should be noted that the system first uses a panoramic positioning monitoring module to collect geographical information about the water area, obtaining the specifications of various parts of the water area (such as length, width, and depth). Based on this information, the system divides the water area into multiple perimeter zones and determines the corresponding grid division method and grid size according to the characteristics of each zone (such as shoals, steep slopes, and deep water areas). Different parts of the water area (such as shoals, steep slopes, deep water edges, and docks) have different impacts on water safety. Therefore, the system designs different division zones according to the characteristics of each part of the water area. Shallow water levels may require larger warning zones because the water flow in shallow areas is slow, but it can also be dangerous, especially when the water level changes significantly. Steep slopes experience rapid water level changes and may require more grid divisions to monitor changes in water flow in a timely manner. Due to the potentially high danger of deep water areas, the system sets smaller grid sizes in the edge areas for more precise monitoring. The area around docks requires special attention and may require special division zones and grid sizes to prevent students from approaching dangerous areas. Based on the size of each division zone, the system further divides these areas into multiple grids. Each grid will become an independent warning zone, and the system will issue an alarm when a student enters these zones.

[0025] In an optional embodiment, based on multiple electronic fence zones and a first positioning dataset, electronic fence spatial mapping data corresponding to each electronic fence zone is generated, including: Determine the boundary range of the electronic fence zone, construct the electronic fence spatial framework based on the boundary range, and divide the electronic fence spatial framework into zones to obtain each electronic fence zone; Label each electronic fence zone with a spatial index, determine the zone number corresponding to each electronic fence zone based on the spatial index, and obtain the initial location association list; For each location point in the first location dataset, a spatial location range is created, and the coordinate boundaries of the spatial location range are determined. Based on the coordinate boundaries of the spatial location range, the electronic fence partition number where the coordinate boundary is located is determined. The initial location association list of the corresponding electronic fence partition is updated to obtain the updated location association list. Specifically, updating the initial location association list of the corresponding electronic fence partition to obtain the updated location association list includes: Obtain the coordinates of the positioning point in the water coordinate system, and determine the coordinate boundaries of the spatial positioning range based on the coordinates. Calculate the numbering range of the electronic fence zone corresponding to each boundary point in the direction of the water boundary and the vertical boundary based on the coordinate boundary of the spatial positioning range; Calculate the number of the electronic fence partition corresponding to each boundary point based on the number range, update the initial location association list of the electronic fence partition with the corresponding number, store the location point coordinates and timestamp data associated with the electronic fence partition, and obtain the updated location association list. Based on the partition attributes of each electronic fence partition, the updated location association list, and the characteristics of the grid warning area, the electronic fence spatial mapping data corresponding to each electronic fence partition is determined.

[0026] It's important to clarify that determining the boundaries of the electronic fence is crucial. For example, in a water area, the boundaries might be determined based on the actual geographical boundaries of the water or the specific area to be monitored (such as a lake or river). Based on these boundaries, a spatial framework for the electronic fence is constructed. This spatial framework is an abstract grid or region partitioning model used to divide a large area into multiple smaller zones for monitoring each zone. For example, suppose an electronic fence is to be set up in a lake area with a total area of ​​1000m². 2The system divides the area into 100 small zones, each 10m x 10m in size, resulting in 100 electronic fence zones, each with clearly defined boundaries. Each electronic fence zone is assigned a spatial index (number) to identify its location and extent. These indices determine which zone corresponds to which physical area. The system retrieves the coordinates of each location point from the positioning dataset and determines its spatial extent based on these coordinates. Each location point corresponds to a specific electronic fence zone number within the spatial framework. The system determines the electronic fence zone number to which the location point belongs based on its coordinates and then updates the "initial positioning association list" for that zone. The initial positioning association list contains the coordinates and timestamps of all location points associated with each zone, allowing for real-time updates of positioning information within each zone. To further refine the positioning, the system calculates the number range in the water coordinate system based on the coordinate boundaries of each location point's spatial positioning range. This means the system not only provides the location but also tracks the zone number in both the vertical and horizontal directions. For example, for a location point (30m, ... (40m), the range of the electronic fence partition number where this point is located may be (3, 4), indicating that the point belongs to area 3 of the water area (the number in the water area coordinate system); after the location association list is updated, the system will generate spatial mapping data of the electronic fence based on the attributes of each electronic fence partition, the updated location association list and the characteristics of the grid warning area; this mapping data can be used for visualization and warning analysis.

