Water quality monitoring robot formation communication network optimization construction method

Abstract

The invention relates to the technical field of water quality monitoring robots, in particular to a water quality monitoring robot formation communication network optimization construction method, which comprises the following steps: S1, determining a water area of a water quality monitoring task; s2, determining coordinates of all water quality monitoring points in the water area of the water quality monitoring task; s3, inputting the formation condition of the water quality monitoring robots; s4, performing task allocation on the water quality detection robot formation, and establishing a task allocation model; s5, solving the task allocation model by using an improved eagle optimization algorithm; s6, establishing a water quality monitoring robot communication full-connectivity model; step S7, performing full communication judgment on the water quality monitoring robot formation communication network; and step S8, constructing and optimizing communication links in a fully connected network state and a non-fully connected network state. The connectivity of a water quality monitoring robot formation communication network is ensured, and the problem of low monitoring efficiency in a large-area water area is solved.

Description

Technical Field

[0001] The invention relates to the technical field of water quality monitoring robots, and in particular to a method for optimizing and constructing a communication network for a water quality monitoring robot formation. Background Art

[0002] Water quality monitoring robots have broad application prospects in water environment monitoring, fishery management and other fields. With the continuous expansion of monitoring scope and the diversification of monitoring scenarios, meeting long-term, dynamic and efficient application requirements has become the key. In order to improve monitoring efficiency, it is usually necessary for multiple water quality monitoring robots to work together and optimize the monitoring process through reasonable task allocation.

[0003] During the water quality monitoring process, water quality monitoring robots need to transmit data through a communication network. At present, the commonly used water quality monitoring methods include randomly deploying fixed anchor nodes on the water surface or directly transmitting data to the base station through water quality monitoring robots. However, these methods have problems such as poor communication effect, high cost, and easy node disconnection. In order to solve these problems, it is necessary to design a dynamic, efficient and low-cost communication network construction method based on the formation distribution of water quality monitoring robots to adapt to the complex water quality monitoring environment. Summary of the invention

[0004] The present invention proposes a method for optimizing the communication network construction of a water quality monitoring robot formation, which is based on an improved Osprey optimization algorithm and aims to improve the data transmission efficiency and task execution efficiency of the water quality monitoring robot formation.

[0005] The water quality monitoring robot is a ship-shaped design equipped with a variety of water quality parameter detection sensors, including pH sensors, conductivity sensors, TDS sensors and water temperature sensors. The water quality monitoring robot is powered by solar energy and packages the collected water quality data and transmits it through its own communication module. Depending on the actual location of the robot, the data can be sent to the base station through direct transmission or multi-hop transmission.

[0006] The present invention is achieved by adopting the following technical solutions:

[0007] A method for optimizing the communication network construction of a water quality monitoring robot formation is implemented by the following steps:

[0008] Step S1: determine the water quality monitoring task water area and establish the water area map and coordinate system;

[0009] Step S2: Determine the coordinates of all water quality monitoring points within the water quality monitoring task area ,in, is the number of water quality monitoring points;

[0010] Step S3: Input the water quality monitoring robot formation situation, the number of water quality monitoring robots is , the water quality robot formation is represented by ;

[0011] Step S4: assigning tasks to the water quality detection robot formation and establishing a task assignment model;

[0012] Step S5: using the Latin hypercube sampling strategy and combining the chaotic mapping mechanism to improve the Osprey optimization algorithm to improve the algorithm performance, and using the improved Osprey optimization algorithm to solve the task allocation model;

[0013] Step S6: Establishing a fully connected communication model for the water quality monitoring robot;

[0014] Step S7: Conducting full connectivity determination on the communication network of the water quality monitoring robot formation;

[0015] Step S8: constructing and optimizing the communication link in the fully connected network state;

[0016] Step S9: construct and optimize the communication link in the non-fully connected network state.

[0017] Furthermore, the step S4: assigning tasks to the water quality detection robot formation and establishing a task assignment model is as follows:

[0018] Step 4.1: , any two water quality monitoring points The distance between them is recorded as:

[0019] (1);

[0020] Step 4.2: Water quality monitoring robot When the speed moves at a constant speed, the instantaneous energy consumption Minimum, complete two water quality monitoring points The corresponding energy consumption is:

[0021] (2);

[0022] Step 4.3: Create decision variables , when the water quality monitoring robot From the water quality monitoring point To the water quality monitoring point season ,otherwise ;

[0023] Step 4.4: Create decision variables , the water quality monitoring robot Water quality monitoring point As your mission point, ,otherwise ;

