CN115019204A - Knowledge graph battlefield target identification method, device, equipment and medium - Google Patents
Knowledge graph battlefield target identification method, device, equipment and medium Download PDFInfo
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Abstract
The present application relates to the field of image recognition, and in particular, to a method, an apparatus, a device, and a medium for identifying a battlefield target of a knowledge graph. The method comprises the following steps: the method comprises the steps of obtaining battlefield image information, carrying out position detection on the battlefield image information, determining image position information of the battlefield image information, determining processing equipment information based on the image position information, summarizing image characteristic information generated by the processing equipment information, inputting the image characteristic information into a battlefield knowledge graph for characteristic identification, and obtaining battlefield target information.
Description
Technical Field
The present application relates to the field of image recognition, and in particular, to a method, an apparatus, a device, and a medium for identifying a battlefield target of a knowledge graph.
Background
Modern war is information war, and the whole battlefield presents characteristics such as diversified operation modes, diversified operation objects, complex and changeable operation environments and the like, so that the command operation difficulty is increased day by day. How to more efficiently and intelligently command and guide the deployment of battles is an important process for the electronic and automatic development of military in China.
At present, when battlefield targets (airplanes, tanks, ships, communication vehicles and the like) are identified, a space-based infrared early warning satellite signal level simulation system and a ground-based radar signal level simulation system are generally adopted, a distributed detection sensor network consisting of a plurality of satellites and a plurality of radars is utilized to perform redundancy and complementary information fusion, the battlefield targets can be collected to a greater extent, and the collected battlefield targets are subjected to target identification so as to estimate the potential states and threats behind the battlefield targets in time.
For the above related technologies, the inventor thinks that when a battlefield target is identified, due to the allopatricity of the distributed sensors, certain uncertainty and ambiguity exist in the acquired target information, so that the accuracy of battlefield target identification is reduced.
Disclosure of Invention
In order to improve accuracy of battlefield target identification, the application provides a battlefield target identification method, a device, equipment and a medium of a knowledge graph.
In a first aspect, the present application provides a method for identifying a battlefield target of a knowledge graph, which adopts the following technical scheme:
a knowledge-graph battlefield target recognition method comprises the following steps:
acquiring battlefield image information;
carrying out position detection on the battlefield image information, and determining the image position information of the battlefield image information;
determining processing device information based on the image location information;
and summarizing the image characteristic information generated by the processing equipment information, and inputting the image characteristic information into a battlefield knowledge graph for characteristic identification to obtain battlefield target information.
By adopting the technical scheme, when battlefield targets are identified, battlefield image information of different positions is obtained, then position detection is carried out on the battlefield image information, image position information corresponding to the battlefield image information is obtained, processing equipment information capable of carrying out image information processing is obtained according to the image position information, then communication connection is carried out through processing equipment corresponding to the processing equipment information, image characteristic information generated by the processing equipment information is collected, the image characteristic information is input into a battlefield knowledge graph for characteristic identification, and final battlefield target information is obtained.
In another possible implementation manner, the determining processing device information based on the image position information includes:
scanning a preset area based on the image position information to obtain first equipment set information;
respectively carrying out signal detection on processing equipment information in the first equipment set information, and determining the equipment signal state of the processing equipment information;
judging whether the equipment signal state meets a preset signal state or not, if not, screening out processing equipment information corresponding to the equipment signal state to obtain second equipment set information;
respectively calculating distance values between processing equipment information in the second equipment set information and the image position information, and determining the signal transmission distance of the processing equipment information;
and judging whether the signal transmission distance meets a preset distance condition, and if so, determining the processing equipment information corresponding to the signal transmission distance.
Through the technical scheme, when the processing equipment information is determined, the preset area is scanned according to the image position information to obtain first equipment set information, then the processing equipment information in the first equipment set information is respectively subjected to signal detection to obtain the equipment signal state of each piece of processing equipment information, then whether the equipment signal state meets the preset signal state is judged, if not, the processing equipment information corresponding to the equipment signal state is screened out to obtain second equipment set information, then the distance value between the processing equipment information in the second equipment set information and the image position information is respectively calculated to determine the signal transmission distance of each piece of processing equipment information, then whether the signal transmission distance meets the preset distance condition is judged, the preset distance condition is the shortest distance value, the processing equipment information with the shortest distance information is selected from the second equipment set information meeting the equipment signal state, the method is beneficial to improving the dissemination efficiency of battlefield image information.
