A hanging map combat method and device based on basic-level governance elements and GIS maps

Abstract

The application discloses a hanging picture combat method and device based on basic-level governance elements and GIS maps, and belongs to the field of digital governance and GIS technologies; the method first physically separates the spatial topology of governance elements from initial semantic attributes, constructs a space-time state hash tree and generates an initial baseline snapshot; then, state change instructions are asynchronously intercepted through a CDC mechanism, combined with a hybrid logic clock to be packaged into a differential event vector, to form a multi-modal space-time event stream; subsequently, invalid shock changes are identified and removed by using Moore neighborhood space collaborative analysis, state folding is performed, and a combat case package is compressed; finally, the case package is analyzed and the baseline is loaded in a virtual sand table, the event stream is played back in a streaming manner to calculate and restore historical situations and output rendering instructions. The technical problems of dirty reading and storage redundancy of historical data in long-period hanging picture combat are solved, and the historical backtracking and sand table deduction of complex combat situations are quickly completed without causing storage expansion and I / O blocking.

Description

Technical Field

[0001] This invention belongs to the field of digital governance and GIS technology, specifically relating to a map-based operational method and device based on grassroots governance elements and GIS maps. Background Technology

[0002] With the deepening of smart city development and grassroots digital governance, the "unified management" platform based on Geographic Information System (GIS) has become a core tool for governments at all levels and grassroots organizations. In practical application scenarios (such as special guarantees for the production and sales of specific agricultural product producing areas, and emergency command for sudden public events), a "map-based operation" model is usually adopted. This model requires the standardized access of multi-source heterogeneous governance elements such as people, houses, land, events, objects, situations, and organizations within the jurisdiction, and their mounting on the corresponding spatial coordinates of the GIS map. Based on this, by selecting specific spatial areas on the front-end interface, task orders are distributed across levels and departments to grid members or collaborating departments, thereby achieving visualized command and dispatch.

[0003] In existing technical implementations, the underlying data architecture typically adopts a decoupled model of "spatial database (such as PostGIS) + relational business database (such as MySQL)". The spatial database is responsible for storing the static location and latest attribute characteristics of various governance elements; the relational business database is used to record status change logs such as "circle selection and dispatch", "task flow", and "supervision results". The front-end engine obtains the latest data through periodic polling or WebSocket and renders the current map situation and risk heat map.

[0004] However, when conducting "historical case preservation and special review studies" for the "map-based operations" special campaign, existing technologies revealed significant underlying data processing deficiencies, specifically: Map-based operations are often a dynamic evolutionary process lasting several weeks or even months (e.g., dynamic risk assessment of crops in a certain region from concentrated production to market sales). During this period, the element attributes on the GIS base map (such as the risk warning color level for a specific park) and the massive number of multi-span scheduling instructions are changing at high frequency and concurrently. In order to preserve the "historical case" of this special operation for subsequent review and analysis, existing technologies usually face two extreme processing paths, both of which have insurmountable technical bottlenecks: 1) Using a "result-oriented archiving" or "independent storage of business logs" approach (loss of spatiotemporal context): The most common practice in existing systems is to export only the final statistical reports, static map screenshots, or retain only the text logs of task assignment and flow in the relational database after a special operation is completed. When the review team wants to revisit the overall risk situation of the map when a key task was issued on a certain day, the system will overlay the historical task logs onto the current GIS base map for rendering. However, since the underlying basic element data (such as land parcels being re-divided, population migration, and updated risk indicators) has objectively changed after the operation, this forced overlay will lead to serious "spatial topological misalignment" and "dirty reading of historical data"; the map situation seen by the review team is completely inconsistent with the actual situation at the time of decision-making, making it impossible to achieve true "sand table simulation-style" high-fidelity restoration.

[0005] 2) The "high-frequency full spatial data snapshot" solution (causing storage disaster and I / O blockage): To solve the aforementioned "dirty read" problem, some systems attempted to perform full snapshot backups of the entire GIS spatial database and business database at a fixed time frequency (e.g., hourly) during operations. However, grassroots governance involves tens of millions of feature point coordinates and multi-dimensional attributes. High-frequency full backups would generate extremely redundant dead data, causing the system storage space to expand exponentially. At the same time, concurrent massive spatial data copying operations would severely consume system I / O resources, and even cause severe lag and delays in the front-end rendering of real-time "map-based operations".