[0024] Step 4.5: Create decision variables , used to control the water quality monitoring robot , the order of water quality testing at water quality monitoring points;

[0025] Step 4.6: Establish a water quality monitoring robot formation task allocation model;

[0026] (3);

[0027] (4);

[0028] in, For the Water quality monitoring task points corresponding to each water quality monitoring robot;

[0029] Constraint 1 limits each water quality monitoring point to only one water quality monitoring robot for testing;

[0030] Constraint 2 restricts the water quality monitoring robot to complete the water quality monitoring point If the detection is completed, it is necessary to proceed to the next monitoring point;

[0031] Constraint 3 limits the task route of each water quality monitoring robot to a complete loop;

[0032] Constraint 4 limits the fairness of task allocation, where The energy consumption difference adjustment coefficient ensures that the energy consumption difference between water quality monitoring robots is limited to an acceptable range.

[0033] Furthermore, the step S5: using the Latin hypercube sampling strategy and combining the chaotic mapping mechanism to improve the Osprey optimization algorithm to improve the algorithm performance, and using the improved Osprey optimization algorithm to solve the task allocation model; specifically as follows:

[0034] Step 5.1: Use the Latin hypercube sampling strategy to improve the population diversity of the osprey optimization algorithm: In the osprey optimization algorithm, the osprey population initialization is performed randomly. After the improvement of the Latin hypercube sampling strategy, the osprey population initialization formula can be rewritten as:

[0035] (5);

[0036] (6);

[0037] in, is the population size; is the dimension of the problem; LHS is generated using Latin hypercube sampling indivual The process of dimensioning samples; It is The sample in Dimension value. and They are Lower and upper bounds on dimensional variables; For interval A random sequence on ;

[0038] Step 5.2: Use the chaotic mapping mechanism to further perturb the osprey population, improve search diversity, and form a new sequence;

[0039] (7);

[0040] in, is the value generated by the chaotic map, Control parameters for mapping.

[0041] Step 5.3: Take the water quality monitoring robot formation task allocation model as the optimization object, let

[0042] (8);

[0043] For each individual osprey , calculate the fitness value ;

[0044] Step 5.4: The osprey hovers to perform a global search, and its position update formula is as follows:

[0045] (9);

[0046] in, is the optimal position of the current osprey individual; ;

[0047] Step 5.5: The osprey approaches the prey for a local search, and the position of the individual osprey is updated as follows:

[0048] (10);

[0049] in, The generated random number is used to adjust the degree to which the individual osprey approaches the optimal solution;

[0050] Step 5.6: After each update, continue to use chaotic mapping to further perturb the individual positions of the ospreys near the optimal solution to avoid the local optimality of the algorithm:

[0051] (11);

[0052] Step 5.7: Determine whether the maximum number of iterations is reached or the fitness value meets the accuracy requirement. If so, output the optimal solution, otherwise return to step 5.4.

[0053] Furthermore, the step S6: establishing a fully connected communication model of the water quality monitoring robot is as follows:

[0054] Step 6.1: At any time , any two water quality monitoring robots , the corresponding positions are , and the distance between them is:

[0055] (12);

[0056] Step 6.2: At any time , water quality monitoring robot To base station The distance is:

[0057] (13);

[0058] Step 6.3: Definition is the maximum distance for establishing communication between two water quality monitoring robots and between a water quality monitoring robot and a base station; then, , when any two water quality monitoring robots The distance between the water quality monitoring robot and the base station meets When , a connection relationship is established between the two;

[0059] Use symbols Water quality monitoring robot Establish a connection, otherwise ; Water quality monitoring robot Establish a connection with the base station, otherwise ;

[0060] Step 6.4: At any time ,like , To transmit data packets to the base station, the water quality monitoring robot communication network link needs to meet the following requirements:

[0061] (14);

[0062] Formula (14) shows that Among the water quality monitoring robots, any water quality monitoring robot can establish a connection with at least one other water quality monitoring robot, and at least one water quality monitoring robot can establish a connection with the base station. The above network is called a fully connected network.

