Battlefield electromagnetic compatibility planning management and control method and system based on knowledge graph

Abstract

The invention relates to the technical field of electromagnetic compatibility management, and provides a battlefield electromagnetic compatibility planning management and control method and system based on a knowledge graph, and the method comprises the following steps: obtaining document data, carrying out the knowledge extraction and knowledge fusion of the document data, and constructing a battlefield electromagnetic compatibility knowledge graph composed of a structured triple form; detecting equipment in the target area through the electromagnetic environment detection network; identifying equipment in the target area to obtain a core entity corresponding to the equipment and attribute parameters of the core entity; and extracting all triads related to the core entity from the battlefield electromagnetic compatibility knowledge graph, and judging whether interference is generated between the core entity and entities in the extracted triads or not through a preset electromagnetic model so as to assist in generating an electromagnetic compatibility scheme. According to the invention, the knowledge graph is used as a central control entity to carry out electromagnetic compatibility intelligent decision-making and processing, all possible relationships in a complex electromagnetic environment are effectively found, and the accuracy of battlefield electromagnetic compatibility planning control is improved.

Description

Technical Field

[0001] The present invention relates to the field of electromagnetic compatibility management technology, and more specifically, to a battlefield electromagnetic compatibility planning and control method and system based on knowledge graph. Background Art

[0002] The overall planning and control capability of battlefield electromagnetic situation is an important part of modern battlefield control capability. How to carry out scientific and effective electromagnetic compatibility planning and management of various types of electronic information equipment and countermeasure equipment in complex electromagnetic environments is a research hotspot in the field of battlefield electromagnetic situation today, and many achievements have been made. These achievements can be roughly divided into three categories, including static partition management, device-level dynamic adaptive management, and coordinated dynamic management.

[0003] Among them, static division management is the traditional way of battlefield electromagnetic compatibility management. It simply relies on manual static planning and management in the frequency domain to divide a certain section of spectrum resources to a specific application or equipment. The main defects of this method are that the spectrum division method is exclusive and monopolistic, which seriously wastes battlefield frequency resources; frequency allocation only considers the artificially set "strong relationship", that is, the main working frequency or frequency band allocation of frequency-using equipment and counter-equipment, but seriously lacks consideration for various non-set relationships, and cannot explore hidden risks; when frequency-using equipment is densely distributed and staggered in the frequency domain, static planning relying solely on frequency planning is difficult to achieve electromagnetic compatibility; the static division management method is an open-loop planning and lacks closed-loop control measures.

[0004] Device-level dynamic adaptive management is a perception method based on highly intelligent devices. This method requires the device to be able to autonomously perceive the surrounding electromagnetic environment, identify potential enemy targets from multi-source intelligence, and obtain the attribute information of potential enemy targets. By analyzing and determining the relationship between various entities including the enemy and our side, the threat level of the potential enemy target is calculated based on the attribute information of the potential enemy target and the relationship between various entities. The electromagnetic situation is predicted by combining the potential enemy target, threat level, our information, and environmental information. That is, device-level dynamic adaptive management relies on group intelligent decision-making theory for efficient electromagnetic compatibility planning and management. This method has high requirements for the perception ability and intelligence level of the device, and the current mainstream battlefield equipment cannot meet the needs.

[0005] The coordinated dynamic management method introduces a central control entity, which uniformly collects perception information and conducts comprehensive processing and analysis through the central control entity, so as to efficiently and dynamically carry out electromagnetic compatibility planning and management. Compared with the other two electromagnetic compatibility methods, the coordinated dynamic management method considers the problem from a global perspective, fully considers the impact of various relationships, and has lower requirements on the perception ability and intelligence level of the equipment itself. It is a more mature electromagnetic compatibility method that better meets the actual battlefield needs. However, the current coordinated dynamic management method still finds it difficult to effectively discover all possible relationships in a complex electromagnetic environment, and has the defects of insufficient visualization effect and insufficient query convenience. Summary of the invention

[0006] In order to overcome the defect that the coordinated dynamic management method described in the above-mentioned prior art is difficult to effectively discover all possible relationships in a complex electromagnetic environment, which affects the accuracy of battlefield electromagnetic compatibility planning and control, the present invention provides a battlefield electromagnetic compatibility planning and control method and system based on a knowledge graph.