[0006] In summary, the core technical problem that urgently needs to be solved in this field is: how to achieve deep spatiotemporal binding of long-cycle, multi-source heterogeneous GIS basic layer states with high-frequency dynamic business command streams without causing exponential expansion of storage space or additional occupation of high-concurrency I / O resources of real-time systems, so as to achieve high-fidelity dynamic reconstruction of complex map combat situations and millisecond-level sand table simulation restoration while avoiding "dirty reading of historical spatial data". Summary of the Invention

[0007] In view of the shortcomings of the prior art, the purpose of this invention is to provide a method and device for combat operations based on a wall map based on grassroots governance elements and GIS maps, thereby solving the problems in the prior art.

[0008] The objective of this invention can be achieved through the following technical solutions: A map-based operational method using grassroots governance elements and GIS maps includes the following steps: At the initial moment, the spatial topology data of the grassroots governance elements to be connected is physically separated from the initial semantic attribute set, and a spatiotemporal state hash tree is constructed based on the spatial topology data. The hash value of the root node of the spatiotemporal state hash tree is extracted to generate an initial spatiotemporal semantic baseline snapshot. Asynchronously intercept the element status change command, extract the change features in the change command and combine them with timestamp and spatial index identifier, encapsulate and generate a standardized differential event vector, and write the differential event vector in append-only mode to form a multimodal spatiotemporal event stream arranged in time sequence; The multimodal spatiotemporal event stream is traversed, and invalid oscillation change events targeting the same spatial index are identified and eliminated based on the underlying spatial topology relationship to perform spatially aware state folding. The valid instructions after folding are differentially compressed and encoded to generate combat case packages that are separated from the real-time business library. In response to the historical retrospective command, the combat case package is parsed to restore the event flow within the target retrospective time period, and the initial spatiotemporal semantic baseline snapshot is loaded in a virtual sandbox space independent of the real-time production environment. The virtual clock engine is driven to stream and replay the restored event flow, calculate the spatial state snapshot at the target time in real time, and output the corresponding vector layer drawing command.

[0009] Furthermore, the process of constructing a spatiotemporal state hash tree includes: Heterogeneous feature homomorphism vectorization is performed on the extracted initial semantic attribute set. Normalization mapping is used for continuous attributes, and one-hot encoding is used for discrete classification features. The vectors are then concatenated to generate a standardized initial dynamic semantic feature vector. Calculate the diagonal distance between the polygon geometric center point and the minimum bounding rectangle of the spatial topology data, and adaptively generate a spatial grid code based on the diagonal distance and a preset resolution. Then, concatenate the grid code with the feature category identifier to generate a globally unified resource identifier. The spatiotemporal state hash tree is constructed according to the GIS grid hierarchy, wherein the initial state feature hash value of the leaf nodes is... The calculation formula is: in, A globally unique resource identifier for the target element. The preset dimensionality reduction feature weight vector, This is the initial dynamic semantic feature vector.

[0010] Furthermore, the process of generating standardized differential event vectors includes: A change data capture mechanism based on the database layer asynchronously intercepts the element status change instructions; Inject a hybrid logic clock as the timestamp into the element status change command. The hybrid logic clock is a binary structure containing a physical time component and a logic counting component. Extract the local physical time of the current node. and the mixed logic clock of the previous event ; Physical time component The update formula is: ; Logical counting components The update formula is: when hour, ;when hour, ; in, This represents the physical time component of the preceding event. The logical count component for the previous event; Generate a change mask vector corresponding to the feature dimension, and perform heterogeneous difference calculation for the feature state change instruction to generate a state difference matrix; The hybrid logic clock, the spatial index identifier, the change mask vector, the state difference matrix, and the opcode are encapsulated into the differential event vector.

[0011] Furthermore, the process of performing spatially aware state folding includes: Set a sliding time window to traverse the multimodal spatiotemporal event stream, and calculate the oscillation frequency and net state offset for the spatial index identifier that generates state changes. Based on the spatial grid code of the spatial index identifier, query its first-order Moiré space neighborhood grid set and calculate the spatial cooperability factor. ; When the trigger condition group is met, it is determined to be an invalid oscillation change event and the intermediate oscillation instructions in the sliding time window are removed, and only the first and last events are retained to complete the state folding.

[0012] Furthermore, the spatial coordination factor The calculation formula is: in, The number of grid cells in the neighborhood grid set of the first-order Moiré space. For indicator functions, The cosine similarity function is used. The state difference matrix of the target element. For adjacent elements within the neighborhood The state difference matrix, This is the similarity threshold.

[0013] Furthermore, the triggering condition group includes: The oscillation frequency is greater than or equal to a preset frequency threshold. The net state offset is less than or equal to a preset net state offset tolerance threshold. No scheduling instruction was triggered during the sliding time window; Furthermore, the spatial coordination factor is less than the preset isolated oscillation threshold.