[0063] Furthermore, the step S7: performing full connectivity determination on the communication network of the water quality monitoring robot formation is as follows:

[0064] Step 7.1: In step S4, the task point matrix corresponding to each water quality monitoring robot is obtained. , and obtain the water quality monitoring robot Corresponding task point matrix In each water quality monitoring point and water quality monitoring robot Corresponding task point matrix The distance between each water quality monitoring point in the , used to store corresponding distance information;

[0065] Step 7.2: If the water quality monitoring robot and The maximum distance between the task points satisfies ,mean and The connection can always be maintained during the task, that is,

[0066] (15);

[0067] in, It takes time for the water quality monitoring robot to complete a round of monitoring tasks;

[0068] Step 7.3: According to the definition of the fully connected network in step S6, combined with equation (15), if at any time, all water quality monitoring robots have at least one data transmission link to the base station, then at all times, the water quality monitoring robot formation constitutes a fully connected network, which is called a full-time fully connected network;

[0069] Step 7.4: If the distance matrix Dissatisfied , but there is , , so that ; In other words, the distance matrix There are two water quality monitoring points in the distance Less than or equal to the connectivity threshold , which means water quality monitoring robot and When the two water quality monitoring points are respectively located, data forwarding can be completed, and accordingly, it is called a time-sharing-full-connectivity network;

[0070] Step 7.5: If the distance matrix middle, , which means water quality monitoring robot and The inability to establish a connection during the entire mission is called a non-fully connected network.

[0071] Furthermore, the step S8: constructing and optimizing the communication link in the fully connected network state is as follows:

[0072] Step 8.1: For the full-time and full-connection network, if the water quality monitoring robot There is a need to send data, by building a Base station location Connection , The equation of the straight line is:

[0073] (16);

[0074] To query the water quality monitoring robot currently connected to it, such as Can be connected to it, then To the straight line The vertical foot The calculation is as follows:

[0075] (17);

[0076] Further seek To base station Distance:

[0077] (18);

[0078] Will As an evaluation metric for relay selection, The smaller the value, the better the water quality monitoring robot The greater the effective distance that the data packet travels, the greater the probability that the water quality monitoring robot will be selected as a relay;

[0079] choose The smallest water quality monitoring robot As a next-hop information relay; similarly, Continue to search for the next relay node according to the above method until the data packet is sent to the base station;

[0080] Step 8.2: For the time-segmented and fully connected network, the waiting time of the water quality monitoring robot needs to be optimized. In step S7.4, there is , , so that , at this time the water quality monitoring robot and A connection can be established if the water quality monitoring robot If you need to send, you can When the water quality monitoring point is reached, wait Arrive at Water quality monitoring points for data transmission; The waiting time for transmission can be optimized and The initial position and speed are reduced;

[0081] when Towards After the data transfer is completed, According to the method of step S8.1 or by waiting, the data packet continues to be forwarded to the next water quality monitoring robot node until it reaches the base station.

[0082] Furthermore, the step S9: constructing and optimizing the communication link in the non-fully connected network state is as follows:

[0083] Step 9.1: In the water quality monitoring robot formation network, if some water quality monitoring robot nodes cannot join the network, it is necessary to add new track points to the route of the node or the surrounding water quality monitoring robot nodes to join them into the data transmission network;

[0084] According to the method of step S7, determine the water quality monitoring robot node Unable to join the network while requesting Corresponding water quality monitoring points In, there is a little Distance water quality monitoring robot Corresponding water quality monitoring points Recently, it is recorded as ;and You can join the network, choose As its relay communication node; otherwise, find the next water quality monitoring robot that meets the above conditions;

[0085] Step 9.2: Water Quality Monitoring Robot and Find a point each , , so that these two points meet the connectivity requirements, and the water quality monitoring robot and The total distance moved is the smallest, and the corresponding model is as follows:

[0086] (19);

[0087] (20);

[0088] The above formula (19) can be solved using the improved Osprey optimization algorithm in step S5; in addition, when there are multiple nodes that need When relaying, the above formula is also applicable, just increase the dimension of the solution;

[0089] Step 9.3: Insert into the matrix as a task point Medium Element Later, as a water quality monitoring robot The track point of Insert into the matrix as a task point Medium Element Later, as a water quality monitoring robot Track points;

[0090] Step 9.4: Water Quality Monitoring Robot According to step S8, data transmission is performed.

[0091] Furthermore, the The value of can be determined based on the channel fading parameters, communication transmission power and antenna gain of the water quality monitoring mission area.

[0092] The structural design of the present invention is reasonable and reliable. Compared with the prior art, the present invention has the following advantages:

[0093] 1. A method for optimizing the communication network construction of a water quality monitoring robot formation solves the problem of low monitoring efficiency of a single water quality monitoring robot in a large area of ​​water. A solution for collaborative tasks of a water quality monitoring robot formation is designed, and a formation task allocation algorithm is designed. An improved Osprey algorithm is proposed to improve task allocation efficiency.

