Method and device for constructing pragmatic interoperation model supporting edge intelligent collaboration

Abstract

The invention relates to the technical field of electronic information engineering, in particular to a pragmatic interoperation model construction method and device supporting edge intelligent collaboration, and the method comprises the steps: constructing an integrated pragmatic interoperation model covering an ontology knowledge base, a formatted instruction set and a collaborative driving engine; a pragmatic interoperation mode in which data interaction and data application are mutually separated in a heterogeneous information system is converted into a joint grammar and semantic pragmatic interoperation mode in which data interaction, understanding and application are integrated, the data interaction efficiency and timeliness in the task collaboration process are improved, and support is provided for real-time and reliable edge intelligent collaboration control. When each subsystem transmits and shares data, the content length of the interaction data and the data interaction process can be optimized by understanding the semantics of the interaction data and the processing rule and process of the interaction data in the collaborative task, the content or (and) the process is simplified under the condition that the efficiency of the collaborative task is not obviously reduced, and the efficiency of the collaborative task is improved. The task cooperation efficiency and the completion success rate are improved.

Description

Technical Field

[0001] The present application relates to the field of electronic information engineering technology, and in particular to a method and device for constructing a pragmatic interoperability model that supports edge intelligence collaboration. Background Art

[0002] Communication in a broad sense needs to solve three basic problems: grammatical problems (accurate transmission of symbols), semantic problems (consistent understanding of meaning) and pragmatic problems (precise control of behavior). Interoperability refers to the ability of two or more information systems to exchange information and use the exchanged information. The two concepts overlap to a certain extent, but they focus on different aspects. The former focuses on information exchange between people with the help of information systems. With the participation of people, information systems can ignore semantic and pragmatic issues. The grammatical information theory established by Shannon has therefore achieved great success and greatly promoted the development of modern communication technology; the latter focuses on information exchange between machines. With the improvement of machine autonomy and intelligence, the problems of grammatical interoperability, semantic interoperability and pragmatic interoperability between heterogeneous machines need to be solved urgently.

[0003] In terms of grammatical interoperability, on the one hand, the existing technology defines the standard interface between the general ground control station and different unmanned aerial vehicles and payloads. On the other hand, the existing Joint Unmanned Systems (JAUS) establishes message sets and data protocols for unmanned ground vehicles, and then expands to more unmanned systems such as unmanned aerial vehicles and unmanned ships to achieve grammatical interoperability of different unmanned systems and payloads.

[0004] In terms of semantic interoperability, existing technologies can realize the automated application configuration and dynamic integration of multiple drones on the one hand, and on the other hand, enable machines to understand and reason about the meaning of sensor data, elevating the interoperability of various systems to a semantic level.

[0005] In terms of pragmatic interoperability, existing technologies, on the one hand, realize intelligent control problems in uncertain environments; on the other hand, they realize pragmatic combinatorial construction of large-scale, diversified, and dynamic simulation systems through formal descriptions of behaviors and descriptions of simulation context constraints.

[0006] In view of this, how to provide a pragmatic interoperability model construction method that supports edge intelligent collaboration, streamline content or simplify processes without significantly reducing the effectiveness of collaborative tasks, and improve task collaboration rate and success rate has become a technical problem that needs to be solved urgently. Summary of the invention

[0007] The embodiments of the present application provide a method for constructing a pragmatic interoperability model that supports edge intelligent collaboration, a device for constructing a pragmatic interoperability model that supports edge intelligent collaboration, an electronic device, and a computer storage medium, which are used to solve the current problems of low task collaboration rate and success rate.

[0008] In a first aspect of an embodiment of the present application, a method for constructing a pragmatic interoperability model supporting edge intelligence collaboration is provided, comprising:

[0009] In the case where at least two devices collaborate to perform a target task, a task collaboration knowledge base and a task collaboration instruction set are created based on the target task, and the task collaboration knowledge base and the task collaboration instruction set are shared with each device, wherein the task collaboration knowledge base carries at least two ontology knowledge bases containing entity features corresponding to each device and entity relationships between each device; the task collaboration instruction set is composed based on a header and instruction content, and the content of each field in the instruction content is determined based on the field content in the interactive data format specified by the metadata in the task collaboration knowledge base, and the length of the field content is adjustable;

[0010] Based on the task collaboration knowledge base and the task collaboration instruction set, each device is managed and controlled in terms of pragmatic behavior, and based on the pragmatic behavior, a task collaboration driving engine is created for each device;

[0011] Based on the task coordination driving engine, determining the interaction instructions corresponding to each device according to the instruction sending rules and instruction receiving rules carried in the task coordination instruction set;

[0012] Based on the task collaboration driving engine, the instruction features of the interaction instructions corresponding to each device participating in the collaborative task are processed to generate new instructions, wherein the instruction features include field content and field length carried in the interaction instruction, and / or the instruction interaction process;

[0013] Based on the new instructions, each device is controlled to collaboratively execute the target task.

