A method and system for unified semantic interoperability of air-ground coordination

Abstract

The application relates to an air-ground cooperative unified semantic operation method and system, wherein the method comprises the following steps: constructing a semantic mapping function and a six-tuple model for unified semantic understanding between air and ground; mapping collected air perception data into a set of logical sentences of semantic statements through the semantic mapping function, and then converting the set of logical sentences into a set of semantic six-tuples based on the six-tuple model; sending the set of semantic six-tuples to a ground perception layer, and directly adding the received set of semantic six-tuples into a ground state pool if the received set of semantic six-tuples is the same as that of the air perception layer. Through the application, the semantic mapping function and the six-tuple model are used to provide a unified representation system for information or instructions between two platforms, so that an identity mapping is formed between an air state pool and a ground state pool, the information exchange structure is simplified, instruction issuing and execution feedback are more explicit and efficient, and the problem of how to improve air-ground cooperative operation efficiency is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of low-altitude operation, in particular to a unified semantic interoperability method and system for air-ground cooperation. BACKGROUND

[0002] With the development of smart cities and intelligent transportation, the demand for the cooperative operation of low-altitude unmanned aerial vehicles, air taxis and ground vehicles is growing, especially in scenarios such as logistics distribution, emergency rescue and unmanned inspection, where low-altitude flight and ground transportation need to work together.

[0003] Currently, low-altitude and ground each often use independent management systems, and low-altitude regulation and ground traffic control lack real-time linkage, making it difficult to share air-ground dynamic information. In other words, air-ground cooperation involves unmanned aerial vehicles, autonomous vehicles, roadside devices and other equipment systems, and the operating environment is complex and variable. Existing models are difficult to support consistent semantic understanding in multi-modal collaboration and dynamic environments, especially in emergency dispatch or emergency task situations. Different systems are often incompatible, data cannot be shared in a timely manner, resulting in slow response to collaboration and even safety hazards.

[0004] Currently, there is no effective solution to how to improve the efficiency of air-ground cooperative operation in the related art. SUMMARY

[0005] Embodiments of the present application provide a unified semantic interoperability method and system for air-ground cooperation to at least solve the problem of how to improve the efficiency of air-ground cooperative operation in the related art.

[0006] In a first aspect, the embodiments of the present application provide a unified semantic interoperability method for air-ground cooperation, which comprises:

[0007] Constructing a semantic mapping function and a six-tuple model, the semantic mapping function and the six-tuple model being used to unify semantic understanding between an air perception layer and a ground perception layer;

[0008] For air perception data collected in the air perception layer, the air perception data is mapped to a set of logical sentences of semantic statements through the semantic mapping function, and the set of logical sentences is converted to a set of semantic six-tuples based on the six-tuple model;

[0009] The set of semantic six-tuples is sent to the ground perception layer, and if the received set of semantic six-tuples is the same as in the air perception layer, the received set of semantic six-tuples is directly added to a ground state pool.

[0010] In some embodiments, constructing a semantic mapping function comprises:

[0011] Building semantic mapping function for semantic understanding between unified air perception layer and ground perception layer wherein, represents air or ground perception data domain, represents logical language of semantic statements, on the basis of a given perception data set the semantic mapping function generates a set of logical sentence collection of semantic statements , is the logical sentence of the nth semantic statement.

[0012] In some embodiments, for air perception data collected in the air perception layer, mapping the air perception data into a logical sentence collection of semantic statements by the semantic mapping function comprises:

[0013] performing feature extraction on the air perception data collected in the air perception layer to obtain a corresponding feature data set;

[0014] by the semantic mapping function , calling a set of semantic reasoning rules convert the feature data set into a logical sentence collection of semantic statements wherein, represents the jth semantic reasoning rule.

[0015] In some embodiments, performing feature extraction on the air perception data collected in the air perception layer to obtain a corresponding feature data set comprises:

[0016] for air perception data collected in the air perception layer, performing feature extraction on the air perception data by a feature extraction function to obtain a corresponding feature data set wherein, represents a feature extraction process, represents perception data collected at time t.

