A rim motor coordinated control method and system

Abstract

The present invention discloses a rim motor collaborative control method and system, and relates to the field of rim motor control technology. The present invention constructs a time series curve of the rim motor operation data, and accurately and quickly identifies the abnormal values ​​in the rim motor operation data in combination with the abnormal value identification coordinate system, and calculates the failure probability of the rim motor with a fault prediction model, thereby achieving an accurate evaluation of the working state of the rim motor; by correcting the abnormal values ​​of the rim motor and clustering, the operating characteristics of the rim motor are accurately identified, and the output characteristics of the rim motor are accurately evaluated. The ship's navigation constraints are constructed through the ship's heading and thrust, so that the rim motor can be iteratively screened based on the accurate evaluation of the working state and output characteristics, thereby achieving accurate collaborative control of the rim motor.

Description

Technical Field

[0001] The present invention belongs to the technical field of wheel rim motor control, and in particular relates to a wheel rim motor coordinated control method and system. Background Art

[0002] With the continuous development and application of ship technology, large ships are now widely used in various ocean voyages and large cargo transportation. In the navigation of ships, rim motors play a vital role. Rim motors provide thrust in different directions to ships, allowing ships to navigate flexibly. However, the thrust provided by a single rim motor is limited. With the increasing popularity of large ships, more and more ships are carrying multiple rim motors to meet the propulsion requirements of large ships. The increase in the number of rim motors also puts higher requirements on the control of rim motors in ships.

[0003] In the current wheel rim motor control, direct control is usually performed based on the equipment manual of the wheel rim motor, which means that the speed and direction of the wheel rim motor are controlled according to the requirements of navigation. However, this control method has major technical defects. For example, the working efficiency of the wheel rim motor is different in different navigation environments, and different navigation environments will damage the working performance of the wheel rim motor, making the coordinated control of the wheel rim motor based only on the requirements of navigation not accurate enough. Therefore, there is an urgent need for a wheel rim motor coordinated control method and system to solve the defects of the prior art. Summary of the invention

[0004] The present invention aims to provide a rim motor collaborative control method and system to solve the above-mentioned technical problems, identify abnormal values ​​of the rim motor operation, evaluate the working state and output characteristics of the rim motor, realize collaborative control of the rim motor based on the working state and output characteristics, and improve the accuracy of the rim motor collaborative control.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a rim motor coordinated control method, comprising:

[0006] Acquire initial operation data of each wheel rim motor in the ship and the target heading and target thrust of the ship, wherein the initial operation data includes: initial power data, initial speed data and initial steering data;

[0007] Constructing a second time series curve according to the initial power data and the initial speed data, and identifying abnormal values ​​in the initial operation data in combination with a preset abnormal value identification coordinate system and rated operation data of the rim motor;

[0008] The abnormal value is input into a preset fault prediction model to obtain a fault probability prediction value of the rim motor, and a working state evaluation value of the rim motor is calculated according to the fault probability prediction value to evaluate the working state of the rim motor; wherein the preset fault prediction model is trained by historical operation data of the rim motor;

[0009] Correcting the initial operation data according to the identified abnormal value to obtain first power data, first speed data and first steering data, and then obtaining first operation data;

[0010] Clustering the first operating data to identify operating characteristics corresponding to the first operating data, and calculating output characteristic evaluation values ​​of the rim motor according to the operating characteristics to complete output characteristic evaluation of the rim motor;

[0011] The ship navigation constraints are constructed according to the target heading and the target thrust, and the rim motors are iteratively screened in combination with the working state evaluation value and the output characteristic evaluation value to obtain an optimal rim motor collaborative control scheme, and the rim motors are collaboratively controlled according to the optimal rim motor collaborative control scheme.

[0012] It can be understood that, compared with the prior art, the present invention constructs a time series curve of the rim motor operating data, and accurately and quickly identifies the abnormal values ​​in the rim motor operating data in combination with the abnormal value identification coordinate system, and calculates the failure probability of the rim motor with the fault prediction model, thereby achieving an accurate assessment of the working state of the rim motor; by correcting the abnormal values ​​of the rim motor and clustering, the operating characteristics of the rim motor can be accurately identified, and the output characteristics of the rim motor can be accurately evaluated. The ship's navigation constraints are constructed through the ship's heading and thrust, so that the rim motor can be iteratively screened based on the accurate assessment of the working state and output characteristics, thereby achieving accurate coordinated control of the rim motor.

[0013] As a preferred solution, the second time series curve is constructed according to the initial power data and the initial speed data, and the abnormal value in the initial operation data is identified in combination with the preset abnormal value identification coordinate system and the rated operation data of the rim motor, specifically including:

[0014] The abnormal value identification coordinate system includes: power as a horizontal axis, rotation speed as a vertical axis and time as a depth axis;

[0015] The rated operating data of the wheel rim motor includes: a rated power set, a rated speed set and a rated steering set;

[0016] Pairing the rated power values ​​in the rated power set with the rated speed values ​​in the rated speed set to obtain a first key-value pair set;

[0017] Substituting the first key-value pair set into an outlier identification coordinate system to construct a first time series curve between the rated power and the rated speed of the rim motor;

[0018] Pairing the initial power value in the initial power data with the initial speed value in the initial speed data in time series to obtain a second key-value pair set;

[0019] Substituting the second key-value pair set into the outlier identification coordinate system to construct a second time series curve between the initial power data and the initial speed data of the rim motor;

[0020] Acquire the coordinate point similarity of the first time series curve and the second time series curve, and identify the power abnormal value and the speed abnormal value in the initial power data and the initial speed data based on the coordinate point similarity;

[0021] Sorting the steering data according to a preset sorting algorithm, and identifying steering anomalies in the initial steering data in combination with the rated steering set;

[0022] The abnormal values ​​in the operation data are obtained according to the abnormal power values, the abnormal speed values ​​and the abnormal steering values.

[0023] As a preferred solution, the obtaining of the coordinate point similarity between the first time series curve and the second time series curve, and identifying the power abnormality value and the speed abnormality value in the initial power data and the initial speed data based on the coordinate point similarity, specifically includes:

[0024] Respectively obtain a first mapping curve and a second mapping curve corresponding to the first time series curve and the second time series curve on a first plane, wherein the first plane is composed of a horizontal axis and a vertical axis;

[0025] Calculate the similarity of the coordinate points of the first mapping curve and the second mapping curve according to a preset similarity calculation algorithm; wherein the preset similarity calculation algorithm includes: Euclidean distance algorithm and cosine similarity algorithm;

[0026] Filtering coordinate points in the first mapping curve whose coordinate point similarity is less than a preset coordinate point similarity threshold to obtain a first coordinate point set;

[0027] The power abnormality value and the rotation speed abnormality value in the initial power data and the initial rotation speed data are obtained according to the first coordinate point set.

