Rule-driven emergency scenario simulation situation visual display method and system

Abstract

This application relates to the field of emergency scenario simulation and visualization technology, and provides a rule-driven method and system for visualizing emergency scenario simulation situations. By setting rules as a combination of triggering conditions and execution actions, with the triggering conditions being multi-dimensional combinations and the execution actions being multi-type action linkages, the system achieves accurate presentation of key situational information. Based on the rule settings consisting of triggering conditions and execution actions, instances are generated and instructions are executed using the characteristics of the rules to control and drive the situational display, thereby achieving flexible configuration of emergency scenario simulation situational visualization. By dynamically loading new or modified rules into the rule engine, the situational display logic is updated in real time.

Description

Technical Field

[0001] This application relates to the field of emergency scenario simulation and visualization technology, and in particular to a rule-driven emergency scenario simulation situation visualization method and system. Background Technology

[0002] Emergency scenario simulation situation visualization is a core supporting technology in daily emergency scenario training, simulation, command and decision-making, drills, equipment testing, and tactical verification. It directly impacts the efficiency of user perception and the accuracy of decision-making in emergency scenarios. In modern emergency scenario simulation systems, the simulation engine continuously outputs massive amounts of multi-dimensional situational data. This data encompasses the static attributes and dynamic states of combat entities, emergency scenario interaction events, spatial relationships, and complex information such as environmental elements. The core objective of emergency scenario simulation situation visualization technology is to collect, process, and transform this dispersed and abstract massive amount of situational data. Through visualization methods such as symbolic representation, graphical rendering, and animation, it presents the underlying patterns and key information of the emergency scenario situation to users intuitively and clearly. This helps users quickly capture emergency scenario dynamics, identify situational changes, and grasp core contradictions, providing reliable situational support for emergency command deployment, emergency plan optimization, and emergency training effectiveness evaluation.

[0003] Traditional emergency scenario simulation situation visualization methods mostly use hard-coded fixed display logic. Because the display logic is strongly coupled with the underlying code, it causes the problem of needing to modify the code and redeploy when the situation display requirements change. Summary of the Invention

[0004] Therefore, it is necessary to provide a rule-driven method and system for visualizing emergency scenario simulation situations to address the aforementioned technical issues.

[0005] A rule-driven method for visualizing emergency scenario simulation situations is provided for emergency scenario simulation systems. The method includes a simulation engine, a rule engine, a rule base, a visualization rendering module, and a rule management module. The method includes the following steps: Receive situation data from the simulation engine and encapsulate the situation data into fact objects that the rule engine can recognize; The rule base loads the corresponding rule set based on the fact object. Each rule in the rule set consists of a triggering condition and an execution action. The triggering condition is a combination of multi-dimensional conditions; the execution action is a linkage of multiple types of actions. The fact object is input into the rule engine. Based on the triggering conditions corresponding to the fact object, the rule engine generates activation rule instances using an incremental matching mechanism and sorts the generated activation rule instances according to a preset strategy. After the sorted activation rule instances are converted into standardized visual instructions, they are input into the visualization rendering module. The visualization rendering module then executes the corresponding actions based on the received standardized visual instructions. The rule management module verifies newly added rules and stores the verified rules in the rule library. The rule library receives the newly added rules after verification, records and stores the rule change information, performs version management of the rule library, and dynamically loads rule changes into the rule engine.

[0006] In one embodiment, situational data is received from the simulation engine, and the situational data is encapsulated into fact objects that the rule engine can recognize, including: Receive situation data from the simulation engine, the situation data including the static attributes, dynamic states and events of the entity; The received situational data is parsed to extract the entity's unique identifier, attribute key-value pairs, spatial coordinates, timestamp, and event description fields. The extracted attribute values ​​are converted into data types supported by the rule engine, the spatial coordinates are subjected to coordinate system transformation, and the timestamps are normalized to the time zone, thus completing the field standardization process. Based on the fact object structure preset by the rule engine, the standardized field association mapping is encapsulated into a fact object containing fixed fields such as entity ID, attribute set, spatiotemporal information and event type.

[0007] In one embodiment, situational data is received from the simulation engine, the situational data including the static attributes, dynamic states, and events of entities, including: Establish a communication link with the simulation engine, wherein the communication link is adapted to a preset network transmission protocol and simulation data interaction format; Configure data subscription parameters, which include the types of entities to be received, event types, and data update frequency; A request command containing the subscription parameters is sent to the simulation engine through the communication link; The simulation engine receives situational data returned by the received request instructions. The situational data includes the static attributes, dynamic states, and events of the entity.

[0008] In one embodiment, the rule base loads a corresponding set of rules based on the fact object, including: Extract key features related to the triggering conditions from the fact object, including attribute set, spatiotemporal information, and event type; Based on the aforementioned key features, rule query keywords corresponding to the triggering conditions of rules in the rule base are generated; By using the indexing system of the rule base, the query keywords are matched with the rules stored in the rule base, and all rules whose triggering conditions contain the key features are filtered out to obtain the rule set corresponding to the fact object.

