AGV simulation model establishment method and system based on digital twinning and medium

Abstract

The invention discloses an AGV simulation model building method and system based on digital twinning and a medium, and relates to the technical field of vehicle simulation models.The AGV simulation model building method comprises the steps that an AGV simulation model is built, and model performance evaluation is conducted on the AGV simulation model; analyzing the driving path difference degree between the historical driving record and the driving simulation record; historical driving records and difference historical driving records in the transportation area are obtained, the simulation accuracy degree of the feature AGV simulation model in the transportation area is evaluated, the difference historical driving records are combined, the feature AGV simulation model is optimized, and a target AGV simulation model is obtained; and obtaining AGV simulation condition data of the transportation area, inputting the AGV simulation condition data into the target AGV simulation model for simulation to obtain a target simulation record, and displaying the target simulation record to a user.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle simulation models, and in particular to a method, system and medium for establishing an AGV vehicle simulation model based on digital twins. Background Art

[0002] Digital twin is a digital technology that creates a virtual model of a physical entity or system to monitor, analyze and optimize its performance in real time. The application of digital twin technology in the establishment of AGV simulation model includes but is not limited to the following benefits: 1. High simulation. Digital twin can build a virtual environment similar to the actual AGV, which can improve the authenticity and reliability of the model; 2. Visual analysis. Through digital twin technology, the operating status of AGV can be visualized in real time, which is convenient for operators to monitor and manage in real time; 3. Digital twin technology can realize data synchronization between AGV virtual model and actual AGV. Through real-time data stream, the virtual model can reflect the real-time status and operation of AGV in the real world.

[0003] At present, when using the established AGV car simulation model for simulation, the common practice is to input the pre-set simulation parameters in the AGV car simulation model to simulate the AGV car. However, this has a big problem. The environments in which the AGV car simulation model is actually used for simulation are different. It is difficult to simulate and test various environments during the establishment of the AGV car simulation model. This not only makes it impossible for users to verify the simulation results of the AGV car simulation model, but also may cause the output results of the AGV car simulation model to be significantly different from the actual results. Summary of the invention

[0004] The purpose of the present invention is to provide a method, system and medium for establishing an AGV trolley simulation model based on digital twins to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for establishing an AGV vehicle simulation model based on digital twins, the method comprising:

[0006] Step S100: construct a simulation cloud platform, obtain the equipment data of the AGV car, obtain the characteristic environment data of the AGV car, establish an AGV car simulation model, perform model performance evaluation on the AGV car simulation model, and obtain a characteristic AGV car simulation model;

[0007] Step S200: Obtain the historical driving record of the AGV in the user's transportation area, simulate the driving state of the AGV in the transportation area based on the historical driving record, use the characteristic AGV simulation model, obtain the driving simulation record, analyze the degree of difference in the driving path between the historical driving record and the driving simulation record, and obtain the difference historical driving record;

[0008] Step S300: Obtain historical driving records and difference historical driving records in the transportation area, evaluate the simulation accuracy of the characteristic AGV car simulation model in the transportation area, and optimize the characteristic AGV car simulation model in combination with the difference historical driving records to obtain the target AGV car simulation model;

[0009] Step S400: Acquire the AGV trolley simulation condition data of the transport area, input the AGV trolley simulation condition data into the target AGV trolley simulation model for simulation, obtain the target simulation record, and display the target simulation record to the user.

[0010] Furthermore, step S100 includes:

[0011] Step S101: Obtaining preset equipment data of the AGV from the cloud platform, the equipment data including data corresponding to various equipment parameters of the AGV;

[0012] Step S102: Obtaining preset characteristic environment data of the AGV from the cloud platform, the characteristic environment data including data corresponding to various environmental parameters;

[0013] Step S103: Based on the equipment data and characteristic environment data, using simulation software, establish an AGV simulation model of the AGV, and obtain the marked simulation condition data for simulation from the cloud platform, wherein the marked simulation condition data includes data corresponding to various simulation condition parameters;

[0014] Step S104: Input the marked simulation condition data into the AGV car simulation model to obtain the driving energy consumption data output by the AGV car simulation model. The driving energy consumption data includes the total distance L traveled by the AGV car after the AGV car simulation model performs simulation. sum , average rate V △ 、Total equipment load H sum和 Total energy consumption of equipment E sum ;

[0015] Step S105: Calculate the characteristic model performance ratio A of the AGV simulation model:

[0016]

[0017] Where g is the acceleration due to gravity; μ is the preset ground friction coefficient; C d is the preset air resistance coefficient; B sum is the total windward area of ​​the AGV;

[0018] Step S106: Evaluate the model performance of the AGV simulation model. The specific evaluation process is to obtain the preset first characteristic model performance ratio A′ of the AGV. 1 , the second characteristic model performance ratio A′ 2 , where A′ 1 >A′ 2 >0, when the feature model performance ratio A′ 1 >A>A′ 2 When , it is determined that the model performance of the AGV car simulation model meets the requirements, and the AGV car simulation model is recorded as the characteristic AGV car simulation model.

