Real-time situation updating and analysis method, device and equipment based on dynamic ontology

Abstract

The present invention discloses a real-time situation update and analysis method, device and equipment based on dynamic ontology. The method includes: constructing a cross-domain joint combat static ontology; obtaining real-time battlefield situation data from different combat domains; directionally capturing target data in the real-time battlefield situation data; performing multi-source data fusion on multiple target data; performing data standardization processing to generate structured situation data; extracting identifiers of all situation instances, and if the situation instance does not exist in the static ontology, adding the situation instance and its attributes to the static ontology and performing fuzzy matching; converting Turtle format files into graph objects; updating the static ontology by adding, deleting and modifying the graph objects; dynamically analyzing and updating the content and forming knowledge accumulation, and using the knowledge accumulation as the input variable for the next multi-source data fusion; and performing reasoning verification on the static ontology and the dynamic ontology. According to the method of the present invention, the degree of automation is high and the situation analysis and decision-making support capabilities are provided.

Description

Technical Field

[0001] The present invention relates to the technical field of situation fusion and analysis decision-making, and in particular to a real-time situation updating and analysis method based on dynamic ontology, a real-time situation updating and analysis device based on dynamic ontology, and an electronic device. Background Art

[0002] Knowledge engineering in the military has always been a core issue in system planning and operational command. Facing the challenges of knowledge discovery and ontology construction in complex combat scenarios such as joint and cross-domain operations, scholars at home and abroad have conducted some research. First, by identifying and recording the human knowledge used by senior military professionals to solve advanced problems, military knowledge can be classified, expressed, shared, and reused, and integrated into automated systems, enabling artificial intelligence to truly play a role in advanced military applications. Second, addressing the interoperability challenges caused by the heterogeneity of multi-source data on the battlefield, ontologies, a consensus framework, can be used to eliminate grammatical and semantic differences.

[0003] However, traditional ontology construction methods are difficult to adapt to the current big data era:

[0004] From the perspective of the autonomous construction process of the ontology model, the degree of expert participation is still relatively high, which limits the portability and learning ability of the ontology, and the degree of automation needs to be improved;

[0005] From the specific implementation of ontology model construction, the efficiency of data extraction, matching and fusion needs to be improved.

[0006] From the perspective of dynamic ontology updating, the accuracy of matching static ontology with target data needs to be improved.

[0007] From the perspective of the meaning of changes in ontology models, in the military field, especially in the combat field, ontology construction focuses on the changes in the inherent attributes of entities, while ignoring the construction of exogenous attributes such as the logical relationships, role cognition, and behavior patterns of entities in the combat process. Therefore, it is difficult to provide deeper semantic support for the cognition and understanding of the dynamic process of the continuous evolution of entities in the combat system over time. Summary of the Invention

[0008] The present invention aims to address at least one of the technical problems existing in the prior art. To this end, the present invention proposes a real-time situation update and analysis method based on a dynamic ontology. This method has the advantages of a high degree of automation, high efficiency in data extraction, matching, and fusion, and high matching accuracy, and provides situation analysis and decision-making support capabilities.

[0009] The present invention also proposes a real-time situation updating and analyzing device based on dynamic ontology.

[0010] The present invention also provides an electronic device.

[0011] According to the first embodiment of the present invention, a real-time situation update and analysis method based on dynamic ontology is used for multi-source battlefield situation awareness and analysis and decision-making in cross-domain joint combat scenarios, wherein the cross-domain joint combat scenarios include land, sea, air, network, and electromagnetic fields. The method includes:

[0012] Constructing a cross-domain joint combat static ontology and converting the cross-domain joint combat static ontology into a Turtle format file;

[0013] Obtain multiple real-time battlefield situation data from different combat domain sources;

[0014] Directedly grabbing target data in the real-time battlefield situation data according to grabbing constraint conditions;

[0015] Performing multi-source data fusion on the plurality of target data, generating multi-source complementary fusion data through data redundancy processing, data association analysis and data merging, including equipment parameter redundancy processing, weapon mount association relationship establishment and similar resource merging and division;

[0016] Performing data standardization on the multi-source complementary fusion data to generate structured situation data, wherein the structured situation data has a consistent format and structure and is provided with an identifier for matching a battlefield situation instance;

[0017] Extracting identifiers of all battlefield situation instances; if the battlefield situation instance does not exist in the cross-domain joint operations static ontology, adding the battlefield situation instance and its attributes to the cross-domain joint operations static ontology, and performing fuzzy matching on the category of the battlefield situation instance based on edit distance similarity, phonetic similarity, and cosine similarity;

[0018] Using a graph method to load and parse the Turtle format file, and convert the Turtle format file into a graph object;

[0019] Updating the cross-domain joint operations static ontology by adding, deleting and modifying the graph objects;

[0020] Dynamically analyzing the updated content of the cross-domain joint operations static ontology and forming knowledge accumulation, and using the knowledge accumulation as an input variable for the next multi-source data fusion;

[0021] Perform reasoning verification on the cross-domain joint combat static ontology and the updated dynamic ontology, including equipment effectiveness analysis, combat readiness deployment, cost-benefit analysis, and combat outcome prediction; wherein the dynamic analysis includes:

[0022] Designing prompt words for a large language model, using the large language model to predict enemy strategies and intentions, and providing strategies for our own troop deployment and firepower allocation;

[0023] The directional crawling includes:

[0024] Obtaining a first readable file containing the capture constraint conditions and a second readable file containing the real-time battlefield situation data;

[0025] Reading the first readable file and converting the grabbing constraint condition into a first dictionary, reading the second readable file and converting the real-time battlefield situation data into a second dictionary;

[0026] For each set of constraint information in the first dictionary, extract the location information and attribute information of the target data, and construct a capture field using the location information and the attribute information;

[0027] For each of the captured fields, capture the target data in the second dictionary;

[0028] The captured target data is converted into a third readable file and outputted.