[0009] In one embodiment, the triggering conditions include entity attribute conditions, spatial relationship conditions, and emergency scenario context environment conditions; The actions performed include symbol replacement, animation triggering, and special effects generation.

[0010] In one embodiment, the fact object is input into a rule engine. Based on the triggering conditions corresponding to the fact object, the rule engine generates activation rule instances using an incremental matching mechanism and sorts the generated activation rule instances according to a preset strategy, including: The fact object is input into the rule engine, which loads the triggering conditions of each rule in the rule set corresponding to the fact object and parses the judgment logic of the triggering conditions. An incremental matching mechanism is used to compare the judgment logic of each field related to the triggering condition in the fact object, and the rule that all judgment logic in the triggering condition is satisfied by the fact object is the activation rule. Create an instance corresponding to each activation rule to obtain an activation rule instance. The activation rule instance includes a unique rule identifier, a condition item that matches the fact object, and a trigger timestamp. The generated activation rule instances are sorted according to a preset strategy.

[0011] In one embodiment, the sorted activation rule instances are converted into standardized visualization instructions and then input into the visualization rendering module. The visualization rendering module executes corresponding actions based on the received standardized visualization instructions, including: Parse each sorted activation rule instance and extract the execution action and parameters of the execution action for each activation rule instance; The extracted execution actions and their parameters are mapped to preset fields of standardized visual instructions. The mapped field information is then integrated according to the preset instruction format specifications and encapsulated into structured standardized visual instructions.

[0012] In one embodiment, the rule management module verifies newly added rules and stores the verified rules in the rule base, including: The rule management module performs syntax and logic checks on newly added rules, and performs structured processing on the newly added rules that pass the checks, extracting the unique identifier, triggering conditions, execution actions and creation timestamp of the newly added rules to form new rule metadata; Based on the rule base storage architecture, the storage path of the newly added rule is determined, and the storage path of the newly added rule corresponds to the triggering condition of the newly added rule; According to the preset storage format of the rule base, the newly added rules and their metadata after structured processing are written to the corresponding storage path of the rule base; The rule management module updates the index information of the rule base, which includes the association between the unique identifier of the rule and its storage path.

[0013] In one embodiment, the rule base receives newly added rules after verification, records and stores rule change information, performs version management of the rule base, and dynamically loads rule changes into the rule engine, including: The rule base receives newly added rules after verification and assigns a unique version identifier to each new or updated rule. The version identifier includes the rule number, version number, and update timestamp. Record rule change information to form a rule version change log. The change information includes the modified content, the identifier of the person making the change, and a description of the reason for the change. According to the pre-defined version storage architecture of the rule base, different versions of rules are stored in their respective version directories; Provides a version search interface to query the target version rules; The rule management module automatically and dynamically loads rule changes into the rule engine.

[0014] A rule-driven emergency scenario simulation situation visualization display system is used for emergency scenario simulation systems, including a simulation engine, a rule engine, a rule base, a visualization rendering module, and a rule management module; The system includes: The data receiving module is used to receive situational data from the simulation engine and encapsulate the situational data into fact objects that the rule engine can recognize; The rule loading module is used by the rule base to load the corresponding rule set based on the fact object. Each rule in the rule set consists of a triggering condition and an execution action. The triggering condition is a combination of multi-dimensional conditions, and the execution action is a linkage of multiple types of actions. The rule matching module is used to input the fact object into the rule engine. The rule engine generates activation rule instances based on the triggering conditions corresponding to the fact object using an incremental matching mechanism, and sorts the generated activation rule instances according to a preset strategy. The instruction generation module is used to convert the sorted activation rule instances into standardized visual instructions and then input them into the visualization rendering module. The visualization rendering module executes the corresponding actions according to the received standardized visual instructions. The rule dynamic change module is used to verify newly added rules through the rule management module and store the verified new rules into the rule library; the rule library receives the newly added rules after verification, records and stores the rule change information, performs version management of the rule library, and dynamically loads the rule changes into the rule engine.

[0015] The aforementioned rule-driven emergency scenario simulation situation visualization method and system, by setting rules as a combination of triggering conditions and execution actions, with triggering conditions being multi-dimensional combinations and execution actions being multi-type action linkages, achieves accurate presentation of key situation information. This improves upon traditional emergency scenario simulation situation visualization methods, which mostly rely on single-condition judgments and single-action displays, failing to meet the information presentation needs of complex emergency scenarios and thus causing omissions of key situation information or unintuitive displays. Furthermore, based on the rule configuration consisting of triggering conditions and execution actions, this invention uses rule characteristics to generate instances and execute instructions to control and drive the situation display, thereby achieving flexible configuration of emergency scenario simulation situation visualization. This improves upon traditional emergency scenario simulation situation visualization methods, which use hard-coded fixed display logic, resulting in strong coupling between the display logic and underlying code, leading to code modification and redeployment when situation display requirements change. Moreover, by dynamically loading new or changed rules into the rule engine, real-time updates of the situation display logic are achieved. This improves upon traditional emergency scenario simulation situation visualization methods, which mostly use static rule deployment, requiring system restarts for rule updates to take effect, thus preventing timely adjustments to the situation display logic during simulation. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a rule-driven emergency scenario simulation situation visualization method in one embodiment. Figure 2 This is a structural block diagram of a rule-driven emergency scenario simulation situation visualization system in one embodiment. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0018] In one embodiment, such as Figure 1 As shown, a rule-driven method for visualizing emergency scenario simulation situations is provided for emergency scenario simulation systems, including a simulation engine, a rule engine, a rule base, a visualization rendering module, and a rule management module. The method includes the following steps: Step 201: Receive situation data from the simulation engine and encapsulate the situation data into fact objects that the rule engine can recognize.