[0014] In a second aspect of an embodiment of the present application, a pragmatic interoperability model construction device supporting edge intelligence collaboration is provided, including:

[0015] A first creation module is configured to create a task collaboration knowledge base and a task collaboration instruction set based on the target task when at least two devices collaborate to perform a target task, and share the task collaboration knowledge base and the task collaboration instruction set with each device, wherein the task collaboration knowledge base carries at least two ontology knowledge bases including entity features corresponding to each device and entity relationships between each device; the task collaboration instruction set is composed based on a header and instruction content, and the content of each field in the instruction content is determined based on the field content in the interactive data format specified by the metadata in the task collaboration knowledge base, and the length of the field content is adjustable;

[0016] A second creation module is configured to perform pragmatic behavior control on each device based on the task collaboration knowledge base and the task collaboration instruction set, and create a task collaboration driving engine for each device based on the pragmatic behavior;

[0017] A determination module is configured to determine the interaction instructions corresponding to each device based on the task coordination driving engine and in accordance with the instruction sending rules and instruction receiving rules carried in the task coordination instruction set;

[0018] A generation module is configured to process the instruction features of the interaction instructions corresponding to each device participating in the collaborative task based on the task collaboration driving engine to generate a new instruction, wherein the instruction features include field content and field length carried in the interaction instruction, and / or instruction interaction process;

[0019] The execution module is configured to control each device to collaboratively execute the target task based on the new instruction.

[0020] According to a third aspect of an embodiment of the present application, a computing device is provided, including:

[0021] Memory and processor;

[0022] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for constructing a pragmatic interoperability model that supports edge intelligent collaboration are implemented.

[0023] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which stores computer-executable instructions, and when the instructions are executed by a processor, the steps of the method for constructing a pragmatic interoperability model that supports edge intelligent collaboration are implemented.

[0024] The present application provides a method for constructing a pragmatic interoperability model that supports edge intelligent collaboration. First, a corresponding task collaboration knowledge base and task collaboration instruction set are created for heterogeneous system devices to collaboratively execute target tasks. Then, based on the created task collaboration knowledge base and task collaboration instruction set, a task collaboration driving engine is created for each device. Secondly, based on the created task collaboration driving engine, the instruction features of the interaction instructions of each device are processed to generate new instructions. Finally, based on the new instructions, each device is controlled to collaboratively execute the target task.

[0025] By applying the method provided in the embodiment of the present application, an integrated pragmatic interoperability model covering an ontology knowledge base, a formatting instruction set, and a collaborative driving engine is constructed to transform the pragmatic interoperability mode in which data interaction and data application are separated from each other in heterogeneous information systems into a joint grammatical semantic pragmatic interoperability mode that integrates data interaction, understanding, and application, thereby improving the efficiency and timeliness of data interaction in the task collaboration process and providing support for real-time, reliable edge intelligent collaborative control. While sharing data transmission, each subsystem can optimize the length of the interactive data content and the data interaction process by understanding the semantics of the interactive data and its processing rules and processes in the collaborative task, thereby streamlining the content or (and) simplifying the process without significantly reducing the effectiveness of the collaborative task, thereby improving the task collaboration efficiency and completion success rate.

[0026] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0028] Figure 1 A flowchart of a method for constructing a pragmatic interoperability model supporting edge intelligence collaboration provided in an embodiment of the present application;

[0029] Figure 2 A schematic diagram of a pragmatic interoperability model in a method for constructing a pragmatic interoperability model supporting edge intelligence collaboration provided in an embodiment of the present application;

[0030] Figure 3 A schematic diagram of another pragmatic interoperability model in a pragmatic interoperability model construction method supporting edge intelligent collaboration provided in an embodiment of the present application;

[0031] Figure 4 A schematic diagram of an ontology knowledge base in a pragmatic interoperability model construction method supporting edge intelligence collaboration provided in an embodiment of the present application;

[0032] Figure 5 A schematic diagram of an interactive instruction set in a method for constructing a pragmatic interoperability model supporting edge intelligence collaboration provided in an embodiment of the present application;

[0033] Figure 6A flowchart of instruction interaction and behavior control in a method for constructing a pragmatic interoperability model supporting edge intelligence collaboration provided in an embodiment of the present application;

[0034] Figure 7 A schematic diagram of a simplified instruction set in a method for constructing a pragmatic interoperability model supporting edge intelligence collaboration provided in an embodiment of the present application;

[0035] Figure 8 A schematic diagram of a simplified task process in a method for constructing a pragmatic interoperability model supporting edge intelligence collaboration provided in an embodiment of the present application;

[0036] Fig. 9 A schematic diagram of a new instruction interaction process in a method for constructing a pragmatic interoperability model supporting edge intelligence collaboration provided in an embodiment of the present application;

[0037] Fig.10 A schematic diagram of the structure of a pragmatic interoperability model building device supporting edge intelligence collaboration provided in an embodiment of the present application;

[0038] Fig.11 A structural block diagram of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0040] The embodiments of the present application provide a method for constructing a pragmatic interoperability model that supports edge intelligent collaboration, a device for constructing a pragmatic interoperability model that supports edge intelligent collaboration, an electronic device, and a computer storage medium, which are used to address the information interaction, understanding, and application problems of heterogeneous unmanned systems in the process of collaborative task execution, and improve the pragmatic data interaction efficiency and task success rate in the task collaboration process.

[0041] See also Figure 1 , Figure 1 A flowchart of a method for constructing a pragmatic interoperability model supporting edge intelligence collaboration provided in an embodiment of the present application. Figure 1 As shown, the specific steps include the following steps.

[0042] Step S102: In the case where at least two devices collaborate to perform a target task, a task collaboration knowledge base and a task collaboration instruction set are created based on the target task, and the task collaboration knowledge base and the task collaboration instruction set are shared with each device, wherein the task collaboration knowledge base carries at least two ontology knowledge bases containing entity features corresponding to each device and entity relationships between each device; the task collaboration instruction set is composed based on a header and instruction content, and the content of each field in the instruction content is determined based on the field content in the interactive data format specified by the metadata in the task collaboration knowledge base, and the length of the field content is adjustable.

