Land, sea, air and space rescue force integration situation display and resource scheduling optimization system

Abstract

The application relates to the field of resource scheduling, and discloses a land-sea-air-space rescue force integrated situation display and resource scheduling optimization system, which comprises a resource integration module, a connected path module, a to-be-processed set module, an initial task module, a feasible path module and a task scheduling module; the resource integration module fuses state and environment data, generates situation data flow, and separates an event information set and an available resource set from the situation data flow; the connected path module generates connected paths and spatial distances of the connected paths according to the positions of events and resources; the to-be-processed set module determines to-be-processed matching pairs by comparing distances and resource coverage mileages; the initial task module judges the event level according to state parameters and event properties, sorts the matching pairs according to the event level, and forms an initial task set; the feasible path module corrects paths by taking environmental obstacles as constraints, and obtains feasible paths and estimated arrival time consumptions; and the task scheduling module finally binds the initial task set by taking paths and time consumptions as space-time constraints, and outputs a final task scheduling set. The application can improve the efficiency of land-sea-air-space rescue force integrated situation display and resource scheduling.

Description

Technical Field

[0001] This invention relates to the field of resource scheduling technology, and in particular to an integrated situation display and resource scheduling optimization system for land, sea, air and space rescue forces. Background Technology

[0002] In existing technologies, the information sources relied upon for rescue operations are often scattered and heterogeneous, making it difficult to effectively integrate and uniformly interpret monitoring data from different departments or platforms. This results in fragmented perception of the disaster site situation, failing to form a real-time, complete panoramic view, and consequently affecting the accurate assessment of the nature and scope of the event, as well as the status of available rescue resources. Consequently, command and decision-making face challenges of incomplete and delayed information from the very beginning.

[0003] Furthermore, traditional resource allocation relies heavily on manual experience or independent planning by individual force units, lacking a global, quantifiable collaborative optimization mechanism. This model not only makes it difficult to quickly match optimal resources with tasks, but also fails to anticipate and resolve potential conflicts between different rescue forces in time and space, easily leading to resource idleness, task delays, or on-site chaos, thus reducing the efficiency and safety of the overall rescue operation. Summary of the Invention

[0004] This invention provides a method for an integrated situational awareness and resource scheduling optimization system for land, sea, air and space rescue forces, the main purpose of which is to solve the problem of low efficiency in the integrated situational awareness and resource scheduling optimization of land, sea, air and space rescue forces.

[0005] To achieve the above objectives, this invention provides an integrated situational awareness and resource scheduling optimization system for land, sea, air, and space rescue forces. The system comprises a risk impact matrix module, a terminal security score generation module, a risk protection strategy generation module, an overall protection strategy module, and a protection generation scheme module, wherein:

[0006] The resource integration module is used to fuse the state data and environmental data of the target process to obtain the situational data stream of the target process, and to separate the event information set and available resource set of the target process according to the type identifier of the data records in the situational data stream.

[0007] The connectivity path module is used to generate the connectivity path and corresponding spatial distance of the target process based on the event geographic location of the event information set and the power location of the available resource set.

[0008] The pending set module is used to determine the pending matching pairs of the target process based on the comparison results between the spatial distance and the coverage mileage corresponding to the carrier type in the available resource set;

[0009] The initial task module is used to determine the event level of the target process based on the state parameters and event nature in the matching pairs to be processed, and sort the matching pairs to be processed by the event level as an index to obtain the initial task set of the target process;

[0010] The feasible path module is used to modify the connectivity path using the environmental obstacle set of the situational data stream as boundary conditions, so as to obtain the feasible path of the target process and the corresponding estimated arrival time.

[0011] The task scheduling module is used to bind the initial task set with the spatial coordinate sequence of the feasible path and the estimated arrival time as spatiotemporal constraints to obtain the task scheduling set of the target process.

[0012] In a preferred embodiment, when the resource integration module performs the fusion of state data and environmental data of the target process to obtain a situational data stream of the target process, and separates the event information set and available resource set of the target process according to the type identifier of the data records in the situational data stream, it is specifically used for:

[0013] The mobile carrier data and monitoring node data of the target process are analyzed to obtain the entity dataset and node status set of the target process;

[0014] The data records of the entity dataset and the node situation set are registered to the geographic information grid of the target process according to the corresponding latitude and longitude coordinates and timestamps, and the data are overlaid to obtain the situation data stream of the target process;

[0015] Based on the disaster identifier of the situation data stream, the geographic coordinates and text description information of the data record are paired to obtain the event information set of the target process;

[0016] The identity code and callable status field of the data record are associated with the platform status identifier of the situational data stream to obtain the available resource set of the target process.

[0017] In a preferred embodiment, when the connectivity module generates the connectivity path and corresponding spatial distance of the target process based on the event geographic location of the event information set and the power location of the available resource set, it is specifically used for:

[0018] Based on the geographic information grid, the coordinates of the event's geographical location are connected with the coordinates of the force's location to obtain the connectivity path of the target process;

[0019] The spherical distance of the connected path is taken as the spatial distance of the target process.

[0020] In a preferred embodiment, when the processing set module determines the matching pairs to be processed in the target process based on the comparison results between the spatial distance and the coverage mileage corresponding to the carrier type in the available resource set, it is specifically used for:

[0021] The maximum operable distance within the mission cycle of the target process is determined based on the carrier type identifier associated with the available resource set and the power location, thus obtaining the coverage mileage of the target process;

[0022] When the spatial distance is less than or equal to the coverage mileage, the information entries of the event information set are combined with the information entries of the available resource set to obtain the target process matching pair to be processed.

[0023] In a preferred embodiment, when the initial task module performs the following steps: It determines the state level of the target process based on the state parameters and event characteristics of the matching pairs to be processed, and sorts the matching pairs to be processed using the state level as an index to obtain the initial task set of the target process, it is specifically used for:

[0024] The state parameters include the typical response speed corresponding to the spatial distance and the carrier type identifier;

[0025] Keyword analysis is performed on the text information in the event information set to identify the event nature that characterizes the severity of the event in the target process;

[0026] A basic level coefficient is set based on the nature of the event, and the delay factor of the target process is calculated based on the spatial distance and the typical response speed.

[0027] The urgency score calculated based on the base level coefficient and the delay factor is converted into the situation level of the target process;

[0028] Using the numerical value of the situation level as the sorting key, the matching pairs to be processed are sorted in descending order to obtain an ordered queue of the target process;

[0029] The information pairs in the ordered queue are encapsulated into structured task entries according to their arrangement order to obtain the initial task set of the target process.

[0030] In a preferred embodiment, the formula for calculating the urgency score is specifically used for:

[0031]

[0032] in, Rate the urgency level. This refers to the weighting coefficient for static events. The basic level coefficient is... The weighting coefficient for response timeliness, The typical response speed is... This is the path complexity reduction factor. For distance nonlinearity sensitive factors, The spatial distance is denoted as .

[0033] In a preferred embodiment, when the feasible path module performs the modification of the connectivity path using the set of environmental obstacles in the situational data stream as boundary conditions to obtain the feasible path of the target process and the corresponding estimated arrival time, it is specifically used for:

[0034] Obstacle marker records are selected from the situational data stream, and the geographic boundary coordinate set of the obstacle marker records is extracted to form the environmental obstacle set of the target process;

[0035] Geometric intersection calculation is performed between the straight line segment of the connected path and the polygonal obstacle region represented by the environmental obstacle set: when the straight line segment is detected to cross the polygonal obstacle region, a polyline path that avoids the boundary of the polygonal obstacle region is searched in the spatial range of the target process, with the coordinates of the force position as the starting point and the coordinates of the event geographical location as the ending point, and the polyline path is determined as the feasible path of the target process.

[0036] The estimated arrival time of the target process is determined based on the spatial coordinate sequence of the feasible path.

[0037] In a preferred embodiment, determining the estimated arrival time of the target process based on the spatial coordinate sequence of the feasible path is specifically used for:

[0038] The total length of the feasible path is obtained by summing the segmented distances between adjacent coordinate points within the spatial coordinate sequence.

[0039] Obtain the carrier identifier of the rescue force associated with the feasible path from the unprocessed matching pairs;

[0040] Based on the carrier identifier, determine the typical cruising speed of the type of rescue force of the matching pair to be processed in the medium environment of the feasible path;

[0041] The ratio of the total length of the feasible path to the typical cruise speed is used as the theoretical travel time of the target process;

[0042] The theoretical travel time is compensated based on the real-time data of the target process to obtain the estimated arrival time of the target process.