[0094] 2. A method for optimizing the construction of a communication network for a water quality monitoring robot formation. Aiming at the problem of constructing a communication network for a water quality monitoring robot formation, a fully connected network model is derived, and the connectivity of the communication network is ensured by optimizing the positions of the water quality monitoring robots. Under the optimized network, all water quality monitoring robots can transmit water quality data to the base station, which improves the reliability of water quality monitoring; it is suitable for the deployment and construction of surface communication networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] Figure 1 It is a schematic diagram of the system framework of the present invention.

[0096] Figure 2It is an implementation flow chart of the present invention.

[0097] Figure 3 It is an implementation flow chart of the improved Osprey optimization algorithm based on the Latin hypercube sampling strategy combined with the chaotic mapping mechanism in the present invention.

[0098] Figure 4 It is a schematic diagram of the relay selection process of the water quality monitoring robot of the present invention.

[0099] Figure 5 This is the water quality robot communication network node deployment result obtained by the present invention. DETAILED DESCRIPTION

[0100] A method for optimizing the communication network construction of a water quality monitoring robot formation is proposed. First, a solution for collaborative tasks of a water quality monitoring robot formation is designed for water quality monitoring in large areas of water. Secondly, an improved Osprey optimization algorithm based on Latin hypercube sampling strategy and chaotic mapping mechanism is developed to allocate tasks to the water quality monitoring robot formation. Then, a fully connected model of the water quality monitoring robot communication network is derived and established, and the water quality monitoring robot communication network is evaluated. Finally, corresponding network connectivity optimization and link optimization models are designed for different network states.

[0101] A method for optimizing the communication network construction of a water quality monitoring robot formation, as shown in the attached Figure 1 , Attachment Figure 2 As shown, the method is implemented by the following steps:

[0102] Step S1: determine the water quality monitoring task water area and establish the water area map and coordinate system;

[0103] Step S2: Determine the coordinates of all water quality monitoring points within the water quality monitoring task area ,in, is the number of water quality monitoring points;

[0104] Step S3: Input the water quality monitoring robot formation situation, the number of water quality monitoring robots is , the water quality robot formation is represented by ;

[0105] Step S4: Assign tasks to the water quality detection robot team and establish a task assignment model; the details are as follows:

[0106] Step 4.1: , any two water quality monitoring points The distance between them is recorded as:

[0107] (1);

[0108] Step 4.2: Water quality monitoring robot When the speed moves at a constant speed, the instantaneous energy consumption Minimum, complete two water quality monitoring points The corresponding energy consumption is:

[0109] (2);

[0110] Step 4.3: Create decision variables , when the water quality monitoring robot From the water quality monitoring point To the water quality monitoring point season ,otherwise ;

[0111] Step 4.4: Create decision variables , the water quality monitoring robot Water quality monitoring point As your mission point, ,otherwise ;

[0112] Step 4.5: Create decision variables , used to control the water quality monitoring robot , the order of water quality testing at water quality monitoring points;

[0113] Step 4.6: Establish a water quality monitoring robot formation task allocation model;

[0114] (3);

[0115] (4);

[0116] The above formula indicates that the quantity is Water quality monitoring task points are assigned to water quality monitoring robots, and determine the order in which each water quality monitoring robot conducts water quality testing at the water quality monitoring points, so that the energy consumption of the water quality monitoring robot formation is minimized; among them, For the Water quality monitoring task points corresponding to each water quality monitoring robot;

[0117] Constraint 1 limits each water quality monitoring point to only one water quality monitoring robot for testing;

[0118] Constraint 2 restricts the water quality monitoring robot to complete the water quality monitoring point If the detection is completed, it is necessary to proceed to the next monitoring point;

[0119] Constraint 3 limits the task route of each water quality monitoring robot to a complete loop;

[0120] Constraint 4 limits the fairness of task allocation, where The energy consumption difference adjustment coefficient ensures that the energy consumption difference between water quality monitoring robots is limited to an acceptable range.