[0014] Furthermore, the process of real-time calculation of the spatial state snapshot up to the target time includes: The change mask vector is used to guide the split operation and calculate the final state of each spatial index identifier at the target time; For continuous numerical feature components, an algebraic accumulator model is used to accumulate the differences in the state difference matrix; For discrete classification feature components, a last write wins over model based on temporal causality is adopted to extract the target state value of the last valid state transition event within the backtracking range as the final state.

[0015] The governance elements include seven categories of characteristics: people, buildings, land, events, organizations, objects, and situations; the method also includes the following business interaction steps: Multi-dimensional element linking and layer construction: In response to custom or combined query commands for the seven feature sets, the target governance elements are filtered and linked to the corresponding spatial index identifiers, and corresponding element layers are added to the GIS map to achieve large-screen visualization. Risk Situation Display: For specific industry cycles, the dynamic semantic attributes of the selected target governance elements are extracted, and the corresponding risk levels are mapped to the color features of the element layers on the map for differentiated display; Spatial selection and task distribution: In response to the area selection operation on the feature layer, identify the target governance elements and associated personnel within the selected area, and generate a wall chart operation instruction containing a personnel list, a task list and an element list; Multi-level scheduling and supervision: The multi-modal spatiotemporal event stream records the flow status of the chart-based combat instructions, enabling cross-level and cross-departmental collaborative scheduling and task supervision; Special Case Archiving: In response to special action orders for a specific time period, the case packaging and space compression steps are performed to save high-fidelity combat case packages for subsequent special review and research.

[0016] A map-based operational device for grassroots governance elements and GIS maps, which executes the above method, includes: The baseline generation module is used to physically separate the spatial topology data of the grassroots governance elements to be connected from the initial semantic attribute set at the initial moment, and construct a spatiotemporal state hash tree based on the spatial topology data, and extract the hash value of the root node of the spatiotemporal state hash tree to generate an initial spatiotemporal semantic baseline snapshot. The incremental capture module is used to asynchronously intercept feature status change instructions, extract the change features in the change instructions and combine them with timestamps and spatial index identifiers to encapsulate and generate standardized differential event vectors, and write the differential event vectors in append-only mode to form a multimodal spatiotemporal event stream arranged in time sequence. The compression and folding module is used to traverse the multimodal spatiotemporal event stream, identify and eliminate invalid oscillation change events targeting the same spatial index based on the underlying spatial topology relationship to perform spatially aware state folding, and perform differential compression encoding on the folded valid instructions to generate combat case packages that are separated from the real-time business library. The sand table calculation module is used to respond to historical retrospective commands, parse the combat case package to restore the event flow within the target retrospective time period, load the initial spatiotemporal semantic baseline snapshot in a virtual sand table space independent of the real-time production environment, drive the virtual clock engine to stream and replay the restored event flow, calculate the spatial state snapshot to the target time in real time, and output the corresponding vector layer drawing commands.

[0017] 9. A computer storage medium storing a readable program, which, when executed, instructs a computing device to perform a map-based operational method based on grassroots governance elements and GIS maps as described above.

[0018] 10. An electronic device, characterized in that it comprises: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, which causes the processor to perform an operation corresponding to the above-described method of combat operations based on grassroots governance elements and GIS maps.

[0019] The beneficial effects of this invention are: 1. This invention achieves the decoupling and dimensionality reduction of spatial vector features and dynamic business features of grassroots governance elements by physically separating spatial topology from initial semantic attributes and constructing a spatiotemporal state hash tree; the initial spatiotemporal semantic baseline snapshot generated based on the grid hierarchy system is extremely lightweight, not only filtering out interference from non-core business attributes, but also providing highly reliable anchor points for subsequent incremental replay, thus solving the problem of exponential expansion of storage space caused by full snapshots from the source.

[0020] 2. This invention generates a multimodal spatiotemporal event stream by asynchronously intercepting change commands and injecting a Hybrid Logic Clock (HLC). It utilizes a database-layer Change Data Capture (CDC) mechanism to avoid frequent overwrite operations on the underlying spatial database, effectively alleviating I / O blocking and rendering stuttering in real-time systems under high concurrency. Simultaneously, HLC timestamps correct clock drift in distributed collaboration, ensuring a rigorous time-series binding between massive, multi-layered scheduling commands and the GIS base layer status, eliminating "dirty reads" of historical data caused by temporal discrepancies. 3. This invention introduces spatially perceptive logical judgments through Moir's neighborhood spatial cooperation analysis and state folding algorithm, and calculates spatial cooperation factors. Identify and remove invalid oscillation changes (such as sensor noise or worthless flicker) targeting the same identifier. This spatial denoising and folding greatly compresses the size of historical case packages without losing key evolutionary trends, achieving high-fidelity archiving that is independent of the real-time business database.