[0043] In a preferred embodiment, when the task scheduling module binds the initial task set with the spatial coordinate sequence of the feasible path and the estimated arrival time as spatiotemporal constraints to obtain the task scheduling set of the target process, it is specifically used for:

[0044] The task elements of the matching pairs to be processed in the initial task set are associated and stored with the spatial coordinate sequence and the field of the estimated arrival time to obtain the segmented task set of the target process;

[0045] Traverse the initial task set, aggregate the segmented task sets, and obtain the preliminary task scheduling set for the target process;

[0046] Based on the feasible path, conflict resolution is performed on the preliminary task scheduling set to obtain the task scheduling set of the target process.

[0047] In a preferred embodiment, when performing conflict resolution on the preliminary task scheduling set based on the feasible path to obtain the task scheduling set for the target process, the specific steps are as follows:

[0048] When the same rescue force in the available resource set is assigned to two or more feasible paths in the task scheduling set, and the estimated arrival time overlaps, the task scheduling set is marked as having a resource time conflict.

[0049] When the feasible paths in the task scheduling set intersect or overlap spatially, and the departure time of each feasible path intersects with the time period calculated based on the estimated arrival time of the intersecting area, the task scheduling set marks the path spatial conflict.

[0050] To address the resource time conflict, the planned start time of the tasks in the task scheduling set is postponed until the time overlap is eliminated, thus obtaining the time-optimized sequence of the target process;

[0051] In response to the path space conflict, alternative feasible paths are searched for tasks in the task scheduling set until the spatial and temporal intersection is eliminated, thus obtaining the path optimization sequence of the target process.

[0052] By merging the time-series optimization sequence and the path optimization sequence, the task scheduling set of the target process is obtained.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. This system achieves accurate, real-time, and integrated perception of all elements of rescue by deeply integrating multi-source heterogeneous data and constructing a unified spatiotemporal benchmark situational data stream. This directly enhances the system's depth and breadth of understanding of complex rescue scenarios, fundamentally improving the accuracy and timeliness of event identification, resource location, and environmental perception, providing a solid and reliable data foundation for all subsequent decisions. Based on this, the system transforms the traditionally experience-dependent scheduling process into an efficient and quantifiable computational process through automated geographic matching, capability assessment, priority ranking, and route planning. This significantly improves the speed and scientific rigor of decision-making from situational awareness to the generation of preliminary task plans, ensuring the agility of emergency response.

[0055] 2. By introducing a conflict detection and automatic mediation mechanism under spatiotemporal constraints, the system achieves global optimization of multi-task parallel scheduling schemes. This ensures that the final generated task scheduling set has inherent consistency and executability in both time and space dimensions, fundamentally avoiding the risks and efficiency losses caused by resource and path conflicts. The application of this technology makes the allocation and use of rescue resources more precise and rational, improving the overall success rate of rescue operations while optimizing the efficiency of limited resources, and enhancing the orderliness, safety, and overall efficiency of large-scale collaborative rescue operations. Attached Figure Description

[0056] Figure 1 This is a system architecture diagram of an integrated land, sea, air and space rescue force situation display and resource scheduling optimization system provided in an embodiment of the present invention;

[0057] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0058] 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 belong to some, but not all, embodiments of the present invention. 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.

[0059] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “said” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0060] Depending on the context, the word "if" or "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0061] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0062] In practice, the server-side equipment deployed in the integrated situational awareness and resource scheduling optimization system for land, sea, air, and space rescue forces may consist of one or more devices. This integrated situational awareness and resource scheduling optimization system can be implemented as: a business instance, a virtual machine, or hardware devices. For example, this integrated situational awareness and resource scheduling optimization system can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, this integrated situational awareness and resource scheduling optimization system can be understood as software deployed on a cloud node, used to provide integrated situational awareness and resource scheduling optimization to various user terminals. Alternatively, this integrated situational awareness and resource scheduling optimization system can also be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing various user terminals. Alternatively, this integrated situational awareness and resource scheduling optimization system can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices set up to provide integrated situational awareness and resource scheduling optimization to various user terminals.

[0063] In terms of implementation, the integrated situational awareness display and resource scheduling optimization system for land, sea, air, and space rescue forces is mutually compatible with the user terminal. Specifically, if the system is implemented as an application installed on a cloud service platform, the user terminal acts as a client establishing a communication connection with that application; or if the system is implemented as a website, the user terminal acts as a webpage; or if the system is implemented as a cloud service platform, the user terminal acts as a mini-program within an instant messaging application.

[0064] like Figure 1 The figure shown is a system architecture diagram of an integrated land, sea, air and space rescue force situation display and resource scheduling optimization system provided in an embodiment of the present invention.

[0065] The integrated land, sea, air, and space rescue force situation display and resource scheduling optimization system 100 described in this invention can be located on a cloud server. In terms of implementation, it can be implemented as one or more service devices, or as an application installed on the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed as a website. Depending on the functions implemented, the integrated land, sea, air, and space rescue force situation display and resource scheduling optimization system 100 may include a resource integration module 101, a connectivity path module 102, a pending processing set module 103, an initial task module 104, a feasible path module 105, and a task scheduling module 106. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by an electronic device's processor and perform a fixed function, stored in the electronic device's memory.

[0066] In this embodiment of the invention, within the integrated situational awareness and resource scheduling optimization system for land, sea, air, and space rescue forces, each of the aforementioned modules can be implemented independently and can call upon other modules. This "calling" can be understood as a module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the integrated situational awareness and resource scheduling optimization system for land, sea, air, and space rescue forces provided by this embodiment of the invention, the applicable scope of the system architecture can be adjusted by adding modules and directly calling them without modifying the program code, achieving cluster-based horizontal expansion to quickly and flexibly expand the system. In practical applications, the aforementioned modules can be set up on the same device or different devices, or they can be set up on virtual devices, such as service instances on a cloud server.

[0067] The following describes, with reference to specific embodiments, each component and specific workflow of the integrated situational awareness and resource scheduling optimization system for land, sea, air, and space rescue forces:

[0068] The resource integration module 101 is used to merge the state data and environmental data of the target process to obtain the situational data stream of the target process, and to separate the event information set and available resource set of the target process according to the type identifier of the data record in the situational data stream.

[0069] In this embodiment of the invention, when the resource integration module performs the fusion of state data and environmental data of the target process to obtain the situational data stream of the target process, and separates the event information set and available resource set of the target process according to the type identifier of the data records in the situational data stream, it is specifically used for:

[0070] The mobile carrier data and monitoring node data of the target process are analyzed to obtain the entity dataset and node status set of the target process;

[0071] The data records of the entity dataset and the node situation set are registered to the geographic information grid of the target process according to the corresponding latitude and longitude coordinates and timestamps, and the data are overlaid to obtain the situation data stream of the target process;

[0072] Based on the disaster identifier of the situation data stream, the geographic coordinates and text description information of the data record are paired to obtain the event information set of the target process;

[0073] The identity code and callable status field of the data record are associated with the platform status identifier of the situational data stream to obtain the available resource set of the target process.

[0074] Specifically, it reads raw data packets from various mobile platforms and fixed monitoring points. Mobile carrier data includes real-time location, speed, heading, and status reports of rescue vehicles, boats, and aircraft. Monitoring node data includes environmental and on-site observation information provided by weather stations, hydrological stations, cameras, satellite remote sensing, etc.

[0075] Specifically, the system operates based on the latitude and longitude coordinates and precise timestamps inherent in each data record. The geographic information grid is a predefined, regular spatial division unit covering the entire task area. The system maps the coordinates of each record to the corresponding grid cell, and simultaneously aligns data within the same time window based on the timestamp.

[0076] Specifically, the system iterates through each data record in the situational data stream, checking if it contains a preset disaster identifier. This identifier could be a specific sensor trigger signal, keyword tag, or abnormal data pattern marker. When a record containing a disaster identifier is identified, the system automatically extracts the geographic coordinate field and related text description information fields contained in that record.

[0077] Specifically, the system iterates through the situational data stream, searching for data records containing platform status identifiers. These identifiers mark that the data originates from a rescue force platform and indicate its status attributes. For each such record found, the system extracts the unique identification field that identifies the platform, such as unit number, vehicle ID, or aircraft tail number. Simultaneously, it extracts the status field indicating whether the platform is currently accessible.

[0078] Furthermore, using pre-defined data parsing rules, entity identifiers, spatial coordinates, timestamps, and attribute fields are extracted from these two types of data. After parsing, the mobile vehicle data is converted into an entity dataset describing the status and location of the rescue forces themselves. The monitoring node data is converted into a node status set describing environmental conditions and event characteristics.

[0079] Furthermore, after completing spatiotemporal registration, data from different sources but located in the same grid cell and the same time slice are merged. For example, the position status of a rescue ship within a grid is overlaid with the wind speed warning information for that area to form a single integrated record. Through this grid-by-grid, time-slice overlay operation, the originally scattered entity datasets and node situation sets are merged into a continuous, spatiotemporally synchronized situation data stream.

