Cross-domain collaborative intelligent management and control system and method

Abstract

The invention relates to the technical field of communication, and provides a cross-domain collaborative intelligent management and control system and method.The cross-domain collaborative intelligent management and control system comprises a distributed intelligent sensing layer, an intelligent processing center layer and an autonomous planning layer, and the distributed intelligent sensing layer, the intelligent processing center layer and the autonomous planning layer are in communication connection through a micro-service bus; when the distributed intelligent sensing layer receives an execution instruction, a node network in a target scene is constructed, and sea-land-air multi-domain data is collected through each node in the node network and pre-processed. And the intelligent processing center layer identifies a target object according to the sea-land-air multi-domain data, generates a scene situation corresponding to a target scene and obtains schedulable resources. And the autonomous planning layer generates a task execution plan of the target scene according to the scene situation and the schedulable resources, generates the task execution control instruction according to the task execution plan, and controls each task execution target object to execute a task on at least one executed task target object. Therefore, efficient and accurate intelligent management and control can be realized.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to an intelligent management and control system and method for cross-domain collaboration. Background Technology

[0002] With the rapid development of network information and communication technologies, traditional control systems face numerous challenges. Currently, traditional control systems rely excessively on manual operation and fixed rules, failing to dynamically and flexibly adapt to complex scenario requirements. For example, in complex combat environments involving missions across land, sea, and air domains, efficient collaborative control and intelligent decision-making are crucial. However, traditional control systems cannot efficiently integrate multi-source data collected from land, sea, and air domains by detection equipment such as drones, unmanned vehicles, and communication countermeasures equipment, resulting in an inaccurate perception of the current environmental situation. Furthermore, due to their over-reliance on manual operation and fixed rules, they cannot flexibly match appropriate control methods to the current mission, achieving optimal resource allocation. Therefore, traditional control systems cannot achieve efficient and accurate intelligent control. Summary of the Invention

[0003] Therefore, it is necessary to provide a cross-domain collaborative intelligent management and control system and method to address the aforementioned technical issues.

[0004] In a first aspect, embodiments of the present invention provide a cross-domain collaborative intelligent management and control system, comprising: a distributed intelligent perception layer, an intelligent processing hub layer, and an autonomous planning layer, wherein the distributed intelligent perception layer, the intelligent processing hub layer, and the autonomous planning layer are connected via a microservice bus.

[0005] The distributed intelligent perception layer is used to construct a node network in the target scene when an execution instruction is received. The node network collects multi-domain data from land, sea and air through each node and performs data preprocessing. The target scene includes multiple pieces of equipment, and each piece of equipment corresponds one-to-one with each node. The execution instruction is used to instruct the formulation of task execution control instructions for the target scene.

[0006] The intelligent processing central layer is used to identify target objects based on the multi-domain data of land, sea and air, generate the scene situation corresponding to the target scene and obtain schedulable resources. The target objects include at least one target object for executing tasks and at least one target object for being executed tasks.

[0007] The autonomous planning layer is used to generate a task execution plan for the target scenario based on the scenario situation and schedulable resources, and to generate task execution control instructions based on the task execution plan. The task execution control instructions are used to control each task execution target object to execute a task on at least one of the task-being target objects.

[0008] In one embodiment, the execution instructions include task execution instructions and target scene determination instructions. The task execution instructions are used to instruct the formulation of task execution control instructions, and the target scene determination instructions are used to instruct the acquisition of target scene parameters.

[0009] In one embodiment, it further includes: a core library, which is communicatively connected to the distributed intelligent perception layer, the intelligent processing hub layer, and the autonomous planning layer. The core library includes: an algorithm library, a database, an equipment library, a map library, a scene library, and a rule library.

