Deep-sea test task scheduling and management method based on multi-platform collaboration

Abstract

The present invention relates to the technical field of deep-sea test task management, and more specifically to a method for scheduling and managing deep-sea test tasks based on multi-platform collaboration, comprising the following steps: S1: generating an environmental adaptability task priority list; S2: generating a multi-platform interface configuration plan based on the priority list in S1; S3: constructing a spatiotemporal conflict probability model and outputting an obstacle avoidance path and resource allocation strategy; S4: generating a data link topology map; and S5: calculating a deep-sea test efficiency index and optimizing the priority list of subsequent test tasks. The present invention achieves intelligent scheduling and efficient execution of deep-sea test tasks through dynamic optimization of task priorities, spatiotemporal conflict prediction, data link adaptive regulation, and efficiency index feedback optimization, thereby improving the stability, communication reliability, and resource utilization of multi-platform collaborative operations.

Description

Technical Field

[0001] The present invention relates to the technical field of deep-sea test task management, and in particular to a deep-sea test task scheduling and management method based on multi-platform collaboration. Background Art

[0002] Deep-sea test missions involve multi-platform collaborative operations, including deep-sea test support vessels, underwater operating platforms and the joint execution of various test equipment, covering multiple sub-tasks such as deployment and installation, energy supply, and data transmission. Due to the extreme pressure, highly dynamic flow field and complex sea conditions in the deep-sea environment, the execution of test missions requires precise task scheduling and resource allocation.

[0003] Existing technologies for deep-sea experimental mission scheduling typically employ static scheduling methods based on fixed task plans. These methods fail to fully account for real-time sea state changes, dynamic equipment status assessment, and the complexity of multi-platform collaborative operations. This leads to problems such as irrational resource allocation, unstable communications, and delayed task priority adjustments during mission execution. Furthermore, current deep-sea task scheduling methods lack a systematic spatiotemporal conflict assessment mechanism, making it difficult to effectively predict and avoid spatial occupancy conflicts between platforms, hindering the smooth execution of missions. Furthermore, existing methods often employ fixed routing strategies for data link topology optimization, making it difficult to adapt to the complex dynamic changes in deep-sea environments, limiting the stability and efficiency of data transmission. Therefore, a deep-sea experimental mission scheduling and management method based on multi-platform collaboration is urgently needed to address these issues. Summary of the Invention

[0004] Based on the above objectives, the present invention provides a deep-sea test task scheduling and management method based on multi-platform collaboration.

[0005] The deep-sea test task scheduling and management method based on multi-platform collaboration includes the following steps:

[0006] S1: Based on the deep-sea test objectives, equipment characteristics, and real-time sea condition data, the test tasks are broken down into deployment and installation, energy supply, and data transmission subtasks. A priority list of environmental adaptability tasks is generated based on the equipment failure risk factor and the dynamic sea condition threshold.

[0007] S2: Based on the priority list in S1, a dynamic protocol conversion interface is designed for the deep-sea test support vessel and underwater operation platform. Based on the device communication protocol type and platform load capacity, a multi-platform interface configuration solution is generated.

[0008] S3: Calls the interface configuration scheme of S2, combines the platform's real-time position, task sequence, and deep-sea flow field prediction data, builds a spatiotemporal conflict probability model, and outputs obstacle avoidance paths and resource allocation strategies;

[0009] S4: Based on the resource allocation strategy of S3, multi-platform sensor data is collected in real time according to a unified data protocol. The data collection frequency and transmission path are dynamically adjusted according to the quality of the communication link to generate a data link topology map.

[0010] S5: Based on the link topology diagram of S4, extract the equipment status parameters and task completion indicators, calculate the deep-sea test efficiency index, and use it to optimize the priority list of subsequent test tasks.

[0011] Optionally, the S1 specifically includes:

[0012] S11: Based on the set deep-sea test objectives, the technical indicators of the mission are decomposed to form a set of mission operation instructions. Based on the task content and operation sequence in the instruction set, the mission operation instructions are clearly divided into deployment and installation instructions, energy supply instructions, and data transmission instructions;

[0013] S12: Obtain the device characteristic parameters of the structural dimensions, mass, power consumption, communication protocol type, and installation method of the equipment involved in the deep-sea test, and build a device characteristic parameter database;

[0014] S13: By deploying buoy sensors and ocean observation platforms in the test sea area, real-time sea condition data such as current speed, wave height, seawater density and seabed topography slope are collected to construct a real-time sequence of sea condition data.

