Method for constructing simulation system model of dual-car dumper

Abstract

The present invention relates to the technical field of equipment simulation analysis, and provides a method for constructing a simulation system model of a dual-car dumper, comprising: acquiring car characteristic data of the dual-car dumper by means of a preset scanning method, simultaneously acquiring, from an equipment database, structural data and operation principle data that correspond to the car characteristic data, and obtaining first data by summarization and outputting the first data; selecting preset simulation software from a simulation tool library, and analyzing and modeling the first data to output a simulation model; configuring simulation operation parameters of the dual-car dumper in the simulation model to obtain configuration parameters, and operating the simulation model on the basis of the configuration parameters to output simulation analysis data; and acquiring, from a historical database, actual operation data matching the configuration parameters, comparing the actual operation data with the simulation analysis data so as to perform verification, and optimizing the simulation model on the basis of a comparison analysis result. The present invention can help improving the accuracy and authenticity of the simulation model, thereby improving the efficiency of system operation, and reducing the costs and risk of actual tests.

Description

Method for building a simulation system model of a double-vehicle tipper Technical Field

[0001] This invention relates to the field of equipment simulation and analysis technology, and in particular to a method for building a simulation system model of a dual-vehicle tipper. Background Technology

[0002] As a key piece of equipment for unloading coal arriving by rail at thermal power plants, the efficient and stable operation of the tippler system is crucial for ensuring fuel supply and safe production. Although the tippler system is largely fully automated, human intervention is still required in case of abnormalities. Therefore, the technical skills of tippler operators and maintenance personnel have a significant impact on the safe operation and unloading efficiency of the tippler.

[0003] Currently, potential problems and risks in the operation of double-vehicle tipplers are difficult to detect. When abnormal situations occur, there are also problems such as uneven technical levels of operators, hidden dangers in some control logic, and insufficient problem-solving capabilities of maintenance personnel, making it difficult to analyze and deal with abnormal situations in a timely and effective manner.

[0004] Therefore, this invention provides a method for building a simulation system model of a dual-vehicle tipper. Summary of the Invention

[0005] This invention provides a method for building a simulation system model of a dual-vehicle tippler, which realizes the simulation operation and performance analysis of the dual-vehicle tippler. It can help improve the accuracy and realism of the simulation model, thereby improving the efficiency of system operation and reducing the cost and risk of actual testing.

[0006] This invention provides a method for building a simulation system model of a dual-vehicle tipper, including:

[0007] Step 1: Obtain vehicle model data of the dual-vehicle tipper using a preset scanning method. At the same time, obtain the structural data and operating principle data corresponding to the vehicle model data from the equipment database, and summarize and output the first data.

[0008] Step 2: Select a preset simulation software from the simulation tool library, analyze and model the first data, and output the simulation model;

[0009] Step 3: Configure the simulation operation parameters of the dual-vehicle tipper in the simulation model to obtain the configuration parameters, and run the simulation model based on the configuration parameters to output simulation analysis data;

[0010] Step 4: Obtain actual running data that matches the configuration parameters from the historical database, compare and verify it with the simulation analysis data, and optimize the simulation model based on the comparison analysis results.

[0011] Preferably, the step of acquiring the vehicle model data of the dual-vehicle tipper through a preset scanning method includes:

[0012] A preset scanning method for scanning a double-vehicle tipper is selected from the method database. Based on the preset scanning method, the identification information and vehicle characteristics of the double-vehicle tipper are scanned, and comprehensive analysis is performed to output vehicle model data.

[0013] Preferably, the step of obtaining the structural data and operating principle data corresponding to the vehicle model data from the equipment database includes:

[0014] The external and internal structure data of the double-vehicle tipper corresponding to the vehicle model data are selected from the equipment database and the structure data is output. At the same time, the operation process and operating logic of the double-vehicle tipper matching the vehicle model data are obtained and the operating principle data is output.

[0015] Preferably, the operation process includes process data for the tipper readjustment, air conditioning, towing platform, main body, and wheel clamp in each control mode.