[0017] In some embodiments, building a six-tuple model comprises:

[0018] Building a six-tuple model for semantic understanding between unified air perception layer and ground perception layer wherein, represents high-level category of semantic statements, represents core predicate or action associating subject with its information in semantic statements, represents specific entity or instance in semantic statements, represents encoding secondary entity or relationship context in semantic statements, represents additional feature value associated with semantic statements, representing a global context or condition of a semantic statement.

[0019] In some embodiments, converting the set of logical statements into a set of semantic six-tuples based on the six-tuple model comprises:

[0020] based on the six-tuple model , the set of logical statements is converted into a set of semantic six-tuples.

[0021] In some embodiments, sending the set of semantic six-tuples to the ground perception layer comprises:

[0022] encapsulating the set of semantic six-tuples into a message by an encoding function , wherein the message contains a designated format message frame, header information and address information;

[0023] sending the message to the ground perception layer by a transmission function .

[0024] In some embodiments, if the received set of semantic six-tuples is identical to that in the ground perception layer, the received set of semantic six-tuples is directly added to the ground state pool comprises:

[0025] receiving a message transmitted from the air perception layer , decoding the message by a decoding function , in the case of no transmission error, the message received by the ground perception layer is equal to the message sent by the air perception layer , i.e. , the decoded set of semantic six-tuples is directly added to the ground state pool .

[0026] In some embodiments, the method comprises:

[0027] for the ground perception data collected in the ground perception layer, mapping the ground perception data into a set of logical statements of semantic statements by the semantic mapping function, and converting the set of logical statements into a set of semantic six-tuples based on the six-tuple model;

[0028] sending the set of semantic six-tuples to the air perception layer, and if the received set of semantic six-tuples is identical to that in the ground perception layer, the received set of semantic six-tuples is directly added to the air state pool .

[0029] In a second aspect, the embodiments of the present application provide an air-ground collaborative unified semantic interoperability system, which is used to execute the method of the first aspect, and comprises an air perception module and a ground semantic interoperability module.

[0030] The air perception module is configured to, for air perception data collected in the air perception layer, map the air perception data into a set of logical sentences of semantic statements by using the semantic mapping function, and convert the set of logical sentences into a set of semantic six-tuples based on the six-tuple model.

[0031] The ground semantic interoperability module is configured to, if the set of semantic six-tuples received from the air perception layer is the same as the set of semantic six-tuples in the ground perception layer, directly add the received set of semantic six-tuples into the ground state pool.

[0032] Compared with the related art, the embodiments of the present application provide an air-ground collaborative unified semantic interoperability method and system. In the method, a semantic mapping function and a six-tuple model are constructed for unified semantic understanding between the air perception layer and the ground perception layer. For air perception data collected in the air perception layer, the air perception data is mapped into a set of logical sentences of semantic statements by using the semantic mapping function, and the set of logical sentences is converted into a set of semantic six-tuples based on the six-tuple model. The set of semantic six-tuples is sent to the ground perception layer. If the received set of semantic six-tuples is the same as the set of semantic six-tuples in the air perception layer, the received set of semantic six-tuples is directly added into the ground state pool. The method realizes semantic consistent information expression and understanding between the air platform and the ground platform, provides a unified representation system for information or instructions between the two platforms by using the semantic mapping function and the six-tuple model, forms an identity mapping between the air state pool and the ground state pool, simplifies the information exchange structure, reduces redundant communication content and semantic ambiguity, makes the instruction issuing and execution feedback more explicit and efficient, significantly improves the information sharing efficiency, and solves the problem of how to improve the air-ground collaborative operation efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0033] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and serve to explain the principles of the application. In the drawings:

[0034] Figure 1 FIG. 1 is a flowchart of the steps of an air-ground collaborative unified semantic interoperability method according to an embodiment of the present application;

[0035] Figure 2 FIG. 2 is a flowchart of the steps of an air-ground collaborative unified semantic interoperability method according to an embodiment of the present application;

[0036] Figure 3 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort belong to the scope of the present application.