[0015] Optionally, the S1 further includes:

[0016] S14: Use the failure mode impact and criticality analysis method to calculate the equipment failure risk factor F based on the equipment's mean time between failures;

[0017] S15: Set dynamic sea condition thresholds based on deep-sea operation safety guidelines ;

[0018] S16: Based on the equipment failure risk factor F and the dynamic threshold of sea conditions Conduct environmental adaptability risk level assessment and calculate the environmental adaptability coefficient. The formula is: , where E is the environmental adaptability coefficient, and are weight coefficients respectively;

[0019] S17: Based on the calculated environmental adaptability coefficient E, the deployment and installation, energy supply, and data transmission instruction sets decomposed in S11 are sorted to generate an environmental adaptability task priority list.

[0020] Optionally, the S2 specifically includes:

[0021] S21: Call the environmental adaptability task priority list generated by S1, determine the communication protocol type of the corresponding device according to the subtask priority from high to low, and obtain the data rate, interface type and communication delay parameters of the protocol;

[0022] S22: Based on the communication interface specifications and data transmission capabilities of the deep-sea test support ship and the underwater operation platform, dynamic protocol conversion interfaces are designed for protocol interconnection. The interfaces include a protocol conversion chip, a level matching circuit, and a data buffer.

[0023] S23: Set the platform load capacity threshold and, based on the platform's energy consumption carrying capacity, data processing capacity, and the interface's real-time bandwidth carrying capacity, specify the maximum data flow value that each platform interface can carry.

[0024] S24: Based on the data rate, communication delay parameters of the device communication protocol and the maximum data flow value of the platform interface, the matching degree of the device protocol and the platform interface capability is compared item by item, and parameter mapping of the protocol conversion interface and the platform interface is performed to generate a multi-platform interface configuration plan.

[0025] Optionally, the S3 specifically includes:

[0026] S31: Obtain the real-time location information of the deep-sea test support ship and the underwater operating platform, extract the platform's longitude and latitude coordinates, operating depth, and heading data, and construct the platform's spatial position vector;

[0027] S32: Generate a task execution time window according to the task timing requirements, establish a time series interval, and form a task time series matrix;

[0028] S33: Based on numerical simulation methods, deep-sea flow field prediction data is obtained, including flow field velocity distribution and flow field direction distribution, and the flow field spatiotemporal distribution matrix is constructed;

[0029] S34: Correlate the platform spatial position vector, the task time sequence matrix, and the flow field spatiotemporal distribution matrix to obtain the spatial intersection subintervals of each task platform overlapping with the flow field area under different task time sequences;

[0030] S35: establishing a spatiotemporal conflict probability model based on the data of the spatial intersection interval, and obtaining a conflict probability index through calculation;

[0031] S36: Setting an obstacle avoidance probability threshold based on the conflict probability index. When the conflict probability index exceeds the obstacle avoidance probability threshold, defining the corresponding platform task as a potential conflict event and outputting a conflict-avoiding path trajectory through a path optimization algorithm.

[0032] S37: Based on the generated obstacle avoidance path trajectory, combined with the platform load capacity and task priority, the platform resource allocation amount is calculated and a resource allocation strategy is generated.

[0033] Optionally, the S35 specifically includes:

[0034] S351: At each discrete time point, based on the range of the spatial intersection sub-interval, calculate the three-dimensional volume occupied by the corresponding intersection sub-interval;

[0035] S352: Determine the duration of the job corresponding to the corresponding time point according to the task timing matrix;

[0036] S353: At the same time point, calculating the three-dimensional volume contained in the actual operating space range of the platform;

[0037] S354: Compare the three-dimensional volume of the intersection sub-interval with the actual working space volume of the platform to obtain the instantaneous conflict probability;

[0038] S355: The instantaneous conflict probabilities corresponding to all discrete time points are weighted and accumulated according to their respective operation durations, and compared with the total duration of all time points to obtain an overall spatiotemporal conflict probability index.

[0039] Optionally, the S37 specifically includes:

[0040] S371: Extracting the actual driving path length and operation time interval of each platform based on the obstacle avoidance path trajectory generated in step S36;

[0041] S372: Obtain the load capacity data of each platform and determine the upper limit of the energy supply capacity and the upper limit of the data transmission bandwidth of each platform;

[0042] S373: Calculate the energy consumption and data transmission volume required for each platform to perform a specific task based on the task priority list and the actual driving path length and operation duration of each task platform;

[0043] S374: Accumulate the energy consumption and data transmission volume required for each platform to perform a specific task, compare the accumulated energy consumption and data transmission volume with the corresponding platform load capacity data, and calculate the remaining margin of the load capacity of each platform when performing the task;

[0044] S375: Based on the remaining margin of the platform's load capacity, balance and allocate the energy and data transmission resources of the task platform, determine the specific energy allocation quota and data bandwidth allocation quota of each platform during the task execution, and thus generate a platform resource allocation strategy.

