Unsteady-state architecture heterogeneous unmanned device control method and system, and storage medium

Abstract

The application discloses a non-steady-state architecture heterogeneous unmanned equipment control method and system and a storage medium, relates to the field of equipment control, and comprises the following steps: collecting the equipment type identifier, real-time position information and real-time running parameter of the heterogeneous unmanned equipment, and generating a state data set; performing architecture situation identification on the non-steady-state architecture to obtain a topology analysis result; performing strategy cooperation on an adaptive message routing mechanism and a preset heterogeneous equipment data conversion rule to obtain an equipment coordination protocol; coordinating and optimizing a motion trajectory adjustment instruction and an inter-equipment cooperative action plan to obtain a control parameter set; performing protocol encapsulation and coding on the control parameter to generate a control instruction; monitoring the response of the control instruction, and updating the monitored instruction response data to the state data set, so that the cooperative operation control of the heterogeneous unmanned equipment is realized; and the application can improve the control efficiency of the non-steady-state architecture heterogeneous unmanned equipment.

Description

Technical Field

[0001] This invention relates to the field of equipment control technology, and in particular to control methods, systems and storage media for heterogeneous unmanned equipment with non-steady-state architectures. Background Technology

[0002] Current control technologies for heterogeneous unmanned equipment are mostly based on steady-state architecture design, failing to adequately adapt to the dynamic changes in equipment topology under unsteady architectures. In the state data acquisition phase, there is often insufficient fusion of multi-source data such as equipment type identification, real-time location, and operating parameters, resulting in incomplete and inaccurate state datasets. Simultaneously, the lack of effective dynamic correlation analysis for situational awareness in unsteady architectures makes it difficult to accurately identify core equipment and assess topology stability. This renders subsequent topology-based control strategy formulation unreliable, limiting the timeliness and adaptability of the overall control response.

[0003] Existing technologies have significant shortcomings in the construction and execution of collaborative control protocols for heterogeneous unmanned devices. On the one hand, the adaptive message routing mechanism and the data conversion rules for heterogeneous devices lack coordination. Dynamic weight allocation and routing adaptation for key communication paths are not combined with dynamic topology changes, and the data semantic conversion lacks precise matching with device types, easily leading to message transmission delays and data interaction failures. On the other hand, during control parameter generation and command execution, the spatiotemporal consistency of motion trajectory adjustment and inter-device collaborative actions is insufficient, the conflict resolution mechanism is imperfect, and the asynchronous monitoring of command responses and status dataset updates further reduce the stability and control efficiency of collaborative operation of heterogeneous unmanned devices, making it difficult to meet the control requirements under non-steady-state architectures. Therefore, how to improve the control efficiency of heterogeneous unmanned devices under non-steady-state architectures has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a control method, system, and storage medium for heterogeneous unmanned equipment with an unsteady architecture.

[0005] In a first aspect, the present invention provides a control method for heterogeneous unmanned equipment with an unsteady architecture, comprising: S1. Collect the device type identifier, real-time location information and real-time operating parameters of the different types of unmanned equipment, and generate the status dataset of the different types of unmanned equipment; S2. Based on the state dataset, perform architecture situation identification on the non-steady-state architecture of the heterogeneous unmanned equipment to obtain the topology analysis results of the heterogeneous unmanned equipment; S3. Based on the topology analysis results, the adaptive message routing mechanism and the preset heterogeneous device data conversion rules are coordinated to obtain the device coordination protocol of the heterogeneous unmanned equipment. S4. Based on the device coordination protocol, coordinate and optimize the motion trajectory adjustment command of the heterogeneous unmanned equipment and the inter-device cooperative action planning to obtain the control parameter set of the heterogeneous unmanned equipment. S5. Encapsulate and encode the control parameter set according to the protocol to generate control instructions for the heterogeneous unmanned equipment; S6. Monitor the response to the control commands and update the monitored command response data to the status dataset to realize the collaborative operation control of the heterogeneous unmanned equipment.

[0006] In a preferred embodiment, the step of collecting the device type identifier, real-time location information, and real-time operating parameters of the heterogeneous unmanned equipment to generate the status dataset of the heterogeneous unmanned equipment includes: Initiate a device capability query request to a different type of unmanned device and receive the device type identifier returned by the different type of unmanned device; Based on inertial measurement data from the satellite positioning system, obtain the real-time positioning information of the heterogeneous unmanned equipment; Extract real-time operating parameters from the operating status of the heterogeneous unmanned equipment; The type identifier, the real-time positioning information, and the real-time operating parameters are fused from multiple sources to obtain the status dataset of the heterogeneous unmanned equipment.

[0007] In a preferred embodiment, the step of performing architectural situation identification on the non-steady-state architecture of the heterogeneous unmanned equipment based on the state dataset to obtain the topology analysis results of the heterogeneous unmanned equipment includes: Obtain the communication connection status and data interaction characteristics among the heterogeneous unmanned devices in the state dataset; Based on the communication connection status and data interaction characteristics, a node relationship diagram of the dynamic association relationship between the different types of unmanned devices is constructed; By mining key nodes in the node relationship graph, the core unmanned equipment that plays a pivotal role in the unsteady architecture is obtained; Based on the changes in the connection status of the core unmanned equipment, the topological stability index of the unsteady architecture is analyzed; By comprehensively evaluating the node relationship diagram, the core unmanned equipment identifier, and the topology stability index, the topology analysis results of the heterogeneous unmanned equipment are obtained.

[0008] In a preferred embodiment, the step of coordinating the adaptive message routing mechanism with preset heterogeneous device data conversion rules based on the topology analysis results to obtain the device coordination protocol for the heterogeneous unmanned devices includes: Based on the topology analysis results, dynamic weight allocation is performed on the key communication paths between the heterogeneous unmanned devices to obtain a path priority list for the heterogeneous unmanned devices. Based on the path priority list, the message forwarding strategy of the adaptive message routing mechanism is adapted and adjusted to obtain the routing configuration of the heterogeneous unmanned equipment. Based on preset heterogeneous device data conversion rules, filter data semantic conversion rules that match the device type identifier; The routing configuration and the data semantic transformation rules are fused together to obtain the device coordination protocol for the heterogeneous unmanned equipment.

[0009] In a preferred embodiment, the step of dynamically weighting the critical communication paths between the heterogeneous unmanned devices based on the topology analysis results to obtain a path priority list for the heterogeneous unmanned devices includes: Based on the connection stability index and delay characteristic parameters in the topology analysis results, the reliability and real-time performance of the communication path between the heterogeneous unmanned devices are evaluated, and path evaluation results are generated. Based on the path evaluation results and the difference in mission criticality between the heterogeneous unmanned devices, a weighting factor for the communication path between the heterogeneous unmanned devices is determined, wherein the calculation formula for the weighting factor is as follows: ; In the formula, For the first The and the first The weighting factor of the communication path between the heterogeneous unmanned devices. The delay sensitivity coefficient in the topology analysis results is... The first in the topology analysis results The and the first The connection stability index of the communication path between the heterogeneous unmanned devices. The preset task criticality penalty coefficient, For the first The and the first The delay characteristic parameters of the communication path between the heterogeneous unmanned devices. For the aforementioned heterogeneous unmanned equipment Task criticality For the aforementioned heterogeneous unmanned equipment Task criticality; Based on the weighting factors, the key communication paths between the heterogeneous unmanned devices are sorted by multi-dimensional utility to obtain a path priority list for the heterogeneous unmanned devices.

[0010] In a preferred embodiment, the step of coordinating and optimizing the motion trajectory adjustment commands and inter-device cooperative action planning of the heterogeneous unmanned equipment based on the device coordination protocol to obtain the control parameter set of the heterogeneous unmanned equipment includes: Based on the device coordination protocol, the feasibility of the motion trajectory adjustment instructions of the heterogeneous unmanned equipment is verified to obtain the trajectory instruction set of the heterogeneous unmanned equipment. A capability matching degree analysis is performed on the inter-device cooperative action planning of the heterogeneous unmanned equipment to obtain the cooperative action sequence of the heterogeneous unmanned equipment. The trajectory instruction set and the cooperative action sequence are spatiotemporally fused to obtain the preliminary control parameters of the heterogeneous unmanned equipment. Based on the device coordination protocol, conflict resolution is performed on the preliminary control parameters to obtain the control parameter set of the heterogeneous unmanned equipment.

