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

Abstract

The invention relates to the technical field of deep sea test task management, in particular to a deep sea test task scheduling and management method based on multi-platform collaboration, comprising the following steps: S1, generating an environmental adaptability task priority list; s2, generating a multi-platform interface configuration scheme based on the priority list in S1; s3, constructing a space-time conflict probability model, and outputting an obstacle avoidance path and a resource allocation strategy; s4, generating a data link topological graph; and S5, calculating a deep sea test efficiency index, and optimizing a subsequent test task priority list. According to the method, intelligent scheduling and efficient execution of deep sea test tasks are realized through task priority dynamic optimization, space-time conflict prediction, data link adaptive regulation and control and efficiency index feedback optimization, and the stability of multi-platform collaborative operation, the communication reliability and the resource utilization rate are improved.

Description

Technical Field

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

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

[0003] In the aspect of deep - sea test mission scheduling in the prior art, a static scheduling method based on a fixed task plan is usually adopted, which fails to fully consider real - time sea - condition changes, dynamic evaluation of equipment status, and the complexity of multi - platform collaborative operations, resulting in problems such as unreasonable resource allocation, unstable communication, and lag in task - priority adjustment during the task - execution process. In addition, the current task - scheduling method for deep - sea operations lacks a systematic spatio - temporal conflict assessment mechanism, making it difficult to effectively predict and avoid spatial occupancy conflicts between platforms, which affects the smooth execution of tasks. At the same time, in the aspect of data - link topology optimization, the existing methods mostly adopt fixed - routing strategies, which are difficult to adapt to the complex dynamic changes in the deep - sea environment, resulting in limited stability and efficiency of data transmission. Therefore, there is an urgent need for a deep - sea test mission scheduling and management method based on multi - platform collaboration to solve the above problems. Summary of the Invention

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

[0005] The deep - sea test mission scheduling and management method based on multi - platform collaboration includes the following steps: S1: According to the deep - sea test objectives, equipment characteristics, and real - time sea - condition data, decompose the test mission into deployment and installation, energy supply, and data - transmission subtasks, and generate an environment - adaptability task - priority list based on the equipment - failure risk coefficient and the sea - condition dynamic threshold. S2: Based on the priority list in S1, design dynamic protocol - conversion interfaces for deep - sea test support vessels and underwater operation platforms, and generate a multi - platform interface configuration scheme according to the equipment - communication protocol type and platform load capacity. S3: Invoke the interface configuration scheme in S2, combine the real - time positions of the platforms, task timings, and deep - sea flow - field prediction data, construct a spatio - temporal conflict - probability model, and output an obstacle - avoidance path and a resource - allocation strategy. S4: Based on the resource allocation strategy in S3, collect multi-platform sensor data in real time according to a unified data protocol, dynamically adjust the data collection frequency and transmission path according to the communication link quality, and generate a data link topology map; S5: Based on the link topology map in S4, extract the device status parameters and task completion degree indicators, calculate the deep-sea test effectiveness index, and use it to optimize the subsequent test task priority list.

[0006] Optionally, S1 specifically includes: S11: According to the set deep-sea test objectives, decompose the technical indicators of the task to form a set of task operation instructions, and clearly divide the task operation instructions into deployment and installation instructions, energy supply instructions, and data transmission instructions according to the task content and operation sequence in the instruction set; S12: Obtain the device characteristic parameters such as the structural dimensions, mass, power consumption, communication protocol type, and installation method of the devices participating in the deep-sea test, and construct a device characteristic parameter database; S13: Through the buoy sensors and ocean observation platforms deployed in the test sea area, collect real-time sea condition data such as sea current speed, wave height, seawater density, and seabed terrain slope in real time, and construct a real-time sequence of sea condition data.

[0007] Optionally, S1 further includes: S14: Adopt the failure mode, effects and criticality analysis method, and calculate the device failure risk coefficient F based on the mean time between failures of the device; S15: Set the dynamic sea condition threshold according to the deep-sea operation safety criteria ; S16: Based on the device failure risk coefficient F and the dynamic sea condition threshold conduct an environmental adaptability risk level assessment, and calculate the environmental adaptability coefficient. The formula is: , where E is the environmental adaptability coefficient, and are the weight coefficients respectively; S17: According to the calculated environmental adaptability coefficient E, sort the deployment and installation, energy supply, and data transmission instruction sets decomposed in S11 to generate an environmental adaptability task priority list.

