A geosot airspace graph data dynamic updating system and method

Abstract

The present application relates to airspace graph updating, in particular to a kind of GeoSOT airspace graph data dynamic updating system and method, space-time reference layer, the multi-source grid data stream of pry dress unit output is as input, unified space-time engine is output using GeoSOT unified space-time mark;Dynamic updating engine layer, on the basis of unified space-time mark, solve the update conflict problem of multiple events concurrent using priority scheduling algorithm based on multiple event collaborative triggering, using adaptive neighborhood radius algorithm based on event influence intensity to optimize local update range, establish the association model of version and traffic light state, support the accurate tracing of traffic light state, AI prediction result is as the input of event trigger update, realize the closed-loop control of " prediction-update-feedback ";The present application can effectively overcome the defects of multi-source data access standardization, single event trigger mechanism, version management and traffic light state evolution disjoint, AI prediction and dynamic updating do not form closed loop, local update range fixed.

Description

Technical Field

[0001] This invention relates to airspace map updating, specifically to a GeoSOT airspace map data dynamic updating system and method. Background Technology

[0002] With the rapid development of the low-altitude economy, the number of low-altitude aircraft such as drones has exploded. The transformation of airspace management from traditional static and extensive management to dynamic, refined, and digital management has become an inevitable trend. The release of GeoSOT Earth Grid and its national standard GB / T 39409-2020 "BeiDou Grid Location Code" provides a unified spatial reference framework for the digital representation of low-altitude airspace.

[0003] However, low-altitude airspace is a highly dynamic environment—aircraft move in real time, weather conditions change rapidly, and temporary traffic control events occur frequently. Airspace map data must have the capability of "real-time updates and dynamic response" to meet the actual needs of low-altitude traffic control. Existing GeoSOT airspace map dynamic update technology mainly suffers from the following problems: 1) Lack of standardization in multi-source data access leads to complex system integration. While existing technologies have enabled the mapping of multi-source data to the GeoSOT grid, the data access layer requires customized interface development for different devices (including radar, spectrum analyzers, photoelectric cameras, weather stations, etc.). The wide variety of sensing devices, different communication protocols, and inconsistent data formats result in long system deployment cycles and high maintenance costs. The root cause of the problem lies in the lack of standardized "hardware skid-mounted" design, the failure to integrate sensing devices with edge computing modules, and the absence of BeiDou grid codes embedded in the hardware layer as a "digital link." 2) The event triggering mechanism is too simple to handle complex collaborative scenarios. Existing event-triggered updates are mainly based on a single event type (such as the entry of a new target). However, in actual airspace management, multiple events often occur concurrently (such as multiple drones entering at the same time, sudden weather changes and temporary control occurring at the same time). The single event triggering mechanism cannot handle the priority and coordination relationship between events, which can easily lead to update conflicts or priority inversion. The root cause of the problem is the lack of a multi-event collaborative triggering model and priority scheduling algorithm, and the lack of a defined event priority calculation function. 3) Version management is disconnected from the evolution of traffic light status. While existing technologies support version management, their version records are mainly for data backtracking and are not deeply linked to the historical evolution of airspace traffic light status. When a security incident occurs, it is difficult to quickly trace "what the traffic light status of a certain grid was at a specific point in time". The root cause of the problem is the lack of a mechanism to link version with changes in traffic light status. 4) AI prediction and dynamic updates have not formed a closed loop. Some existing technologies mention "combining AI prediction to optimize airspace resource allocation", but the AI ​​prediction results (such as trajectory prediction results, conflict prediction results, etc.) do not directly participate in the triggering of the dynamic update mechanism. For example, when AI predicts that a conflict will occur in 5 seconds, existing technologies will not trigger the grid status pre-update in advance to actively avoid the conflict. The root cause of the problem is the lack of a closed-loop control mechanism of "prediction-update-feedback". 5) Fixed local update range, lacking adaptive capability. Existing technologies mainly use fixed neighborhood radii (such as 26 neighborhoods) for local updates, without considering the propagation characteristics of changes in airspace state. Some events (such as designating a large no-fly zone) should trigger a larger range of grid state updates, while minor events (such as updating the position of a single UAV) only require a small range of grid state updates. The root of the problem is that the neighborhood radius is set to a fixed value and is not effectively correlated with the intensity and type of the event's impact. Summary of the Invention

[0004] (a) Technical problems to be solved To address the aforementioned shortcomings of existing technologies, this invention provides a GeoSOT spatial map data dynamic update system and method, which can effectively overcome the deficiencies of existing technologies, such as insufficient standardization of multi-source data access, a single event triggering mechanism, disconnect between version management and traffic light state evolution, lack of closed loop between AI prediction and dynamic update, and fixed local update range.