[0010] In one embodiment, the distributed intelligent sensing layer includes: an equipment access unit, a node networking unit, and a data preprocessing unit;

[0011] The equipment access unit is used to execute control commands for the task, establish communication connections between each piece of equipment and the intelligent management and control system based on the connection information of each piece of equipment in the equipment library, determine commands for the target scenario, determine and configure the target scenario parameters in the scenario library;

[0012] The node networking unit is used to construct the node network based on the self-organizing networking protocol for multiple devices that have established communication connections, and to collect multi-domain data of sea, land and air through each node. The multi-domain data of sea, land and air includes: sea data, land data and airspace data.

[0013] The data preprocessing unit is used to clean the noise in the multi-domain data of land, sea and air, and to perform spatiotemporal alignment processing on the cleaned data according to the actual target objects stored in the map library.

[0014] In one embodiment, the node networking unit is further configured to store multi-domain data (sea, land, and air) into the database.

[0015] In one embodiment, the intelligent processing central layer includes: a target object identification unit, a scene situation generation unit, and a schedulable resource acquisition unit;

[0016] The target object recognition unit is used to schedule multiple target object recognition models stored in the algorithm library, identify each target object based on the multi-domain data of land, sea and air, and locate and track each target object. The target object also includes at least one obstacle target object.

[0017] The scene situation generation unit is used to determine the priority execution level of each of the at least one execution task target object, at least one execution task target object, and at least one obstacle target object, and generate the scene situation based on the priority execution level. The scene situation is used to indicate the state and movement trajectory of at least one execution task target object, at least one execution task target object, and at least one obstacle target object.

[0018] The schedulable resource acquisition unit is used to acquire the schedulable resources of the target object for the execution task.

[0019] In one embodiment, the autonomous planning layer includes: a task execution planning generation unit and a control instruction generation unit;

[0020] The task execution plan generation unit is used to generate a task execution plan for the target scenario based on the scenario situation and schedulable resources, and store the task execution plan in the database;

[0021] The control instruction generation unit is used to generate the task execution control instruction according to the task execution plan, send the task execution control instruction to at least one target object through the microservice bus, and control at least one task execution target object to execute a task on at least one of the task-being target objects.

[0022] In one embodiment, the autonomous planning layer further includes a dynamic visualization unit; the dynamic visualization unit is used to receive the task execution plan and visualize it.

[0023] In one embodiment, it further includes: an optimization module, which includes: a historical data acquisition unit and an analysis and verification unit;

[0024] The historical data acquisition unit is used to acquire historical multi-domain data on land, sea and air, as well as corresponding task execution plans, from the database.

[0025] The analysis and verification unit is used to analyze and verify historical multi-domain data (sea, land, and air) and corresponding task execution plans, and generate optimization instructions. The optimization instructions are used to instruct the generation of the next task execution plan.

[0026] Secondly, embodiments of the present invention provide an intelligent management and control method for cross-domain collaboration, comprising:

[0027] Upon receiving an execution instruction, a node network in the target scenario is constructed. Data from multiple domains (land, sea, and air) is collected and preprocessed through each node in the node network. The target scenario includes multiple pieces of equipment, each of which corresponds one-to-one with each node. The execution instruction is used to instruct the formulation of task execution control instructions for the target scenario.

[0028] Based on the multi-domain data of land, sea and air, target objects are identified, a scene situation corresponding to the target scene is generated, and schedulable resources are obtained. The target objects include at least one target object for executing a task and at least one target object for being executed a task.

[0029] Based on the scenario situation and schedulable resources, a task execution plan for the target scenario is generated. Based on the task execution plan, a task execution control instruction is generated. The task execution control instruction is used to control each task execution target object to execute a task on at least one of the task-being target objects.