[0008] Optionally, S2 specifically includes: S21: Call the environmental adaptability task priority list generated by S1, determine the corresponding device communication protocol type according to the sub-task 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 vessel and the underwater operation platform respectively, design dynamic protocol conversion interfaces for protocol interconnection. The interfaces include protocol conversion chips, level matching circuits, and data buffers; S23: Set the platform load capacity threshold, and clarify the maximum data traffic value that each platform interface can carry according to the platform's energy consumption bearing capacity, data processing capacity, and the real-time bandwidth bearing capacity of the interface; S24: According to the data rate, communication delay parameters of the device communication protocol and the maximum data traffic value of the platform interface, compare item by item the matching degree between the device protocol and the platform interface capabilities, and perform parameter mapping between the protocol conversion interface and the platform interface to generate a multi-platform interface configuration plan.

[0009] Optionally, the specific steps of S3 are as follows: S31: Obtain the real-time position information of the deep-sea test support vessel and the underwater operation platform, extract the platform's latitude and longitude coordinates, operation depth, and heading data, and construct a platform space 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: Obtain deep-sea flow field prediction data based on numerical simulation methods, including flow field velocity distribution and flow field direction distribution, and construct a flow field spatio-temporal distribution matrix; S34: Perform correlation calculations on the platform space position vector, task time series matrix, and flow field spatio-temporal distribution matrix to obtain the spatial intersection sub-intervals where each task platform overlaps with the flow field area at different task timings; S35: Establish a spatio-temporal conflict probability model based on the data of the spatial intersection interval, and calculate the conflict probability index through calculation; S36: According to the conflict probability index, set an obstacle avoidance probability threshold. When the conflict probability index exceeds this obstacle avoidance probability threshold, define the corresponding platform task as a potential conflict event, and output an obstacle avoidance path trajectory through a path optimization algorithm; S37: According to the generated obstacle avoidance path trajectory, combined with the platform load capacity and task priority, calculate the platform resource allocation amount and generate a resource allocation strategy.

[0010] Optionally, the specific steps of S35 are as follows: S351: At each discrete time point, calculate the three-dimensional volume occupied by the corresponding intersection sub-interval according to the range of the spatial intersection sub-interval; S352: According to the task timing matrix, determine the operation duration corresponding to the corresponding time point; S353: At the same time point, calculate the three-dimensional volume contained in the actual operation space range of the platform; S354: Compare the three-dimensional volume of the intersection sub-interval with the actual operating space volume of the platform to obtain the instantaneous conflict probability; S355: Weight and accumulate the instantaneous conflict probabilities corresponding to all discrete time points according to their respective operation durations, and correspond to the total duration of all time points to obtain the overall spatio-temporal conflict probability index.

[0011] Optionally, the S37 specifically includes: S371: Extract the actual driving path lengths and operation time intervals of each platform according to 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: Combine the task priority list, and calculate the energy consumption and data transmission volume required for each platform to execute specific tasks according to 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 execute specific tasks, compare it with the load capacity data of the corresponding platform, and calculate the remaining margin of the load capacity of each platform when executing tasks; S375: According to the remaining margin of the load capacity of the platform, 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 process, so as to generate the platform resource allocation strategy.

[0012] Optionally, the S4 specifically includes: S41: According to the resource allocation strategy generated in S3, determine each platform and device node participating in the test task, and obtain the spatial position information of all nodes; S42: Real-time collect the communication link quality data of the sensors on each node through a unified data protocol, including signal strength, data transmission rate and communication delay parameters; S43: Conduct correlation analysis on the spatial position information and communication link quality data of each node, establish a topological structure with nodes as vertices and communication links as edges, and calculate the connection reliability of the communication links between any two adjacent nodes; S44: Determine the optimal communication path between each node according to the connection reliability of the communication links between nodes, and form a complete data transmission path set; S45: Based on the nodes and communication links, combine the data transmission path set to construct a complete data link topology diagram.