[0028] As a preferred solution, the step of sorting the turning data according to a preset sorting algorithm and identifying turning abnormal values ​​in the initial turning data in combination with the rated turning set specifically includes:

[0029] Obtaining a maximum rated steering value and a minimum rated steering value in the rated steering set;

[0030] The maximum rated steering value and the minimum rated steering value are added to the initial steering data, and the initial steering data is sorted according to a preset sorting algorithm to obtain sorted steering data; wherein the preset sorting algorithm includes: a bubble sorting algorithm;

[0031] The steering values ​​in a first interval in the sorted steering data are eliminated to obtain the steering abnormality values ​​in the initial steering data, wherein the first interval is determined by the maximum rated steering value and the minimum rated steering value.

[0032] As a preferred solution, the abnormal value is input into a preset fault prediction model to obtain a fault probability prediction value of the rim motor, and the working state evaluation value of the rim motor is calculated according to the fault probability prediction value to evaluate the working state of the rim motor, specifically including:

[0033] Inputting the power abnormal value, the speed abnormal value and the steering abnormal value into a preset fault prediction model respectively to obtain a fault probability prediction value of the rim motor, wherein the fault probability prediction value of the rim motor includes: a power fault probability prediction value, a speed fault probability prediction value and a steering fault probability prediction value;

[0034] Obtaining the power failure coefficient, speed failure coefficient and steering failure coefficient corresponding to the power failure probability prediction value, speed failure probability prediction value and steering failure probability prediction value respectively;

[0035] According to the power failure coefficient, the speed failure coefficient and the steering failure coefficient, the working state evaluation value of the rim motor is calculated in combination with the power failure probability prediction value, the speed failure probability prediction value and the steering failure probability prediction value;

[0036] The preset working state evaluation table is queried, and the working state evaluation value is combined to complete the evaluation of the working state of the rim motor.

[0037] This preferred solution constructs a time series curve of the rim motor operating data, and combines it with the outlier identification coordinate system to accurately and quickly identify the outliers in the rim motor operating data, and calculates the failure probability of the rim motor with the fault prediction model, thereby achieving an accurate assessment of the working status of the rim motor, and providing a basis for subsequent accurate coordinated control of the rim motor.

[0038] As a preferred solution, clustering the first operating data to identify operating characteristics corresponding to the first operating data, and calculating the output characteristic evaluation value of the rim motor according to the operating characteristics to complete the output characteristic evaluation of the rim motor specifically includes:

[0039] Iteratively clustering the first power data, the first speed data and the first steering data respectively to obtain a power clustering result, a speed clustering result and a steering clustering result; wherein the power clustering result, the speed clustering result and the steering clustering result each include: a plurality of clusters and cluster centers corresponding to the clusters;

[0040] Filter the cluster with the most feature points in the power clustering results, the speed clustering results, and the steering clustering results, and obtain the first cluster center corresponding to the cluster with the most feature points;

[0041] Acquire operation characteristics corresponding to the first operation data according to the first cluster center, wherein the operation characteristics include: an optimal power value, an optimal speed value, and an optimal steering value;

[0042] The output characteristic evaluation value of the rim motor is determined according to the optimal power value, the optimal speed value and the optimal steering value, and the output characteristic of the rim motor is evaluated in combination with a preset output characteristic evaluation table.

[0043] This preferred solution corrects the abnormal values ​​of the rim motor and performs clustering to accurately identify the operating characteristics of the rim motor, and then accurately evaluates the output characteristics of the rim motor, providing a basis for subsequent accurate coordinated control of the rim motor.

[0044] As a preferred solution, the ship navigation constraint is constructed according to the target heading and the target thrust, and the rim motor is iteratively screened in combination with the working state evaluation value and the output characteristic evaluation value to obtain an optimal rim motor collaborative control scheme, and the rim motor is collaboratively controlled according to the optimal rim motor collaborative control scheme, specifically including:

[0045] Sorting the rim motors according to the working state evaluation value and the output characteristic evaluation value, and assigning a corresponding priority value to each rim motor according to the sorting result and in combination with a preset priority value table;

[0046] Constructing a rim node corresponding to each rim motor according to the optimal speed value and the optimal steering value, and connecting the rim nodes to construct a coordinated control graph of the rim motor; wherein the attribute value of the edge between the nodes is composed of the priority value of the rim node;

[0047] The ship navigation constraint is constructed according to the target heading and the target thrust, and the path optimization is performed on the coordination control graph with the goal of minimizing the path priority value to obtain the optimal path;

[0048] The optimal speed, optimal power and optimal steering of the rim motor corresponding to each rim node in the optimal path are extracted to obtain an optimal rim motor collaborative control scheme, and the rim motor is collaboratively controlled according to the optimal rim motor collaborative control scheme.

[0049] This preferred solution constructs ship navigation constraints through the ship's heading and thrust, so that the rim motors can be iteratively screened based on accurate evaluation of the working status and output characteristics, thereby achieving accurate coordinated control of the rim motors.

[0050] Accordingly, an embodiment of the present invention provides a rim motor cooperative control system, comprising: a data acquisition module, an abnormal value identification module, a working state evaluation module, an abnormal value correction module, a working characteristic evaluation module and a rim motor cooperative control module;

[0051] The data acquisition module is used to acquire the initial operation data of each wheel rim motor in the ship and the target heading and target thrust of the ship, wherein the initial operation data includes: initial power data, initial speed data and initial steering data;

[0052] The abnormal value identification module is used to construct a second time series curve according to the initial power data and the initial speed data, and identify abnormal values ​​in the initial operation data in combination with a preset abnormal value identification coordinate system and the rated operation data of the rim motor;

[0053] The working state evaluation module is used to input the abnormal value into a preset fault prediction model to obtain a fault probability prediction value of the rim motor, and calculate the working state evaluation value of the rim motor according to the fault probability prediction value to evaluate the working state of the rim motor; wherein the preset fault prediction model is trained by historical operation data of the rim motor;

[0054] The abnormal value correction module is used to correct the initial operation data according to the identified abnormal value to obtain first power data, first speed data and first steering data, and then obtain first operation data;

[0055] The working characteristic evaluation module is used to cluster the first operating data to identify the operating characteristics corresponding to the first operating data, and calculate the output characteristic evaluation value of the rim motor according to the operating characteristics to complete the output characteristic evaluation of the rim motor;

[0056] The rim motor collaborative control module is used to construct ship navigation constraints according to the target heading and target thrust, and iteratively screen the rim motors in combination with the working state evaluation value and the output characteristic evaluation value to obtain an optimal rim motor collaborative control scheme, and collaboratively control the rim motors according to the optimal rim motor collaborative control scheme.