[0007] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0008] A battlefield electromagnetic compatibility planning and control method based on knowledge graph includes the following steps:

[0009] S1. After acquiring document data, perform knowledge extraction and knowledge fusion on it to construct a battlefield electromagnetic compatibility knowledge graph consisting of structured triples;

[0010] S2. Detect the equipment in the target area through the electromagnetic environment detection network;

[0011] S3. Identify the devices in the target area and obtain the core entities and attribute parameters corresponding to the devices;

[0012] S4. Extract all triples related to the core entity from the battlefield electromagnetic compatibility knowledge graph, and determine whether the core entity and the entities in the extracted triples interfere with each other through a preset electromagnetic model, so as to assist in generating an electromagnetic compatibility solution.

[0013] As a preferred solution, in the S1 step, the specific steps include: S1.1, performing knowledge extraction on the acquired document data to obtain the equipment data, equipment attribute data and relationship data in the document data; S1.2, performing knowledge fusion on the extracted equipment data, equipment attribute data and relationship data, taking the equipment data and equipment attribute data as entities respectively, and taking the relationship data as the relationship between entities, constructing a battlefield electromagnetic compatibility knowledge graph composed of structured triples of entity-relationship-entity, and storing it in the graph database.

[0014] As a preferred solution, the document data includes design documents and tactical instructions for frequency-using equipment, design documents and tactical instructions for various types of interference countermeasure equipment, national military standards related to electronic countermeasures, domestic and foreign electromagnetic compatibility related standards and actual engineering experience documents.

[0015] As a preferred solution, entities constructed with device data include sensor devices, synchronous positioning devices, computer devices, radio devices, network communication devices, security protection devices and other types of devices; entities constructed with device attribute data include basic attributes, functional attributes and performance attributes.

[0016] As a preferred solution, the basic attributes include device identification, system-level resource identifier, device name, device physical location, department, device type, device manufacturer, available status and registration time; the functional attributes include the functional category of the device, as well as the attribute parameters of each category of function and its operations on the attributes; the performance attributes include the name, description, unit and value range of the performance indicators of the device.

[0017] As a preferred embodiment, the relationship data includes set relationships and non-set relationships; wherein, the set relationship includes a preset antagonistic relationship between devices and an electromagnetic information transmission relationship between devices, and the antagonistic relationship includes a matching relationship between devices in time domain, space domain and frequency domain; the non-set relationship includes an interference relationship between devices or between devices and the environment, and the interference relationship includes friendly neighbor co-frequency interference, adjacent channel interference, out-of-band interference, high-order harmonics, intermodulation interference, multipath interference, ground clutter, sea clutter and meteorological clutter.

[0018] As a preferred solution, in the step S4, the step of determining whether the core entity and the entity in the extracted triplet generate interference by using a preset electromagnetic model includes: obtaining the values ​​of various parameters under the current environment, calculating the interference power P generated when the signal emitted by the core entity i enters the receiver of the entity j in the extracted triplet ij , and the interference effect threshold pt of entity j in the extracted triple j ; The interference power P ij and interference effect threshold pt j Input the preset electromagnetic model for judgment; the expression of the electromagnetic model is as follows:

[0019]

[0020] Where LP i is the attribute parameter of core entity i, LP j is the attribute parameter of entity j in the extracted triple;

[0021] Among them, when the interference power Pij Greater than the interference effect threshold pt j , it is judged that core entity i has interference with entity j, otherwise there is no interference.

[0022] As a preferred solution, the step S1 also includes the following steps: using Neo4j to visualize the constructed battlefield electromagnetic compatibility knowledge graph.

[0023] Furthermore, the present invention also proposes a battlefield electromagnetic compatibility planning and control system based on knowledge graph, and applies the battlefield electromagnetic compatibility planning and control method based on knowledge graph proposed in any of the above technical solutions, which includes a data acquisition module, a knowledge graph construction module, a detection module, an identification module, and a control module.