[0043] It should be noted that the embodiments of the present application can monitor the various devices participating in the collaborative task based on the monitoring and control platform; or one of the devices participating in the collaborative task can be designated as the monitoring and control platform to monitor all devices.

[0044] Taking the example of UAV A and unmanned vehicle B performing a collaborative search and attack mission for target D, and monitoring and control platform C monitoring all interaction information between UAV A and unmanned vehicle B during the mission execution, it should be noted that when UAV A and / or unmanned vehicle B encounter abnormal situations, the management personnel can send control instructions to UAV A and / or unmanned vehicle B through monitoring and control platform C, and ensure the safety of the unmanned platform through human intervention.

[0045] In the embodiment of the present application, the number of unmanned equipment is not limited to two, and the type of unmanned platform is not limited to drones and unmanned vehicles, but can also be unmanned boats, underwater unmanned submersibles, etc.

[0046] In the embodiment of the present application, the monitoring and control platform C is not necessary. In the case where the monitoring and control platform C exists, a simplified model including an ontology knowledge base and a task coordination instruction set is deployed on the monitoring and control platform C. Figure 2 , Figure 2 A schematic diagram of a pragmatic interoperability model in a method for constructing a pragmatic interoperability model that supports edge intelligent collaboration provided in an embodiment of the present application.

[0047] Specifically, in the case where at least two devices collaborate to perform a target task, based on the target task, creating a task collaboration knowledge base and a task collaboration instruction set includes: in the case where at least two devices collaborate to perform a target task, monitoring the interaction information of each device in the process of collaboratively performing the target task through a monitoring and control platform; based on the interaction information, creating a task collaboration knowledge base and a task collaboration instruction set in the monitoring and control platform, and sharing the task collaboration knowledge base and the task collaboration instruction set to each device participating in the collaborative task.

[0048] In the absence of a monitoring and control platform C, one of the devices participating in the collaborative task is first determined as the monitoring and control platform, and then the simplified model of the ontology knowledge base and the task collaborative instruction set is deployed on each device, see Figure 3 , Figure 3 A schematic diagram of another pragmatic interoperability model in a pragmatic interoperability model construction method supporting edge intelligent collaboration provided in an embodiment of the present application.

[0049] Specifically, in the case where at least two devices collaborate to perform a target task, based on the target task, creating a task collaboration knowledge base and a task collaboration instruction set, including: in the case where at least two devices collaborate to perform a target task, using a first device as a monitoring and control platform to monitor interaction information during the process of collaboratively performing the target task by each device; based on the interaction information, creating an ontology knowledge base and a task collaboration instruction set in each device respectively, and sharing the ontology knowledge base and the task collaboration instruction set to each device participating in the collaborative task.

[0050] Therefore, the contents of the constructed task collaboration knowledge base (or ontology knowledge base) and task collaboration instruction set need to be shared with drone A and unmanned vehicle B in advance.

[0051] In an embodiment of the present application, creating a task collaboration knowledge base based on the target task includes: using the first device as a decision-making platform, creating a task collaboration knowledge base based on the target task, wherein the task collaboration knowledge base includes a first ontology knowledge base and a second ontology knowledge base; triggering the first device to execute a first position reporting instruction, a target identification instruction, and a target reporting instruction based on the first ontology knowledge base; triggering the second device to execute a second position reporting instruction, an electromagnetic interference instruction, and a fire strike instruction based on the second ontology knowledge base.

[0052] It should be noted that the task collaboration knowledge base here is a knowledge base generated after ontology modeling of entities and relationships between entities that need to interact with data during collaborative tasks. The task collaboration knowledge base carries the ontology knowledge base corresponding to all devices participating in the collaborative task. For example, drone A supports instructions such as position reporting, target identification, and target reporting; unmanned vehicle B supports instructions such as position reporting, electromagnetic interference, and fire strike. Among them, the position report instruction is generated periodically and autonomously; the target report instruction is generated periodically after the target is identified; the target identification instruction, electromagnetic interference instruction, and fire strike instruction are triggered after the collaborative decision of the unmanned equipment platform.

[0053] The ontology knowledge base here includes but is not limited to entity name, entity description, entity type, entity state, entity behavior, entity relationship and entity capability, etc.; by instantiating the specific unmanned platforms and payloads in each ontology knowledge base, a collaborative ontology knowledge base is formed.

[0054] In the embodiment provided by the present application, the target D of the collaborative mission is not a self-propelled artillery, the target recognition function of the drone A can identify the target D, and the electromagnetic interference and firepower strike of the unmanned vehicle B have the ability to interfere with and strike the target D.

[0055] It should be noted that UAV A and UAV B use satellite navigation positioning with a positioning accuracy of 10 meters. After UAV A identifies target D, the positioning accuracy of target D is 30 meters. The electromagnetic interference range of UAV B is a hemisphere with a radius of 1000 meters and a sphere with a radius of 500 meters. The ontology knowledge base constructed based on the above information can be found in Figure 4 ,in, Figure 4 A schematic diagram of an ontology knowledge base in a method for constructing a pragmatic interoperability model that supports edge intelligence collaboration provided in an embodiment of the present application.

[0056] In an embodiment of the present application, creating a task coordination instruction set based on the target task includes: using the first device as a decision-making platform, creating a task coordination instruction set based on the target task, wherein the task coordination instruction set includes a first task coordination instruction set and a second task coordination instruction set; according to the first task coordination instruction set, controlling the first device to periodically send first position report information and control command information to the second device; according to the second task coordination instruction set, controlling the second device to periodically send second position report information and control command response information to the first device.