[0045] Optionally, the S4 specifically includes:

[0046] S41: Based on the resource allocation strategy generated in S3, determine the platforms and device nodes participating in the test task and obtain the spatial location information of all nodes;

[0047] S42: collecting communication link quality data of sensors on each node in real time through a unified data protocol, including signal strength, data transmission rate, and communication delay parameters;

[0048] S43: Correlation analysis is performed on the spatial location information of each node and the communication link quality data, a topological structure is established with nodes as vertices and communication links as edges, and the connection reliability of the communication link between any two adjacent nodes is calculated;

[0049] S44: Determine the optimal communication path between the nodes based on the connection reliability of the communication link between the nodes to form a complete data transmission path set;

[0050] S45: Based on nodes and communication links, combined with the data transmission path set, a complete data link topology map is constructed.

[0051] Optionally, the S5 specifically includes:

[0052] S51: Based on the data link topology map generated in step S4, the status data of each node device is obtained in real time, including device power consumption, operating time, and fault alarm information, and the device status parameters are extracted;

[0053] S52: According to the actual progress of the tasks executed on each platform, the planned task duration and the actual task completion duration are obtained, and the task completion index Q is calculated;

[0054] S53: Calculate the deep-sea test effectiveness index by combining equipment status parameters and task completion indicators ; The formula is: , where The actual number of devices that experienced equipment failure; is the total number of devices participating in the test; are the weight coefficients of task progress and equipment failure, respectively, and satisfy ;

[0055] S54: Based on the calculated deep-sea test effectiveness index E, dynamically adjust the priority of each task in the environmental adaptability task priority list generated in S1, and update and generate a new test task priority list.

[0056] Optionally, the S54 specifically includes:

[0057] S541: Deep sea test effectiveness index calculated based on S53 , and compare it with the preset test performance benchmark value Make comparisons;

[0058] when When , the environmental adaptability task priority list generated by S1 remains unchanged;

[0059] when When , the priority score of each task in the environmental adaptability task priority list generated in step S1 is adjusted; the specific adjustment formula is: , where is the adjusted priority score of the j-th task; To adjust the priority score of the jth task; Adjust sensitivity coefficients for priorities;

[0060] S542: The task priority score adjusted by S541 is Rearrange from high to low to form an optimized environmental adaptability task priority list.

[0061] Beneficial effects of the present invention:

[0062] The present invention realizes the intelligent scheduling of deep-sea test tasks through task decomposition and the construction of an environmental adaptability task priority list; adopts a dynamic protocol conversion interface, combines the equipment communication protocol and the platform load capacity, and generates a multi-platform interface configuration scheme to ensure equipment adaptability and improve operational efficiency; at the same time, based on real-time position, task timing and deep-sea flow field prediction data, a spatiotemporal conflict probability model is established to accurately evaluate and avoid space occupancy conflicts, optimize the operation path, and improve the safety and coordination of task execution.

[0063] The present invention calculates the deep-sea test efficiency index by extracting equipment status and task completion indicators, and optimizes the task priority list based on the index to achieve dynamic optimization of task scheduling. Compared with existing technologies, it can achieve intelligent coordination of task scheduling, resource allocation and data transmission under complex sea conditions, thereby improving test efficiency and execution reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0065] Figure 1 Schematic diagram of a method for scheduling and managing deep-sea test tasks according to an embodiment of the present invention;

[0066] Figure 2 4 is a flow chart of calculating the conflict probability index according to an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0068] like Figure 1-Figure 2 As shown, the deep-sea test task scheduling and management method based on multi-platform collaboration includes the following steps:

[0069] S1: Based on the deep-sea test objectives, equipment characteristics, and real-time sea condition data, the test tasks are broken down into deployment and installation, energy supply, and data transmission subtasks. A priority list of environmental adaptability tasks is generated based on the equipment failure risk factor and the dynamic sea condition threshold.

[0070] S2: Based on the priority list in S1, a dynamic protocol conversion interface is designed for deep-sea test support vessels and underwater operating platforms. Based on the device communication protocol type and platform load capacity, a multi-platform interface configuration scheme is generated to complete the structure-power-control adaptation of the equipment and platform.

[0071] S3: Calls the interface configuration scheme of S2, combines the platform's real-time position, task sequence, and deep-sea flow field prediction data, builds a spatiotemporal conflict probability model, and outputs obstacle avoidance paths and resource allocation strategies;

[0072] S4: Based on the resource allocation strategy of S3, multi-platform sensor data is collected in real time according to a unified data protocol. The data collection frequency and transmission path are dynamically adjusted according to the quality of the communication link to generate a data link topology map.

[0073] S5: Based on the link topology diagram of S4, extract the equipment status parameters and task completion indicators, calculate the deep-sea test efficiency index, and use it to optimize the priority list of subsequent test tasks.

