A method for allocating environmental observation tasks based on multiple underwater unmanned equipment

Abstract

The present invention belongs to the technical field of environmental observation task allocation, and discloses an environmental observation task allocation method based on multiple underwater unmanned equipment, including S1, constructing a multi-heterogeneous underwater unmanned platform environmental observation efficiency evaluation algorithm; S2, constructing an underwater unmanned platform collaborative environmental observation task allocation optimization algorithm model; S3, constructing a problem analysis and optimization mathematical model; The present invention realizes the task allocation function of the underwater unmanned platform cluster by designing a multi-heterogeneous underwater unmanned platform environmental observation efficiency evaluation algorithm and an underwater unmanned platform collaborative environmental observation task allocation optimization algorithm model. Through task allocation, the environmental observation task list of each unmanned equipment is determined, and the optimization algorithm is used to ensure that the observation task assigned to each equipment can ensure the optimal coordination for the entire equipment system.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental observation task allocation, and in particular to an environmental observation task allocation method based on multiple underwater unmanned equipment. Background Art

[0002] Task allocation is one of the important contents of operations research and optimization. Reasonable allocation of tasks for individuals in the system can greatly save costs, thereby improving the overall performance of the system. Underwater unmanned equipment is one of the important tools for human exploration and utilization of marine resources, scientific exploration and research. Among them, new underwater unmanned equipment such as buoys, underwater gliders, wave energy gliders, and underwater robots (autonomous underwater vehicles, referred to as "AUVs") have been widely used in complex scenarios such as marine information reconnaissance and seabed resource exploration. These tasks are often highly time-sensitive and have certain requirements for the quality of task completion. Therefore, it is difficult for one underwater unmanned equipment to complete the task successfully, and multiple underwater unmanned equipment are required to work together to complete the above tasks.

[0003] When using multiple underwater unmanned equipment to coordinate and cooperate for marine information reconnaissance, seabed resource exploration, etc., different types / models of underwater unmanned equipment (buoys, underwater gliders, wave energy gliders, AUVs, etc.) have different application methods, environmental constraints, and environmental observation capabilities. Therefore, facing different types and numbers of underwater unmanned equipment, how to effectively utilize limited underwater unmanned equipment resources and achieve the best marine observation effect is one of the problems that need to be solved in this field. Summary of the invention

[0004] The present invention aims to provide an environmental observation task allocation method based on multiple underwater unmanned equipment, and realizes the task allocation function of underwater unmanned platform clusters by designing a multi-heterogeneous underwater unmanned platform environmental observation efficiency evaluation algorithm and an underwater unmanned platform collaborative environmental observation task allocation optimization algorithm model. Through task allocation, the environmental observation task list of each unmanned equipment is determined, and the optimization algorithm is used to ensure that the observation tasks assigned to each equipment can ensure the optimal coordination for the entire equipment system.

[0005] In order to achieve the above object, the present invention provides the following technical solution: a method for allocating environmental observation tasks based on multiple underwater unmanned equipment, comprising the following steps:

[0006] S1. Construct a multi-heterogeneous underwater unmanned platform environment observation performance evaluation algorithm. According to the application scenarios of multi-heterogeneous underwater unmanned platform environment observation, define the observation task set. and unmanned observation equipment collection Unmanned observation equipment To complete the observation task , forming a pair ;

[0007] S2. Construct an optimization algorithm model for the allocation of underwater unmanned platform collaborative environment observation tasks

[0008] Pairing of unmanned observation equipment and observation tasks obtained in step S1 , each pairing has a corresponding cost function and the observed return function , the two form a comprehensive cost-effectiveness function and is the balancing coefficient; and according to the purpose of task allocation, find the equipment-task pairing scheme to make the overall cost-effectiveness optimal. The model is:

[0009]

[0010] In the formula, In , different task numbers Corresponding device serial number Can be the same, indicating that the same device can perform multiple tasks; n is the number of observation tasks, and m is the number of observation devices;

[0011] S3. Constructing problem analysis and optimization mathematical models

[0012] The problem analysis and optimization mathematical model are transformed into a path search problem between grids; among them, there are Tasks to be completed, for each task there is candidate devices are used to complete the task, and each candidate task allocation scheme is a path scheme between grids. The overall cost-effectiveness of the construction path is:

[0013]

[0014] By comparing each candidate path Overall cost-effectiveness , get the optimal task allocation path , to solve the task allocation problem.