[0005] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: A GeoSOT spatial map data dynamic update system, the system architecture includes: The skid-mounted sensing layer, based on a hardware skid-mounted design, integrates sensing devices and edge computing modules into a standardized skid-mounted unit. The spatiotemporal reference layer takes the multi-source grid data stream output by the skid-mounted unit as input and uses the GeoSOT unified spatiotemporal engine to output a unified spatiotemporal identifier. The dynamic update engine layer, based on a unified spatiotemporal identifier, adopts a priority scheduling algorithm based on multi-event collaborative triggering to solve the update conflict problem when multiple events are concurrent, adopts an adaptive neighborhood radius algorithm based on the event impact intensity to optimize the local update range, establishes a correlation model between version and traffic light status, supports accurate traceability of traffic light status, and uses AI prediction results as input for event-triggered updates to achieve closed-loop control of "prediction-update-feedback". The traffic light control layer manages the grid status and presents the traffic light status in a visual format. The application service layer, based on a unified grid interface, supports plug-and-play application services including route planning, conflict warning, and emergency response.

[0006] Preferably, the skid-mounted sensing layer includes a skid-mounted unit, which includes: Sensing devices are configured with corresponding sensors according to their type, including radar skid-mounted units, spectrum skid-mounted units, and optoelectronic skid-mounted units, which integrate radar, spectrum analyzer, and optoelectronic camera into skid-mounted units respectively. Edge computing module with built-in BeiDou grid code processing chip; The communication module supports 4G / 5G / fiber optic communication; The timing module supports BeiDou high-precision timing.

[0007] Preferably, the skid-mounted unit performs the following processing while acquiring data: 1) Obtain the device's BeiDou grid code indicating the current location of the sensing device; 2) Obtain the relative position of the target, get the corresponding latitude and longitude coordinates, and convert them into the target's BeiDou grid code; 3) Obtain the BeiDou time synchronization timestamp; 4) Output standardized multi-source grid data streams; Through a hardware skid-mounted design, the sensing data is grid-encoded and spatiotemporally aligned at the source, enabling rapid mapping between "physical space and digital space". The application service layer is plug-and-play based on a unified grid interface.

[0008] Preferably, the dynamic update engine layer includes: The event triggering and priority scheduling module uses a priority scheduling algorithm based on multi-event collaborative triggering to solve the update conflict problem when multiple events are concurrent; The adaptive local update module uses an adaptive neighborhood radius algorithm based on the intensity of event impact to optimize the local update range; The version management module establishes a correlation model between versions and traffic light status, supporting accurate tracing of traffic light status. The AI ​​prediction and closed-loop feedback module uses the AI ​​prediction results as input for event-triggered updates, achieving closed-loop control of "prediction-update-feedback".

[0009] Preferably, the event triggering and priority scheduling module employs a priority scheduling algorithm based on multi-event collaborative triggering to resolve update conflicts during concurrent events, including: S11. Define the set of event types: including new target entry events, target location update events, target departure events, temporary control events, weather change events, and AI prediction events; S12, Event Priority Calculation: The priority P of the i-th eventi It is determined by the following three factors: 1) Urgency level The highest level of control is determined by the type of event, with temporary control events being the most severe. 2) Time urgency T i The smaller the time difference between the time the event occurred and the current time, the higher the score. 3) Spatial influence range R i The number of affected grid cells is determined by the number of grid cells affected. The priority P of the i-th event i Calculate using the following formula: ; in, The value should be a very small positive number to avoid a denominator of 0. , , All are weighting coefficients; S13, Trigger Update: When the priority P of the i-th event... i Greater than the priority threshold θ trigger When the time comes, an update is triggered; S14. Multi-event collaborative processing: For multiple events that trigger updates simultaneously, a priority queue is used for scheduling. Arrange all events in descending order of priority, and process the grid updates triggered by each event in turn.

[0010] Preferably, the adaptive local update module optimizes the local update range using an adaptive neighborhood radius algorithm based on the event impact intensity, including: S21. Quantification of the intensity of event impact: 1) For the target location update event, its event impact strength I is: ; Where Δx is the displacement vector of the target. Indicates the length of the modulus, △ grid The grid side length; 2) For temporary control events, the event impact intensity I is: ; Among them, R control The radius of the controlled area; 3) For meteorological change events, the event impact intensity I is: ; Among them, A weather The area affected; S22. Calculation of Adaptive Neighborhood Radius: The adaptive neighborhood radius r is calculated using the following formula: ; Where r0 is the basic radius and k is the proportionality coefficient; S23. Determining the Neighborhood Grid Set: For a grid g, its neighborhood grid set N(g) is: ; Where g' is the candidate grid, dist(·) is the grid spatial distance function, which uses GeoSOT encoding to quickly calculate the implicit spatial relationships. dist(g,g') represents the spatial distance between the calculated grid g and the candidate grid g'. This indicates that grid g belongs to the set of grids g affected by the same event. changed ; S24. For multiple grids affected by the same event, the final update range A is the union of the sets of neighboring grids of each grid: .