[0013] Optionally, the S5 specifically includes: S51: Based on the data link topology diagram generated in step S4, the status data of each node device is obtained in real time, including device power consumption, running time, and fault alarm information, and 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 degree index Q is calculated; S53: Combining the device status parameters and the task completion degree index, calculate the deep-sea test effectiveness index ; The formula is: , where in the formula, is the number of devices with actual device failures; is the total number of devices participating in the test; are the weight coefficients of the task progress and the device failure respectively, and satisfy ; S54: Based on the calculated deep-sea test effectiveness index E, the priorities of the tasks in the environmental adaptability task priority list generated in S1 are dynamically adjusted, and a new test task priority list is updated and generated.

[0014] Optionally, the S54 specifically includes: S541: According to the deep-sea test effectiveness index calculated in S53, compare it with the preset test effectiveness benchmark threshold ; When , keep the environmental adaptability task priority list generated in S1 unchanged; When , adjust the priority scores of each task in the environmental adaptability task priority list generated in step S1; The specific adjustment formula is: , where in the formula, is the adjusted priority score of the jth task; is the priority score of the jth task before adjustment; is the priority adjustment sensitivity coefficient; S542: Re-sort the adjusted task priority scores from high to low to form an optimized environmental adaptability task priority list.

[0015] Advantages of the present invention: The present invention realizes the intelligent scheduling of deep - sea test tasks through task decomposition and the construction of an environment - adaptability task priority list; adopts a dynamic protocol conversion interface, combines the device communication protocol with the platform load capacity to generate a multi - platform interface configuration scheme, ensures device adaptability, and improves operation efficiency; meanwhile, based on real - time position, task timing, and deep - sea flow field prediction data, establishes a spatio - temporal conflict probability model, accurately evaluates and avoids spatial occupancy conflicts, optimizes the operation path, and enhances the safety and coordination of task execution.

[0016] The present invention extracts device status and task completion degree indicators, calculates the deep - sea test effectiveness index, and optimizes the task priority list based on this index to achieve dynamic optimization of task scheduling; compared with the prior art, it can achieve intelligent coordination of task scheduling, resource allocation, and data transmission under complex sea conditions, improving test efficiency and execution reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a schematic diagram of the deep - sea test task scheduling and management method according to an embodiment of the present invention; Figure 2 It is a schematic diagram of the process for calculating the conflict probability index according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The following will describe the present invention in detail with reference to the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well - known technologies, those skilled in the art can also adopt other alternative methods for implementation; moreover, the drawing part is only for more specifically describing the embodiments, and is not intended to specifically limit the present invention.

[0020] As Figure 1 - Figure 2 shown, the deep - sea test task scheduling and management method based on multi - platform collaboration includes the following steps: S1: According to the deep - sea test objectives, device characteristics, and real - time sea condition data, decompose the test tasks into sub - tasks of deployment and installation, energy supply, and data transmission, and generate an environment - adaptability task priority list based on the device failure risk coefficient and the dynamic threshold of sea conditions; S2: Based on the priority list in S1, design dynamic protocol conversion interfaces for the deep-sea test support ship and the underwater operation platform, and generate a multi-platform interface configuration plan according to the device communication protocol type and platform load capacity to complete the three-terminal adaptation of the structure-power-control of the device and the platform; S3: Invoke the interface configuration plan in S2, combine the real-time position of the platform, the task time sequence, and the deep-sea flow field prediction data to construct a spatio-temporal conflict probability model, and output an obstacle avoidance path and a resource allocation strategy; S4: Based on the resource allocation strategy in S3, collect multi-platform sensor data in real time according to a unified data protocol, dynamically adjust the data collection frequency and transmission path according to the communication link quality, and generate a data link topology map; S5: Based on the link topology map in S4, extract the device status parameters and task completion degree indicators, calculate the deep-sea test effectiveness index, and use it to optimize the subsequent test task priority list.

[0021] S1 specifically includes: S11: According to the set deep-sea test objectives, decompose the technical indicators of the task to form a set of task operation instructions, and clearly divide the task operation instructions into deployment and installation instructions, energy supply instructions, and data transmission instructions according to the task content and operation sequence in the instruction set; S12: Obtain the device characteristic parameters such as the structural dimensions, mass, power consumption, communication protocol type, and installation method of the devices participating in the deep-sea test, and construct a device characteristic parameter database; S13: Through the buoy sensors and ocean observation platforms deployed in the test sea area, collect real-time sea condition data such as sea current velocity, wave height, seawater density, and seabed terrain slope in real time, and construct a real-time sequence of sea condition data.