[0015] Furthermore, in S2, the cost function Used to describe unmanned observation equipment Perform observation missions The cost to be paid; cost function Including time cost, risk cost and economic cost, each cost element forms a single cost function through hierarchical weighting ;

[0016] Among them, the time cost model is constructed to distinguish the device types in the path search problem between grids, and the estimated time cost is calculated by the initial position of the device. Run to the observation mission target position The time required in the middle; the risk cost comes from the threat of complex marine environment to equipment and the equipment's own failure rate; the whole set of cost functions obtained by hierarchical weighting for:

[0017]

[0018] In the formula, Indicates the equipment sequence assigned to the entire task sequence , each All from the device collection Select from; n is the number of observation tasks, m is the number of observation equipment;

[0019] Revenue Function Used to describe the environmental observation benefits obtained by the equipment when performing tasks; specifically including the importance of the task and the degree to which the equipment observation can meet the task completion; the whole set of benefit functions obtained by hierarchical weighting for:

[0020]

[0021] In the formula, the task number is accumulated conduct, Indicates the equipment sequence assigned to the entire task sequence , each From device collection Select from; n is the number of observation tasks, m is the number of observation equipment;

[0022] Cost-effectiveness function For each pairing of observation equipment and observation tasks , comprehensive consideration ,in, and is the balancing factor.

[0023] Furthermore, the time cost estimation method of the fixed environment observation equipment buoy is to distinguish whether it has been assigned to a certain observation task , if an observation task has been assigned , then the time cost for , indicating that fixed equipment can only be assigned a task once; if the fixed equipment has not been deployed, the time cost of its task allocation is is 0.

[0024] Furthermore, the time cost estimation method for the underwater mobile unmanned platform task allocation is as follows: considering the starting and ending positions and the voyage in the grid, the influence of the ocean current in the route area, and the remaining endurance of the mobile equipment; the time cost model of the underwater glider is constructed as follows:

[0025]

[0026] The time cost model of wave energy glider is:

[0027]

[0028] The time cost model of AUV is:

[0029]

[0030] In the formula, is the number of grids that the mobile equipment passes through, is the estimated time of the navigation grid obtained based on the motion model of the maneuverable equipment under the influence of the ocean current, The remaining battery life of the device.

[0031] Furthermore, the risk cost of the fixed observation equipment buoy comes from the gap between the sea depth and seabed slope environmental factors of the deployment location and the environmental adaptation parameters of the equipment. When the environmental adaptation parameters of the fixed detection equipment are suitable for the environment of the sea area at the mission target location, the risk cost is 0; when the fixed equipment is not suitable for the sea area environment of the target location, the risk cost is ; The risk estimation formula for fixed observation equipment is constructed as follows:

[0032] .

[0033] Furthermore, the risk cost of underwater glider equipment of underwater mobile unmanned platform comes from the loss of control of underwater glider due to complex and harsh ocean currents, and the "profile" movement of underwater glider when the sea depth on the path is shallow. The method for estimating the risk cost is: statistically analyze the grid set of underwater gliders. The number of grids where the ocean current exceeds the threshold or the sea depth is shallower than the threshold is denoted as The estimated result is , weight It is used to balance the comparability of various costs; the risk estimation formula of underwater glider equipment is constructed as follows:

[0034] .

[0035] Furthermore, the risk cost of the underwater mobile unmanned platform wave energy glider equipment comes from the probability that the turbulent waves will damage its surface components or the surface and underwater connecting cables; the risk estimation method is to count the wave energy glider passing through the grid set The number of grids where the wave height exceeds the threshold is denoted by The estimated result is , weight It is used to balance the comparability of various costs; the risk estimation formula of wave energy glider equipment is constructed as follows:

[0036] .

[0037] Furthermore, the risk cost of underwater mobile unmanned platform AUV equipment comes from the risk of AUV underwater navigation hitting the bottom due to shallow water depth and complex underwater terrain. The risk estimation method is: statistical AUV navigation grid set The number of grids that simultaneously satisfy the water depth shallower than the threshold and the terrain depth changes dramatically is denoted as The estimated result is , weight It is used to balance the comparability of various costs; the AUV equipment risk estimation formula is constructed as follows:

[0038] .

[0039] Furthermore, the importance of the task determines the benefit of completing the task, which depends on the observation task The importance of the task is inherent to the nature of the observation task and has nothing to do with the assigned equipment; the importance is proposed by the observation demander and the benefits of completing the task are determined by the observation demander. It is divided into 5 levels: 0 means no benefit and unimportant, 1 means moderately important, 2 means relatively important, 3 means somewhat important, and 4 means very important.