[0011] Preferably, the version management module establishes a correlation model between versions and traffic light status, supporting accurate tracing of traffic light status, including: S31. Version Definition: Set the version number for the spatial domain graph updated and generated at time t. The version includes: 1) Mesh state set g t ={(g, )}, Let g be the grid state at time t; 2) Traffic light status set l t ={(g, )}, Let g be the traffic light state at time t; S32, Version Difference Calculation: 1) Difference in mesh state between adjacent versions △g t for: ; Among them, g t g t-1 These represent the sets of grid states contained in the spatial domain graphs updated at time t and t-1, respectively, where \ represents the set difference operation; 2) Differences in traffic light status between adjacent versions △l t for: ; Among them, l t l t-1 The traffic light state sets contained in the spatial domain maps updated at time t and t-1, respectively; S33. Traffic light state history evolution: By traversing the version chain, the traffic light state time series H of grid g can be reconstructed.g : ; Where T is the target time series set.

[0012] Preferably, the AI ​​prediction and closed-loop feedback module uses the AI ​​prediction result as input for event-triggered updates, realizing closed-loop control of "prediction-update-feedback", including: S41. Target Trajectory Prediction: Based on historical trajectory data, Kalman filtering or LSTM networks are used to predict the future position of the target. For uniformly accelerated motion models: ; in, Let x(t) be the predicted position vector of the target at a future time t+Δt, x(t) be the actual position vector of the target at time t, v(t) be the velocity vector of the target at time t, a(t) be the acceleration vector of the target at time t, and Δt be the prediction time interval. S42. Conflict Probability Calculation: Based on the predicted position vector of the target at a future time, calculate the conflict probability P between target i and target j within the time window △T. ij,△T : ; Wherein, △t n Let N be the time step size for the nth sampling point, and N be the number of sampling points. This indicates that the target i is calculated at a future time t+Δt. n Predicted position vector With target j at future time t+△t n Predicted position vector The Euclidean distance between them, D safe I(·) is the safety distance threshold, and I(·) is the indicator function. The function value is 1 when the condition in parentheses is true, and 0 otherwise. S43. Pre-update triggering mechanism: The grid state update is triggered in advance when any of the following conditions are met: 1) The probability of conflict between target i and target j within the time window △T, P ij,△T Greater than the conflict probability threshold θ conflict ; 2) The target is about to enter the obstacle zone, that is: ; in, This represents the predicted position vector of the target at a future time t+Δt. With respect to the actual position vector x of the obstacle obstacle The Euclidean distance between them, D warning This is the warning distance threshold; Pre-update events are added to a priority queue and processed in conjunction with real-time events.

[0013] Preferably, the application service layer is based on a unified mesh interface and supports plug-and-play application services including route planning, conflict early warning, and emergency response, including: S51. Unified Mesh Interface Definition: Defines a standardized API interface. get_grid_status(grid_code,time): Queries the status of a specified grid at a specified time. subscribe_grid_update(grid_code, callback): Subscribes to the state updates of the specified grid. get_grid_history(grid_code, start_time, end_time): Retrieves the historical state sequence of the specified grid. S52. Service Registration and Discovery: Newly developed application services register with the platform through the registration center, declaring their required data types and grid ranges. The platform automatically pushes the status data of the relevant grids based on the registration information. S53, Plug and Play Process: New application service launched, calling the registration interface; The platform verifies the legitimacy of the service and assigns access credentials; The platform establishes a data channel with the dynamic update engine layer based on service needs; The application service begins receiving real-time status data from the grid without requiring modifications to the platform's core code.

[0014] A method for dynamically updating GeoSOT spatial map data includes the following steps: S1. Based on hardware skid-mounted design, the sensing device and edge computing module are integrated into a standardized skid-mounted unit; S2. Use the multi-source grid data stream output from the skid-mounted unit as input, and use the GeoSOT unified spatiotemporal engine to output a unified spatiotemporal identifier. S3. Based on a unified spatiotemporal identifier, a priority scheduling algorithm based on multi-event collaborative triggering is adopted to solve the update conflict problem when multiple events are concurrent. An adaptive neighborhood radius algorithm based on the event impact intensity is adopted to optimize the local update range. A correlation model between version and traffic light status is established to support accurate tracing of traffic light status. AI prediction results are used as input for event-triggered updates to achieve closed-loop control of "prediction-update-feedback". S4. Manage the grid status and present the traffic light status in a visual format; S5 provides users with application services including route planning, conflict warning, and emergency response based on a unified grid interface.