[0057] It should be noted that in the process of creating a task collaboration instruction set, the data content that needs to be interacted in the collaborative task process is formatted and defined, and the metadata in the knowledge base is used to specify the arrangement order of each field in the interactive data format, but the length of the content of each field is not mandatory. At the same time, sending rules and receiving rules are specified for different types of interactive data, including but not limited to status instructions, control instructions, request instructions, etc., to form a task collaboration instruction set.

[0058] In the embodiment provided by the present application, drone A needs to send its own position report information, target report information obtained by target recognition, and control command information to the unmanned vehicle. Unmanned vehicle B needs to send its own position report information and control command response information.

[0059] In addition, in the embodiment of the present application, the interactive information between drone A and unmanned vehicle B is designed using the instruction format of "header + instruction content", and each field in the instruction content is defined using metadata in the ontology knowledge base.

[0060] It should be noted that the header stipulates the field order and field length, while the instruction content only stipulates the order of the fields and does not forcibly specify the length of each field. Therefore, the actual length of the instruction can be flexibly adjusted according to different tasks, different types of unmanned platforms or payloads.

[0061] For ease of use, the initial default length of each field can be given when agreeing on the field order. At the same time, according to the different types of instructions, the sending rules and receiving rules of various types of instructions are specified. For the interactive instruction set designed based on the knowledge base, see Figure 5 ,in, Figure 5 A schematic diagram of an interactive instruction set in a method for constructing a pragmatic interoperability model that supports edge intelligence collaboration provided in an embodiment of the present application.

[0062] Step S104: Based on the task collaboration knowledge base and the task collaboration instruction set, pragmatic behavior management is performed on each device, and based on the pragmatic behavior, a task collaboration driving engine is created for each device.

[0063] Step S106: Based on the task collaboration driving engine, the interaction instructions corresponding to each device are determined according to the instruction sending rules and instruction receiving rules carried in the task collaboration instruction set.

[0064] In an embodiment of the present application, based on the task collaboration driving engine, the interaction instructions corresponding to each device are determined according to the instruction sending rules and instruction receiving rules carried in the task collaboration instruction set, including: the first task collaboration driving engine corresponding to the first device generates a corresponding instruction according to the instruction sending rules, and sends it to the second device, wherein the corresponding instruction carries the entity characteristics corresponding to the first device; the second device processes the received corresponding instruction according to the instruction receiving rules, and executes the corresponding instruction.

[0065] It should be noted that the initial content and state of the collaborative driving engine here are the same, but the collaborative driving engine learns and infers new content due to algorithm calls during operation, so collaborative driving engine A and collaborative driving engine B are used to distinguish them.

[0066] However, if a collaborative driving engine, such as collaborative driving engine A, infers new content, another collaborative driving engine, namely collaborative driving engine B, can also infer the same content, and the two may be inconsistent simply because the algorithm is running at different times. For example, after the collaborative engine A of drone A understands the mission intent, it optimizes the length of the position report instruction and forms a new position report instruction format. At this time, the content is in the collaborative engine A, but not in the collaborative engine B. However, after drone A uses the new format to send to unmanned vehicle B, when collaborative engine B cannot decode it with the default instruction format, it can infer the new instruction format used by collaborative driving engine A, thereby correctly decoding it, and updating and storing the new instruction format in collaborative engine B. At this time, the content of collaborative driving engines A and B is consistent.

[0067] In actual applications, the task collaboration driving engine is constructed and operated, and its own pragmatic behavior control is performed based on the shared task collaboration knowledge base and task collaboration instruction set, including instruction sending, instruction receiving, load control and instruction optimization (instruction format and instruction interaction process, etc.), forming a collaborative driving engine. During operation, the collaborative driving engine understands and learns the task intent from the instruction interaction situation, and simplifies the content and length of the interactive instructions or simplifies the instruction interaction process based on the entity capabilities and entity behaviors of each participating node.

[0068] In the embodiments provided in the present application, pragmatic behaviors such as instruction generation and sending, instruction reception and processing, and platform and payload behavior control are performed according to the ontology knowledge base and collaborative instruction set to promote drone A and unmanned vehicle B to collaboratively perform tasks.

[0069] The collaborative driving engine of UAV A periodically sends position report instructions according to the knowledge in the ontology knowledge base, with a period of 1 second. The length of each field in the instruction content adopts the default length of the field with instruction type 01 in the collaborative instruction set. Similarly, unmanned vehicle B periodically sends position report instructions with a period of 5 seconds, and each field in the instruction format adopts the default length.

[0070] When drone A is numbered 1001 and its latitude and longitude altitude is (longitude, latitude, altitude) = (128.24, 35.45, 350), the position report command sent by drone A is 0x000E0001FFFF03E90003E95B31612A326AF37C0000015E (hexadecimal);

[0071] When the number of unmanned vehicle B is 1002 and the longitude and latitude high position is (longitude, latitude, altitude) = (127.38, 34.54, 0), the position report command sent by unmanned vehicle B is 0x000E0001FFFF03EA0003EA5A94D242311FA156 (hexadecimal).

[0072] After unmanned vehicle B and drone A receive each other's position report instructions, their collaborative drive engines decode them according to the default instruction format of instruction type 01, and obtain drone A's number 1001 and position (longitude, latitude, altitude) = (128.24, 35.45, 350), and unmanned vehicle B's number 1002 and position (127.38, 34.54, 0).

[0073] During the mission execution process, the pragmatic behaviors involved by UAV A and UAV B mainly include starting target identification, starting electromagnetic interference, and starting fire strikes.