[0019] Step 202: The rule base loads the corresponding rule set based on the fact object. Each rule in the rule set consists of a triggering condition and an execution action. The triggering condition is a combination of multi-dimensional conditions; the execution action is a linkage of multiple types of actions.

[0020] Step 203: Input the fact object into the rule engine. Based on the triggering conditions corresponding to the fact object, the rule engine generates activation rule instances using an incremental matching mechanism and sorts the generated activation rule instances according to a preset strategy.

[0021] Step 204: After converting the sorted activation rule instances into standardized visualization instructions, input them into the visualization rendering module. The visualization rendering module then executes the corresponding actions based on the received standardized visualization instructions.

[0022] Step 205: Verify the newly added rules through the rule management module and store the verified new rules into the rule library; the rule library receives the newly added rules after verification, records the change information of the stored rules, performs version management of the rule library, and dynamically loads the rule changes into the rule engine.

[0023] The aforementioned rule-driven emergency scenario simulation situation visualization method, by setting rules as a combination of triggering conditions and execution actions, with triggering conditions being multi-dimensional combinations and execution actions being multi-type action linkages, achieves accurate presentation of key situation information. This improves upon traditional emergency scenario simulation situation visualization methods, which mostly rely on single-condition judgments and single-action displays, failing to meet the information presentation needs of complex emergency scenarios and thus causing omissions of key situation information or unintuitive displays. Furthermore, based on the rule configuration consisting of triggering conditions and execution actions, this invention uses rule characteristics to generate instances and execute instructions to control and drive the situation display, thereby achieving flexible configuration of emergency scenario simulation situation visualization. This improves upon traditional methods that use hard-coded fixed display logic, leading to strong coupling between the display logic and underlying code, resulting in code modification and redeployment when situation display requirements change. Moreover, by dynamically loading new or changed rules into the rule engine, real-time updates of the situation display logic are achieved. This addresses the problem of traditional emergency scenario simulation situation visualization methods, which mostly use static rule deployment, requiring system restarts for rule updates to take effect, thus preventing timely adjustments to the situation display logic during simulation.

[0024] In one embodiment, the triggering conditions include entity attribute conditions, spatial relationship conditions, and emergency scenario context conditions; The actions performed include symbol replacement, animation triggering, and special effects generation.

[0025] In one embodiment, step 201 involves receiving situational data from the simulation engine and encapsulating the situational data into factual objects that the rule engine can recognize, including: Receive situational data from the simulation engine, which includes the static attributes, dynamic states, and events of entities; The received situational data is parsed to extract the entity's unique identifier, attribute key-value pairs, spatial coordinates, timestamp, and event description fields. The extracted attribute values ​​are converted into data types supported by the rule engine, the spatial coordinates are subjected to coordinate system transformation, and the timestamps are normalized to the time zone, thus completing the field standardization process. Based on the fact object structure preset by the rule engine, the standardized field association mapping is encapsulated into a fact object containing fixed fields such as entity ID, attribute set, spatiotemporal information and event type.

[0026] In this embodiment, the extracted attribute key-value pairs, spatial coordinates, and timestamps are standardized, and then the standardized fields are associated and mapped, encapsulated into a fact object structure preset by the rule engine. This allows the rule engine to perform matching directly based on the preset structure without having to handle data format issues, reducing unnecessary calculations and improving matching speed. Furthermore, by placing the data format conversion, standardization, and encapsulation work outside the rule engine and simulation engine, the simulation engine and rule engine do not need to concern themselves with each other's data details; they only need to follow their respective interface specifications, effectively reducing inter-system dependencies.