[0015] (III) Beneficial Effects Compared with the prior art, the GeoSOT spatial map data dynamic update system and method provided by the present invention have the following beneficial effects: 1) Hardware skid-mounted design enables rapid deployment and standardized access. This invention is based on a hardware skid-mounted design, which integrates sensing devices with edge computing modules and completes BeiDou grid code encoding at the source. Compared with existing solutions that require customized interfaces to access multi-source data, the skid-mounted unit of this invention outputs a standardized multi-source grid data stream, reducing system deployment time from several months to 2 hours and maintenance costs by more than 80%. It uses BeiDou grid code as a "digital link" to achieve rapid mapping between "physical space and digital space". 2) Multi-event collaborative triggering enhances dynamic response capabilities The priority scheduling algorithm based on multi-event collaborative triggering proposed in this invention solves the update conflict problem when multiple events occur concurrently. Compared with the existing schemes based on single-event triggering, the update accuracy of this invention is improved to 99.5%, and update conflicts are reduced by 90%. By calculating event priorities, it can ensure that emergency events are handled first, thus protecting airspace safety. 3) Adaptive local update optimizes computational efficiency This invention proposes an adaptive neighborhood radius algorithm based on the intensity of event impact. Compared with the existing scheme that uses a fixed 26 neighborhoods, the average number of update grids in this invention is reduced by 65%, the computational resource consumption is reduced by 60%, and the update response time can still be controlled within 2 seconds in high-density flight scenarios (more than 100 flights / s). 4) Version - Traffic light association model supports accurate traceability This invention establishes a correlation model between the version and the traffic light status, supporting accurate tracing of airspace status. During accident review, the traffic light status time series H can be queried. g Quickly reconstruct the airspace situation at the time of the incident to provide a basis for determining responsibility. Although some existing technologies support data backtracking, they cannot directly correlate with traffic light status. 5) AI prediction-update closed loop enables proactive defense. This invention incorporates AI prediction results into the update trigger mechanism, achieving closed-loop control of "prediction-update-feedback". When the "conflict probability P" is reached... ij,△T Greater than the conflict probability threshold θ conflict When a target is about to enter an obstacle zone, the system updates the relevant grid status in advance to proactively avoid potential conflicts. Compared with the passive response updates of existing technologies, the proactive defense capability of this invention can significantly reduce the conflict rate. 6) The software platform supports plug-and-play functionality. This invention is based on a unified grid interface using BeiDou grid codes, enabling plug-and-play upper-layer application services. New services can be launched without modifying the platform's core code; users only need to declare their requirements through the registration interface, and the platform automatically pushes the relevant grid status data. This architecture shortens the system expansion cycle from months to days, providing flexible technical support for the rapid development of the low-altitude economy. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0017] Figure 1 This is a schematic diagram of the system of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] The following section describes the specific architecture of the GeoSOT spatial map data dynamic update system provided by this invention, using concrete examples (e.g.) Figure 1 (As shown) and its technical effects. The system architecture includes: The skid-mounted sensing layer, based on a hardware skid-mounted design, integrates sensing devices and edge computing modules into a standardized skid-mounted unit. The spatiotemporal reference layer takes the multi-source grid data stream output by the skid-mounted unit as input and uses the GeoSOT unified spatiotemporal engine to output a unified spatiotemporal identifier. The dynamic update engine layer, based on a unified spatiotemporal identifier, adopts a priority scheduling algorithm based on multi-event collaborative triggering to solve the update conflict problem when multiple events are concurrent, adopts an adaptive neighborhood radius algorithm based on the event impact intensity to optimize the local update range, establishes a correlation model between version and traffic light status, supports accurate traceability of traffic light status, and uses AI prediction results as input for event-triggered updates to achieve closed-loop control of "prediction-update-feedback". The traffic light control layer manages the grid status and presents the traffic light status in a visual format. The application service layer, based on a unified grid interface, supports plug-and-play application services including route planning, conflict warning, and emergency response.

[0020] I. Skid-mounted sensing layer The skid-mounted sensing layer includes skid-mounted units, which include: Sensing devices are configured with corresponding sensors according to their type, including radar skid-mounted units, spectrum skid-mounted units, and optoelectronic skid-mounted units, which integrate radar, spectrum analyzer, and optoelectronic camera into skid-mounted units respectively. Edge computing module with built-in BeiDou grid code processing chip; The communication module supports 4G / 5G / fiber optic communication; The timing module supports BeiDou high-precision timing.