[0024] In the present technical solution, the data acquisition module is used to obtain original document data; the knowledge graph construction module is used to perform knowledge extraction and knowledge fusion on the collected original document data, and construct a battlefield electromagnetic compatibility knowledge graph composed of structured triples; the detection module is used to detect the equipment in the target area through the electromagnetic environment detection network; the identification module is used to identify the equipment in the detected target area, and obtain the core entity and its attribute parameters corresponding to the equipment; the management and control module is used to extract all triples related to the core entity from the battlefield electromagnetic compatibility knowledge graph constructed by the knowledge graph construction module, and judge whether the core entity interferes with the entity in the extracted triple through a preset electromagnetic model, so as to assist in generating an electromagnetic compatibility solution.

[0025] As a preferred solution, the system also includes a visualization module for visually displaying the battlefield electromagnetic compatibility knowledge graph constructed by the knowledge graph construction module using Neo4j technology.

[0026] Compared with the prior art, the beneficial effects of the technical solution of the present invention are: the present invention uses the knowledge graph as the central control entity to perform electromagnetic compatibility intelligent decision-making and processing, establishes an electromagnetic compatibility knowledge graph based on the information monitored by the equipment in real time and the knowledge provided by the original database, and uses the knowledge graph-based method to find and predict the relationship between enemy equipment, our own equipment and the environment on the battlefield, effectively discover all possible relationships in a complex electromagnetic environment, and improve the accuracy of battlefield electromagnetic compatibility planning and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a battlefield electromagnetic compatibility planning and control method based on knowledge graph according to an embodiment of the present invention.

[0028] Figure 2 This is a schematic diagram of the relationship between entities in the battlefield electromagnetic compatibility knowledge graph of an embodiment of the present invention.

[0029] Figure 3 This is an example diagram of the battlefield electromagnetic compatibility knowledge graph of an embodiment of the present invention.

[0030] Figure 4 This is an architecture diagram of a battlefield electromagnetic compatibility planning and control system based on a knowledge graph according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

[0032] In order to better illustrate the present embodiment, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0033] It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0034] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0035] Example 1

[0036] This embodiment proposes a battlefield electromagnetic compatibility planning and control method based on knowledge graph, such as Figure 1 As shown, it is a flow chart of the battlefield electromagnetic compatibility planning and control method based on knowledge graph of this embodiment.

[0037] The battlefield electromagnetic compatibility planning and control method based on knowledge graph proposed in this embodiment includes the following steps:

[0038] S1. After acquiring the document data, knowledge extraction and knowledge fusion are performed to construct a battlefield electromagnetic compatibility knowledge graph composed of structured triples.

[0039] Basic theories, relevant documents and practical engineering experience are the data sources for building knowledge graphs.

[0040] In an optional embodiment, the document data used to construct the battlefield electromagnetic compatibility knowledge graph includes design documents and tactical instructions for frequency-using equipment, design documents and tactical instructions for various types of interference countermeasures equipment, national military standards related to electronic countermeasures, domestic and foreign electromagnetic compatibility-related standards, and actual engineering experience documents.

[0041] S2. Detect the equipment in the target area through the electromagnetic environment detection network.

[0042] The electromagnetic spectrum detection network mainly detects atmospheric propagation conditions and obtains environmental parameter information related to the electromagnetic model in real time.

[0043] S3. Identify the devices in the target area and obtain the core entities and attribute parameters corresponding to the devices.

[0044] S4. Extract all triples related to the core entity from the battlefield electromagnetic compatibility knowledge graph, and determine whether the core entity and the entities in the extracted triples interfere with each other through a preset electromagnetic model, so as to assist in generating an electromagnetic compatibility solution.

[0045] In an optional embodiment, the specific steps of constructing a battlefield electromagnetic compatibility knowledge graph composed of structured triples include:

[0046] S1.1. Perform knowledge extraction on the acquired document data to obtain device data, device attribute data and relationship data in the document data.

[0047] S1.2. Perform knowledge fusion on the extracted equipment data, equipment attribute data and relationship data. Take the equipment data and equipment attribute data as entities respectively, and take the relationship data as the relationship between entities. Construct a battlefield electromagnetic compatibility knowledge graph consisting of structured triples of entity-relationship-entity and store it in the graph database.