[0011] In a preferred embodiment, the step of protocol encapsulating and encoding the control parameter set to generate control commands for the heterogeneous unmanned equipment includes: Based on the device coordination protocol, the control parameter set is serialized to obtain a standardized data packet for the heterogeneous unmanned equipment. Based on the routing configuration, the standardized data packets are reconstructed using route-driven methods to obtain protocol-compatible instructions for the standardized data packets; The protocol-compatible instructions are encapsulated into control flow to obtain the control instructions for the heterogeneous unmanned equipment.

[0012] In a preferred embodiment, the step of monitoring the response to the control commands and updating the monitored command response data to the status dataset to achieve collaborative operation control of the heterogeneous unmanned equipment includes: The system monitors the response to the control commands sent by the heterogeneous unmanned equipment to obtain the actual response status data corresponding to the control commands. The actual response status data is compared with the expected status of the control command to obtain a consistency report of the control command. Based on the consistency report, the control effectiveness of the actual response status data is measured to obtain the effectiveness evaluation result of the control command; The status dataset is dynamically updated based on the performance evaluation results and the actual response status data. Compared with the prior art, the present invention has the following beneficial effects: 1. This invention accurately collects equipment type identifiers, real-time location information, and real-time operating parameters of heterogeneous unmanned equipment, and performs multi-source data fusion to generate a state dataset, providing comprehensive and accurate data support for subsequent control processes. Based on this state dataset, it performs architecture situation identification on unsteady architectures, clearly constructs a node relationship graph of dynamic connections between devices, accurately identifies core unmanned equipment, and analyzes topology stability indicators to form reliable topology analysis results. Furthermore, by combining the topology analysis results with the adaptive message routing mechanism and heterogeneous device data conversion rules, it generates a highly adaptable device coordination protocol, effectively improving the basic reliability and specificity of heterogeneous unmanned equipment control under unsteady architectures.

[0013] 2. This invention coordinates and optimizes motion trajectory adjustment commands and inter-device collaborative action planning based on a device coordination protocol. Through feasibility verification, capability matching analysis, and spatiotemporal consistency fusion, it generates a set of control parameters after conflict resolution, ensuring the rationality and accuracy of the control parameters. The control parameter set is then encapsulated and encoded using a protocol to generate control commands, ensuring compatible transmission and effective execution of commands between different types of devices. Simultaneously, the response to the control commands is monitored, and the monitored command response data is updated to the status dataset in a timely manner, forming a dynamic closed-loop control. This significantly improves the efficiency of collaborative operation of different types of unmanned devices and enhances the stability and effectiveness of device control under non-steady-state architectures. Attached Figure Description

[0014] Figure 1 A flowchart illustrating a non-steady-state architecture heterogeneous unmanned equipment control method according to an embodiment of the present invention; Figure 2 This is a functional block diagram of a non-steady-state architecture heterogeneous unmanned equipment control method provided in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0015] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0016] This application provides a method for controlling heterogeneous unmanned devices with a non-steady-state architecture. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for controlling heterogeneous unmanned devices with a non-steady-state architecture can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0017] Reference Figure 1 The diagram shown is a flowchart illustrating a heterogeneous unmanned equipment control method with a non-steady-state architecture according to an embodiment of the present invention. In this embodiment, the heterogeneous unmanned equipment control method with a non-steady-state architecture includes: S1. Collect the device type identifier, real-time location information and real-time operating parameters of the different types of unmanned equipment, and generate the status dataset of the different types of unmanned equipment; In this embodiment of the invention, the step of collecting the device type identifier, real-time location information, and real-time operating parameters of heterogeneous unmanned equipment to generate the status dataset of the heterogeneous unmanned equipment includes: Initiate a device capability query request to a different type of unmanned device and receive the device type identifier returned by the different type of unmanned device; Based on inertial measurement data from the satellite positioning system, obtain the real-time positioning information of the heterogeneous unmanned equipment; Extract real-time operating parameters from the operating status of the heterogeneous unmanned equipment; The type identifier, the real-time positioning information, and the real-time operating parameters are fused from multiple sources to obtain the status dataset of the heterogeneous unmanned equipment.

[0018] Specifically, the system first determines the communication protocol compatible with the heterogeneous unmanned equipment, constructs a device capability query request packet according to the protocol specifications, and the request packet contains a unique device identifier and a clear "device capability query" instruction field. Then, the request packet is sent to the target heterogeneous unmanned equipment through the device's preset communication link. After receiving the request packet, the heterogeneous unmanned equipment's receiving module parses the identifier and instruction in the packet. After confirming that it is a legitimate capability query request, it extracts the preset device type identifier from the hardware configuration file stored locally on the device, constructs a response packet according to the same MQTT protocol, writes the device type identifier into the "device type" field of the response packet, and sends it back to the system through the original communication link. After receiving the response packet, the system's parsing module extracts the content of the "device type" field and finally obtains and stores the device type identifier returned by the heterogeneous unmanned equipment.

[0019] Furthermore, the heterogeneous unmanned equipment is equipped with a BeiDou-3 satellite positioning module and an inertial measurement unit. The satellite positioning module receives navigation signals in real time and calculates the distance between the equipment and each satellite using the satellite orbit parameters and signal propagation time in the signals. Based on these distance data, the longitude and latitude coordinates of the equipment are initially determined. At the same time, the inertial measurement unit collects the acceleration and angular velocity data of the equipment in real time. When the positioning accuracy decreases due to factors such as building obstruction of satellite signals, the collected acceleration data is used to calculate the position offset of the equipment, and the angular velocity data is used to correct the direction of motion of the equipment, compensating for the coordinates initially calculated by the satellite positioning module, and obtaining precise location information including longitude, latitude, and altitude. The equipment sends this precise location information to the system through the communication module. After receiving it, the system checks whether the data fields are complete and whether the values ​​are within a reasonable range. After the verification is passed, the real-time positioning information of the heterogeneous unmanned equipment is obtained.

[0020] Furthermore, each core functional component of the heterogeneous unmanned equipment is equipped with a dedicated sensor. For example, the motor of the power system is equipped with a motor speed sensor, the battery of the energy system is equipped with a battery remaining power sensor, and the propeller of the flight system is equipped with a propeller speed sensor. These sensors collect the operating data of the corresponding components at a fixed frequency. The motor speed sensor converts the mechanical signal of the motor rotation into an electrical signal to obtain the real-time speed. The battery remaining power sensor calculates the real-time remaining power by detecting changes in battery voltage and current. The propeller speed sensor obtains the real-time speed by capturing the propeller rotation cycle. The control unit of the equipment processes the raw data collected by these sensors, removes abnormal values ​​that are outside the normal range, and converts the raw electrical signals into digital signals. Then, it filters out data directly related to the equipment's operating status, such as the real-time motor speed, the real-time remaining battery power, and the real-time propeller speed, from the processed digital signals. These are the real-time operating parameters. Subsequently, these real-time operating parameters are sent to the system end through the equipment's communication link. After the system end confirms that the parameters match the equipment type and that the values ​​conform to the equipment's operating logic, the extraction of real-time operating parameters is completed.

[0021] Furthermore, a unified timestamp calibration standard is established on the system side. Using the time the system receives real-time positioning information as a benchmark, the reception times of the device type identifier and real-time operating parameters are uniformly adjusted to this benchmark time, ensuring complete correspondence of the three types of data in the time dimension. Next, a structured data fusion template is created, containing three core fields: "Device Type Identifier," "Real-time Positioning Information," and "Real-time Operating Parameters." The calibrated device type identifier is filled into the "Device Type Identifier" field, the longitude, latitude, and altitude values ​​of the real-time positioning information are filled into the corresponding sub-fields, and the values ​​of the real-time operating parameters are filled into the corresponding sub-fields. Afterward, the system side checks the correlation of the data within the template, confirming that the device type identifier matches the real-time operating parameters and that the real-time positioning information conforms to the device's usage scenario. Once the verification is passed, all data within the template is integrated into a complete device status record. After summarizing these status records from multiple dissimilar unmanned devices, the status dataset of the dissimilar unmanned devices is obtained. Finally, this status dataset is stored in the system's relational database to ensure stable data retrieval and subsequent use.