[0121] Step S5: Using the Latin hypercube sampling strategy and combining the chaotic mapping mechanism to improve the Osprey optimization algorithm to improve the algorithm performance, and using the improved Osprey optimization algorithm to solve the task allocation model; the details are as follows:

[0122] Step 5.1: Use the Latin hypercube sampling strategy to improve the population diversity of the osprey optimization algorithm: In the osprey optimization algorithm, the osprey population initialization is performed randomly. After the improvement of the Latin hypercube sampling strategy, the osprey population initialization formula can be rewritten as:

[0123] (5);

[0124] (6);

[0125] in, is the population size; is the dimension of the problem; LHS is generated using Latin hypercube sampling indivual The process of dimensioning samples; It is The sample in Dimension value. and They are Lower and upper bounds on dimensional variables; For interval A random sequence on ;

[0126] Step 5.2: Use the chaotic mapping mechanism to further perturb the osprey population, improve search diversity, and form a new sequence;

[0127] (7);

[0128] in, is the value generated by the chaotic map, Control parameters for mapping.

[0129] Step 5.3: Take the water quality monitoring robot formation task allocation model as the optimization object, let

[0130] (8);

[0131] For each individual osprey , calculate the fitness value ;

[0132] Step 5.4: The osprey hovers to perform a global search, and its position update formula is as follows:

[0133] (9);

[0134] in, is the optimal position of the current osprey individual; ;

[0135] Step 5.5: The osprey approaches the prey for a local search, and the position of the individual osprey is updated as follows:

[0136] (10);

[0137] in, The generated random number is used to adjust the degree to which the individual osprey approaches the optimal solution;

[0138] Step 5.6: After each update, continue to use chaotic mapping to further perturb the individual positions of the ospreys near the optimal solution to avoid the local optimality of the algorithm:

[0139] (11);

[0140] Step 5.7: Determine whether the maximum number of iterations is reached or the fitness value meets the accuracy requirement. If so, output the optimal solution, otherwise return to step 5.4.

[0141] The improved Osprey optimization algorithm implementation flow chart is as follows: Figure 3 Through the above process, the task allocation of the water quality monitoring team and the monitoring task points and corresponding task paths of each water quality monitoring robot are obtained. .

[0142] Step S6: Establish a fully connected communication model for the water quality monitoring robot; the details are as follows:

[0143] Step 6.1: At any time , any two water quality monitoring robots , the corresponding positions are , and the distance between them is:

[0144] (12);

[0145] Step 6.2: At any time , water quality monitoring robot To base station The distance is:

[0146] (13);

[0147] Step 6.3: Definition The maximum distance for establishing communication between two water quality monitoring robots and between a water quality monitoring robot and a base station; The value of can be determined according to the channel fading parameters, communication transmission power and antenna gain of the water quality monitoring mission area; then, , when any two water quality monitoring robots The distance between the water quality monitoring robot and the base station meets When , a connection relationship is established between the two;

[0148] Use symbols Water quality monitoring robot Establish a connection, otherwise ; Water quality monitoring robot Establish a connection with the base station, otherwise ;

[0149] Step 6.4: At any time ,like , To transmit data packets to the base station, the water quality monitoring robot communication network link needs to meet the following requirements:

[0150] (14);

[0151] Formula (14) shows that Among the water quality monitoring robots, any water quality monitoring robot can establish a connection with at least one other water quality monitoring robot, and at least one water quality monitoring robot can establish a connection with the base station. The above network is called a fully connected network.

[0152] Step S7: Conduct full connectivity determination on the communication network of the water quality monitoring robot formation; details are as follows:

[0153] Step 7.1: In step S4, the task point matrix corresponding to each water quality monitoring robot is obtained. , and obtain the water quality monitoring robot Corresponding task point matrix In each water quality monitoring point and water quality monitoring robot Corresponding task point matrix The distance between each water quality monitoring point in the , used to store corresponding distance information;

[0154] Step 7.2: If the water quality monitoring robot and The maximum distance between the task points satisfies ,mean and The connection can always be maintained during the task, that is,

[0155] (15);

[0156] in, It takes time for the water quality monitoring robot to complete a round of monitoring tasks;

[0157] Step 7.3: According to the definition of the fully connected network in step S6, combined with equation (15), if at any time, all water quality monitoring robots have at least one data transmission link to the base station, then at all times, the water quality monitoring robot formation constitutes a fully connected network, which is called a full-time fully connected network;

[0158] Step 7.4: If the distance matrix Dissatisfied , but there is , , so that ; In other words, the distance matrix There are two water quality monitoring points in the distance Less than or equal to the connectivity threshold , which means water quality monitoring robot and When the two water quality monitoring points are respectively located, data forwarding can be completed, and accordingly, it is called a time-sharing-full-connectivity network;

[0159] Step 7.5: If the distance matrix middle, , which means water quality monitoring robot and The inability to establish a connection during the entire mission is called a non-fully connected network.