[0029] According to the real-time situation update and analysis method based on dynamic ontology in an embodiment of the present invention, by establishing a general workflow for ontology construction and updating, it is possible to realize the automatic standardized construction of data into ontology models, which is conducive to improving the degree of automation; the use of intelligent data integration and standardization technology can improve the efficiency of data extraction, matching and fusion; the use of a multi-dimensional fuzzy weighted matching algorithm to improve the accuracy of matching between the static ontology of cross-domain joint operations and target data; and the use of a large language model to analyze and interpret the dynamically updated part of the static ontology of cross-domain joint operations, and then provide predictions and suggestions to guide the decision-making process.

[0030] In addition, the real-time situation update and analysis method based on dynamic ontology according to an embodiment of the present invention also has the following additional technical features:

[0031] According to some embodiments of the present invention, constructing a cross-domain joint operations static ontology includes:

[0032] Collect literature, expert knowledge and historical data based on needs analysis;

[0033] Extract and integrate effective data;

[0034] Carry out hierarchical design and relationship construction to form conceptual modeling;

[0035] Generate the cross-domain joint combat static ontology.

[0036] According to some embodiments of the present invention, the multi-source data fusion includes:

[0037] Obtaining a plurality of the third readable files and converting the third readable files into a third dictionary;

[0038] For each key of any two of the third dictionaries, if the keys of the two third dictionaries exist and have the same key value, the key value is updated using the result of fusion using the complementary fusion function; otherwise, the key value of the two third dictionaries is updated with the key value of the existing key.

[0039] According to some embodiments of the present invention, the data normalization process includes:

[0040] The multi-source complementary fusion data is subjected to data flattening, data cleaning, data conversion, deduplication, anomaly detection and error correction.

[0041] According to some embodiments of the present invention, the fuzzy matching is performed according to the following formula:

[0042]

[0043] Where, s is the comprehensive similarity, s ∈[0,1]; is the edit distance similarity; is the phonetic similarity; is the cosine similarity; A 、 B 、 C is the weighting coefficient, A + B + C =1;

[0044] If the strings are of different lengths, then A Greater than B ; If the strings are the same length, then A Less than B .

[0045] According to some embodiments of the present invention, the prompt of the large language model is "You are a senior military tactical analyst with extensive experience in battlefield situational awareness and analysis. Please carefully analyze the updated content of the cross-domain joint operations static ontology, evaluate the number and location of military units / equipment of both sides, intelligence data, communication status, and combat objectives, and based on this analysis, speculate on the tactical actions that the enemy may take against us and how we should formulate a response strategy."

[0046] The analysis includes:

[0047] Changes in enemy and friendly forces' combat objectives, troop strength, weapon and equipment quantity and location, intelligence collection, communication capabilities, and other important situational information; analysis results include:

[0048] Enemy tactics: Based on dynamically updated enemy situational information, the specific tactical actions the enemy is preparing to take include raids, reconnaissance, and long-range strikes.

[0049] Our response strategy: Our response measures include strengthening air defense capabilities, increasing reconnaissance and surveillance, and taking actions to confuse the enemy;

[0050] The situation data used for the dynamic analysis include: changes in the physical status of both the enemy and our side, including alert status, standby status, intelligence collection activities, equipment status, and combat level.

[0051] According to the second aspect of the present invention, the real-time situation update and analysis device based on dynamic ontology is used for multi-source battlefield situation perception and analysis decision-making in cross-domain joint combat scenarios, and the cross-domain joint combat scenarios include land, sea, air, network and electromagnetic fields. The device includes: a construction module, the construction module is used to construct a cross-domain joint combat static ontology and convert the cross-domain joint combat static ontology into a Turtle format file; an acquisition module, the acquisition module is used to obtain multiple real-time battlefield situation data from different combat fields; a directional capture module, the directional capture module is used to capture target data in the real-time battlefield situation data according to capture constraints; a multi-source data fusion module, the multi-source data fusion module is used to fusion multiple real-time battlefield situation data. The target data is multi-source data fused, and multi-source complementary fusion data is generated through data redundancy processing, data association analysis and data merging, including equipment parameter redundancy processing, weapon mount association relationship establishment and similar resource merging and division; a data standardization processing module, the data standardization processing module is used to perform data standardization processing on the multi-source complementary fusion data to generate structured situation data, the format and structure of the structured situation data are consistent and are provided with an identifier for matching battlefield situation instances; an extraction module, the extraction module is used to extract the identifiers of all the battlefield situation instances, and if the battlefield situation instance does not exist in the cross-domain joint operation static ontology, the battlefield situation instance and its attributes are added to the cross-domain joint operation static ontology In the static ontology, the categories of the battlefield situation instances are fuzzy matched according to the edit distance similarity, pinyin similarity and cosine similarity; an update module, the update module uses a graph method to load and parse the Turtle format file, and converts the Turtle format file into a graph object; the update module updates the cross-domain joint combat static ontology by adding, deleting and modifying the graph object; a dynamic analysis module, the dynamic analysis module is used to dynamically analyze the updated content of the cross-domain joint combat static ontology and form knowledge accumulation, and use the knowledge accumulation as the input variable of the next multi-source data fusion; wherein, the dynamic analysis module is also used to design the prompt words of the large language model, and use the large language model Predict the enemy's strategy and intention, and provide our own troop deployment and firepower allocation strategy; a reasoning verification module, the reasoning verification module is used to perform reasoning verification on the cross-domain joint combat static ontology and the updated dynamic ontology, including equipment effectiveness analysis, combat readiness deployment, cost-effectiveness analysis and combat result prediction; wherein, the directional crawling module includes: a directional acquisition module, the directional acquisition module is used to acquire a first readable file containing the crawling constraint conditions and a second readable file containing the real-time battlefield situation data; a directional reading module, the directional reading module is used to read the first readable file and convert the crawling constraint conditions into a first dictionary, read the second readable file and convert the real-time battlefield situation data into a second dictionary;a directional extraction module configured to extract the location information and attribute information of the target data for each set of constraint information in the first dictionary, and construct a capture field using the location information and attribute information; and a capture output module configured to capture the target data from the second dictionary for each capture field, and to convert the captured target data into a third readable file and output the file.