In another possible implementation manner, the summarizing the image feature information generated by the processing device information and inputting the image feature information into a battlefield knowledge graph for feature recognition further includes:
acquiring battlefield data information, wherein the battlefield data information comprises terrain image information and battle tool image information;
and constructing a battlefield knowledge graph based on the battlefield data information.
According to the technical scheme, before the image characteristic information is identified, the terrain image information and the fighting tool image information of a battlefield are collected in advance by adopting the image collecting device (such as an unmanned aerial vehicle), then the collected terrain image information and the collected fighting tool image information are obtained, and a battlefield map is built according to the terrain image information and the fighting tool image information, so that the image characteristic information can be identified subsequently.
In another possible implementation manner, the performing position detection on the battlefield image information, determining image position information of the battlefield image information, and then further includes:
determining region level information based on the image position information, wherein the region level information is used for representing grade information of a region where the image position information is located;
comparing the region level information with a standard region level table to determine the acquisition frequency of acquiring the battlefield image information;
and controlling a preset acquisition device to acquire the battlefield image information according to the acquisition frequency.
Through the technical scheme, when the control is preset to acquire the battlefield image information by the acquisition device, the regional level information of the battlefield is determined according to the image position information, then the regional level information is compared with the standard regional level table, the acquisition frequency for acquiring the battlefield image information is determined, and then the battlefield image information of the acquisition device is preset according to the acquisition frequency control to be acquired, so that the effect of controlling the acquisition frequency of the preset acquisition device is achieved, and the real-time performance of the information is improved.
In another possible implementation manner, the determining regional level information based on the image position information further includes:
and judging whether the region level information accords with preset region level information or not, and if so, labeling the image position information.
Through the technical scheme, when the regional level information of the image position information is judged, the regional level information is matched with the preset regional level information, whether the current regional level information accords with the preset regional level information or not is determined, and when the current regional level information accords with the preset regional level information, the image position information is marked, so that the battlefield personnel can be informed of improving the attention degree of the image position information.
In another possible implementation manner, the summarizing the image feature information generated by the processing device information, and inputting the image feature information into a knowledge graph for feature recognition to obtain battlefield target information, and then further includes:
counting the quantity of the battlefield targets in the battlefield target information to obtain battlefield scale information;
judging whether the scale in the battlefield scale information reaches a preset battlefield scale or not, and if so, judging whether the battlefield target in the battlefield target information runs to obtain target advancing information;
and predicting the battlefield target information by combining the battlefield scale information and the target traveling information to obtain characteristic intention information.
According to the technical scheme, when characteristic intention information of a battlefield is predicted according to battlefield target information, the quantity of battlefield targets in the battlefield target information is counted to obtain battlefield scale information, whether the scale in the battlefield scale information reaches the preset battlefield scale is judged, when the scale in the battlefield scale information reaches the preset battlefield scale, the battlefield targets in the battlefield target information are judged to run to obtain target running information, and then the battlefield target information is predicted by combining the battlefield scale information and the target running information to obtain the characteristic intention information, so that battlefield personnel can predict the movement of enemies conveniently.
In another possible implementation manner, the predicting the battlefield target information by combining the battlefield scale information and the target travel information to obtain the characteristic intention information, and then further includes:
carrying out hazard grade evaluation on the characteristic intention information to generate a characteristic hazard grade;
and judging whether the characteristic hazard level exceeds a preset hazard level, if so, generating an early warning instruction, and controlling an early warning device to perform early warning.
Through the technical scheme, when the characteristic intention information is subjected to hazard assessment, the characteristic hazard grade is determined according to the characteristic intention of enemies in the characteristic intention information, then whether the characteristic hazard grade exceeds the preset hazard grade or not is determined, and when the characteristic hazard grade is met, an early warning instruction is generated to control an early warning device to perform early warning, so that the effect of early warning the hazards of the enemies is achieved.
In a second aspect, the present application provides a battlefield target recognition device of a knowledge graph, which adopts the following technical solution:
a knowledge-graph battlefield object recognition device comprising:
the information acquisition module is used for acquiring battlefield image information;
the position determining module is used for carrying out position detection on the battlefield image information and determining the image position information of the battlefield image information;
a device determination module for determining processing device information based on the image location information;
and the characteristic identification module is used for summarizing the image characteristic information generated by the processing equipment information and inputting the image characteristic information into the battlefield knowledge graph for characteristic identification to obtain the battlefield target information.