[0027] Specifically, in one embodiment, situational data is received from the simulation engine. This data includes static attributes, dynamic states, and events of entities. Static attributes include Red Team vehicle A, belonging to the Red Team, and a maneuver radius of 50km. Dynamic states include real-time location coordinates of 116.4°E, 39.9°N, maneuver speed of 40km / h, and damage level of 30%. Events include a maneuver event, occurring at simulation time 01:23:45, and associated entities Red Team vehicle A and Blue Team vehicle B. The received situational data is parsed, extracting the entity's unique identifier (T-001), attribute key-value pairs including model = vehicle A, belonging to the Red Team, damage level = 30%, spatial coordinates of 116.4°E, 39.9°N, altitude of 50m, timestamp of simulation time 01:23:45, and event description fields including a maneuver event and Blue Team ID A-003. The parsed fields are standardized: attribute values ​​are converted into string, numeric, and boolean data types supported by the rule engine; spatial coordinates are converted from the simulation engine's local coordinate system to the geodetic coordinate system; and timestamps are normalized to UTC timezone. Following the rule engine's preset fact object structure, the standardized fields are associated and mapped, encapsulated into a fact object containing fixed fields such as entity ID, attribute set, spatiotemporal information, and event type. An example of the JSON format for this fact object is as follows: {"Entity ID": "T-001", "Attribute Set": {"Model": "Vehicle A", "Owner": "Red Party", "Damage Level": 30}, "Spatiotemporal Information": {"Longitude": 116.4", "Latitude": 39.9", "Altitude": 50", "Timestamp": "2024-01-01T01:23:45Z"}, "Event Type": "Motor Event"} In one embodiment, situational data is received from the simulation engine. This situational data includes the static attributes, dynamic states, and events of the entities, including: Establish a communication link with the simulation engine, wherein the communication link is adapted to a preset network transmission protocol and simulation data interaction format; Configure data subscription parameters, which include the types of entities to be received, event types, and data update frequency; Send a request command containing subscription parameters to the simulation engine via the communication link; The system receives situational data returned by the simulation engine based on the received request instructions. The situational data includes the static attributes, dynamic states, and events of the entities.

[0028] In this embodiment, by configuring data subscription parameters that include entity type and event type, the request instructions received by the rule engine only contain the required core data. This reduces the interference of irrelevant data on subsequent processing and avoids a decrease in matching efficiency caused by the rule engine processing a large amount of useless data. Simultaneously, configuring the data update frequency in the data subscription parameters avoids unrestrained data pushes that could lead to network congestion and rule engine overload, ensuring stable system operation.

[0029] Specifically, in one embodiment, a bidirectional communication link is established with the simulation engine. The network transmission protocol adapted to the communication link is TCP / IP, and the simulation data interaction format is Protocol Buffers to ensure the stability and efficiency of data transmission. Data subscription parameters are configured, including the types of entities to be received, events, and a data update frequency of 10Hz. A request command containing the subscription parameters is sent to the simulation engine through the aforementioned communication link. After responding, the simulation engine continuously pushes situational data. The situational data returned by the simulation engine is received, and the situational data includes the static attributes, dynamic states, and events of the entities.

[0030] In one embodiment, step 202, the rule base loads the corresponding rule set based on the fact object, including: Extract key features related to the triggering conditions from the fact object. These key features include attribute sets, spatiotemporal information, and event types. Generate rule query keywords that correspond to the triggering conditions of rules in the rule base based on key features; By using the indexing system of the rule base, the query keywords are matched with the rules stored in the rule base, and all rules whose triggering conditions contain the key features are filtered out to obtain the rule set corresponding to the fact object.

[0031] In this embodiment, key features related to the triggering conditions in the fact object are extracted to generate rule query keywords, enabling the rule engine to perform accurate matching based on the generated query keywords without having to traverse all rules. This allows the rule engine to run stably and efficiently in complex, large-scale rule scenarios.

[0032] Specifically, in one embodiment, key features related to the triggering conditions are extracted from the factual object. These key features include attribute sets, spatiotemporal information, and event type. The attribute set information includes ownership = Red Team, model = Vehicle A, damage level = 30, spatial coordinates of longitude 116.4° and latitude 39.9°, and event type information of motor event. Rule query keywords are generated based on these key features: “Red Team,” “Vehicle A,” “Motor event,” and “116.4°E-39.9°N.” These query keywords correspond to the triggering condition features of rules in the rule base. Using the rule base's indexing system, the query keywords are matched with the rules stored in the rule base to filter out rules whose triggering conditions contain the aforementioned key features. Finally, two relevant rules are selected, forming a rule set. Each rule in the rule set consists of a triggering condition and an execution action, as follows: Rule 1: The triggering condition is a combination of multi-dimensional conditions, including entity attribute conditions (ownership = red party, model = vehicle A), spatial relationship conditions (distance from the target entity ≤ 5km), and emergency scenario context environment conditions (weather condition = sunny). The execution action is a multi-type action linkage, including symbol replacement (changing the color of the vehicle A icon from blue to red), animation trigger (playing the vehicle A motion animation for 2 seconds), and special effect generation (displaying a yellow warning halo at the location of vehicle A with display parameters of a motion radius of 30 pixels and transparency of 60%). Rule 2: The triggering condition is a combination of multi-dimensional conditions, including entity attribute conditions (ownership = red team, damage level ≥ 30%), spatial relationship conditions (outside the supply area), and emergency scenario context environment conditions (communication link status = normal). The execution action is a multi-type action linkage, including symbol replacement (adding a damage mark to the upper right corner of the vehicle A icon), animation trigger (playing a damaged shaking animation of vehicle A for 1.5 seconds), and special effect generation (displaying a red damage warning box around vehicle A with display parameters of 50 pixels on each side and 2 pixels on the border).