[0074] S1 specifically includes:

[0075] S11: Based on the set deep-sea test objectives, the technical indicators of the mission are decomposed to form a set of mission operation instructions. Based on the task content and operation sequence in the instruction set, the mission operation instructions are clearly divided into deployment and installation instructions, energy supply instructions, and data transmission instructions;

[0076] S12: Obtain the device characteristic parameters of the structural dimensions, mass, power consumption, communication protocol type, and installation method of the equipment involved in the deep-sea test, and build a device characteristic parameter database;

[0077] S13: By deploying buoy sensors and ocean observation platforms in the test sea area, real-time sea condition data such as current speed, wave height, seawater density and seabed topography slope are collected to construct a real-time sequence of sea condition data.

[0078] The S1 also includes:

[0079] S14: Use the Failure Mode Effects and Criticality Analysis (FMECA) method to calculate the equipment failure risk factor F based on the mean time between failures of the equipment. The formula is: , where MTBF is the mean time between failures of equipment, is the equipment structure complexity coefficient, Deploy depth factor for the device;

[0080] ,in, The number of key components of the equipment; The complexity coefficient of the connection between equipment components (the value range is 1.0~2.5, and different connection methods have different empirical values, such as 1.2 for welded connection, 1.5 for bolted connection, and 2.0 for integrated connection); is the degree of equipment standardization (range 0.5~2.0, higher values indicate lower standardization);

[0081] ,in, The actual deployment depth of the device; Design the equipment to withstand depth;

[0082] S15: Set dynamic sea condition thresholds based on deep-sea operation safety guidelines , whose expression is: ,in, is the current velocity threshold, is the wave height threshold, is the seawater density threshold;

[0083] S16: Based on the equipment failure risk factor F and the dynamic threshold of sea conditions Conduct environmental adaptability risk level assessment and calculate the environmental adaptability coefficient. The formula is: , where E is the environmental adaptability coefficient, and are weight coefficients, and ;

[0084] S17: Based on the calculated environmental adaptability coefficient E, the deployment and installation, energy supply, and data transmission instruction sets decomposed in S11 are sorted to generate a priority list of environmental adaptability tasks. The above steps clarify the task decomposition standards, combine the characteristic parameters of the equipment itself and real-time sea conditions data, and use specific algorithms to achieve accurate assessment of the task risk level, which can more scientifically guide the execution sequence of tasks and improve the reliability of task execution in complex deep-sea environments.

[0085] S2 specifically includes:

[0086] S21: Call the environmental adaptability task priority list generated by S1, determine the communication protocol type of the corresponding device according to the subtask priority from high to low, and obtain the data rate, interface type and communication delay parameters of the protocol;

[0087] S22: Based on the communication interface specifications and data transmission capabilities of the deep-sea test support ship and the underwater operation platform, dynamic protocol conversion interfaces are designed for protocol interconnection. The interfaces include a protocol conversion chip, a level matching circuit, and a data buffer.

[0088] S23: Set a platform load capacity threshold and, based on the platform's energy consumption carrying capacity, data processing capacity, and the interface's real-time bandwidth carrying capacity, determine the maximum data flow value that each platform interface can carry. The maximum data flow value of the platform interface is calculated as follows: , where The maximum data flow rate that the platform interface can carry; The energy consumption carrying capacity of the platform interface; The interface power consumption corresponding to the unit data flow; Provides platform interface data processing capabilities; The data processing consumption corresponding to the unit data flow; Real-time bandwidth carrying capacity of the platform interface;

[0089] S24: Based on the data rate, communication delay parameters of the device communication protocol and the maximum data flow value of the platform interface, the matching degree of the device protocol and the platform interface capability is compared item by item, and parameter mapping of the protocol conversion interface and the platform interface is performed to generate a multi-platform interface configuration scheme that meets the requirements of structural adaptation, power adaptation and control adaptation.

[0090] S24 specifically includes:

[0091] S241: Obtain the data rate and communication delay parameters of the device communication protocol, and call the maximum data flow value of the platform interface obtained in step S23 to calculate the data rate matching index between the protocol and the platform interface , the specific calculation formula is: , where is the data rate matching index; The maximum data flow rate that the platform interface can carry; The data rate required by the device communication protocol;

[0092] S242: Calculate the delay matching index between the protocol and the platform interface based on the device communication delay parameter and the real-time communication delay index of the platform interface. , the specific calculation formula is: , where; It is the real-time communication delay indicator of the platform interface; The maximum communication delay allowed by the device communication protocol;

[0093] S243: Perform comprehensive weighted calculation on the calculated data rate matching index and delay matching index to obtain the total matching index between the device protocol and the platform interface. The calculation formula is: , where It is the overall matching index between the device protocol and the platform interface; and are the weighting coefficients of data rate and delay matching respectively;

[0094] S244: Based on the total matching index of the device protocol and the platform interface, the parameter mapping relationship between the device protocol and the platform interface is determined, and then the configuration parameters of the protocol conversion interface are generated to form a multi-platform interface configuration scheme that meets the requirements of structural adaptation, power adaptation, and control adaptation. The above steps effectively improve the adaptation accuracy between the device communication protocol and the platform interface through the matching calculation and the determination of the parameter mapping relationship, and realize the precise configuration of the multi-platform communication interface parameters in the deep-sea test.