By adopting the technical scheme, when battlefield targets are identified, battlefield image information of different positions is obtained, then position detection is carried out on the battlefield image information, image position information corresponding to the battlefield image information is obtained, processing equipment information capable of carrying out image information processing is obtained according to the image position information, then communication connection is carried out through processing equipment corresponding to the processing equipment information, image characteristic information generated by the processing equipment information is collected, the image characteristic information is input into a battlefield knowledge graph for characteristic identification, and final battlefield target information is obtained.
In a possible implementation manner, when determining processing device information based on the image position information, the device determination module is specifically configured to:
scanning a preset area based on the image position information to obtain first equipment set information;
respectively carrying out signal detection on processing equipment information in the first equipment set information, and determining the equipment signal state of the processing equipment information;
judging whether the equipment signal state meets a preset signal state or not, if not, screening out processing equipment information corresponding to the equipment signal state to obtain second equipment set information;
respectively calculating distance values between processing equipment information in the second equipment set information and the image position information, and determining the signal transmission distance of the processing equipment information;
and judging whether the signal transmission distance meets a preset distance condition, and if so, determining the processing equipment information corresponding to the signal transmission distance.
In another possible implementation manner, the apparatus further includes: a data acquisition module and a map building module, wherein,
the data acquisition module is used for acquiring battlefield data information, and the battlefield data information comprises topographic image information and battle tool image information;
and the map building module is used for building a battlefield knowledge map based on the battlefield data information.
In another possible implementation manner, the apparatus further includes: a region determining module, a region comparing module and a frequency control module, wherein,
the region determining module is configured to determine region level information based on the image location information, where the region level information is used to indicate level information of a region where the image location information is located;
the region comparison module is used for comparing the region level information with a standard region level table and determining the acquisition frequency of acquiring the battlefield image information;
and the frequency control module is used for controlling a preset acquisition device to acquire the battlefield image information according to the acquisition frequency.
In another possible implementation manner, the apparatus further includes: a grade determination module, wherein,
and the grade judging module is used for judging whether the region grade information accords with preset region grade information or not, and if so, labeling the image position information.
In another possible implementation manner, the apparatus further includes: a quantity statistic module, a scale judging module and an intention predicting module, wherein,
the quantity counting module is used for counting the quantity of the battlefield targets in the battlefield target information to obtain battlefield scale information;
the scale judgment module is used for judging whether the scale in the battlefield scale information reaches a preset battlefield scale or not, and if so, driving judgment is carried out on a battlefield target in the battlefield target information to obtain target traveling information;
the intention prediction module is used for predicting the battlefield target information by combining the battlefield scale information and the target traveling information to obtain characteristic intention information.
In another possible implementation manner, the apparatus further includes: a grade generation module and an early warning module, wherein,
the grade generation module is used for carrying out hazard grade evaluation on the characteristic intention information to generate a characteristic hazard grade;
the early warning module is used for judging whether the characteristic hazard level exceeds a preset hazard level, and if so, generating an early warning instruction and controlling an early warning device to carry out early warning.
In a third aspect, the present application provides an electronic device, which adopts the following technical solutions:
an electronic device, comprising:
at least one processor;
a memory;
at least one application, wherein the at least one application is stored in the memory and configured to be executed by the at least one processor, the at least one application configured to: and executing the battlefield target identification method of the knowledge graph.
In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solutions:
a computer-readable storage medium, comprising: a computer program is stored which can be loaded by a processor and which implements the method of knowledge-graph battlefield object identification described above.
To sum up, the application comprises the following beneficial technical effects:
1. when a battlefield target is identified, battlefield image information of different positions is obtained, then position detection is carried out on the battlefield image information to obtain image position information corresponding to the battlefield image information, processing equipment information capable of carrying out image information processing is obtained according to the image position information, then communication connection is carried out through the processing equipment corresponding to the processing equipment information, image characteristic information generated by the processing equipment information is collected and collected, the image characteristic information is input into a battlefield knowledge map for characteristic identification, and final battlefield target information is obtained;
2. when processing equipment information is determined, scanning a preset area according to image position information to obtain first equipment set information, respectively carrying out signal detection on the processing equipment information in the first equipment set information to obtain an equipment signal state of each piece of processing equipment information, then judging whether the equipment signal state meets the preset signal state, if not, screening out the processing equipment information corresponding to the equipment signal state to obtain second equipment set information, respectively calculating a distance value between the processing equipment information in the second equipment set information and the image position information, determining a signal transmission distance of each piece of processing equipment information, then judging whether the signal transmission distance meets a preset distance condition, wherein the preset distance condition is a shortest distance value, and selecting the processing equipment information with the shortest distance from the second equipment set information meeting the equipment signal state, the method is beneficial to improving the dissemination efficiency of battlefield image information.