[0057] As a preferred solution, the working characteristic evaluation module includes: a working characteristic evaluation unit;

[0058] The working characteristic evaluation unit is used to iteratively cluster the first power data, the first speed data and the first steering data respectively to obtain a power clustering result, a speed clustering result and a steering clustering result; wherein the power clustering result, the speed clustering result and the steering clustering result each include: a plurality of clusters and cluster centers corresponding to the clusters;

[0059] Filter the cluster with the most feature points in the power clustering results, the speed clustering results, and the steering clustering results, and obtain the first cluster center corresponding to the cluster with the most feature points;

[0060] Acquire operation characteristics corresponding to the first operation data according to the first cluster center, wherein the operation characteristics include: an optimal power value, an optimal speed value, and an optimal steering value;

[0061] The output characteristic evaluation value of the rim motor is determined according to the optimal power value, the optimal speed value and the optimal steering value, and the output characteristic of the rim motor is evaluated in combination with a preset output characteristic evaluation table.

[0062] As a preferred solution, the wheel rim motor cooperative control module includes: a wheel rim motor cooperative control unit;

[0063] The rim motor cooperative control unit is used to sort the rim motors according to the working state evaluation value and the output characteristic evaluation value, and assign a corresponding priority value to each rim motor according to the sorting result in combination with a preset priority value table;

[0064] Constructing a rim node corresponding to each rim motor according to the optimal speed value and the optimal steering value, and connecting the rim nodes to construct a coordinated control graph of the rim motor; wherein the attribute value of the edge between the nodes is composed of the priority value of the rim node;

[0065] The ship navigation constraint is constructed according to the target heading and the target thrust, and the path optimization is performed on the coordination control graph with the goal of minimizing the path priority value to obtain the optimal path;

[0066] The optimal speed, optimal power and optimal steering of the rim motor corresponding to each rim node in the optimal path are extracted to obtain an optimal rim motor collaborative control scheme, and the rim motor is collaboratively controlled according to the optimal rim motor collaborative control scheme.

[0067] It can be understood that, compared with the prior art, this system constructs a time series curve of the rim motor operating data, and combines the outlier identification coordinate system to accurately and quickly identify the outliers in the rim motor operating data, and calculates the failure probability of the rim motor with the fault prediction model, thereby achieving an accurate assessment of the working state of the rim motor; by correcting the outliers of the rim motor and clustering them, the operating characteristics of the rim motor can be accurately identified, and the output characteristics of the rim motor can be accurately evaluated. By constructing the ship's navigation constraints through the ship's heading and thrust, the rim motor can be iteratively screened based on the accurate assessment of the working state and output characteristics, thereby achieving accurate coordinated control of the rim motor. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 : A flowchart of a method for coordinated control of a wheel rim motor provided by an embodiment of the present invention;

[0069] Figure 2 : A schematic structural diagram of a wheel rim motor collaborative control system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] Embodiment 1

[0072] Please refer to Figure 1 , which is a step flow chart of a wheel rim motor cooperative control method provided in an embodiment of the present invention, including steps S101 to S106.

[0073] Step S101: acquiring initial operation data of each wheel rim motor in the ship and the target heading and target thrust of the ship, wherein the initial operation data includes initial power data, initial rotation speed data and initial steering data.

[0074] Step S102: constructing a second time series curve according to the initial power data and the initial speed data, and identifying abnormal values ​​in the initial operation data in combination with a preset abnormal value identification coordinate system and the rated operation data of the rim motor.

[0075] In this embodiment, the second time series curve is constructed according to the initial power data and the initial speed data, and the abnormal value in the initial operation data is identified in combination with the preset abnormal value identification coordinate system and the rated operation data of the rim motor, specifically including:

[0076] The abnormal value identification coordinate system includes: power as a horizontal axis, rotation speed as a vertical axis and time as a depth axis;

[0077] The rated operating data of the wheel rim motor includes: a rated power set, a rated speed set and a rated steering set;

[0078] Pairing the rated power values ​​in the rated power set with the rated speed values ​​in the rated speed set to obtain a first key-value pair set;

[0079] Substituting the first key-value pair set into an outlier identification coordinate system to construct a first time series curve between the rated power and the rated speed of the rim motor;

[0080] Pairing the initial power value in the initial power data with the initial speed value in the initial speed data in time series to obtain a second key-value pair set;

[0081] Substituting the second key-value pair set into the outlier identification coordinate system to construct a second time series curve between the initial power data and the initial speed data of the rim motor;

[0082] Acquire the coordinate point similarity of the first time series curve and the second time series curve, and identify the power abnormal value and the speed abnormal value in the initial power data and the initial speed data based on the coordinate point similarity;

[0083] Sorting the steering data according to a preset sorting algorithm, and identifying steering anomalies in the initial steering data in combination with the rated steering set;

[0084] The abnormal values ​​in the operation data are obtained according to the abnormal power values, the abnormal speed values ​​and the abnormal steering values.

[0085] It should be noted that the first key-value pair in the first key-value pair set is composed of the rated power and rated speed of the rim motor. Specifically, the equipment manual of the rim motor can be queried to obtain the relationship between the rated power and the rated speed, that is, the theoretical rated speed of the rim motor at a certain rated power, so as to construct the first key-value pair set. The second key-value pair in the second key-value pair set refers to the initial power value and initial speed value of the rim motor at the same time, which can be obtained by the sensor installed on the rim motor. By collecting the initial power value and initial speed value at the same time, the pairing in the time series is completed, so as to construct the second key-value pair set. By collecting the operating data of the rim motor in real time, the data accuracy of the rim motor can be ensured.

[0086] It should be noted that, since the first key-value pair set is obtained through rated operation data, this embodiment sets the value of the depth axis of each coordinate point in the first time series curve to 0, so that the first time series curve becomes the reference curve. The values ​​of the coordinate points in the second time series curve are power, speed and time, respectively, so that the second time series curve can reflect the changes in power and speed of the rim motor over time.