[0021] Specifically, the skid-mounted unit performs the following processing while collecting data: 1) Obtain the device's BeiDou grid code indicating the current location of the sensing device; 2) Obtain the relative position of the target (including drones, etc.), get the corresponding latitude and longitude coordinates, and convert them into the target Beidou grid code; 3) Obtain the BeiDou time synchronization timestamp (accurate to 0.01s); 4) Output standardized multi-source grid data streams; Through a hardware skid-mounted design, the sensing data is grid-encoded and spatiotemporally aligned at the source, enabling rapid mapping between "physical space and digital space". The application service layer is plug-and-play based on a unified grid interface.

[0022] II. Dynamically Update Engine Layer The dynamic update engine layer includes: The event triggering and priority scheduling module uses a priority scheduling algorithm based on multi-event collaborative triggering to solve the update conflict problem when multiple events are concurrent; The adaptive local update module uses an adaptive neighborhood radius algorithm based on the intensity of event impact to optimize the local update range; The version management module establishes a correlation model between versions and traffic light status, supporting accurate tracing of traffic light status. The AI ​​prediction and closed-loop feedback module uses the AI ​​prediction results as input for event-triggered updates, achieving closed-loop control of "prediction-update-feedback".

[0023] i. Event Triggering and Priority Scheduling Module The event triggering and priority scheduling module employs a priority scheduling algorithm based on multi-event collaborative triggering to resolve update conflicts during concurrent events, including: S11. Define the set of event types: including new target entry events, target location update events, target departure events, temporary control events, weather change events, and AI prediction events; S12, Event Priority Calculation: The priority P of the i-th event i It is determined by the following three factors: 1) Urgency level The highest level of control is determined by the type of event, with temporary control events being the most severe. 2) Time urgency T i The smaller the time difference between the time the event occurred and the current time, the higher the score. 3) Spatial influence range R i The number of affected grid cells is determined by the number of grid cells affected. The priority P of the i-th event i Calculate using the following formula: ; in, The value should be a very small positive number to avoid a denominator of 0. , , All are weighting coefficients; S13, Trigger Update: When the priority P of the i-th event... i Greater than the priority threshold θ trigger When the time comes, an update is triggered; S14. Multi-event collaborative processing: For multiple events that trigger updates simultaneously, a priority queue is used for scheduling. Arrange all events in descending order of priority, and process the grid updates triggered by each event in turn.

[0024] Adaptive local update module The adaptive local update module employs an adaptive neighborhood radius algorithm based on the intensity of event impact to optimize the local update range, including: S21. Quantification of the intensity of event impact: 1) For the target location update event, its event impact strength I is: ; Where Δx is the displacement vector of the target. Indicates the length of the modulus, △ grid The grid side length; 2) For temporary control events, the event impact intensity I is: ; Among them, R control The radius of the controlled area; 3) For meteorological change events, the event impact intensity I is: ; Among them, A weather The area affected; S22. Calculation of Adaptive Neighborhood Radius: The adaptive neighborhood radius r is calculated using the following formula: ; Where r0 is the base radius (usually the radius of the inscribed circle of one grid), and k is the scaling factor; S23. Determining the Neighborhood Grid Set: For a grid g, its neighborhood grid set N(g) is: ; Where g' is the candidate grid, dist(·) is the grid spatial distance function, which uses GeoSOT encoding to quickly calculate the implicit spatial relationships. dist(g,g') represents the spatial distance between the calculated grid g and the candidate grid g'. This indicates that grid g belongs to the set of grids g affected by the same event. changed ; S24. For multiple grids affected by the same event, the final update range A is the union of the sets of neighboring grids of each grid: .

[0025] Version Management Module The version management module establishes a correlation model between versions and traffic light status, supporting precise traceability of traffic light status, including: S31. Version Definition: Set the version number for the spatial domain graph updated and generated at time t. The version includes: 1) Mesh state set g t ={(g, )}, Let g be the grid state at time t; 2) Traffic light status set l t ={(g, )}, Let g be the traffic light state at time t; S32, Version Difference Calculation: 1) Difference in mesh state between adjacent versions △g t for: ; Among them, g t g t-1 These represent the sets of grid states contained in the spatial domain graphs updated at time t and t-1, respectively, where \ represents the set difference operation; 2) Differences in traffic light status between adjacent versions △lt for: ; Among them, l t l t-1 The traffic light state sets contained in the spatial domain maps updated at time t and t-1, respectively; S33. Traffic light state history evolution: By traversing the version chain, the traffic light state time series H of grid g can be reconstructed. g : ; Where T is the target time series set.