[0021] 4. This invention utilizes a streaming replay and calculation model guided by a modified mask. Within a memory-level virtual sandbox, the system abandons the coarse calculation of uniform summation and instead uses a modified mask to process continuous features (algebraic accumulation) and discrete features (finally written to the winning LWW). This precise calculation scheme, tailored to the heterogeneous characteristics of data, combined with GeoJSON vector tile stream output, achieves millisecond-level high-fidelity reconstruction of the GIS situation at a specified historical moment, providing a decision-making and analysis environment with "sandbox simulation" reconstruction capabilities for complex map-based operations. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of the wall map-based combat method of the present invention, which is based on grassroots governance elements and GIS maps. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Example 1 like Figure 1 As shown, a map-based operational method using grassroots governance elements and GIS maps includes the following steps: S1, Data preprocessing and baseline anchoring; At the initial stage of the wall chart operation The spatial and semantic features of the grassroots governance elements to be accessed are decoupled and reduced, and the spatial topology data and dynamic semantic attributes of the elements are physically separated. A global unified resource identifier is generated for the separated spatial topology data, and a spatiotemporal state hash tree is constructed based on the GIS grid level. The leaf nodes of the spatiotemporal state hash tree store the global unified resource identifier and the feature hash value of the initial state to generate a lightweight initial spatiotemporal semantic baseline snapshot. The specific steps include: S11, Physical stripping and homomorphic vectorization At the initial stage of the wall chart operation Extract the spatial vector coordinate set C of the seven types of governance elements and the initial semantic attribute set. Physical stripping is performed in the underlying memory. Specifically, for the seven feature sets of people, houses, places, events, organizations, objects, and emotions, the rules for expressing their spatial topological data and semantic attributes are as follows: (1) People and objects: Spatial topology is expressed as the coordinates of the moving point; semantic attributes include personnel / object ID, risk score, and organization.

[0026] (2) Event and sentiment elements: Spatial topology is expressed as fixed points or line strings of the event location; semantic attributes include event type, urgency, and sentiment score.

[0027] (3) Building, land and organizational elements: Spatial topology is expressed as static polygon or polyhedral boundary; semantic attributes include building use, land parcel type, organizational level, etc.

[0028] For the stripped Perform homomorphic vectorization of heterogeneous features: use normalized mapping for continuous attributes (such as risk scores) and one-hot encoding for discrete categorical attributes (such as feature categories and warning colors), and concatenate them to generate a standardized initial dynamic semantic feature vector. .

[0029] S12, Generate a Global Uniform Resource Identifier For the stripped spatial topology data The method calculates the diagonal distance D between the geometric center point of the polygon and its minimum bounding rectangle (MBR). Based on D, it adaptively generates a spatial grid code (Geohash) at a preset resolution. For polygonal or area features spanning multiple Geohash grids (such as large plots), this method uses the grid containing the geometric center point of the feature as the primary index calculation reference. Simultaneously, based on the diagonal distance D, it dynamically reduces the Geohash grid resolution level (i.e., expands the spatial coverage of a single grid) until a single target grid at that resolution level can completely enclose the MBR of the feature. Through the above adaptive dimensionality reduction matching, it ensures that any polygonal feature spanning multiple grids is mapped to a single Geohash code of a unique level, which is then concatenated with the feature category identifier to generate a globally unique resource identifier. This ensures that a single polygonal feature has a unique primary leaf node mounting position in the spatiotemporal state hash tree, avoiding redundant calculations during aggregation.

[0030] S13, Construct a spatiotemporal state hash tree to generate a baseline. The spatiotemporal state hash tree is constructed strictly according to the hierarchical system of the GIS grid (such as the 32-ary tree structure of Geohash). For leaf nodes, the characteristic hash value of their initial state is... The calculation formula is: in, A pre-defined dimensionality reduction feature weight vector is used to filter out non-core business attributes. For non-leaf nodes, a hash calculation is performed after strictly sorting all their child nodes in ascending order of their spatial encoding alphabet. Finally, the root node hash value is extracted to generate and solidify a lightweight initial spatiotemporal semantic baseline snapshot.

[0031] S2, Situational Evolution and Incremental Capture; During the evolution cycle of the map-based operations, intercept the feature status change commands triggered by front-end GIS map interaction and business gateway, and transform the high-frequency, multi-source, heterogeneous underlying commands into standardized differential event vectors; inject a timestamp and a spatial index identifier that matches the global unified resource identifier into each differential event vector, thereby transforming business status transitions into a multimodal spatiotemporal event stream strictly arranged in time sequence to avoid overwriting the underlying spatial database.