[0038] Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present application, and for those of ordinary skill in the art, the present application can be applied to other similar scenarios without creative effort based on the accompanying drawings. In addition, it can be understood that although the efforts made in the development process can be complex and lengthy, for those of ordinary skill in the art related to the content disclosed in the present application, some designs, manufacturing or production changes based on the technical content disclosed in the present application are only routine technical means and should not be understood as insufficient disclosure of the present application.

[0039] In the present application, the phrase "embodiments" means that the specific features, structures or characteristics described in conjunction with the embodiments can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor is it an independent or alternative embodiment to other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in the present application can be combined with other embodiments without conflict.

[0040] Unless otherwise defined, technical terms and scientific terms used in the present application shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terms "a", "an", "one", "this", and similar referents in the context of describing the application are to be construed to be inclusive, not exclusive. For example, the phrases "comprising a", "comprising one", "comprising the", and "comprising one of" are not limited to meaning that the referenced element is the only element in the group of elements that the phrase refers to. The use of the term "about" in the context of describing the application is intended to refer to the approximate value of a numerical quantity, and is not intended to mean "not more than" or "not less than". The use of the term "comprising" in the context of describing the application is intended to mean that the composition or method includes the recited elements, but not excluding additional elements. The use of the term "connected" in the context of describing the application is intended to mean that the two elements are in direct or indirect contact with each other. The use of the term "multiple" in the context of describing the application is intended to mean two or more. The use of the term "and / or" in the context of describing the application is intended to mean that the associated objects can exist independently of one another, or in any combination. The use of the term "first", "second", "third", etc. in the context of describing the application is intended to distinguish similar objects, and does not imply a specific order or sequence.

[0041] The embodiment of the present application provides a unified semantic interoperability method for air-ground cooperation, Figure 1 is a step flow chart of the unified semantic interoperability method for air-ground cooperation according to the embodiment of the present application, as shown in the figure, the method comprises the following steps: Figure 1 The method comprises the following steps:

[0042] In step S102, a semantic mapping function and a six-tuple model are constructed, and the semantic mapping function and the six-tuple model are used for unified semantic understanding between an air perception layer and a ground perception layer;

[0043] It should be noted that, based on the construction of the semantic mapping function and the six-tuple model in step S102, the information expression and understanding between the air platform and the ground platform are consistent in semantics, the semantic mapping function and the six-tuple model are used to provide a unified representation system for information or instructions between the two platforms, so that an identity mapping is formed between the air state pool and the ground state pool, the information exchange structure is simplified, the redundant communication content and semantic ambiguity are reduced, the instruction issuing and execution feedback are more clear and efficient, the information sharing efficiency is significantly improved, and the multi-agent joint decision and rapid response capability are enhanced.

[0044] Step S102 specifically comprises the following steps:

[0045] In step S1021, a semantic mapping function for unified semantic understanding between an air perception layer and a ground perception layer is constructed wherein, represents the aerial or ground perception data domain, represents a logical language of semantic statements, on the basis of a given perception data set the semantic mapping function generates a set of logical sentence collections of semantic statements , is the nth logical sentence of semantic statements.

[0046] Step S1022, construct a six-tuple model of semantic understanding between the unified aerial perception layer and the ground perception layer wherein, represents a high-level category of semantic statements, represents a core predicate or action that associates the subject with its information in the semantic statement, represents a specific entity or instance in the semantic statement, represents a secondary entity or relationship context in the encoded semantic statement, represents an additional feature value associated with the semantic statement, represents a global context or condition of the semantic statement.

[0047] It should be noted that this six-tuple model encapsulates the core elements in the logical sentence collection of semantic statements in a standardized format. Specifically, each element in the collection is defined as follows:

[0048] (category): represents a high-level category of semantic statements. is selected from a finite set of context tags, defined according to the content of .

[0049] (action / property): represents a core predicate or action that associates the subject with its information. In a report-type message, is a property, while in an instruction-type message, represents an action. is an element in the set of predicates defined. Here is considered as an identifier of the stated or requested content, the selection of can be represented as where represents the extracted original unstructured semantic content, and the function achieves semantic matching by selecting the keywords that best describe the data relationship.