[0087] In this embodiment, the obtaining of the coordinate point similarity between the first time series curve and the second time series curve, and identifying the power abnormality value and the speed abnormality value in the initial power data and the initial speed data based on the coordinate point similarity, specifically includes:

[0088] Respectively obtain a first mapping curve and a second mapping curve corresponding to the first time series curve and the second time series curve on a first plane, wherein the first plane is composed of a horizontal axis and a vertical axis;

[0089] Calculate the similarity of the coordinate points of the first mapping curve and the second mapping curve according to a preset similarity calculation algorithm; wherein the preset similarity calculation algorithm includes: Euclidean distance algorithm and cosine similarity algorithm;

[0090] Filtering coordinate points in the first mapping curve whose coordinate point similarity is less than a preset coordinate point similarity threshold to obtain a first coordinate point set;

[0091] The power abnormality value and the rotation speed abnormality value in the initial power data and the initial rotation speed data are obtained according to the first coordinate point set.

[0092] It should be noted that by comparing the similarity of coordinate points through the similarity obtaining algorithm, the abnormal power and abnormal speed of the rim motor during operation can be quickly and accurately screened out, thereby achieving an accurate assessment of the working state of the rim motor.

[0093] In this embodiment, the step of sorting the turn data according to a preset sorting algorithm and identifying turn abnormal values ​​in the initial turn data in combination with the rated turn set specifically includes:

[0094] Obtaining a maximum rated steering value and a minimum rated steering value in the rated steering set;

[0095] The maximum rated steering value and the minimum rated steering value are added to the initial steering data, and the initial steering data is sorted according to a preset sorting algorithm to obtain sorted steering data; wherein the preset sorting algorithm includes: a bubble sorting algorithm;

[0096] The steering values ​​in a first interval in the sorted steering data are eliminated to obtain the steering abnormality values ​​in the initial steering data, wherein the first interval is determined by the maximum rated steering value and the minimum rated steering value.

[0097] Step S103: input the abnormal value into a preset fault prediction model to obtain a fault probability prediction value of the rim motor, and calculate the working state evaluation value of the rim motor according to the fault probability prediction value to evaluate the working state of the rim motor; wherein the preset fault prediction model is trained by the historical operation data of the rim motor.

[0098] In this embodiment, the abnormal value is input into a preset fault prediction model to obtain a fault probability prediction value of the rim motor, and the working state evaluation value of the rim motor is calculated according to the fault probability prediction value to evaluate the working state of the rim motor, specifically including:

[0099] Inputting the power abnormal value, the speed abnormal value and the steering abnormal value into a preset fault prediction model respectively to obtain a fault probability prediction value of the rim motor, wherein the fault probability prediction value of the rim motor includes: a power fault probability prediction value, a speed fault probability prediction value and a steering fault probability prediction value;

[0100] Obtaining the power failure coefficient, speed failure coefficient and steering failure coefficient corresponding to the power failure probability prediction value, speed failure probability prediction value and steering failure probability prediction value respectively;

[0101] According to the power failure coefficient, the speed failure coefficient and the steering failure coefficient, the working state evaluation value of the rim motor is calculated in combination with the power failure probability prediction value, the speed failure probability prediction value and the steering failure probability prediction value;

[0102] The preset working state evaluation table is queried, and the working state evaluation value is combined to complete the evaluation of the working state of the rim motor.

[0103] In an optional embodiment, the expression of the working status evaluation value is:

[0104] ,

[0105] in, , and are the power failure coefficient, speed failure coefficient and steering failure coefficient respectively, , and They are the power failure probability prediction value, speed failure probability prediction value and steering failure probability prediction value respectively.

[0106] It should be noted that the working status evaluation table includes different working status evaluation value intervals and the working status corresponding to each working status evaluation value interval, wherein the working status includes: full working status, overload working status and no-load working status; wherein, the definition and division of the working status can be determined by the specific actual situation, and this embodiment will not go into details about this.

[0107] It should be noted that the fault prediction model of the present invention can be a machine learning model or a deep learning model, wherein the machine learning model includes: decision tree, support vector machine, random forest and neural network, and the deep learning model includes: convolutional neural network and recurrent neural network. In the current machine learning model and deep learning model applications, there are already many applications that make predictions by acquiring historical data, and the present application obtains the fault probability prediction value through the historical operation data of the rim motor, which can be understood as a specific application of the machine learning model and the deep learning model application. There are currently many technologies that can be implemented, so this embodiment will not be described in detail here.

[0108] Step S104: correcting the initial operation data according to the identified abnormal value to obtain first power data, first speed data and first steering data, and further obtaining first operation data.

[0109] In an optional embodiment, the initial operation data is corrected. In this embodiment, the abnormal value is replaced by the average value of the non-abnormal value at the previous sampling moment and the next sampling moment of the abnormal value, so as to correct the abnormal value.

[0110] Step S105: clustering the first operating data to identify operating characteristics corresponding to the first operating data, and calculating the output characteristic evaluation value of the rim motor according to the operating characteristics to complete the output characteristic evaluation of the rim motor.

[0111] In this embodiment, clustering the first operating data to identify operating characteristics corresponding to the first operating data, and calculating the output characteristic evaluation value of the rim motor according to the operating characteristics to complete the output characteristic evaluation of the rim motor specifically includes:

[0112] Iteratively clustering the first power data, the first speed data and the first steering data respectively to obtain a power clustering result, a speed clustering result and a steering clustering result; wherein the power clustering result, the speed clustering result and the steering clustering result each include: a plurality of clusters and cluster centers corresponding to the clusters;

[0113] Filter the cluster with the most feature points in the power clustering results, the speed clustering results, and the steering clustering results, and obtain the first cluster center corresponding to the cluster with the most feature points;

[0114] Acquire operation characteristics corresponding to the first operation data according to the first cluster center, wherein the operation characteristics include: an optimal power value, an optimal speed value, and an optimal steering value;

[0115] The output characteristic evaluation value of the rim motor is determined according to the optimal power value, the optimal speed value and the optimal steering value, and the output characteristic of the rim motor is evaluated in combination with a preset output characteristic evaluation table.