[0052] According to the real-time situation update and analysis device based on dynamic ontology in an embodiment of the present invention, by establishing a general workflow for ontology construction and updating, it is possible to realize the automatic standardized construction of data into ontology models, which is conducive to improving the degree of automation; the use of intelligent data integration and standardization technology can improve the efficiency of data extraction, matching and fusion; the use of a multi-dimensional fuzzy weighted matching algorithm to improve the accuracy of matching between the static ontology of cross-domain joint operations and target data; and the use of a large language model to analyze and interpret the dynamically updated part of the static ontology of cross-domain joint operations, and then provide predictions and suggestions to guide the decision-making process.

[0053] According to some embodiments of the present invention, the building block comprises:

[0054] A demand analysis module, wherein the demand analysis module is used to perform demand analysis;

[0055] A static data collection module, wherein the static data collection module is used to collect literature, expert knowledge and historical data;

[0056] A static data fusion module, which is used to extract and fuse effective data;

[0057] A conceptual modeling module, wherein the conceptual modeling module is used to carry out hierarchical design and relationship construction;

[0058] An ontology construction module is used to generate the cross-domain joint combat static ontology.

[0059] According to an embodiment of the third aspect of the present invention, an electronic device includes a processor and a memory, wherein the processor and the memory are connected, and the memory is used to store a computer program. When the computer program is executed by the processor, the real-time situation update and analysis method based on dynamic ontology as described in the embodiment of the first aspect of the present invention is implemented.

[0060] The electronic device according to the embodiment of the present invention can realize the automatic standardized construction of data into ontology models by establishing a general workflow for ontology construction and updating, which is conducive to improving the degree of automation; the use of intelligent data integration and standardization technology can improve the efficiency of data extraction, matching and fusion; the use of a multi-dimensional fuzzy weighted matching algorithm to improve the accuracy of matching between the static ontology of cross-domain joint operations and target data; and the use of a large language model to analyze and interpret the dynamically updated part of the static ontology of cross-domain joint operations, and then provide predictions and suggestions to guide the decision-making process.

[0061] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 2. It is a schematic diagram of a general workflow for ontology construction and update according to an embodiment of the present invention;

[0063] Figure 2 is an architectural diagram of a real-time situation update and analysis method based on dynamic ontology according to an embodiment of the present invention;

[0064] Figure 3 is a schematic diagram of multi-source data fusion according to an embodiment of the present invention;

[0065] Figure 4 is a structural block diagram of comprehensive similarity according to an embodiment of the present invention;

[0066] Figure 5 2. It is a schematic diagram of the upper and lower supports of the static ontology of cross-domain joint operations according to an embodiment of the present invention;

[0067] Figure 6 It is a mapping relationship diagram between real-time battlefield situation data and cross-domain joint combat static ontology according to an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0069] The following describes a dynamic ontology-based real-time situation update and analysis method according to a first embodiment of the present invention with reference to the accompanying drawings. The method is used for multi-source battlefield situation awareness and analysis and decision-making in cross-domain joint combat scenarios, including land, sea, air, cyber, and electromagnetic domains.

[0070] like Figures 1-6As shown, the real-time situation update and analysis method based on dynamic ontology according to an embodiment of the present invention includes:

[0071] Collect literature, expert knowledge and historical data based on needs analysis;

[0072] Extract and integrate effective data;

[0073] Carry out hierarchical design and relationship construction to form conceptual modeling;

[0074] Perform semantic modeling based on entities, relationships, and events in combat scenarios to generate a cross-domain joint combat static ontology, and convert the cross-domain joint combat static ontology into a Turtle format file;

[0075] Obtain multiple real-time battlefield situation data from different combat domain sources;

[0076] Directed crawling of target data in real-time battlefield situation data based on crawling constraints; Directed crawling includes:

[0077] Obtain a first readable file containing crawling constraints and a second readable file containing real-time battlefield situation data, where the first readable file and the second readable file may be JSON files;

[0078] Reading a first readable file and converting the crawling constraints into a first dictionary, reading a second readable file and converting the real-time battlefield situation data into a second dictionary;

[0079] For each set of constraint information in the first dictionary, extract the location information and attribute information of the target data, and construct a capture field using the location information and attribute information;

[0080] For each captured field, capture the target data in the second dictionary;

[0081] The captured target data is converted into a third readable file and output. The third readable file may be a JSON file.

[0082] First, to better represent and process complex data structures, data encoding and decoding methods are used to convert constraints and real-time battlefield situation data into a nested dictionary format, enabling efficient data organization and management. Second, information extraction and semantic understanding techniques are used to extract location keys and constraints from the data. Location keys specify the storage location of target data, while constraints describe the requirements for filtering target data, such as its attribute information.

[0083] Based on the parsed location keys and constraints, you can construct crawl fields. These define the location and constraints of the target data to be crawled, and serve as key parameters for subsequent data extraction. Crawl fields can be constructed using techniques such as data query languages ​​or regular expressions to accurately locate and filter target data.

[0084] Finally, through data extraction, the acquired target data is converted into JSON format to realize target data extraction in large-scale situation data scenarios.

[0085] Perform multi-source data fusion on multiple target data, generate multi-source complementary fusion data through data redundancy processing, data association analysis and data merging, including equipment parameter redundancy processing, weapon mount association relationship establishment and similar resource merging and division;

[0086] Perform data standardization on multi-source complementary fusion data to generate structured situation data. The format and structure of structured situation data are consistent and are equipped with identifiers for matching battlefield situation instances. This ensures data consistency and integrity, breaks down data silos, enables cross-system and cross-platform data exchange and sharing, and improves data integration efficiency and effectiveness.

[0087] Extract identifiers of all battlefield situation instances. If the battlefield situation instance does not exist in the cross-domain joint operations static ontology, add the battlefield situation instance and its attributes to the cross-domain joint operations static ontology, and perform fuzzy matching on the category of the battlefield situation instance based on edit distance similarity, pinyin similarity, and cosine similarity.