[0040] Furthermore, the task completion degree includes the observation time requirement and the observation element requirement; each task will be rewarded after 100% completion. Due to the performance constraints of the assigned equipment, the environmental observation can only be partially completed. Let the completion degree be , then the device Execute the task The income is:

[0041] .

[0042] Furthermore, the observation time requires the timeliness of the completion of the observation task and the continuity of the observation time. The timeliness of the equipment arrival time is because some ocean phenomena change or even disappear over time, and their observation needs to be limited to a certain time window of their life cycle. If it is not possible to observe them within the time window, the observation effect and value will be lost. The estimation model for the timeliness of the arrival time is constructed as follows:

[0043]

[0044] In the formula, For estimation equipment Arrival Mission The moment of the specified location; For the task Request to start observing time, For the task The time window in which delays are allowed;

[0045] The observation time continuity examines whether the continuous observation time of the equipment can meet the duration requirements set by the task. If it cannot be reached, the continuity index will be reduced according to the time ratio; the equipment's own endurance Determined by the device's own parameters, when the device arrives at the task location and starts to perform environmental observation The planned water entry time is recorded as , the remaining battery life of the device ;Task The required observation time is , the model of observation time persistence is constructed as:

[0046]

[0047] According to the timeliness of arrival and observation time The two elements of continuity are taken into account, and the degree of satisfaction of the observation time requirement is obtained as follows:

[0048]

[0049] In the formula, and They are the coefficients of the importance of the two factors of timeliness and continuity of task observation time.

[0050] Furthermore, the observation element requires the task Determine, including temperature profile and salinity profile; when the task When a temperature profile observation is required, first inspect the equipment Whether it has the ability to observe temperature. If not, the degree of satisfaction of temperature profile observation is 0. If it has the ability to observe temperature, continue to investigate the depth range of temperature observation of the equipment. , comparison task Required temperature observation depth range , get the device The temperature observation range can cover the task Ratio of the observed temperature range , the degree of satisfaction of temperature profile observation requirements is estimated as:

[0051]

[0052] The degree of satisfaction of salinity profile observation is estimated as:

[0053]

[0054] Follow-up temperature profile observation satisfaction and salinity profile observations meet the , the degree of satisfaction of the observation element requirements is obtained as follows:

[0055]

[0056] In the formula, and are the coefficients that balance the importance of the two tasks, temperature observation and salinity observation.

[0057] Furthermore, in S3, the method for constructing the path search problem between grids is:

[0058] First establish the observation task and unmanned observation equipment The "grid points" are formed to form a pairing model for each task and each unmanned device; then a model connecting the "edges" between the grid points is established, and the evaluation function of each "edge" is composed of the cost function , profit function and cost-effectiveness function combination; finally, use the grid path search method to find the set of "edges" connecting each "grid point" to form an optimized task allocation plan.

[0059] The beneficial effects of the technical solution are:

[0060] 1. The present invention provides a method for allocating environmental observation tasks based on multiple underwater unmanned equipment. By designing a multi-heterogeneous underwater unmanned platform environmental observation performance evaluation model, it can accurately evaluate the observation capabilities and efficiency of different types and models of underwater unmanned equipment in different environments. Based on this evaluation, the task allocation optimization algorithm can accurately allocate tasks to the unmanned equipment that is most suitable for performing the task, thereby maximizing the advantages of each device, reducing resource redundancy and waste, and improving overall resource utilization efficiency;

[0061] 2. The present invention provides an environmental observation task allocation method based on multiple underwater unmanned equipment. Through the collaborative environmental observation task allocation optimization algorithm, different types of underwater unmanned equipment (such as buoys, underwater gliders, wave energy gliders, AUVs, etc.) can form an efficient and complementary observation network. These devices are allocated to the best observation position according to task requirements, realizing multi-source data fusion and cross-validation, and significantly improving the comprehensiveness, accuracy and real-time performance of ocean observations. This collaborative mechanism effectively solves the problem of limited observation capabilities of a single device and enhances the effectiveness of the overall observation system.