[0095] S3 specifically includes:

[0096] S31: Obtain the real-time location information of the deep-sea test support ship and the underwater operating platform, extract the platform's longitude and latitude coordinates, operating depth, and heading data, and construct the platform's spatial position vector;

[0097] S32: Generate a task execution time window according to the task timing requirements, establish a time series interval, and form a task time series matrix;

[0098] S33: Based on numerical simulation methods, deep-sea flow field prediction data is obtained, including flow field velocity distribution and flow field direction distribution, and the flow field spatiotemporal distribution matrix is constructed;

[0099] S34: Correlate the platform spatial position vector, the task time sequence matrix, and the flow field spatiotemporal distribution matrix to obtain the spatial intersection subintervals of each task platform overlapping with the flow field area under different task time sequences;

[0100] Specifically, the task time series matrix is first discretized to obtain a series of discrete time points ;in, represents the i-th discrete time point;

[0101] Then, at each discrete time point Next, based on the platform spatial position vector, the spatial coordinates of the platform at this time point are defined as , D represents the operating influence radius of the platform at that time point, and the platform space area set is constructed , the expression is:

[0102] , where x, y, z represent the coordinates of any point in the space to be solved;

[0103] Then, at each discrete time point Next, extract the flow field space region set corresponding to the time point from the flow field spatiotemporal distribution matrix ,in Indicates that all the deep sea flow field prediction data are satisfied Coordinate point;

[0104] Then, for each discrete time point , the platform space area is aggregated and the flow field spatial region collection Perform set intersection operation to obtain spatial intersection subintervals , the expression is: ;

[0105] Finally, each discrete time point is sorted in chronological order. The corresponding spatial intersection subinterval The complete spatial intersection interval of each task platform overlapping with the flow field area under different task timings is obtained by merging.

[0106] S35: establishing a spatiotemporal conflict probability model based on the data of the spatial intersection interval, and obtaining a conflict probability index through calculation;

[0107] S36: Setting an obstacle avoidance probability threshold based on the conflict probability index. When the conflict probability index exceeds the obstacle avoidance probability threshold, defining the corresponding platform task as a potential conflict event and outputting a conflict-avoiding path trajectory through a path optimization algorithm.

[0108] S37: Based on the generated obstacle avoidance path trajectory, combined with the platform load capacity and task priority, the platform resource allocation amount is calculated and a resource allocation strategy is generated; through the above steps, under different sea conditions and multi-platform collaborative operation conditions, the time and space conflict detection and avoidance between the deep-sea operation platform and the support ship can be effectively realized, thereby improving the safety of multi-platform collaborative operations and the efficiency of task execution.

[0109] S35 specifically includes:

[0110] S351: At each discrete time point, based on the range of the spatial intersection sub-interval, calculate the three-dimensional volume occupied by the corresponding intersection sub-interval;

[0111] S352: Determine the duration of the job corresponding to the corresponding time point according to the task timing matrix;

[0112] S353: At the same time point, calculating the three-dimensional volume contained in the actual operating space range of the platform;

[0113] S354: Compare the three-dimensional volume of the intersection sub-interval with the actual working space volume of the platform to obtain the instantaneous conflict probability;

[0114] S355: The instantaneous conflict probabilities corresponding to all discrete time points are weighted and accumulated according to their respective operation durations, and compared with the total duration of all time points to obtain an overall spatiotemporal conflict probability index.

[0115] Specifically, first, based on the spatial intersection subinterval obtained by S34 , calculate each discrete time point The volume of the intersection subinterval of the lower space is: , where is the volume of the spatial intersection subinterval at the i-th discrete time point;

[0116] Then, according to the task timing matrix, determine the duration of each task platform at the zth discrete time point , in hours;

[0117] Then, according to the actual operating space area set of the platform at the i-th discrete time point , calculate the corresponding actual working space volume, the formula is: , where is the actual operating space volume of the platform at the i-th discrete time point;

[0118] Then, according to the above calculation, the volume of the spatial intersection subinterval , duration and actual working space volume , calculate the instantaneous conflict probability at the i-th discrete time point, the calculation formula is: , where is the instantaneous conflict probability at the i-th discrete time point;

[0119] S355: The instantaneous conflict probability of all discrete time points Perform time-weighted accumulation to obtain the total space-time conflict probability index, which is expressed as: , where is the total spatiotemporal conflict probability index; N is the total number of discrete time points; and the spatiotemporal conflict probability model expression established thereby is: .