[0048] Furthermore, entities constructed with device data include sensor devices (such as radars), synchronous positioning devices (such as Beidou receivers), computer devices, radio devices, network communication devices, security protection devices, and other types of devices. Entities constructed with device attribute data include basic attributes, functional attributes, and performance attributes.

[0049] Among them, the basic attributes include device identification (used to identify the unique ID of device-level resources), affiliated system-level resource identifier, device name, device physical location, department, device type, device manufacturer, available status and registration time.

[0050] In this embodiment, the basic attributes of the basic device resources describe the uniqueness and availability of the device and its model, so that users can quickly understand the overall situation of the device-level resources. Among them, the required attributes include the device name and device identification. The configurable attributes include the affiliated system-level resource identifier, the physical location of the device, the department to which it belongs, the device type, the available status, and the registration time.

[0051] Functional attributes are used to describe the functions of device-level resources. Each function should be described in a classified manner. Each category should include attributes (parameter items) and their operations on the attributes (functional method items). Functional items reflect the capabilities of device-level resources, and parameter items reflect the parameters required to implement a certain function.

[0052] Performance attributes are used to describe the quantitative results of the functional characteristics of device-level resources, reflecting the degree of realization of the resource function, the duration of function maintenance and the scope of function applicability. Each type of resource should have a unified measurement standard, which must include information such as the name, description, unit and value range of the performance indicator.

[0053] In an optional embodiment, the relationship data includes set relationships and non-set relationships.

[0054] In this embodiment, the goal of battlefield electromagnetic compatibility planning and management is to minimize mutual influence as much as possible, to achieve maximum efficiency in harmonious coexistence, and to effectively avoid the influence of enemy countermeasures. In essence, it is to explore the relationship between equipment and equipment, and between equipment and environment. In the electromagnetic compatibility knowledge graph of this embodiment, complex relationships are classified into two categories: set relationships and non-set relationships.

[0055] The set relationship includes a preset confrontation relationship between devices and an electromagnetic information transmission relationship between devices. The confrontation relationship includes a matching relationship between devices in the time domain, space domain and frequency domain.

[0056] The set relationship is an explicit "strong relationship" that is artificially set and therefore easy to find. In electromagnetic compatibility, it is mainly manifested as an adversarial relationship, or an electromagnetic information transmission relationship between frequency-using equipment.

[0057] The non-set relationship includes the interference relationship between devices or between devices and the environment, and the interference relationship includes friendly neighbor co-frequency interference, adjacent channel interference, out-of-band interference, higher harmonics, intermodulation interference, multipath interference, ground clutter, sea clutter and meteorological clutter.

[0058] The non-set relationship is an invisible "weak relationship". The discovery of this type of relationship is relatively complex, difficult to achieve manually and easy to be missed.

[0059] like Figure 2 The figure shows the relationship between entities in the battlefield electromagnetic compatibility knowledge graph. The thick solid line represents the electromagnetic information transmission relationship or confrontation relationship (i.e., the set relationship) between frequency-using equipment, and the thin dotted line represents the non-set relationship between frequency-using equipment.

[0060] The non-set relationship in this embodiment is calculated based on the connection relationship between points in the knowledge graph and the corresponding model to determine whether there is an interference relationship.

[0061] In an optional embodiment, the step of determining whether the core entity and the entities in the extracted triplet interfere with each other by using a preset electromagnetic model includes:

[0062] Get the values ​​of each parameter in the current environment and calculate the interference power P generated when the signal sent by the core entity i enters the receiver of the entity j in the extracted triplet ij , and the interference effect threshold pt of entity j in the extracted triple j .

[0063] Among them, the interference power P ij The calculation formula is as follows:

[0064] P R =P T +G1+G2-LL r -L p

[0065] L r =32.5+20lgf+20lgR

[0066] Where P R (dBm) The interference signal power received by the interference radar, the interference radar antenna transmission power is P T (dBm), the transmitting antenna gain is G1 (dBm), and the interference radar receiving antenna gain is G2 (dBm). L (dBm) is the weighted loss, including rain and snow loss, atmospheric absorption loss, feeder loss, etc., and is generally taken as 15dB in the calculation. r (dBm) is the spatial propagation loss, which is related to frequency and distance and can be calculated using the following formula. Where f (MHz) is the interference radar transmission frequency, and R (km) is the signal propagation distance. p Polarization loss is the loss caused by the different polarization modes of the two radars. It is generally taken as 20dB in calculation.