[0032] The specific steps of S2 include: S21, Asynchronous Interception and Timestamp Injection During the evolution cycle, a database-layer Change Data Capture (CDC) mechanism asynchronously intercepts state change commands, avoiding overwrite operations on the underlying spatial database. Specifically, the system deploys a CDC parsing component (such as parsing MySQL's Binlog or PostgreSQL's WAL log) at the database layer. Since purely interactive commands (such as map zooming, panning, and layer selection) are rendered only in front-end memory and do not generate database write operations, they are naturally isolated on the physical link. For the database side, the system formulates filtering rules based on a whitelist of business tables and Data Manipulation Language (DML) types: only when the operation involves a specified governance element relationship table and the operation type is INSERT, UPDATE, or DELETE is it determined to be a change command. The CDC mechanism ensures that all underlying data change commands that meet the whitelist rules are read sequentially by directly subscribing to the underlying database transaction log and using an offset ACK mechanism, completely eliminating missed or false interceptions caused by application-layer network fluctuations.

[0033] To address distributed clock drift caused by multi-span collaboration, a hybrid logic clock (HLC) is injected into the instructions as a rigorous timestamp. The hybrid logic clock is a binary structure containing a physical time component and a logical counting component, i.e. ; The specific logic is as follows: extract the local physical time of the current node. and the mixed logic clock of the previous event ; Physical time component The update formula is: Logical counting components The update logic is as follows: in, This represents the physical time component of the preceding event. This is the logical count component of the previous event.

[0034] S22, Heterogeneous Differential Computation and Mask Generation To address the heterogeneous nature of multiple sources, a change mask vector corresponding to the feature dimension is generated. Based on the mask, algebraic subtraction is performed on the continuous numerical feature components to calculate the difference, and the state transition tuples that represent the transformation from old to new are extracted from the discrete classification feature components. These are then combined to form the state difference matrix. .

[0035] S23 (Generate and arrange the spatiotemporal event stream): Combine the mixed logic clock... (Timestamp), Global Uniform Resource Identifier for Target Elements (Spatial index identifier), change mask vector State difference matrix and the operation code that triggers the change Encapsulated as a standardized differential event vector : It is written in "append-only" mode and transformed into a multimodal spatiotemporal event stream that is strictly arranged in time sequence.

[0036] S3, Case Packaging and Space Compression; In response to the special review and archiving instruction for the aforementioned map-based operations, the initial spatiotemporal semantic baseline snapshot and the multimodal spatiotemporal event stream are extracted and redundantly merged and differentially compressed. By sliding through the multimodal spatiotemporal event stream, invalid oscillation change events targeting the same spatial index are identified and eliminated based on the underlying spatial topology relationship. Spatial-aware state folding is performed, and valid instructions are differentially compressed and encoded to generate a high-fidelity operational case package that is independent of the real-time business database. Specifically, S3 includes the following steps: S31, Spatial Co-operation Analysis Based on Moir Neighborhood Extract baseline snapshots and event streams, and set a sliding time window. Traverse the spatiotemporal event flow; where the size of the sliding time window W is dynamically configured based on the specific business scenario of the "wall chart operation", for example, it is configured to be 1 to 5 minutes in the emergency command phase of sudden public events, and 1 to 4 hours in the long-cycle inspection phase of agricultural product production and sales.

[0037] Target identifier that generates state change Calculate its oscillation frequency Offset from net state At the same time, based on its underlying spatial topology (i.e. (Geohash encoding), query its first-order Moir space neighborhood grid set. And calculate the spatial co-operation factor. : in, The number of grid cells in the neighborhood grid set of the first-order Moiré space. For indicator functions; This is a cosine similarity function used to assess the true extent of the spread of space disasters; This represents the state difference matrix of the target element. For adjacent elements within the neighborhood The state difference matrix, This is the similarity threshold.

[0038] S32, Perform spatial awareness state folding If and only if Exceeding the threshold Below the threshold, without external scheduling instructions triggered, and When (i.e., the neighborhood does not change synchronously and is judged as an isolated invalid oscillation change event), the intermediate oscillation instructions within the window are removed, and the spatial perception state folding is completed; among them.

[0039] Only if the following condition group is met: (1) ( (2) The threshold for the state oscillation frequency is preferably 5 to 12 times per window); ( (3) No scheduling instruction was triggered during the period (the net state offset tolerance threshold is preferably 0.01 to 0.05); (4) and spatial coordination factor ( For isolated oscillation threshold, the preferred value is... Only when the vast majority of surrounding nodes do not experience similar oscillations does the system confirm that this is an isolated sensor noise or worthless flicker, and then performs state folding in the underlying event stream (removing intermediate oscillation event vectors within the window and retaining only the beginning and end).