[0022] In summary, this step involves initiating a device capability query request containing device identification information and query instructions to a different type of unmanned device. After being transmitted to the device via a preset communication link, the device parses the request and extracts a preset device type identifier from its local configuration file. This identifier is then transmitted back via the original link, and finally, the device type identifier is received and confirmed, thus completing the acquisition of the device type identifier.

[0023] In summary, this step relies on the satellite positioning module and inertial measurement unit carried by the heterogeneous unmanned equipment. The satellite positioning module receives satellite signals to calculate the initial position, and the inertial measurement unit collects acceleration and angular velocity data to compensate and correct the initial position. After data verification, the real-time positioning information of the equipment, including longitude, latitude, and altitude, is obtained.

[0024] In summary, this step utilizes dedicated sensors mounted on the core components of various types of unmanned equipment to collect raw operational data. After the equipment control unit processes outliers and converts signal formats, it filters out data directly related to the equipment's operating status. After verifying the data's compatibility with the equipment type, it completes the extraction of the equipment's real-time operating parameters.

[0025] In summary, this step first unifies the timestamps of equipment type identification, real-time positioning information, and real-time operating parameters, then fills in the structured fusion template and checks the data correlation. After the verification is passed, it is integrated into the status record of a single device. After the status records of multiple devices are summarized, the status dataset of different types of unmanned equipment is obtained, and finally stored in the designated database.

[0026] S2. Based on the state dataset, perform architecture situation identification on the non-steady-state architecture of the heterogeneous unmanned equipment to obtain the topology analysis results of the heterogeneous unmanned equipment; In this embodiment of the invention, the step of performing architectural situation identification on the non-steady-state architecture of the heterogeneous unmanned equipment based on the state dataset to obtain the topology analysis results of the heterogeneous unmanned equipment includes: Obtain the communication connection status and data interaction characteristics among the heterogeneous unmanned devices in the state dataset; Based on the communication connection status and data interaction characteristics, a node relationship diagram of the dynamic association relationship between the different types of unmanned devices is constructed; By mining key nodes in the node relationship graph, the core unmanned equipment that plays a pivotal role in the unsteady architecture is obtained; Based on the changes in the connection status of the core unmanned equipment, the topological stability index of the unsteady architecture is analyzed; By comprehensively evaluating the node relationship diagram, the core unmanned equipment identifier, and the topology stability index, the topology analysis results of the heterogeneous unmanned equipment are obtained.

[0027] Specifically, from a relational database storing heterogeneous unmanned equipment status datasets, fields related to inter-device communication are selected, including "communication link status," "data transmission timestamp," "transmission data type," and "data volume." The "communication link status" field directly indicates whether communication between two devices has been established; "connected" indicates a communication connection exists, while "disconnected" indicates no communication connection. This allows us to obtain the communication connection status between heterogeneous unmanned equipment. Simultaneously, the number of transmissions between two connected devices within a fixed unit of time is counted to determine the data interaction frequency. The "data type" and "data volume" of each transmission are extracted, and the frequency, data type, and data volume are integrated to form the data interaction characteristics between heterogeneous unmanned equipment. Finally, the communication connection status and data interaction characteristics are obtained.

[0028] Furthermore, a unique node identifier is assigned to each device in the heterogeneous unmanned equipment status dataset, with each identifier corresponding one-to-one with the device type identifier. On the canvas of the visualization drawing tool, each node identifier is presented as a circular icon, serving as a node in the node relationship graph. Based on the acquired communication connection status, if the communication link status of two devices is "connected," a straight line is used to connect the corresponding two node icons, forming an edge in the node relationship graph. The data interaction characteristics corresponding to the connection are labeled on each edge, including the frequency of data interaction, the main data types transmitted, and the average data volume per transaction. Through the above operations, a node relationship graph of the dynamic association between heterogeneous unmanned equipment is constructed.

[0029] Furthermore, the number of edges connected to each node in the node relationship graph is counted one by one, and this number is the connection degree of the node. The connection degree of each node is recorded and sorted from largest to smallest. The nodes with the highest connection degree are selected as candidate core nodes. Then, all edges corresponding to each candidate core node are simulated to be disconnected, and the structural changes of the node relationship graph are observed. If disconnecting a candidate node causes the node relationship graph to split into multiple independent subgraphs, and the number of nodes in the subgraphs accounts for a large proportion of the original total number of nodes, then the candidate node is determined to have a key supporting role in the overall relationship. The unmanned equipment corresponding to the candidate nodes that meet the above judgment conditions is identified as the core unmanned equipment that plays a pivotal role in the non-steady-state architecture, and the identification information of the core unmanned equipment is recorded.

[0030] Furthermore, using a set statistical period as the unit, the connection status of the core unmanned equipment is recorded in real time for each time period within the period, including the number of nodes connected to the core unmanned equipment, the duration of the connection, and the number of connection interruptions within that time period. The connection retention rate of the core unmanned equipment is calculated by dividing the duration of the connection in each time period by the total duration of that time period to obtain the connection retention rate for each time period, and then taking the average connection retention rate over the entire statistical period as the overall connection retention rate. The total number of connection interruptions of the core unmanned equipment over the entire statistical period is counted and divided by the total number of time periods in the period to obtain the average interruption frequency per time period. The overall connection retention rate and the average interruption frequency per time period are used as core topology stability indicators, where a higher overall connection retention rate and a lower average interruption frequency per time period indicate stronger topology stability of the non-steady-state architecture. The topology stability indicators of the non-steady-state architecture are obtained through the above calculations and analyses.

[0031] Furthermore, the overall structure presented by the node relationship diagram is first analyzed to clarify the main association paths between different types of unmanned equipment, the composition of each sub-association network, and the distribution of equipment, and to determine whether there is a structural risk of over-reliance on a single node. Then, combined with the core unmanned equipment identifier, the position of the core unmanned equipment in the main association paths is confirmed, its connection effect on each sub-association network is analyzed, and the architectural impact range that a failure of the core equipment may cause is assessed. Finally, the topology stability index is compared with the preset stability threshold to determine the stability level of the current architecture. The structural analysis results, the core equipment role assessment results, and the stability level determination results are integrated to form a comprehensive report that includes the association structure of different types of unmanned equipment, core equipment information, and architectural stability assessment. This report is the topology analysis result of different types of unmanned equipment.

[0032] In summary, this step filters out information related to communication between devices from the status dataset of heterogeneous unmanned devices, determines the communication connection status by judging whether the communication link is connected, and integrates data interaction frequency, transmitted data type and data volume to form data interaction characteristics, and finally completes the acquisition of these two pieces of information.

[0033] In summary, this step uses the communication connection status and data interaction characteristics between different types of unmanned devices as a basis to assign a corresponding node identifier to each device and present it with an icon. Straight lines are used to connect the communicating nodes to form edges, and data interaction characteristics are marked on the edges to construct a node relationship graph of dynamic association between devices.

[0034] In summary, this step first counts and sorts the connectivity degree of each node in the node relationship graph, selects the nodes with the highest connectivity degree as candidate core nodes, then observes the changes in the graph structure by simulating disconnecting the candidate nodes, determines the nodes that play a key supporting role in the relationship, and finally obtains the core unmanned equipment that plays a pivotal role in the non-steady-state architecture.

[0035] In summary, this step is based on the changes in the connection status of the core unmanned equipment within a set period. By calculating information such as connection retention rate and interruption frequency, it analyzes and determines the topology stability index that can reflect the stability of the non-steady-state architecture.

[0036] In summary, this step first outlines the overall structure and relationships of the node relationship diagram, then analyzes the role and impact of the core unmanned equipment, and finally combines topology stability indicators and preset thresholds to determine the architecture stability level. These analysis results are then integrated and evaluated to obtain the topology analysis results for different types of unmanned equipment.