[0088] Use graph method to load and parse Turtle format files, and convert Turtle format files into graph objects;

[0089] Update the cross-domain joint operations static ontology by adding, deleting and modifying graph objects;

[0090] Dynamically analyze the updated content of the static ontology of cross-domain joint operations and form knowledge accumulation, and use the knowledge accumulation as the input variable for the next multi-source data fusion to meet the needs of dynamic data changes and adaptive growth, and further improve the integration and utilization capabilities of battlefield data. Among them, dynamic analysis includes:

[0091] Design prompts for the large language model, using it to predict enemy strategies and intentions and provide strategies for our own troop deployment and firepower allocation. For example, a prompt might read, "You are a senior military tactical analyst with extensive experience in battlefield situational awareness and analysis. Please carefully analyze the updated content of the cross-domain joint operations static ontology, assessing the number and location of both sides' military units / equipment, intelligence data, communications status, and operational objectives. Based on this analysis, infer the enemy's possible tactical actions against us and how we should formulate our response strategies."

[0092] The analysis includes:

[0093] Changes in enemy and friendly forces' combat objectives, troop strength, weapon and equipment quantity and location, intelligence collection, communication capabilities, and other important situational information; analysis results include:

[0094] Enemy tactics: Based on dynamically updated enemy situational information, the specific tactical actions the enemy is preparing to take include raids, reconnaissance, and long-range strikes.

[0095] Our response strategy: Our response measures include strengthening air defense capabilities, increasing reconnaissance and surveillance, and taking actions to confuse the enemy;

[0096] Situational data used for dynamic analysis include: changes in the physical status of both the enemy and our side, including alert status, standby status, intelligence collection activities, equipment status, and combat level.

[0097] The following is an example of the output of a large language model.

[0098] Possible enemy tactics: Based on their dynamic updates, they may be preparing for rapid strikes, surprise attacks, and long-range strikes. For example, they may use tactical assault vehicles for surprise attacks while simultaneously using missile launchers for long-range strikes. Increased enemy radar activity may be an attempt to better monitor our movements and guide their strikes.

[0099] Our response strategy: We should strengthen air defense capabilities, deploy air defense missile systems, enhance electronic warfare capabilities, intensify surveillance, and implement actions to confuse the enemy. Specifically, we should immediately strengthen frontline air defense capabilities by deploying more air defense missile systems and close-in defense weapons. At the same time, we should enhance electronic warfare capabilities to disrupt and destroy enemy radar and communications systems. Furthermore, we should strengthen monitoring of enemy movements and utilize drones and other reconnaissance methods to obtain more accurate intelligence for a timely response. At the tactical level, we can consider actions to confuse the enemy, such as distracting them with simulated attacks or setting traps and ambushes along possible enemy attack routes.

[0100] In some specific embodiments of the present invention, enemy: The distance between enemy tactical assault vehicles and our base has shortened to 5 kilometers, the number of missile launchers has increased significantly, and the frequency and duration of enemy radar activation have increased, indicating that the enemy may be preparing for some form of military action. Our: Our intelligence gathering capabilities have improved, but the intensity of electronic interference on the front line is high, which may affect the accuracy of intelligence and the stability of communications.

[0101] Finally, the static ontology and the updated dynamic ontology of cross-domain joint operations are reasoned and verified, including equipment effectiveness analysis, combat readiness deployment, cost-benefit analysis, and combat outcome prediction.

[0102] The real-time situation update and analysis method based on a dynamic ontology according to an embodiment of the present invention addresses the characteristics of situation data in complex scenarios, such as multi-source heterogeneity, complex and intertwined relationship attributes, spatiotemporal discontinuity, and incomplete information. By establishing a universal workflow for ontology construction and updating, the combat system is dynamically constructed using fragmented data as a supplement. This reduces expert involvement and enables the automatic and standardized construction of data into the ontology model, further improving the scalability and portability of the ontology and facilitating increased automation. Intelligent data integration and standardization technologies are employed to enhance the efficiency of data extraction, matching, and fusion. A multidimensional fuzzy weighted matching algorithm is employed to improve the accuracy of matching static ontology data with target data for cross-domain joint operations. A large language model is employed to analyze and interpret the dynamically updated portion of the static ontology for cross-domain joint operations, enabling recognition and understanding of the dynamic evolution of entities over time within the combat system, and providing predictions of enemy tactics and strategic choices for our own forces.

[0103] Specifically, establishing Figure 1 The figure shows the general workflow for ontology construction and updating. The left side of the figure shows the static ontology construction process, and the right side shows the dynamic ontology update process. White boxes (such as requirements analysis, literature, expert knowledge, historical data, hierarchical design, relationship construction, conceptual modeling, ontology construction, and cross-domain joint operations static ontology) represent the specific steps for static ontology construction. Grid boxes (such as real-time situation, ontology update, dynamic ontology, dynamic analysis, and knowledge accumulation) represent the specific steps for dynamic ontology update. Gray boxes (such as data collection, data fusion, and reasoning verification) represent the common steps for both static ontology construction and dynamic ontology update.

[0104] The construction of the static ontology of cross-domain joint operations begins with demand analysis. By collecting literature, expert knowledge, and historical data, effective data is extracted and integrated. Then, two key steps, hierarchical design and relationship construction, are carried out to achieve conceptual modeling. Finally, with the help of ontology knowledge modeling tools, the ontology file model is generated, laying the foundation for subsequent reasoning and verification.

[0105] Based on the static ontology of cross-domain joint operations, real-time battlefield situation data is collected, and fusion is carried out by using targeted capture of large-scale target data, multi-source data fusion, and data standardization processing. Relationship mapping and updating are carried out based on the multi-dimensional fuzzy weighted matching algorithm. The updated content of the static ontology of cross-domain joint operations is analyzed with the help of a large language model to form a knowledge accumulation that includes the enemy's combat intentions, our own tactics and strategies, and resource allocation plans.

[0106] like Figure 2 As shown, based on the static ontology of cross-domain joint operations and combined with fragmented target data obtained from real-time battlefield situation data, the static ontology of cross-domain joint operations is dynamically updated and improved to reflect the latest intelligence and battlefield situation. Specifically, it converts semi-structured data into structured data; supports the addition and deletion of ontology classes and the creation of subclass-parent class relationships; adds and deletes instance data attributes and object properties, and establishes subordinate relationships; and focuses on adding annotations for instances and their attributes, completing instance core attributes, constructing complex object relationships between instances, and adaptively matching instance categories. Intelligent analysis and decision-making encompasses process logic, spatiotemporal relationships, and situation analysis. For example, the input is complete situation information in hierarchical JSON format and a static ontology of cross-domain joint operations in TTL format; the output is an updated dynamic ontology in TTL format.