[0033] In one embodiment, step 203 involves inputting the fact object into the rule engine. Based on the triggering conditions corresponding to the fact object, the rule engine generates activation rule instances using an incremental matching mechanism and sorts the generated activation rule instances according to a preset strategy, including: The fact object is input into the rule engine, which loads the triggering conditions of each rule in the rule set corresponding to the fact object and parses the judgment logic of the triggering conditions. An incremental matching mechanism is used to compare the judgment logic of each field related to the triggering condition in the fact object, and the rule that all judgment logic in the triggering condition is satisfied by the fact object is the activation rule. Create an instance corresponding to each activation rule to obtain an activation rule instance. The activation rule instance includes a unique rule identifier, a condition item that matches the fact object, and a trigger timestamp. The generated activation rule instances are sorted according to a preset strategy.

[0034] In this embodiment, an incremental matching mechanism is used to compare and judge each field related to the triggering condition in the fact object, thus eliminating the need to traverse the entire rule base and significantly reducing the amount of matching calculations. By defining the criteria for activation rules, it is ensured that only rules that meet all conditions will generate instances, preventing invalid rules that partially meet the conditions from participating in subsequent processes, and also reducing system resource consumption. At the same time, the generated activation rule instances are sorted according to a preset strategy, thereby prioritizing the processing of core tasks and avoiding disordered execution when multiple rules are activated simultaneously.

[0035] Specifically, in one embodiment, the rule engine loads the triggering conditions of each rule in the rule set corresponding to the fact object. It parses the rules to obtain the judgment logic for Rule 1: the distance between Red Team vehicle A and the target entity is ≤5km in clear weather; and the judgment logic for Rule 2: Red Team vehicle A with damage ≥30% is outside the resupply area when communication is normal. Based on the incremental matching mechanism, only the fields related to the triggering conditions in the fact object are compared, without traversing the entire rule base. The attribute set information of the fact object, including Red Team, vehicle A, damage level 30%, spatial coordinates 116.4°E-39.9°N, and event type (motor event), is compared item by item with the judgment logic of the parsed triggering conditions. After comparison, the factual object satisfies all the judgment logic of Rule 1: the party is Red, the model is Vehicle A, the weather conditions are confirmed to be sunny, and the distance to the target entity is 3.2km ≤ 5km through simulation data. It also satisfies all the judgment logic of Rule 2: the party is Red, the damage level is 30%, the communication link status is normal, and the spatial coordinates confirm it is outside the resupply area. Therefore, both rules are determined to be active. An instance is created for each active rule. The instance of Rule 1 includes the rule unique identifier R-001, the conditions matching the factual object including Red, Vehicle A, sunny weather, distance 3.2km, and trigger timestamp 2024-01-01T01:23:45Z; the instance of Rule 2 includes the rule unique identifier R-002, the conditions matching the factual object including Red, damage level 30%, normal communication, outside the resupply area, and trigger timestamp 2024-01-01T01:23:45Z. Activation rule instances are sorted according to a preset strategy, which prioritizes the severity of the event. Rule 1, which is related to maneuver events, has a higher priority than rule 2, which is related to damage warnings. The sorting result is that rule 1 instances come first, followed by rule 2 instances.

[0036] In one embodiment, step 204 involves converting the sorted activation rule instances into standardized visualization instructions and inputting them into the visualization rendering module. The visualization rendering module then executes the corresponding actions based on the received standardized visualization instructions, including: Parse each sorted activation rule instance and extract the execution action and parameters of the execution action for each activation rule instance; The extracted execution actions and their parameters are mapped to preset fields of standardized visual instructions. The mapped field information is then integrated according to the preset instruction format specifications and encapsulated into structured standardized visual instructions.

[0037] In this embodiment, the execution actions and parameters of the activation rule instances are extracted and mapped to preset fields of standardized visualization instructions. This ensures that the standardized visualization instructions contain core elements, preventing a disconnect between rule logic and visualization effects. Simultaneously, standardized visualization instructions enable the visualization rendering module to directly adapt to the rendering logic, reducing unnecessary calculations and shortening the time required for visualization presentation.