[0037] S3. Based on the topology analysis results, the adaptive message routing mechanism and the preset heterogeneous device data conversion rules are coordinated to obtain the device coordination protocol of the heterogeneous unmanned equipment. In this embodiment of the invention, the step of coordinating the adaptive message routing mechanism with preset heterogeneous device data conversion rules based on the topology analysis results to obtain the device coordination protocol for the heterogeneous unmanned devices includes: Based on the topology analysis results, dynamic weight allocation is performed on the key communication paths between the heterogeneous unmanned devices to obtain a path priority list for the heterogeneous unmanned devices. Based on the path priority list, the message forwarding strategy of the adaptive message routing mechanism is adapted and adjusted to obtain the routing configuration of the heterogeneous unmanned equipment. Based on preset heterogeneous device data conversion rules, filter data semantic conversion rules that match the device type identifier; The routing configuration and the data semantic transformation rules are fused together to obtain the device coordination protocol for the heterogeneous unmanned equipment.

[0038] Based on the topology analysis results, dynamic weight allocation is performed on the critical communication paths between the heterogeneous unmanned devices to obtain a path priority list for the heterogeneous unmanned devices, including: Based on the connection stability index and delay characteristic parameters in the topology analysis results, the reliability and real-time performance of the communication path between the heterogeneous unmanned devices are evaluated, and path evaluation results are generated. Based on the path evaluation results and the difference in mission criticality between the heterogeneous unmanned devices, a weighting factor for the communication path between the heterogeneous unmanned devices is determined, wherein the calculation formula for the weighting factor is as follows: ; In the formula, For the first The and the first The weighting factor of the communication path between the heterogeneous unmanned devices. The delay sensitivity coefficient in the topology analysis results is... The first in the topology analysis results The and the first The connection stability index of the communication path between the heterogeneous unmanned devices. The preset task criticality penalty coefficient, For the first The and the first The delay characteristic parameters of the communication path between the heterogeneous unmanned devices. For the aforementioned heterogeneous unmanned equipment Task criticality For the aforementioned heterogeneous unmanned equipment Task criticality; Based on the weighting factors, the key communication paths between the heterogeneous unmanned devices are sorted by multi-dimensional utility to obtain a path priority list for the heterogeneous unmanned devices.

[0039] Specifically, from the topology analysis results of heterogeneous unmanned equipment, the confirmed key communication paths between the devices are extracted. These paths must include paths connecting core unmanned equipment and paths showing high connection retention rates in the topology stability index. Subsequently, two core criteria for weight allocation are determined: first, the topology stability of the path, using the connection retention rate in the topology analysis results as the criterion; the higher the connection retention rate, the higher the basic weight of the path; second, the importance of the data carried by the path. If the path transmits core operating parameters, additional weight is added; if it transmits ordinary status data, no additional weight is added. The final weight is calculated for each key communication path according to the above criteria. The basic weight and the additional weight are added to obtain the total weight of each path. Then, all key communication paths are arranged in descending order of total weight and compiled into a list containing path name, corresponding weight, and device identifiers at both ends of the path. This list is the path priority list for heterogeneous unmanned equipment.

[0040] Furthermore, the current default message forwarding strategy of the adaptive message routing mechanism is first obtained, clarifying its original path selection logic and forwarding triggering conditions. Then, the path priority list is used as the basis for adjustment, setting the highest priority path in the list as the primary message forwarding path, the second highest priority path as the backup forwarding path, and subsequent paths as lower-level backup paths in order of priority. At the same time, combined with the connection status change characteristics of the core unmanned equipment in the topology analysis results, the switching conditions for forwarding paths are set. That is, when the connection status of the primary path is displayed as "disconnected", the adaptive message routing mechanism automatically triggers the switching process, switching the message forwarding path to the second highest priority path. The adjusted path selection order, switching conditions, and corresponding forwarding execution requirements are uniformly organized into a document containing specific operating specifications. This document is the routing configuration for heterogeneous unmanned equipment.

[0041] Furthermore, a data conversion rule library for heterogeneous equipment is pre-established. The rule library is categorized and stored according to equipment type identifiers. Each equipment type identifier corresponds to a set of independent data conversion rules. Each set of rules includes the mapping relationship between the equipment-specific data fields and the system's general data fields, data format conversion standards, and other semantic conversion-related content. The equipment type identifier of the current equipment is extracted from the status dataset of heterogeneous unmanned equipment. Using this identifier as a search keyword, a matching query is performed in the pre-set heterogeneous equipment data conversion rule library to find the rule category directory that completely corresponds to the identifier. All data field mapping relationships, format conversion standards, and other content stored in this directory are completely extracted to form a set of exclusive rules for the current equipment. This set is the data semantic conversion rule that matches the equipment type identifier.

[0042] Furthermore, first, a unified document structure for the device coordination protocol is determined. The document must include two core chapters: "Route Coordination Specification" and "Data Conversion Coordination Specification." The path priority ordering, preferred message forwarding path, and backup path switching conditions in the routing configuration are broken down and written into the "Route Coordination Specification" chapter, clarifying the applicable scenarios and execution requirements for each routing rule. Next, the field mapping relationships and data format conversion standards in the data semantic conversion rules are organized according to the data processing flow and written into the "Data Conversion Coordination Specification" chapter, clarifying the specific conversion methods for data before and after transmission for each device type. After completing the content of both chapters, it is checked and confirmed that the data transmitted by the forwarding path specified in the "Route Coordination Specification" can accurately match the semantic conversion rules for the corresponding device type in the "Data Conversion Coordination Specification," avoiding situations where the forwarded data lacks corresponding conversion rules. Finally, the document undergoes format standardization and content verification to ensure that all clauses are clearly stated and conflict-free, forming a complete standardized document. This document constitutes the device coordination protocol for heterogeneous unmanned equipment.

[0043] Specifically, from the topology analysis results of heterogeneous unmanned equipment, connection stability indicators and latency parameters corresponding to each critical communication path are extracted. Connection stability indicators include the path's connection hold rate and connection interruption frequency, while latency parameters include the complete transmission time from the sending end to the receiving end. When evaluating path reliability, based on connection stability indicators, paths with high connection hold rates and low connection interruption frequencies indicate that they can continuously maintain communication during operation and are less prone to disconnection, thus being classified as high reliability. Paths with low connection hold rates and high connection interruption frequencies are prone to frequent disconnections, thus being classified as low reliability. When evaluating path real-time performance, based on latency parameters, paths with short data transmission times can quickly complete data transmission, meeting the equipment's requirements for data timeliness, thus being classified as high real-time performance. Paths with longer transmission times exhibit significant data transmission delays, thus being classified as low real-time performance. For each critical communication path, reliability and real-time performance levels are determined. The path identifier, reliability level, and real-time performance level are then integrated into a structured document, which constitutes the path evaluation results.

[0044] Furthermore, the criticality distinction criteria for data transmission tasks of different types of unmanned equipment are first clarified. Data transmission tasks during equipment operation are divided into core tasks and ordinary tasks. Core tasks include equipment control command transmission and critical operating parameter feedback. These tasks directly affect the safe operation of the equipment, and their criticality is set to high. Ordinary tasks include reporting non-core equipment status, which has a smaller impact on equipment operational safety, and their criticality is set to low. A correspondence rule is established between path evaluation results, task criticality, and weighting factors: paths with high reliability, high real-time performance, and carrying core tasks have the highest weighting factor; paths with high reliability and high real-time performance but carrying ordinary tasks have the second highest weighting factor; paths with low reliability, low real-time performance, and carrying ordinary tasks have the lowest weighting factor; other paths are matched with corresponding weighting factor levels according to the principle of "reliability takes precedence over real-time performance, and task criticality takes precedence over path evaluation results." For each critical communication path, a unique weighting factor is determined based on its path evaluation result and the criticality of the task it carries, according to the above rules. This weighting factor directly reflects the overall importance of the path in equipment communication.

[0045] Furthermore, a multi-dimensional utility ranking is conducted based on the weight factors of each critical communication path. During the ranking process, the weight factor levels are compared first. Paths with higher weight factor levels represent stronger overall utility in ensuring communication reliability, meeting real-time requirements, and supporting critical tasks, and thus rank higher in the ranking sequence. Paths with lower weight factor levels have weaker overall utility and rank lower. If paths have the same weight factor level, their real-time performance levels are further compared, with the path having a higher real-time performance level ranking higher. If the real-time performance levels are also the same, their reliability levels are compared, with the path having a higher reliability level ranking higher. After ranking all critical communication paths, the specific information for each path is compiled into a list, which must include the path identifier, the corresponding weight factor, the path evaluation result, and the type of task it undertakes. This list constitutes the path priority list for heterogeneous unmanned equipment.