[0027] In an optional embodiment, determining the student's approach rate within a first preset time period based on location trajectory data and electronic fence spatial mapping data includes: Based on the water boundaries of each electronic fence zone, the electronic fence zone number to which each trajectory point in the location trajectory data belongs is determined by a spatial point-surface matching algorithm. Calculate the straight-line distance from each trajectory point to the nearest water boundary, where the straight-line distance is obtained by calculating the Euclidean distance between the coordinates of the trajectory point and the boundary coordinates of the water boundary; Add a timestamp, the electronic fence zone number to which it belongs, and the straight-line distance to the water boundary to each trajectory point, and arrange them in chronological order to obtain a sequence of trajectory points with zone identifiers; Extract position change feature vectors from the trajectory point sequence with partition identifiers. The position change feature vectors include the trajectory point movement direction angle, the time interval between adjacent trajectory points, and the distance change across partition boundaries. The movement direction angle in the position change feature vector is compared with the preset water area pointing angle to calculate the direction consistency coefficient. When the direction consistency coefficient is greater than the preset direction threshold, it is determined to be a valid approach movement. For a pair of trajectory points that are effectively approaching each other, calculate the change in the straight-line distance between adjacent trajectory points and compare it with the corresponding time interval to obtain the instantaneous approach rate; The preset water distance attenuation coefficient is multiplied by the instantaneous approach rate to obtain the corrected instantaneous approach rate sequence, wherein the water distance attenuation coefficient increases as the distance to the water boundary decreases; Calculate the weighted average of all corrected instantaneous approach rates within the first preset time period to obtain the student's average approach rate within the first preset time period. The weighting weight is inversely proportional to the distance from the corresponding trajectory point to the water boundary.

[0028] It should be noted that each trajectory point has coordinates; through spatial point-to-polygon matching algorithms (e.g., point-to-polygon matching), it can be determined which electronic fence zone each trajectory point belongs to; the zone consists of the boundary of the water area and the defined electronic fence area; for example, suppose there is an electronic fence area (such as a lake), the lake area is divided into multiple zones (e.g., zone A, zone B); the coordinates of each trajectory point will be matched with these zones to determine which zone it belongs to; for example, trajectory point (30, 40) is located in zone A, while trajectory point (50, 60) is located in zone B; the straight-line distance from each trajectory point to the boundary of the water area is calculated, usually using Euclidean distance calculation; for example, suppose trajectory point (30, 40) is closest to the boundary of the water area (e.g., the boundary of the lake) at point (35, 40). If 40 is 5 meters away, then the straight-line distance from this point to the water boundary is 5 meters. Each trajectory point needs to be accompanied by a timestamp (recording the time the location occurred), its partition number (e.g., region A, region B), and its distance to the water boundary. All trajectory points are arranged in chronological order to form a sequence of trajectory points with partition identifiers. For example, suppose that at a certain time (e.g., 14:30 on August 15, 2023), trajectory point (30, 40) belongs to region A and its straight-line distance to the water boundary is 5 meters. Then, the trajectory point with partition identifiers is: (30, 40, 14:30, Area A, 5 meters); Extract position change features from the trajectory point sequence with zone identifiers, mainly including: Movement direction angle: the direction between two adjacent trajectory points; usually expressed as an angle; Time interval: the time difference between two adjacent trajectory points; Distance change across zone boundaries: if a trajectory point crosses a zone boundary, record the distance change; Compare the movement direction angle with the preset water direction angle; If the two directions are close, calculate the direction consistency coefficient; When the direction consistency coefficient is greater than a certain preset threshold, it indicates that the trajectory point is effectively approaching the water boundary; For example: Assuming the preset water direction angle is 0 degrees (pointing to the water boundary), and the direction angle of the trajectory point is 5 degrees, calculate the direction... Consistency coefficient; if the consistency coefficient is greater than a certain preset threshold (e.g., 0.9), the trajectory point is considered to be effectively approaching; for a pair of trajectory points that have effectively approached, the change in the straight-line distance between adjacent trajectory points is calculated and compared with the time interval to obtain the instantaneous approach rate; the instantaneous approach rate represents the rate at which the object approaches the water boundary; the instantaneous approach rate is multiplied by a preset water distance attenuation coefficient; the water distance attenuation coefficient increases as the distance to the water boundary decreases; thus, the rate of approaching the water boundary is enhanced; calculate the weighted average of all corrected instantaneous approach rates within a preset time period; the weight is inversely proportional to the distance of the trajectory point from the water boundary, and the closer the trajectory point is, the greater its weight.