[0016] Preferably, before summarizing and outputting the first data, the method further includes:

[0017] The system acquires real-time environmental data of the dual-vehicle tipper in a real-world scenario by using a pre-set sensor array, and outputs equipment operation-environment comparison data by combining the operating data of the dual-vehicle tipper at the same time.

[0018] Preferably, step 2 includes:

[0019] At the same time, the simulation analysis requirements of the user are obtained, and the preset simulation software is selected from the simulation tool library in combination with the user's tool selection instructions;

[0020] The first data is input into a preset simulation software for analysis and modeling to obtain a first model, and the first model that meets the preset performance conditions is output as a simulation model.

[0021] Preferably, step 3 includes:

[0022] Based on the simulation analysis requirements, the application scenarios of the simulation model are determined, and scenario configuration information is output.

[0023] Meanwhile, based on the simulation analysis requirements, the initial operating parameters and simulation environment parameters of the dual-vehicle tipper in the simulation analysis model are determined, and the operating parameter configuration information and environment parameter configuration information are output respectively.

[0024] The scenario configuration information, runtime parameter configuration information, and environmental parameter configuration information are summarized, and the initial configuration data is output.

[0025] The initial configuration data is input into the simulation model and run to obtain the simulation analysis results within a preset time period. At the same time, the process data of the double-vehicle tipper during the operation of the simulation model is obtained, and the simulation analysis process data is output.

[0026] Based on the initial configuration data, simulation analysis results, and simulation analysis process data, and combined with the preset animation presentation components in the simulation model, the process animation of the dual-vehicle tipper during the operation of the simulation model is output.

[0027] Feature extraction is performed on the simulation analysis results and simulation process data, and feature matching is performed on the extracted features in combination with a preset abnormal feature library. Features that meet the preset matching conditions are summarized and output as a second feature set.

[0028] By combining the preset feature-state mapping table, the abnormal operating state of the double-car tipper corresponding to the second feature set is identified and determined, and a state analysis table is output.

[0029] Keywords in the status analysis table are identified and extracted, and the corresponding handling strategies and methods are selected from the strategy and method database in combination with the preset keyword-strategy comparison table, and the status-strategy-method comparison table is output.

[0030] Based on the state-strategy-method comparison table, the abnormal operating states of the dual-vehicle tipper are handled, and the handling process of the abnormal operating states is analyzed in combination with preset effect analysis indicators, and the abnormal handling effect analysis results are output.

[0031] Preferably, step 4 includes:

[0032] Historical data matching the first data are selected from the historical database, and historical data with a similarity greater than the first preset degree are output as primary reference data. At the same time, historical data with a similarity less than the first preset degree but greater than the second preset degree are output as secondary reference data.

[0033] The primary reference data and secondary reference data are compared and verified with the first data respectively, and the comparison and verification results are output.

[0034] Simultaneously, based on the preset optimization indicators selected from the indicator database, the comparison and verification results are optimized and analyzed, and the optimization analysis results are output. The optimization analysis results include model parameter optimization, work process optimization, personnel operation optimization, control logic optimization, and environmental parameter optimization.

[0035] Based on the optimization analysis results, matching optimization strategies and methods are selected from the strategy and method database. The simulation model parameters, as well as the operation process, personnel operation, control logic, and environmental parameters of the double-vehicle tipper, are optimized based on the optimization strategies and methods. At the same time, optimization logs are output.

[0036] Based on the optimization logs, and in conjunction with the optimization strategies and methods, the simulation model parameters, personnel operation manual, and the operation process, control logic, and environmental parameters of the double-vehicle tipper are adjusted.