[0046] Furthermore, As the first The and the first The weighting factor of the communication path between two different types of unmanned devices is the final result obtained by calculating other parameters using this formula. Its value directly reflects the overall importance of the communication path between the two devices. The topology analysis results are derived from heterogeneous unmanned equipment; specifically, they are extracted from the topology analysis results and related to the first... The and the first The connection stability index corresponding to the communication path directly associated with each type of unmanned device includes information that reflects the continuity of path communication, such as the path connection retention rate and connection interruption frequency. The delay sensitivity coefficient, derived from the topology analysis results of heterogeneous unmanned equipment, is a delay sensitivity coefficient explicitly given in the topology analysis results and used to measure the degree of influence of path delay on weight. The value of this coefficient is determined by the evaluation results of the equipment communication delay requirements during the topology analysis process. It is a preset task criticality penalty coefficient, which is set in advance according to the overall plan for task allocation of different types of unmanned equipment before the weight factor is calculated. Its value is used to control the degree of influence of the difference in task criticality between the two devices on the weight factor.

[0047] Furthermore, The topology analysis results derived from heterogeneous unmanned equipment are extracted from the topology analysis results. The and the first The delay characteristic parameter of the communication path between three different types of unmanned devices. This parameter is specifically the delay characteristic parameter of the data from the first... The device transmits data to the first device via the path. The full duration required for each device. It is the first The mission criticality of a different type of unmanned equipment is determined based on the type of mission it undertakes. If the equipment undertakes a core mission, the mission criticality is high; if it undertakes a general mission, the mission criticality is low. It is the first The mission criticality of different types of unmanned equipment, and the method of determination. Consistency means determining the level of criticality of a task based on the type of task it undertakes.

[0048] Furthermore, the numerator of the formula is composed of and negative Multiply The result is obtained by multiplying the two parts to the power of the power, where Reflecting the first The and the first The stability of the communication path between devices. A higher value indicates more stable path communication; negative Multiply The power of this power reflects the impact of path delay characteristics. The larger the value, the smaller the value of this part, and the lower the overall molecule value. Therefore, the molecule reflects the positive contribution of the reliability and real-time performance of the path to the weight.

[0049] Furthermore, the denominator of the formula consists of 1 and Multiply and The absolute value of the difference is obtained by adding the two parts, where and The absolute value of the difference reflects the first The and the first The greater the difference in the criticality of the tasks of each device, the larger this value will be. As a preset penalty coefficient, the impact of this difference on the denominator is amplified or reduced; the larger the denominator value, the greater the overall weighting factor. The smaller the value, the more the denominator reflects the negative penalty effect of the difference in the criticality of the two devices' tasks on the weight. This formula integrates multiple factors affecting the importance of a communication path into a unified calculation process by comprehensively considering path connection stability and real-time performance in the numerator and the difference in the criticality of the devices' tasks in the denominator. The final result is... Able to accurately reflect the first The and the first The comprehensive effectiveness of communication paths between different types of unmanned devices provides a quantitative basis for the subsequent weight allocation and priority ranking of key communication paths.

[0050] In summary, this step is based on the topology analysis results of heterogeneous unmanned equipment. For the critical communication paths between the equipment, dynamic weight allocation is carried out in combination with factors such as path stability and the criticality of the mission. By clarifying the weight levels and sorting them, a path priority list of heterogeneous unmanned equipment that can reflect the importance of the path is finally obtained.

[0051] In summary, this step uses the path priority list as the core basis to adapt and adjust the original message forwarding strategy of the adaptive message routing mechanism, clarify the preferred forwarding path, the backup path and the path switching conditions, and organize the adjusted path selection logic and execution requirements into a standardized document, thereby obtaining the routing configuration for different types of unmanned equipment.

[0052] In summary, this step is based on a pre-defined rule base for the conversion of heterogeneous equipment data. It extracts equipment type identifiers from the status dataset of heterogeneous unmanned equipment, uses these identifiers as search keywords to match the corresponding rule categories in the rule base, filters out content that includes field mapping relationships and format conversion standards, and finally obtains data semantic conversion rules that match the equipment type identifiers.

[0053] In summary, this step first determines the unified document structure of the device coordination protocol, incorporates the path selection and switching rules in the routing configuration into the routing coordination chapter, incorporates the field mapping and format standards in the data semantic conversion rules into the data conversion chapter, verifies the matching between the two, completes document integration and verification, and finally obtains the device coordination protocol for heterogeneous unmanned devices.

[0054] In summary, this step extracts the connection stability index and delay characteristic parameters of the communication path from the topology analysis results, judges the path reliability based on the connection stability index, judges the path real-time performance based on the delay characteristic parameters, integrates the path identification, reliability and real-time performance judgment results, and finally generates the path evaluation result.

[0055] In summary, this step first classifies the criticality of data transmission tasks of different types of unmanned equipment, then establishes the corresponding rules for path evaluation results, task criticality, and weight factors, and determines the corresponding weight factors according to the evaluation results of each critical communication path and the criticality of the task it carries.

[0056] In summary, this step uses the weight factors of critical communication paths as the core to sort the utility in multiple dimensions. It prioritizes sorting by weight factor level, and when the levels are the same, it compares the real-time performance and reliability levels in turn. The sorted paths and related information are compiled into a list, and finally a path priority list for different types of unmanned equipment is obtained.

[0057] S4. Based on the device coordination protocol, coordinate and optimize the motion trajectory adjustment command of the heterogeneous unmanned equipment and the inter-device cooperative action planning to obtain the control parameter set of the heterogeneous unmanned equipment. In this embodiment of the invention, the step of coordinating and optimizing the motion trajectory adjustment commands and inter-device collaborative action planning of the heterogeneous unmanned equipment based on the device coordination protocol to obtain the control parameter set of the heterogeneous unmanned equipment includes: Based on the device coordination protocol, the feasibility of the motion trajectory adjustment instructions of the heterogeneous unmanned equipment is verified to obtain the trajectory instruction set of the heterogeneous unmanned equipment. A capability matching degree analysis is performed on the inter-device cooperative action planning of the heterogeneous unmanned equipment to obtain the cooperative action sequence of the heterogeneous unmanned equipment. The trajectory instruction set and the cooperative action sequence are spatiotemporally fused to obtain the preliminary control parameters of the heterogeneous unmanned equipment. Based on the device coordination protocol, conflict resolution is performed on the preliminary control parameters to obtain the control parameter set of the heterogeneous unmanned equipment.

[0058] Specifically, constraints related to motion trajectory adjustment commands are extracted from the device coordination protocol, including communication path stability requirements in the routing configuration, command format specifications in the data semantic conversion rules, and spatial range limitations corresponding to high-reliability paths in the path priority list. Motion trajectory adjustment commands for heterogeneous unmanned devices to be verified are obtained, such as flight coordinate commands for drones and driving route commands for unmanned vehicles. First, the command format is verified to conform to the data semantic conversion rules, confirming that the coordinate units and speed units in the command are consistent with the parameter requirements corresponding to the device type identifier. If the altitude unit in the drone command is "meters" and conforms to its data conversion standard, the format verification passes. Next, the communication path on which the command depends is verified to meet the routing configuration requirements. The path corresponding to the status feedback data that needs to be transmitted during command execution is checked to see if it is within the high-priority path list specified in the protocol, ensuring that the path can maintain stable communication to support real-time command adjustments. Finally, the motion range corresponding to the command is verified to match the device's capabilities. Combined with parameters such as the device's maximum motion radius and maximum speed in the status data, it is determined whether the motion required by the command is within the device's achievable range. All verified motion trajectory adjustment instructions are categorized and organized by device type to form a structured list containing instruction content, execution time period, and dependency path. This list is the trajectory instruction set for different types of unmanned equipment.