[0029] In an optional embodiment, extracting position change feature vectors from a sequence of trajectory points with partition identifiers includes: Calculate the coordinate difference between two adjacent trajectory points, and calculate the movement direction angle of the trajectory point using the arctangent function, where the movement direction angle is based on the direction pointing to the water boundary as 0 degrees. Calculate the timestamp differences between adjacent trajectory points, generate a time interval sequence, and calculate the mean and variance of the time intervals; Identify track point pairs that cross the boundaries of electronic fence zones in a sequence of track points, and calculate the distance change of the track point pairs on both sides of the electronic fence zone boundaries; By combining the movement direction angle, statistical parameters of the time interval sequence, and changes in cross-regional distance, a position change feature vector is obtained.

[0030] It should be noted that the coordinate difference between two adjacent trajectory points represents the direction of the object's movement; the arctangent function is used to calculate the direction angle of movement of the trajectory point; this angle is relative to the direction of the water boundary, which is set to 0 degrees (i.e., the positive direction); if the trajectory point moves towards the water boundary, the angle is close to 0 degrees; if it moves away from the water boundary, the angle is larger; for example, suppose there are two trajectory points, point A (coordinates: 30, 40) and point B (coordinates: 35, 40). 45) The direction of movement between these two points points towards the water boundary; if the angle between the direction of movement from point A to point B and the water boundary is close to 0 degrees, then the direction angle may be 0 degrees (indicating that the object is moving along the boundary); if it deviates from the boundary direction, the direction angle may be 45 degrees or other values; calculate the time difference between adjacent trajectory points to generate a time interval sequence; then, calculate the mean and variance of these time intervals; the mean indicates that the time between trajectory points is roughly consistent, while the variance reflects the fluctuation of the time interval; if the variance is large, it indicates that the object's speed changes significantly; if the variance is small, it indicates that the speed changes slightly; for example: suppose there are three adjacent trajectory points with timestamps of 14:00:00, 14:00:05, and 14:00:10; the time intervals are 5 seconds and 5 seconds respectively; the mean is 5 seconds, and the variance is 0 (because the time interval is consistent); if the latter time interval is 10 seconds, the variance will increase, indicating that the speed has changed; in the electronic fence, different areas The system is divided into multiple zones. By analyzing the trajectory point sequence, it is determined which trajectory points cross the zone boundaries. Trajectory points that cross boundaries require special attention, and their distance changes between the two zones are calculated. This allows assessment of whether an object is moving closer to or further away from certain specific areas. For example, suppose there is a trajectory point in zone A (point A) and another trajectory point in zone B (point B). If point A is located on the edge of zone A and point B is located on the other side of zone B, then these two trajectory points have crossed the boundaries of the electronic fence. The distance change between them is calculated to assess whether the object has moved significantly between the two zones. Features such as the movement direction angle, statistical parameters of the time interval (mean and variance), and the distance change across zones are combined to generate a position change feature vector. This feature vector integrates the movement direction, speed change (through statistical analysis of the time interval), and whether the trajectory point crosses the zone boundary, thus providing rich information for subsequent analysis (such as behavior prediction, path planning, etc.).

[0031] In one alternative embodiment, the environmental data includes weather conditions and time-of-day characteristics; Based on environmental data, environmental regulation parameters are generated, including: Based on the weather conditions, weather impact parameters are generated. These parameters are positively correlated with the severity of severe weather and are the ratio of the actual weather risk value to the preset weather safety threshold. Based on the characteristics of the time period, time period impact parameters are generated. Among them, the time period impact parameters are positively correlated with the degree of correlation of dangerous time periods. The time period impact parameters are the ratio of the actual time period risk value to the preset time period safety threshold. Environmental regulation parameters are generated based on weather impact parameters and time-period impact parameters; among them, the generation of environmental regulation parameters based on weather impact parameters and time-period impact parameters includes: Determine the first weight value for the weather impact parameter; determine the second weight value for the time period impact parameter; The weather impact parameters are weighted together with the first weight value to obtain the first weighting coefficient; The time period influence parameter is weighted by the second weight value to obtain the second weighting coefficient; The first weighting coefficient and the second weighting coefficient are combined to generate environmental regulation parameters.