[0026] AI prediction and closed-loop feedback module The AI ​​prediction and closed-loop feedback module uses AI prediction results as input for event-triggered updates, achieving closed-loop control of "prediction-update-feedback," including: S41. Target Trajectory Prediction: Based on historical trajectory data, Kalman filtering or LSTM networks are used to predict the future position of the target. For uniformly accelerated motion models: ; in, Let x(t) be the predicted position vector of the target at a future time t+Δt, x(t) be the actual position vector of the target at time t, v(t) be the velocity vector of the target at time t, a(t) be the acceleration vector of the target at time t, and Δt be the prediction time interval. S42. Conflict Probability Calculation: Based on the predicted position vector of the target at a future time, calculate the conflict probability P between target i and target j within the time window △T. ij,△T : ; Wherein, △t n Let N be the time step size for the nth sampling point, and N be the number of sampling points. This indicates that the target i is calculated at a future time t+Δt. n Predicted position vector With target j at future time t+△t n Predicted position vector The Euclidean distance between them, D safe I(·) is the safety distance threshold, and I(·) is the indicator function. The function value is 1 when the condition in parentheses is true, and 0 otherwise. S43. Pre-update triggering mechanism: The grid state update is triggered in advance when any of the following conditions are met: 1) The probability of conflict between target i and target j within the time window △T, P ij,△T Greater than the conflict probability threshold θ conflict; 2) The target is about to enter the obstacle zone, that is: ; in, This represents the predicted position vector of the target at a future time t+Δt. With respect to the actual position vector x of the obstacle obstacle The Euclidean distance between them, D warning This is the warning distance threshold; Pre-update events are added to a priority queue and processed in conjunction with real-time events.

[0027] III. Application Service Layer The application service layer, based on a unified mesh interface, supports plug-and-play application services including route planning, conflict warning, and emergency response, including: S51. Unified Mesh Interface Definition: Defines a standardized API interface. get_grid_status(grid_code,time): Queries the status of a specified grid at a specified time. subscribe_grid_update(grid_code, callback): Subscribes to the state updates of the specified grid. get_grid_history(grid_code, start_time, end_time): Retrieves the historical state sequence of the specified grid. S52. Service Registration and Discovery: Newly developed application services register with the platform through the registration center, declaring their required data types and grid ranges. The platform automatically pushes the status data of the relevant grids based on the registration information. S53, Plug and Play Process: New application service launched, calling the registration interface; The platform verifies the legitimacy of the service and assigns access credentials; The platform establishes a data channel with the dynamic update engine layer based on service needs; The application service begins receiving real-time status data from the grid without requiring modifications to the platform's core code.

[0028] Based on the aforementioned GeoSOT spatial map data dynamic update system, this invention also discloses a GeoSOT spatial map data dynamic update method, comprising the following steps: S1. Based on hardware skid-mounted design, the sensing device and edge computing module are integrated into a standardized skid-mounted unit; S2. Use the multi-source grid data stream output from the skid-mounted unit as input, and use the GeoSOT unified spatiotemporal engine to output a unified spatiotemporal identifier. S3. Based on a unified spatiotemporal identifier, a priority scheduling algorithm based on multi-event collaborative triggering is adopted to solve the update conflict problem when multiple events are concurrent. An adaptive neighborhood radius algorithm based on the event impact intensity is adopted to optimize the local update range. A correlation model between version and traffic light status is established to support accurate tracing of traffic light status. AI prediction results are used as input for event-triggered updates to achieve closed-loop control of "prediction-update-feedback". S4. Manage the grid status and present the traffic light status in a visual format; S5 provides users with application services including route planning, conflict warning, and emergency response based on a unified grid interface.

[0029] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A GeoSOT spatial map data dynamic update system, characterized in that: The system architecture includes: The skid-mounted sensing layer, based on a hardware skid-mounted design, integrates sensing devices and edge computing modules into a standardized skid-mounted unit. The spatiotemporal reference layer takes the multi-source grid data stream output by the skid-mounted unit as input and uses the GeoSOT unified spatiotemporal engine to output a unified spatiotemporal identifier. The dynamic update engine layer, based on a unified spatiotemporal identifier, adopts a priority scheduling algorithm based on multi-event collaborative triggering to solve the update conflict problem when multiple events are concurrent, adopts an adaptive neighborhood radius algorithm based on the event impact intensity to optimize the local update range, establishes a correlation model between version and traffic light status, supports accurate tracing of traffic light status, and uses AI prediction results as input for event-triggered updates to achieve closed-loop control of "prediction-update-feedback". The traffic light control layer manages the grid status and presents the traffic light status in a visual format. The application service layer, based on a unified grid interface, supports plug-and-play application services including route planning, conflict warning, and emergency response.