[0059] Furthermore, core capability parameters of each device are extracted from the state dataset of heterogeneous unmanned devices. The parameter categories are determined based on the device type identifier. For example, the core capability parameters of a drone include maximum payload, maximum flight altitude, and endurance; the core capability parameters of an unmanned vehicle include maximum speed, obstacle-crossing altitude, and cargo capacity. These parameters directly reflect the range of actions the device can perform. A pre-defined inter-device collaborative action planning scheme is obtained. This scheme contains multiple action items that require cooperation from multiple devices, such as "after the drone transports supplies to the target area, the unmanned vehicle arrives at the target area to receive the supplies." Capability matching analysis is performed on each action item. The requirements of the action item are first broken down. For example, "drone transporting supplies" needs to match its maximum payload capacity, confirming that the weight of the supplies does not exceed the drone's maximum payload; "unmanned vehicle arriving at the target area" needs to match its maximum speed and endurance, confirming that the unmanned vehicle can travel from its current location to the target area within the drone's transport time. All action items that pass the capability matching analysis are arranged in chronological order of execution time to ensure that the action of the next device can be connected in a timely manner after the action of the previous device is completed, forming an action list sorted by time axis. This list is the collaborative action sequence of different types of unmanned equipment.

[0060] Furthermore, a unified spatiotemporal reference is established. The time reference is based on the system's real-time clock, and the spatial reference is based on the coordinate system of the satellite positioning system, ensuring that the trajectory command set and the coordinated action sequence are integrated within the same spatiotemporal framework. The execution time node and spatial position parameters of each command in the trajectory command set are extracted; simultaneously, the execution time requirements and spatial position requirements of each action in the coordinated action sequence are extracted. The time nodes of the trajectory commands are aligned with the time requirements of the coordinated actions, and the synchronization between the UAV and the unmanned vehicle at that moment is checked. If a deviation exists, the execution speed of the trajectory commands is adjusted to ensure that both arrive at the designated position simultaneously at 15:00. The spatial position of the trajectory commands is then checked to ensure consistency with the spatial requirements of the coordinated actions, confirming that they belong to the same target area in space, thus avoiding positional deviations that could lead to coordination failure. The spatiotemporally aligned trajectory commands and coordinated actions are integrated, generating parameter entries for each device that include execution time, spatial coordinates, and action content. The parameter entries of all devices are then summarized to form the preliminary control parameters for dissimilar types of unmanned equipment.

[0061] Furthermore, the basis for conflict resolution is extracted from the device coordination protocol, including path priority rules in the routing configuration, command priority division in the data conversion rules, and the guarantee requirements of core unmanned equipment in the topology analysis results. Initial control parameter conflict investigation focuses on three types of conflicts: first, time conflicts, where multiple devices' control parameters require the use of the same high-priority communication path at the same time, leading to insufficient path bandwidth; second, spatial conflicts, where the movement trajectories of two devices point to the same spatial coordinates at the same time, posing a collision risk; and third, command conflicts, where the same device receives two different action commands, such as simultaneously requiring the drone to ascend and descend. For time conflicts, based on the path priority list in the protocol, high-priority paths are allocated to the control parameters of the core unmanned equipment, while the control parameters of other devices are adjusted to use the second-highest priority path. For spatial conflicts, the execution time of one device is fine-tuned by referring to the time sequence of the coordinated action sequence. For command conflicts, according to the command priority in the data conversion rules, high-priority commands are retained, and low-priority commands are discarded. After resolving all conflicts, the control parameters are verified a second time to confirm that they comply with all constraints of the equipment coordination protocol. Finally, they are organized into a complete parameter document according to the equipment type. This document is the control parameter set for different types of unmanned equipment.

[0062] In summary, this step is based on the constraints in the equipment coordination protocol. The feasibility of the motion trajectory adjustment instructions to be verified is checked from three aspects: format, dependent path, and motion range. The verified instructions are organized according to equipment type, and finally the trajectory instruction set of different types of unmanned equipment is obtained.

[0063] In summary, this step first extracts the core capability parameters of each device from the state dataset, then breaks down the action requirements of the preset collaborative action plan, matches the device capabilities one by one to complete the capability matching degree analysis, arranges the actions that have passed the analysis in chronological order, and finally obtains the collaborative action sequence of different types of unmanned devices.

[0064] In summary, this step first establishes a unified spatiotemporal reference, then extracts the execution time and space parameters of the trajectory instruction set and the time and space requirements of the cooperative action sequence, and ensures consistency between the two by aligning time nodes and verifying spatial positions. After integration, parameter entries are generated for each device, and finally, preliminary control parameters for heterogeneous unmanned devices are obtained.

[0065] In summary, this step uses the equipment coordination protocol as the basis for conflict resolution. First, it checks for time, space, and command conflicts in the preliminary control parameters. Then, it resolves the conflicts in a targeted manner according to the protocol rules. The resolved parameters are then verified a second time. After confirming that they comply with the protocol constraints, they are compiled into a document, and finally, the control parameter set of different types of unmanned equipment is obtained.

[0066] S5. Encapsulate and encode the control parameter set according to the protocol to generate control instructions for the heterogeneous unmanned equipment; In this embodiment of the invention, the step of protocol encapsulating and encoding the control parameter set to generate control instructions for the heterogeneous unmanned equipment includes: Based on the device coordination protocol, the control parameter set is serialized to obtain a standardized data packet for the heterogeneous unmanned equipment. Based on the routing configuration, the standardized data packets are reconstructed using route-driven methods to obtain protocol-compatible instructions for the standardized data packets; The protocol-compatible instructions are encapsulated into control flow to obtain the control instructions for the heterogeneous unmanned equipment.

[0067] Specifically, data serialization rules are extracted from the equipment coordination protocol. These rules clearly define the data organization format, field arrangement order, and data encoding format. Next, the control parameter sets of heterogeneous unmanned equipment are disassembled, and the parameters are classified according to the equipment type identifier. The control parameters of each equipment are split into independent parameter units, and each parameter unit contains the equipment identifier, specific control parameters, and the corresponding execution time. Then, according to the serialization rules, the split parameter units are converted one by one into JSON format strings, ensuring that the name of each field is completely consistent with the protocol specification. After the conversion is completed, each JSON string is checked to see if it meets the UTF-8 encoding requirements, confirming that the field order does not deviate from the protocol rules. All compliant JSON format data are summarized by equipment type to form a structured data set containing multiple equipment parameter units. This data set is the standardized data package for heterogeneous unmanned equipment.

[0068] Furthermore, key information is extracted from the routing configuration of heterogeneous unmanned devices, including the source device address, destination device address, specified communication protocol type, and header fields required by the protocol for data packet transmission. Next, standardized data packets are parsed, extracting the source and destination device identifiers for each parameter unit. Based on the address mapping relationship in the routing configuration, the device identifiers are converted into corresponding network addresses to determine the communication protocol required for the data packet. Subsequently, the standardized data packets are reconstructed according to the format requirements of the specified communication protocol, adding the required protocol fields to the packet header and embedding the JSON format data of the standardized data packets as the protocol data body. After reconstruction, the format and content of the header fields are checked to ensure they conform to the routing configuration requirements, confirming that the device address mapping is correct and the communication protocol type is consistent with the routing configuration. The resulting reconstructed data is the protocol-compatible instruction for the standardized data packets.

[0069] Furthermore, a fixed structure for control flow encapsulation is determined. This structure comprises three parts: an instruction header, a data area, and a checksum area. The instruction header must include the instruction type, total instruction length, and instruction number. The data area stores the complete content of the protocol-compatible instruction, and the checksum area stores the checksum generated using the CRC checksum method. First, the complete content of the protocol-compatible instruction is filled into the data area, ensuring that the content of the data area is completely consistent with the protocol-compatible instruction, with no omissions or extra characters. Next, the checksum is calculated. The calculation process starts from the first byte of the instruction header and accumulates all bytes of the header and data area sequentially. The accumulated result is modulo a preset fixed value, and the result is the CRC checksum, which is then filled into the checksum area. Subsequently, the contents of each part are assembled in the order of "instruction header - data area - checksum area," ensuring that there are no gaps between the parts and that the value of the total instruction length field completely matches the actual total number of bytes after assembly. Finally, the overall structure after assembly is checked to confirm that the instruction type field is correct, the instruction number is unique, and the checksum matches the calculation result. The resulting complete structured instruction is the control instruction for the heterogeneous unmanned equipment.

[0070] In summary, this step first extracts data serialization rules from the device coordination protocol, then breaks down the control parameter set into independent parameter units according to the device type identifier, then converts the parameter units into specified format strings according to the rules and checks the encoding and field order, and finally summarizes the data that meets the requirements to form a structured set containing multiple device parameter units, ultimately obtaining a standardized data packet for heterogeneous unmanned equipment.