[0032] It should be noted that weather impact parameters reflect the degree of weather's influence on the environment; severe weather (such as storms, heavy snow, etc.) will increase the value of weather impact parameters. Specifically, weather impact parameters are calculated by comparing the current actual weather risk value with a preset safety threshold; that is, the closer the current weather conditions are to or exceed the safety threshold, the higher the weather impact parameter. Time-of-day impact parameters measure the probability of dangerous events occurring within a specific time period; dangerous time periods (such as late at night or peak traffic hours) usually have higher risks, therefore the correlation between time-of-day impact parameters and dangerous time periods is positive. Similar to weather impact parameters, time-of-day impact parameters are calculated by comparing the actual time-of-day risk value with a preset safety threshold; for example: assuming a specific time period (e.g., 8 pm to 10 pm) is considered a high-risk time period, and the preset safety threshold is a time-of-day risk value of 40; if the actual time-of-day risk value is 45, then the time-of-day impact parameter is 45 / 40. =1.125 indicates that the risk during this period is slightly higher than the preset safety standard; the environmental adjustment parameter is a parameter that combines the effects of weather and time period, and is used to measure the overall safety status of the environment; in order to generate this parameter, it is necessary to consider the importance of each influencing factor and assign it a corresponding weight; usually, the impact of weather and time period may have different degrees of influence on the environment, so we assign them different weight values; two weighting coefficients are calculated by weighting, and finally they are combined to generate the final environmental adjustment parameter.

[0033] In an optional embodiment, obtaining the regional hazard factor around the risky water area includes: Acquire hydrological characteristic data of risky waters and generate natural hazard coefficients based on the hydrological characteristic data; Obtain monitoring data of surrounding facilities in high-risk waters, and generate facility hazard coefficients based on the monitoring data. The monitoring data of surrounding facilities includes at least one of the following: integrity of guardrails, clarity of warning signs, and completeness of rescue equipment. The regional risk coefficient of the risky water area is generated by weighted summation of the natural risk coefficient and the facility risk coefficient.

[0034] It should be noted that hydrological characteristic data reflects the natural conditions of a body of water, such as water flow velocity, water depth, and wave intensity. These characteristics directly affect the degree of danger of the water body. Extremely rapid currents or excessive water depths may lead to a higher natural risk. The natural risk coefficient is calculated based on this hydrological data and reflects the risks posed by the natural environment of the water body. The generated natural risk coefficient based on this data may be relatively high; for example, a value of 0.8 indicates a high risk from the natural environment of the water body. Facilities around the water body (such as guardrails, warning signs, and rescue equipment) play a crucial role in ensuring safety. If these facilities are incomplete, difficult to identify, or inadequate... This may increase the risk; the facility hazard factor is assessed based on the condition of at least one critical facility; common facility inspection items include the integrity of guardrails, the clarity of warning signs, and the availability of rescue equipment; in order to comprehensively assess the hazard of the water area, it is necessary to weight and sum the natural hazard factor and the facility hazard factor; different factors may have different effects on the overall hazard, so it is necessary to assign a weight to each factor; for example, the natural conditions of the water area may be more important than the condition of the facilities, so the natural hazard factor may have a higher weight; through this weighted summation method, a comprehensive "regional hazard factor" is generated, which reflects the risk level of the entire water area.

[0035] In an optional embodiment, adjusting the first distance change based on environmental adjustment parameters to obtain the student's second distance change over a second preset time period includes: The second distance change is obtained by weighting the first distance change with environmental adjustment parameters. The second distance change is adjusted based on the regional risk factor to obtain the student's target distance change within the second preset time period, including: The target distance change is obtained by weighting the second distance change with the regional risk coefficient.