[0080] Furthermore, the textual description information originates from text summaries of monitoring reports, text transcribed from speech, or brief descriptions entered manually. After extraction, the system creates a pairing relationship between this set of geographic coordinates and textual description information, forming an event information entry. The collection of all identified and paired event information entries constitutes the event information set of the target process.

[0081] Furthermore, the identity code in the same record is associated with the callable status field to form a resource status entry. By aggregating all such entries, a set of available resources for the target process is generated. This set clearly lists the identities of all rescue forces and their current callable status.

[0082] In summary, the system has achieved a preliminary sorting and classification of dynamic information on rescue forces and environmental situation information, providing standardized and consistent input for subsequent data fusion, avoiding analytical obstacles caused by inconsistent data formats or ambiguous meanings, and improving the system's ability to accept and understand multi-source information.

[0083] In summary, it eliminates the spatiotemporal inconsistencies of data from different sources, constructs an integrated situational view of the location, status, and environment of rescue forces, provides a real-time, coherent, and spatially visualized data foundation for subsequent event identification and resource scheduling, and enhances the comprehensiveness and accuracy of situational awareness.

[0084] In summary, it can quickly and automatically identify emergencies from massive amounts of dynamic data, clarify their location and nature, reduce the time and error of manual judgment, ensure that the rescue system can grasp the target events that need to be responded to in a timely and accurate manner, and provide a clear and complete list of events for subsequent task generation and priority determination.

[0085] In summary, the system dynamically integrates information on all available rescue forces in real time, including their identity, location, and readiness status. This provides an accurate and reliable resource pool for matching resources with tasks, avoiding scheduling conflicts or delays caused by unclear resource status, and improving the precision of rescue resource management and scheduling efficiency.

[0086] The connectivity path module 102 is used to generate the connectivity path and corresponding spatial distance of the target process based on the event geographical location of the event information set and the power location of the available resource set.

[0087] In this embodiment of the invention, when generating the connectivity path and corresponding spatial distance of the target process based on the event geographic location of the event information set and the power location of the available resource set, it is specifically used for:

[0088] Based on the geographic information grid, the coordinates of the event's geographical location are connected with the coordinates of the force's location to obtain the connectivity path of the target process;

[0089] The spherical distance of the connected path is taken as the spatial distance of the target process.

[0090] Specifically, the geographic information grid covers the entire mission area and divides it into a series of fixed-size, uniquely coded grid cells. Event geographic coordinates are derived from the event information set, which records the latitude and longitude of the incident location. Force location coordinates are derived from the available resource set, which records the latitude and longitude of the current location of various rescue platforms. Upon receiving coordinate pairs from the event information set and the available resource set, the system first maps each coordinate point to its corresponding geographic information grid cell.

[0091] Specifically, when processing each connected path generated in the previous step, the latitude and longitude values ​​of its starting and ending points are extracted. Since the Earth is an approximate sphere, the curvature of the Earth's surface must be taken into account when measuring the distance between two points over a large geographical area. Therefore, the system uses a method of calculating spherical distances to obtain the true spatial distance.

[0092] Furthermore, this mapping process is accomplished by calculating the relationship between coordinate points and the boundaries of each grid cell to determine the cell code to which it belongs. After mapping, the system creates a connection for each pair of event locations and force locations. This connection is represented internally by a virtual, branchless straight line segment. The starting point of this line segment is the center point or mapping point of the grid cell where the force location is located, and the ending point is the center point or mapping point of the grid cell where the event location is located. This virtual straight line segment established in the grid space, from the rescue force point to the event point, is defined and generated as a connected path for the target process. The system generates such a connected path for each pair of potentially matching events and resources, thus forming a set of connected paths, providing the basic geometric objects for subsequent distance calculations and feasibility screening.

[0093] Furthermore, the latitude and longitude coordinates of the two points are converted from degrees to radians. Next, based on the principles of spherical trigonometry, the system treats the two points as points on a sphere, and the central angle formed by them and the center of the sphere can be calculated using the converted latitude and longitude radians. The size of this central angle reflects the radian of the great circle formed by the two points on the Earth's surface. Then, the system multiplies this central angle by a fixed constant value representing the average radius of the Earth, chosen based on a standard Earth model. Through this multiplication, the system obtains the length of the great circle between the two points, expressed in units of length, which is the spherical distance. For each connected path, the system performs the above calculation process once and assigns the result—a specific length value—to the connected path as its corresponding spatial distance.

[0094] In summary, this approach establishes initial, unambiguous geometric connections in a standardized and computable manner for all subsequent distance calculations and feasibility analyses. This method transforms complex spatial relationship calculations into processing well-defined geometric objects, avoiding the chaos and performance degradation caused by inconsistent coordinate systems or directly calculating all point pair combinations. It lays a solid foundation for the system to quickly and efficiently process a large number of potential pairings of events and resources.

[0095] In summary, it provides a realistic geographical distance measurement between rescue forces and incident points, rather than a simple straight-line distance. This is crucial for large-scale cross-domain rescue operations across land, sea, air, and space, because it takes into account the Earth's curvature, making the calculated spatial distance more consistent with the actual mileage required for the vehicles to travel. This results in more accurate and reliable subsequent key decision-making bases such as matching and screening based on coverage mileage and time estimation based on response speed, effectively improving the feasibility and rationality of dispatch plans in real geographical environments.

[0096] The pending set module 103 is used to determine the pending matching pair of the target process based on the comparison result between the spatial distance and the coverage mileage corresponding to the available resource set carrier type.

[0097] In this embodiment of the invention, when determining the matching pair to be processed in the target process based on the comparison result between the spatial distance and the coverage mileage corresponding to the available resource centralized carrier type, it is specifically used for:

[0098] The maximum operable distance within the mission cycle of the target process is determined based on the carrier type identifier associated with the available resource set and the power location, thus obtaining the coverage mileage of the target process;

[0099] When the spatial distance is less than or equal to the coverage mileage, the information entries of the event information set are combined with the information entries of the available resource set to obtain the target process matching pair to be processed.

[0100] Specifically, the carrier type identifier is a unique code used to distinguish different categories of rescue platforms, such as a string or numeric identifier representing a specific type of land vehicle, sea vessel, aircraft, or spacecraft. The system maintains a carrier performance configuration library, which uses the carrier type identifier as a unique key and predefines the maximum operational distance for each carrier type under a standard mission cycle. This distance is a fixed value determined based on factors such as the carrier's design endurance, typical refueling cycle, average operating speed, and the maximum allowed continuous operating time for the mission. When the system processes the available resource set, it reads each resource entry one by one and precisely extracts the carrier type identifier field bound to the force location information.

[0101] Specifically, the system iterates through all possible combinations of events and rescue forces. For each combination, the following operations are performed: the pre-calculated spatial distance value for that combination is retrieved from storage, along with the corresponding coverage mileage value for that rescue force. The system then compares these values ​​to determine if the spatial distance is less than or equal to the coverage mileage. This comparison is a rigorous arithmetic operation. If the condition is met, it indicates that the rescue force's operational radius is sufficient to cover the event location within the mission period, demonstrating preliminary geographical feasibility for response.

[0102] Furthermore, using this carrier type identifier as the query condition, a precise match is performed in the carrier performance configuration database to retrieve the corresponding maximum operational distance value. This search process is a direct, one-to-one mapping retrieval, ensuring that each rescue force obtains a definite distance value. The system assigns this retrieved value to the entry for that rescue force, as its coverage mileage within the current mission cycle.

[0103] Furthermore, once the comparison conditions are met, the system locates the complete information entry corresponding to the event from the event information set. This entry includes all fields such as the event's unique identifier, the event's geographical coordinates, and textual description. Simultaneously, the system locates the complete information entry corresponding to the rescue force from the available resource set, including all fields such as the force's unique identifier, force location coordinates, carrier type identifier, and availability status. The system copies and integrates all data content from these two independent entries into a new, unified data structure. This structure simultaneously and completely contains all information about both the event and the rescue force, implicitly implying the pairing relationship between them. This newly created data unit is a matching pair to be processed.

[0104] In summary, this provides an objective and quantifiable basis for subsequent matching and screening, ensuring that dispatch decisions are based on the actual physical capabilities of rescue forces. This avoids the risk of assigning unreachable tasks due to ignoring vehicle performance limitations, and also prevents the inefficient use of high-capacity resources or the overload assignment of low-capacity resources. Thus, it improves the scientific nature and reliability of task allocation from the source, which is a key prerequisite for achieving accurate resource matching.