[0160] Step S8: construct and optimize the communication link in the fully connected network state; Figure 4 As shown, the details are as follows:

[0161] Step 8.1: For the full-time and full-connection network, if the water quality monitoring robot There is a need to send data, by building a Base station location Connection , The equation of the straight line is:

[0162] (16);

[0163] To query the water quality monitoring robot currently connected to it, such as Can be connected to it, then To the straight line The vertical foot The calculation is as follows:

[0164] (17);

[0165] Further seek To base station Distance:

[0166] (18);

[0167] Will As an evaluation metric for relay selection, The smaller the value, the better the water quality monitoring robot The greater the effective distance that the data packet travels, the greater the probability that the water quality monitoring robot will be selected as a relay;

[0168] choose The smallest water quality monitoring robot As a next-hop information relay; similarly, Continue to search for the next relay node according to the above method until the data packet is sent to the base station;

[0169] Step 8.2: For the time-segmented and fully connected network, the waiting time of the water quality monitoring robot needs to be optimized. In step S7.4, there is , , so that , at this time the water quality monitoring robot and A connection can be established if the water quality monitoring robot If you need to send, you can When the water quality monitoring point is reached, wait Arrive at Water quality monitoring points for data transmission; The waiting time for transmission can be optimized and The initial position and speed are reduced;

[0170] when Towards After the data transfer is completed, According to the method of step S8.1 or by waiting, the data packet continues to be forwarded to the next water quality monitoring robot node until it reaches the base station.

[0171] Step S9: construct and optimize the communication link in the non-fully connected network state; the details are as follows:

[0172] Step 9.1: In the water quality monitoring robot formation network, if some water quality monitoring robot nodes cannot join the network, it is necessary to add new track points to the route of the node or the surrounding water quality monitoring robot nodes to join them into the data transmission network;

[0173] According to the method of step S7, determine the water quality monitoring robot node Unable to join the network while requesting Corresponding water quality monitoring points In, there is a little Distance water quality monitoring robot Corresponding water quality monitoring points Recently, it is recorded as ;and You can join the network, choose As its relay communication node; otherwise, find the next water quality monitoring robot that meets the above conditions;

[0174] Step 9.2: Water Quality Monitoring Robot and Find a point each , , so that these two points meet the connectivity requirements, and the water quality monitoring robot and The total distance moved is the smallest, and the corresponding model is as follows:

[0175] (19);

[0176] (20);

[0177] The above formula (19) can be solved using the improved Osprey optimization algorithm in step S5; in addition, when there are multiple nodes that need When relaying, the above formula is also applicable, just increase the dimension of the solution;

[0178] Step 9.3: Insert into the matrix as a task point Medium Element Later, as a water quality monitoring robot The track point of Insert into the matrix as a task point Medium Element Later, as a water quality monitoring robot Track points;

[0179] Step 9.4: Water Quality Monitoring Robot According to step S8, data transmission is performed.

[0180] As attached Figure 5 As shown, according to the above process, the present invention obtains a formation consisting of 9 water quality monitoring robots within a range of 10km×10km, and the corresponding task allocation results and communication link diagram.

[0181] From the above results, it can be seen that the method of the present invention can realize the task planning of the water quality monitoring robot formation, and improve the connectivity of the network to ensure the smooth transmission of water quality monitoring data. This has positive significance for the field of water quality monitoring.

[0182] In the description of the present invention, it is necessary to understand that the indicated orientation or positional relationship is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0183] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing and constructing a communication network for a water quality monitoring robot formation, characterized in that: This method is implemented using the following steps: Step S1: determine the water quality monitoring task water area and establish the water area map and coordinate system; Step S2: Determine the coordinates of all water quality monitoring points within the water quality monitoring task area ,in, is the number of water quality monitoring points; Step S3: Input the water quality monitoring robot formation situation, the number of water quality monitoring robots is , the water quality robot formation is represented by ; Step S4: assigning tasks to the water quality detection robot formation and establishing a task assignment model; Step S5: using the Latin hypercube sampling strategy and combining the chaotic mapping mechanism to improve the Osprey optimization algorithm to improve the algorithm performance, and using the improved Osprey optimization algorithm to solve the task allocation model; Step S6: Establishing a fully connected communication model of the water quality monitoring robot; Step S7: Conducting full connectivity determination on the communication network of the water quality monitoring robot formation; Step S8: constructing and optimizing the communication link in the fully connected network state; Step S9: construct and optimize the communication link in the non-fully connected network state.