2. The GeoSOT spatial map data dynamic update system according to claim 1, characterized in that: The skid-mounted sensing layer includes skid-mounted units, which include: Sensing devices are configured with corresponding sensors according to their type, including radar skid-mounted units, spectrum skid-mounted units, and optoelectronic skid-mounted units, which integrate radar, spectrum analyzer, and optoelectronic camera into skid-mounted units respectively. Edge computing module with built-in BeiDou grid code processing chip; The communication module supports 4G / 5G / fiber optic communication; The timing module supports BeiDou high-precision timing.

3. The GeoSOT spatial map data dynamic update system according to claim 2, characterized in that: The skid-mounted unit performs the following processing while collecting data: 1) Obtain the device's BeiDou grid code indicating the current location of the sensing device; 2) Obtain the relative position of the target, get the corresponding latitude and longitude coordinates, and convert them into the target's BeiDou grid code; 3) Obtain the BeiDou time synchronization timestamp; 4) Output standardized multi-source grid data streams; Through hardware skid-mounted design, the sensing data is grid-encoded and spatiotemporally aligned at the source, enabling rapid mapping between "physical space and digital space". The application service layer is plug-and-play based on a unified grid interface.

4. The GeoSOT spatial map data dynamic update system according to claim 1, characterized in that: The dynamic update engine layer includes: The event triggering and priority scheduling module uses a priority scheduling algorithm based on multi-event collaborative triggering to solve the update conflict problem when multiple events are concurrent; The adaptive local update module uses an adaptive neighborhood radius algorithm based on the intensity of event impact to optimize the local update range; The version management module establishes a correlation model between versions and traffic light status, supporting accurate tracing of traffic light status. The AI ​​prediction and closed-loop feedback module uses the AI ​​prediction results as input for event-triggered updates, achieving closed-loop control of "prediction-update-feedback".

5. The GeoSOT spatial map data dynamic update system according to claim 4, characterized in that: The event triggering and priority scheduling module employs a priority scheduling algorithm based on multi-event collaborative triggering to resolve update conflicts during concurrent events, including: S11. Define the set of event types: including new target entry events, target location update events, target departure events, temporary control events, weather change events, and AI prediction events; S12, Event Priority Calculation: The priority P of the i-th event i It is determined by the following three factors: 1) Urgency level The highest level of control is determined by the type of event, with temporary control events being the most severe. 2) Time urgency T i The smaller the time difference between the time the event occurred and the current time, the higher the score. 3) Spatial influence range R i The number of affected grid cells is determined by the number of grid cells affected. The priority P of the i-th event i Calculate using the following formula: ； in, The value should be a very small positive number to avoid a denominator of 0. , , All are weighting coefficients; S13, Trigger Update: When the priority P of the i-th event... i Greater than the priority threshold θ trigger When the time comes, an update is triggered; S14. Multi-event collaborative processing: For multiple events that trigger updates simultaneously, a priority queue is used for scheduling. Arrange all events in descending order of priority, and process the grid updates triggered by each event in turn.

6. The GeoSOT spatial map data dynamic update system according to claim 4, characterized in that: The adaptive local update module employs an adaptive neighborhood radius algorithm based on the intensity of event impact to optimize the local update range, including: S21. Quantification of the intensity of event impact: 1) For the target location update event, its event impact strength I is: ； Where Δx is the displacement vector of the target. Indicates the length of the modulus, △ grid The grid side length; 2) For temporary control events, the event impact intensity I is: ； Among them, R control The radius of the controlled area; 3) For meteorological change events, the event impact intensity I is: ； Among them, A weather The area affected; S22. Calculation of Adaptive Neighborhood Radius: The adaptive neighborhood radius r is calculated using the following formula: ； Where r0 is the basic radius and k is the proportionality coefficient; S23. Determining the Neighborhood Grid Set: For a grid g, its neighborhood grid set N(g) is: ； Where g' is the candidate grid, dist(·) is the grid spatial distance function, which uses GeoSOT encoding to quickly calculate the implicit spatial relationships. dist(g,g') represents the spatial distance between the calculated grid g and the candidate grid g'. This indicates that grid g belongs to the set of grids g affected by the same event. changed ; S24. For multiple grids affected by the same event, the final update range A is the union of the sets of neighboring grids of each grid: 。 7. The GeoSOT spatial map data dynamic update system according to claim 4, characterized in that: The version management module establishes a correlation model between versions and traffic light status, supporting accurate tracing of traffic light status, including: S31. Version Definition: Set the version number for the spatial domain graph updated and generated at time t. The version includes: 1) Mesh state set g t ={(g, )}, Let g be the grid state at time t; 2) Traffic light status set l t ={(g, )}, Let g be the traffic light state at time t; S32, Version Difference Calculation: 1) Difference in mesh state between adjacent versions △g t for: ； Among them, g t g t-1 These represent the sets of grid states contained in the spatial domain graphs updated at time t and t-1, respectively, where \ represents the set difference operation; 2) Differences in traffic light status between adjacent versions △l t for: ； Among them, l t l t-1 The traffic light state sets contained in the spatial domain maps updated at time t and t-1, respectively; S33. Traffic light state history evolution: By traversing the version chain, the traffic light state time series H of grid g can be reconstructed. g : ； Where T is the target time series set.