[0050] (Instance / Identifier): is the specific entity or instance involved in the semantic statement, i.e. the subject or main object of the message. Here we define from the identity domain and generate by the identity function: , represents the function of assigning a uniform identifier using target identification or tracking data. Taking the unmanned inspection scene as an example, when the unmanned aerial vehicle detects a new target, a new is assigned so that the ground system can accurately refer to the same entity. This uniform identification is crucial for interoperability, and air and ground must use the same identifier to refer to the same entity.

[0051] (Relation Identifier): represents the secondary entity or relation context in the semantic statement. is selected from the relation descriptor or the domain of the received identifier: , the function determines the corresponding relation element.

[0052] (Feature Value): represents additional feature values associated with the semantic content. is a numerical value or structured data that quantifies or refines the statement content, taken from the feature domain: , is a function that calculates quantitative parameters from task requirements.

[0053] (Global Context): summarizes the global context or conditions in which the semantic information is located, including spatio-temporal reference, environmental conditions, and background knowledge required for correct interpretation . Here we use to represent elements such as observation timestamp, geographic coordinate system, and pointer to shared ontology / knowledge base, and as an element of the context state space set , represented as: , is the collected context data.

[0054] In step S104, for the aerial perception data collected in the aerial perception layer, the aerial perception data is mapped to a set of logical sentences of semantic statements through a semantic mapping function, and then the set of logical sentences is converted into a set of semantic six-tuple based on the six-tuple model;

[0055] It should be noted that,Figure 2 This is a flowchart illustrating the air-ground cooperative unified semantic interoperability method according to an embodiment of this application, as shown below. Figure 2 As shown, the semantic mapping process perceived in step S104 is deployed on the terminal device (airborne processor or edge computing module connected to sensors, such as low-altitude aircraft, autonomous vehicles, roadside equipment). The terminal's perception module acquires and preprocesses the raw perception data. Subsequently, the semantic reasoning module applies rules. generate The final result It is a machine-interpretable description with explicit and structured features.

[0056] Step S104 specifically includes the following steps:

[0057] Step S1041: Extract features from the air perception data collected in the air perception layer to obtain the corresponding feature dataset;

[0058] Specifically, in step S1041, for the aerial sensing data collected in the aerial sensing layer, a feature extraction function is used. Feature extraction is performed on the aerial sensing data to obtain the corresponding feature dataset. ,in, This represents the feature extraction process. This represents the sensing data collected at time t.

[0059] Step S1042, through semantic mapping function Call the semantic reasoning rule set Transform the feature dataset into a set of logical statements that express semantic meaning. ,in, Let j represent the j-th semantic reasoning rule.

[0060] It should be noted that each semantic reasoning rule This describes how to transform raw data patterns into semantic domain knowledge. Each rule represents an implication or transformation relation: if... If true, then the proposition is derived. .in, Indicates conditions or patterns on the data. These are the logical statements that need to be added. In other words, the set It contains all semantic statements Its prerequisites This is satisfied by the current perception data (airborne or ground-based). Taking an unmanned inspection scenario as an example, the perception layer (airborne or ground-based) detects a vehicle and generates a [data / data]. The statement indicates the existence of a "vehicle" type object with a definite identifier; the perception layer rules associate sensor measurements with attributes to generate a... The statements, all these inferred facts are uniformly integrated into In this process, a complete semantic description of perception is formed.

[0061] Step S1043, based on the six-tuple model Set of logical statements Convert to a set of semantic six-tuples.

[0062] It should be noted that six-tuple modeling provides a unified representation system for information or instructions between heterogeneous systems, constructing complete semantics through a specific formal process. Under this unified architecture, the mapping between the internal representation of the airborne system and the understanding of the ground system forms an identity mapping, i.e. This enables air-ground systems to operate collaboratively at the semantic level, ensuring the effective transmission of information and instructions.

[0063] Step S106: Send the semantic six-tuple set to the ground perception layer. If the received semantic six-tuple set is the same as that in the air perception layer, then add the received semantic six-tuple set directly to the ground state pool.

[0064] Step S106 specifically includes the following steps:

[0065] Step S1061, through the encoding function Encapsulate the set of semantic six-tuples into a message. Among them, the message Include The specified format message frame, header information, and address information.