[0019] Further, step S200 includes:

[0020] Step S201: Obtain the user who uses the characteristic AGV car simulation model, set the unit time, obtain the historical driving record of the AGV car in the user's transportation area, and obtain the driving path set D = {D 1 ,D 2 ,...,D m}, where the two-dimensional coordinate points of the AGV car at the 1st, 2nd, ..., mth unit time after driving in the historical driving record;

[0021] Step S202: Acquire characteristic simulation condition data of the AGV from the historical driving records, the characteristic simulation condition data including data corresponding to various simulation condition parameters, use the characteristic AGV simulation model to simulate the driving state of the AGV in the transportation area, obtain the driving simulation record, and obtain the simulated driving path F from the driving simulation record. 1 ,F 2 ,...,F n}, where the two-dimensional coordinate points of the AGV in the driving simulation record at the 1st, 2nd, ..., nth unit time after the simulated driving;

[0022] Step S203: Analyze the degree of difference between the driving paths of the historical driving records and the driving simulation records. The specific analysis process is to construct a matrix W based on the driving path set D and the simulated driving path F, and set the first element in the matrix W to 0, where the element W in the αth row and the βth column in the matrix W is α,β =L(D α ,F β ), where L(D α ,Fβ ) is the two-dimensional coordinate point D corresponding to the αth element in the driving path set D α , the two-dimensional coordinate point F corresponding to the βth element in the simulated driving path F β The Euclidean distance between them is as follows:

[0023]

[0024] Among them, x α ,y α Represented as two-dimensional coordinate points D α The horizontal and vertical coordinates of x β ,y β Represented as two-dimensional coordinate points F β The horizontal and vertical coordinates of

[0025] Step S204: define a cumulative distance matrix Q, wherein the element Q in the i-th row and i-th column of the cumulative distance matrix Q is obtained. i,j =W i,j +min{Q i-1,j ,Q i,j-1 ,Q i-1,j-1}, where Q i-1,j Represented as the element in the i-1th row and jth column of the cumulative distance matrix Q, Q i,j-1 Represented as the element in the i-th row and j-1-th column of the cumulative distance matrix Q, Q i-1,j Represented as the element in the i-1th row and jth column of the cumulative distance matrix Q, Q i-1,j-1 Represented as the element in the i-1th row and j-1th column of the cumulative distance matrix Q;

[0026] Step S205: Obtain the last element Q in the lower right corner of the cumulative distance matrix Q m,n Corresponding values, calculate the characteristic path difference value R between the historical driving record and the driving simulation record:

[0027]

[0028] When the characteristic path difference value R is greater than the preset characteristic path difference threshold, it is determined that there is a difference in the driving path of the AGV between the historical driving record and the driving simulation record, and the historical driving record is recorded as the difference historical driving record of the transportation area.

[0029] Furthermore, step S300 includes:

[0030] Step S301: Obtain the total number U of several historical driving records simulated by the characteristic AGV vehicle simulation model in the transportation area sum , obtain the total number of different historical driving records in the transportation area U′ sum;

[0031] Step S302: Evaluate the simulation accuracy of the characteristic AGV car simulation model in the transportation area. The specific evaluation process is to calculate the characteristic simulation accuracy score P of the characteristic AGV car simulation model in the transportation area = (U sum -U′ sum ) / U sum ;

[0032] Step S303: When the feature simulation accuracy score P of the characteristic AGV car simulation model in the transportation area is greater than or equal to the preset feature simulation accuracy score threshold, it is determined that the feature AGV car simulation model is accurate in simulating the transportation area, and the feature AGV car simulation model is recorded as the target AGV car simulation model in the transportation area;

[0033] Step S304: When the feature simulation accuracy score P is less than the preset feature simulation accuracy score threshold, the feature AGV car simulation model is determined to be inaccurate in simulating the transport area, and the feature AGV car simulation model is optimized. The specific optimization process is:

[0034] Obtain the difference historical driving records in the transportation area, obtain characteristic simulation condition data from the difference historical driving records, import the characteristic simulation condition data into the characteristic AGV car simulation model, obtain the characteristic simulation driving records of the difference historical driving records, and obtain the average values ​​corresponding to various performance indicators from the characteristic simulation driving records;