[0038] Specifically, in one embodiment, the two sorted activation rule instances are parsed. The execution actions of rule 1 are extracted, including symbol replacement, animation triggering, and effect generation, along with corresponding parameters including target entity identifier T-001, action type = motor association, execution duration 2 seconds, and display parameters = red icon + 30-pixel yellow halo. The execution actions of rule 2 are also extracted, including symbol replacement, animation triggering, and effect generation, along with corresponding parameters including target entity identifier T-001, action type = damage association, execution duration 1.5 seconds, and display parameters = damage identifier + 50-pixel red prompt box. The extracted execution actions and parameters are mapped to preset fields of standardized visualization instructions. The instruction identifier is generated according to the instruction type-timestamp format, the target object ID is T-001, the corresponding action codes are symbol replacement = S001, animation triggering = A002, and effect generation = E003, and the parameters are configured as the specific execution parameters for each action. The execution priority is consistent with the sorting of the activation rule instances. Following the preset instruction format specification, i.e., JSON format, the mapped field information is integrated and encapsulated into structured, standardized visualization instructions. This instruction format is adapted to the parsing requirements of OpenGL-based visualization rendering modules. Specific instructions are as follows: {"Instruction Identifier": "VIS-20240101012345-001", "Target Object ID": "T-001", "Action Code": ["S001", "A002", "E003"], "Parameter Configuration": {"Symbol Replacement": "Red Vehicle A Icon", "Animation Trigger"} {"Animation for 2 seconds", "Effects Generation": "Yellow halo 30 pixels"}, "Execution Priority": 1}, {"Instruction Identifier": "VIS-20240101012345-002", "Target Object ID": "T-001", "Motion Code": ["S001", "A002", "E003"], "Parameter Configuration": {"Symbol Replacement": "Add Damage Identifier", "Animation Trigger": "Damage Jitter 1.5 seconds", "Effects Generation": "Red Warning Box 50 pixels"}, "Execution Priority": 2} In one embodiment, step 205 involves verifying the newly added rules through the rule management module and storing the verified new rules into the rule base, including: The rule management module performs syntax and logic checks on newly added rules, and performs structured processing on the newly added rules that pass the checks, extracting the unique identifier, triggering conditions, execution actions and creation timestamp of the newly added rules to form new rule metadata; Based on the rule base storage architecture, the storage path of the new rule is determined, and the storage path of the new rule corresponds to the triggering condition of the new rule. According to the preset storage format of the rule base, the newly added rules and their metadata after structured processing are written to the corresponding storage path of the rule base; The rule management module updates the rule base index information, which includes the association between the rule's unique identifier and its storage path.

[0039] In this embodiment, syntactic and logical validation of newly added rules is performed to prevent erroneous rules from entering the rule base and causing matching errors in the rule engine. By setting the association between the unique identifier of a rule and its storage path in the index information, duplicate rule storage or incorrect storage paths are avoided.

[0040] Specifically, in one embodiment, when an administrator adds a new rule through the visual interface of the rule management module, the rule content includes the following trigger conditions: Red Team vehicle A, speed ≥ 60 km / h, emergency scenario context environment condition = nighttime; and the execution actions: replacing the symbol with a highlighted green icon, triggering an animation to create a fast movement trajectory, and generating a speed warning sign. The rule management module performs syntax validation on the new rule, checking the format of the condition expression and the completeness of the action parameters to confirm that there are no syntax errors. Simultaneously, it performs logical validation to check for conflicts with existing rules and to determine whether the trigger conditions are duplicated or contradictory with other rules in the same scenario. After validation, no logical conflicts are found. The new rule that passes the syntax and logic validation is then structured, extracting the rule's unique identifier R-003, trigger condition features including Red Team vehicle A, speed ≥ 60 km / h, and nighttime, execution action features including a highlighted green icon, a fast movement trajectory, a speed warning sign, and a creation timestamp 2024-01-01T09:00:00Z, forming rule metadata. The rule-based storage architecture uses a hierarchical storage system based on entity type and event type. The storage path for this new rule is determined as rule base / red team / vehicle A / motorized event, corresponding to the rule's trigger condition feature category. Following the rule base's preset storage format (JSON), the structured rule and its metadata are written to the corresponding storage path. The rule management module updates the rule base's index information, adding new index entries that include the association between the rule's unique identifier R-003 and the storage path rule base / red team / vehicle A / motorized event, ensuring fast subsequent querying and loading.

[0041] In one embodiment, in step 205, the rule base receives the newly added rules after verification, records and stores the rule change information, performs version management on the rule base, and dynamically loads the rule changes into the rule engine, including: The rule base receives newly added rules after verification and assigns a unique version identifier to each new or updated rule. The version identifier includes the rule number, version number, and update timestamp. Record rule change information to form a rule version change log. The change information includes the modified content, the identifier of the person who made the change, and an explanation of the reason for the change. According to the pre-defined version storage architecture of the rule base, different versions of rules are stored in their respective version directories; Provides a version search interface to query the target version rules; The rule management module automatically and dynamically loads rule changes into the rule engine.

[0042] In this embodiment, by storing different versions of rules in their respective version directories, historical versions are ensured to be traceable; the rule management module automatically and dynamically loads rule changes into the rule engine, so that the rule engine can work according to the changed rules without restarting.