[0074] Since UAV A and UAV B are equal roles, it is necessary to designate UAV A or UAV B as the decision-making platform in advance, or during the mission, UAV A and UAV B adopt a certain negotiation process to determine the decision-making platform in this mission.

[0075] For example, UAV A is designated as the decision-making platform, and control commands are issued at the appropriate time. In this example, when UAV A takes off, target recognition can be started. At this time, information interaction occurs within the UAV A platform, and does not involve information interaction between UAV A and unmanned vehicle B.

[0076] When UAV A identifies target D, cooperative driving engine A generates a target report command and sends it periodically with a cycle of 10 seconds. Each field in the command format uses the default length.

[0077] If the target D is between 10 km and 20 km away from the unmanned vehicle B, the drone A sends a coordinated control instruction to the unmanned vehicle B, and the control command is electromagnetic interference. If the target D is less than 10 km away from the unmanned vehicle B, the drone A sends a coordinated control instruction to the unmanned vehicle B for a fire strike.

[0078] The target reporting instructions and collaborative control instructions sent by UAV A all use the default instruction format of the corresponding type in the collaborative instruction set. The instruction interaction and behavior control process during the mission execution is as follows: Figure 6 As shown, Figure 6 A flowchart of command interaction and behavior control in a method for building a pragmatic interoperability model supporting edge intelligent collaboration provided in an embodiment of the present application, through which drone A and unmanned vehicle B can collaboratively complete target recognition, electromagnetic interference and fire strike on target D.

[0079] In the above-mentioned process of identifying, interfering with and striking the target D, the collaborative driving engines A and B autonomously learn the mission intent (not necessarily the complete mission intent) through the interactive command content, that is, the drone A and the unmanned vehicle B should collaborate to complete target identification, electromagnetic interference and fire strike on targets of artillery type, thereby shortening the command length and simplifying the command interaction process.

[0080] Step S108: Based on the task collaboration driving engine, the instruction features of the interaction instructions corresponding to each device participating in the collaborative task are processed to generate new instructions, wherein the instruction features include the field content and field length carried in the interaction instructions, and / or the instruction interaction process.

[0081] In an embodiment of the present application, the task collaboration driving engine is based on which the instruction features of the interactive instructions corresponding to each device participating in the collaborative task are processed to generate new instructions, including: based on the task collaboration driving engine, the instruction interaction situations corresponding to each device participating in the collaborative task are understood and learned to determine the instruction features of the interactive instructions; according to the entity features corresponding to each device participating in the task collaboration, the instruction features corresponding to the interactive instructions are processed to generate a first new instruction, wherein the first new instruction is an instruction obtained by simplifying the field content and field length corresponding to the instruction; according to the entity features corresponding to each device participating in the task collaboration, the instruction interaction process corresponding to the interactive instruction is processed to generate a second new instruction, wherein the second new instruction is an instruction obtained by simplifying the instruction interaction process.

[0082] In the embodiment provided by the present application, for the collaborative driving engine A, it can be obtained from the ontology knowledge base that the satellite navigation positioning accuracy of the drone A is 10 meters, and the positioning accuracy of the drone A for the target D is 30 meters. Therefore, the lengths of the longitude, latitude, and altitude fields in the position report instruction can be shortened to 22 bits, 21 bits, and 10 bits, respectively, and the corresponding position accuracy is about 9.5 meters (the longitude range is -180° to 180°, the latitude range is -90° to 90°, and the altitude range is 0 to 10,000 meters); the target report can be The lengths of the longitude, latitude, and altitude fields in the target reporting instruction are shortened to 21 bits, 20 bits, and 9 bits, respectively, and the corresponding position accuracy is about 19.1 meters (the longitude, latitude, and altitude ranges are the same as above); considering that the electromagnetic interference range of the unmanned vehicle B is 1000 meters and the fire strike damage range is 500 meters, the lengths of the longitude, latitude, and altitude fields in the target reporting instruction can be further shortened to 17 bits, 16 bits, and 5 bits, respectively, and the corresponding position accuracy is about 312.5 meters (the longitude, latitude, and altitude ranges are the same as above).

[0083] Therefore, the collaborative driving engine A can infer and generate a new reduced instruction set such as Figure 7 As shown, Figure 7 A schematic diagram of a simplified instruction set in a method for constructing a pragmatic interoperability model that supports edge intelligence collaboration provided in an embodiment of the present application.

[0084] For the collaborative driving engine B, it can be obtained by combining the task intent and the ontology knowledge base that the unmanned vehicle B is responsible for implementing electromagnetic interference and fire strikes on targets of artillery type in the collaborative task. Therefore, once the location information of the corresponding target is obtained, the collaborative driving engine B can autonomously start electromagnetic interference or fire strikes according to its own electromagnetic interference range and fire strike range, without having to implement the corresponding behavior after receiving a collaborative control instruction with an electromagnetic interference control command or a collaborative control instruction with a fire strike control command. The collaborative driving engine B infers and generates a new simplified task flow or instruction interaction flow such as Figure 8 As shown, Figure 8 A schematic diagram of a simplified task process in a method for constructing a pragmatic interoperability model that supports edge intelligence collaboration provided in an embodiment of the present application.

[0085] Step S110: Based on the new instruction, control each device to collaboratively execute the target task.

[0086] In actual applications, each system or subsystem uses a streamlined new instruction format or a simplified new instruction interaction process to perform tasks. The collaborative driving engines of other members infer and decode the instruction format of the received instructions based on the collaborative knowledge base and the task collaborative instruction set, and record and store the new instruction format for use in subsequent task processes. The new instruction format and new interaction process can also be iteratively optimized in subsequent task processes.