[0105] In summary, this process effectively screened geographical accessibility, automatically eliminating task options that were simply infeasible due to excessive distance, significantly narrowing down the scope of subsequent fine-grained planning and sorting. This not only reduced the system's computational burden and improved the efficiency of scheduling generation, but more importantly, it ensured that every pair in the generated set of pending matches met the most basic power reachability condition, laying a solid foundation for constructing a feasible initial task set.

[0106] The initial task module 104 is used to determine the event level of the target process based on the state parameters and event nature in the matching pairs to be processed, and sort the matching pairs to be processed using the event level as an index to obtain the initial task set of the target process.

[0107] In this embodiment of the invention, the step of determining the event level of the target process based on the state parameters and event properties in the matching pairs to be processed, and sorting the matching pairs to be processed using the event level as an index to obtain the initial task set of the target process, is specifically used for:

[0108] The state parameters include the typical response speed corresponding to the spatial distance and the carrier type identifier;

[0109] Keyword analysis is performed on the text information in the event information set to identify the event nature that characterizes the severity of the event in the target process;

[0110] A basic level coefficient is set based on the nature of the event, and the delay factor of the target process is calculated based on the spatial distance and the typical response speed.

[0111] The urgency score calculated based on the base level coefficient and the delay factor is converted into the situation level of the target process;

[0112] Using the numerical value of the situation level as the sorting key, the matching pairs to be processed are sorted in descending order to obtain an ordered queue of the target process;

[0113] The information pairs in the ordered queue are encapsulated into structured task entries according to their arrangement order to obtain the initial task set of the target process.

[0114] Specifically, the typical response speed is obtained as follows: the system queries a predefined carrier performance configuration library based on the carrier type identifier recorded in the matching pair to be processed. This library explicitly associates a fixed value with each carrier type identifier, which is the average travel speed of that type of carrier to the incident site in a standard environment and task mode. This fixed value is the typical response speed.

[0115] Specifically, the text information field in the event information set contains a textual description of the event. The system internally maintains an event-related keyword library, which maps different keywords or phrases to specific event-related categories. The system reads the event text description information associated with the matching pair to be processed, performs word segmentation on the text, and divides it into independent word or phrase units.

[0116] Specifically, based on the event nature identified in the previous step, a preset mapping table is queried. This mapping table assigns a fixed numerical coefficient to each specific event nature. This numerical coefficient is called the basic level coefficient, which directly reflects the inherent severity level of the event nature, independent of location and resources.

[0117] Specifically, a static urgency component is obtained by multiplying the base level coefficient by a fixed coefficient representing the static severity weight of the event. Simultaneously, a fixed calculation method is used to process the delay factor, for example, converting it into a timeliness impact score that is inversely proportional to or non-linearly related to time. This timeliness impact score is then multiplied by a fixed coefficient representing the response timeliness weight to obtain a dynamic urgency component. Finally, the system adds the static urgency component and the dynamic urgency component to obtain a comprehensive urgency score.

[0118] Specifically, for each pair of matches to be processed in the list, its determined state level is extracted. This state level is typically quantified into a comparable numerical value. Then, the system sorts all the pairs of matches in the list according to these state level values. The sorting follows a descending order, meaning that pairs with higher state level values ​​are listed earlier in the list, and those with lower values ​​are listed later.

[0119] Specifically, for the currently processed matching pair, the system reads all the information it contains. This information comes from the original event information entries and available resource entries, including all relevant data fields such as event identifier, event location, event description, event nature, force identifier, force location, carrier type, state parameters, and calculated event level. The system then fills these scattered data fields into the corresponding designated positions in a predefined structured task entry template.

[0120] Furthermore, by performing a lookup operation, the typical response speed value obtained is associated with the matching pair to be processed, thereby completing the preparation of the state parameters.

[0121] Furthermore, these words or phrases are compared one by one with entries in the event nature keyword database. When a word in the text is found to be a perfect match with an entry in the keyword database or to satisfy a preset semantic matching rule, the system determines that the event has the event nature corresponding to that keyword entry. By traversing the entire text and summarizing the natures indicated by all matched keywords, the system finally determines one or more event nature tags used to characterize the severity of the event.

[0122] Furthermore, the system acquires the spatial distance and typical response speed values ​​from the matching pairs to be processed, and divides the spatial distance value by the typical response speed value, performing a division operation. The result of this division operation is a value in units of time, representing the time required for the rescue force to reach the incident scene at its typical speed under ideal linear motion without obstruction. This calculation result is defined as the delay factor. The larger the delay factor, the longer the theoretical response time.

[0123] Furthermore, after obtaining the urgency score, the system converts it into a situation level. The conversion method is based on a preset numerical interval division table: this table defines several continuous or discontinuous urgency score intervals, each interval corresponding to a discrete situation level. The system compares the calculated urgency score with these intervals to determine the interval to which it belongs, and uses the level label corresponding to that interval as the final situation level determined for the matching pair to be processed.

[0124] Furthermore, this sorting process is implemented using standard comparison sorting algorithms, such as quicksort or mergesort. These algorithms repeatedly compare the state level values ​​of different matching pairs and swap their positions in the list accordingly, until all elements in the entire list are arranged in descending order of state level. Once sorted, this new list of matching pairs to be processed, arranged in descending order of state level, is defined as the ordered queue of the target process.

[0125] Furthermore, this template defines a fixed format for task entries, ensuring that each task entry contains data items of the same type and order. Once populated, a complete structured task entry is generated, integrating all relevant attributes of the task and implicitly indicating its processing order. The system repeats this process until all matching pairs in the ordered queue have been processed. Finally, the system collects all sequentially generated structured task entries into a new set, which becomes the initial task set for the target process.

[0126] In summary, it provides an objective and calculable unified input for all subsequent timeliness assessments, transforming the two key physical elements of how far and how fast in rescue missions into numerical values ​​that the system can directly process. This ensures that priority judgments are based on a consistent and comparable quantitative foundation, avoiding inconsistencies in decision-making caused by relying on subjective experience or vague descriptions.

[0127] In summary, it can quickly and accurately extract key features that determine the basic urgency of an event from unstructured text information, achieving objective and standardized classification of event severity, reducing reliance on manual interpretation, and ensuring consistency and repeatability of severity assessments across different events.

[0128] In summary, the urgency of a task is quantified using both static and dynamic dimensions: a base level coefficient solidifies the severity of the event itself, while a delay factor quantifies the time pressure caused by geographical distance and response capabilities. This separate quantification approach allows the system to clearly distinguish and comprehensively address both the inherent urgency of an event and the external response challenges.

[0129] In summary, it provides a unified, sortable priority metric that reflects not only the static severity of an event but also the dynamic impact of response timeliness. Converting continuous scores into discrete levels simplifies subsequent sorting and classification operations, making task priorities clearer and easier to manage.

[0130] In summary, a clear task processing order queue is automatically generated, ensuring that the system prioritizes the most urgent and pressing task combinations. This quantified ranking avoids blind and arbitrary resource allocation and is the core mechanism for optimizing overall rescue response efficiency and achieving the principle of "handling urgent matters first."

[0131] In summary, a standardized and clearly defined task list was produced that can be directly used for subsequent in-depth planning. Each entry integrates all necessary information such as events, resources, and priorities, providing a clear and orderly input to the scheduling system. It is a key output of the entire scheduling process as it transitions from the evaluation and screening phase to the detailed planning phase.

[0132] In this embodiment of the invention, the formula for calculating the urgency score is specifically used for:

[0133]

[0134] in, Rate the urgency level. This refers to the weighting coefficient for static events. The basic level coefficient is... The weighting coefficient for response timeliness, The typical response speed is... This is the path complexity reduction factor. For distance nonlinearity sensitive factors, The spatial distance is described.

[0135] Specifically, the weighting coefficients for static events and response time are fixed values ​​pre-set and stored in the configuration library by the system, used to adjust the importance ratio of different factors in the calculation. The basic level coefficient is derived from the keyword analysis process of the text description field in the event information set. After identifying the nature of the event by matching the preset keyword library, the system directly assigns the value according to the mapping table between the nature and the coefficient. The typical response speed is obtained by querying the carrier performance configuration library through the carrier identifier of the rescue force. The library defines standard speed values ​​for each carrier type in a specific medium environment. The path complexity reduction coefficient is calculated based on the spatial coordinate sequence of feasible paths. The system analyzes the curvature of the path and the degree of obstacle avoidance, and generates a coefficient value between zero and one according to a predefined rule set. The distance nonlinearity sensitivity factor is a constant parameter built into the system, used to adjust the nonlinear shape of the influence of spatial distance on the response time. The spatial distance is a spherical distance value obtained directly from the connected path generated in the previous steps, which represents the actual ground length between the starting point of the rescue force and the endpoint of the event.