[0107] Combat scenarios involve different military services and departments, with numerous participating units and diverse data sources. Different units may use different systems, equipment, and communication protocols, resulting in heterogeneous data formats, necessitating data fusion and conversion. Therefore, given the multi-source, heterogeneous, and fragmented nature of combat scenario data, multi-source heterogeneous data fusion technology is employed to support the dynamic updating of subsequent entities and support command decision-making needs.

[0108] Data fusion typically involves steps such as data quality management, matching, alignment, and integration. During the dynamic update phase of the static ontology of cross-domain joint operations, the received situational data is large in scale, hierarchically nested, and has complex relationship attributes. This requires targeted capture of target data under large-scale situations, complementary fusion of multi-source data, and standardized processing of heterogeneous data. This fusion of fragmented multi-source data lays the foundation for the dynamic update of the static ontology of cross-domain joint operations.

[0109] In large-scale situation data, obtaining designated target information is the basis for dynamic ontology updating. The accuracy and comprehensiveness of the extracted data affect the completeness of the updated battlefield ontology knowledge model.

[0110] In combat scenarios, with numerous combat units and weapons and equipment, data comes from a wide range of sources and may be incomplete. Multi-source data complementary fusion technology integrates and fuses multiple data files from different sources to improve data quality, accuracy, and usability. This allows the full complementarity between different data sources to be fully utilized, resulting in more comprehensive and global information.

[0111] In some embodiments of the present invention, multi-source data fusion includes:

[0112] Obtaining a plurality of third readable files and converting the third readable files into a third dictionary;

[0113] For each key of any two third dictionaries, if the keys of both third dictionaries exist and have the same key value, the key value is updated using the result of fusion using the complementary fusion function; otherwise, the key values ​​of the two third dictionaries are updated with the key value of the existing key. Figure 3 In the process, data redundancy processing, data association analysis, and data merging are performed on n data sources to generate multi-source complementary fusion data. For example, multiple subsystems and multiple engines output battlefield situation files, that is, multiple subsystems and multiple engines become data source nodes.

[0114] Data fused from multiple sources often has differences in format, structure, and quality. Before use, data cleaning, data conversion, deduplication, anomaly detection, and error correction are usually required to ensure consistent format and structure across multiple sources and address data quality issues.

[0115] Real-time battlefield situation data is typically stored in a nested JSON format, containing complex hierarchical structures. The first step in standardizing heterogeneous data is data flattening, also known as data flattening. Data flattening is a crucial step in data preprocessing. Tree traversal algorithms or recursive methods can be used to flatten the data into a table-like structure and populate it into the constructed data frame.

[0116] The construction rules for the cross-domain joint operations static ontology stipulate that instance names must be unique and contain no illegal characters, such as "#" or " / ." However, instance names in situation information are allowed to be repeated, and there are no naming restrictions. Therefore, before using situation data to update the cross-domain joint operations static ontology, data cleaning is required to number instances with the same name and remove illegal symbols.

[0117] After a series of processing, information consistent with the static ontology construction rules of cross-domain joint operations can be obtained, providing high-quality situation information for dynamic updates.

[0118] The cross-domain joint operations static ontology is established after highly conceptualized and abstracted combat experience, intelligence, and initial information. Therefore, the judgment of instances and the setting of class names will be subject to human subjective influence to a certain extent, making them different from the instances in the situation data. In order to solve the problem of difficulty in matching situation data information with the cross-domain joint operations static ontology, the ontology update technology based on fuzzy matching is the key to whether the situation information can be updated to the cross-domain joint operations static ontology. The information in the situation data to be updated to the cross-domain joint operations static ontology is mostly in the form of strings. Due to the wide range of naming and diverse naming methods, the names contain special characters such as Chinese characters, letters, and symbols, which brings many challenges to accurate matching. Since the edit distance similarity between air-to-air missiles and ground-to-ground missiles is the same as the edit distance similarity between air-to-air missiles and air-to-air missiles, and the pinyin similarity cannot handle the problem of matching strings with the same pronunciation but different characters, cosine similarity is used to make up for the limitations of the first two matching methods. Cosine similarity is based on text content and does not consider the order of characters. It only focuses on the frequency of word occurrence and the angle between vectors, and is not sensitive to the order of text arrangement. Therefore, Figure 4 As shown, fuzzy matching is performed according to the following formula:

[0119]

[0120] Where, s is the comprehensive similarity, s ∈[0,1]; s The larger the value, the higher the similarity between the two. s =1 means the two are completely consistent. is the edit distance similarity; is the phonetic similarity; is the cosine similarity; A 、 B 、 C is the weighting coefficient, A + B + C = 1. If the lengths of the strings are different, then A Greater than B ; If the strings are the same length, then A Less than B Thus, the accuracy of comparison can be improved, defects can be compensated for, the similarity between two strings can be measured relatively comprehensively, and finally fuzzy matching of static ontology for cross-domain joint operations can be achieved.

[0121] The static ontology for cross-domain joint operations is constructed based on professional knowledge and industry domain rules, combined with initial data and typical scenarios. By defining ontology classes and attributes, it establishes a hierarchy, relationships, and constraints between knowledge. This static ontology primarily represents and stores domain knowledge and cannot automatically adapt to new knowledge and situational changes. Therefore, a dynamic ontology is required to enable knowledge updates. Dynamic updates primarily focus on the relationship between the static ontology for cross-domain joint operations and real-time battlefield situation data.