[0071] In summary, this step first extracts key information such as source and destination device addresses, communication protocol types, and protocol header fields from the routing configuration. Then, it parses the standardized data packets to obtain device identifiers and converts them into corresponding network addresses. Subsequently, it adds required fields to the header of the data packets according to the specified communication protocol format, embeds the standardized data packets as the data body, and after checking that the header fields and address mappings are correct, it finally obtains the protocol compatibility instructions for the standardized data packets.

[0072] In summary, this step first determines the control flow encapsulation structure, which includes an instruction header, a data area, and a check area. Then, protocol-compatible instructions are filled into the data area. A check code is generated by accumulating the bytes in the header and data areas and taking the modulo, and then filled into the check area. The parts are assembled in a fixed order and their consistency is checked, ultimately yielding the control instructions for heterogeneous unmanned equipment.

[0073] S6. Monitor the response to the control commands and update the monitored command response data to the status dataset to realize the collaborative operation control of the heterogeneous unmanned equipment.

[0074] In this embodiment of the invention, the step of monitoring the response to the control command and updating the monitored command response data to the status dataset to realize the collaborative operation control of the heterogeneous unmanned equipment includes: The system monitors the response to the control commands sent by the heterogeneous unmanned equipment to obtain the actual response status data corresponding to the control commands. The actual response status data is compared with the expected status of the control command to obtain a consistency report of the control command. Based on the consistency report, the control effectiveness of the actual response status data is measured to obtain the effectiveness evaluation result of the control command; The status dataset is dynamically updated based on the performance evaluation results and the actual response status data.

[0075] Specifically, through the pre-set feedback communication channels of different types of unmanned equipment, real-time status feedback data is received from the equipment after executing control commands. This feedback data includes equipment identification, response time, current position, and core operating parameters. The received feedback data undergoes integrity verification to confirm the presence of all necessary fields. If the equipment identification or response time is missing, a feedback request is re-initiated until complete data is obtained. Next, the data format is verified to conform to the data semantic conversion rules in the equipment coordination protocol; for example, whether the units of the position coordinates match those in the control commands, and whether the values ​​of the operating parameters are within the normal operating range of the equipment. The verified feedback data is categorized by equipment identification. The feedback data from each equipment is organized into a structured data unit containing "equipment identification - response time - current position - core operating parameters." The sum of all structured data units from all equipment constitutes the actual response status data corresponding to the control commands.

[0076] Furthermore, preset expected state parameters are extracted from the control commands. These parameters represent the target states that the control commands expect the device to achieve, including expected movement position, expected operating parameters, and expected response time range. Each expected state parameter is associated with a corresponding device identifier. Each field in the actual response state data is compared one by one with the expected state parameters. For example, the actual position coordinates of the UAV are compared spatially with the expected position coordinates in the control commands to determine if the actual position falls within a reasonable error range around the expected position; the actual driving speed of the unmanned vehicle is compared numerically with the expected driving speed to determine if the expected speed requirement is met. The results of each comparison and the specific difference values ​​are recorded, and the comparison results are organized by device identifier to form a document containing the device identifier, expected state, actual state, comparison results, and difference details. This document serves as the consistency report of the control commands.

[0077] Furthermore, based on the comparison results in the consistency report, if the actual state of a device completely matches the expected state, it is preliminarily determined that the device's execution efficiency of control commands meets the standard. If some fields do not match but the differences are small, the device's response time is considered. If the response time is within the expected range, the efficiency is still considered basically met. If the difference is large or the response time exceeds the expected range, the efficiency is considered substandard. Simultaneously, the percentage of devices meeting the efficiency standard among all types of unmanned equipment is statistically analyzed. Combined with the timeliness of the compliant devices' responses, the overall efficiency level is comprehensively determined, categorized as "Excellent," "Qualified," and "Needs Optimization." The efficiency level, task completion rate, details of compliant devices, and problem analysis of substandard devices are compiled into a structured result, which is the efficiency evaluation result of the control commands.

[0078] Furthermore, the data update strategy is first determined based on the performance evaluation results: if the performance evaluation result is "excellent" or "qualified," the actual response status data is directly written into the status dataset; if it is "to be optimized," a "performance to be optimized" label is added after the data entry for the corresponding device while writing the actual response status data, indicating the details of the differences in the non-compliant fields. The old data entry for the device corresponding to the actual response status data is found in the status dataset, and the "real-time location information" and "real-time operating parameters" fields in the old entry are replaced with the corresponding fields in the actual response status data. Simultaneously, the "data update time" of the entry is updated to the current operation time, and a "performance evaluation result" field is added at the end of the entry, filled with the current performance evaluation result. After the update is completed, the status dataset is checked for completeness to confirm that all updated entries include the device identifier, updated status data, update time, and performance evaluation result, with no missing fields or data mismatches. This ensures that the status dataset accurately reflects the current actual operating status of different types of unmanned equipment, completing the dynamic update of the status dataset.

[0079] In summary, this step receives status feedback data after the execution of control commands through the preset feedback communication channel of the heterogeneous unmanned equipment, performs integrity and format verification on the data, classifies and organizes the verified data into structured data units according to the equipment identifier, and finally obtains the actual response status data corresponding to the control commands.

[0080] In summary, this step extracts the expected state parameters associated with the device identifier from the control command, compares each field of the actual response state data with the corresponding expected parameters one by one and records the results and differences, organizes the comparison information by device identifier, and forms a document containing the device identifier, expected and actual states, comparison results and difference details, and finally obtains a consistency report of the control command.

[0081] In summary, this step focuses on the comparison results of the consistency report, combines the equipment response time to determine whether the performance of a single device meets the standard, counts the percentage of compliant devices and determines the overall performance level, analyzes and organizes the performance level, compliance details and non-compliance issues into a structured result, and finally obtains the performance evaluation result of the control command.

[0082] In summary, this step determines the data update strategy based on the performance evaluation results, matches old entries in the status dataset with device identifiers and replaces relevant fields, adds data update time and performance evaluation results, completes the update after integrity checks, and finally achieves dynamic updating of the status dataset.

[0083] like Figure 2 The diagram shown is a functional block diagram of a reference information generation system based on artificial intelligence and smart home provided in an embodiment of the present invention.

[0084] The heterogeneous unmanned equipment control system 100 with an unsteady architecture described in this invention can be installed in a storage medium. Depending on the functions implemented, the heterogeneous unmanned equipment control system 100 with an unsteady architecture may include a state data acquisition module 101, an architecture situation identification module 102, a protocol coordination generation module 103, a control parameter synthesis module 104, an instruction encapsulation and encoding module 105, and a response monitoring and update module 106. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the storage medium's processor and perform a fixed function, stored in the storage medium's memory.

[0085] In this embodiment, the functions of each module / unit are as follows: The status data acquisition module 101 is used to collect the device type identifier, real-time location information and real-time operating parameters of the different types of unmanned equipment, and generate the status dataset of the different types of unmanned equipment. The architecture situation identification module 102 is used to identify the architecture situation of the non-steady-state architecture of the heterogeneous unmanned equipment based on the state dataset, and obtain the topology analysis results of the heterogeneous unmanned equipment. The protocol coordination generation module 103 is used to coordinate the adaptive message routing mechanism with the preset heterogeneous device data conversion rules based on the topology analysis results to obtain the device coordination protocol of the heterogeneous unmanned device. The control parameter synthesis module 104 is used to coordinate and optimize the motion trajectory adjustment command and inter-device collaborative action planning of the heterogeneous unmanned equipment based on the equipment coordination protocol, so as to obtain the control parameter set of the heterogeneous unmanned equipment. The instruction encapsulation and encoding module 105 is used to encapsulate and encode the control parameter set according to the protocol to generate control instructions for the heterogeneous unmanned equipment. The response monitoring and update module 106 is used to monitor the response to the control command and update the monitored command response data to the status dataset, thereby realizing the collaborative operation control of the heterogeneous unmanned equipment.