[0035] Step S305: Obtain the average values ​​corresponding to the various performance indicators in the difference historical driving records, calculate the characteristic AGV car simulation model, and calculate the characteristic simulation error value K of the difference historical driving records:

[0036]

[0037] Among them, S z is the average value corresponding to the zth performance indicator in the difference historical driving record; S′ z is the average value corresponding to the zth performance indicator in the characteristic simulation driving record; γ is the total number of performance indicators in the difference historical driving record;

[0038] Step S306: Calculate the characteristic AGV car simulation model, and the marking simulation error ζ in the transportation area:

[0039]

[0040] Where η represents the total number of different historical driving records in the transportation area; K ε It is represented as the characteristic AGV car simulation model, and the characteristic simulation error value of the εth difference historical driving record;

[0041] Step S307: adjusting various model parameters of the characteristic AGV car simulation model, and calculating the marking simulation error of the adjusted characteristic AGV car simulation model in the transportation area, until the marking simulation error is less than the preset marking simulation error threshold, determining that the optimization of the characteristic AGV car simulation model is completed, and recording the characteristic AGV car simulation model as the target AGV car simulation model in the transportation area;

[0042] In the above steps, the characteristic AGV car simulation model is calculated, and the characteristic simulation accuracy score in the transportation area is used to determine whether the characteristic AGV car simulation model is used in the transportation area. Not only is the determination speed very fast, but the data for determination has the highest degree of fit with the transportation area, and it can better reflect whether the characteristic AGV car simulation model is suitable for the transportation area, thereby providing data support for the following characteristic AGV car simulation model, which is conducive to improving the simulation accuracy of the characteristic AGV car simulation model.

[0043] Furthermore, step S400 includes:

[0044] Step S401: obtaining a target AGV car simulation model in the transport area, and obtaining the AGV car simulation condition data of the user taking the transport area as the driving scene, wherein the AGV car simulation condition data includes data corresponding to various simulation condition parameters;

[0045] Step S402: Input the AGV car simulation condition data into the target AGV car simulation model to obtain the target simulation record, and display the target simulation record to the user through the cloud platform.

[0046] In order to better implement the above method, a AGV car simulation model establishment system is also proposed, which includes a model evaluation module, a difference analysis module, a model optimization module, and a model simulation module;

[0047] The model evaluation module is used to evaluate the model performance of the AGV car simulation model and obtain the characteristic AGV car simulation model;

[0048] A difference analysis module is used to analyze the difference degree of the driving path between the historical driving record and the driving simulation record to obtain the difference historical driving record;

[0049] The model optimization module is used to optimize the characteristic AGV car simulation model to obtain the target AGV car simulation model;

[0050] The model simulation module is used to obtain the simulation condition data of the AGV car in the transportation area, input the simulation condition data of the AGV car into the target AGV car simulation model for simulation, obtain the target simulation record, and display the target simulation record to the user.

[0051] Further, the model evaluation module includes a simulation model building unit and a model evaluation unit;

[0052] The simulation model building unit is used to obtain the equipment data of the AGV car, obtain the characteristic environment data of the AGV car, and build a simulation model of the AGV car;

[0053] The model evaluation unit is used to calculate the characteristic model performance ratio of the AGV car simulation model, and perform model performance evaluation on the AGV car simulation model according to the characteristic model performance ratio to obtain the characteristic AGV car simulation model.

[0054] Further, the difference analysis module includes a characteristic path difference value unit and a difference analysis unit;

[0055] The characteristic path difference value unit is used to obtain the historical driving records of the AGV in the transportation area, obtain the driving simulation records, and calculate the characteristic path difference value between the historical driving records and the driving simulation records;

[0056] The difference analysis unit is used to analyze the degree of difference in driving paths between the historical driving record and the driving simulation record according to the characteristic path difference value, and obtain the difference historical driving record.

[0057] Further, the model optimization module includes a simulation accuracy evaluation unit and a model optimization unit;

[0058] The simulation accuracy evaluation unit is used to evaluate the simulation accuracy of the characteristic AGV car simulation model in the transportation area;

[0059] The model optimization unit is used to optimize the characteristic AGV car simulation model to obtain the target AGV car simulation model of the transportation area.

[0060] A medium stores computer instructions. When the computer instructions are executed by a processor, a method for establishing an AGV car simulation model based on digital twins can be implemented.