[0037] This invention provides a method for building a simulation system model for a dual-vehicle tipper. First, the invention acquires vehicle model data using a preset scanning method and combines this data with structural and operational principle data from an equipment database to output initial data. Then, appropriate simulation software is selected from a simulation tool library to analyze and model the initial data, generating a simulation model. Next, the simulation operating parameters of the dual-vehicle tipper in the simulation model are configured, and the simulation model is run to output simulation analysis data. Finally, actual operating data is obtained from a historical database and compared with the simulation analysis data for verification. The simulation model is then optimized based on the comparative analysis results to improve its accuracy and reliability. This invention, through the establishment and operation of the simulation model and the comparison and verification with actual operating data, achieves simulated operation and performance analysis of a dual-vehicle tipper. This helps improve the accuracy and realism of the simulation model, thereby increasing system operating efficiency, reducing the cost and risk of actual testing, and providing reliable support for the optimization of dual-vehicle tippers. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0039] Figure 1 is a flowchart illustrating the method for building a simulation system model of a dual-vehicle tipper provided in an embodiment of the present invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0041] As shown in Figure 1, the method for building a simulation system model of a dual-vehicle tipper provided in this embodiment of the invention includes:

[0042] Step 1: Obtain vehicle model data of the dual-vehicle tipper using a preset scanning method. At the same time, obtain the structural data and operating principle data corresponding to the vehicle model data from the equipment database, and summarize and output the first data.

[0043] Step 2: Select a preset simulation software from the simulation tool library, analyze and model the first data, and output the simulation model;

[0044] Step 3: Configure the simulation operation parameters of the double-vehicle tipper in the simulation model, obtain the configuration parameters, run the simulation model based on the configuration parameters, and output simulation analysis data;

[0045] Step 4: Obtain actual running data that matches the configuration parameters from the historical database, compare and verify it with the simulation analysis data, and optimize the simulation model based on the comparison analysis results.

[0046] In this embodiment, the preset scanning method is a preset method for acquiring vehicle model data of a dual-vehicle tipper.

[0047] In this embodiment, vehicle model data describes the characteristics and specifications of the dual-vehicle tipper, such as size, weight, and structure.

[0048] In this embodiment, the device database is a database that stores various device-related data, including structural data and operating principle data.

[0049] In this embodiment, structural data refers to data describing the internal structure, components, and constituent elements of the dual-vehicle tippler.

[0050] In this embodiment, the operating principle data includes data explaining the operating principle, working mode, and related parameters of the dual-vehicle tippler.

[0051] In this embodiment, the first data is the output of the summaries of the acquired vehicle model data, structural data, and operating principle data.

[0052] In this embodiment, the simulation tool library is a library that stores various simulation software and tools.

[0053] In this embodiment, the preset simulation software is: pre-selected software for modeling and simulation, such as WINCC and Unitypro;

[0054] In this embodiment, the simulation model is a virtual model of the dual-vehicle tipper established based on the first data, used to simulate its operation and behavior, such as the simulation model of the encoder, the eight-axis counter, etc.

[0055] In this embodiment, the simulation operating parameters are parameters used to configure the simulation model to simulate the operation of the dual-vehicle tippler, including equipment operating parameters and personnel operation data.

[0056] In this embodiment, the configuration parameters are specific parameter values ​​set according to the simulation operation parameters.

[0057] In this embodiment, the simulation analysis data is the data generated after the simulation model runs, which is used to analyze the performance and behavior of the dual-vehicle tipper.

[0058] In this embodiment, the historical database is a database that stores past actual operational data.

[0059] In this embodiment, the actual operating data refers to the data collected during the actual operation of the dual-vehicle tipper, which is used to compare and verify with the simulation analysis data.

[0060] The implementation principle and beneficial effects of this embodiment are as follows: First, the invention acquires vehicle model data using a preset scanning method, and combines this data with structural and operational principle data from the equipment database to output first data. Then, appropriate simulation software is selected from the simulation tool library to analyze and model the first data, generating a simulation model. Next, the simulation operating parameters of the dual-vehicle tipper in the simulation model are configured, and the simulation model is run to output simulation analysis data. Finally, actual operating data is obtained from the historical database and compared with the simulation analysis data. The simulation model is optimized based on the comparative analysis results to improve its accuracy and reliability. This invention, through the establishment and operation of the simulation model, combined with comparative verification using actual operating data, achieves the simulation operation and performance of the dual-vehicle tipper. This helps improve the accuracy and realism of the simulation model, thereby increasing the efficiency of system operation, reducing the cost and risk of actual testing, and providing reliable support for the optimization of the dual-vehicle tipper.