2. A method for optimizing and constructing a communication network for a water quality monitoring robot formation according to claim 1, characterized in that: The step S4: assigning tasks to the water quality detection robot team and establishing a task assignment model; the details are as follows: Step 4.1: , any two water quality monitoring points The distance between them is recorded as: (1)； Step 4.2: Water quality monitoring robot When the speed moves at a constant speed, the instantaneous energy consumption Minimum, complete two water quality monitoring points The corresponding energy consumption is: (2)； Step 4.3: Create decision variables , when the water quality monitoring robot From the water quality monitoring point To the water quality monitoring point season ,otherwise ; Step 4.4: Create decision variables , the water quality monitoring robot Water quality monitoring point As your mission point, ,otherwise ; Step 4.5: Create decision variables , used to control the water quality monitoring robot , the order of water quality testing at water quality monitoring points; Step 4.6: Establish a water quality monitoring robot formation task allocation model; (3)； (4)； in, For the Water quality monitoring task points corresponding to each water quality monitoring robot; Constraint 1 limits each water quality monitoring point to only one water quality monitoring robot for testing; Constraint 2 restricts the water quality monitoring robot to complete the water quality monitoring point If the detection is completed, it is necessary to proceed to the next monitoring point; Constraint 3 limits the task route of each water quality monitoring robot to a complete loop; Constraint 4 limits the fairness of task allocation, where The energy consumption difference adjustment coefficient ensures that the energy consumption difference between water quality monitoring robots is limited to an acceptable range.

3. A method for optimizing and constructing a communication network for a water quality monitoring robot formation according to claim 2, characterized in that: The step S5: using the Latin hypercube sampling strategy and combining the chaotic mapping mechanism to improve the Osprey optimization algorithm to improve the algorithm performance, and using the improved Osprey optimization algorithm to solve the task allocation model; the details are as follows: Step 5.1: Use the Latin hypercube sampling strategy to improve the population diversity of the osprey optimization algorithm: In the osprey optimization algorithm, the osprey population initialization is performed randomly. After the improvement of the Latin hypercube sampling strategy, the osprey population initialization formula can be rewritten as: (5)； (6)； in, is the population size; is the dimension of the problem; LHS is generated using Latin hypercube sampling indivual The process of dimensioning samples; It is The sample in Dimension value; and They are Lower and upper bounds on dimensional variables; For interval A random sequence on ; Step 5.2: Use the chaotic mapping mechanism to further perturb the osprey population, improve search diversity, and form a new sequence; (7)； in, is the value generated by the chaotic map, To map control parameters; Step 5.3: Take the water quality monitoring robot formation task allocation model as the optimization object, let (8)； For each individual osprey , calculate the fitness value ; Step 5.4: The osprey hovers to perform a global search, and its position update formula is as follows: (9)； in, is the optimal position of the current osprey individual; ; Step 5.5: The osprey approaches the prey for a local search, and the position of the individual osprey is updated as follows: (10)； in, The generated random number is used to adjust the degree to which the individual osprey approaches the optimal solution; Step 5.6: After each update, continue to use chaotic mapping to further perturb the individual positions of the ospreys near the optimal solution to avoid the local optimality of the algorithm: (11)； Step 5.7: Determine whether the maximum number of iterations is reached or the fitness value meets the accuracy requirement. If so, output the optimal solution, otherwise return to step 5.

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4. A method for optimizing and constructing a communication network for a water quality monitoring robot formation according to claim 3, characterized in that: The step S6: establishing a fully connected communication model for the water quality monitoring robot; the details are as follows: Step 6.1: At any time , any two water quality monitoring robots , the corresponding positions are , and the distance between them is: (12)； Step 6.2: At any time , water quality monitoring robot To base station The distance is: (13)； Step 6.3: Definition is the maximum distance for establishing communication between two water quality monitoring robots and between a water quality monitoring robot and a base station; then, , when any two water quality monitoring robots The distance between the water quality monitoring robot and the base station meets When , a connection relationship is established between the two; Use symbols Water quality monitoring robot Establish a connection, otherwise ; Water quality monitoring robot Establish a connection with the base station, otherwise ; Step 6.4: At any time ,like , To transmit data packets to the base station, the water quality monitoring robot communication network link needs to meet the following requirements: (14)； Formula (14) shows that Among the water quality monitoring robots, any water quality monitoring robot can establish a connection with at least one other water quality monitoring robot, and at least one water quality monitoring robot can establish a connection with the base station. The above network is called a fully connected network.