[0061] Compared with the prior art, the beneficial effects of the present invention are: the present invention realizes the intelligent establishment of the AGV trolley simulation model, and when using the AGV trolley simulation model to perform simulation in a non-transportation area, simulation can be performed based on the historical driving records of the actual AGV trolley in the transportation area to obtain a driving simulation record, and by analyzing the degree of difference in the driving paths between the historical driving records and the driving simulation records, it is verified whether the AGV trolley simulation model is accurate in the transportation area, and the AGV trolley simulation model can also be optimized and adjusted, so that the established AGV trolley simulation model can be applicable to various environments and various AGV trolleys, further greatly improving the accuracy of the AGV trolley simulation model. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a method flow chart of the method, system and medium for establishing the AGV car simulation model based on digital twin of the present invention;

[0063] Figure 2 It is a module schematic diagram of the method, system and medium for establishing the AGV car simulation model based on digital twin of the present invention. DETAILED DESCRIPTION

[0064] 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.

[0065] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution, a method for establishing an AGV vehicle simulation model based on digital twins, the method comprising:

[0066] Step S100: construct a simulation cloud platform, obtain the equipment data of the AGV car, obtain the characteristic environment data of the AGV car, establish an AGV car simulation model, perform model performance evaluation on the AGV car simulation model, and obtain a characteristic AGV car simulation model;

[0067] Wherein, step S100 includes:

[0068] Step S101: Obtaining preset equipment data of the AGV from the cloud platform, the equipment data including data corresponding to various equipment parameters of the AGV;

[0069] For example, various equipment parameters include the vehicle weight of the AGV, the maximum speed of the vehicle, etc.

[0070] Step S102: Obtaining preset characteristic environment data of the AGV from the cloud platform, the characteristic environment data including data corresponding to various environmental parameters;

[0071] Step S103: Based on the equipment data and characteristic environment data, using simulation software, establish an AGV simulation model of the AGV, and obtain the marked simulation condition data for simulation from the cloud platform, wherein the marked simulation condition data includes data corresponding to various simulation condition parameters;

[0072] For example, the motor power, battery capacity, maximum acceleration, etc. of the AGV;

[0073] Step S104: Input the marked simulation condition data into the AGV car simulation model to obtain the driving energy consumption data output by the AGV car simulation model. The driving energy consumption data includes the total distance L traveled by the AGV car after the AGV car simulation model performs simulation. sum , average rate V △ 、Total equipment load H sum和 Total energy consumption of equipment E sum ;

[0074] Step S105: Calculate the characteristic model performance ratio A of the AGV simulation model:

[0075]

[0076] Where g is the acceleration due to gravity; μ is the preset ground friction coefficient; C d is the preset air resistance coefficient; B sum is the total windward area of ​​the AGV;

[0077] Step S106: Evaluate the model performance of the AGV simulation model. The specific evaluation process is to obtain the preset first characteristic model performance ratio A′ of the AGV. 1 , the second characteristic model performance ratio A′ 2 , where A′ 1 >A′ 2 >0, when the feature model performance ratio A′ 1 >A>A′ 2 When , it is determined that the model performance of the AGV car simulation model meets the requirements, and the AGV car simulation model is recorded as the characteristic AGV car simulation model;

[0078] Step S200: Obtain the historical driving record of the AGV in the user's transportation area, simulate the driving state of the AGV in the transportation area based on the historical driving record, use the characteristic AGV simulation model, obtain the driving simulation record, analyze the degree of difference in the driving path between the historical driving record and the driving simulation record, and obtain the difference historical driving record;

[0079] Wherein, step S200 includes:

[0080] Step S201: Obtain the user who uses the characteristic AGV car simulation model, set the unit time, obtain the historical driving record of the AGV car in the user's transportation area, and obtain the driving path set D = {D 1 ,D 2 ,...,D m}, where the two-dimensional coordinate points of the AGV car at the 1st, 2nd, ..., mth unit time after driving in the historical driving record;

[0081] Step S202: Acquire characteristic simulation condition data of the AGV from the historical driving records, the characteristic simulation condition data including data corresponding to various simulation condition parameters, use the characteristic AGV simulation model to simulate the driving state of the AGV in the transportation area, obtain the driving simulation record, and obtain the simulated driving path F from the driving simulation record. 1 ,F 2 ,...,F n}, where the two-dimensional coordinate points of the AGV in the driving simulation record at the 1st, 2nd, ..., nth unit time after the simulated driving;

[0082] Step S203: Analyze the degree of difference between the driving paths of the historical driving records and the driving simulation records. The specific analysis process is to construct a matrix W based on the driving path set D and the simulated driving path F, and set the first element in the matrix W to 0, where the element W in the αth row and the βth column in the matrix W is α,β =L(D α ,F β ), where L(D α ,F β ) is the two-dimensional coordinate point D corresponding to the αth element in the driving path set D α , the two-dimensional coordinate point F corresponding to the βth element in the simulated driving path F β The Euclidean distance between them is as follows:

[0083]