[0067] The threshold of radar interference effect is often related to many factors, including the minimum receiving power of the receiver, accumulated loss, frequency mismatch loss, etc. The following formula is often used to calculate the threshold in research:

[0068] P min =S min +L a +L f +D

[0069] L a =λlgn

[0070] Where S min (dBm) is the minimum detectable power of the interfered radar receiver, which is related to the radar's noise coefficient and other parameters and is directly given in the simulation. r (dB) is the receiver accumulation loss, where λ is a constant, which is 8 when the receiver uses coherent integration and 7 when non-coherent integration is used. n is the number of interference signal pulses received by the receiver during the beam dwell period, which is calculated using the following formula:

[0071]

[0072] where f PRF1(KHz) is the pulse repetition frequency of the interference signal, θ1 and θ2 are the beam widths of the two interference parties, ω1 and ω2 represent the beam scanning speeds of the two interference parties, and a plus sign is used when the two rotate in the same direction and a minus sign is used when the two rotate in opposite directions. Frequency mismatch loss L f It is caused by the mismatch between the interference signal frequency and the intermediate frequency of the interfered radar. When the two do not overlap at all, it takes infinity. When there is overlap, it is calculated as follows:

[0073]

[0074] where d f1 (MHz) is the overlapping frequency bandwidth, d f2 (MHz) is the bandwidth of the jamming radar's transmitted signal.

[0075] Furthermore, the interference power P ij and interference effect threshold pt j Input the preset electromagnetic model for judgment; the expression of the electromagnetic model is as follows:

[0076]

[0077] Where LP i is the attribute parameter of core entity i, LP j is the attribute parameter of entity j in the extracted triple.

[0078] Among them, when the interference power P ij Greater than the interference effect threshold pt j , it is judged that core entity i has interference with entity j, otherwise there is no interference.

[0079] After determining the core entity, this embodiment extracts all function triplets related to the core entity from the constructed knowledge graph, determines whether interference will occur between entities through the electromagnetic model, assists in generating an electromagnetic compatibility plan, adjusts the working status of frequency-using equipment, and further updates the electromagnetic situation.

[0080] In another optional embodiment, the following steps are also included: using Neo4j to visualize the constructed battlefield electromagnetic compatibility knowledge graph.

[0081] like Figure 3 The figure shows an example of battlefield electromagnetic compatibility knowledge graph. This embodiment uses a graph database to construct a knowledge graph, which reduces the time required to query whether there is an interference relationship between devices and improves system convenience.

[0082] In the coordinated dynamic electromagnetic compatibility planning and management method based on knowledge graph proposed in this embodiment, an electromagnetic compatibility knowledge graph is established according to the information monitored by the equipment in real time and the knowledge provided by the original database. The knowledge graph-based method searches and predicts non-human set relationships in complex electromagnetic environments, and generates electromagnetic compatibility plans for decision-making.

[0083] This embodiment uses the knowledge graph as the central control entity to make intelligent decisions and processes for electromagnetic compatibility. The entire system forms a dynamic closed-loop control, and the resulting electromagnetic compatibility solution has global and intelligent characteristics. At the same time, this embodiment uses the knowledge graph to comprehensively and intuitively discover and reveal the relationship between enemy equipment, our equipment, and the environment on the battlefield, avoiding the omission of complex and invisible "weak relationships", effectively discovering all possible relationships in a complex electromagnetic environment, and improving the accuracy of battlefield electromagnetic compatibility planning and control.

[0084] Example 2

[0085] This embodiment applies the battlefield electromagnetic compatibility planning and control method based on knowledge graph proposed in Embodiment 1 to perform battlefield electromagnetic compatibility planning and control on an electromagnetic compatibility scheme.