[0120] S37 specifically includes:

[0121] S371: Extracting the actual driving path length and operation time interval of each platform based on the obstacle avoidance path trajectory generated in step S36;

[0122] S372: Obtain the load capacity data of each platform and determine the upper limit of the energy supply capacity and the upper limit of the data transmission bandwidth of each platform;

[0123] S373: Calculate the energy consumption and data transmission volume required for each platform to perform a specific task based on the task priority list and the actual driving path length and operation duration of each task platform;

[0124] S374: Accumulate the energy consumption and data transmission volume required for each platform to perform a specific task, compare the accumulated energy consumption and data transmission volume with the corresponding platform load capacity data, and calculate the remaining margin of the load capacity of each platform when performing the task;

[0125] S375: Based on the remaining margin of the platform's load capacity, balance and allocate the mission platform's energy and data transmission resources, determine the specific energy allocation quota and data bandwidth allocation quota of each platform during the mission execution, and thus generate a platform resource allocation strategy; through the above steps, based on the obstacle avoidance path, fully consider the platform's actual available resources and mission priorities, and achieve accurate scheduling and allocation of multi-platform resources, thereby improving the mission execution efficiency and overall collaborative operation capabilities of deep-sea experiments.

[0126] S4 specifically includes:

[0127] S41: Based on the resource allocation strategy generated in S3, determine the platforms and device nodes participating in the test task and obtain the spatial location information of all nodes;

[0128] S42: collecting communication link quality data of sensors on each node in real time through a unified data protocol, including signal strength, data transmission rate, and communication delay parameters;

[0129] S43: Correlate and analyze the spatial location information of each node with the communication link quality data, establish a topological structure with nodes as vertices and communication links as edges, and calculate the connection reliability of the communication link between any two adjacent nodes; the formula is: Where, is the connection reliability of the communication link between node i and node j; is the signal strength between node i and node j; is the data transmission rate between node i and node j; is the communication delay between node i and node j;

[0130] S44: Determine the optimal communication path between the nodes based on the connection reliability of the communication link between the nodes to form a complete data transmission path set;

[0131] S45: Based on nodes and communication links, combined with a set of data transmission paths, a complete data link topology diagram is constructed; the above steps clarify the communication connection relationship between multiple platforms and device nodes, improve the reliability of data collection and transmission, and provide accurate support for real-time data management and subsequent test performance evaluation.

[0132] S5 specifically includes:

[0133] S51: Based on the data link topology map generated in step S4, the status data of each node device is obtained in real time, including device power consumption, operating time, and fault alarm information, and the device status parameters are extracted;

[0134] S52: Based on the actual progress of the tasks executed on each platform, the planned task duration and the actual task completion duration are obtained, and the task completion index Q is calculated. The formula is: , where Q is the task completion index; The actual completion time of the task; Plan the duration of the task;

[0135] S53: Calculate the deep-sea test effectiveness index by combining equipment status parameters and task completion indicators ; The formula is: , where The actual number of devices that experienced equipment failure; is the total number of devices participating in the test; are the weight coefficients of task progress and equipment failure, respectively, and satisfy ;

[0136] S54: Based on the calculated deep-sea test efficiency index E, dynamically adjust the priority of each task in the environmental adaptability task priority list generated by S1, and update and generate a new test task priority list; the above steps form a clear deep-sea test efficiency evaluation through real-time monitoring and quantitative analysis of equipment status parameters and task completion, which is conducive to optimizing and adjusting the execution order and resource allocation plan of subsequent test tasks.

[0137] S54 specifically includes:

[0138] S541: Deep sea test effectiveness index calculated based on S53 , and compare it with the preset test performance benchmark value Make comparisons;

[0139] when When , the environmental adaptability task priority list generated by S1 remains unchanged;

[0140] when When , the priority score of each task in the environmental adaptability task priority list generated in step S1 is adjusted; the specific adjustment formula is: , where is the priority score of the j-th task after adjustment; To adjust the priority score of the jth task; Adjust sensitivity coefficients for priorities;

[0141] S542: The task priority score adjusted by S541 is Rearrange from high to low to form an optimized environmental adaptability task priority list; the above steps realize real-time dynamic optimization of the task priority list through dynamic feedback of the deep-sea test efficiency index, effectively improving the adaptability and execution efficiency of the test tasks.