[0036] It should be noted that the first distance change may represent the actual change in a student's behavior over a certain period of time (e.g., the change in the student's distance from the target location); environmental adjustment parameters may be external factors such as temperature, humidity, noise, traffic conditions, etc.; these factors may affect the student's performance; therefore, the first distance change needs to be combined with these environmental factors and weighted to obtain an adjusted value, called the "second distance change"; for example, suppose the first distance change of a student from the starting point to the target location is 10 meters; if there is a strong wind in the environment (e.g., a wind speed of 10 meters per second), the student's walking speed may be affected; therefore, when calculating the second distance change, we will consider the impact of wind speed on the student (e.g., wind speed reduces the student's actual walking speed by 10%); after weighted adjustment, the second distance change may be 9 meters; the regional hazard coefficient reflects the risk level of the current environment or area (e.g., high traffic accident rate, unstable weather, etc.); this coefficient will affect the student's behavioral changes because high-risk areas may cause students to take more precautions, slow their actions, or take safer routes when reaching the target; therefore, the regional hazard coefficient The second distance change will be adjusted to ensure that students' behavior meets safety requirements. For example, suppose a student is crossing a busy area (e.g., near a street) with a high risk factor (let's say 0.8). In this case, the student's walking speed in the area will be affected; they may choose to detour or slow down to ensure safety. Therefore, the second distance change (e.g., 9 meters) will be adjusted according to the area's risk factor, for example, to 7.2 meters, indicating that the student's target walking distance in the high-risk area is reduced to ensure safety. In the final calculation, the second distance change and the area's risk factor are combined again, and the final target distance change is obtained through weighting. This target value reflects the actual change in the student's target behavior after considering environmental and safety risks. For example, suppose the second distance change is 9 meters and the area's risk factor is 0.8. After weighting, the target distance change may be further reduced. For example, according to the weighted result, the target distance change becomes 7.5 meters, indicating that the student should adjust their target behavior to reach a distance of 7.5 meters within the preset time period based on the actual situation and environmental safety factors.

[0037] Example 2, please refer to Figure 2 This invention provides a technical solution: a water area drowning prevention early warning system, applicable to the aforementioned water area drowning prevention early warning method, comprising: Positioning monitoring unit 1 is used to determine the first positioning dataset corresponding to several grid early warning areas around the risky water area based on the panoramic positioning monitoring modules pre-deployed around the water area. The spatial mapping unit 2 is used to generate spatial mapping data of each electronic fence partition based on multiple electronic fence partitions and the first positioning dataset; wherein, the electronic fence partition uses the length of the corresponding grid warning area along the water boundary as the partition width to form a closed protection area around the risky water area; The rate calculation unit 3 is used to obtain the location trajectory data of students in the vicinity of the risk water area within a first preset time period based on the wearable device, and to determine the approach rate of students within the first preset time period based on the location trajectory data and the electronic fence spatial mapping data. Distance prediction unit 4 is used to predict the first distance change of a student within a second preset time period based on the approach rate, wherein the second preset time period is after the first preset time period. Environmental weighting unit 5 is used to acquire environmental data around the risky water area during a second preset time period, determine environmental adjustment parameters based on the environmental data, adjust the first distance change based on the environmental adjustment parameters, and obtain the second distance change of the student during the second preset time period. The water area early warning unit 6 is used to obtain the regional hazard coefficient around the risky water area, adjust the second distance change amount according to the regional hazard coefficient, and obtain the target distance change amount of the student in the second preset time period; when the target distance change amount of the student is less than the preset safe distance, an early warning information is generated and sent to the monitoring terminal.

[0038] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A method for early warning of drowning prevention in water areas, characterized in that, include: Based on the panoramic positioning monitoring modules pre-deployed around the water area, the first positioning dataset corresponding to several grid early warning areas around the risky water area is determined; Based on multiple electronic fence zones and the first positioning dataset, electronic fence spatial mapping data corresponding to each electronic fence zone is generated; wherein, the electronic fence zone uses the length of the corresponding grid warning area along the water boundary as the zone width to form a closed protective area around the risky water area; The location trajectory data of students within the vicinity of the risky water area is obtained by the wearable device within a first preset time period. Based on the location trajectory data and the electronic fence spatial mapping data, the approach rate of the students within the first preset time period is determined. The student's first distance change within a second preset time period is predicted based on the approach rate, wherein the second preset time period is after the first preset time period; Obtain environmental data of the area surrounding the risky waters during the second preset time period; determine environmental adjustment parameters based on the environmental data; adjust the first distance change based on the environmental adjustment parameters to obtain the second distance change of the student during the second preset time period. The risk coefficient of the area surrounding the risky water area is obtained, and the second distance change is adjusted according to the risk coefficient to obtain the target distance change of the student within the second preset time period; when the target distance change of the student is less than the preset safe distance, an early warning message is generated and sent to the monitoring terminal.

2. The method for early warning of drowning prevention in water areas according to claim 1, characterized in that, The panoramic positioning monitoring module consists of multiple BeiDou positioning base stations; at least two BeiDou positioning base stations are set up and evenly distributed around the water area to collect positioning data from various locations around the water area to obtain panoramic positioning data; wherein, the panoramic positioning data is used to characterize the satellite positioning signals around the water area.