[0116] It should be noted that the clustering methods for iterative clustering of the first power data, the first speed data and the first steering data include: K-Means clustering, hierarchical clustering and mean shift clustering; through iterative clustering, the distribution characteristics of the first power data, the first speed data and the first steering data of the rim motor can be mined, and then the optimal power value, optimal speed value and optimal steering value of the rim motor can be obtained through the cluster center, so as to accurately characterize the output characteristics of the rim motor.

[0117] In an optional embodiment, determining the output characteristic evaluation value of the rim motor according to the optimal power value, the optimal speed value and the optimal steering value specifically includes:

[0118] The rated power set, the rated speed set and the rated steering set are sorted from small to large to obtain a sorted rated power set, a sorted rated speed set and a sorted rated steering set;

[0119] Obtain the power position percentage value, speed percentage value and steering percentage value of the optimal power value, the optimal speed value and the optimal steering value in the sorted rated power set, the sorted rated speed set and the sorted rated steering set respectively;

[0120] The power position percentage value, the speed percentage value, and the direction percentage value are summed to obtain the output characteristic evaluation value.

[0121] Step S106: constructing ship navigation constraints according to the target heading and target thrust, and iteratively screening the rim motors in combination with the working state evaluation value and the output characteristic evaluation value to obtain an optimal rim motor collaborative control scheme, and collaboratively controlling the rim motors according to the optimal rim motor collaborative control scheme.

[0122] In this embodiment, the ship navigation constraint is constructed according to the target heading and the target thrust, and the rim motor is iteratively screened in combination with the working state evaluation value and the output characteristic evaluation value to obtain an optimal rim motor collaborative control scheme, and the rim motor is collaboratively controlled according to the optimal rim motor collaborative control scheme, specifically including:

[0123] Sorting the rim motors according to the working state evaluation value and the output characteristic evaluation value, and assigning a corresponding priority value to each rim motor according to the sorting result and in combination with a preset priority value table;

[0124] Constructing a rim node corresponding to each rim motor according to the optimal speed value and the optimal steering value, and connecting the rim nodes to construct a coordinated control graph of the rim motor; wherein the attribute value of the edge between the nodes is composed of the priority value of the rim node;

[0125] The ship navigation constraint is constructed according to the target heading and the target thrust, and the path optimization is performed on the coordination control graph with the goal of minimizing the path priority value to obtain the optimal path;

[0126] The optimal speed, optimal power and optimal steering of the rim motor corresponding to each rim node in the optimal path are extracted to obtain an optimal rim motor collaborative control scheme, and the rim motor is collaboratively controlled according to the optimal rim motor collaborative control scheme.

[0127] In an optional embodiment, the sorting of the rim motors according to the working state evaluation value and the output characteristic evaluation value specifically includes:

[0128] First, the rim motors are preliminarily sorted in descending order according to the working state evaluation values. When the working state evaluation values ​​are the same, the output characteristic evaluation values ​​are compared and the output characteristic evaluation values ​​are sorted again in descending order to obtain the sorting result.

[0129] It should be noted that the attribute value of the edge between the rim nodes is obtained by the average value of the priority values ​​of the two rim nodes. Path optimization can be achieved by ant colony algorithm, breadth-first search algorithm and depth-first search algorithm.

[0130] The ship navigation constraints include: speed constraint and heading constraint; the speed constraint means that the sum of the speeds of all rim motors in the optimal rim motor collaborative control scheme is greater than or equal to the target speed corresponding to the target thrust; the heading constraint means that the total thrust direction corresponding to the total steering of all rim motors with the center of the ship as the origin is the same as the target heading.

[0131] This embodiment constructs a time series curve of the rim motor operation data, and combines the abnormal value identification coordinate system to accurately and quickly identify the abnormal values ​​in the rim motor operation data, and calculates the failure probability of the rim motor with the fault prediction model, thereby achieving an accurate assessment of the working state of the rim motor; by correcting the abnormal values ​​of the rim motor and clustering, the operating characteristics of the rim motor are accurately identified, and the output characteristics of the rim motor are accurately evaluated. The ship's navigation constraints are constructed through the ship's heading and thrust, so that the rim motor can be iteratively screened based on the accurate assessment of the working state and output characteristics, and accurate coordinated control of the rim motor is achieved.

[0132] Embodiment 2

[0133] Please refer to Figure 2 , which is a structural schematic diagram of a rim motor cooperative control system provided in an embodiment of the present invention, including: a data acquisition module 201, an abnormal value identification module 202, a working state evaluation module 203, an abnormal value correction module 204, a working characteristic evaluation module 205 and a rim motor cooperative control module 206.

[0134] The data acquisition module 201 is used to acquire the initial operation data of each wheel rim motor in the ship and the target heading and target thrust of the ship. The initial operation data includes: initial power data, initial speed data and initial steering data.

[0135] The abnormal value identification module 202 is used to construct a second time series curve according to the initial power data and the initial speed data, and identify abnormal values ​​in the initial operation data in combination with a preset abnormal value identification coordinate system and the rated operation data of the rim motor.

[0136] In this embodiment, the outlier identification module 202 includes: an outlier identification unit;

[0137] The abnormal value identification unit is used for the abnormal value identification coordinate system including: power as a horizontal axis, rotation speed as a vertical axis and time as a depth axis;

[0138] The rated operating data of the wheel rim motor includes: a rated power set, a rated speed set and a rated steering set;

[0139] Pairing the rated power values ​​in the rated power set with the rated speed values ​​in the rated speed set to obtain a first key-value pair set;

[0140] Substituting the first key-value pair set into an outlier identification coordinate system to construct a first time series curve between the rated power and the rated speed of the rim motor;

[0141] Pairing the initial power value in the initial power data with the initial speed value in the initial speed data in time series to obtain a second key-value pair set;

[0142] Substituting the second key-value pair set into the outlier identification coordinate system to construct a second time series curve between the initial power data and the initial speed data of the rim motor;

[0143] Acquire the coordinate point similarity of the first time series curve and the second time series curve, and identify the power abnormal value and the speed abnormal value in the initial power data and the initial speed data based on the coordinate point similarity;

[0144] Sorting the steering data according to a preset sorting algorithm, and identifying steering anomalies in the initial steering data in combination with the rated steering set;

[0145] The abnormal values ​​in the operation data are obtained according to the abnormal power values, the abnormal speed values ​​and the abnormal steering values.