[0030] The technical solution provided by the embodiments of the present invention has the following advantages compared with the prior art:

[0031] This invention provides a cross-domain collaborative intelligent management and control system, comprising a distributed intelligent perception layer, an intelligent processing hub layer, and an autonomous planning layer. These layers are interconnected via a microservice bus. The distributed intelligent perception layer controls multiple devices across multiple domains (land, sea, and air) corresponding to the target scenario to collect multi-domain data. The intelligent processing hub layer integrates and analyzes this multi-domain data to determine the current scenario's situation and available resources. Finally, the autonomous planning layer formulates a task execution plan based on the current scenario's situation and available resources, generating task execution control commands to control each task-executing target object to execute tasks on at least one target object. This effectively integrates multi-domain data for the current target scenario, accurately perceives the current environmental situation, and avoids the over-reliance on manual operation and fixed rules in existing technologies, which cannot flexibly match suitable management and control methods. It maximizes resource allocation, thereby achieving efficient and accurate intelligent management and control. Attached Figure Description

[0032] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 A schematic diagram of a cross-domain collaborative intelligent management and control system provided in an embodiment of the present invention;

[0035] Figure 2 This is a flowchart illustrating an intelligent management and control method for cross-domain collaboration provided in an embodiment of the present invention. Detailed Implementation

[0036] To better understand the above-mentioned objectives, features, and advantages of the present invention, the solutions of the present invention will be further described below. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other.

[0037] Many specific details are set forth in the following description in order to provide a full understanding of the invention, but the invention may also be practiced in other ways different from those described herein; obviously, the embodiments in the specification are only some embodiments of the invention, and not all embodiments.

[0038] With the rapid development of network information and communication technologies, traditional control systems face numerous challenges. These systems rely excessively on manual operation and fixed rules, making them unable to dynamically and flexibly adapt to complex scenarios. For example, in complex combat environments involving missions across land, sea, and air domains, efficient collaborative control and intelligent decision-making are crucial. However, traditional control systems struggle to efficiently integrate multi-source data collected from land, sea, and air domains by detection equipment such as drones, unmanned vehicles, and communication countermeasures equipment, resulting in inaccurate environmental situational awareness. Furthermore, their over-reliance on manual operation and fixed rules prevents them from flexibly matching appropriate control methods to the specific mission and achieving optimal resource allocation. Therefore, traditional control systems fail to achieve efficient and accurate intelligent control.

[0039] Therefore, this invention provides a cross-domain collaborative intelligent management and control system, including a distributed intelligent perception layer, an intelligent processing hub layer, and an autonomous planning layer. The distributed intelligent perception layer, intelligent processing hub layer, and autonomous planning layer are connected via a microservice bus. The distributed intelligent perception layer controls multiple devices in the sea, land, and air domains corresponding to the target scenario to collect multi-domain data. The intelligent processing hub layer integrates and analyzes the multi-domain data to determine the current target scenario's situation and available resources. Finally, the autonomous planning layer formulates a task execution plan based on the current target scenario's situation and available resources, thereby generating task execution control instructions to control each task execution target object to execute tasks on at least one target object. This effectively integrates multi-domain data for the current target scenario, accurately perceives the current environmental situation, and avoids the excessive reliance on manual operation and fixed rules in existing technologies, which cannot flexibly match suitable management and control methods. It maximizes resource allocation, thereby achieving efficient and accurate intelligent management and control.

[0040] In one embodiment, such as Figure 1 As shown, Figure 1 A schematic diagram of a cross-domain collaborative intelligent management and control system provided in an embodiment of the present invention is shown below. Figure 1 As shown, the cross-domain collaborative intelligent management and control system includes: a distributed intelligent perception layer 11, an intelligent processing hub layer 12, and an autonomous planning layer 13. The distributed intelligent perception layer 11, the intelligent processing hub layer 12, and the autonomous planning layer 13 are connected through a microservice bus. The microservice bus, as the "nervous system" of the intelligent management and control system, is responsible for message passing, service invocation, and data flow between the layers.

[0041] The distributed intelligent perception layer 11 is used to construct a node network in the target scene when an execution instruction is received, and to collect multi-domain data on land, sea and air through each node in the node network and perform data preprocessing.

[0042] In this context, the target scenario refers to the environment in which the current mission is being performed, such as a combat scenario. A target scenario includes multiple pieces of equipment, including data-collecting detection equipment, mission-executing target equipment, or mission-being target equipment. For example, in a combat scenario, the mission-executing target equipment could be equipment controlled by our side performing a combat mission, and the mission-being target could be equipment being used to execute a combat mission. However, this is not a limitation; the present invention is not specifically restrictive, and those skilled in the art can set it according to the actual situation.