[0084] Among them, x α ,y αRepresented as two-dimensional coordinate points D α The horizontal and vertical coordinates of x β ,y β Represented as two-dimensional coordinate points F β The horizontal and vertical coordinates of

[0085] Step S204: define a cumulative distance matrix Q, wherein the element Q in the i-th row and i-th column of the cumulative distance matrix Q is obtained. i,j =W i,j +min{Q i-1,j ,Q i,j-1 ,Q i-1,j-1}, where Q i-1,j Represented as the element in the i-1th row and jth column of the cumulative distance matrix Q, Q i,j-1 Represented as the element in the i-th row and j-1-th column of the cumulative distance matrix Q, Q i-1,j Represented as the element in the i-1th row and jth column of the cumulative distance matrix Q, Q i-1,j-1 Represented as the element in the i-1th row and j-1th column of the cumulative distance matrix Q;

[0086] Step S205: Obtain the last element Q in the lower right corner of the cumulative distance matrix Q m,n Corresponding values, calculate the characteristic path difference value R between the historical driving record and the driving simulation record:

[0087]

[0088] When the characteristic path difference value R is greater than the preset characteristic path difference threshold, it is determined that there is a difference in the driving path of the AGV between the historical driving record and the driving simulation record, and the historical driving record is recorded as the difference historical driving record of the transportation area;

[0089] Step S300: Obtain historical driving records and difference historical driving records in the transportation area, evaluate the simulation accuracy of the characteristic AGV car simulation model in the transportation area, and optimize the characteristic AGV car simulation model in combination with the difference historical driving records to obtain the target AGV car simulation model;

[0090] Wherein, step S300 includes:

[0091] Step S301: Obtain the total number U of several historical driving records simulated by the characteristic AGV vehicle simulation model in the transportation area sum , obtain the total number of different historical driving records in the transportation area U′ sum ;

[0092] Step S302: Evaluate the simulation accuracy of the characteristic AGV car simulation model in the transportation area. The specific evaluation process is to calculate the characteristic simulation accuracy score P of the characteristic AGV car simulation model in the transportation area = (U sum -U′ sum ) / U sum ;

[0093] Step S303: When the feature simulation accuracy score P of the characteristic AGV car simulation model in the transportation area is greater than or equal to the preset feature simulation accuracy score threshold, it is determined that the feature AGV car simulation model is accurate in simulating the transportation area, and the feature AGV car simulation model is recorded as the target AGV car simulation model in the transportation area;

[0094] Step S304: When the feature simulation accuracy score P is less than the preset feature simulation accuracy score threshold, the feature AGV car simulation model is determined to be inaccurate in simulating the transport area, and the feature AGV car simulation model is optimized. The specific optimization process is:

[0095] Obtain the difference historical driving records in the transportation area, obtain characteristic simulation condition data from the difference historical driving records, import the characteristic simulation condition data into the characteristic AGV car simulation model, obtain the characteristic simulation driving records of the difference historical driving records, and obtain the average values ​​corresponding to various performance indicators from the characteristic simulation driving records;

[0096] For example, various performance indicators include the total length of the driving route and completion time;

[0097] Step S305: Obtain the average values ​​corresponding to the various performance indicators in the difference historical driving records, calculate the characteristic AGV car simulation model, and calculate the characteristic simulation error value K of the difference historical driving records:

[0098]

[0099] Among them, S z is the average value corresponding to the zth performance indicator in the difference historical driving record; S′ z is the average value corresponding to the zth performance indicator in the characteristic simulation driving record; γ is the total number of performance indicators in the difference historical driving record;

[0100] For example, γ is represented by 4, S 1 Expressed as 30; S 2 Expressed as 20; S 3 Expressed as 15; S 4 Expressed as 10; S′ 1 Expressed as 33;;S′ 2 Expressed as 23;;S′ 3 Expressed as 17; S′4 Expressed as 11; Calculate the characteristic AGV car simulation model, and the characteristic simulation error value K of the difference historical driving record:

[0101]

[0102] Step S306: Calculate the characteristic AGV car simulation model, and the marking simulation error ζ in the transportation area:

[0103]

[0104] Where η represents the total number of different historical driving records in the transportation area; K ε It is represented as the characteristic AGV car simulation model, and the characteristic simulation error value of the εth difference historical driving record;

[0105] Step S307: adjusting various model parameters of the characteristic AGV car simulation model, and calculating the marking simulation error of the adjusted characteristic AGV car simulation model in the transportation area, until the marking simulation error is less than the preset marking simulation error threshold, determining that the optimization of the characteristic AGV car simulation model is completed, and recording the characteristic AGV car simulation model as the target AGV car simulation model in the transportation area;

[0106] For example, various model parameters include PID control parameters, etc.