[0066] It should be noted that, taking the unmanned inspection scenario as an example, the drone's communication module will use the encoding algorithm defined above. Encapsulate this semantic tuple into a message ,Include The specified format message frame, with header / address information appended (e.g., for drones). The sender is identified as the sender and the ground station as the receiver, and the system is prepared for transmission to ensure that all semantic content can be serialized.

[0067] Step S1062, via transfer function The message Send to the ground sensing layer.

[0068] It should be noted that, taking the unmanned inspection scenario as an example, the message... Transmitted to the target receiver via an air-to-ground communication network. We model the transmission as a function. , the function has a delay and potential noise when transmitting messages through the channel: . Ideally, under the error-free condition, the message reaches the receiving end as . If the message header contains the destination address, the intermediate device will use the address to forward In the air-ground communication scenario, the radio transmitter of the unmanned aerial vehicle will send the message to the ground base station, and then forward it to the control server through the wired network.

[0069] Step S1063, receiving the message transmitted by the air perception layer , the message is decoded by the decoding function , in the case of no transmission error, the message received by the ground perception layer is equal to the message sent by the air perception layer , that is , the decoded semantic six-tuple set is directly added to the ground state pool .

[0070] It should be noted that, taking the unmanned inspection scenario as an example, when the ground system receives the message , its interoperability module unpacks and parses the semantic information, that is , the decoded semantic six-tuple set is directly added to the ground state pool . Table 1 is a pseudo code example table corresponding to the above algorithm steps.

[0071] Table 1

[0072]

[0073] Through the above steps in the embodiments of the present application, the semantic consistent information expression and understanding between the air platform and the ground platform is realized, the semantic mapping function and the six-tuple model are used to provide a unified representation system for the information or instructions between the two platforms, so that the identity mapping is formed between the air state pool and the ground state pool, the information exchange structure is simplified, the redundant communication content and semantic ambiguity are reduced, the instruction issuing and execution feedback are more clear and efficient, the information sharing efficiency is significantly improved, and the problem of how to improve the air-ground collaborative operation efficiency is solved.

[0074] Specifically, due to the lack of unified semantic model and expression mechanism, the low-altitude and ground intelligent agents often face the problems of inconsistent information structure, incompatible semantics, and difficult cross-platform interaction in the process of collaborative operation, resulting in complex system integration, low communication efficiency, and understanding deviation of collaborative behavior. Especially in the dynamic scene of multi-agent heterogeneous collaboration, the semantic inconsistency problem is further aggravated, which seriously restricts the interoperability and task execution efficiency of the system. The invention proposes a unified semantic interoperability method, which constructs a semantic six-tuple model based on ontology theory, combines standardized semantic message structure and multi-layer semantic interaction mechanism, and realizes consistent semantic expression and understanding between different platforms. Through this method, all kinds of air-ground intelligent agents in the system can perform unified semantic analysis and fusion on heterogeneous data, significantly improving semantic understanding and data compatibility, fundamentally solving the problem of "can transmit but cannot understand", and reducing the system integration cost. Further, with the help of the standardized semantic interface constructed by this method, different intelligent agents can realize high-consistency intention recognition and collaborative decision-making, ensuring the accuracy of task execution and the overall coordination of the system. The unified semantic framework further simplifies the information exchange structure, reduces redundant communication content and semantic ambiguity, makes the system command issuance and execution feedback more clear and efficient, significantly improves the information sharing efficiency, and enhances the multi-agent joint decision-making and rapid response capability.

[0075] The embodiment of the present application provides a unified semantic interoperability method for air-ground collaboration. The above embodiment converts the air perception data collected in the air perception layer into a semantic six-tuple set and then adds it to the ground state pool. The embodiment converts the ground perception data collected in the ground perception layer into a semantic six-tuple set and then adds it to the air state pool. In other words, the algorithm steps used in the two embodiments are basically the same, but the data flow is opposite. The method of the present embodiment comprises the following steps:

[0076] For the ground perception data collected in the ground perception layer, the ground perception data is mapped to a set of logical sentences of semantic statements through a semantic mapping function, and then the set of logical sentences is converted into a set of semantic six-tuples based on the six-tuple model.