[0043] Each of the aforementioned equipment and nodes in the node network corresponds one-to-one, and the execution instructions are used to instruct the task execution control commands for the specified target scenario.

[0044] The aforementioned multi-domain data encompasses sea area data, land area data, and air area data. For example, air area data may include obstacle information data, personnel activity data, and electromagnetic signal data collected by drones. Land area data may include ambient temperature data, humidity data, sound data, and status data of the target object performing the task collected by ground sensors. However, this invention is not limited to these categories, and those skilled in the art can set the data according to the actual situation.

[0045] The intelligent processing central layer 12 is used to identify target objects based on multi-domain data from land, sea and air, generate scene situation corresponding to the target scene, and acquire schedulable resources.

[0046] The target objects include at least one task-executing target object and at least one task-being target object. The scenario situation is used to indicate the state and movement trajectory of at least one task-executing target object, at least one task-being target object, and at least one obstacle target object.

[0047] Scheduled resources refer to resources that can be scheduled and used in the current target scenario. For example, following the above embodiments, for combat scenarios, scheduled resources may be, for example, scheduled combat equipment and personnel, but are not limited thereto. This invention does not specifically limit the scope, and those skilled in the art can set it according to the actual situation.

[0048] The autonomous planning layer 13 is used to generate task execution plans for the target scenario based on the scenario situation and schedulable resources, and to generate task execution control instructions based on the task execution plans.

[0049] Among them, the task execution control instructions are used to control each task execution target object to execute a task on at least one task target object.

[0050] Specifically, when the intelligent control system receives an execution command, it constructs a node network in the target scene through the distributed intelligent perception layer 11. Each node in the node network corresponds one-to-one with multiple pieces of equipment in the target scene. After the node network is constructed, it collects multi-domain data from land, sea, and air through each node and performs data preprocessing. The preprocessed data is then sent to the intelligent processing hub layer 12. The intelligent processing hub layer 12 identifies target objects based on the received multi-domain data and generates a scene situation corresponding to the target scene based on the identified multiple target objects, as well as acquiring schedulable resources. It should be noted that the target objects include at least one execution task target object and at least one task-being target object. The intelligent processing hub layer 12 sends the scene situation and schedulable resources to the autonomous planning layer 13. The autonomous planning layer 13 generates a task execution plan corresponding to the target scene based on the scene situation and schedulable resources, and then generates task execution control commands based on the task execution plan. The generated task execution control commands are sent to each execution task target object in the target scene, thereby controlling each execution task target object to perform task operations on one or more task-being target objects.

[0051] In summary, the cross-domain collaborative intelligent management and control system provided in this embodiment includes a distributed intelligent perception layer, an intelligent processing hub layer, and an autonomous planning layer. These three layers are connected via a microservice bus. The distributed intelligent perception layer controls multiple devices across multiple domains (land, sea, and air) corresponding to the target scenario to collect multi-domain data. The intelligent processing hub layer integrates and analyzes this multi-domain data to determine the current situation and available resources of the target scenario. Finally, the autonomous planning layer formulates a task execution plan based on the current situation and available resources, generating task execution control commands to control each task execution target to perform tasks on at least one target object. This effectively integrates multi-domain data for the current target scenario, accurately perceives the current environmental situation, and avoids the over-reliance on manual operation and fixed rules in existing technologies, which cannot flexibly match suitable management and control methods. It maximizes resource allocation, thereby achieving efficient and accurate intelligent management and control.

[0052] Optionally, based on the above embodiments, in some embodiments of the present invention, the intelligent control system further includes: a core library, which is communicatively connected to the distributed intelligent perception layer, the intelligent processing central layer, and the autonomous planning layer. The core library is used to store various types of data, models, and algorithms, and includes: an algorithm library, a database, an equipment library, a map library, a scene library, and a rule library.