Drawings
FIG. 1 is a schematic flow chart of a knowledge-graph battlefield target identification method according to an embodiment of the present application;
FIG. 2 is a block diagram of a knowledge-graph battlefield object identification method according to an embodiment of the present application;
fig. 3 is a schematic diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to figures 1-3.
A person skilled in the art, after reading the present description, may make modifications to the embodiments as required, without any inventive contribution thereto, but shall be protected by the patent laws within the scope of the claims of the present application.
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In addition, the term "and/or" herein is only one kind of association relationship describing the association object, and means that there may be three kinds of relationships, for example, a and/or B, and may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship, unless otherwise specified.
The embodiments of the present application will be described in further detail with reference to the drawings attached hereto.
The embodiment of the application provides a battlefield target identification method of a knowledge graph, which is executed by electronic equipment, wherein the electronic equipment can be a server or terminal equipment, the server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and a cloud server for providing cloud computing service. The terminal device may be a smart phone, a tablet computer, a notebook computer, a desktop computer, and the like, but is not limited thereto, the terminal device and the server may be directly or indirectly connected through a wired or wireless communication manner, and an embodiment of the present application is not limited thereto, as shown in fig. 1, the method includes:
step S10, battlefield image information is acquired.
In this application embodiment, gather image information through the image acquisition device who sets up in every different battlefields in advance, image acquisition device includes unmanned aerial vehicle and aircraft, is provided with the camera that is used for the image to shoot on unmanned aerial vehicle and the aircraft respectively.
Specifically, the image acquisition device is connected with the communication end of the electronic equipment in the embodiment of the present application, so that the electronic equipment can directly acquire battlefield image information acquired by the image acquisition device, and the specific connection mode of the image acquisition device and the electronic equipment includes: the image acquisition device communicates with the base station in a close range, battlefield image information is sent to the base station, and then the electronic equipment is in communication connection with the corresponding base station to acquire the battlefield image information.
Step S11 is performed to detect the position of the battlefield image information and to determine the image position information of the battlefield image information.
Specifically, the battlefield image information includes a terrain image, a battle instrument image, temperature information and latitude and longitude information, wherein the terrain image includes: land, sky, and sea, the combat tool image includes: the temperature information represents the temperature value of the image acquisition device, the latitude and longitude information represents the geographic position of the image acquisition device, and the image position information of the battlefield image information is determined according to the specific ground latitude and longitude information.
In step S12, the processing device information is determined based on the image position information.
For the embodiment of the present application, the processing device information is an image processing device, and specifically includes: an image graphics processor and a computer. Also, the information receiving end of the image processing apparatus is connected to the base station set forth in step S10.
Specifically, a satellite positioning scanning technology is adopted to position an accurate position in image position information, area scanning is carried out by taking the image position information as a center, and surrounding processing equipment information in the image position information is determined.
And step S13, summarizing the image characteristic information generated by the processing equipment information, and inputting the image characteristic information into the battlefield knowledge graph for characteristic recognition to obtain battlefield target information.
In particular, a knowledge graph is a structured semantic knowledge base used to quickly describe concepts and their interrelationships in the physical world. The knowledge graph is converted into a simple and clear triple of entities, relations and entities by effectively processing, processing and integrating the data of the complicated document, and finally a great deal of knowledge is aggregated, so that the quick response and reasoning of the knowledge are realized. The knowledge graph has two construction modes of top-down and bottom-up. The top-down construction is to extract ontology and mode information from high-quality data by means of structured data sources such as encyclopedic websites and the like, and add the ontology and mode information into a knowledge base; the bottom-up construction is that a resource mode is extracted from publicly acquired data by a certain technical means, a new mode with higher confidence coefficient is selected, and the new mode is added into a knowledge base after manual review.
In the embodiment of the application, the battlefield knowledge map comprises terrain image information and battle tool image information, and the image characteristic information generated by the image processing equipment is input into the battlefield knowledge map for characteristic recognition to obtain battlefield target information.
The embodiment of the application provides a battlefield target identification method of a knowledge graph, which is used for acquiring battlefield image information of different positions when identifying battlefield targets, then position detection is carried out on the battlefield image information to obtain image position information corresponding to the battlefield image information, processing equipment information capable of carrying out image information processing is obtained according to the image position information, then, the image characteristic information generated by the processing equipment information is collected through the communication connection of the processing equipment corresponding to the processing equipment information, inputting the image characteristic information into a battlefield knowledge graph for characteristic identification to obtain final battlefield target information, by adopting the mode of same-region image processing and information gathering and collecting, the uncertainty and the fuzziness of target information are improved, so that the accuracy of battlefield target identification is improved, and the information query efficiency is improved.