8. The GeoSOT spatial map data dynamic update system according to claim 4, characterized in that: The AI ​​prediction and closed-loop feedback module uses the AI ​​prediction results as input for event-triggered updates, achieving closed-loop control of "prediction-update-feedback," including: S41. Target Trajectory Prediction: Based on historical trajectory data, Kalman filtering or LSTM networks are used to predict the future position of the target. For uniformly accelerated motion models: ； in, Let x(t) be the predicted position vector of the target at a future time t+Δt, x(t) be the actual position vector of the target at time t, v(t) be the velocity vector of the target at time t, a(t) be the acceleration vector of the target at time t, and Δt be the prediction time interval. S42. Conflict Probability Calculation: Based on the predicted position vector of the target at a future time, calculate the conflict probability P between target i and target j within the time window △T. ij,△T : ； Wherein, △t n Let N be the time step size for the nth sampling point, and N be the number of sampling points. This indicates that the target i is calculated at a future time t+Δt. n Predicted position vector With target j at future time t+△t n Predicted position vector The Euclidean distance between them, D safe I(·) is the safety distance threshold, and I(·) is the indicator function. The function value is 1 when the condition in parentheses is true, and 0 otherwise. S43. Pre-update triggering mechanism: The grid state update is triggered in advance when any of the following conditions are met: 1) The probability of conflict between target i and target j within the time window △T, P ij,△T Greater than the conflict probability threshold θ conflict ; 2) The target is about to enter the obstacle zone, that is: ； in, This represents the predicted position vector of the target at a future time t+Δt. With respect to the actual position vector x of the obstacle obstacle The Euclidean distance between them, D warning This is the warning distance threshold; Pre-update events are added to a priority queue and processed in conjunction with real-time events.

9. The GeoSOT spatial map data dynamic update system according to claim 1, characterized in that: The application service layer is based on a unified mesh interface and supports plug-and-play application services including route planning, conflict warning, and emergency response, including: S51. Unified Mesh Interface Definition: Defines a standardized API interface. get_grid_status(grid_code,time): Queries the status of a specified grid at a specified time. subscribe_grid_update(grid_code, callback): Subscribes to the state updates of the specified grid. get_grid_history(grid_code, start_time, end_time): Retrieves the historical state sequence of the specified grid. S52. Service Registration and Discovery: Newly developed application services register with the platform through the registration center, declaring their required data types and grid ranges. The platform automatically pushes the status data of the relevant grids based on the registration information. S53, Plug and Play Process: New application services are launched, and the registration interface is called; The platform verifies the legitimacy of the service and assigns access credentials; The platform establishes a data channel with the dynamic update engine layer based on service needs; The application service begins receiving real-time status data from the grid without requiring modifications to the platform's core code.

10. A method for dynamically updating GeoSOT spatial map data, applicable to the GeoSOT spatial map data dynamic update system of claim 1, characterized in that: Includes the following steps: S1. Based on hardware skid-mounted design, the sensing device and edge computing module are integrated into a standardized skid-mounted unit; S2. Use the multi-source grid data stream output from the skid-mounted unit as input, and use the GeoSOT unified spatiotemporal engine to output a unified spatiotemporal identifier. S3. Based on a unified spatiotemporal identifier, a priority scheduling algorithm based on multi-event collaborative triggering is adopted to solve the update conflict problem when multiple events are concurrent. An adaptive neighborhood radius algorithm based on the event impact intensity is adopted to optimize the local update range. A correlation model between version and traffic light status is established to support accurate tracing of traffic light status. AI prediction results are used as input for event-triggered updates to achieve closed-loop control of "prediction-update-feedback". S4. Manage the grid status and present the traffic light status in a visual format; S5 provides users with application services including route planning, conflict warning, and emergency response based on a unified grid interface.

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