[0053] The algorithm library stores core algorithm packages for target recognition and performance evaluation, such as multi-target recognition models, localization models, and interference resource optimization allocation models, and supports dynamic invocation.

[0054] The database is used to store preprocessed multi-domain data from land, sea, and air, such as spectrum data and environmental data.

[0055] The equipment library is used to store connection information of multiple equipment in the target scenario that are connected to the system, such as interface protocols and status information.

[0056] The map library is used to provide digital map information and geospatial benchmarks, support geographic environment data queries, and enable spatiotemporal alignment of multi-domain data (land, sea, and air).

[0057] The scene library is used to store target scene parameters, such as the target scene configuration.

[0058] The rule base is used to store fixed execution task rules, performance evaluation criteria, security constraints and other logical rules.

[0059] Thus, the cross-domain collaborative intelligent management and control system provided by this invention, by setting up a core library including an algorithm library, a database, an equipment library, a map library, a scene library, and a rule library, can achieve data sharing during the subsequent acquisition of task execution plans, thereby improving the accuracy of intelligent management and control.

[0060] Optionally, based on the above embodiments, in some embodiments of the present invention, the distributed intelligent sensing layer 11 includes: an equipment access unit, a node networking unit, and a data preprocessing unit.

[0061] The equipment access unit executes control commands for the mission, establishes communication connections between each piece of equipment and the intelligent management and control system based on the connection information of each piece of equipment in the equipment library, and determines the commands for the target scenario and the target scenario parameters in the scenario library.

[0062] The connection information can include interface protocols and status information.

[0063] The node networking unit constructs a node network based on a self-organizing networking protocol for multiple pieces of equipment that establish communication connections, and collects multi-domain data from land, sea and air through each node.

[0064] The data preprocessing unit cleans the noise in the multi-domain data of land, sea and air, and performs spatiotemporal alignment on the cleaned data based on the actual target objects stored in the map library.

[0065] Specifically, when the distributed intelligent perception layer receives execution commands input by the user, the equipment access unit responds to the task execution control commands included in the execution commands, obtains the connection information of each piece of equipment from the equipment library, such as interface protocols and status information, and establishes communication connections between each piece of equipment and the intelligent management and control system through the microservice bus based on the connection information. It also responds to the target scene determination commands included in the execution commands, determines the target scene parameters in the scene library, and configures them. The node networking unit, for multiple pieces of equipment that have established communication connections, constructs a node network based on a self-organizing networking protocol, controls each node to collect multi-domain data (sea, land, and air) from the target scene, and transmits it to the data preprocessing unit. The data preprocessing unit cleans the noise in the received multi-domain data and performs spatiotemporal alignment processing on the cleaned data based on the actual target objects stored in the map library.

[0066] For example, the above-mentioned spatiotemporal alignment processing of the cleaned data based on the actual target objects stored in the map library can be performed as follows: Continuing from the above embodiments, in a combat scenario, for the same collected object such as a school, a drone is used to collect the corresponding school data in the airspace, and a ground sensor is used to collect the corresponding school data in the land area. In order to realize the school data in the airspace and the school data in the land area, the actual data corresponding to the school can be obtained from the map library, and the alignment processing can be achieved based on the actual data. However, it is not limited to this. The present invention is not specifically limited, and those skilled in the art can set it according to the actual situation.

[0067] It should be noted that after acquiring multi-domain data from land, sea, and air, the multi-domain data is stored in the database.

[0068] Optionally, based on the above embodiments, in some embodiments of the present invention, the intelligent processing central layer 12 includes: a target object recognition unit, a scene situation generation unit, and a schedulable resource acquisition unit;

[0069] The target object recognition unit is used to schedule multiple target object recognition models stored in the algorithm library, identify each target object based on multi-domain data of land, sea and air, and locate and track each target object.

[0070] The aforementioned target objects also include at least one obstacle target object, such as a building or a mountain, but are not limited thereto. This invention is not specifically limited, and those skilled in the art can set it according to the actual situation.