[0146] In this embodiment, the abnormal value identification unit includes: a power abnormal value and a rotation speed abnormal value identification subunit;

[0147] The power abnormal value and speed abnormal value identification subunit is used to respectively obtain a first mapping curve and a second mapping curve corresponding to the first time series curve and the second time series curve on a first plane, wherein the first plane is composed of a horizontal axis and a vertical axis;

[0148] Calculate the similarity of the coordinate points of the first mapping curve and the second mapping curve according to a preset similarity calculation algorithm; wherein the preset similarity calculation algorithm includes: Euclidean distance algorithm and cosine similarity algorithm;

[0149] Filtering coordinate points in the first mapping curve whose coordinate point similarity is less than a preset coordinate point similarity threshold to obtain a first coordinate point set;

[0150] The power abnormality value and the rotation speed abnormality value in the initial power data and the initial rotation speed data are obtained according to the first coordinate point set.

[0151] In this embodiment, the outlier identification unit includes: a steering outlier identification subunit;

[0152] The steering abnormality value identification subunit is used to obtain the maximum rated steering value and the minimum rated steering value of the rated steering set;

[0153] The maximum rated steering value and the minimum rated steering value are added to the initial steering data, and the initial steering data is sorted according to a preset sorting algorithm to obtain sorted steering data; wherein the preset sorting algorithm includes: a bubble sorting algorithm;

[0154] The steering values ​​in a first interval in the sorted steering data are eliminated to obtain the steering abnormality values ​​in the initial steering data, wherein the first interval is determined by the maximum rated steering value and the minimum rated steering value.

[0155] The working status assessment module 203 is used to input the abnormal value into a preset fault prediction model to obtain a fault probability prediction value of the rim motor, and calculate the working status assessment value of the rim motor according to the fault probability prediction value to evaluate the working status of the rim motor; wherein the preset fault prediction model is trained by historical operating data of the rim motor.

[0156] In this embodiment, the working status evaluation module 203 includes: a working status evaluation unit;

[0157] The working state evaluation unit is used to input the power abnormal value, the speed abnormal value and the steering abnormal value into a preset fault prediction model respectively to obtain a fault probability prediction value of the rim motor, wherein the fault probability prediction value of the rim motor includes: a power fault probability prediction value, a speed fault probability prediction value and a steering fault probability prediction value;

[0158] Obtaining the power failure coefficient, speed failure coefficient and steering failure coefficient corresponding to the power failure probability prediction value, speed failure probability prediction value and steering failure probability prediction value respectively;

[0159] According to the power failure coefficient, the speed failure coefficient and the steering failure coefficient, the working state evaluation value of the rim motor is calculated in combination with the power failure probability prediction value, the speed failure probability prediction value and the steering failure probability prediction value;

[0160] The preset working state evaluation table is queried, and the working state evaluation value is combined to complete the evaluation of the working state of the rim motor.

[0161] The abnormal value correction module 204 is used to correct the initial operation data according to the identified abnormal value to obtain first power data, first speed data and first steering data, and further obtain first operation data.

[0162] The working characteristic evaluation module 205 is used to cluster the first operating data to identify the operating characteristics corresponding to the first operating data, and calculate the output characteristic evaluation value of the rim motor according to the operating characteristics to complete the output characteristic evaluation of the rim motor.

[0163] In this embodiment, the working characteristic evaluation module 205 includes: a working characteristic evaluation unit;

[0164] The working characteristic evaluation unit is used to iteratively cluster the first power data, the first speed data and the first steering data respectively to obtain a power clustering result, a speed clustering result and a steering clustering result; wherein the power clustering result, the speed clustering result and the steering clustering result each include: a plurality of clusters and cluster centers corresponding to the clusters;

[0165] Filter the cluster with the most feature points in the power clustering results, the speed clustering results, and the steering clustering results, and obtain the first cluster center corresponding to the cluster with the most feature points;

[0166] Acquire operation characteristics corresponding to the first operation data according to the first cluster center, wherein the operation characteristics include: an optimal power value, an optimal speed value, and an optimal steering value;

[0167] The output characteristic evaluation value of the rim motor is determined according to the optimal power value, the optimal speed value and the optimal steering value, and the output characteristic of the rim motor is evaluated in combination with a preset output characteristic evaluation table.

[0168] The rim motor collaborative control module 206 is used to construct the ship navigation constraints according to the target heading and the target thrust, and iteratively screen the rim motors in combination with the working state evaluation value and the output characteristic evaluation value to obtain the optimal rim motor collaborative control scheme, and collaboratively control the rim motors according to the optimal rim motor collaborative control scheme.

[0169] In this embodiment, the wheel rim motor cooperative control module 206 includes: a wheel rim motor cooperative control unit;

[0170] The rim motor cooperative control unit is used to sort the rim motors according to the working state evaluation value and the output characteristic evaluation value, and assign a corresponding priority value to each rim motor according to the sorting result in combination with a preset priority value table;

[0171] Constructing a rim node corresponding to each rim motor according to the optimal speed value and the optimal steering value, and connecting the rim nodes to construct a coordinated control graph of the rim motor; wherein the attribute value of the edge between the nodes is composed of the priority value of the rim node;

[0172] The ship navigation constraint is constructed according to the target heading and the target thrust, and the path optimization is performed on the coordination control graph with the goal of minimizing the path priority value to obtain the optimal path;

[0173] The optimal speed, optimal power and optimal steering of the rim motor corresponding to each rim node in the optimal path are extracted to obtain an optimal rim motor collaborative control scheme, and the rim motor is collaboratively controlled according to the optimal rim motor collaborative control scheme.

[0174] This embodiment constructs a time series curve of the rim motor operation data, and combines the abnormal value identification coordinate system to accurately and quickly identify the abnormal values ​​in the rim motor operation data, and calculates the failure probability of the rim motor with the fault prediction model, thereby achieving an accurate assessment of the working state of the rim motor; by correcting the abnormal values ​​of the rim motor and clustering, the operating characteristics of the rim motor are accurately identified, and the output characteristics of the rim motor are accurately evaluated. The ship's navigation constraints are constructed through the ship's heading and thrust, so that the rim motor can be iteratively screened based on the accurate assessment of the working state and output characteristics, and accurate coordinated control of the rim motor is achieved.

[0175] In summary, the embodiment of the present invention constructs a time series curve of the rim motor operating data, and accurately and quickly identifies the abnormal values ​​in the rim motor operating data in combination with the abnormal value identification coordinate system, and calculates the failure probability of the rim motor with the fault prediction model, thereby achieving an accurate evaluation of the working state of the rim motor; by correcting the abnormal values ​​of the rim motor and clustering, the operating characteristics of the rim motor are accurately identified, and the output characteristics of the rim motor are accurately evaluated. The ship's navigation constraints are constructed through the ship's heading and thrust, so that the rim motor can be iteratively screened based on the accurate evaluation of the working state and output characteristics, thereby achieving accurate coordinated control of the rim motor.