[0086] Assume that in a certain electromagnetic compatibility scheme design, the battlefield situation is as follows:

[0087] (1) The Red Army has a total of 20 different radars, which are divided into four subgroups according to the frequency band, namely lg1, lg2, lg3 and lg4. The subgroups can be assumed to be non-interfering with each other, but there may be mutual influence between the radars within a subgroup.

[0088] (2) The blue team has a total of 20 jammers of different types, which are divided into five subgroups according to the working frequency bands, namely jg1, jg2, jg3, jg4, and jg5. The subgroups can be considered to have no interference with each other, but there may be mutual influence between the jammers within a subgroup.

[0089] (3) Both parties configured the attributes such as working mode and parameters, and carried out and deployed them with the support of geographic information system. The corresponding natural and electromagnetic background environment was also configured and deployed.

[0090] The working modes and parameter information of radars and jammers are extracted, and the knowledge obtained from various technical documents is combined to construct entities in the knowledge graph. Then, the functional relationship between entities is determined based on the electromagnetic model, and a complete knowledge graph is constructed. It is displayed using Neo4j. The display effect is as follows: Figure 3 shown.

[0091] The functional relationship between entities is further described based on the electromagnetic model. Here, it is assumed that the core entity and the possible interference entity are both radars.

[0092] Assume that in a certain subgroup, the performance attribute of radar i is LP i ={T i ,F i ,S i ,Sig i ,E i}, where T i It is the time domain working parameter, including the power on / off time, etc.; F i It is the frequency domain working parameter, focusing on the working frequency range, whether it is frequency agile, the frequency conversion pattern and law; S i It is the airspace working parameter, focusing on the scanning airspace range, scanning mode, beam shape parameters, and deployment location, etc.; P i is the polarization working parameter, including the polarization angle; Sig i It is the signal pattern parameter, which mainly includes signal pattern, pulse group parameter, inter-pulse parameter and intra-pulse parameter; E i is the signal energy parameter, focusing on transmitter power, RF loss, receiver sensitivity, etc. Similarly, the performance attributes of radar j are described as LP j ={T j ,F j ,S j ,Sig j ,E j}, then the following expression is obtained according to the electromagnetic model:

[0093]

[0094] When it is necessary to calculate whether radar i interferes with radar j, first obtain the values ​​of various parameters in the current environment, and then calculate the interference power P of radar i signal entering the radar j receiver based on the connection function from i to j. ij As well as the threshold of radar interference effect, by comparing the interference power with the threshold, it is determined whether radar i interferes with radar j.

[0095] Example 3

[0096] This embodiment proposes a battlefield electromagnetic compatibility planning and control system based on knowledge graph, and applies the battlefield electromagnetic compatibility planning and control method based on knowledge graph proposed in Example 1. Figure 4 As shown, this is an architecture diagram of the battlefield electromagnetic compatibility planning and control system based on knowledge graph of this embodiment.

[0097] The battlefield electromagnetic compatibility planning and control system based on knowledge graph proposed in this embodiment includes a data acquisition module 100, a knowledge graph construction module 200, a detection module 300, an identification module 400, and a control module 500.

[0098] In this embodiment, the data collection module 100 is used to obtain original document data. In the specific implementation process, data collection is achieved through a web crawler or manual input of document data.

[0099] The knowledge graph construction module 200 is used to perform knowledge extraction and knowledge fusion on the collected original document data, and construct a battlefield electromagnetic compatibility knowledge graph composed of structured triples.

[0100] During the specific implementation process, the knowledge graph construction module 200 extracts knowledge from the acquired document data to obtain the equipment data, equipment attribute data and relationship data in the document data, and then performs knowledge fusion on the extracted equipment data, equipment attribute data and relationship data, and takes the equipment data and equipment attribute data as entities respectively, and takes the relationship data as the relationship between entities, to construct a battlefield electromagnetic compatibility knowledge graph composed of structured triples of entity-relationship-entity, and store it in the graph database.

[0101] In an optional embodiment, entities constructed with device data include sensor devices (such as radars), synchronous positioning devices (such as Beidou receivers), computer devices, radio devices, network communication devices, security protection devices, and other types of devices. Entities constructed with device attribute data include basic attributes, functional attributes, and performance attributes.