[0062] 3. The present invention provides a method for allocating environmental observation tasks based on multiple underwater unmanned devices. Under limited resource conditions, the optimization algorithm is used to ensure that the tasks assigned to each unmanned device can not only meet the observation needs, but also minimize energy consumption and extend the endurance time, thereby reducing the total cost of task execution. This includes not only the energy consumption cost of the equipment, but also the subsequent costs such as maintenance and replacement, which is particularly important for long-term, large-scale ocean observation projects;

[0063] 4. The present invention provides an environmental observation task allocation method based on multiple underwater unmanned equipment. Through a flexible task allocation mechanism, the task allocation strategy can be dynamically adjusted according to the hydrological conditions, weather conditions and other factors in different sea areas to ensure that the unmanned equipment can still work stably in harsh environments, thereby improving the robustness and adaptability of the overall system. Improve the ability to cope with complex environments;

[0064] 5. The present invention provides an environmental observation task allocation method based on multiple underwater unmanned equipment, which cross-integrates multiple disciplines and fields (marine science, automatic control, artificial intelligence, big data analysis, etc.). It can not only promote the innovation and development of related technologies, but also provide new ideas and impetus for the research and development, manufacturing, application and industrial upgrading of underwater unmanned equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A cost-effectiveness function model constructed by a method for allocating environmental observation tasks based on multiple underwater unmanned equipments according to the present invention;

[0066] Figure 2A grid diagram of time cost evaluation of an environmental observation task allocation method based on multiple underwater unmanned equipments according to the present invention;

[0067] Figure 3 A schematic diagram of the direction of ocean currents and equipment in a grid in a method for allocating environmental observation tasks based on multiple underwater unmanned equipment according to the present invention;

[0068] Figure 4 The present invention is a schematic diagram of a method for allocating environmental observation tasks based on multiple underwater unmanned equipments, which converts task allocation into a path search problem. DETAILED DESCRIPTION

[0069] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments:

[0070] A method for allocating environmental observation tasks based on multiple underwater unmanned equipment includes the following steps:

[0071] S1. Constructing a multi-heterogeneous underwater unmanned platform environmental observation performance evaluation algorithm

[0072] Define the observation task set according to the application scenarios of multi-heterogeneous underwater unmanned platform environment observation and unmanned observation equipment collection ; And equipped with unmanned observation equipment To complete the observation task , forming a pair ;

[0073] S2. Construct an optimization algorithm model for the allocation of underwater unmanned platform collaborative environment observation tasks

[0074] Pairing of unmanned observation equipment and observation tasks obtained in step S1 Each pairing has a corresponding cost function and the observed return function , the two form a comprehensive cost-effectiveness function , and is the balancing coefficient; and according to the purpose of task allocation, find the equipment-task pairing scheme to make the overall cost-effectiveness optimal. The model is:

[0075]

[0076] In the formula, In , different task numbers Corresponding device serial number Can be the same, indicating that the same device can perform multiple tasks; n is the number of observation tasks, and m is the number of observation devices;

[0077] Among them, the cost function Used to describe unmanned observation equipment Perform observation missions The cost to be paid; cost function Including time cost, risk cost and economic cost, each cost element forms a single cost function through hierarchical weighting ;

[0078] The time cost estimation method of the fixed environment observation equipment buoy is: distinguish whether it has been assigned to a certain observation task , if an observation task has been assigned , then the time cost for , indicating that fixed equipment can only be assigned a task once; if the fixed equipment has not been deployed, the time for its task assignment becomes is 0;

[0079] The time cost estimation of underwater mobile unmanned platform task allocation should take into account multiple constraints such as the starting and ending positions and the voyage, the influence of ocean currents in the route area, and the remaining endurance of the mobile equipment; Figure 2 As shown in the figure, the time cost of underwater mobile unmanned platform task allocation is estimated by first constructing a grid and dividing the grid into geographic space. Each cell is a rectangle with a length and width of 5 nautical miles, forming a grid set of geographic space. , let the grid point where the starting point is , the grid where the target end point is The arrow indicates the need to maneuver from the starting point to the target end point. The direction and speed of the current must be taken into account. Within the grid, the direction and speed of the current are as follows: Figure 3 As shown; the time cost model of the underwater glider is constructed as follows:

[0080]

[0081] The time cost model of wave energy glider is:

[0082]

[0083] The time cost model of AUV is:

[0084]

[0085] In the formula, is the number of grids that the mobile equipment passes through, is the estimated time of the navigation grid obtained based on the motion model of the maneuverable equipment under the influence of the ocean current, The remaining battery life of the device;