[0107] Step S400: Acquire the AGV trolley simulation condition data of the transport area, input the AGV trolley simulation condition data into the target AGV trolley simulation model for simulation, obtain the target simulation record, and display the target simulation record to the user;

[0108] Wherein, step S400 includes:

[0109] Step S401: obtaining a target AGV car simulation model in the transport area, and obtaining the AGV car simulation condition data of the user taking the transport area as the driving scene, wherein the AGV car simulation condition data includes data corresponding to various simulation condition parameters;

[0110] Step S402: inputting the AGV trolley simulation condition data into the target AGV trolley simulation model to obtain the target simulation record, and displaying the target simulation record to the user through the cloud platform;

[0111] In order to better implement the above method, a AGV car simulation model establishment system is also proposed, which includes a model evaluation module, a difference analysis module, a model optimization module, and a model simulation module;

[0112] The model evaluation module is used to evaluate the model performance of the AGV car simulation model and obtain the characteristic AGV car simulation model;

[0113] A difference analysis module is used to analyze the difference degree of the driving path between the historical driving record and the driving simulation record to obtain the difference historical driving record;

[0114] The model optimization module is used to optimize the characteristic AGV car simulation model to obtain the target AGV car simulation model;

[0115] The model simulation module is used to obtain the simulation condition data of the AGV car in the transportation area, input the simulation condition data of the AGV car into the target AGV car simulation model for simulation, obtain the target simulation record, and display the target simulation record to the user;

[0116] Among them, the model evaluation module includes a simulation model building unit and a model evaluation unit;

[0117] The simulation model building unit is used to obtain the equipment data of the AGV car, obtain the characteristic environment data of the AGV car, and build a simulation model of the AGV car;

[0118] The model evaluation unit is used to calculate the characteristic model performance ratio of the AGV car simulation model, and perform model performance evaluation on the AGV car simulation model according to the characteristic model performance ratio to obtain the characteristic AGV car simulation model;

[0119] Wherein, the difference analysis module includes a characteristic path difference value unit and a difference analysis unit;

[0120] The characteristic path difference value unit is used to obtain the historical driving records of the AGV in the transportation area, obtain the driving simulation records, and calculate the characteristic path difference value between the historical driving records and the driving simulation records;

[0121] A difference analysis unit, used for analyzing the degree of difference in driving paths between the historical driving record and the driving simulation record according to the characteristic path difference value, and obtaining a difference historical driving record;

[0122] Among them, the model optimization module includes a simulation accuracy evaluation unit and a model optimization unit;

[0123] The simulation accuracy evaluation unit is used to evaluate the simulation accuracy of the characteristic AGV car simulation model in the transportation area;

[0124] The model optimization unit is used to optimize the characteristic AGV car simulation model to obtain the target AGV car simulation model of the transportation area.

[0125] A medium stores computer instructions. When the computer instructions are executed by a processor, a method for establishing an AGV car simulation model based on digital twins can be implemented.

[0126] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for establishing an AGV vehicle simulation model based on digital twins, characterized in that: The method comprises: Step S100: construct a simulation cloud platform, obtain the equipment data of the AGV car, obtain the characteristic environment data of the AGV car, establish an AGV car simulation model, perform model performance evaluation on the AGV car simulation model, and obtain a characteristic AGV car simulation model; Step S200: obtaining the historical driving record of the AGV in the user's transportation area, simulating the driving state of the AGV in the transportation area based on the historical driving record using the characteristic AGV simulation model to obtain a driving simulation record, analyzing the degree of difference in driving paths between the historical driving record and the driving simulation record to obtain a difference historical driving record; Step S300: Obtain historical driving records and difference historical driving records in the transport area, evaluate the simulation accuracy of the characteristic AGV trolley simulation model in the transport area, and optimize the characteristic AGV trolley simulation model in combination with the difference historical driving records to obtain a target AGV trolley simulation model; Step S400: Acquire the AGV trolley simulation condition data of the transport area, input the AGV trolley simulation condition data into the target AGV trolley simulation model for simulation, obtain the target simulation record, and display the target simulation record to the user.