In a possible implementation manner of the embodiment of the present application, the step S12 specifically includes a step S121 (not shown), a step S122 (not shown), a step S123 (not shown), a step S124 (not shown), and a step S125 (not shown), wherein,
step S121, scanning a preset area based on the image position information to obtain first equipment set information.
Specifically, the preset area is a circular area with the image position information as the center of a circle and a radius of 5 kilometers, processing equipment information in the circular area is scanned to obtain a plurality of processing equipment, and then the equipment information corresponding to the processing equipment is put into a set to form first equipment set information. For example: denote the first device set information by a, the plurality of processing devices are denoted by (a, b, c, d, e.), then a = { a, b, c, d, e. }.
Step S122, performing signal detection on the processing device information in the first device set information, and determining a device signal state of the processing device information.
Specifically, the device signal state of the processing device information is determined by a signal detector provided in a preset area in advance.
Step S123, determining whether the device signal state satisfies the preset signal state, and if not, screening out the processing device information corresponding to the device signal state to obtain second device set information.
Specifically, after signal detection is performed on processing device information in the first device set information by the signal detector, a device signal state corresponding to the processing device information is obtained, and the device signal state is represented by "1, 0, X, and Z", where 1 and 0 are undoubtedly the signal state that exists really, Z is used to represent the high impedance state, and X is used to represent the uncertain state. And (4) presetting a signal state as '1', screening out the signals which do not meet '1' by judging whether the signal state of the equipment meets the preset signal state, and obtaining the residual second equipment set information of the first equipment set information.
In step S124, distance values between the processing device information and the image position information in the second device set information are respectively calculated, and the signal transmission distance of the processing device information is determined.
In an embodiment of the present application, processing device information includes: a processing device name, a processing device type, a processing device status, and a processing device longitude and latitude.
Specifically, the processing device information in the second device set information is listed, and the distance between the longitude and latitude of the processing device and the longitude and latitude of the battlefield image information is calculated one by one. For example: the longitude and latitude of the processing equipment are (X1, Y1), the longitude and latitude of the battlefield image information are (X2, Y2), wherein X1, X2 are longitude, Y1 and Y2 are latitude, the longitude is converted into radian ([ 3.1415926/180) according to the requirement of a calculation program, the radius of the earth is R =6371.0KM, and then the signal transmission distance D = R [ cos (Y1) ] cos (Y2) ] cos (X1-X2) + sin (Y1) ] sin (Y2) ].
And step S125, judging whether the signal transmission distance meets a preset distance condition, and if so, determining the processing equipment information corresponding to the signal transmission distance.
Specifically, the preset distance condition is that the distance is shortest, and the processing device information with the shortest longitude and latitude from the battlefield image information is obtained by comparing the calculated signal transmission distances one by one.
In a possible implementation manner of the embodiment of the present application, step S13 further includes step Sa (not shown in the figure) and step Sb (not shown in the figure), wherein,
and step Sa, acquiring battlefield data information, wherein the battlefield data information comprises terrain image information and battle tool image information.
Specifically, the method for acquiring the battlefield data information comprises the following steps: electronic data are directly collected and acquired, and non-electronic data are imported in a region scanning identification mode, for example: and through a textbook page turning mode, carrying out regional image feature recognition and keyword extraction matched with the regional image feature recognition, and carrying out check import.
And step Sb, establishing a battlefield knowledge graph based on the battlefield data information.