[0086] The risk cost of the fixed observation equipment buoy comes from the gap between the sea depth and seabed slope environmental factors of the deployment location and the environmental adaptation parameters of the equipment. When the environmental adaptation parameters of the fixed detection equipment are suitable for the environment of the sea area at the mission target location, the risk cost is 0; when the fixed equipment is not suitable for the sea area environment of the target location, the risk cost is ; The risk estimation formula for fixed observation equipment is constructed as follows:

[0087]

[0088] The risk cost of underwater glider equipment of underwater mobile unmanned platform comes from the loss of control of underwater glider due to complex and harsh ocean current environment, and the "profile" movement of underwater glider when the sea depth on the path is shallow. The method of estimating the risk cost is: statistically analyzing the grid set of underwater gliders The number of grids where the ocean current exceeds the threshold or the sea depth is shallower than the threshold is denoted as The estimated result is , weight It is used to balance the comparability of various costs; the risk estimation formula of underwater glider equipment is constructed as follows:

[0089]

[0090] The risk cost of underwater mobile unmanned platform wave energy glider equipment comes from the probability that the turbulent waves will damage its surface components or the surface and underwater connecting cables. The risk estimation method is to: count the wave energy glider passing through the grid set The number of grids where the wave height exceeds the threshold is denoted by The estimated result is , weight It is used to balance the comparability of various costs; the risk estimation formula of wave energy glider equipment is constructed as follows:

[0091]

[0092] The risk cost of underwater mobile unmanned platform AUV equipment comes from the risk of AUV underwater navigation hitting the bottom due to shallow water depth and complex underwater terrain. The risk estimation method is: statistical AUV navigation grid set The number of grids that simultaneously satisfy the water depth shallower than the threshold and the terrain depth changes dramatically is denoted as The estimated result is , weight It is used to balance the comparability of various costs; the AUV equipment risk estimation formula is constructed as follows:

[0093]

[0094] In the path search problem between grids, the time cost model is constructed to distinguish the device types and estimate the initial position of the device. Run to the observation mission target position The time required in the middle; the risk cost comes from the threat of complex marine environment to equipment and the equipment's own failure rate; the whole set of cost functions obtained by hierarchical weighting for:

[0095]

[0096] In the formula, Indicates the equipment sequence assigned to the entire task sequence , each All from the device collection Select from, n is the number of observation tasks, m is the number of observation equipment;

[0097] Revenue Function Used to describe the environmental observation benefits obtained by the equipment when performing tasks; specifically, it includes the importance of the task and the degree to which the equipment observation can meet the task completion;

[0098] The importance of the task determines the benefit of completing the task, which depends on the observation task The importance of the task is inherent to the nature of the observation task and has nothing to do with the assigned equipment; the importance is proposed by the observation demander and the benefits of completing the task are determined by the observation demander. It is divided into 5 levels: 0 means no benefit and unimportant, 1 means moderately important, 2 means relatively important, 3 means somewhat important, and 4 means very important;

[0099] The degree of task completion includes observation time requirements and observation element requirements; each task will be rewarded after 100% completion Due to the performance constraints of the assigned equipment, the environmental observation can only be partially completed. Let the completion degree be , then the device Execute the task The income is:

[0100] The observation time requirement takes into account the timeliness of the completion of the observation task and the continuity of the observation time. The timeliness of the equipment arrival time is because some ocean phenomena change or even disappear over time, and their observation needs to be limited to a certain time window in their life cycle. If it is not possible to observe them within the time window, the observation effect and value will be lost. The estimation model for the timeliness of the arrival time is constructed as follows:

[0101]

[0102] In the formula, For estimation equipment Arrival Mission The moment of the specified location; For the task Request to start observing time, For the task The time window in which delays are allowed;

[0103] The observation time continuity examines whether the continuous observation time of the equipment can meet the duration requirements set by the task. If it cannot be reached, the continuity index will be reduced according to the time ratio; the equipment's own endurance Determined by the device's own parameters, when the device arrives at the task location and starts to perform environmental observation The planned water entry time is recorded as , the remaining battery life of the device ;Task The required observation time is , the model of observation time persistence is constructed as:

[0104]

[0105] According to the timeliness of arrival and observation time The two elements of continuity are taken into account, and the degree of satisfaction of the observation time requirement is obtained as follows:

[0106]

[0107] In the formula, and They are the coefficients of the importance of the two elements of timeliness and continuity of task observation time;