[0022] S1 also includes: S14: Adopt the Failure Mode, Effects and Criticality Analysis (FMECA) method, and calculate the device failure risk coefficient F based on the mean time between failures of the device. The formula is: , where MTBF is the mean time between failures of the device, is the device structure complexity coefficient, is the device deployment depth coefficient; , where is the number of key components of the device; is the connection complexity coefficient between device components (the value range is 1.0 - 2.5, and different connection methods have different empirical values, such as 1.2 for welded connections, 1.5 for bolt connections, and 2.0 for integrated connections); is the standardization degree of the device (the value range is 0.5 - 2.0, and the higher the value, the lower the standardization degree); , where is the actual deployment depth of the device; is the designed tolerance depth of the device; S15: Set the dynamic threshold of sea conditions according to the deep-sea operation safety guidelines , and its expression is: , where is the sea current speed threshold, is the wave height threshold, is the sea water density threshold; S16: Based on the device failure risk coefficient F and the dynamic threshold of sea conditions carry out the environmental adaptability risk level assessment, calculate the environmental adaptability coefficient, and the formula is: , where E is the environmental adaptability coefficient, and are the weight coefficients respectively, and ; S17: According to the calculated environmental adaptability coefficient E, sort the instruction sets of the deployment and installation category, energy supply category, and data transmission category decomposed in S11 to generate an environmental adaptability task priority list; the above steps clarify the task decomposition criteria, combine the characteristic parameters of the device itself and the real-time sea condition data, and accurately evaluate the task risk level with a specific algorithm, which can more scientifically guide the execution order of tasks and improve the reliability of task execution in the deep-sea complex environment.

[0023] S2 specifically includes: S21: Call the environmental adaptability task priority list generated by S1, determine the corresponding communication protocol type of the device according to the sub-task 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 respectively, design a dynamic protocol conversion interface for protocol interconnection, and the interface includes a protocol conversion chip, a level matching circuit, and a data buffer; S23: Set the platform load capacity threshold, and clarify the maximum data traffic value that each platform interface can carry according to the platform energy consumption bearing capacity, data processing capacity, and the real-time bandwidth bearing capacity of the interface; among them, the formula for the maximum data traffic value of the platform interface is: , in the formula, is the maximum data traffic value that the platform interface can carry; is the platform interface energy consumption bearing capacity; is the interface power consumption corresponding to the unit data traffic; is the platform interface data processing capacity; is the data processing consumption corresponding to the unit data traffic; is the real-time bandwidth carrying capacity of the platform interface; S24: According to the data rate, communication delay parameters of the device communication protocol and the maximum data traffic value of the platform interface, compare item by item the matching degree between the device protocol and the platform interface capabilities, and perform parameter mapping between the protocol conversion interface and the platform interface to generate a multi-platform interface configuration scheme that meets the requirements of structural adaptation, power adaptation, and control adaptation.

[0024] S24 specifically includes: S241: Obtain the data rate and communication delay parameters of the device communication protocol, and call the maximum data traffic value of the platform interface obtained in step S23 to calculate the data rate matching degree index between the protocol and the platform interface , and the specific calculation formula is: , where is the data rate matching degree index; is the maximum data traffic value that the platform interface can carry; is the data rate required by the device communication protocol; S242: Based on the device communication delay parameters and the real-time communication delay index of the platform interface, calculate the delay matching degree index between the protocol and the platform interface , and the specific calculation formula is: , where; is the real-time communication delay index of the platform interface; is the maximum communication delay allowed by the device communication protocol; S243: Perform comprehensive weighted calculation on the calculated data rate matching degree index and delay matching degree index to obtain the total matching degree index between the device protocol and the platform interface. The calculation formula is: , where is the total matching degree index between the device protocol and the platform interface; and are the weighted coefficients of the data rate and delay matching degrees respectively; S244: According to the total matching degree index between the device protocol and the platform interface, determine the parameter mapping relationship between the device protocol and the platform interface, and then generate the configuration parameters of the protocol conversion interface to form a multi-platform interface configuration scheme that meets the requirements of structural adaptation, power adaptation, and control adaptation; through the above steps of matching degree calculation and determination of parameter mapping relationship, the adaptation accuracy between the device communication protocol and the platform interface is effectively improved, and the accurate configuration of multi-platform communication interface parameters in deep-sea tests is realized.