[0040] The system constructs a dynamic threshold mapping table for different element types and business stages: for high-sensitivity elements (such as people's urgent social security requests, sudden event alarms) or high-frequency business outbreaks, a lower oscillation frequency threshold τ (such as 3 times / window) and a lower offset tolerance threshold ε (such as 0.01) are set to retain more details; for low-sensitivity elements (such as the static attribute fine-tuning of land) or business stability periods, a higher oscillation frequency threshold τ (such as 15 times / window) and a higher tolerance threshold ε (such as 0.1) are set to maximize the compression of storage space.

[0041] S33, Perform differential compression encoding to generate case packages. For valid instructions retained after folding, the timestamp is encoded using a Delta-of-Delta algorithm. Perform incremental compression, based on Construct a spatial prefix trie to compress the spatial index identifier, and then... Sparse matrix storage is performed, and finally, redundancy is merged to generate a high-fidelity combat case package that is independent of the real-time business database.

[0042] S4, memory-level sandbox reconstruction and restoration; In response to the historical backtracking command, the high-fidelity combat case package is parsed, and a virtual sandbox space independent of the real-time production environment is created in memory. The initial spatiotemporal semantic baseline snapshot is deserialized and loaded in the virtual sandbox space. Based on the received arbitrary historical backtracking timestamp, the virtual clock engine is driven to stream and replay the multimodal spatiotemporal event stream on the basis of the baseline snapshot. The spatial state snapshot corresponding to the historical backtracking timestamp is calculated in real time and mapped to the vector layer drawing command of the front-end GIS map to dynamically render and generate a high-fidelity GIS situation and task distribution topology map at a specified historical moment.

[0043] Specifically, S4 includes the following steps: S41, Clock Mapping and Sandbox Initialization Respond to the historical backtracking command issued by the review personnel (including the target physical timestamp) The system first parses the high-fidelity combat case package generated in step S3, and then allocates a virtual sandbox space in an isolated memory heap. The system then sets the target's physical timestamp. Mapped to the target hybrid logic clock boundary in the underlying event stream (Taking physical time from the event stream) (Maximum logic clock value).

[0044] Subsequently, the initial spatiotemporal semantic baseline snapshot from deserialization loading step S1 is used to extract the feature nodes. Initial standardized semantic feature vector To construct the initial underlying state base of the sandbox.

[0045] S42, Streaming Replay and Real-Time Calculation Based on Changed Mask The system drives a virtual clock engine to extract the logical clock from the event stream. All difference event vectors .

[0046] For each specific governance element Instead of using a unified summation mechanism, it utilizes a modified mask vector. Guide the split operation and calculate its value in The final state at any given moment : 1) For continuous numerical eigencomponents (Such as risk assessment scores, dynamic population density, etc.), the algebraic accumulator model is used for calculation: in, For the first The difference of this continuous feature in the sub-events; As an indicator function, the difference is accumulated only if the logic clock is within the backtrack range and the mask is hit.

[0047] 2) For discrete classification feature components (Such as risk warning colors, multi-cross collaborative work order status, etc.), the final state snapshot is calculated using a time-series causal last-write-wins (LWW) coverage model: Among them, subscript The index representing the last valid state transition event within the backtracking range of this discrete feature is calculated using the following formula: in, Extracted from this The state transition tuple for the next event The target state value in the equation (e.g., "red" in the change from yellow to red).

[0048] S43, GIS Situation Mapping and Dynamic Rendering Layer Generation After completing the state machine calculations in memory, the system iterates through all active states in the virtual sandbox. and its final state vector after calculation .

[0049] The algorithm is based on The built-in Geohash encoding (generated in step S1) restores its underlying spatial topology and maps key discrete features in the final state vector (such as “risk color”) to the fill-color attribute of a GIS rendering engine (such as MapboxGL or Cesium), and maps continuous features (such as “risk score”) to the fill-opacity or height stretch attribute.

[0050] Finally, it is encapsulated into a GeoJSON vector tile stream (VectorTiles) carrying a Properties dictionary and output to the front end to complete the specified historical moment. Millisecond-level high-fidelity rendering of situation and task topology diagrams.