[0136] Furthermore, the urgency score is calculated by combining a static event urgency component and a dynamic response timeliness component. When calculating the static event urgency component, the system performs a multiplication operation, multiplying the weighting coefficient of the static event by the base level coefficient to obtain the component value. When calculating the dynamic response timeliness component, the system first performs a multiplication operation, multiplying the typical response speed by the path complexity reduction factor to obtain a product 1. Then, another multiplication operation is performed, multiplying the distance nonlinearity sensitivity factor by the spatial distance to obtain a product 2. Next, an addition operation is performed, adding the product 1 and the product 2 to obtain a sum, which serves as the denominator. Then, a division operation is performed, dividing the product 1 by this sum to obtain a ratio value between zero and one. Finally, another multiplication operation is performed, multiplying this ratio value by the response timeliness weighting coefficient to obtain the dynamic response timeliness component. The system finally performs an addition operation, adding the static event urgency component and the dynamic response timeliness component; the sum obtained is the urgency score. This score comprehensively reflects the inherent severity of the event and the timeliness challenges faced by the rescue response; a higher score indicates a more urgent task.

[0137] In general, as the base level coefficient increases, the urgency component of static events increases linearly, leading to an overall increase in urgency scores. When spatial distance increases, the product two also increases, which increases the denominator in the calculation of the dynamic response timeliness component, resulting in a decrease in the ratio value and ultimately a decrease in the dynamic response timeliness component, thus dragging down the overall urgency score. When typical response speed or path complexity reduction coefficient increases, the product one increases. Although this slightly increases the denominator, the magnitude of the numerator increase dominates the increase in the ratio value, thus increasing the dynamic response timeliness component and pushing the overall urgency score higher. The weight coefficients of static events and response timeliness, as fixed multipliers, determine the scaling magnitude of the corresponding components; the larger the coefficient value, the greater the contribution weight of that component to the total score. When the distance nonlinearity sensitivity factor increases, it amplifies the influence of spatial distance in the product two, strengthening the weakening effect of distance on the dynamic response timeliness component, making the curve of urgency score decreasing with increasing distance steeper. Overall, the calculation model ensures that the more severe the event, the greater the potential for rescue response, and the smaller the geographical distance, the higher the urgency score generated.

[0138] The feasible path module 104 is used to modify the connectivity path using the environmental obstacle set of the situational data stream as boundary conditions, so as to obtain the feasible path of the target process and the corresponding estimated arrival time.

[0139] In this embodiment of the invention, when the environmental obstacle set of the situational data stream is used as a boundary condition to correct the connectivity path and obtain the feasible path of the target process and the corresponding estimated arrival time, it is specifically used for:

[0140] Obstacle marker records are selected from the situational data stream, and the geographic boundary coordinate set of the obstacle marker records is extracted to form the environmental obstacle set of the target process;

[0141] Geometric intersection calculation is performed between the straight line segment of the connected path and the polygonal obstacle region represented by the environmental obstacle set: when the straight line segment is detected to cross the polygonal obstacle region, a polyline path that avoids the boundary of the polygonal obstacle region is searched in the spatial range of the target process, with the coordinates of the force position as the starting point and the coordinates of the event geographical location as the ending point, and the polyline path is determined as the feasible path of the target process.

[0142] The estimated arrival time of the target process is determined based on the spatial coordinate sequence of the feasible path.

[0143] Specifically, the system iterates through each data record in the situational data stream, checking if it contains an identifier explicitly marked as an obstacle or having an equivalent indicative function. This identifier distinguishes data describing environmental obstacles from data describing events, resources, and other elements. Once an obstacle marker record is identified, the system extracts a set of geographic boundary coordinates describing the spatial extent of the obstacle. This coordinate set typically consists of a series of sequentially arranged latitude and longitude coordinates that, when connected sequentially, enclose a closed polygonal region.

[0144] Specifically, the system constructs a line segment by connecting the start and end coordinates of the straight line segment of the connected path, and then connects the sequence of boundary coordinates of the polygonal obstacle region to form a closed polygon. The system determines whether this line segment crosses the interior of the polygon by checking if the line segment intersects any boundary edge of the polygon and whether the endpoints of the line segment are located inside the polygon. Intersection detection is achieved by calculating the intersection points of the line segment with the straight lines containing each boundary edge and determining whether these intersection points are simultaneously within the actual line segment range of both the line segment and the boundary edge. When a straight line segment of the connected path is detected to cross a polygonal obstacle region, the system determines that the original straight path is infeasible.

[0145] Specifically, the total length of the feasible path is calculated by sequentially calculating the spherical distance between any two adjacent coordinate points in the sequence, in a manner identical to the method used to calculate the spherical distance of connected paths. Then, the spherical distances between all adjacent points are summed, and the final value obtained is the total length of the feasible path. Next, the system obtains the carrier identifier of the rescue force from the unprocessed matching pairs associated with the feasible path.

[0146] Furthermore, the extracted sequence of coordinate points, along with the unique identifier of the obstacle, is stored as an independent polygonal obstacle region description object. The system repeats the above identification, extraction, and storage process for all obstacle marker records, summarizing all the resulting polygonal obstacle region description objects into a single set. This set, containing the spatial boundary information of all identified obstacles, constitutes the environmental obstacle set for the target process.

[0147] Furthermore, the system initiates a polyline path search process: using the force location coordinates as the absolute starting point and the event location coordinates as the absolute ending point, it attempts to connect the starting point, the ending point, and a series of intermediate points to form a polyline within the pre-defined spatial range of the target process. The core rule of the search is that each segment of the polyline cannot intersect the interior of any polygonal obstacle region. The system constructs a polyline that satisfies the condition of not crossing obstacles by using several pre-generated candidate path points distributed within the spatial range, or by generating new path points along the outside of obstacle boundaries and attempting to connect these points. The first successfully found polyline that connects the starting point and the ending point and whose segments avoid all obstacle regions is determined by the system as a feasible path for the target process between the event and the resource. If the original connected path does not cross any obstacles, the straight line segment itself is directly determined as a feasible path.

[0148] Furthermore, based on this carrier identifier, the system queries the carrier performance configuration library to obtain the typical cruising speed of this type of rescue force when performing missions in the main medium environment of the current feasible path. This is a preset fixed value. Subsequently, the system calculates the theoretical travel time: dividing the total length of the feasible path by the typical cruising speed of the rescue force, performing a division operation, yields a theoretical value in time. Finally, the system performs time compensation based on real-time data of the target process to obtain the estimated arrival time: by accessing the real-time data stream, it considers the impact of current and predicted environmental factors on the typical cruising speed, these factors being represented by speed adjustment coefficients; the system multiplies the theoretical travel time by a speed influence coefficient that integrates these real-time factors, or directly adds a fixed delay time based on historical and real-time data, thereby correcting the theoretical time. The final time value obtained after compensation and adjustment is the estimated arrival time of the target process.

[0149] In summary, a precise, machine-understandable digital map of geographic restricted areas was dynamically constructed. This allows the system to clearly identify which areas are impassable (such as no-fly zones, disaster epicenters, and traffic disruption sections) when planning routes, thus transforming complex real-world constraints into clear digital boundary conditions. This lays a crucial environmental awareness foundation for generating safe and compliant rescue routes.

[0150] In summary, this ensures that the planned routes for each pair of matches are not only geometrically connected, but also physically feasible and safe, avoiding the planning of meaningless paths through dangerous or impossible areas, and greatly improving the practical feasibility and reliability of the scheduling scheme.

[0151] In summary, this method provides a more accurate and reliable estimate of task execution time. Compared to calculations that simply use straight-line distance and nominal speed, this method comprehensively considers the actual mileage added by detours, the actual performance of the vehicle in a specific medium, and the impact of the current environment. This makes the estimated arrival time output closer to the actual operation, providing high-quality and highly reliable critical time input for subsequent task sequencing, resource conflict detection, and overall scheduling timing optimization.

[0152] In this embodiment of the invention, the step of determining the estimated arrival time of the target process based on the spatial coordinate sequence of the feasible path is specifically used for:

[0153] The total length of the feasible path is obtained by summing the segmented distances between adjacent coordinate points within the spatial coordinate sequence.

[0154] Obtain the carrier identifier of the rescue force associated with the feasible path from the unprocessed matching pairs;

[0155] Based on the carrier identifier, determine the typical cruising speed of the type of rescue force of the matching pair to be processed in the medium environment of the feasible path;

[0156] The ratio of the total length of the feasible path to the typical cruise speed is used as the theoretical travel time of the target process;

[0157] The theoretical travel time is compensated based on the real-time data of the target process to obtain the estimated arrival time of the target process.