[0025] S3 specifically includes: S31: Obtain the real-time position information of the deep-sea test support ship and the underwater operation platform, extract the platform longitude and latitude coordinates, operation depth, and heading data, and construct the platform space 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: Obtain deep - sea flow field prediction data based on the numerical simulation method, including the flow field velocity distribution and the flow field direction distribution, and construct a flow field spatio - temporal distribution matrix; S34: Correlate and calculate the platform space position vector, the task time series matrix, and the flow field spatio - temporal distribution matrix to obtain the spatial intersection sub - intervals where each task platform overlaps with the flow field region at different task time sequences; Specifically, first perform discrete processing on the task time series matrix to obtain a series of discrete time points ; where represents the i - th discrete time point; Then, at each discrete time point , based on the platform space position vector, define the spatial coordinates of the platform at this time point as , use D to represent the operation influence radius of the platform at this time point, and construct the platform space region set , and the expression is: , where x, y, z represent the coordinates of any point in the space to be solved; Next, at each discrete time point , extract the flow field space region set corresponding to this time point from the flow field spatio - temporal distribution matrix, where represents all coordinate points that satisfy the deep - sea flow field prediction data; Subsequently, for each discrete time point , perform an intersection operation on the platform space region set and the flow field space region set to obtain the spatial intersection sub - interval , and the expression is: ; Finally, merge the spatial intersection sub - intervals corresponding to each discrete time point in chronological order to obtain the complete spatial intersection interval where each task platform overlaps with the flow field region at different task time sequences.

[0026] S35: Establish a spatio - temporal conflict probability model based on the data of the spatial intersection interval, and calculate the conflict probability index through calculation; S36: Set an obstacle avoidance probability threshold according to the conflict probability index. When the conflict probability index exceeds this obstacle avoidance probability threshold, define the corresponding platform task as a potential conflict event, and output the path trajectory for avoiding conflicts through the path optimization algorithm; S37: Based on the generated obstacle avoidance path trajectory, combined with the platform load capacity and task priority, calculate the platform resource allocation amount and generate a resource allocation strategy; Through the above steps, under different sea conditions and multi-platform collaborative operation conditions, the conflict detection and avoidance between the deep-sea operation platform and the support ship in terms of time and space can be effectively realized, improving the safety and task execution efficiency of multi-platform collaborative operations.

[0027] S35 specifically includes: S351: At each discrete time point, calculate the three-dimensional volume occupied by the corresponding intersection sub-interval according to the range of the spatial intersection sub-interval; S352: According to the task timing matrix, determine the operation duration corresponding to the corresponding time point; S353: At the same time point, calculate the three-dimensional volume included in the actual operation space range of the platform; S354: Compare the three-dimensional volume of the intersection sub-interval with the actual operation space volume of the platform to obtain the instantaneous conflict probability; S355: Weight and accumulate the instantaneous conflict probabilities corresponding to all discrete time points according to their respective operation durations, and correspond to the total duration of all time points to obtain the overall spatio-temporal conflict probability index.

[0028] Specifically, first, based on the spatial intersection sub-interval obtained from S34 , calculate the volume of the spatial intersection sub-interval at each discrete time point , and the formula is: , where is the volume of the spatial intersection sub-interval at the i-th discrete time point; Then, according to the task timing matrix, determine the duration length of each task platform at the z-th discrete time point, with the unit of hour; Next, according to the set of actual operation space regions of the platform at the i-th discrete time point , calculate the corresponding actual operation space volume, and the formula is: , where is the actual operation space volume of the platform at the i-th discrete time point; Subsequently, according to the volume of the spatial intersection sub-interval, the duration length and the actual operation space volume calculated above, calculate the instantaneous conflict probability at the i-th discrete time point, and the calculation formula is: , where is the instantaneous conflict probability at the i-th discrete time point; S355: The instantaneous conflict probabilities of all discrete time points Perform time-weighted accumulation to obtain the total spatio-temporal conflict probability index, and the expression is: , where is the total spatio-temporal conflict probability index; N is the total number of discrete time points; and the spatio-temporal conflict probability model expression established therefrom is: .