[0102] Among them, basic attributes include device identification (used to identify the unique ID of device-level resources), affiliated system-level resource identifier, device name, device physical location, department, device type, device manufacturer, available status and registration time. Functional attributes include the functional category of the device, as well as the attribute parameters of each category of functions and their operations on the attributes. Performance attributes include the name, description, unit and value range of the performance indicators of the device.

[0103] Relationship data includes set relationships and non-set relationships. Among them, the set relationship includes the preset confrontation relationship between devices and the electromagnetic information transmission relationship between devices. The confrontation relationship includes the matching relationship between devices in the time domain, space domain and frequency domain. The non-set relationship includes the interference relationship between devices or between devices and the environment. The interference relationship includes friendly neighbor co-frequency interference, adjacent channel interference, out-of-band interference, high-order harmonics, intermodulation interference, multipath interference, ground clutter, sea clutter and meteorological clutter.

[0104] The detection module 300 in this embodiment is used to detect devices in the target area through an electromagnetic environment detection network, and then send them to the identification module 400.

[0105] In the specific implementation process, the detection module 300 mainly detects atmospheric propagation conditions and obtains environmental parameter information related to the electromagnetic model in real time.

[0106] The identification module 400 in this embodiment is used to identify the device in the detected target area, obtain the core entity and its attribute parameters corresponding to the device, and then send them to the management and control module 500.

[0107] The management and control module 500 in this embodiment is used to extract all triples related to the core entity from the battlefield electromagnetic compatibility knowledge graph constructed by the knowledge graph construction module 200, and judge whether the core entity and the entities in the extracted triples interfere with each other through a preset electromagnetic model, so as to assist in generating an electromagnetic compatibility plan.

[0108] In an optional embodiment, the expression of the electromagnetic model preset in the control module 500 is:

[0109]

[0110] Where LP i is the attribute parameter of core entity i, LP j is the attribute parameter of entity j in the extracted triple.

[0111] After determining the core entity, the management and control module 500 extracts all function triplets related to the core entity from the constructed knowledge graph, determines whether interference will occur between entities through the electromagnetic model, assists in generating electromagnetic compatibility solutions, adjusts the working status of frequency-using equipment, and further updates the electromagnetic situation.

[0112] In another optional embodiment, the knowledge graph construction module 200 transmits the battlefield electromagnetic compatibility knowledge graph it constructs to the visualization module 600. The visualization module 600 visualizes the battlefield electromagnetic compatibility knowledge graph constructed by the knowledge graph construction module 200 using Neo4j technology, allowing users to comprehensively and intuitively discover and reveal the relationship between enemy equipment, friendly equipment and the environment on the battlefield, reducing the time required to query whether there is an interference relationship between devices and improving system convenience.

[0113] The same or similar reference numerals correspond to the same or similar components;

[0114] The terms used in the drawings to describe positional relationships are only used for illustrative purposes and should not be construed as limiting this patent;

[0115] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A battlefield electromagnetic compatibility planning and control method based on knowledge graph, characterized in that: The following steps are involved: S1. After acquiring document data, perform knowledge extraction and knowledge fusion on it to construct a battlefield electromagnetic compatibility knowledge graph consisting of structured triples; S2. Detect the equipment in the target area through the electromagnetic environment detection network; S3. Identify the devices in the target area and obtain the core entities and attribute parameters corresponding to the devices; S4. Extract all triples related to the core entity from the battlefield electromagnetic compatibility knowledge graph, and determine whether the core entity and the entities in the extracted triples interfere with each other through a preset electromagnetic model, so as to assist in generating an electromagnetic compatibility solution.

2. The battlefield electromagnetic compatibility planning and control method based on knowledge graph according to claim 1 is characterized in that: In the step S1, the specific steps include: S1.

1. Perform knowledge extraction on the acquired document data to obtain device data, device attribute data and relationship data in the document data; S1.