2. The method for establishing an AGV vehicle simulation model based on digital twin according to claim 1 is characterized in that: The step S100 includes: Step S101: obtaining preset equipment data of the AGV from the cloud platform, wherein the equipment data includes data corresponding to various equipment parameters of the AGV; Step S102: Acquire preset characteristic environment data of the AGV from the cloud platform, wherein the characteristic environment data includes data corresponding to various environmental parameters; Step S103: Based on the device data and characteristic environment data, using simulation software, establish an AGV simulation model of the AGV, and obtain the marked simulation condition data for simulation from the cloud platform, wherein the marked simulation condition data includes data corresponding to various simulation condition parameters; Step S104: Input the marked simulation condition data into the AGV car simulation model to obtain the driving energy consumption data output by the AGV car simulation model, wherein the driving energy consumption data includes the total distance L traveled by the AGV car after the AGV car simulation model performs simulation. sum , average rate V △ 、Total equipment load H sum和 Total energy consumption of equipment E sum ; Step S105: Calculate the characteristic model performance ratio A of the AGV simulation model: Where g is the acceleration due to gravity; μ is the preset ground friction coefficient; C d is the preset air resistance coefficient; B sum is the total windward area of ​​the AGV; Step S106: Perform model performance evaluation on the AGV trolley simulation model. The specific evaluation process is to obtain the preset first characteristic model performance ratio A′1 and second characteristic model performance ratio A′2 of the AGV trolley, wherein A′1>A′2>0. When the characteristic model performance ratio A′1>A>A′2, it is determined that the model performance of the AGV trolley simulation model meets the requirements, and the AGV trolley simulation model is recorded as the characteristic AGV trolley simulation model.

3. The method for establishing an AGV simulation model based on digital twin according to claim 1 is characterized in that: The step S200 includes: Step S201: Obtain the user who uses the characteristic AGV car simulation model, set the unit time, obtain the historical driving record of the AGV car in the transportation area of ​​the user, and obtain the driving path set D = {D1, D2, ..., D m }, wherein the two-dimensional coordinate points of the AGV in the historical driving record at the 1st, 2nd, ..., mth unit time after driving; Step S202: Acquire characteristic simulation condition data of the AGV from the historical driving record, the characteristic simulation condition data including data corresponding to various simulation condition parameters, use the characteristic AGV simulation model to simulate the driving state of the AGV in the transportation area, obtain a driving simulation record, and obtain a simulated driving path F={F1, F2, ..., F n }, wherein the two-dimensional coordinate points of the AGV in the driving simulation record at the 1st, 2nd, ..., nth unit time length after the simulated driving; Step S203: Analyze the degree of difference between the driving paths of the historical driving record and the driving simulation record. The specific analysis process is to construct a matrix W based on the driving path set D and the simulated driving path F, and set the first element in the matrix W to 0, wherein the element W in the αth row and the βth column of the matrix W is α,β =L(D α ,F β ), where L(D α ,F β ) is the two-dimensional coordinate point D corresponding to the αth element in the driving path set D α , the two-dimensional coordinate point F corresponding to the βth element in the simulated driving path F β The Euclidean distance between them is as follows: Among them, x α ,y α Respectively represented as the two-dimensional coordinate point D α The horizontal and vertical coordinates of x β ,y β Respectively represented as the two-dimensional coordinate point F β The horizontal and vertical coordinates of Step S204: define a cumulative distance matrix Q, wherein the element Q in the i-th row and i-th column of the cumulative distance matrix Q is obtained. i,j =W i,j +min{Q i-1,j ,Q i,j-1 ,Q i-1,j-1 }, where Q i-1,j Represented as the element in the i-1th row and jth column of the cumulative distance matrix Q, Q i,j-1 It is represented by the element of the i-th row and j-1-th column in the cumulative distance matrix Q, Q i-1,j Represented as the element in the i-1th row and jth column of the cumulative distance matrix Q, Q i-1,j-1 Represented as the element in the i-1th row and j-1th column in the cumulative distance matrix Q; Step S205: Obtain the last element Q in the lower right corner of the cumulative distance matrix Q m,n The corresponding values ​​are used to calculate the characteristic path difference value R between the historical driving record and the driving simulation record: When the characteristic path difference value R is greater than a preset characteristic path difference threshold, it is determined that there is a difference in the driving path of the AGV between the historical driving record and the driving simulation record, and the historical driving record is recorded as a difference historical driving record of the transportation area.