[0087] In the embodiment provided in the present application, instruction sending and receiving are processed according to the new reduced instruction set. Figure 7 The simplified length 1 in the collaborative drive engine sends the position report instruction. At this time, the collaborative drive engine B cannot decode correctly according to the default length. It can be inferred that the collaborative drive engine A uses a new instruction format to send instructions. At this time, logical reasoning can be used to obtain the position report instruction format in the collaborative drive engine A. The fuzzy matching method can also be used to match the optimal length field by field based on the field order, field meaning and typical value range of the position report instruction in the collaborative instruction set, combined with the previous longitude and latitude high position information of the drone A, so as to infer the new instruction format used. Similarly, other instructions, such as the new target report instruction format, can also be inferred by the same method.

[0088] In another embodiment of the present application, when there is a new collaborative task or an existing collaborative task is optimized, the task collaboration knowledge base, the task collaboration instruction set and the task collaboration driving engine can be iteratively updated simultaneously or separately, including but not limited to adding new entities or relationships to the collaborative ontology knowledge base, adding new interactive data formats to the task collaboration instruction set, adding new instruction optimization algorithms to the task collaboration driving engine, etc., to enhance the pragmatic interoperability of heterogeneous information systems.

[0089] In the embodiment provided in the present application, the task is continued according to the new command interaction process. Drone A identifies a new target E, and unmanned vehicle B is subject to electromagnetic interference from target E. Its wireless link with drone A is unstable, and communication is intermittent. At this time, according to the previous task process, unmanned vehicle B may fail the task due to failure to transmit collaborative control instructions. However, since drone A periodically sends target report instructions for target E, as long as unmanned vehicle B receives a target report instruction once, collaborative drive engine B can autonomously initiate electromagnetic interference and fire strikes at the appropriate location, reducing command interaction time and improving mission success rate. Fig. 9 As shown, Fig. 9 A schematic diagram of the new instruction interaction process in a method for constructing a pragmatic interoperability model that supports edge intelligence collaboration provided in an embodiment of the present application.

[0090] By constructing an integrated pragmatic interoperability model covering an ontology knowledge base, a formatted instruction set, and a collaborative driving engine, the pragmatic interoperability mode in which data interaction and data application are separated in heterogeneous information systems is transformed into a joint grammatical-semantic pragmatic interoperability mode that integrates data interaction, understanding, and application. This makes the information interaction content more refined and the information interaction process more concise during task collaboration, thereby improving the efficiency of intelligent collaborative control.

[0091] The pragmatic interoperability model construction method provided in the embodiment of the present application constructs an integrated pragmatic interoperability model covering an ontology knowledge base, a formatting instruction set and an algorithm-driven engine. While sharing data transmission, each subsystem can understand the semantics of the interactive data and its processing rules and processes in the collaborative task, and optimize the interactive data content length and data interaction process based on the task collaborative instruction set and the task collaborative driving engine, thereby streamlining the content or (and) simplifying the process without significantly reducing the effectiveness of the collaborative task, thereby improving the task collaboration efficiency and success rate.

[0092] Corresponding to the above method embodiment, this specification also provides an embodiment of a pragmatic interoperability model construction device supporting edge intelligence collaboration, Fig.10 A schematic diagram of a structure of a pragmatic interoperability model construction device supporting edge intelligence collaboration provided in an embodiment of the present application. Fig.10 As shown, the device comprises:

[0093] The first creation module 1002 is configured to create a task collaboration knowledge base and a task collaboration instruction set based on the target task when at least two devices collaborate to perform a target task, and share the task collaboration knowledge base and the task collaboration instruction set with each device, wherein the task collaboration knowledge base carries at least two ontology knowledge bases including entity features corresponding to each device and entity relationships between each device; the task collaboration instruction set is composed based on a header and instruction content, and the content of each field in the instruction content is determined based on the field content in the interactive data format specified by the metadata in the task collaboration knowledge base, and the length of the field content is adjustable;

[0094] The second creation module 1004 is configured to perform pragmatic behavior control on each device based on the task collaboration knowledge base and the task collaboration instruction set, and create a task collaboration driving engine for each device based on the pragmatic behavior;

[0095] The determination module 1006 is configured to determine the interaction instructions corresponding to each device based on the task coordination driving engine and according to the instruction sending rules and instruction receiving rules carried in the task coordination instruction set;

[0096] The generating module 1008 is configured to process the instruction features of the interaction instructions corresponding to each device participating in the collaborative task based on the task collaborative driving engine, and generate a new instruction, wherein the instruction features include the field content and field length carried in the interaction instruction, and / or the instruction interaction process;

[0097] The execution module 1010 is configured to control each device to collaboratively execute the target task based on the new instruction.

[0098] In an optional embodiment, the first creation module 1002 is further configured to:

[0099] Using the first device as a decision-making platform, based on the target task, creating a task collaboration knowledge base, wherein the task collaboration knowledge base includes a first ontology knowledge base and a second ontology knowledge base;

[0100] triggering the first device to execute a first position reporting instruction, a target identification instruction, and a target reporting instruction according to the first ontology knowledge base;

[0101] According to the second ontology knowledge base, the second device is triggered to execute the second position reporting instruction, the electromagnetic interference instruction and the fire strike instruction.