[0051] Example 2 This embodiment proposes a map-based operational device based on grassroots governance elements and GIS maps, specifically including: The baseline generation module is used to physically separate the spatial topology data of the grassroots governance elements to be connected from the initial semantic attribute set at the initial moment, and construct a spatiotemporal state hash tree based on the spatial topology data, and extract the hash value of the root node of the spatiotemporal state hash tree to generate an initial spatiotemporal semantic baseline snapshot. The incremental capture module is used to asynchronously intercept feature status change instructions, extract the change features in the change instructions and combine them with timestamps and spatial index identifiers to encapsulate and generate standardized differential event vectors, and write the differential event vectors in append-only mode to form a multimodal spatiotemporal event stream arranged in time sequence. The compression and folding module is used to traverse the multimodal spatiotemporal event stream, identify and eliminate invalid oscillation change events targeting the same spatial index based on the underlying spatial topology relationship to perform spatially aware state folding, and perform differential compression encoding on the folded valid instructions to generate combat case packages that are separated from the real-time business library. The sand table calculation module is used to respond to historical retrospective commands, parse the combat case package to restore the event flow within the target retrospective time period, load the initial spatiotemporal semantic baseline snapshot in a virtual sand table space independent of the real-time production environment, drive the virtual clock engine to stream and replay the restored event flow, calculate the spatial state snapshot to the target time in real time, and output the corresponding vector layer drawing commands.

[0052] Based on a similar inventive concept, embodiments of the present invention also provide a computer storage medium storing a readable program that, when run by a processor, can execute the aforementioned method of map-based warfare using grassroots governance elements and GIS maps.

[0053] Based on a similar inventive concept, this invention provides an electronic device, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-mentioned map-based combat method based on grassroots governance elements and GIS maps.

[0054] Based on a similar inventive concept, this invention also provides a computer program product, including computer instructions, which instruct a computing device to perform the operations corresponding to the above-described method of map-based warfare using grassroots governance elements and GIS maps.

[0055] The methods of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code originally stored on a remote recording medium or a non-transitory machine-readable medium and subsequently stored on a local recording medium, downloaded over a network. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code that, when accessed and executed by the computer, processor, or hardware, implements the methods described herein. Furthermore, when a general-purpose computer accesses the code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for performing the methods shown herein.

[0056] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A map-based operational method using grassroots governance elements and GIS maps, characterized in that: Includes the following steps: At the initial moment, the spatial topology data of the grassroots governance elements to be connected is physically separated from the initial semantic attribute set, and a spatiotemporal state hash tree is constructed based on the spatial topology data. The hash value of the root node of the spatiotemporal state hash tree is extracted to generate an initial spatiotemporal semantic baseline snapshot. Asynchronously intercept the element status change command, extract the change features in the change command and combine them with timestamp and spatial index identifier, encapsulate and generate a standardized differential event vector, and write the differential event vector in append-only mode to form a multimodal spatiotemporal event stream arranged in time sequence; The multimodal spatiotemporal event stream is traversed, and invalid oscillation change events targeting the same spatial index are identified and eliminated based on the underlying spatial topology relationship to perform spatially aware state folding. The valid instructions after folding are differentially compressed and encoded to generate combat case packages that are separated from the real-time business library. In response to the historical retrospective command, the combat case package is parsed to restore the event flow within the target retrospective time period, and the initial spatiotemporal semantic baseline snapshot is loaded in a virtual sandbox space independent of the real-time production environment. The virtual clock engine is driven to stream and replay the restored event flow, calculate the spatial state snapshot at the target time in real time, and output the corresponding vector layer drawing command.

2. The map-based operational method based on grassroots governance elements and GIS maps according to claim 1, characterized in that, The process of constructing a spatiotemporal state hash tree includes: Heterogeneous feature homomorphism vectorization is performed on the extracted initial semantic attribute set. Normalization mapping is used for continuous attributes, and one-hot encoding is used for discrete classification features. The vectors are then concatenated to generate a standardized initial dynamic semantic feature vector. Calculate the diagonal distance between the polygon geometric center point and the minimum bounding rectangle of the spatial topology data, and adaptively generate a spatial grid code based on the diagonal distance and a preset resolution. Then, concatenate the grid code with the feature category identifier to generate a globally unified resource identifier. The spatiotemporal state hash tree is constructed according to the GIS grid hierarchy, wherein the initial state feature hash value of the leaf nodes is... The calculation formula is: in, A globally unique resource identifier for the target element. The preset dimensionality reduction feature weight vector, This is the initial dynamic semantic feature vector.

3. The method for combat operations based on grassroots governance elements and GIS maps according to claim 1, characterized in that, The process of generating standardized differential event vectors includes: A change data capture mechanism based on the database layer asynchronously intercepts the element status change instructions; Inject a hybrid logic clock as the timestamp into the element status change command. The hybrid logic clock is a binary structure containing a physical time component and a logic counting component. Extract the local physical time of the current node. and the mixed logic clock of the previous event ; Physical time component The update formula is: ; Logical counting components The update formula is: when hour, ;when hour, ; in, This represents the physical time component of the preceding event. The logical count component for the previous event; Generate a change mask vector corresponding to the feature dimension, and perform heterogeneous difference calculation for the feature state change instruction to generate a state difference matrix; The hybrid logic clock, the spatial index identifier, the change mask vector, the state difference matrix, and the opcode are encapsulated into the differential event vector.