[0086] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0087] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0088] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0089] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0090] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A control method for heterogeneous unmanned equipment with an unsteady architecture, characterized in that, include: S1. Collect the device type identifier, real-time location information and real-time operating parameters of the different types of unmanned equipment, and generate the status dataset of the different types of unmanned equipment; S2. Based on the state dataset, perform architecture situation identification on the non-steady-state architecture of the heterogeneous unmanned equipment to obtain the topology analysis results of the heterogeneous unmanned equipment. S3. Based on the topology analysis results, the adaptive message routing mechanism and the preset heterogeneous device data conversion rules are coordinated to obtain the device coordination protocol of the heterogeneous unmanned equipment. S4. Based on the device coordination protocol, coordinate and optimize the motion trajectory adjustment command of the heterogeneous unmanned equipment and the inter-device cooperative action planning to obtain the control parameter set of the heterogeneous unmanned equipment. S5. Encapsulate and encode the control parameter set according to the protocol to generate control instructions for the heterogeneous unmanned equipment; S6. Monitor the response to the control commands and update the monitored command response data to the status dataset to realize the collaborative operation control of the heterogeneous unmanned equipment.

2. The control method for heterogeneous unmanned equipment with an unsteady architecture as described in claim 1, characterized in that, The process involves collecting device type identifiers, real-time location information, and real-time operating parameters of different types of unmanned equipment to generate a status dataset for those different types of unmanned equipment, including: Initiate a device capability query request to a different type of unmanned device and receive the device type identifier returned by the different type of unmanned device; Based on inertial measurement data from the satellite positioning system, obtain the real-time positioning information of the heterogeneous unmanned equipment; Extract real-time operating parameters from the operating status of the heterogeneous unmanned equipment; The type identifier, the real-time positioning information, and the real-time operating parameters are fused from multiple sources to obtain the status dataset of the heterogeneous unmanned equipment.

3. The control method for heterogeneous unmanned equipment with an unsteady architecture as described in claim 1, characterized in that, Based on the state dataset, the non-steady-state architecture of the heterogeneous unmanned equipment is identified to obtain the topology analysis results of the heterogeneous unmanned equipment, including: Obtain the communication connection status and data interaction characteristics among the heterogeneous unmanned devices in the state dataset; Based on the communication connection status and data interaction characteristics, a node relationship diagram of the dynamic association relationship between the different types of unmanned devices is constructed; By mining key nodes in the node relationship graph, the core unmanned equipment that plays a pivotal role in the unsteady architecture is obtained; Based on the changes in the connection status of the core unmanned equipment, the topological stability index of the unsteady architecture is analyzed; By comprehensively evaluating the node relationship diagram, the core unmanned equipment identifier, and the topology stability index, the topology analysis results of the heterogeneous unmanned equipment are obtained.

4. The control method for heterogeneous unmanned equipment with an unsteady architecture as described in claim 1, characterized in that, The step involves coordinating the adaptive message routing mechanism with preset heterogeneous device data conversion rules based on the topology analysis results to obtain the device coordination protocol for the heterogeneous unmanned devices, including: Based on the topology analysis results, dynamic weight allocation is performed on the key communication paths between the heterogeneous unmanned devices to obtain a path priority list for the heterogeneous unmanned devices. Based on the path priority list, the message forwarding strategy of the adaptive message routing mechanism is adapted and adjusted to obtain the routing configuration of the heterogeneous unmanned equipment. Based on preset heterogeneous device data conversion rules, filter data semantic conversion rules that match the device type identifier; The routing configuration and the data semantic transformation rules are fused together to obtain the device coordination protocol for the heterogeneous unmanned equipment.

5. The control method for heterogeneous unmanned equipment with an unsteady architecture as described in claim 4, characterized in that, Based on the topology analysis results, dynamic weight allocation is performed on the critical communication paths between the heterogeneous unmanned devices to obtain a path priority list for the heterogeneous unmanned devices, including: Based on the connection stability index and delay characteristic parameters in the topology analysis results, the reliability and real-time performance of the communication path between the heterogeneous unmanned devices are evaluated, and path evaluation results are generated. Based on the path evaluation results and the difference in mission criticality between the heterogeneous unmanned devices, a weighting factor for the communication path between the heterogeneous unmanned devices is determined, wherein the calculation formula for the weighting factor is as follows: ； In the formula, For the first The and the first The weighting factor of the communication path between the heterogeneous unmanned devices. The delay sensitivity coefficient in the topology analysis results is... The first in the topology analysis results The and the first The connection stability index of the communication path between the heterogeneous unmanned devices. The preset task criticality penalty coefficient, For the first The and the first The delay characteristic parameters of the communication path between the heterogeneous unmanned devices. For the aforementioned heterogeneous unmanned equipment Task criticality For the aforementioned heterogeneous unmanned equipment Task criticality; Based on the weighting factors, the key communication paths between the heterogeneous unmanned devices are sorted by multi-dimensional utility to obtain a path priority list for the heterogeneous unmanned devices.

6. The control method for heterogeneous unmanned equipment with an unsteady architecture as described in claim 1, characterized in that, Based on the device coordination protocol, the motion trajectory adjustment commands and inter-device cooperative action planning of the heterogeneous unmanned devices are coordinated and optimized to obtain the control parameter set of the heterogeneous unmanned devices, including: Based on the device coordination protocol, the feasibility of the motion trajectory adjustment instructions of the heterogeneous unmanned equipment is verified to obtain the trajectory instruction set of the heterogeneous unmanned equipment. A capability matching degree analysis is performed on the inter-device cooperative action planning of the heterogeneous unmanned equipment to obtain the cooperative action sequence of the heterogeneous unmanned equipment. The trajectory instruction set and the cooperative action sequence are spatiotemporally fused to obtain the preliminary control parameters of the heterogeneous unmanned equipment. Based on the device coordination protocol, conflict resolution is performed on the preliminary control parameters to obtain the control parameter set of the heterogeneous unmanned equipment.

7. The control method for heterogeneous unmanned equipment with an unsteady architecture as described in claim 1, characterized in that, The step of encapsulating and encoding the control parameter set according to a protocol to generate control commands for the heterogeneous unmanned equipment includes: Based on the device coordination protocol, the control parameter set is serialized to obtain a standardized data packet for the heterogeneous unmanned equipment. Based on the routing configuration, the standardized data packets are reconstructed using route-driven methods to obtain protocol-compatible instructions for the standardized data packets; The protocol-compatible instructions are encapsulated into control flow to obtain the control instructions for the heterogeneous unmanned equipment.

8. The control method for heterogeneous unmanned equipment with an unsteady architecture as described in claim 1, characterized in that, The step of monitoring the response to the control commands and updating the monitored command response data to the status dataset to achieve collaborative operation control of the heterogeneous unmanned equipment includes: The system monitors the response to the control commands sent by the heterogeneous unmanned equipment to obtain the actual response status data corresponding to the control commands. The actual response status data is compared with the expected status of the control command to obtain a consistency report of the control command. Based on the consistency report, the control effectiveness of the actual response status data is measured to obtain the effectiveness evaluation result of the control command; The status dataset is dynamically updated based on the performance evaluation results and the actual response status data.

9. A heterogeneous unmanned equipment control system with an unsteady architecture, characterized in that, include: The status data acquisition module is used to collect the device type identifier, real-time location information and real-time operating parameters of different types of unmanned equipment, and generate the status dataset of the different types of unmanned equipment. The architecture situation identification module is used to identify the unsteady architecture of the heterogeneous unmanned equipment based on the state dataset, and obtain the topology analysis results of the heterogeneous unmanned equipment. The protocol coordination generation module is used to coordinate the adaptive message routing mechanism with the preset heterogeneous device data conversion rules based on the topology analysis results to obtain the device coordination protocol of the heterogeneous unmanned device. The control parameter synthesis module is used to coordinate and optimize the motion trajectory adjustment instructions and inter-device collaborative action planning of the heterogeneous unmanned equipment based on the equipment coordination protocol, so as to obtain the control parameter set of the heterogeneous unmanned equipment. The instruction encapsulation and encoding module is used to encapsulate and encode the control parameter set according to the protocol to generate control instructions for the heterogeneous unmanned equipment. The response monitoring and update module is used to monitor the response to the control commands and update the monitored command response data to the status dataset, thereby realizing the collaborative operation control of the heterogeneous unmanned equipment.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 8.

Images