5. A method for optimizing and constructing a communication network for a water quality monitoring robot formation according to claim 4, characterized in that: The step S7: performing full connectivity determination on the communication network of the water quality monitoring robot formation; specifically as follows: Step 7.1: In step S4, the task point matrix corresponding to each water quality monitoring robot is obtained. , and obtain the water quality monitoring robot Corresponding task point matrix In each water quality monitoring point and water quality monitoring robot Corresponding task point matrix The distance between each water quality monitoring point in the , used to store corresponding distance information; Step 7.2: If the water quality monitoring robot and The maximum distance between the task points satisfies ,mean and The connection can always be maintained during the task, that is, (15)； in, It takes time for the water quality monitoring robot to complete a round of monitoring tasks; Step 7.3: According to the definition of the fully connected network in step S6, combined with equation (15), if at any time, all water quality monitoring robots have at least one data transmission link to the base station, then at all times, the water quality monitoring robot formation constitutes a fully connected network, which is called a full-time fully connected network; Step 7.4: If the distance matrix Dissatisfied , but there is , , so that ; In other words, the distance matrix There are two water quality monitoring points in the distance Less than or equal to the connectivity threshold , which means water quality monitoring robot and When the two water quality monitoring points are respectively located, data forwarding can be completed, and accordingly, it is called a time-sharing-full-connectivity network; Step 7.5: If the distance matrix middle, , which means water quality monitoring robot and The inability to establish a connection during the entire mission is called a non-fully connected network.

6. A method for optimizing and constructing a communication network for a water quality monitoring robot formation according to claim 5, characterized in that: The step S8: constructing and optimizing the communication link in the fully connected network state; the details are as follows: Step 8.1: For the full-time and full-connection network, if the water quality monitoring robot There is a need to send data, by building a Base station location Connection , The equation of the straight line is: (16)； To query the water quality monitoring robot currently connected to it, such as Can be connected to it, then To the straight line The vertical foot The calculation is as follows: (17)； Further seek To base station Distance: (18)； Will As an evaluation metric for relay selection, The smaller the value, the better the water quality monitoring robot The greater the effective distance that the data packet travels, the greater the probability that the water quality monitoring robot will be selected as a relay; choose The smallest water quality monitoring robot Act as a next-hop information relay; Similarly, Continue to search for the next relay node according to the above method until the data packet is sent to the base station; Step 8.2: For the time-segmented and fully connected network, the waiting time of the water quality monitoring robot needs to be optimized. In step S7.4, there is , , so that , at this time the water quality monitoring robot and A connection can be established if the water quality monitoring robot If you need to send, you can When there are water quality monitoring points, wait Arrive at Water quality monitoring points for data transmission; The waiting time for transmission can be optimized and The initial position and speed are reduced; when Towards After the data transfer is completed, According to the method of step S8.1 or by waiting, the data packet continues to be forwarded to the next water quality monitoring robot node until it reaches the base station.

7. A method for optimizing and constructing a communication network for a water quality monitoring robot formation according to claim 6, characterized in that: The step S9: constructing and optimizing the communication link in the non-fully connected network state; the details are as follows: Step 9.1: In the water quality monitoring robot formation network, if some water quality monitoring robot nodes cannot join the network, it is necessary to add new track points to the route of the node or the surrounding water quality monitoring robot nodes to join them into the data transmission network; According to the method of step S7, determine the water quality monitoring robot node Unable to join the network while requesting Corresponding water quality monitoring points In, there is a little Distance water quality monitoring robot Corresponding water quality monitoring points Recently, it is recorded as ;and You can join the network, choose As its relay communication node; otherwise, find the next water quality monitoring robot that meets the above conditions; Step 9.2: Water Quality Monitoring Robot and Find a point each , , so that these two points meet the connectivity requirements, and the water quality monitoring robot and The total distance moved is the smallest, and the corresponding model is as follows: (19)； (20)； The above formula (19) can be solved using the improved Osprey optimization algorithm in step S5; in addition, when there are multiple nodes that need When relaying, the above formula also applies, just increase the dimension of the solution; Step 9.3: Insert into the matrix as a task point Medium Element Later, as a water quality monitoring robot The track point of Insert into the matrix as a task point Medium Element Later, as a water quality monitoring robot Track points; Step 9.4: Water Quality Monitoring Robot According to step S8, data transmission is performed.

8. The method for optimizing and constructing a communication network for a water quality monitoring robot formation according to claim 4, characterized in that: Said The value of can be determined based on the channel fading parameters, communication transmission power and antenna gain of the water quality monitoring mission area.

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