2. Perform knowledge fusion on the extracted equipment data, equipment attribute data and relationship data. Take the equipment data and equipment attribute data as entities respectively, and take the relationship data as the relationship between entities. Construct a battlefield electromagnetic compatibility knowledge graph consisting of structured triples of entity-relationship-entity and store it in the graph database.

3. The battlefield electromagnetic compatibility planning and control method based on knowledge graph according to claim 2 is characterized in that: The document data includes design documents and tactical instructions for frequency-using equipment, design documents and tactical instructions for various types of interference countermeasure equipment, national military standards related to electronic countermeasures, domestic and foreign electromagnetic compatibility related standards and actual engineering experience documents.

4. The battlefield electromagnetic compatibility planning and control method based on knowledge graph according to claim 2 is characterized in that: Entities constructed with device data include sensor devices, synchronous positioning devices, computer devices, radio devices, network communication devices, security protection devices and other types of equipment; entities constructed with device attribute data include basic attributes, functional attributes and performance attributes.

5. The battlefield electromagnetic compatibility planning and control method based on knowledge graph according to claim 4 is characterized in that: The basic attributes include device identification, system-level resource identifier, device name, physical location of the device, department to which it belongs, device type, device manufacturer, available status and registration time; the functional attributes include the functional category of the device, as well as the attribute parameters of each category of function and its operations on the attributes; the performance attributes include the name, description, unit and value range of the performance indicators of the device.

6. The battlefield electromagnetic compatibility planning and control method based on knowledge graph according to claim 2 is characterized in that: The relationship data includes set relationships and non-set relationships; The set relationship includes a preset confrontation relationship between devices and an electromagnetic information transmission relationship between devices, and the confrontation relationship includes a matching relationship between devices in the time domain, space domain and frequency domain; The non-set relationship includes the interference relationship between devices or between devices and the environment, and the interference relationship includes friendly neighbor co-frequency interference, adjacent channel interference, out-of-band interference, higher harmonics, intermodulation interference, multipath interference, ground clutter, sea clutter and meteorological clutter.

7. The battlefield electromagnetic compatibility planning and control method based on knowledge graph according to any one of claims 1 to 6, characterized in that: In the step S4, the step of determining whether the core entity and the entities in the extracted triplet interfere with each other by using a preset electromagnetic model includes: Get the values ​​of each parameter in the current environment and calculate the interference power P generated when the signal sent by the core entity i enters the receiver of the entity j in the extracted triplet ij , and the interference effect threshold pt of entity j in the extracted triple j ; The interference power P ij and interference effect threshold pt j Input the preset electromagnetic model for judgment; the expression of the electromagnetic model is as follows: Where LP i is the attribute parameter of core entity i, LP j is the attribute parameter of entity j in the extracted triple; Among them, when the interference power P ij Greater than the interference effect threshold pt j , it is judged that core entity i has interference with entity j, otherwise there is no interference.

8. The battlefield electromagnetic compatibility planning and control method based on knowledge graph according to claim 7 is characterized in that: The S1 step also includes the following steps: using Neo4j to visualize the constructed battlefield electromagnetic compatibility knowledge graph.

9. A battlefield electromagnetic compatibility planning and control system based on knowledge graph, applying the battlefield electromagnetic compatibility planning and control method based on knowledge graph according to any one of claims 1 to 8, characterized in that: include: A data acquisition module, used to obtain original document data; The knowledge graph construction module is used to extract and fuse knowledge from the collected original document data and construct a battlefield electromagnetic compatibility knowledge graph composed of structured triples; A detection module, used to detect devices in the target area through an electromagnetic environment detection network; An identification module is used to identify the devices in the detected target area and obtain the core entities and attribute parameters corresponding to the devices; The management and control module is used to extract all triples related to the core entity from the battlefield electromagnetic compatibility knowledge graph constructed by the knowledge graph construction module, determine whether the core entity and the entities in the extracted triples interfere with each other through a preset electromagnetic model, and assist in generating an electromagnetic compatibility plan.

10. The battlefield electromagnetic compatibility planning and control system based on knowledge graph according to claim 9 is characterized in that: The system also includes a visualization module for visually displaying the battlefield electromagnetic compatibility knowledge graph constructed by the knowledge graph construction module using Neo4j technology.

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