[0158] Specifically, starting from the first point in the sequence, each pair of adjacent coordinate points is processed sequentially. For each pair of adjacent points, the system uses a fixed method for calculating spherical distance: the latitude and longitude values ​​of the two points are converted from degrees to radians, and then the central angle radians between the two points and the Earth's center are calculated based on these radian values ​​using the principles of spherical trigonometry. Finally, this central angle radian is multiplied by the Earth's average radius constant to obtain the precise spherical distance between the pair of adjacent points. This result is a piecewise distance.

[0159] Specifically, each feasible path, upon generation, is uniquely bound to a specific pair of matching data to be processed. This pair contains information about the combination of a specific event and specific rescue forces. Based on this binding relationship, the system locates the corresponding data structure of the matching pair to be processed. Within this data structure, the system accesses and extracts the information entries for the available resource set.

[0160] Specifically, the system accomplishes this by querying a predefined, structured performance database. This database uses the carrier identifier as the primary key and stores standard performance parameters for each rescue carrier type under different media environments. After obtaining the carrier identifier, the system inputs it into the performance database as a precise query condition. Simultaneously, the system analyzes the spatial coordinate sequence of the current feasible path: by determining the main area type covered by the path's coordinate points, it identifies the media environment in which the path is located.

[0161] Specifically, the system retrieves the total length of the calculated feasible path from storage, expressed in units of length. Simultaneously, it retrieves the typical cruise speed value determined in the previous step that matches the task, expressed as the distance traveled per unit time. The system then performs a division calculation, using the total length value as the dividend and the typical cruise speed value as the divisor.

[0162] Specifically, the system continuously receives real-time data streams related to the mission area. These data include, but are not limited to, current meteorological information, marine hydrological information, traffic conditions, and the carrier's own real-time status reports. The system has a built-in compensation model that calculates a speed impact coefficient or an additional time delay based on specific fields or combinations of indicators from these real-time data.

[0163] Furthermore, after calculating the segmented distance between a pair of points, it is immediately accumulated into a summation variable. Next, the system moves to the next pair of adjacent coordinate points, repeating the exact same spherical distance calculation process and accumulating the new segmented distance value into the summation variable. The system processes this sequentially until the last pair of adjacent points in the sequence has been calculated. At this point, the final value in the accumulated summation variable represents the actual length of the entire polyline path formed by connecting all the coordinate points in sequence on the Earth's surface. This value is assigned and stored by the system as the total length of the feasible path.

[0164] Furthermore, within this information entry, the system searches for and reads a field named "Carrier Type Identifier." This field contains a predefined code or string that uniquely distinguishes different rescue platform types. The code or string extracted by the system is the carrier identifier of the rescue force directly related to the rescue mission served by the currently feasible path.

[0165] Furthermore, the system performs a joint query on the database using both the vehicle identifier and the media environment. The database returns the typical cruising speed value corresponding to that vehicle in that media environment. This is a fixed value representing the sustainable speed that this type of force can typically maintain when performing tasks in the corresponding environment.

[0166] Furthermore, this calculation directly yields a result in units of time. This result represents the time required for the rescue force to reach its destination from the starting point under ideal conditions, assuming it strictly follows the planned feasible route, travels at its typical cruising speed in this environment, and experiences no delays.

[0167] Furthermore, if the system detects headwinds in the path area and the wind force reaches a specific threshold, it will adjust the effective cruising speed according to preset rules to calculate a new, slower speed value; alternatively, the system can directly add a fixed travel delay time to a certain segment of the path based on historical data under similar conditions. The system applies this compensation model to correct the theoretical travel time: either recalculating the travel time using the adjusted speed value, or adding the calculated additional delay time to the theoretical travel time. The new time value obtained after this compensation calculation is an estimate that more closely approximates the actual situation, incorporating ideal conditions and current practical constraints. The system determines this final value as the estimated arrival time of the target process.

[0168] In summary, this provides accurate index keys for subsequent performance parameter queries. This allows the system to directly associate a specific planned path with the capabilities of a particular type of rescue platform, ensuring that the acquisition of key parameters such as speed is highly targeted and accurate, and avoiding fundamental deviations in time estimation due to incorrect resource type identification.

[0169] In summary, this method matches the target task with standard speed parameters that best suit its actual operational scenario. Compared to using a general average speed, this approach distinguishes between different vehicle types and the performance differences of the same vehicle in different environments. This makes the calculated travel time benchmark closer to the actual operational capabilities of this type of force in this type of environment, improving the rationality and discriminativeness of the time prediction model.

[0170] In summary, it provides a clear and reproducible baseline time estimate. This theoretical value, stripped of real-time dynamic interference, provides the ideal or optimal value of the time required for task execution purely from the perspective of physical distance and nominal carrier performance. It establishes an objective comparison benchmark for subsequent compensation and correction and is the core calculation link of the entire time estimate logic.

[0171] In summary, static theoretical calculations are transformed into dynamic, realistic comprehensive estimates. This makes the estimated arrival time output by the system no longer a rigid ideal value, but a more flexible and reliable prediction that can respond to real-time situational changes. This significantly enhances the adaptability of the scheduling scheme to complex real-world factors and improves the executability and reliability of the overall scheduling sequence.

[0172] The task scheduling module 105 is used to bind the initial task set with the spatial coordinate sequence of the feasible path and the estimated arrival time as spatiotemporal constraints to obtain the task scheduling set of the target process.

[0173] In this embodiment of the invention, when binding the initial task set with the spatial coordinate sequence of the feasible path and the estimated arrival time as spatiotemporal constraints to obtain the task scheduling set of the target process, the specific usage is as follows:

[0174] The task elements of the matching pairs to be processed in the initial task set are associated and stored with the spatial coordinate sequence and the field of the estimated arrival time to obtain the segmented task set of the target process;

[0175] Traverse the initial task set, aggregate the segmented task sets, and obtain the preliminary task scheduling set for the target process;

[0176] Based on the feasible path, conflict resolution is performed on the preliminary task scheduling set to obtain the task scheduling set of the target process.

[0177] Specifically, the system first reads a task entry sequentially from the initial task set. This entry contains all task elements in a structured manner, including event information, resource information, and situation level. Next, the system uses the implicit event-resource pairings within this entry to locate two specific data objects previously generated and stored within the system for that pairing: one is a sequence of spatial coordinates describing the feasible path, and the other is the calculated estimated arrival time. The system then creates a new data container and copies all task elements of the current task entry into this new container.

[0178] Specifically, starting with the first task entry in the initial task set, the process proceeds sequentially to the last entry. For each task entry encountered, the system does not process it directly. Instead, it triggers and waits for the segmented task set generation process described in the previous section to generate a corresponding segmented task set entry for that task entry. After generating a segmented task set entry, the system immediately adds this entry to a dedicated collection container. This container is initialized to empty before the traversal begins. The system then processes the next task entry in the initial task set, similarly generating its segmented task set entry and adding it to the same collection container.

[0179] Specifically, during the conflict detection phase, the system examines each segmented task set entry in the initial task scheduling set. The system performs resource time conflict detection: it compares the rescue force identification field in different entries. When the same rescue force identification appears in two or more entries, it further compares the estimated arrival times associated with these entries. The system calculates the task execution time period implied by each time period field. If any of the calculated time periods overlap, a resource time conflict is determined, and all involved entries are marked with a resource time conflict flag.

[0180] Furthermore, the system stores the located spatial coordinate sequence and estimated arrival time as two new, independent fields in this new container, establishing a strong association with the task elements within the container. This new container constitutes a complete segmented task set entry, integrating task description, planned route, and estimated time. The system repeats the above process of reading, locating, creating containers, copying elements, and adding path and time fields for each task entry in the initial task set, generating a corresponding enhanced segmented task set entry for each original task.

[0181] Furthermore, this process is repeated until all task entries in the initial task set have been processed. When the iteration ends, the collection container has gathered all the segmented task set entries corresponding to each initial task in the original task order. The system uses this complete set containing all integrated task entries as the initial task scheduling set for the target process.

[0182] Furthermore, the system performs path space conflict detection: it compares the spatial coordinate sequences of feasible paths for different items, and uses geometric calculations to determine whether these paths intersect or overlap in two-dimensional or three-dimensional space. If paths physically intersect, the system calculates the specific time periods during which rescue forces for different tasks are expected to pass through the intersection area based on the planned start time and estimated duration of each task. If the calculated time periods overlap, a path space conflict is determined, and the relevant items are marked with a path space conflict identifier. In the conflict resolution phase, for resource time conflicts, the system employs a time offset method: for multiple conflicting tasks targeting the same rescue force, it postpones the planned start time of lower-priority tasks according to their situation level and original order until the time periods of all tasks no longer overlap, forming a time-ordered optimized sequence. For path space conflicts, the system employs a path replanning method: for tasks with spatial intersection conflicts, it searches for alternative routes for their feasible paths, replaces the original paths with new feasible paths, and recalculates the estimated duration until the spatial and temporal intersections are eliminated, forming a path-ordered optimized sequence. Finally, the system merges all task entries in the timing optimization sequence and path optimization sequence obtained after conflict resolution, removes conflict markers, and forms a unified, conflict-free task list. This list is then determined as the task scheduling set for the final target process.