Specifically, the concept of the knowledge graph is firstly proposed by google, the purpose of the knowledge graph is to describe various entities and concepts existing in the real world and the relationship among the entities and the concepts, and in the actual building process, the knowledge graph mainly comprises six steps of knowledge modeling, knowledge storage, knowledge extraction, knowledge fusion, knowledge calculation and knowledge application. The knowledge modeling is based on application attributes, knowledge characteristics and actual requirements of industries, service abstraction and service modeling are carried out according to modes of a knowledge graph, and entity definition, relation definition and attribute definition are mainly carried out. Knowledge storage, the raw data types of a knowledge graph generally have three categories: structured Data (structured Data), such as a relational database; unstructured data such as pictures, audio, video; semi-structured data, such as XML, JSON, encyclopedia, currently, the mainstream knowledge storage solution includes both unitary and hybrid storage. The storage mode generally has two options, one is to store through a standard storage format such as RDF (resource description framework), and Jena and the like are commonly used. Alternatively, a graph database is used for storage, such as Neo4j, which is commonly used. Information extraction (information extraction) is a technique for automatically extracting structured information such as entities, relationships, and entity attributes from semi-structured and unstructured data. The key technology comprises the following steps: entity extraction, relationship extraction and attribute extraction. Knowledge fusion, namely, after acquiring entities, relationships and attribute information of the entities from original data through information extraction, logic attribution and redundant/error filtering are required to be performed on the data through knowledge fusion. Namely, two processes of entity linkage and knowledge combination are needed to realize. Knowledge computation, after extraction through information and fusion of knowledge, has achieved a series of basic factual expressions obtained from the original scrambled data. The next step is to obtain a structured and networked knowledge system and an updating mechanism through knowledge calculation. Knowledge application, namely, through the 5 links, a knowledge map of a special field is constructed, and after the knowledge map is combined with field data and a business scene in a specific application form, an actual assisting enterprise obtains an actual commercial value in the field.
In a possible implementation manner of the embodiment of the present application, step S11 further includes step S111 (not shown), step S112 (not shown), and step S113 (not shown), wherein,
step S111 specifies region level information indicating level information of a region where the image position information is located, based on the image position information.
Specifically, the regions of the battlefield are classified in advance, for example: grade 5 for severe battlefield form, grade 4 for severe battlefield form, grade 3 for moderate battlefield form, grade 2 for moderate battlefield form and grade 1 for stable battlefield form. And determining the position of the battlefield according to the image position information, and then determining the region level information corresponding to the battlefield position.
And step S112, comparing the region level information with a standard region level table, and determining the acquisition frequency of the acquired battlefield image information.
Specifically, the acquisition frequencies of different battlefield image information corresponding to different regional grade information are embodied in a standard regional grade table, and each regional grade with different grades corresponds to different image acquisition frequencies one to one.
And S113, controlling a preset acquisition device to acquire battlefield image information according to the acquisition frequency.
In a possible implementation manner of the embodiment of the present application, step S111 (not shown in the figure) is followed by step S1111 (not shown in the figure), wherein,
step S1111, judging whether the region level information conforms to the preset region level information, and if so, labeling the image position information.
Specifically, the preset regional level information is 4-level and 5-level.
In a possible implementation manner of the embodiment of the present application, step S13 further includes step S131 (not shown), step S132 (not shown), and step S133 (not shown), wherein,
step S131, carrying out quantity statistics on the battlefield targets in the battlefield target information to obtain battlefield scale information.
Specifically, the battlefield scale information is obtained by carrying out scale statistics on the number of the battle tools embodied in the battlefield target information.
Step S132, judging whether the scale in the battlefield scale information reaches the preset battlefield scale, and if so, judging the driving of the battlefield target in the battlefield target information to obtain target traveling information.
Specifically, the driving direction and speed of the battlefield target are judged through battlefield image information acquired twice in a neighboring mode.
And step S133, predicting the battlefield target information by combining the battlefield scale information and the target traveling information to obtain characteristic intention information.
Specifically, the characteristic intention information of the battlefield target information is predicted from the battlefield scale information and the target travel information, for example: the size of the battlefield target is 1000 tanks, the driving direction is towards the friend base, and the predicted characteristic intention information initiates an attack for the enemy to the friend base.
In a possible implementation manner of the embodiment of the present application, step S133 (not shown in the figure) further includes step S1331 (not shown in the figure) and step S1332 (not shown in the figure), wherein,
and step S1331, carrying out hazard grade evaluation on the characteristic intention information to generate a characteristic hazard grade.
Specifically, the characteristic intention information is subjected to hazard ranking, such as: the hazard level of the intention of invading the friend base in the characteristic intention information is 5 grades, and the hazard level of the intention of invading the friend stationing point is 4 grades.
And step S1332, judging whether the characteristic hazard level exceeds a preset hazard level, if so, generating an early warning instruction, and controlling an early warning device to perform early warning.
Specifically, the preset hazard level is 4 levels, and when the characteristic hazard level exceeds 4 levels, the early warning device is controlled to give an early warning, and comprises a buzzer, a warning lamp and the like.
The above embodiments describe a method for identifying a battlefield target by using a knowledge graph from the perspective of a method flow, and the following embodiments describe a device for identifying a battlefield target by using a knowledge graph from the perspective of a virtual module or a virtual unit, which will be described in detail in the following embodiments.