[0108] Observation element requirements are determined by the task Determine, including temperature profile and salinity profile; when the task When a temperature profile observation is required, first inspect the equipment Whether it has the ability to observe temperature. If not, the degree of satisfaction of temperature profile observation is 0. If it has the ability to observe temperature, continue to investigate the depth range of temperature observation of the equipment. , comparison task Required temperature observation depth range , get the device The temperature observation range can cover the task Ratio of the observed temperature range , the degree of satisfaction of temperature profile observation requirements is estimated as:

[0109]

[0110] The degree of satisfaction of salinity profile observation is estimated as:

[0111]

[0112] Follow-up temperature profile observation satisfaction and salinity profile observations meet the , the degree of satisfaction of the observation element requirements is obtained as follows:

[0113]

[0114] In the formula, and are the coefficients for balancing the importance of the two tasks, temperature observation and salinity observation;

[0115] The whole set of profit functions obtained by hierarchical weighting for:

[0116]

[0117] In the formula, the task number is accumulated conduct, Indicates the equipment sequence assigned to the entire task sequence , each From device collection Select from; n is the number of observation tasks, m is the number of observation equipment;

[0118] Cost-effectiveness function For each pairing of observation equipment and observation tasks , comprehensive consideration ,in, and is the balancing coefficient; the cost-effectiveness function model constructed is as follows Figure 1 As shown;

[0119] S3. Constructing problem analysis and optimization mathematical models

[0120] The problem analysis and optimization mathematical model are transformed into a path search problem between grids; among them, there are Tasks to be completed, for each task there is Candidate devices are used to complete the task. Each candidate task allocation scheme is a path scheme between grids. Each candidate task allocation scheme is a path formed by connecting various devices from left to right along the task direction in the figure, such as Figure 4 As shown, according to the path The overall cost-effectiveness of the construction path is:

[0121]

[0122] By comparing each candidate path Overall cost-effectiveness , get the optimal task allocation path , to solve the task allocation problem; among them, the method of constructing the path search problem between grids is:

[0123] First establish the observation task and unmanned observation equipment The "grid points" are formed to form a pairing model for each task and each unmanned device; then a model connecting the "edges" between the grid points is established, and the evaluation function of each "edge" is composed of the cost function , profit function and cost-effectiveness function combination; finally, use the grid path search method to find the set of "edges" connecting each "grid point" to form an optimized task allocation plan.

[0124] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the technical solution of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for allocating environmental observation tasks based on multiple underwater unmanned equipment, characterized in that: The following steps are involved: S1. Constructing a multi-heterogeneous underwater unmanned platform environment observation performance evaluation algorithm According to the application scenario of multi-heterogeneous underwater unmanned platform environment observation, define the observation task set Y = {y1, y2, ... y n } and the set of unmanned observation equipment X = {x1, x2, …x m }; and set up unmanned observation equipment x i Used to complete the observation task y j , forming a pair D ij = <x i ,y j > S2. Construct an optimization algorithm model for the allocation of underwater unmanned platform collaborative environment observation tasks According to the pairing D of the unmanned observation equipment and the observation task obtained in step S1 ij = <x i ,y j >, each pairing has a corresponding cost function C(D ij ) and the observed return function R(D ij ), the two form a comprehensive cost-effectiveness function S ij =αR(D ij )-βC(D ij ), α and β are balancing coefficients; and according to the purpose of task allocation, find the equipment-task pairing scheme to make the overall cost-effectiveness optimal. The model is: In the formula, i j j∈{1,2,…,m}, different task numbers j correspond to device numbers i j Can be the same, indicating that the same device can perform multiple tasks; n is the number of observation tasks, and m is the number of observation devices; Cost function C(D ij ) is used to describe unmanned observation equipment x i Perform observation task y i The cost required; cost function C(D ij ) includes time cost, risk cost and economic cost. Each cost element is weighted in a hierarchical manner to form a single cost function CO(D ij ); Among them, the time cost model is constructed to distinguish the device types in the path search problem between grids, and the estimated time cost model is constructed by the initial position p of the device. s (x i ) runs to the observation mission target position p e (x i ) The time required in the middle; the risk cost comes from the threat of complex marine environment to equipment and the equipment's own failure rate; the whole set of cost functions CO(D) obtained by hierarchical weighting ij )for: In the formula, i j j represents the equipment sequence [i1,i2,…,i j ,…,i n ], each i j are all selected from the device set [1,…,m]; n is the number of observation tasks, m is the number of observation devices; Revenue function R(D ij ) is used to describe the environmental observation benefits obtained by the equipment when performing tasks; specifically, it includes the importance of the task and the degree to which the equipment observation can meet the task completion; the whole set of benefit functions obtained by hierarchical weighting for: In the formula, the accumulation is performed on task number j, i j j represents the equipment sequence [i1,i2,…,i j ,…,i n ], each i j Select from the device set [1,…,m]; n is the number of observation tasks, m is the number of observation devices; Cost-effectiveness function S ij (D ij ) For each pairing of observation equipment and observation task D ij , taking into account S ij =αR(D ij )-βC(D ij ), where α and β are balancing coefficients; S3. Constructing problem analysis and optimization mathematical models The problem analysis and optimization mathematical model is transformed into a path search problem between grids; there are n tasks to be completed, and for each task there are m candidate devices to complete the task. Each candidate task allocation scheme is a path scheme between grids. c ={ <x c1 ,y1>, <x c2 ,y2>,… <x cj ,y j >… <x cn ,y n >}The overall cost-effectiveness of the construction path is: By comparing each candidate path X c The overall cost-effectiveness ratio S c , get the optimal task allocation path Realize the solution of task allocation problem.