[0122] In the simulation and deduction system, the relationship between the cross-domain joint operations static ontology and the real-time battlefield situation data is bidirectional. On the one hand, the situation data is the source of knowledge for constructing the cross-domain joint operations static ontology; on the other hand, the relationship in the cross-domain joint operations static ontology also provides support for the association and fusion of situation data, making autonomous data association possible. For example, the synonymous relationship between the DBID (DataBase Identifier, a unique identifier of the database) in the real-time battlefield situation data and the class to which the instance belongs can guide the mapping of the corresponding fields. The cognitive ability of the cross-domain joint operations static ontology provides certain support for decision-making, and the execution results during system operation will also provide feedback to the cross-domain joint operations static ontology to further optimize and improve the quality of knowledge. Therefore, if Figure 5 As shown in the figure, a good cross-domain joint combat static ontology can support system applications upward and integrate situational data downward, playing an important role in the construction of combat system models.

[0123] The difficulty in dynamically adaptively updating the static ontology of cross-domain joint operations lies in the mapping relationship between real-time battlefield situation data and ontology information. This invention adopts a comprehensive update method to update the static ontology of cross-domain joint operations based on patterns and rules. Manually defined rules are matched with standardized real-time battlefield situation data to identify the subject, object, or predicate of instance data triples. Classes in the static ontology of cross-domain joint operations are abstractions and classifications of entities. They generally remain unchanged when the situation changes, and are primarily used to determine the category to which instance data belongs during dynamic updates. Instances in the static ontology of cross-domain joint operations are the focus of dynamic updates, including the addition and value update of instances and their attributes. For updating data attributes and relationship attributes, a predefined attribute set based on the static ontology of cross-domain joint operations is fuzzy matched and updated with the attribute key values ​​in the real-time battlefield situation data. If attributes in the real-time battlefield situation data are not defined in the static ontology of cross-domain joint operations, they are first added to the annotation module for model completeness. Then, reminders and alerts are issued, and the attribute category is determined after semantic understanding and judgment.

[0124] The dynamic ontology is implemented on the basis of the cross-domain joint operations static ontology. The cross-domain joint operations static ontology is built based on the Resource Description Framework (RDF) model. Any entity that may exist in the battlefield can be represented as a resource in the RDF model using the W3C standard. The semi-structured situation data after fragmented fusion and heterogeneous standardization is in a two-layer nested form. The data primary key is the unique identifier of the instance, and the internal value contains the instance name and attributes. The cross-domain joint operations static ontology is mapped to the semi-structured situation data according to the knowledge understanding, and the attribute alignment and relationship alignment are achieved according to some key identifiers. After determining the mapping object of the situation data, it can be accurately updated to the cross-domain joint operations static ontology to realize the dynamic update of the cross-domain joint operations static ontology, that is, the dynamic ontology. Construct a rule-based mapping relationship between real-time battlefield situation data and the cross-domain joint operations static ontology, such as Figure 6 shown.

[0125] The dynamic nature of the cross-domain joint operations static ontology update lies in its ability to dynamically adjust and update the model based on changes in external situational data, maintaining consistency and accuracy with the actual situation. When new concepts, relationships, or attributes emerge, the cross-domain joint operations static ontology can be expanded and updated by adding corresponding triples to reflect these changes. The adaptability of the cross-domain joint operations static ontology update lies in its use of fuzzy matching, which enables adaptive and precise updates to different types of instances and their attributes. These two factors complement each other, and the updated cross-domain joint operations static ontology can better reflect the battlefield situation, further enhance the ability to integrate and utilize battlefield data, and support commanders' tactical decision-making.

[0126] During combat, data sources are fragmented, dynamic, low-quality, and disorganized, posing severe challenges to data analysis and knowledge discovery for complex combat processes. To address this, this paper proposes a real-time situation update and analysis method based on dynamic ontology. This method uses a static ontology for cross-domain joint operations as the data model layer to form standardized constraints for data fusion. This allows for dynamic adjustment of the static ontology for cross-domain joint operations based on multiple data matching methods as new data sources are imported, achieving dynamic linking of the static ontology for cross-domain joint operations and seamless data fusion, while also reflecting the spatiotemporal relationships among combat formations, combat targets, and combat elements.

[0127] According to the second embodiment of the present invention, the real-time situation update and analysis device based on dynamic ontology is used for multi-source battlefield situation awareness and analysis decision-making in cross-domain joint combat scenarios, and the cross-domain joint combat scenarios include land, sea, air, network and electromagnetic fields. The device includes: a construction module, an acquisition module, a directional capture module, a multi-source data fusion module, a data standardization processing module, an extraction module, an update module, a dynamic analysis module and an inference verification module. For example, the real-time situation update and analysis device based on dynamic ontology can be an open source large language model application development platform, and the general workflow of ontology construction and update is constructed based on the open source large language model application development platform, which can be used to Figure 1 The complex tasks in the ontology are decomposed into multiple subtasks, reducing the complexity of each link and thus improving the efficiency of ontology update.

[0128] Specifically, the construction module is used to construct a cross-domain joint operations static ontology and convert it into a Turtle format file. The acquisition module is used to acquire multiple real-time battlefield situation data from different combat domains. The targeted capture module targets target data within the real-time battlefield situation data based on capture constraints. The multi-source data fusion module performs multi-source data fusion on multiple target data. This fusion generates multi-source complementary fused data through data redundancy processing, data association analysis, and data merging, including equipment parameter redundancy processing, weapon mount association relationship establishment, and similar resource merging and division. The data standardization module performs data standardization on the multi-source complementary fused data to generate structured situation data. The structured situation data has a consistent format and structure and is equipped with identifiers for matching battlefield situation instances. The extraction module extracts the identifiers of all battlefield situation instances. If a battlefield situation instance does not exist in the cross-domain joint operations static ontology, the battlefield situation instance and its attributes are added to the cross-domain joint operations static ontology. Fuzzy matching is then performed on the categories of the battlefield situation instances based on edit distance similarity, phonetic similarity, and cosine similarity. The update module uses a graph approach to load and parse Turtle format files, converting them into graph objects. The update module updates the cross-domain joint operations static ontology by adding, deleting, and modifying graph objects. The dynamic analysis module dynamically analyzes the updated content of the cross-domain joint operations static ontology and accumulates knowledge, using this accumulated knowledge as an input variable for the next multi-source data fusion. The dynamic analysis module can be constructed based on the prompt engineering technology of a large language model to design prompt words for the large language model. This large language model is then used to predict enemy strategies and intentions and provide strategies for friendly troop deployment and firepower allocation. The reasoning and verification module performs reasoning and verification on the cross-domain joint operations static ontology and the updated dynamic ontology, including equipment effectiveness analysis, combat readiness deployment, cost-benefit analysis, and combat outcome prediction.