[0071] The scenario situation generation unit is used to determine the priority level of each target object for executing at least one task, at least one target object being executed, and at least one obstacle target object, and generate a scenario situation based on the priority level.

[0072] Here, priority level refers to the priority of the task execution target object when performing task operations on the task-being target object. For example, following the above embodiments, in a combat scenario, this priority can be determined based on the degree of harm caused by the task-being target object to the task-executing target object; the higher the degree, the higher the priority. However, it is not limited to this, and the present invention is not specifically limited; those skilled in the art can set it according to the actual situation.

[0073] The schedulable resource acquisition unit is used to acquire schedulable resources for the target object of the execution task.

[0074] Specifically, the target object recognition unit schedules multiple target object recognition models stored in the algorithm library, inputs multi-domain data from land, sea, and air into the corresponding target object recognition models, identifies each target object in the target scene, and locates and tracks each target object. The scene situation generation unit, for at least one executing task target object, at least one task-being target object, and at least one obstacle target object, first determines the priority execution level of each task-being target object, and generates a scene situation based on the priority execution level, indicating the state and trajectory of each executing task target object, task-being target object, and obstacle target object. Finally, the schedulable resource acquisition unit acquires the schedulable resources of the executing task target object and sends the scene situation and schedulable resources to the autonomous planning layer, enabling the autonomous planning layer to generate a task execution plan based on the scene situation and schedulable resources, achieving intelligent management and control.

[0075] Optionally, based on the above embodiments, in some embodiments of the present invention, the autonomous planning layer 13 includes: a task execution planning generation unit and a control instruction generation unit.

[0076] The task execution planning generation unit is used to generate a task execution plan for the target scenario based on the scenario situation and schedulable resources, and store the task execution plan in the database.

[0077] The control instruction generation unit is used to generate task execution control instructions according to the task execution plan, and send the task execution control instructions to at least one target object through the microservice bus to control at least one target object to execute a task on at least one target object.

[0078] Specifically, the task execution planning generation unit generates a task execution plan for the target scenario based on the received scenario situation and schedulable resources, stores the task execution plan in the database, and sends it to the control instruction generation unit. The control instruction generation unit generates task execution control instructions based on the received task execution plan, and sends the task execution control instructions to at least one target object through the microservice bus, so as to control at least one task execution target object to execute tasks on at least one task being executed target object.

[0079] Optionally, based on the above embodiments, in some embodiments of the present invention, the autonomous planning layer 13 further includes: a dynamic visualization unit; the dynamic visualization unit is used to receive the task execution plan and visualize it. This allows the user to observe the task execution plan in the current target scene in real time, improving the user experience.

[0080] Optionally, based on the above embodiments, in some embodiments of the present invention, reference continues to be made to... Figure 1 As shown, the intelligent control system also includes an optimization module 14, which includes a historical data acquisition unit and an analysis and verification unit.

[0081] The historical data acquisition unit is used to retrieve historical multi-domain data on land, sea, and air from the database, as well as the corresponding task execution plan.

[0082] The analysis and verification unit is used to analyze and verify historical multi-domain data (sea, land, and air) and corresponding task execution plans, and generate optimization instructions.

[0083] The optimization instruction is used to instruct the generation of an optimized task execution plan for the next task.

[0084] Specifically, the historical data acquisition unit retrieves historical multi-domain data (sea, land, and air) and corresponding task execution plans from the database and sends them to the analysis and verification unit. The analysis and verification unit analyzes and verifies the received historical multi-domain data and corresponding task execution plans, generates optimization instructions, and feeds these instructions back to ensure that the task execution plan can be optimized upon receiving the next execution instruction.

[0085] For example, the analysis and verification of historical multi-domain data (sea, land, and air) and corresponding task execution plans can be achieved through technologies such as the Practical Byzantine Fault Tolerance (PBFT) improved consensus algorithm, federated learning, Long Short-Term Memory (LSTM) anomaly detection network, and blockchain. For specific implementation methods, please refer to existing technologies, which will not be elaborated on here.