[0029] S37 specifically includes: S371: Extract the actual driving path length and operation time interval of each platform according to 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: Combine the task priority list, and calculate the energy consumption and data transmission volume required for each platform to execute a specific task according to 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 execute a specific task, compare it with the corresponding platform load capacity data, and calculate the remaining margin of the load capacity of each platform when executing the task; S375: Balance and allocate the energy and data transmission resources of the task platform according to the remaining margin of the load capacity of the platform, determine the specific energy allocation quota and data bandwidth allocation quota of each platform during the task execution process, so as to generate a platform resource allocation strategy; Through the above steps, based on the obstacle avoidance path, fully considering the actual available resources of the platform and the task priority, the precise scheduling and allocation of multi-platform resources can be realized, thereby improving the task execution efficiency and overall collaborative operation ability of the deep-sea test.

[0030] S4 specifically includes: S41: Determine each platform and device node participating in the test task according to the resource allocation strategy generated in S3, and obtain the spatial position information of all nodes; S42: Real-time collect the communication link quality data of the sensors on each node through a unified data protocol, including signal strength, data transmission rate and communication delay parameters; S43: Conduct correlation analysis on the spatial position information and communication link quality data of each node, establish a topological structure with nodes as vertices and communication links as edges, and calculate the connection reliability of the communication links 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; S44: Determine the optimal communication paths between nodes according to the connection reliability of the communication links between nodes, and form a complete set of data transmission paths; S45: Based on the nodes and communication links, combined with the set of data transmission paths, construct a complete data link topology diagram; The above steps clarify the communication connection relationships between multi-platforms and device nodes, improve the reliability of data collection and transmission, and provide accurate support for real-time data management and subsequent test effectiveness evaluation.

[0031] S5 specifically includes: S51: Based on the data link topology diagram generated in step S4, obtain the status data of each node device in real time, including device power consumption, running time, and fault alarm information, and extract device status parameters; S52: According to the actual progress of the tasks executed by each platform, obtain the planned task duration and the actual completed task duration, and calculate the task completion degree index Q. The formula is: , where Q is the task completion degree index; is the actual completed task duration; is the planned task duration; S53: Combine the device status parameters and the task completion degree index to calculate the deep-sea test effectiveness index ; The formula is: , where is the number of devices with actual device failures; is the total number of devices participating in the test; are the weight coefficients of the task progress and device failures respectively, and satisfy ; S54: According to the calculated deep-sea test effectiveness index E, dynamically adjust the priorities of the tasks in the environmental adaptability task priority list generated in S1, and update and generate a new test task priority list; The above steps form a clear deep-sea test effectiveness evaluation through the real-time monitoring and quantitative analysis of device status parameters and task completion degrees, which is conducive to optimizing and adjusting the execution order and resource allocation plan of subsequent test tasks.

[0032] S54 specifically includes: S541: According to the deep-sea test effectiveness index calculated in S53, compare it with the preset test effectiveness benchmark threshold ; When , keep the environmental adaptability task priority list generated in S1 unchanged; When , adjust the priority scores of each task in the environmental adaptability task priority list generated in step S1; The specific adjustment formula is: , where is the priority score of the adjusted j-th task; is the priority score of the j-th task before adjustment; is the priority adjustment sensitivity coefficient; S542: Reorder the priority scores of the tasks adjusted by S541 from high to low to form an optimized priority list of environmental adaptability tasks; The above steps realize the real-time dynamic optimization of the task priority list through the dynamic feedback of the deep-sea test effectiveness index, effectively improving the self-adaptability and execution efficiency of the test tasks.

[0033] The present invention covers any substitutions, modifications, equivalent methods, and solutions made on the essence and scope of the present invention. For the public to have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, and those skilled in the art can fully understand the present invention without these detailed descriptions. Additionally, well-known methods, processes, procedures, components, and circuits are not described in detail to avoid unnecessary confusion to the essence of the present invention.