[0129] The directional crawling module includes: a directional acquisition module, which is used to acquire a first readable file containing crawling constraints and a second readable file containing real-time battlefield situation data; a directional reading module, which is used to read the first readable file and convert the crawling constraints into a first dictionary, and read the second readable file and convert the real-time battlefield situation data into a second dictionary; a directional extraction module, which is used to extract the location information and attribute information of the target data for each set of constraint information in the first dictionary, and construct a crawling field using the location information and attribute information; a crawling output module, which is used to crawl target data in the second dictionary for each crawling field; and is used to convert the crawled target data into a third readable file and output it.

[0130] According to the real-time situation update and analysis device based on dynamic ontology in an embodiment of the present invention, by establishing a general workflow for ontology construction and updating, it is possible to realize the automatic standardized construction of data into ontology models, which is conducive to improving the degree of automation; the use of intelligent data integration and standardization technology can improve the efficiency of data extraction, matching and fusion; the use of a multi-dimensional fuzzy weighted matching algorithm to improve the accuracy of matching between the static ontology of cross-domain joint operations and target data; and the use of a large language model to analyze and interpret the dynamically updated part of the static ontology of cross-domain joint operations, and then provide predictions and suggestions to guide the decision-making process.

[0131] According to some embodiments of the present invention, the construction module includes: a requirements analysis module, a static data collection module, a static data fusion module, a conceptual modeling module, and an ontology construction module. The requirements analysis module is used to perform requirements analysis. The static data collection module is used to collect literature, expert knowledge, and historical data. The static data fusion module is used to extract and fuse valid data. The conceptual modeling module is used to perform hierarchical design and relationship construction. The ontology construction module is used to generate a static ontology for cross-domain joint operations.

[0132] An electronic device according to an embodiment of a third aspect of the present invention includes a processor and a memory. The processor and the memory are connected, and the memory is used to store a computer program. When the computer program is executed by the processor, the real-time situation update and analysis method based on dynamic ontology as described in the embodiment of the first aspect of the present invention is implemented.

[0133] The electronic device according to the embodiment of the present invention can realize the automatic standardized construction of data into ontology models by establishing a general workflow for ontology construction and updating, which is conducive to improving the degree of automation; the use of intelligent data integration and standardization technology can improve the efficiency of data extraction, matching and fusion; the use of a multi-dimensional fuzzy weighted matching algorithm to improve the accuracy of matching between the static ontology of cross-domain joint operations and target data; and the use of a large language model to analyze and interpret the dynamically updated part of the static ontology of cross-domain joint operations, and then provide predictions and suggestions to guide the decision-making process.

[0134] Other structures and operations of the electronic device according to the embodiment of the present invention are known to those skilled in the art and will not be described in detail here.

[0135] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0136] In the description of the present invention, "a first feature" or "a second feature" may include one or more such features. A first feature being "above" or "below" a second feature may include the first and second features being in direct contact, or may also include the first and second features being in contact not directly but through another feature therebetween. A first feature being "above," "above," or "above" a second feature may include the first feature being directly above or obliquely above the second feature, or may simply mean that the first feature is at a higher level than the second feature.

[0137] It should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections, indirect connections through an intermediate medium, or internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0138] Throughout this specification, references to terms such as "one embodiment," "some embodiments," "specific embodiments," "example," or "some examples" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0139] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A real-time situation update and analysis method based on dynamic ontology for multi-source battlefield situation awareness and analysis decision-making in cross-domain joint combat scenarios, wherein the cross-domain joint combat scenarios include land, sea, air, network and electromagnetic fields, characterized in that: The method comprises: Constructing a cross-domain joint combat static ontology and converting the cross-domain joint combat static ontology into a Turtle format file; Obtain multiple real-time battlefield situation data from different combat domain sources; Directedly grabbing target data in the real-time battlefield situation data according to grabbing constraint conditions; Performing multi-source data fusion on the plurality of target data, generating multi-source complementary fusion data through data redundancy processing, data association analysis and data merging, including equipment parameter redundancy processing, weapon mount association relationship establishment and similar resource merging and division; Performing data standardization on the multi-source complementary fusion data to generate structured situation data, wherein the structured situation data has a consistent format and structure and is provided with an identifier for matching a battlefield situation instance; Extracting identifiers of all battlefield situation instances; if the battlefield situation instance does not exist in the cross-domain joint operations static ontology, adding the battlefield situation instance and its attributes to the cross-domain joint operations static ontology, and performing fuzzy matching on the category of the battlefield situation instance based on edit distance similarity, phonetic similarity, and cosine similarity; Using a graph method to load and parse the Turtle format file, and convert the Turtle format file into a graph object; Updating the cross-domain joint operations static ontology by adding, deleting and modifying the graph objects; Dynamically analyzing the updated content of the cross-domain joint operations static ontology and forming knowledge accumulation, and using the knowledge accumulation as an input variable for the next multi-source data fusion; Perform reasoning verification on the cross-domain joint combat static ontology and the updated dynamic ontology, including equipment effectiveness analysis, combat readiness deployment, cost-benefit analysis, and combat outcome prediction; wherein the dynamic analysis includes: Designing prompt words for a large language model, using the large language model to predict enemy strategies and intentions, and providing strategies for our own troop deployment and firepower allocation; The directional crawling includes: Obtaining a first readable file containing the capture constraint conditions and a second readable file containing the real-time battlefield situation data; Reading the first readable file and converting the grabbing constraint condition into a first dictionary, reading the second readable file and converting the real-time battlefield situation data into a second dictionary; For each set of constraint information in the first dictionary, extract the location information and attribute information of the target data, and construct a capture field using the location information and the attribute information; For each of the captured fields, capture the target data in the second dictionary; The captured target data is converted into a third readable file and outputted.