[0043] Specifically, in one embodiment, a unique version identifier is assigned to the newly added rule R-003. The version identifier format is rule number-version sequence number-update timestamp, specifically R-003-V1-20240101090000. When the administrator subsequently modifies the execution action of this rule, i.e., changes the highlighted green icon to a highlighted blue icon, the rule management module records the rule change information, including the modification content (symbol color changed from green to blue), the modifier identifier (Admin-001), and the reason for the change (to adapt to the visual recognition requirements of high-speed maneuvering at night), forming a rule version change log. According to the preset version storage architecture of the rule base, the modified rule is stored as version V2 in the rule base / red team / vehicle A / motor event / V2 directory, while version V1 data is retained in the corresponding V1 directory to ensure the traceability of historical versions. The rule base provides a version retrieval interface, allowing administrators to query target version rules by rule number R-003, version identifier R-003-V1, or update timestamp 2024-01-01T09:00:00. If a rollback is needed, a version rollback operation can be performed based on the query results to restore to version V1. After rule version V2 is updated, there is no need to restart the simulation system or rule engine; the rule management module automatically and dynamically loads the updated rule into the Drools rule engine, and subsequently received situational data can be directly matched against the updated rule.

[0044] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0045] In one embodiment, such as Figure 2 As shown, a rule-driven emergency scenario simulation situation visualization display system is provided for emergency scenario simulation systems, including a simulation engine, a rule engine, a rule base, a visualization rendering module, and a rule management module; The system includes: The data receiving module 901 is used to receive situational data from the simulation engine and encapsulate the situational data into fact objects that the rule engine can recognize.

[0046] The rule loading module 902 is used to load the corresponding rule set based on the fact object in the rule base. Each rule in the rule set consists of a triggering condition and an execution action. The triggering condition is a combination of multi-dimensional conditions; the execution action is a linkage of multiple types of actions.

[0047] The rule matching module 903 is used to input fact objects into the rule engine. The rule engine generates activation rule instances based on the triggering conditions corresponding to the fact objects using an incremental matching mechanism, and sorts the generated activation rule instances according to a preset strategy.

[0048] The instruction generation module 904 is used to convert the sorted activation rule instances into standardized visual instructions and then input them into the visualization rendering module. The visualization rendering module executes the corresponding actions according to the received standardized visual instructions.

[0049] The rule dynamic change module 905 is used to verify newly added rules through the rule management module and store the verified new rules into the rule library; the rule library receives the newly added rules after verification, records the change information of the stored rules, performs version management of the rule library, and dynamically loads the rule changes into the rule engine.

[0050] Specific limitations regarding the rule-driven emergency scenario simulation situation visualization system can be found in the limitations of the rule-driven emergency scenario simulation situation visualization method described above, and will not be repeated here. Each module in the aforementioned rule-driven emergency scenario simulation situation visualization system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0051] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0052] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A rule-driven method for visualizing emergency scenario simulation situations, characterized in that, For emergency scenario simulation systems, including simulation engine, rule engine, rule base, visualization rendering module and rule management module; The method includes the following steps: Receive situation data from the simulation engine and encapsulate the situation data into fact objects that the rule engine can recognize; The rule base loads the corresponding rule set based on the fact object. Each rule in the rule set consists of a triggering condition and an execution action. The triggering condition is a combination of multi-dimensional conditions; the execution action is a linkage of multiple types of actions. The fact object is input into the rule engine. Based on the triggering conditions corresponding to the fact object, the rule engine generates activation rule instances using an incremental matching mechanism and sorts the generated activation rule instances according to a preset strategy. After the sorted activation rule instances are converted into standardized visual instructions, they are input into the visualization rendering module. The visualization rendering module then executes the corresponding actions based on the received standardized visual instructions. The rule management module verifies newly added rules and stores the verified rules in the rule library. The rule library receives the newly added rules after verification, records and stores the rule change information, performs version management of the rule library, and dynamically loads rule changes into the rule engine.

2. The rule-driven emergency scenario simulation situation visualization method according to claim 1, characterized in that, Receive situational data from the simulation engine and encapsulate the situational data into fact objects that the rule engine can recognize, including: Receive situation data from the simulation engine, the situation data including the static attributes, dynamic states and events of the entity; The received situational data is parsed to extract the entity's unique identifier, attribute key-value pairs, spatial coordinates, timestamp, and event description fields. The extracted attribute values ​​are converted into data types supported by the rule engine, the spatial coordinates are subjected to coordinate system transformation, and the timestamps are normalized to the time zone, thus completing the field standardization process. Based on the fact object structure preset by the rule engine, the standardized field association mapping is encapsulated into a fact object containing fixed fields such as entity ID, attribute set, spatiotemporal information and event type.

3. The rule-driven emergency scenario simulation situation visualization method according to claim 2, characterized in that, Receive situational data from the simulation engine, the situational data including the static attributes, dynamic states, and events of entities, including: Establish a communication link with the simulation engine, wherein the communication link is adapted to a preset network transmission protocol and simulation data interaction format; Configure data subscription parameters, which include the types of entities to be received, event types, and data update frequency; A request command containing the subscription parameters is sent to the simulation engine through the communication link; The simulation engine receives situational data returned by the received request instructions. The situational data includes the static attributes, dynamic states, and events of the entity.