4. The method for establishing an AGV vehicle simulation model based on digital twin according to claim 3 is characterized in that: The step S300 includes: Step S301: Obtain the total number U of several historical driving records simulated by the characteristic AGV vehicle simulation model in the transportation area. sum , obtain the total number U′ of each difference historical driving record in the transportation area sum ; Step S302: Evaluate the simulation accuracy of the characteristic AGV car simulation model in the transportation area. The specific evaluation process is to calculate the characteristic simulation accuracy score P of the characteristic AGV car simulation model in the transportation area = (U sum -U′ sum ) / U sum ; Step S303: When the feature simulation accuracy score P of the characteristic AGV trolley simulation model in the transportation area is greater than or equal to the preset feature simulation accuracy score threshold, it is determined that the feature AGV trolley simulation model accurately simulates the transportation area, and the feature AGV trolley simulation model is recorded as the target AGV trolley simulation model of the transportation area; Step S304: When the feature simulation accuracy score P is less than the preset feature simulation accuracy score threshold, it is determined that the feature AGV trolley simulation model is inaccurate in simulating the transport area, and the feature AGV trolley simulation model is optimized. The specific optimization process is: Obtaining difference historical driving records in the transportation area, obtaining characteristic simulation condition data from the difference historical driving records, importing the characteristic simulation condition data into the characteristic AGV trolley simulation model, obtaining characteristic simulation driving records of the difference historical driving records, and obtaining average values ​​corresponding to various performance indicators from the characteristic simulation driving records; Step S305: Obtain the average values ​​corresponding to the various performance indicators in the difference historical driving records, calculate the characteristic AGV car simulation model, and calculate the characteristic simulation error value K of the difference historical driving records: Among them, S z is the average value corresponding to the zth performance indicator in the difference historical driving record; S′ z is the average value corresponding to the zth performance indicator in the characteristic simulation driving record; γ is the total number of performance indicators in the difference historical driving record; Step S306: Calculate the marking simulation error ζ of the characteristic AGV simulation model in the transportation area: Wherein, η represents the total number of difference historical driving records in the transportation area; K ε It is represented by the characteristic simulation error value of the characteristic AGV car simulation model for the εth difference historical driving record; Step S307: Adjust the various model parameters of the characteristic AGV car simulation model, and calculate the marking simulation error of the adjusted characteristic AGV car simulation model in the transportation area, until the marking simulation error is less than the preset marking simulation error threshold, and determine that the optimization of the characteristic AGV car simulation model is completed, and record the characteristic AGV car simulation model as the target AGV car simulation model of the transportation area.

5. The method for establishing an AGV vehicle simulation model based on digital twin according to claim 4 is characterized in that: The step S400 includes: Step S401: obtaining a target AGV car simulation model of the transport area, and obtaining the AGV car simulation condition data of the user taking the transport area as a driving scene, wherein the AGV car simulation condition data includes data corresponding to various simulation condition parameters; Step S402: input the AGV trolley simulation condition data into the target AGV trolley simulation model to obtain a target simulation record, and display the target simulation record to the user through the cloud platform.

6. An AGV trolley simulation model establishment system, used to execute the AGV trolley simulation model establishment method based on digital twins as described in any one of claims 1 to 5, characterized in that: The system includes a model evaluation module, a difference analysis module, a model optimization module, and a model simulation module; The model evaluation module is used to perform model performance evaluation on the AGV car simulation model to obtain a characteristic AGV car simulation model; The difference analysis module is used to analyze the degree of difference in the driving path between the historical driving record and the driving simulation record to obtain a difference historical driving record; The model optimization module is used to optimize the characteristic AGV car simulation model to obtain a target AGV car simulation model; The model simulation module is used to obtain the AGV car simulation condition data of the transportation area, input the AGV car simulation condition data into the target AGV car simulation model for simulation, obtain the target simulation record, and display the target simulation record to the user.

7. The AGV vehicle simulation model establishment system according to claim 6, characterized in that: The model evaluation module includes a simulation model building unit and a model evaluation unit; The simulation model building unit is used to acquire the equipment data of the AGV car, acquire the characteristic environment data of the AGV car, and build a simulation model of the AGV car; The model evaluation unit is used to calculate the characteristic model performance ratio of the AGV trolley simulation model, and perform model performance evaluation on the AGV trolley simulation model according to the characteristic model performance ratio to obtain a characteristic AGV trolley simulation model.

8. The AGV vehicle simulation model establishment system according to claim 6, characterized in that: The difference analysis module includes a characteristic path difference value unit and a difference analysis unit; The characteristic path difference value unit is used to obtain the historical driving record of the AGV in the transportation area, obtain the driving simulation record, and calculate the characteristic path difference value between the historical driving record and the driving simulation record; The difference analysis unit is used to analyze the degree of difference in driving paths between the historical driving record and the driving simulation record according to the characteristic path difference value, so as to obtain a difference historical driving record.

9. The AGV vehicle simulation model establishment system according to claim 6, characterized in that: The model optimization module includes a simulation accuracy evaluation unit and a model optimization unit; The simulation accuracy evaluation unit is used to evaluate the simulation accuracy of the characteristic AGV trolley simulation model in the transportation area; The model optimization unit is used to optimize the characteristic AGV trolley simulation model to obtain the target AGV trolley simulation model of the transportation area.

10. A medium, characterized in that The medium stores computer instructions, and when the computer instructions are executed by the processor, the method for establishing an AGV vehicle simulation model based on digital twins according to any one of claims 1 to 5 can be implemented.

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