2. The real-time situation update and analysis method based on dynamic ontology according to claim 1 is characterized in that: The construction of the cross-domain joint combat static ontology includes: Collect literature, expert knowledge and historical data based on needs analysis; Extract and integrate effective data; Carry out hierarchical design and relationship construction to form conceptual modeling; Generate the cross-domain joint combat static ontology.

3. The real-time situation update and analysis method based on dynamic ontology according to claim 1 is characterized in that: The multi-source data fusion includes: Obtaining a plurality of the third readable files and converting the third readable files into a third dictionary; For each key of any two of the third dictionaries, if the keys of the two third dictionaries exist and have the same key value, the key value is updated using the result of fusion using the complementary fusion function; otherwise, the key value of the two third dictionaries is updated with the key value of the existing key.

4. The real-time situation update and analysis method based on dynamic ontology according to claim 1 is characterized in that: The data standardization process includes: The multi-source complementary fusion data is subjected to data flattening, data cleaning, data conversion, deduplication, anomaly detection and error correction.

5. The real-time situation update and analysis method based on dynamic ontology according to claim 1 is characterized in that: The fuzzy matching is performed according to the following formula: Where, s is the comprehensive similarity, s ∈[0,1]; is the edit distance similarity; is the phonetic similarity; is the cosine similarity; A 、 B 、 C is the weighting coefficient, A + B + C =1; If the strings are of different lengths, then A Greater than B ; If the strings are the same length, then A Less than B .

6. The real-time situation update and analysis method based on dynamic ontology according to claim 1 is characterized in that: The prompt for the large language model is "You are a senior military tactical analyst with extensive experience in battlefield situational awareness and analysis. Please carefully analyze the updated content of the cross-domain joint operations static ontology, assessing the number and location of both sides' military units / equipment, intelligence data, communication status, and combat objectives. Based on this analysis, speculate on the enemy's possible tactical actions against us and how we should formulate a response strategy." The analysis includes: Changes in enemy and friendly forces' combat objectives, troop strength, weapon and equipment quantity and location, intelligence collection, communication capabilities, and other important situational information; analysis results include: Enemy tactics: Based on dynamically updated enemy situational information, the specific tactical actions the enemy is preparing to take include raids, reconnaissance, and long-range strikes. Our response strategy: Our response measures include strengthening air defense capabilities, increasing reconnaissance and surveillance, and taking actions to confuse the enemy; The situation data used for the dynamic analysis include: changes in the physical status of both the enemy and our side, including alert status, standby status, intelligence collection activities, equipment status, and combat level.

7. A real-time situation update and analysis device based on dynamic ontology, used for multi-source battlefield situation awareness and analysis and decision-making in cross-domain joint combat scenarios, wherein the cross-domain joint combat scenarios include land, sea, air, network and electromagnetic fields, characterized in that: The device comprises: A construction module, wherein the construction module is used to construct a cross-domain joint combat static ontology and convert the cross-domain joint combat static ontology into a Turtle format file; An acquisition module, the acquisition module is used to acquire multiple real-time battlefield situation data from different combat domains; A directional grabbing module, which directionally grabs target data in the real-time battlefield situation data according to grabbing constraints; A multi-source data fusion module is used to perform multi-source data fusion on the plurality of target data, and generate multi-source complementary fusion data through data redundancy processing, data association analysis and data merging, including equipment parameter redundancy processing, weapon mount association relationship establishment and similar resource merging and division; a data standardization processing module, the data standardization processing module being used to perform data standardization processing on the multi-source complementary fusion data to generate structured situation data, wherein the structured situation data has a consistent format and structure and is provided with an identifier for matching a battlefield situation instance; an extraction module, the extraction module being configured to extract identifiers of all battlefield situation instances; if the battlefield situation instance does not exist in the cross-domain joint operations static ontology, the battlefield situation instance and its attributes are added to the cross-domain joint operations static ontology; and fuzzy matching is performed on the category of the battlefield situation instance based on edit distance similarity, phonetic similarity, and cosine similarity; An update module, wherein the update module uses a graph method to load and parse the Turtle format file and convert the Turtle format file into a graph object; the update module updates the cross-domain joint operations static ontology by adding, deleting, and modifying the graph object; A dynamic analysis module, which is used to dynamically analyze the updated content of the cross-domain joint operations static ontology and form knowledge accumulation, and use the knowledge accumulation as an input variable for the next multi-source data fusion; wherein the dynamic analysis module is also used to design prompt words for the large language model, use the large language model to predict enemy strategies and intentions, and provide our own troop deployment and firepower allocation strategies; A reasoning and verification module, which is used to perform reasoning and verification on the cross-domain joint combat static ontology and the updated dynamic ontology, including equipment effectiveness analysis, combat readiness deployment, cost-benefit analysis, and combat outcome prediction; Wherein, the directional grasping module includes: a directional acquisition module, the directional acquisition module being used to acquire a first readable file containing the capture constraint conditions and a second readable file containing the real-time battlefield situation data; a directional reading module, configured to read the first readable file and convert the grabbing constraints into a first dictionary, and to read the second readable file and convert the real-time battlefield situation data into a second dictionary; a directional extraction module, configured to extract, for each set of constraint information in the first dictionary, location information and attribute information of the target data, and construct a capture field using the location information and the attribute information; A capture and output module is used to capture the target data in the second dictionary for each of the capture fields; and to convert the captured target data into a third readable file and output the file.

8. The real-time situation update and analysis device based on dynamic ontology according to claim 7 is characterized in that: The building blocks include: A demand analysis module, wherein the demand analysis module is used to perform demand analysis; A static data collection module, wherein the static data collection module is used to collect literature, expert knowledge and historical data; A static data fusion module, which is used to extract and fuse effective data; A conceptual modeling module, wherein the conceptual modeling module is used to carry out hierarchical design and relationship construction; An ontology construction module is used to generate the cross-domain joint combat static ontology.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, the processor and the memory are connected, the memory is used to store a computer program, and when the computer program is executed by the processor, the real-time situation update and analysis method based on dynamic ontology as described in any one of claims 1-6 is implemented.

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