[0034] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A deep-sea test mission scheduling and management method based on multi-platform collaboration, characterized in that, It includes the following steps: S1: According to the deep - sea test objectives, equipment characteristics, and real - time sea condition data, decompose the test tasks into sub - tasks of deployment and installation, energy supply, and data transmission, and generate an environmental adaptability task priority list based on the equipment failure risk coefficient and the dynamic threshold of sea conditions; S2: Based on the priority list in S1, design dynamic protocol conversion interfaces for deep - sea test support vessels and underwater operation platforms, and generate a multi - platform interface configuration plan according to the equipment communication protocol type and platform load capacity; S3: Invoke the interface configuration plan in S2, combine the real - time position of the platform, task timing, and deep - sea flow field prediction data, construct a spatio - temporal conflict probability model, and output an obstacle - avoidance path and resource allocation strategy; S4: Based on the resource allocation strategy in S3, collect multi - platform sensor data in real - time according to a unified data protocol, dynamically adjust the data collection frequency and transmission path according to the communication link quality, and generate a data - link topology map; S5: Based on the link topology map in S4, extract equipment status parameters and task completion degree indicators, calculate the deep - sea test effectiveness index, and use it to optimize the subsequent test task priority list.

2. The method for scheduling and managing deep-sea test tasks based on multi-platform collaboration according to claim 1, wherein, The specific content of S1 includes: S11: According to the set deep - sea test objectives, decompose the technical indicators of the tasks to form a set of task operation instructions, and clearly divide the task operation instructions into deployment and installation - type instructions, energy - supply - type instructions, and data - transmission - type instructions according to the task content and operation sequence in the instruction set; S12: Obtain the equipment characteristic parameters such as the structural dimensions, mass, power consumption, communication protocol type, and installation method of the equipment participating in the deep - sea test, and construct an equipment characteristic parameter database; S13: Through the buoy sensors and ocean observation platforms deployed in the test sea area, collect real - time sea condition data such as sea current velocity, wave height, seawater density, and seabed terrain slope in real - time, and construct a real - time sea condition data sequence.

3. The deep-sea test task scheduling and management method based on multi-platform collaboration according to claim 2, characterized in that S1 also includes: S14: Adopt the failure mode, effects, and criticality analysis method, and calculate the equipment failure risk coefficient F based on the mean time between failures of the equipment; S15: Set the dynamic threshold of sea conditions according to the deep-sea operation safety guidelines ; S16: Based on the equipment failure risk coefficient F and the dynamic threshold of sea conditions conduct an environmental adaptability risk level assessment, calculate the environmental adaptability coefficient, and the formula is: , where E is the environmental adaptability coefficient, and are the weight coefficients respectively; S17: According to the calculated environmental adaptability coefficient E, sort the instruction sets of deployment and installation - type, energy - supply - type, and data - transmission - type decomposed in S11 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, characterized in that, The specific content of S2 includes: S21: Invoke the environmental adaptability task priority list generated in S1, determine the corresponding equipment communication protocol type according to the sub - task priority from high to low, and obtain the data rate, interface type, and communication delay parameters of the protocol; S22: Based on the respective communication interface specifications and data transmission capabilities of the deep - sea test support vessel and the underwater operation platform, design dynamic protocol conversion interfaces for protocol interconnection respectively. The interfaces include protocol conversion chips, level - matching circuits, and data buffers; S23: Set the platform load capacity threshold, and clarify the maximum data traffic value that each platform interface can carry according to the platform energy consumption bearing capacity, data processing capacity, and real - time bandwidth bearing capacity of the interface; S24: According to the data rate of the device communication protocol, the communication delay parameter, and the maximum data traffic value of the platform interface, compare item by item the matching degree between the device protocol and the platform interface capabilities, and perform parameter mapping between the protocol conversion interface and the platform interface 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, characterized in that The specific steps of S3 are as follows: S31: Obtain the real-time position information of the deep-sea test support ship and the underwater operation platform, extract the platform's longitude and latitude coordinates, operation depth, and heading data, and construct a platform 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: Obtain deep-sea flow field prediction data based on numerical simulation methods, including flow field velocity distribution and flow field direction distribution, and construct a flow field spatio-temporal distribution matrix; S34: Perform correlation calculations on the platform spatial position vector, the task time series matrix, and the flow field spatio-temporal distribution matrix to obtain the spatial intersection sub-intervals where each task platform overlaps with the flow field area under different task timings; S35: According to the data of the spatial intersection interval, establish a spatio-temporal conflict probability model, and calculate the conflict probability index through calculation; S36: According to the conflict probability index, set an obstacle avoidance probability threshold. When the conflict probability index exceeds this obstacle avoidance probability threshold, define the corresponding platform task as a potential conflict event, and output an obstacle avoidance conflict path trajectory through a path optimization algorithm; S37: According to the generated obstacle avoidance path trajectory, combined with the platform load capacity and task priority, calculate the platform resource allocation amount, and generate a resource allocation strategy.