[0142] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0143] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for scheduling and managing deep-sea test tasks based on multi-platform collaboration, characterized in that: The following steps are involved: S1: Based on the deep-sea test objectives, equipment characteristics, and real-time sea condition data, the test tasks are broken down into deployment and installation, energy supply, and data transmission subtasks. A priority list of environmental adaptability tasks is generated based on the equipment failure risk factor and the dynamic sea condition threshold. S2: Based on the priority list in S1, a dynamic protocol conversion interface is designed for the deep-sea test support vessel and underwater operation platform. Based on the device communication protocol type and platform load capacity, a multi-platform interface configuration solution is generated. S3: Calls the interface configuration scheme of S2, combines the platform's real-time position, task sequence, and deep-sea flow field prediction data, builds a spatiotemporal conflict probability model, and outputs obstacle avoidance paths and resource allocation strategies; The S3 specifically includes: S31: Obtain the real-time location information of the deep-sea test support ship and the underwater operating platform, extract the platform's longitude and latitude coordinates, operating depth, and heading data, and construct the platform's spatial position vector; S32: Generate a task execution time window according to the task timing requirements, establish a time series interval, and form a task time series matrix; S33: Based on numerical simulation methods, deep-sea flow field prediction data is obtained, including flow field velocity distribution and flow field direction distribution, and the flow field spatiotemporal distribution matrix is constructed; S34: Correlate the platform spatial position vector, the task time sequence matrix, and the flow field spatiotemporal distribution matrix to obtain the spatial intersection subintervals of each task platform overlapping with the flow field area under different task time sequences; S35: establishing a spatiotemporal conflict probability model based on the data of the spatial intersection interval, and obtaining a conflict probability index through calculation; S36: Setting an obstacle avoidance probability threshold based on the conflict probability index. When the conflict probability index exceeds the obstacle avoidance probability threshold, defining the corresponding platform task as a potential conflict event and outputting a conflict-avoiding path trajectory through a path optimization algorithm. S37: Based on the generated obstacle avoidance path trajectory, combined with the platform load capacity and task priority, the platform resource allocation amount is calculated and a resource allocation strategy is generated; The S35 specifically includes: S351: At each discrete time point, based on the range of the spatial intersection sub-interval, calculate the three-dimensional volume occupied by the corresponding intersection sub-interval; S352: Determine the duration of the job corresponding to the corresponding time point according to the task timing matrix; S353: At the same time point, calculating the three-dimensional volume contained in the actual operating space range of the platform; S354: Compare the three-dimensional volume of the intersection sub-interval with the actual working space volume of the platform to obtain the instantaneous conflict probability; S355: The instantaneous conflict probabilities corresponding to all discrete time points are weighted and accumulated according to their respective operation durations, and compared with the total duration of all time points to obtain an overall spatiotemporal conflict probability index; S4: Based on the resource allocation strategy of S3, multi-platform sensor data is collected in real time according to a unified data protocol. The data collection frequency and transmission path are dynamically adjusted according to the quality of the communication link to generate a data link topology map. S5: Based on the link topology diagram of S4, extract the equipment status parameters and task completion indicators, calculate the deep-sea test efficiency index, and use it to optimize the priority list of subsequent test tasks.

2. The method for scheduling and managing deep-sea test tasks based on multi-platform collaboration according to claim 1 is characterized in that: Said S1 specifically includes: S11: Based on the set deep-sea test objectives, the technical indicators of the mission are decomposed to form a set of mission operation instructions. Based on the task content and operation sequence in the instruction set, the mission operation instructions are clearly divided into deployment and installation instructions, energy supply instructions, and data transmission instructions; S12: Obtain the device characteristic parameters of the structural dimensions, mass, power consumption, communication protocol type, and installation method of the equipment involved in the deep-sea test, and build a device characteristic parameter database; S13: By deploying buoy sensors and ocean observation platforms in the test sea area, real-time sea condition data such as current speed, wave height, seawater density and seabed topography slope are collected to construct a real-time sequence of sea condition data.

3. The method for scheduling and managing deep-sea test tasks based on multi-platform collaboration according to claim 2 is characterized in that: Said S1 further comprises: S14: Use the failure mode impact and criticality analysis method and the mean time between failures of the equipment as the basis to calculate the equipment failure risk factor F; the formula is: , where MTBF is the mean time between failures of equipment, is the equipment structure complexity coefficient, Deploy depth factor for the device; ,in, is the number of equipment components; is the connection complexity coefficient between device components; The degree of equipment standardization; ,in, The actual deployment depth of the device; Design the equipment to withstand depth; S15: Set dynamic sea condition thresholds based on deep-sea operation safety guidelines ; S16: Based on the equipment failure risk factor F and the dynamic threshold of sea conditions Conduct environmental adaptability risk level assessment and calculate the environmental adaptability coefficient. The formula is: , where E is the environmental adaptability coefficient, and are weight coefficients respectively; S17: Based on the calculated environmental adaptability coefficient E, the deployment and installation, energy supply, and data transmission instruction sets decomposed in S11 are sorted to generate an environmental adaptability task priority list.