[0086] Thus, the embodiments provided by the present invention, by analyzing and verifying historical multi-domain data of land, sea and air, as well as the corresponding task execution plans, optimize the next task execution plan and further improve the accuracy of intelligent management and control.

[0087] In one embodiment, such as Figure 2The diagram illustrates a flowchart of a cross-domain collaborative intelligent management and control method, applied to the cross-domain collaborative intelligent management and control system described in the above embodiments, including:

[0088] S201, upon receiving the execution instruction, constructs a node network in the target scenario, collects multi-domain data from land, sea, and air through each node in the node network, and performs data preprocessing.

[0089] The target scenario includes multiple pieces of equipment, each piece of equipment corresponds to a node, and the execution instructions are used to instruct the task execution control instructions for the target scenario.

[0090] S202 identifies target objects based on multi-domain data from land, sea, and air, generates the scene situation corresponding to the target scene, and acquires schedulable resources.

[0091] The target object includes at least one target object for executing the task and at least one target object for the task being executed.

[0092] S203 generates a task execution plan for the target scenario based on the scenario situation and schedulable resources, and generates task execution control instructions based on the task execution plan.

[0093] Among them, the task execution control instructions are used to control each task execution target object to execute a task on at least one task target object.

[0094] The cross-domain collaborative intelligent management and control method provided in this embodiment is applied to a cross-domain collaborative intelligent management and control system. The system includes a distributed intelligent perception layer, an intelligent processing hub layer, and an autonomous planning layer. The distributed intelligent perception layer, the intelligent processing hub layer, and the autonomous planning layer are connected through a microservice bus. The distributed intelligent perception layer controls multiple equipment in the sea, land, and air domains corresponding to the target scenario to collect multi-domain data. The intelligent processing hub layer integrates and analyzes the multi-domain data to determine the current scenario situation and schedulable resources. Finally, the autonomous planning layer formulates a task execution plan based on the current scenario situation and schedulable resources, thereby generating task execution control instructions to control each task execution target object to execute tasks on at least one target object. This effectively integrates multi-domain data for the current target scenario, accurately perceives the current environmental situation, and avoids the excessive reliance on manual operation and fixed rules in existing technologies, which cannot flexibly match appropriate management and control methods. It maximizes resource allocation and thus achieves efficient and accurate intelligent management and control.

[0095] It should be understood that, although Figure 2The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 2 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0096] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static random access memory (SRAM) and dynamic random access memory (DRAM), etc.

[0097] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0098] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A cross-domain collaborative intelligent control system, characterized in that, include: The system comprises a distributed intelligent perception layer, an intelligent processing hub layer, and an autonomous planning layer, which are connected via a microservice bus. The distributed intelligent perception layer is used to construct a node network in the target scene when an execution instruction is received. The node network collects multi-domain data from land, sea and air through each node and performs data preprocessing. The target scene includes multiple pieces of equipment, and each piece of equipment corresponds one-to-one with each node. The execution instruction is used to instruct the formulation of task execution control instructions for the target scene. The intelligent processing central layer is used to identify target objects based on the multi-domain data of land, sea and air, generate the scene situation corresponding to the target scene and obtain schedulable resources. The target objects include at least one target object for executing tasks and at least one target object for being executed tasks. The autonomous planning layer is used to generate a task execution plan for the target scenario based on the scenario situation and schedulable resources, and to generate task execution control instructions based on the task execution plan. The task execution control instructions are used to control each task execution target object to execute a task on at least one of the task-being target objects.

2. The intelligent management and control system for cross-domain collaboration according to claim 1, characterized in that, The execution instructions include task execution instructions and target scene determination instructions. The task execution instructions are used to instruct the formulation of task execution control instructions, and the target scene determination instructions are used to instruct the acquisition of target scene parameters.

3. The intelligent management and control system for cross-domain collaboration according to claim 1, characterized in that, Also includes: The core library is communicatively connected to the distributed intelligent perception layer, the intelligent processing hub layer, and the autonomous planning layer. The core library includes: an algorithm library, a database, an equipment library, a map library, a scene library, and a rule library.