[0102] In an optional embodiment, the first creation module 1002 is further configured to:

[0103] Using the first device as a decision-making platform, based on the target task, creating a task coordination instruction set, wherein the task coordination instruction set includes a first task coordination instruction set and a second task coordination instruction set;

[0104] According to the first task coordination instruction set, control the first device to periodically send first position report information and control command information to the second device;

[0105] According to the second task coordination instruction set, the second device is controlled to periodically send second position report information and control command response information to the first device.

[0106] In an optional embodiment, the determining module 1006 is further configured to:

[0107] The first task collaborative driving engine corresponding to the first device generates a corresponding instruction according to the instruction sending rule, and sends the instruction to the second device, wherein the corresponding instruction carries the entity characteristics corresponding to the first device;

[0108] The second device processes the received corresponding instruction according to the instruction receiving rule and executes the corresponding instruction.

[0109] In an optional embodiment, the generating module 1008 is further configured to:

[0110] Based on the task collaboration driving engine, the command interaction situations corresponding to the devices participating in the collaborative task are understood and learned, and the command features of the interactive commands are determined;

[0111] Processing the instruction feature corresponding to the interactive instruction according to the entity features corresponding to each device participating in the task collaboration to generate a first new instruction, wherein the first new instruction is an instruction obtained by simplifying the field content and field length corresponding to the instruction;

[0112] The instruction interaction process corresponding to the interactive instruction is processed according to entity characteristics corresponding to each device participating in task collaboration to generate a second new instruction, wherein the second new instruction is an instruction obtained by simplifying the instruction interaction process.

[0113] In an optional embodiment, the first creation module 1002 is further configured to:

[0114] In the case where at least two devices cooperate to perform a target task, the first device is used as a monitoring and control platform to monitor interaction information during the process of each device cooperatively performing the target task;

[0115] Based on the interactive information, an ontology knowledge base and a task coordination instruction set are respectively created in each device, and the ontology knowledge base and the task coordination instruction set are shared with each device participating in the coordination task.

[0116] In an optional embodiment, the first creation module 1002 is further configured to:

[0117] In the case where at least two devices cooperate to perform a target task, monitoring the interaction information of each device in the process of cooperatively performing the target task through the monitoring and control platform;

[0118] Based on the interaction information, a task collaboration knowledge base and a task collaboration instruction set are created in the monitoring and control platform, and the task collaboration knowledge base and the task collaboration instruction set are shared with each device participating in the collaborative task.

[0119] By constructing an integrated pragmatic interoperability model covering an ontology knowledge base, a formatted instruction set, and a collaborative driving engine, the pragmatic interoperability mode in which data interaction and data application are separated in heterogeneous information systems is transformed into a joint grammatical-semantic pragmatic interoperability mode that integrates data interaction, understanding, and application. This makes the information interaction content more refined and the information interaction process more concise during task collaboration, thereby improving the efficiency of intelligent collaborative control.

[0120] Fig.11 This is a block diagram of a computing device provided in an embodiment of the present application. The components of the computing device 1100 include but are not limited to a memory 1110 and a processor 1120. The processor 1120 is connected to the memory 1110 via a bus 1130, and the database 1150 is used to store data.

[0121] The computing device 1100 also includes an access device 1140, which enables the computing device 1100 to communicate via one or more networks 1160. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 1140 may include one or more of any type of network interface (e.g., a network interface card (NIC)) of wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a world-wide interoperability for microwave access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, and a near field communication (NFC).

[0122] In one embodiment of the present specification, the above components of the computing device 1100 and Fig.11 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Fig.11 The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0123] The computing device 1100 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 1100 may also be a mobile or stationary server.

[0124] Among them, the processor 1120 is used to execute the following computer-executable instructions, which, when executed by the processor, implement the steps of the above-mentioned method for constructing a pragmatic interoperability model that supports edge intelligent collaboration.

[0125] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computing device embodiment, since it is basically similar to the embodiment of the pragmatic interoperability model construction method supporting edge intelligent collaboration, the description is relatively simple, and the relevant parts can be referred to the partial description of the embodiment of the pragmatic interoperability model construction method supporting edge intelligent collaboration.

[0126] An embodiment of the present specification also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for constructing a pragmatic interoperability model that supports edge intelligent collaboration.

[0127] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computer-readable storage medium embodiment, since it is basically similar to the embodiment of the pragmatic interoperability model construction method supporting edge intelligent collaboration, the description is relatively simple, and the relevant parts can be referred to the partial description of the embodiment of the pragmatic interoperability model construction method supporting edge intelligent collaboration.

[0128] An embodiment of the present specification also provides a computer program, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned method for constructing a pragmatic interoperability model that supports edge intelligent collaboration.

[0129] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computer program embodiment, since it is basically similar to the embodiment of the method for building a pragmatic interoperability model supporting edge intelligence collaboration, the description is relatively simple, and the relevant parts can be referred to the partial description of the embodiment of the method for building a pragmatic interoperability model supporting edge intelligence collaboration.

[0130] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0131] The computer instructions include computer program codes, which may be in source code form, object code form, executable files or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0132] It should be noted that the above is a description of a specific embodiment of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of the present specification.