The embodiment of the present application provides a knowledge graph battlefield target recognition device, as shown in fig. 2, the device 20 may specifically include: an information acquisition module 21, a location determination module 22, a decision device determination module 23, and a feature identification module 24, wherein,
an information acquisition module 21, configured to acquire battlefield image information;
the position determining module 22 is used for detecting the position of the battlefield image information and determining the image position information of the battlefield image information;
a device determination module 23 for determining processing device information based on the image position information;
and the feature identification module 24 is used for summarizing the image feature information generated by processing the equipment information, and inputting the image feature information into the battlefield knowledge graph for feature identification to obtain battlefield target information.
In a possible implementation manner of the embodiment of the present application, when determining the processing device information based on the image location information, the device determining module 23 is specifically configured to:
scanning a preset area based on the image position information to obtain first equipment set information;
respectively carrying out signal detection on the processing equipment information in the first equipment set information, and determining the equipment signal state of the processing equipment information;
judging whether the equipment signal state meets a preset signal state or not, if not, screening out processing equipment information corresponding to the equipment signal state to obtain second equipment set information;
respectively calculating distance values between the processing equipment information and the image position information in the second equipment set information, and determining the signal transmission distance of the processing equipment information;
and judging whether the signal transmission distance meets a preset distance condition, and if so, determining the processing equipment information corresponding to the signal transmission distance.
In another possible implementation manner of the embodiment of the present application, the apparatus 20 further includes: a data acquisition module and a map building module, wherein,
the data acquisition module is used for acquiring battlefield data information, and the battlefield data information comprises terrain image information and battle tool image information;
and the map building module is used for building a battlefield knowledge map based on the battlefield data information.
In another possible implementation manner of the embodiment of the present application, the apparatus 20 further includes: a region determining module, a region comparing module and a frequency control module, wherein,
the region determining module is used for determining region level information based on the image position information, and the region level information is used for indicating the level information of a region where the image position information is located;
the region comparison module is used for comparing the region level information with a standard region level table and determining the acquisition frequency of acquiring the battlefield image information;
and the frequency control module is used for controlling the preset acquisition device to acquire battlefield image information according to the acquisition frequency.
In another possible implementation manner of the embodiment of the present application, the apparatus 20 further includes: a grade determination module, wherein,
and the grade judging module is used for judging whether the region grade information accords with the preset region grade information or not, and if so, labeling the image position information.
In another possible implementation manner of the embodiment of the present application, the apparatus 20 further includes: a quantity counting module, a scale judging module and an intention predicting module, wherein,
the quantity counting module is used for carrying out quantity counting on the battlefield targets in the battlefield target information to obtain battlefield scale information;
the scale judgment module is used for judging whether the scale in the battlefield scale information reaches the preset battlefield scale or not, and if so, driving judgment is carried out on battlefield targets in the battlefield target information to obtain target traveling information;
and the intention prediction module is used for predicting the battlefield target information by combining the battlefield scale information and the target traveling information to obtain the characteristic intention information.
In another possible implementation manner of the embodiment of the present application, the apparatus 20 further includes: a grade generation module and an early warning module, wherein,
the grade generation module is used for carrying out hazard grade evaluation on the characteristic intention information to generate a characteristic hazard grade;
and the early warning module is used for judging whether the characteristic hazard level meets a preset hazard level, and if so, generating an early warning instruction and controlling the early warning device to carry out early warning.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The embodiment of the present application also introduces an electronic apparatus from the perspective of a physical device, as shown in fig. 3, an electronic apparatus 300 shown in fig. 3 includes: a processor 301 and a memory 303. Wherein the processor 301 is coupled to the memory 303, such as via bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that the transceiver 304 is not limited to one in practical applications, and the structure of the electronic device 300 is not limited to the embodiment of the present application.
The Processor 301 may be a CPU (Central Processing Unit), a general-purpose Processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other Programmable logic device, a transistor logic device, a hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor 301 may also be a combination of computing functions, e.g., comprising one or more microprocessors, a combination of a DSP and a microprocessor, or the like.
The Memory 303 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical Disc storage, optical Disc storage (including Compact Disc, laser Disc, optical Disc, digital versatile Disc, blu-ray Disc, etc.), a magnetic Disc storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to these.
The memory 303 is used for storing application program codes for executing the scheme of the application, and the processor 301 controls the execution. The processor 301 is configured to execute application program code stored in the memory 303 to implement the aspects illustrated in the foregoing method embodiments.
Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. But also a server, etc. The electronic device shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The foregoing is only a partial embodiment of the present application, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present application, and these modifications and decorations should also be regarded as the protection scope of the present application.
Claims (10)
1. A battlefield target identification method of knowledge graph is characterized by comprising
Acquiring battlefield image information;
carrying out position detection on the battlefield image information, and determining the image position information of the battlefield image information;
determining processing device information based on the image location information;
and summarizing the image characteristic information generated by the processing equipment information, and inputting the image characteristic information into a battlefield knowledge graph for characteristic identification to obtain battlefield target information.
2. The method of claim 1, wherein determining processing device information based on the image location information comprises:
scanning a preset area based on the image position information to obtain first equipment set information;
respectively carrying out signal detection on processing equipment information in the first equipment set information, and determining the equipment signal state of the processing equipment information;
judging whether the equipment signal state meets a preset signal state or not, if not, screening out processing equipment information corresponding to the equipment signal state to obtain second equipment set information;
respectively calculating distance values between processing equipment information in the second equipment set information and the image position information, and determining the signal transmission distance of the processing equipment information;
and judging whether the signal transmission distance meets a preset distance condition, and if so, determining the processing equipment information corresponding to the signal transmission distance.
3. The method of claim 1, wherein the aggregating the image feature information generated by the processing device information and inputting the image feature information into a battlefield knowledge graph for feature recognition further comprises:
acquiring battlefield data information, wherein the battlefield data information comprises topographic image information and battle tool image information;
and constructing a battlefield knowledge graph based on the battlefield data information.
4. The method of claim 1, wherein said detecting the position of the battlefield image information, determining the image position information of the battlefield image information, then further comprises:
determining region level information based on the image position information, wherein the region level information is used for indicating level information of a region where the image position information is located;
comparing the region level information with a standard region level table to determine the acquisition frequency of acquiring the battlefield image information;
and controlling a preset acquisition device to acquire the battlefield image information according to the acquisition frequency.
5. The method of claim 4, wherein determining regional level information based on the image location information further comprises:
and judging whether the region level information accords with preset region level information or not, and if so, labeling the image position information.
6. The method of claim 1, wherein the summarizing image feature information generated from the processing device information and inputting the image feature information into a knowledge graph for feature recognition to obtain battlefield target information further comprises:
counting the quantity of the battlefield targets in the battlefield target information to obtain battlefield scale information;
judging whether the scale in the battlefield scale information reaches a preset battlefield scale or not, if so, judging the driving of a battlefield target in the battlefield target information to obtain target traveling information;
and predicting the battlefield target information by combining the battlefield scale information and the target traveling information to obtain characteristic intention information.
7. The method of claim 6, wherein said predicting the battlefield target information in combination with the battlefield size information and target travel information to obtain feature intent information, further comprising:
carrying out hazard grade evaluation on the characteristic intention information to generate a characteristic hazard grade;
and judging whether the characteristic hazard level exceeds a preset hazard level, if so, generating an early warning instruction, and controlling an early warning device to perform early warning.
8. A knowledge-graph battlefield object recognition device, comprising:
the information acquisition module is used for acquiring battlefield image information;
the position determining module is used for carrying out position detection on the battlefield image information and determining the image position information of the battlefield image information;
a device determination module for determining processing device information based on the image location information;
and the characteristic identification module is used for summarizing the image characteristic information generated by the processing equipment information and inputting the image characteristic information into the battlefield knowledge graph for characteristic identification to obtain the battlefield target information.
9. An electronic device, comprising:
at least one processor;
a memory;
at least one application, wherein the at least one application is stored in the memory and configured to be executed by the at least one processor, the at least one application configured to: a method of battlefield object identification implementing the knowledge-graph of any one of claims 1 to 7.
10. A computer-readable storage medium on which a computer program is stored, which, when the computer program is executed in a computer, causes the computer to execute the method for identifying a battlefield object of a knowledge-graph according to any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115542318A (en) * | 2022-10-12 | 2022-12-30 | 南京航空航天大学 | Air-ground combined multi-domain detection system and method for unmanned aerial vehicle group target |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115542318A (en) * | 2022-10-12 | 2022-12-30 | 南京航空航天大学 | Air-ground combined multi-domain detection system and method for unmanned aerial vehicle group target |
| CN115542318B (en) * | 2022-10-12 | 2024-01-09 | 南京航空航天大学 | Unmanned aerial vehicle group target-oriented air-ground combined multi-domain detection system and method |
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