[0077] The set of semantic six-tuples is sent to the air perception layer. If the received set of semantic six-tuples is the same as that in the ground perception layer, the received set of semantic six-tuples is directly added to the air state pool .

[0078] It should be noted that the steps shown in the above process or the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0079] The embodiment of the application provides an air-ground cooperative unified semantic interoperation system, which comprises an air perception module and a ground semantic interoperation module.

[0080] The air perception module is configured to map air perception data collected in an air perception layer into a set of logical sentences of semantic statements by using a semantic mapping function, and convert the set of logical sentences into a set of semantic six-tuples based on a six-tuple model.

[0081] The ground semantic interoperation module is configured to add the received set of semantic six-tuples directly into a ground state pool if the received set of semantic six-tuples is the same as the set of semantic six-tuples sent to the ground perception layer.

[0082] Through the air perception module and the ground semantic interoperation module in the embodiment of the application, the information expression and understanding between the air platform and the ground platform are consistent in semantics, a unified representation system is provided for the information or instructions between the two platforms by using the semantic mapping function and the six-tuple model, an identical mapping is formed between the air state pool and the ground state pool, the information exchange structure is simplified, the redundant communication content and semantic ambiguity are reduced, the instruction issuing and execution feedback are more explicit and efficient, the information sharing efficiency is significantly improved, and the problem of how to improve the air-ground cooperative operation efficiency is solved.

[0083] It should be noted that each of the above modules can be a functional module or a program module, and can be implemented by software or hardware. For the modules implemented by hardware, each of the above modules can be located in the same processor, or each of the above modules can be located in different processors in any combination.

[0084] The embodiment provides an electronic device, comprising a memory and a processor, the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any of the method embodiments.

[0085] Optionally, the electronic device can further comprise a transmission device and an input-output device, wherein the transmission device is connected with the processor, and the input-output device is connected with the processor.

[0086] Optionally, the electronic device further comprises a processor, a memory, a network interface, a display screen and an input device connected through a system bus. The processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The network interface of the electronic device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement an air-ground collaborative unified semantic interoperability method. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen. The input device of the electronic device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the electronic device, or an external keyboard, touchpad or mouse, etc.

[0087] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation manners, and this embodiment will not be described here.

[0088] In addition, in combination with the air-ground collaborative unified semantic interoperability method in the above embodiments, the embodiments of the present application can provide a storage medium for implementation. The storage medium stores a computer program; the computer program is executed by the processor to implement any one of the air-ground collaborative unified semantic interoperability methods in the above embodiments.

[0089] In one embodiment, Figure 3 is a schematic diagram of the internal structure of an electronic device according to the embodiments of the present application, as Figure 3 indicated, an electronic device is provided, which can be a server, and the internal structure diagram thereof can be as Figure 3 indicated. The electronic device comprises a processor, a network interface, an internal memory and a non-volatile memory connected through an internal bus, wherein the non-volatile memory stores an operating system, a computer program and a database. The processor is configured to provide computing and control capabilities, the network interface is configured to communicate with an external terminal through a network connection, the internal memory is configured to provide an environment for the operating system and the computer program to run, the computer program is executed by the processor to implement an air-ground collaborative unified semantic interoperability method, and the database is configured to store data.

[0090] Those skilled in the art can understand that Figure 3 the structure shown in the above embodiment is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device can comprise more or fewer components than those shown in the diagram, or combine certain components, or have a different arrangement of components.

[0091] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments of each method. Any reference to memory, storage, database or other medium used in each embodiment provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0092] Those skilled in the art should understand that each technical feature of the above-mentioned embodiments can be combined arbitrarily, and in order to make the description simple, each technical feature in the above-mentioned embodiments is not described all possible combinations, however, as long as the combination of these technical features does not exist contradictory, it should be considered as the scope of the present application.

[0093] The above-mentioned embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the patent of the present application should be subject to the appended claims.