2. The method for allocating environmental observation tasks based on multiple underwater unmanned equipment according to claim 1, characterized in that: The time cost estimation method of the fixed environment observation equipment buoy is: distinguish whether it has been assigned to a certain observation task y q , if it has been assigned to an observation task y q , then the time cost C TS is ∞, indicating that the fixed equipment can only be assigned a task once; if the fixed equipment has not been deployed, the time cost of its task allocation is C TS is 0.

3. The method for allocating environmental observation tasks based on multiple underwater unmanned equipment according to claim 2 is characterized in that: The time cost estimation method for underwater mobile unmanned platform task allocation is as follows: considering the starting and ending positions and the voyage in the grid, the influence of ocean currents in the route area, and the remaining endurance of the mobile equipment; the time cost model of the underwater glider is constructed as follows: The time cost model of wave energy glider is: The time cost model of AUV is: Where N is the number of grids that the mobile equipment passes through, t i E is the estimated time of the navigation grid obtained based on the motion model of the maneuverable equipment under the influence of the ocean current, T The remaining battery life of the device.

4. The method for allocating environmental observation tasks based on multiple underwater unmanned equipment according to claim 3 is characterized in that: The risk cost of the fixed observation equipment buoy comes from the gap between the sea depth and seabed slope environmental factors of the deployment location and the environmental adaptation parameters of the equipment. When the environmental adaptation parameters of the fixed detection equipment are suitable for the environment of the sea area of ​​the mission target location, the risk cost is 0; when the fixed equipment is not suitable for the sea area environment of the target location, the risk cost is ∞; the risk estimation formula of the fixed observation equipment is constructed as follows:

5. The method for allocating environmental observation tasks based on multiple underwater unmanned equipment according to claim 4 is characterized in that: The risk cost of underwater glider equipment of underwater mobile unmanned platforms comes from the loss of control of underwater gliders due to complex and harsh ocean currents, and the "profile" movement of underwater gliders when the sea depth on the path is shallow. The method for estimating the risk cost is as follows: Count the number of underwater gliders flying through the grid set L D The number of grids where the ocean current exceeds the threshold or the sea depth is shallower than the threshold, denoted as N rg The estimated result is η rg ·N rg , weight η rg It is used to balance the comparability of various costs; the risk estimation formula of underwater glider equipment is constructed as follows: C RG (x i ,p s ,p e )6η rg ·N rg 。 。 6. The method for allocating environmental observation tasks based on multiple underwater unmanned equipment according to claim 5 is characterized in that: The risk cost of underwater mobile unmanned platform wave energy glider equipment comes from the probability that the turbulent waves will damage its surface components or the surface and underwater connecting cables. The risk estimation method is to count the number of grid sets L where the wave energy glider passes. D The number of grids where the wave height exceeds the threshold, denoted as N rw The estimated result is η rw ·N rw , weight η rw It is used to balance the comparability of various costs; the risk estimation formula of wave energy glider equipment is constructed as follows: C RW (x i ,p s ,p e )6η rw ·N rw 。 。 7. The method for allocating environmental observation tasks based on multiple underwater unmanned equipment according to claim 6 is characterized in that: The risk cost of underwater mobile unmanned platform AUV equipment comes from the risk of AUV underwater navigation hitting the bottom due to shallow water depth and complex underwater terrain. The risk estimation method is: statistical AUV navigation grid set L D The number of grids that simultaneously satisfy the water depth shallower than the threshold and the terrain depth changes dramatically is denoted as N ru The estimated result is η ru ·N ru , weight η ru It is used to balance the comparability of various costs; the AUV equipment risk estimation formula is constructed as follows: C RU (x i ,p s ,p e )6η ru ·N ru 。 。 8. The method for allocating environmental observation tasks based on multiple underwater unmanned equipment according to claim 7, characterized in that: The importance of the task determines the benefit of completing the task, which depends on the observed task y j The importance of the task is inherent to the observation task and has nothing to do with the assigned equipment. The importance is proposed by the observation demander and the task completion benefit R T (D j ) is divided into 5 levels: 0 means no benefit and unimportant, 1 means moderately important, 2 means relatively important, 3 means somewhat important, and 4 means very important.