3. The method for early warning of drowning prevention in water areas according to claim 2, characterized in that, Before determining the first location dataset corresponding to several grid warning areas around the risky water area based on the panoramic positioning monitoring module pre-deployed around the water area, the method further includes: The panoramic positioning monitoring module is used to determine multiple surrounding areas corresponding to the water area specifications, as well as the grid size corresponding to each of the surrounding areas; wherein each of the surrounding areas corresponds to the surrounding area of ​​each water area; the water area includes at least one of shallow water, steep slope, deep water edge, and dock; Based on the grid size, the corresponding surrounding area is divided into grids to obtain several grid warning areas.

4. The water area drowning prevention early warning method according to claim 3, characterized in that, Based on multiple electronic fence zones and the first positioning dataset, electronic fence spatial mapping data corresponding to each of the electronic fence zones is generated, including: Determine the boundary range of the electronic fence zone, construct the electronic fence spatial framework based on the boundary range, and divide the electronic fence spatial framework into zones to obtain each electronic fence zone; A spatial index is labeled for each of the electronic fence zones, and the zone number corresponding to each of the electronic fence zones is determined according to the spatial index to obtain an initial location association list; For each location point in the first location dataset, a spatial location range is created, and the coordinate boundaries of the spatial location range are determined. Based on the coordinate boundaries of the spatial location range, the electronic fence partition number where the coordinate boundaries are located is determined. The initial location association list of the corresponding electronic fence partition is updated to obtain an updated location association list. The updating of the initial location association list of the corresponding electronic fence partition to obtain the updated location association list includes: Obtain the coordinates of the positioning point in the water coordinate system, and determine the coordinate boundaries of the spatial positioning range based on the coordinates. Calculate the numbering range of the electronic fence zone corresponding to each boundary point in the direction of the water boundary and the vertical boundary based on the coordinate boundary of the spatial positioning range. Calculate the number of the electronic fence partition corresponding to each boundary point according to the number range, update the initial location association list of the electronic fence partition with the corresponding number, store the location point coordinates and timestamp data associated with the electronic fence partition, and obtain the updated location association list. Based on the partition attributes of each electronic fence partition, the updated location association list, and the characteristics of the grid warning area, the electronic fence spatial mapping data corresponding to each electronic fence partition is determined.

5. A method for early warning of drowning prevention in water areas according to claim 4, characterized in that, Determining the student's approach rate within the first preset time period based on the location trajectory data and the electronic fence spatial mapping data includes: Based on the water boundaries of each electronic fence zone, the electronic fence zone number to which each trajectory point in the location trajectory data belongs is determined by a spatial point-surface matching algorithm. Calculate the straight-line distance from each trajectory point to the nearest water boundary, wherein the straight-line distance is obtained by calculating the Euclidean distance between the coordinates of the trajectory point and the boundary coordinates of the water boundary; Add a timestamp, the electronic fence zone number to which it belongs, and the straight-line distance to the water boundary to each trajectory point, and arrange them in chronological order to obtain a sequence of trajectory points with zone identifiers; Extract position change feature vectors from the trajectory point sequence with partition identifiers, wherein the position change feature vectors include the trajectory point movement direction angle, the time interval between adjacent trajectory points, and the distance change across partition boundaries; The movement direction angle in the position change feature vector is compared with the preset water area pointing angle, and the direction consistency coefficient is calculated. When the direction consistency coefficient is greater than the preset direction threshold, it is determined to be a valid approach movement. For the effective approach movement trajectory point pair, calculate the change in straight-line distance between adjacent trajectory points and compare it with the corresponding time interval to obtain the instantaneous approach rate; The preset water distance attenuation coefficient is multiplied by the instantaneous approach rate to obtain the corrected instantaneous approach rate sequence, wherein the water distance attenuation coefficient increases as the distance to the water boundary decreases; Calculate the weighted average of all the corrected instantaneous approach rates within the first preset time period to obtain the average approach rate of the student within the first preset time period, wherein the weighting weight is inversely proportional to the distance from the corresponding trajectory point to the water boundary.