[0133] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0134] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The optional embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that technicians in the relevant technical field can well understand and use this specification. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A method for constructing a pragmatic interoperability model supporting edge intelligence collaboration, characterized in that: include: In the case where at least two devices collaborate to perform a target task, a task collaboration knowledge base and a task collaboration instruction set are created based on the target task, and the task collaboration knowledge base and the task collaboration instruction set are shared with each device, wherein the task collaboration knowledge base carries at least two ontology knowledge bases containing entity features corresponding to each device and entity relationships between each device; the task collaboration instruction set is composed based on a header and instruction content, and the content of each field in the instruction content is determined based on the field content in the interactive data format specified by the metadata in the task collaboration knowledge base, and the length of the field content is adjustable; Based on the task collaboration knowledge base and the task collaboration instruction set, each device is managed and controlled in terms of pragmatic behavior, and based on the pragmatic behavior, a task collaboration driving engine is created for each device; Based on the task coordination driving engine, determining the interaction instructions corresponding to each device according to the instruction sending rules and instruction receiving rules carried in the task coordination instruction set; Based on the task collaboration driving engine, the instruction features of the interaction instructions corresponding to each device participating in the collaborative task are processed to generate new instructions, wherein the instruction features include field content and field length carried in the interaction instruction, and / or the instruction interaction process; Based on the new instructions, each device is controlled to collaboratively execute the target task.

2. The method according to claim 1, characterized in that The step of creating a task collaboration knowledge base based on the target task includes: Using the first device as a decision-making platform, based on the target task, creating a task collaboration knowledge base, wherein the task collaboration knowledge base includes a first ontology knowledge base and a second ontology knowledge base; triggering the first device to execute a first position reporting instruction, a target identification instruction, and a target reporting instruction according to the first ontology knowledge base; According to the second ontology knowledge base, the second device is triggered to execute the second position reporting instruction, the electromagnetic interference instruction and the fire strike instruction.

3. The method according to claim 1, characterized in that The step of creating a task coordination instruction set based on the target task includes: Using the first device as a decision-making platform, based on the target task, creating a task coordination instruction set, wherein the task coordination instruction set includes a first task coordination instruction set and a second task coordination instruction set; According to the first task coordination instruction set, control the first device to periodically send first position report information and control command information to the second device; According to the second task coordination instruction set, the second device is controlled to periodically send second position report information and control command response information to the first device.

4. The method according to claim 1, characterized in that: The determining, based on the task coordination driving engine and according to the instruction sending rule and instruction receiving rule carried in the task coordination instruction set, the interaction instruction corresponding to each device includes: The first task collaborative driving engine corresponding to the first device generates a corresponding instruction according to the instruction sending rule, and sends the instruction to the second device, wherein the corresponding instruction carries the entity characteristics corresponding to the first device; The second device processes the received corresponding instruction according to the instruction receiving rule and executes the corresponding instruction.

5. The method according to claim 1, characterized in that The step of processing the instruction features of the interaction instructions corresponding to each device participating in the collaborative task based on the task collaborative driving engine to generate a new instruction includes: Based on the task collaboration driving engine, the command interaction situations corresponding to the devices participating in the collaborative task are understood and learned, and the command features of the interactive commands are determined; Processing the instruction feature corresponding to the interactive instruction according to the entity features corresponding to each device participating in the task collaboration to generate a first new instruction, wherein the first new instruction is an instruction obtained by simplifying the field content and field length corresponding to the instruction; The instruction interaction process corresponding to the interactive instruction is processed according to entity characteristics corresponding to each device participating in task collaboration to generate a second new instruction, wherein the second new instruction is an instruction obtained by simplifying the instruction interaction process.

6. The method according to claim 1, characterized in that In the case where at least two devices cooperate to perform a target task, creating a task cooperation knowledge base and a task cooperation instruction set based on the target task includes: In the case where at least two devices cooperate to perform a target task, the first device is used as a monitoring and control platform to monitor interaction information during the process of each device cooperatively performing the target task; Based on the interactive information, an ontology knowledge base and a task coordination instruction set are respectively created in each device, and the ontology knowledge base and the task coordination instruction set are shared with each device participating in the coordination task.

7. The method according to claim 1, characterized in that In the case where at least two devices cooperate to perform a target task, creating a task cooperation knowledge base and a task cooperation instruction set based on the target task includes: In the case where at least two devices cooperate to perform a target task, monitoring the interaction information of each device in the process of cooperatively performing the target task through the monitoring and control platform; Based on the interaction information, a task collaboration knowledge base and a task collaboration instruction set are created in the monitoring and control platform, and the task collaboration knowledge base and the task collaboration instruction set are shared with each device participating in the collaborative task.

8. A pragmatic interoperability model construction device supporting edge intelligence collaboration, characterized in that: include: A first creation module is configured to create a task collaboration knowledge base and a task collaboration instruction set based on the target task when at least two devices collaborate to perform a target task, and share the task collaboration knowledge base and the task collaboration instruction set with each device, wherein the task collaboration knowledge base carries at least two ontology knowledge bases including entity features corresponding to each device and entity relationships between each device; the task collaboration instruction set is composed based on a header and instruction content, and the content of each field in the instruction content is determined based on the field content in the interactive data format specified by the metadata in the task collaboration knowledge base, and the length of the field content is adjustable; A second creation module is configured to perform pragmatic behavior control on each device based on the task collaboration knowledge base and the task collaboration instruction set, and create a task collaboration driving engine for each device based on the pragmatic behavior; A determination module is configured to determine the interaction instructions corresponding to each device based on the task coordination driving engine and in accordance with the instruction sending rules and instruction receiving rules carried in the task coordination instruction set; A generation module is configured to process the instruction features of the interaction instructions corresponding to each device participating in the collaborative task based on the task collaboration driving engine to generate a new instruction, wherein the instruction features include field content and field length carried in the interaction instruction, and / or instruction interaction process; The execution module is configured to control each device to collaboratively execute the target task based on the new instruction.

9. A computer device, characterized in that: The computer device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the method according to any one of claims 1 to 7 when executed by the processor.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an implementation program for information transmission, and when the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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