4. The method for scheduling and managing deep-sea test tasks based on multi-platform collaboration according to claim 1 is characterized in that: The S2 specifically includes: S21: Call the environmental adaptability task priority list generated by S1, determine the communication protocol type of the corresponding device according to the subtask priority from high to low, and obtain the data rate, interface type and communication delay parameters of the protocol; S22: Based on the communication interface specifications and data transmission capabilities of the deep-sea test support ship and the underwater operation platform, dynamic protocol conversion interfaces are designed for protocol interconnection. The interfaces include a protocol conversion chip, a level matching circuit, and a data buffer. S23: Set the platform load capacity threshold and, based on the platform's energy consumption carrying capacity, data processing capacity, and the interface's real-time bandwidth carrying capacity, specify the maximum data flow value that each platform interface can carry. S24: Based on the data rate, communication delay parameters of the device communication protocol and the maximum data flow value of the platform interface, the matching degree of the device protocol and the platform interface capability is compared item by item, and parameter mapping of the protocol conversion interface and the platform interface is performed to generate a multi-platform interface configuration plan.

5. The method for scheduling and managing deep-sea test tasks based on multi-platform collaboration according to claim 1 is characterized in that: The S37 specifically includes: S371: Extracting the actual driving path length and operation time interval of each platform based on the obstacle avoidance path trajectory generated in step S36; S372: Obtain the load capacity data of each platform and determine the upper limit of the energy supply capacity and the upper limit of the data transmission bandwidth of each platform; S373: Calculate the energy consumption and data transmission volume required for each platform to perform a specific task based on the task priority list and the actual driving path length and operation duration of each task platform; S374: Accumulate the energy consumption and data transmission volume required for each platform to perform a specific task, compare the accumulated energy consumption and data transmission volume with the corresponding platform load capacity data, and calculate the remaining margin of the load capacity of each platform when performing the task; S375: Based on the remaining margin of the platform's load capacity, balance and allocate the energy and data transmission resources of the task platform, determine the specific energy allocation quota and data bandwidth allocation quota of each platform during the task execution, and thus generate a platform resource allocation strategy.

6. The method for scheduling and managing deep-sea test tasks based on multi-platform collaboration according to claim 1 is characterized in that: The S4 specifically includes: S41: Based on the resource allocation strategy generated in S3, determine the platforms and device nodes participating in the test task and obtain the spatial location information of all nodes; S42: collecting communication link quality data of sensors on each node in real time through a unified data protocol, including signal strength, data transmission rate, and communication delay parameters; S43: Correlation analysis is performed on the spatial location information of each node and the communication link quality data, a topological structure is established with nodes as vertices and communication links as edges, and the connection reliability of the communication link between any two adjacent nodes is calculated; S44: Determine the optimal communication path between the nodes based on the connection reliability of the communication link between the nodes to form a complete data transmission path set; S45: Based on nodes and communication links, combined with the data transmission path set, a complete data link topology map is constructed.

7. The method for scheduling and managing deep-sea test tasks based on multi-platform collaboration according to claim 1 is characterized in that: The S5 specifically includes: S51: Based on the data link topology map generated in step S4, the status data of each node device is obtained in real time, including device power consumption, operating time, and fault alarm information, and the device status parameters are extracted; S52: According to the actual progress of the tasks executed on each platform, the planned task duration and the actual task completion duration are obtained, and the task completion index Q is calculated; S53: Calculate the deep-sea test effectiveness index by combining equipment status parameters and task completion indicators ; The formula is: , where The actual number of devices that experienced equipment failure; is the total number of devices participating in the test; are the weight coefficients of task progress and equipment failure, respectively, and satisfy ; S54: Based on the calculated deep-sea test effectiveness index E, dynamically adjust the priority of each task in the environmental adaptability task priority list generated in S1, and update and generate a new test task priority list.

8. The method for scheduling and managing deep-sea test tasks based on multi-platform collaboration according to claim 7 is characterized in that: The S54 specifically includes: S541: Deep sea test effectiveness index calculated based on S53 , and compare it with the preset test performance benchmark value Make comparisons; when When , the environmental adaptability task priority list generated by S1 remains unchanged; when When , the priority score of each task in the environmental adaptability task priority list generated in step S1 is adjusted; the specific adjustment formula is: , where is the priority score of the j-th task after adjustment; To adjust the priority score of the jth task; Adjust sensitivity coefficients for priorities; S542: The task priority score adjusted by S541 is Rearrange from high to low to form an optimized environmental adaptability task priority list.

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