Claims

1. A unified semantic interoperability method for air-ground collaboration, characterized in that, The method comprises: Constructing semantic mapping functions for semantic understanding between unified air and ground perception layers wherein, denotes the air or ground perception data domain, denotes a logical language of semantic statements, on the basis of a given perception data set the semantic mapping function generates a set of logical sentences of a set of semantic statements , is the logical sentence for the nth semantic statement. Building a six-tuple model for semantic understanding between unified air perception layer and ground perception layer wherein, represents a high-level category of semantic statement, represents a core predicate or action linking a subject with its information in a semantic statement, represents a specific entity or instance in a semantic statement, represents a secondary entity or relationship context in a semantic statement, represents an additional feature value associated with a semantic statement, represents a global context or condition of a semantic statement, the semantic mapping function and the six-tuple model are used for semantic understanding between unified air perception layer and ground perception layer; For the air awareness data collected in the air awareness layer, the air awareness data is mapped into a set of logical sentences of semantic statements through the semantic mapping function, and the set of logical sentences is converted into a set of semantic six-tuples based on the six-tuple model; The set of semantic six-tuples is sent to the ground awareness layer, and if the received set of semantic six-tuples is the same as that in the air awareness layer, the received set of semantic six-tuples is directly added to the ground state pool.

2. The method of claim 1, wherein, For the air awareness data collected in the air awareness layer, the air awareness data is mapped into a set of logical sentences of semantic statements through the semantic mapping function, and the set of logical sentences is converted into a set of semantic six-tuples based on the six-tuple model; The air awareness data collected in the air awareness layer is subjected to feature extraction to obtain a corresponding feature data set; by the semantic mapping function invoking a semantic reasoning rule set converting the feature data set into a logical sentence set of semantic statements wherein, denotes the jth semantic reasoning rule.

3. The method of claim 2, wherein, The air awareness data collected in the air awareness layer is subjected to feature extraction to obtain a corresponding feature data set, which comprises: For the air perception data collected in the air perception layer, the feature extraction function is used to extract the feature data set corresponding to the air perception data The feature extraction function is used to extract the feature data set corresponding to the air perception data Wherein, The feature extraction process is represented by The perception data collected at time t is represented by 4. The method of claim 1, wherein, The set of logical sentences is converted into a set of semantic six-tuples based on the six-tuple model, which comprises: based on the six tuple model the logical sentence set is converted into a semantic six tuple set.

5. The method of claim 1, wherein, The set of semantic six-tuples is sent to the ground awareness layer, which comprises: by an encoding function encapsulating the semantic sextuple set into a message wherein the message contains a specified format message frame, header information and address information; Through the transfer function The message is sent to the ground perception layer.

6. The method of claim 5, wherein, If the received set of semantic six-tuples is the same as that in the air awareness layer, the received set of semantic six-tuples is directly added to the ground state pool, which comprises: receiving the message transmitted from the air perception layer decoding the message by a decoding function in case of error-free transmission, the message received by the ground perception layer is equal to the message transmitted by the air perception layer i.e. adding the decoded semantic six-tuple set directly to the ground state pool .​​ 7. The method of claim 1, wherein, The method comprises: For the ground awareness data collected in the ground awareness layer, the ground awareness data is mapped into a set of logical sentences of semantic statements through the semantic mapping function, and the set of logical sentences is converted into a set of semantic six-tuples based on the six-tuple model; sending the set of semantic sextuples to the air perception layer, and if the received set of semantic sextuples is identical to the one in the ground perception layer, directly adding the received set of semantic sextuples to the air state pool in the air perception layer.

8. An air-ground cooperative unified semantic interoperability system, characterized by, The system is used to execute the method of any one of claims 1 to 7, and the system comprises an air awareness module and a ground semantic interoperation module; The air awareness module is used to, for the air awareness data collected in the air awareness layer, map the air awareness data into a set of logical sentences of semantic statements through the semantic mapping function, and convert the set of logical sentences into a set of semantic six-tuples based on the six-tuple model; The ground semantic interoperation module is used to, if the set of semantic six-tuples sent to the ground awareness layer is the same as that in the air awareness layer, directly add the received set of semantic six-tuples to the ground state pool.

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