4. The rule-driven emergency scenario simulation situation visualization method according to claim 3, characterized in that, The rule base loads the corresponding rule set based on the fact object, including: Extract key features related to the triggering conditions from the fact object, including attribute set, spatiotemporal information, and event type; Based on the aforementioned key features, rule query keywords corresponding to the triggering conditions of rules in the rule base are generated; By using the indexing system of the rule base, the query keywords are matched with the rules stored in the rule base, and all rules whose triggering conditions contain the key features are filtered out to obtain the rule set corresponding to the fact object.

5. The rule-driven emergency scenario simulation situation visualization method according to claim 4, characterized in that, The triggering conditions include entity attribute conditions, spatial relationship conditions, and emergency scenario context environment conditions; The actions performed include symbol replacement, animation triggering, and special effects generation.

6. The rule-driven emergency scenario simulation situation visualization method according to claim 4, characterized in that, The fact object is input into the rule engine. Based on the triggering conditions corresponding to the fact object, the rule engine generates activation rule instances using an incremental matching mechanism, and sorts the generated activation rule instances according to a preset strategy, including: The fact object is input into the rule engine, which loads the triggering conditions of each rule in the rule set corresponding to the fact object and parses the judgment logic of the triggering conditions. An incremental matching mechanism is used to compare the judgment logic of each field related to the triggering condition in the fact object, and the rule that all judgment logic in the triggering condition is satisfied by the fact object is the activation rule. Create an instance corresponding to each activation rule to obtain an activation rule instance. The activation rule instance includes a unique rule identifier, a condition item that matches the fact object, and a trigger timestamp. The generated activation rule instances are sorted according to a preset strategy.

7. The rule-driven emergency scenario simulation situation visualization method according to claim 6, characterized in that, After the sorted activation rule instances are converted into standardized visualization instructions, they are input into the visualization rendering module. The visualization rendering module executes the corresponding actions based on the received standardized visualization instructions, including: Parse each sorted activation rule instance and extract the execution action and parameters of the execution action for each activation rule instance; The extracted execution actions and their parameters are mapped to preset fields of standardized visual instructions. The mapped field information is then integrated according to the preset instruction format specifications and encapsulated into structured standardized visual instructions.

8. The rule-driven emergency scenario simulation situation visualization method according to claim 7, characterized in that, Newly added rules are validated through the rule management module, and those that pass validation are stored in the rule base, including: The rule management module performs syntax and logic checks on newly added rules, and performs structured processing on the newly added rules that pass the checks, extracting the unique identifier, triggering conditions, execution actions and creation timestamp of the newly added rules to form new rule metadata; Based on the rule base storage architecture, the storage path of the newly added rule is determined, and the storage path of the newly added rule corresponds to the triggering condition of the newly added rule; According to the preset storage format of the rule base, the newly added rules and their metadata after structured processing are written to the corresponding storage path of the rule base; The rule management module updates the index information of the rule base, which includes the association between the unique identifier of the rule and its storage path.

9. The rule-driven emergency scenario simulation situation visualization method according to claim 8, characterized in that, The rule base receives newly added rules after verification, records and stores rule change information, manages rule base versions, and dynamically loads rule changes into the rule engine, including: The rule base receives newly added rules after verification and assigns a unique version identifier to each new or updated rule. The version identifier includes the rule number, version number, and update timestamp. Record rule change information to form a rule version change log. The change information includes the modified content, the identifier of the person making the change, and a description of the reason for the change. According to the pre-defined version storage architecture of the rule base, different versions of rules are stored in their respective version directories; Provides a version search interface to query the target version rules; The rule management module automatically and dynamically loads rule changes into the rule engine.

10. A rule-driven emergency scenario simulation situation visualization system, characterized in that, For emergency scenario simulation systems, including simulation engine, rule engine, rule base, visualization rendering module and rule management module; The system includes: The data receiving module is used to receive situational data from the simulation engine and encapsulate the situational data into fact objects that the rule engine can recognize; The rule loading module is used by the rule base to load the corresponding rule set based on the fact object. Each rule in the rule set consists of a triggering condition and an execution action. The triggering condition is a combination of multi-dimensional conditions, and the execution action is a linkage of multiple types of actions. The rule matching module is used to input the fact object into the rule engine. The rule engine generates activation rule instances based on the triggering conditions corresponding to the fact object using an incremental matching mechanism, and sorts the generated activation rule instances according to a preset strategy. The instruction generation module is used to convert the sorted activation rule instances into standardized visual instructions and then input them into the visualization rendering module. The visualization rendering module executes the corresponding actions according to the received standardized visual instructions. The rule dynamic change module is used to verify newly added rules through the rule management module and store the verified new rules into the rule library; the rule library receives the newly added rules after verification, records and stores the rule change information, performs version management of the rule library, and dynamically loads the rule changes into the rule engine.

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