[0183] In summary, this approach elevates task planning information from abstract to concrete, creating an independent data packet containing complete spatiotemporal constraints for each task to be executed. This ensures that each task has a clear, executable route and reliable time expectations during subsequent scheduling, avoiding planning gaps caused by scattered or missing information, and providing a unified and rich data unit foundation for the system's refined scheduling and conflict analysis.

[0184] In summary, a comprehensive but unoptimized initial scheduling blueprint was constructed. This preliminary task scheduling set fully reflects all currently identified task requirements and their preliminary planning schemes, serving as the starting point for the system's global review and optimization. It aggregates scattered, independent task plans into a holistic view, enabling the system to assess potential resource competition, spatiotemporal conflicts, and other issues between tasks from a global perspective, providing the necessary operational objects and scope for subsequent conflict detection and resolution.

[0185] In summary, this ensured the internal consistency and executability of the final scheduling plan. By proactively identifying and resolving issues such as the duplicate use of the same resource or the spatiotemporal overlap of different paths, this step optimized the initial, potentially contradictory plan into a feasible solution that avoids conflicts in resource timing and spatial path usage. This significantly improved the operability of the scheduling instructions, avoided the risks of resource contention or route collisions during mission execution, and is a crucial final step in ensuring the orderly, efficient, and safe conduct of the entire rescue operation.

[0186] In this embodiment of the invention, when performing conflict mediation on the preliminary task scheduling set based on the feasible path to obtain the task scheduling set of the target process, it is specifically used for:

[0187] When the same rescue force in the available resource set is assigned to two or more feasible paths in the task scheduling set, and the estimated arrival time overlaps, the task scheduling set is marked as having a resource time conflict.

[0188] When the feasible paths in the task scheduling set intersect or overlap spatially, and the departure time of each feasible path intersects with the time period calculated based on the estimated arrival time of the intersecting area, the task scheduling set marks the path spatial conflict.

[0189] To address the resource time conflict, the planned start time of the tasks in the task scheduling set is postponed until the time overlap is eliminated, thus obtaining the time-optimized sequence of the target process;

[0190] In response to the path space conflict, alternative feasible paths are searched for tasks in the task scheduling set until the spatial and temporal intersection is eliminated, thus obtaining the path optimization sequence of the target process.

[0191] By merging the time-series optimization sequence and the path optimization sequence, the task scheduling set of the target process is obtained.

[0192] Specifically, the system sequentially extracts the rescue force identifier recorded in each task entry. This identifier uniquely corresponds to a specific rescue unit within the available resource set. The system establishes a mapping table in memory to record the indexes of all task entries where each rescue force identifier appears. When the system finds that the same rescue force identifier appears in two or more task entries, it triggers a detailed time analysis of that group of entries. The system reads the estimated arrival time field and the task's implicit planned start time from these entries, and calculates the absolute time period expected to be occupied by each task based on the start time and the arrival time, i.e., the interval from the start time to the start time plus the estimated arrival time. The system compares these time periods pairwise to check if they overlap on the timeline.

[0193] Specifically, the task scheduling set is traversed to extract the spatial coordinate sequence of feasible paths stored in each task entry; for any two different feasible paths, the system performs geometric intersection detection: this is achieved by calculating whether there is an intersection point between all continuous line segments of the two paths, that is, checking whether the extension of one line segment intersects with another line segment and the intersection point is located within the body of both line segments.

[0194] Specifically, all task entries involving the same rescue force conflict are grouped and extracted from the conflict list; the system sorts these task entries according to their original situation level, with higher-level tasks having higher priority, and tasks with the same level can refer to their original order in the initial task set; the system keeps the planned start time of the highest priority task unchanged as a baseline; for the next priority task, the system sets its planned start time to a fixed safety interval after the end of the time period of the previous task, which is a preset minimum amount of time to ensure force conversion or preparation.

[0195] Specifically, for each pair of tasks with path space conflicts, the system selects the task with lower priority for path replanning. The system uses the original force position coordinates of the task as the starting point and the event geographical location coordinates as the ending point, and calls the path search algorithm again. The core constraint of this search is that the newly found feasible path must avoid the spatial intersection area with the path of the higher priority task. This is achieved by temporarily treating the area near the path of the higher priority task as a virtual obstacle zone. The path search algorithm adds this virtual obstacle zone to the original set of environmental obstacles, and then re-executes the previously used polyline path search process to find a new path that connects the starting point and the ending point without crossing any real or virtual obstacles.

[0196] Specifically, first, all task entries from the timing optimization sequence are loaded into a new collection container. These entries have resolved resource time conflicts and have updated scheduled start times. Next, the system loads task entries from the path optimization sequence, but needs to handle potential overlaps: because the two sequences may contain different updated versions of the same original task. The system matches tasks based on their unique identifiers.

[0197] Furthermore, as long as any two time periods overlap, the system determines that a resource time conflict has occurred. The system then attaches a clear resource time conflict status mark to all task entries involved in the conflict and records these entries in a special conflict list. However, the fact that such conflict entries exist in the task scheduling set itself as a whole has been recorded and marked by the system.

[0198] Furthermore, if at least one such intersection is detected, it is determined that the two paths intersect or overlap spatially. After determining the spatial intersection, the system performs temporal intersection analysis: it obtains the planned start time and estimated arrival time of the tasks belonging to the two paths, calculates the approximate arrival time interval of each task from the starting point to the aforementioned spatial intersection point, and obtains this calculation by proportionally converting the total path length to the estimated arrival time. The system compares the arrival time intervals of the two tasks at the intersection point to determine whether they overlap in time. If the time intervals overlap, it is determined that a path spatial conflict has occurred. The system adds a path spatial conflict status mark to the task entries corresponding to these two paths and records the conflict details. Similarly, the task scheduling set is thus marked as having such a conflict.

[0199] Furthermore, the system uses this new start time, combined with the original estimated arrival time of the task, to recalculate its time period. Then, the system checks the overlap of this new time period with the time periods of all adjusted tasks again. If there is still an overlap, the start time of the task is postponed until its time period has no overlap with the time periods of all higher priority and adjusted tasks. The system processes all task groups with resource time conflicts in this way. After processing, the time arrangements of all tasks no longer overlap. The system arranges these adjusted task items in the new time order to form a new task list without time conflicts. This list is defined as the time-series optimization sequence.

[0200] Furthermore, if a new path is found, the system calculates its total length and the new estimated arrival time, and updates the spatial coordinate sequence and estimated arrival time field of the feasible path in the task entry. Then, the system uses the new path and time to perform spatial and temporal intersection checks with all related tasks again. If the conflict is still not resolved, the system may try to replan the path for another conflicting task, or further adjust the planned start time for fine-tuning. This process is iterated until all pairs of path spatial conflicts are eliminated. Finally, all tasks have mutually non-conflicting paths, and the set of these updated task entries is defined as the path optimization sequence.

[0201] Furthermore, for tasks that also exist in the path optimization sequence, the system uses the updated planned start time of the task in the time-series optimization sequence, but combines the updated feasible path spatial coordinate sequence and the corresponding estimated arrival time recalculated based on the new path, forming the final entry for the task. For tasks that exist only in the time-series optimization sequence, their entries are directly added to the final set. The system ensures that each merged task entry contains the latest, conflict-free time schedule and path planning. Finally, the system sorts all tasks in this merged set according to the order of their finally determined planned start times, forming a unified and coherent task list. This list is the final task scheduling set for the target process.

[0202] In summary, this system enables proactive and accurate detection of resource over-allocation or scheduling conflicts. This avoids resource timing contradictions that might go unnoticed due to human oversight or computational complexity, ensuring that the system is aware of timing conflicts in the scheduling scheme before subsequent optimization. It provides a clear and specific set of target problems for targeted resource timing optimization and is the primary detection mechanism to ensure the executability of the scheduling scheme in the time dimension.

[0203] In summary, this extends safety early warning from simple path planning to the spatiotemporal coordination level. This enables the system to proactively detect the risk that different rescue forces may intersect or converge in the same spatial area at a specific time, thereby actively avoiding potential safety hazards such as traffic congestion, collisions, or mutual interference. It is a key spatial situational awareness and conflict early warning link for improving the safety and smoothness of multi-task parallel execution.

[0204] In summary, this paper presents a direct and effective method for serializing resource scheduling. When resources cannot be increased, by rearranging task timing, it ensures that each resource is allocated only one task at a time, thus fundamentally resolving resource timing conflicts. This guarantees the clarity, orderliness, and executable nature of scheduling instructions on the timeline, making it a core timing adjustment technique for optimizing resource utilization and achieving pipelined task execution.