4. The map-based operational method based on grassroots governance elements and GIS maps according to claim 1, characterized in that, The process of performing spatially aware state folding includes: Set a sliding time window to traverse the multimodal spatiotemporal event stream, and calculate the oscillation frequency and net state offset for the spatial index identifier that generates state changes. Based on the spatial grid code of the spatial index identifier, query its first-order Moiré space neighborhood grid set and calculate the spatial cooperability factor. ; When the trigger condition group is met, it is determined to be an invalid oscillation change event and the intermediate oscillation instructions in the sliding time window are removed, and only the first and last events are retained to complete the state folding.

5. The method for map-based operations based on grassroots governance elements and GIS maps according to claim 4, characterized in that, The spatial coordination factor The calculation formula is: in, The number of grid cells in the neighborhood grid set of the first-order Moiré space. For indicator functions, The cosine similarity function is used. The state difference matrix of the target element. For adjacent elements within the neighborhood The state difference matrix, This is the similarity threshold.

6. The method for map-based operations based on grassroots governance elements and GIS maps according to claim 4, characterized in that, The trigger condition group includes: The oscillation frequency is greater than or equal to a preset frequency threshold. The net state offset is less than or equal to a preset net state offset tolerance threshold. No scheduling instruction was triggered during the sliding time window; Furthermore, the spatial coordination factor is less than the preset isolated oscillation threshold.

7. The method for map-based operations based on grassroots governance elements and GIS maps according to claim 3, characterized in that, The process of real-time calculation of a spatial state snapshot up to the target time includes: The change mask vector is used to guide the split operation and calculate the final state of each spatial index identifier at the target time; For continuous numerical feature components, an algebraic accumulator model is used to accumulate the differences in the state difference matrix; For discrete classification feature components, a last write wins over model based on temporal causality is adopted to extract the target state value of the last valid state transition event within the backtracking range as the final state.

8. The method for combat operations based on grassroots governance elements and GIS maps according to claim 1, characterized in that, The governance elements include seven categories of characteristics: people, buildings, land, events, organizations, objects, and situations; the method also includes the following business interaction steps: Multi-dimensional element linking and layer construction: In response to custom or combined query commands for the seven feature sets, the target governance elements are filtered and linked to the corresponding spatial index identifiers, and corresponding element layers are added to the GIS map to achieve large-screen visualization. Risk Situation Display: For specific industry cycles, the dynamic semantic attributes of the selected target governance elements are extracted, and the corresponding risk levels are mapped to the color features of the element layers on the map for differentiated display; Spatial selection and task distribution: In response to the area selection operation on the feature layer, identify the target governance elements and associated personnel within the selected area, and generate a wall chart operation instruction containing a personnel list, a task list and an element list; Multi-level scheduling and supervision: The multi-modal spatiotemporal event stream records the flow status of the chart-based combat instructions, enabling cross-level and cross-departmental collaborative scheduling and task supervision; Special Case Archiving: In response to special action orders for a specific time period, the case packaging and space compression steps are performed to save high-fidelity combat case packages for subsequent special review and research.

9. A wall-map-based operational device based on grassroots governance elements and GIS maps, comprising the method described in any one of claims 1-8, characterized in that, include: The baseline generation module is used to physically separate the spatial topology data of the grassroots governance elements to be connected from the initial semantic attribute set at the initial moment, and construct a spatiotemporal state hash tree based on the spatial topology data, and extract the hash value of the root node of the spatiotemporal state hash tree to generate an initial spatiotemporal semantic baseline snapshot. The incremental capture module is used to asynchronously intercept feature status change instructions, extract the change features in the change instructions and combine them with timestamps and spatial index identifiers to encapsulate and generate standardized differential event vectors, and write the differential event vectors in append-only mode to form a multimodal spatiotemporal event stream arranged in time sequence. The compression and folding module is used to traverse the multimodal spatiotemporal event stream, identify and eliminate invalid oscillation change events targeting the same spatial index based on the underlying spatial topology relationship to perform spatially aware state folding, and perform differential compression encoding on the folded valid instructions to generate combat case packages that are separated from the real-time business library. The sand table calculation module is used to respond to historical retrospective commands, parse the combat case package to restore the event flow within the target retrospective time period, load the initial spatiotemporal semantic baseline snapshot in a virtual sand table space independent of the real-time production environment, drive the virtual clock engine to stream and replay the restored event flow, calculate the spatial state snapshot to the target time in real time, and output the corresponding vector layer drawing commands.

10. An electronic device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform an operation corresponding to the map-based combat method based on grassroots governance elements and GIS maps as described in any one of claims 1-8.

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