6. The method for scheduling and managing deep-sea test tasks based on multi-platform collaboration according to claim 5, wherein The specific steps of S35 are as follows: S351: At each discrete time point, according to the range of the spatial intersection sub-interval, calculate the three-dimensional volume occupied by the corresponding intersection sub-interval; S352: According to the task timing matrix, determine the operation duration corresponding to the corresponding time point; S353: At the same time point, calculate the three-dimensional volume included in the actual operation space range of the platform; S354: Compare the three-dimensional volume of the intersection sub-interval with the actual operation space volume of the platform to obtain the instantaneous conflict probability; S355: Weight and accumulate the instantaneous conflict probabilities corresponding to all discrete time points according to their respective operation durations, and correspond to the total duration of all time points to obtain the overall spatio-temporal conflict probability index.

7. The method for deep-sea test task scheduling and management based on multi-platform collaboration according to claim 6, characterized in that, The specific steps of S37 are as follows: S371: According to the obstacle avoidance path trajectory generated in step S36, extract the actual driving path length and operation time interval of each platform; 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: Combined with the task priority list, according to the actual driving path length and operation duration of each task platform, calculate the energy consumption and data transmission volume required for each platform to execute specific tasks; S374: Accumulate the energy consumption and data transmission volume required for each platform to execute specific tasks, compare it with the corresponding platform load capacity data, and calculate the remaining margin of the load capacity of each platform when executing tasks; S375: Based on the remaining margin of the platform's load capacity, balance and allocate the energy and data transmission resources of the mission platforms, determine the specific energy allocation quotas and data bandwidth allocation quotas for each platform during the mission execution, so as to generate a platform resource allocation strategy.

8. The method for scheduling and managing deep-sea test tasks based on multi-platform collaboration according to claim 1, wherein The specific steps of S4 are as follows: S41: According to the resource allocation strategy generated by S3, determine the various platforms and device nodes participating in the test mission, and obtain the spatial location information of all nodes; S42: Real-time collect the communication link quality data of the sensors on each node through a unified data protocol, including signal strength, data transmission rate, and communication delay parameters; S43: Conduct correlation analysis on the spatial location information and communication link quality data of each node, establish a topological structure with nodes as vertices and communication links as edges, and calculate the connection reliability of the communication links between any two adjacent nodes; S44: Determine the optimal communication paths between each node according to the connection reliability of the communication links between nodes, and form a complete set of data transmission paths; S45: Based on the nodes and communication links, combined with the set of data transmission paths, construct a complete data link topology graph.

9. The deep-sea test task scheduling and management method based on multi-platform collaboration according to claim 1, wherein, The specific steps of S5 are as follows: S51: Based on the data link topology graph generated in step S4, real-time obtain the status data of each node device, including device power consumption, running time, and fault alarm information, and extract device status parameters; S52: According to the actual progress of each platform in executing the mission, obtain the planned mission duration and the actual mission completion duration, and calculate the mission completion degree index Q; S53: Calculate the deep - sea test effectiveness index by combining the equipment status parameters and the task completion degree index ; The formula is: , where is the number of equipment with actual equipment failures; is the total number of equipment 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 efficiency index E, dynamically adjust the priorities of the tasks in the environmental adaptability task priority list generated by S1, and update and generate a new test mission priority list.

10. The method for scheduling and managing deep-sea test tasks based on multi-platform collaboration according to claim 9, characterized in that, The specific steps of S54 are as follows: S541: Deep-sea test effectiveness index calculated according to S53 , and compare it with the preset intermediate value of the test effectiveness benchmark ; When maintain the environmental adaptability task priority list generated by S1 unchanged; When the priority scores of each task in the environmental adaptability task priority list generated in step S1 are adjusted; the specific adjustment formula is: , where is the priority score of the j-th task after adjustment; is the priority score of the j-th task before adjustment; is the priority adjustment sensitivity coefficient; S542: Reorder the task priority scores adjusted by S541 from high to low to form an optimized environmental adaptability task priority list.

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