4. The intelligent management and control system for cross-domain collaboration according to claim 3, characterized in that, The distributed intelligent sensing layer includes: an equipment access unit, a node networking unit, and a data preprocessing unit. The equipment access unit is used to execute control commands for the task, establish communication connections between each piece of equipment and the intelligent management and control system based on the connection information of each piece of equipment in the equipment library, determine commands for the target scenario, determine and configure the target scenario parameters in the scenario library; The node networking unit is used to construct the node network based on the self-organizing networking protocol for multiple devices that have established communication connections, and to collect multi-domain data of sea, land and air through each node. The multi-domain data of sea, land and air includes: sea data, land data and airspace data. The data preprocessing unit is used to clean the noise in the multi-domain data of land, sea and air, and to perform spatiotemporal alignment processing on the cleaned data according to the actual target objects stored in the map library.

5. The intelligent management and control system for cross-domain collaboration according to claim 4, characterized in that, The node networking unit is also used to store multi-domain data (sea, land, and air) into the database.

6. The intelligent control system for cross-domain collaboration according to claim 3, characterized in that, The intelligent processing central layer includes: a target object recognition unit, a scene situation generation unit, and a schedulable resource acquisition unit; The target object recognition unit is used to schedule multiple target object recognition models stored in the algorithm library, identify each target object based on the multi-domain data of land, sea and air, and locate and track each target object. The target object also includes at least one obstacle target object. The scene situation generation unit is used to determine the priority execution level of each of the at least one execution task target object, at least one execution task target object, and at least one obstacle target object, and generate the scene situation based on the priority execution level. The scene situation is used to indicate the state and movement trajectory of at least one execution task target object, at least one execution task target object, and at least one obstacle target object. The schedulable resource acquisition unit is used to acquire the schedulable resources of the target object for the execution task.

7. The intelligent management and control system for cross-domain collaboration according to claim 3, characterized in that, The autonomous planning layer includes: a task execution planning generation unit and a control instruction generation unit; The task execution plan generation unit is used to generate a task execution plan for the target scenario based on the scenario situation and schedulable resources, and store the task execution plan in the database; The control instruction generation unit is used to generate the task execution control instruction according to the task execution plan, send the task execution control instruction to at least one target object through the microservice bus, and control at least one task execution target object to execute a task on at least one of the task-being target objects.

8. The intelligent management and control system for cross-domain collaboration according to claim 7, characterized in that, The autonomous planning layer further includes a dynamic visualization unit; the dynamic visualization unit is used to receive the task execution plan and visualize it.

9. The intelligent control system for cross-domain collaboration according to claim 3, characterized in that, Also includes: The optimization module includes: a historical data acquisition unit and an analysis and verification unit; The historical data acquisition unit is used to acquire historical multi-domain data on land, sea and air, as well as corresponding task execution plans, from the database. The analysis and verification unit is used to analyze and verify historical multi-domain data (sea, land, and air) and corresponding task execution plans, and generate optimization instructions. The optimization instructions are used to instruct the generation of the next task execution plan.

10. A cross-domain collaborative intelligent management and control method, characterized in that, include: Upon receiving an execution instruction, a node network in the target scenario is constructed. Data from multiple domains (land, sea, and air) is collected and preprocessed through each node in the node network. The target scenario includes multiple pieces of equipment, each of which corresponds one-to-one with each node. The execution instruction is used to instruct the formulation of task execution control instructions for the target scenario. Based on the multi-domain data of land, sea and air, target objects are identified, a scene situation corresponding to the target scene is generated, and schedulable resources are obtained. The target objects include at least one target object for executing a task and at least one target object for being executed a task. Based on the scenario situation and schedulable resources, a task execution plan for the target scenario is generated. Based on the task execution plan, a task execution control instruction is generated. The task execution control instruction is used to control each task execution target object to execute a task on at least one of the task-being target objects.

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