[0176] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A rim motor coordinated control method, characterized in that: include: Acquire initial operation data of each wheel rim motor in the ship and the target heading and target thrust of the ship, wherein the initial operation data includes: initial power data, initial speed data and initial steering data; Constructing a second time series curve according to the initial power data and the initial speed data, and identifying abnormal values ​​in the initial operation data in combination with a preset abnormal value identification coordinate system and rated operation data of the rim motor; The abnormal value is input into a preset fault prediction model to obtain a fault probability prediction value of the rim motor, and a working state evaluation value of the rim motor is calculated according to the fault probability prediction value to evaluate the working state of the rim motor; wherein the preset fault prediction model is trained by historical operation data of the rim motor; Correcting the initial operation data according to the identified abnormal value to obtain first power data, first speed data and first steering data, and then obtaining first operation data; Clustering the first operating data to identify operating characteristics corresponding to the first operating data, and calculating output characteristic evaluation values ​​of the rim motor according to the operating characteristics to complete output characteristic evaluation of the rim motor; Constructing ship navigation constraints according to the target heading and target thrust, iteratively screening the rim motors in combination with the working state evaluation value and the output characteristic evaluation value to obtain an optimal rim motor collaborative control scheme, and collaboratively controlling the rim motors according to the optimal rim motor collaborative control scheme; Among them, the second time series curve is constructed according to the initial power data and the initial speed data, and the abnormal value in the initial operation data is identified in combination with the preset abnormal value identification coordinate system and the rated operation data of the rim motor, specifically including: the abnormal value identification coordinate system includes: power as the horizontal axis, speed as the vertical axis and time as the depth axis; the rated operation data of the rim motor includes: a rated power set, a rated speed set and a rated steering set; the rated power value in the rated power set and the rated speed value in the rated speed set are paired to obtain a first key-value pair set; the first key-value pair set is substituted into the abnormal value identification coordinate system to construct a first time series curve between the rated power and the rated speed of the rim motor; the initial power The initial power value in the data is paired with the initial speed value in the initial speed data in a time series to obtain a second key-value pair set; the second key-value pair set is substituted into the abnormal value identification coordinate system to construct a second time series curve between the initial power data and the initial speed data of the rim motor; the coordinate point similarity of the first time series curve and the second time series curve is obtained, and the power abnormal value and the speed abnormal value in the initial power data and the initial speed data are identified based on the coordinate point similarity; the steering data is sorted according to a preset sorting algorithm, and the steering abnormal value in the initial steering data is identified in combination with the rated steering set; the abnormal value in the operating data is obtained according to the power abnormal value, the speed abnormal value and the steering abnormal value.

2. A rim motor coordinated control method as claimed in claim 1, characterized in that: The obtaining of the coordinate point similarity between the first time series curve and the second time series curve, and identifying the power abnormality value and the speed abnormality value in the initial power data and the initial speed data based on the coordinate point similarity, specifically includes: Respectively obtain a first mapping curve and a second mapping curve corresponding to the first time series curve and the second time series curve on a first plane, wherein the first plane is composed of a horizontal axis and a vertical axis; Calculate the similarity of the coordinate points of the first mapping curve and the second mapping curve according to a preset similarity calculation algorithm; wherein the preset similarity calculation algorithm includes: Euclidean distance algorithm and cosine similarity algorithm; Filtering coordinate points in the first mapping curve whose coordinate point similarity is less than a preset coordinate point similarity threshold to obtain a first coordinate point set; The power abnormality value and the rotation speed abnormality value in the initial power data and the initial rotation speed data are obtained according to the first coordinate point set.

3. A rim motor coordinated control method as claimed in claim 2, characterized in that: The step of sorting the turning data according to a preset sorting algorithm and identifying turning abnormal values ​​in the initial turning data in combination with the rated turning set specifically includes: Obtaining a maximum rated steering value and a minimum rated steering value in the rated steering set; The maximum rated steering value and the minimum rated steering value are added to the initial steering data, and the initial steering data is sorted according to a preset sorting algorithm to obtain sorted steering data; wherein the preset sorting algorithm includes: a bubble sorting algorithm; The steering values ​​in a first interval in the sorted steering data are eliminated to obtain the steering abnormality values ​​in the initial steering data, wherein the first interval is determined by the maximum rated steering value and the minimum rated steering value.

4. A rim motor coordinated control method as claimed in claim 1, characterized in that: The step of inputting the abnormal value into a preset fault prediction model to obtain a fault probability prediction value of the rim motor, and calculating a working state evaluation value of the rim motor according to the fault probability prediction value to evaluate the working state of the rim motor specifically includes: Inputting the power abnormal value, the speed abnormal value and the steering abnormal value into a preset fault prediction model respectively to obtain a fault probability prediction value of the rim motor, wherein the fault probability prediction value of the rim motor includes: a power fault probability prediction value, a speed fault probability prediction value and a steering fault probability prediction value; Obtaining the power failure coefficient, speed failure coefficient and steering failure coefficient corresponding to the power failure probability prediction value, speed failure probability prediction value and steering failure probability prediction value respectively; According to the power failure coefficient, the speed failure coefficient and the steering failure coefficient, the working state evaluation value of the rim motor is calculated in combination with the power failure probability prediction value, the speed failure probability prediction value and the steering failure probability prediction value; The preset working state evaluation table is queried, and the working state evaluation value is combined to complete the evaluation of the working state of the rim motor.

5. A rim motor coordinated control method as claimed in claim 1, characterized in that: The clustering of the first operating data to identify the operating characteristics corresponding to the first operating data, and calculating the output characteristic evaluation value of the rim motor according to the operating characteristics to complete the output characteristic evaluation of the rim motor specifically includes: Iteratively clustering the first power data, the first speed data and the first steering data respectively to obtain a power clustering result, a speed clustering result and a steering clustering result; wherein the power clustering result, the speed clustering result and the steering clustering result each include: a plurality of clusters and cluster centers corresponding to the clusters; Filter the cluster with the most feature points in the power clustering results, the speed clustering results, and the steering clustering results, and obtain the first cluster center corresponding to the cluster with the most feature points; Acquire operation characteristics corresponding to the first operation data according to the first cluster center, wherein the operation characteristics include: an optimal power value, an optimal speed value, and an optimal steering value; The output characteristic evaluation value of the rim motor is determined according to the optimal power value, the optimal speed value and the optimal steering value, and the output characteristic of the rim motor is evaluated in combination with a preset output characteristic evaluation table.