[0205] In summary, the system provides a flexible solution to spatial conflicts without significantly delaying task execution. By adjusting local paths, the system can resolve competition between different tasks on spatial channels within the constraints of fixed resource availability and time windows, maintaining concurrent task execution capabilities while eliminating potential spatial security risks. This demonstrates the flexibility and optimization capabilities of the scheduling system in finding feasible solutions under complex spatiotemporal constraints.

[0206] In summary, the final integration and verification of the scheduling scheme were completed. By decoupling and then merging the time and spatial dimensions, the system can produce a complete, globally coordinated action plan that is consistent in both resource usage timing and spatial path occupancy. This marks the transformation from local optimization to global optimization. The final output task scheduling set is a highly operable, safe, and efficient set of rescue operation instructions that can be directly used for command and execution.

[0207] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0208] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0209] Finally, it should be noted that 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A system for integrated situational awareness display and resource scheduling optimization of land, sea, air, and space rescue forces, characterized in that, The system includes a resource integration module, a connectivity path module, a task set module, an initial task module, a feasible path module, and a task scheduling module, wherein: The resource integration module is used to merge the state data and environmental data of the target process to obtain the situational data stream of the target process, and to separate the event information set and available resource set of the target process according to the type identifier of the data record in the situational data stream. The connectivity path module is used to generate the connectivity path and corresponding spatial distance of the target process based on the event geographic location of the event information set and the power location of the available resource set. The pending set module is used to determine the pending matching pairs of the target process based on the comparison results between the spatial distance and the coverage mileage corresponding to the carrier type in the available resource set; The initial task module is used to determine the event level of the target process based on the state parameters and event nature in the matching pairs to be processed, and sort the matching pairs to be processed by the event level as an index to obtain the initial task set of the target process; The feasible path module is used to modify the connectivity path using the environmental obstacle set of the situational data stream as boundary conditions, so as to obtain the feasible path of the target process and the corresponding estimated arrival time. The task scheduling module is used to bind the initial task set with the spatial coordinate sequence of the feasible path and the estimated arrival time as spatiotemporal constraints to obtain the task scheduling set of the target process. When the module for the set of objects to be processed determines the matching pairs to be processed for the target process based on the comparison results between the spatial distance and the coverage mileage corresponding to the carrier type in the available resource set, it is specifically used for: The maximum operable distance within the mission cycle of the target process is determined based on the carrier type identifier associated with the available resource set and the power location, thus obtaining the coverage mileage of the target process; When the spatial distance is less than or equal to the coverage mileage, the information entries of the event information set are combined with the information entries of the available resource set to obtain the target process matching pair to be processed; When the initial task module performs the following steps: It determines the state level of the target process based on the state parameters and event characteristics of the matching pairs to be processed, and sorts the matching pairs to be processed using the state level as an index to obtain the initial task set of the target process, it is specifically used for: The state parameters include the typical response speed corresponding to the spatial distance and the carrier type identifier; Keyword analysis is performed on the text information in the event information set to identify the event nature that characterizes the severity of the event in the target process; A basic level coefficient is set based on the nature of the event, and the delay factor of the target process is calculated based on the spatial distance and the typical response speed. The urgency score calculated based on the base level coefficient and the delay factor is converted into the situation level of the target process; Using the numerical value of the situation level as the sorting key, the matching pairs to be processed are sorted in descending order to obtain an ordered queue of the target process; The information pairs of the ordered queue are encapsulated into structured task entries according to the order of arrangement to obtain the initial task set of the target process; The formula for calculating the urgency score is specifically used for: ； in, Rate the urgency level. This refers to the weighting coefficient for static events. The basic level coefficient is... The weighting coefficient for response timeliness, The typical response speed is... This is the path complexity reduction factor. For distance nonlinearity sensitive factors, The spatial distance is described.

2. The integrated situation display and resource scheduling optimization system for land, sea, air and space rescue forces as described in claim 1, characterized in that, When the resource integration module performs the fusion of state data and environmental data of the target process to obtain the situational data stream of the target process, and separates the event information set and available resource set of the target process according to the type identifier of the data records in the situational data stream, it is specifically used for: The mobile carrier data and monitoring node data of the target process are analyzed to obtain the entity dataset and node status set of the target process; The data records of the entity dataset and the node situation set are registered to the geographic information grid of the target process according to the corresponding latitude and longitude coordinates and timestamps, and the data are overlaid to obtain the situation data stream of the target process; Based on the disaster identifier of the situation data stream, the geographic coordinates and text description information of the data record are paired to obtain the event information set of the target process; The identity code and callable status field of the data record are associated with the platform status identifier of the situational data stream to obtain the available resource set of the target process.

3. The integrated situation display and resource scheduling optimization system for land, sea, air and space rescue forces as described in claim 2, characterized in that, When the connectivity module generates the connectivity path and corresponding spatial distance of the target process based on the event geographic location of the event information set and the power location of the available resource set, it is specifically used for: Based on the geographic information grid, the coordinates of the event's geographical location are connected with the coordinates of the force's location to obtain the connectivity path of the target process; The spherical distance of the connected path is taken as the spatial distance of the target process.

4. The integrated situational awareness and resource scheduling optimization system for land, sea, air, and space rescue forces as described in claim 1, characterized in that, When the feasible path module executes the modification of the connectivity path using the environmental obstacle set of the situational data stream as boundary conditions to obtain the feasible path of the target process and the corresponding estimated arrival time, it is specifically used for: Obstacle marker records are selected from the situational data stream, and the geographic boundary coordinate set of the obstacle marker records is extracted to form the environmental obstacle set of the target process; Geometric intersection calculation is performed between the straight line segment of the connected path and the polygonal obstacle region represented by the environmental obstacle set: when the straight line segment is detected to cross the polygonal obstacle region, a polyline path that avoids the boundary of the polygonal obstacle region is searched in the spatial range of the target process, with the coordinates of the force position as the starting point and the coordinates of the event geographical location as the ending point, and the polyline path is determined as the feasible path of the target process. The estimated arrival time of the target process is determined based on the spatial coordinate sequence of the feasible path.

5. The integrated situational awareness and resource scheduling optimization system for land, sea, air, and space rescue forces as described in claim 4, characterized in that, The method of determining the estimated arrival time of the target process based on the spatial coordinate sequence of the feasible path is specifically used for: The total length of the feasible path is obtained by summing the segmented distances between adjacent coordinate points within the spatial coordinate sequence. Obtain the carrier identifier of the rescue force associated with the feasible path from the unprocessed matching pairs; Based on the carrier identifier, determine the typical cruising speed of the type of rescue force of the matching pair to be processed in the medium environment of the feasible path; The ratio of the total length of the feasible path to the typical cruise speed is used as the theoretical travel time of the target process; The theoretical travel time is compensated based on the real-time data of the target process to obtain the estimated arrival time of the target process.

6. The integrated situational awareness and resource scheduling optimization system for land, sea, air, and space rescue forces as described in claim 1, characterized in that, When the task scheduling module binds the initial task set with the spatial coordinate sequence of the feasible path and the estimated arrival time as spatiotemporal constraints to obtain the task scheduling set of the target process, it is specifically used for: The task elements of the matching pairs to be processed in the initial task set are associated and stored with the spatial coordinate sequence and the field of the estimated arrival time to obtain the segmented task set of the target process; Traverse the initial task set, aggregate the segmented task sets, and obtain the preliminary task scheduling set for the target process; Based on the feasible path, conflict resolution is performed on the preliminary task scheduling set to obtain the task scheduling set of the target process.

7. The integrated situational awareness and resource scheduling optimization system for land, sea, air, and space rescue forces as described in claim 6, characterized in that, When resolving conflicts in the preliminary task scheduling set based on the feasible path to obtain the task scheduling set for the target process, the specific purpose is as follows: When the same rescue force in the available resource set is assigned to two or more feasible paths in the task scheduling set, and the estimated arrival time overlaps, the task scheduling set is marked as having a resource time conflict. When the feasible paths in the task scheduling set intersect or overlap spatially, and the departure time of each feasible path intersects with the time period calculated based on the estimated arrival time of the intersecting area, the task scheduling set marks the path spatial conflict. To address the resource time conflict, the planned start time of the tasks in the task scheduling set is postponed until the time overlap is eliminated, thus obtaining the time-optimized sequence of the target process; In response to the path space conflict, alternative feasible paths are searched for tasks in the task scheduling set until the spatial and temporal intersection is eliminated, thus obtaining the path optimization sequence of the target process. By merging the time-series optimization sequence and the path optimization sequence, the task scheduling set of the target process is obtained.

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