6. A method for early warning of drowning prevention in water areas according to claim 5, characterized in that, Extracting position change feature vectors from the sequence of trajectory points with partition identifiers includes: Calculate the coordinate difference between two adjacent trajectory points, and calculate the movement direction angle of the trajectory point using the arctangent function, wherein the movement direction angle is based on the direction pointing to the water boundary as 0 degrees. Calculate the timestamp differences between adjacent trajectory points, generate a time interval sequence, and calculate the mean and variance of the time intervals; Identify the pairs of trajectory points that cross the boundaries of the electronic fence zones in the trajectory point sequence, and calculate the distance change of the trajectory point pairs on both sides of the electronic fence zone boundaries; The position change feature vector is obtained by combining the statistical parameters of the movement direction angle, the time interval sequence, and the cross-regional distance change.

7. A method for early warning of drowning prevention in water areas according to claim 6, characterized in that, The environmental data includes weather conditions and time-of-day characteristics; Based on the environmental data, environmental regulation parameters are generated, including: Based on the weather conditions, weather impact parameters are generated, wherein the weather impact parameters are positively correlated with the severity of severe weather, and the weather impact parameters are the ratio of the actual weather risk value to the preset weather safety threshold. Based on the time period characteristics, a time period impact parameter is generated, wherein the time period impact parameter is positively correlated with the degree of correlation of dangerous time periods, and the time period impact parameter is the ratio of the actual time period risk value to the preset time period safety threshold; Environmental regulation parameters are generated based on the weather impact parameters and the time period impact parameters; wherein, generating environmental regulation parameters based on the weather impact parameters and the time period impact parameters includes: Determine the first weight value corresponding to the weather impact parameter; determine the second weight value corresponding to the time period impact parameter; The weather impact parameters are weighted together with the first weight value to obtain the first weighting coefficient; The time period influence parameter is weighted by the second weight value to obtain the second weighting coefficient; The first weighting coefficient and the second weighting coefficient are fused to generate environmental adjustment parameters.

8. A method for early warning of drowning prevention in water areas according to claim 7, characterized in that, Obtaining the regional hazard coefficient around the aforementioned risky water area includes: Obtain hydrological characteristic data of the risky water area, and generate a natural hazard coefficient based on the hydrological characteristic data; Obtain monitoring data of surrounding facilities in the risky water area, and generate facility hazard coefficients based on the monitoring data. The monitoring data of surrounding facilities includes at least one of the following: integrity of guardrails, clarity of warning signs, and completeness of rescue equipment. The regional risk coefficient of the risky water area is generated by weighted summation of the natural risk coefficient and the facility risk coefficient.

9. A method for early warning of drowning prevention in water areas according to claim 8, characterized in that, Adjusting the first distance change based on the environmental adjustment parameters yields the second distance change for the student within the second preset time period, including: The first distance change is weighted with the environmental adjustment parameter to obtain the second distance change; The second distance change is adjusted based on the regional risk coefficient to obtain the target distance change of the student within the second preset time period, including: The target distance change is obtained by weighting the second distance change with the area hazard coefficient.

10. A water area drowning prevention early warning system, applicable to the water area drowning prevention early warning method according to any one of claims 1-9, characterized in that, include: The positioning and monitoring unit is used to determine the first positioning dataset corresponding to several grid warning areas around the risky water area based on the panoramic positioning and monitoring modules pre-deployed around the water area. The spatial mapping unit is used to generate electronic fence spatial mapping data corresponding to each of the multiple electronic fence partitions and the first positioning dataset; wherein, the electronic fence partition uses the length of the corresponding grid warning area along the water boundary as the partition width to form a closed protection area around the risky water area. The rate calculation unit is used to obtain the location trajectory data of the student in the vicinity of the risky water area within a first preset time period based on the wearable device, and to determine the student's approach rate within the first preset time period based on the location trajectory data and the electronic fence spatial mapping data. A distance prediction unit is used to predict the first distance change of the student within a second preset time period based on the approach rate, wherein the second preset time period is after the first preset time period; An environmental weighting unit is used to acquire environmental data around the risky water area during the second preset time period, determine environmental adjustment parameters based on the environmental data, adjust the first distance change amount based on the environmental adjustment parameters, and obtain the second distance change amount of the student during the second preset time period. The water area early warning unit is used to obtain the regional danger coefficient around the risky water area, adjust the second distance change amount according to the regional danger coefficient, and obtain the target distance change amount of the student within the second preset time period; when the target distance change amount of the student is less than the preset safe distance, an early warning information is generated and sent to the monitoring terminal.

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