6. A rim motor coordinated control method as claimed in claim 5, characterized in that: The ship navigation constraint is constructed according to the target heading and the target thrust, and the rim motor is iteratively screened in combination with the working state evaluation value and the output characteristic evaluation value to obtain an optimal rim motor collaborative control scheme, and the rim motor is collaboratively controlled according to the optimal rim motor collaborative control scheme, specifically including: Sorting the rim motors according to the working state evaluation value and the output characteristic evaluation value, and assigning a corresponding priority value to each rim motor according to the sorting result and in combination with a preset priority value table; Constructing a rim node corresponding to each rim motor according to the optimal speed value and the optimal steering value, and connecting the rim nodes to construct a coordinated control graph of the rim motor; wherein the attribute value of the edge between the nodes is composed of the priority value of the rim node; The ship navigation constraint is constructed according to the target heading and the target thrust, and the path optimization is performed on the coordination control graph with the goal of minimizing the path priority value to obtain the optimal path; The optimal speed, optimal power and optimal steering of the rim motor corresponding to each rim node in the optimal path are extracted to obtain an optimal rim motor collaborative control scheme, and the rim motor is collaboratively controlled according to the optimal rim motor collaborative control scheme.

7. A wheel rim motor coordinated control system, characterized in that: include: Data acquisition module, abnormal value identification module, working state evaluation module, abnormal value correction module, working characteristic evaluation module and wheel rim motor coordinated control module; The data acquisition module is used to acquire the initial operation data of each wheel rim motor in the ship and the target heading and target thrust of the ship, wherein the initial operation data includes: initial power data, initial speed data and initial steering data; The abnormal value identification module is used to construct a second time series curve according to the initial power data and the initial speed data, and identify abnormal values ​​in the initial operation data in combination with a preset abnormal value identification coordinate system and the rated operation data of the rim motor; The working state evaluation module is used to input the abnormal value into a preset fault prediction model to obtain a fault probability prediction value of the rim motor, and calculate the working state evaluation value of the rim motor according to the fault probability prediction value to evaluate the working state of the rim motor; wherein the preset fault prediction model is trained by historical operation data of the rim motor; The abnormal value correction module is used to correct the initial operation data according to the identified abnormal value to obtain first power data, first speed data and first steering data, and then obtain first operation data; The working characteristic evaluation module is used to cluster the first operating data to identify the operating characteristics corresponding to the first operating data, and calculate the output characteristic evaluation value of the rim motor according to the operating characteristics to complete the output characteristic evaluation of the rim motor; The rim motor cooperative control module is used to construct a ship navigation constraint according to the target heading and the target thrust, and iteratively screen the rim motors in combination with the working state evaluation value and the output characteristic evaluation value to obtain an optimal rim motor cooperative control scheme, and cooperatively control the rim motors according to the optimal rim motor cooperative control scheme; Among them, the second time series curve is constructed according to the initial power data and the initial speed data, and the abnormal value in the initial operation data is identified in combination with the preset abnormal value identification coordinate system and the rated operation data of the rim motor, specifically including: the abnormal value identification coordinate system includes: power as the horizontal axis, speed as the vertical axis and time as the depth axis; the rated operation data of the rim motor includes: a rated power set, a rated speed set and a rated steering set; the rated power value in the rated power set and the rated speed value in the rated speed set are paired to obtain a first key-value pair set; the first key-value pair set is substituted into the abnormal value identification coordinate system to construct a first time series curve between the rated power and the rated speed of the rim motor; the initial power The initial power value in the data is paired with the initial speed value in the initial speed data in a time series to obtain a second key-value pair set; the second key-value pair set is substituted into the abnormal value identification coordinate system to construct a second time series curve between the initial power data and the initial speed data of the rim motor; the coordinate point similarity of the first time series curve and the second time series curve is obtained, and the power abnormal value and the speed abnormal value in the initial power data and the initial speed data are identified based on the coordinate point similarity; the steering data is sorted according to a preset sorting algorithm, and the steering abnormal value in the initial steering data is identified in combination with the rated steering set; the abnormal value in the operating data is obtained according to the power abnormal value, the speed abnormal value and the steering abnormal value.

8. A wheel rim motor cooperative control system as claimed in claim 7, characterized in that: The work characteristic evaluation module comprises: a work characteristic evaluation unit; The working characteristic evaluation unit is used to iteratively cluster the first power data, the first speed data and the first steering data respectively to obtain a power clustering result, a speed clustering result and a steering clustering result; wherein the power clustering result, the speed clustering result and the steering clustering result each include: a plurality of clusters and cluster centers corresponding to the clusters; Filter the cluster with the most feature points in the power clustering results, the speed clustering results, and the steering clustering results, and obtain the first cluster center corresponding to the cluster with the most feature points; Acquire operation characteristics corresponding to the first operation data according to the first cluster center, wherein the operation characteristics include: an optimal power value, an optimal speed value, and an optimal steering value; The output characteristic evaluation value of the rim motor is determined according to the optimal power value, the optimal speed value and the optimal steering value, and the output characteristic of the rim motor is evaluated in combination with a preset output characteristic evaluation table.

9. A wheel rim motor cooperative control system as claimed in claim 8, characterized in that: The wheel rim motor cooperative control module comprises: a wheel rim motor cooperative control unit; The rim motor cooperative control unit is used to sort the rim motors according to the working state evaluation value and the output characteristic evaluation value, and assign a corresponding priority value to each rim motor according to the sorting result in combination with a preset priority value table; Constructing a rim node corresponding to each rim motor according to the optimal speed value and the optimal steering value, and connecting the rim nodes to construct a coordinated control graph of the rim motor; wherein the attribute value of the edge between the nodes is composed of the priority value of the rim node; The ship navigation constraint is constructed according to the target heading and the target thrust, and the path optimization is performed on the coordination control graph with the goal of minimizing the path priority value to obtain the optimal path; The optimal speed, optimal power and optimal steering of the rim motor corresponding to each rim node in the optimal path are extracted to obtain an optimal rim motor collaborative control scheme, and the rim motor is collaboratively controlled according to the optimal rim motor collaborative control scheme.

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