9. The method for allocating environmental observation tasks based on multiple underwater unmanned equipment according to claim 8, characterized in that: The degree of task completion includes the observation time requirement and the observation element requirement; each task will be rewarded R after 100% completion. T (D j ); Due to the parameter performance constraints of the assigned equipment, the environmental observation can only be partially completed, and the completion degree is set to R F (D ij ), then device x i Execute task y j The income is: R(D ij )=R T (D j )·R F (D ij )。 10. The method for allocating environmental observation tasks based on multiple underwater unmanned equipment according to claim 9, characterized in that: The observation time requirement takes into account the timeliness of the completion of the observation task and the continuity of the observation time. The timeliness of the equipment arrival time is because some ocean phenomena change or even disappear over time, and their observation needs to be limited to a certain time window in their life cycle. If it is not possible to observe them within the time window, the observation effect and value will be lost. The estimation model for the timeliness of the arrival time is constructed as follows: In the formula, t ia To estimate the equipment x i Arrival mission y j The time at the specified location; t pi For task y j Required observation start time, t pw For task y j The time window in which delays are allowed; The observation time continuity examines whether the continuous observation time of the equipment can meet the duration requirements set by the task. If not, the continuity index is reduced according to the time ratio; the equipment's own endurance t le (x i ) is determined by the device's own parameters. When the device reaches the task location and starts to perform environmental observation, i The planned water entry time is t lw (x i ), the remaining battery life of the device t ll (x i ) = t le (x i )-t lw (x i ); Task y j The required observation time is t lt (y j ), the model of observation time persistence is constructed as: According to the timeliness of arrival ti (x i ,y j ) and observation time r tl (x i ,y j ) and the degree of satisfaction of the observation time requirement is: r t (x i ,y j )=α ti ·r ti (x i ,y j )+α tl ·r tl (x i ,y j ) In the formula, α ti and α tl They are the coefficients of the importance of the two factors of timeliness and continuity of task observation time.

11. The method for allocating environmental observation tasks based on multiple underwater unmanned equipment according to claim 10, characterized in that: The observation element requirements are determined by task y j Determine, including temperature profile and salinity profile; when task y j When a temperature profile observation is required, first inspect the equipment x i Whether it has the ability to observe temperature. If not, the degree of satisfaction of temperature profile observation is 0. If it has the ability to observe temperature, continue to investigate the depth range of temperature observation of the equipment. td (x i ), comparison task y j Required temperature observation depth rangee pt (y j ), get device x i The temperature observation range can cover task y j The ratio of the temperature observation range tr (x i ,y j ), the degree of satisfaction of temperature profile observation requirements is estimated as: The degree of satisfaction of salinity profile observation is estimated as: Follow-up temperature profile observation satisfaction degree r et (x i ,y j ) and salinity profile observations satisfy the degree r es (x i ,y j ), the satisfaction degree of the observation element requirements is obtained as: r e (x i ,y j )=α et ·r et (x i ,y j )+α es ·r es (x i ,y j ) In the formula, α et and α es are the coefficients that balance the importance of the two tasks, temperature observation and salinity observation.

12. The method for allocating environmental observation tasks based on multiple underwater unmanned equipment according to claim 11, characterized in that: In S3, the method for constructing the path search problem between grids is: First establish the observation task y j and unmanned observation equipmentx i The "grid points" are formed to form a pairing model for each task and each unmanned device; then a model connecting the "edges" between the grid points is established, and the evaluation function of each "edge" is composed of the cost function C(D ij ), the profit function R(D ij ) and cost-effectiveness function S ij =αR(D ij )-βC(D ij ) combination; finally, the grid path search method is used to find the set of "edges" connecting each "grid point" to form an optimized task allocation plan.

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