[0061] The method for building a simulation system model of a dual-vehicle tipper provided in this embodiment of the invention obtains vehicle model data of the dual-vehicle tipper through a preset scanning method, including:

[0062] A preset scanning method for scanning a double-vehicle tipper is selected from the method database. Based on the preset scanning method, the identification information and vehicle characteristics of the double-vehicle tipper are scanned, and comprehensive analysis is performed to output vehicle model data.

[0063] In this embodiment, the method database is a database that stores various preset scanning methods for scanning a dual-vehicle tipper.

[0064] In this embodiment, the identification information is a unique identifier or identification information for the dual-vehicle tipper, which can be used to distinguish different vehicle models or equipment, such as the serial number of the dual-vehicle tipper, manufacturer information, etc.

[0065] In this embodiment, vehicle characteristics refer to data describing the features or characteristics of the dual-vehicle tipper, such as size, weight, and functions.

[0066] The implementation principle and beneficial effects of this embodiment are as follows: This invention utilizes a preset scanning method in the method database to scan a dual-vehicle tipper, extracting identification information and vehicle model characteristics, and outputting vehicle model data after comprehensive analysis. This invention can effectively obtain vehicle model data for dual-vehicle tippers, providing an accurate foundation for subsequent simulation model building and performance analysis, saving time and costs, improving the efficiency and accuracy of model building, and providing reliable support for the optimization of dual-vehicle tippers.

[0067] The method for building a simulation system model of a dual-vehicle tipper provided in this embodiment of the invention obtains structural data and operating principle data corresponding to vehicle model data from an equipment database, including:

[0068] The external and internal structural data of the double-vehicle tipper corresponding to the vehicle model data are selected from the equipment database and the structural data is output. At the same time, the operation process and operating logic of the double-vehicle tipper matching the vehicle model data are obtained and the operating principle data is output.

[0069] In this embodiment, the external and internal structural data includes detailed data on the external and internal structure of the double-car tipper, including the location, connection method, and materials of each component, such as the steel structure frame, hydraulic cylinder location, and sensor arrangement of the double-car tipper.

[0070] In this embodiment, the work process is as follows: the operation process and steps of the double-car tipper in actual work, covering the work process from start to finish, such as the lifting, rotating, and placing operations of the double-car tipper;

[0071] In this embodiment, the operating logic is the operating logic and control principle of the dual-vehicle tipper during operation, including sensor feedback, control system, etc. For example, the dual-vehicle tipper adjusts the lifting speed, rotation angle, etc., based on sensor feedback.

[0072] The implementation principle and beneficial effects of this embodiment are as follows: By acquiring structural and operational principle data from an equipment database, this invention provides a deeper understanding of the internal structure and working principle of a dual-vehicle tippler. This data is crucial for establishing accurate simulation models and simulating actual operation. By acquiring external and internal structural data as well as operational principle data, this invention enables a more comprehensive understanding of the structure and operation of the dual-vehicle tippler, facilitating the establishment of more realistic and accurate simulation models. This improves the accuracy and reliability of the simulation system and provides stronger support for system optimization.

[0073] The method for building a simulation system model of a double-vehicle tipper provided in this embodiment of the invention includes the workflow data of tipper readjustment, air conditioning, towing platform, body, and wheel clamp in each control mode.

[0074] In this embodiment, the tippler is readjusted: the process of readjusting or resetting the dual tippler to ensure its normal operation, for example, recalibrating the horizontal position and angle of the dual tippler;

[0075] In this embodiment, the air conditioning system is part of the process for handling no-load or no-load conditions during the operation of the dual-vehicle tipper.

[0076] In this embodiment, the traction platform is a platform or device used by the double-car tipper to tow or move vehicles, and performs different operations under different control modes.

[0077] In this embodiment, the main body refers to the main part of the dual-vehicle tipper, including the body structure and main functional components;

[0078] In this embodiment, the wheel clamp is a device on the double-car tipper used to clamp or fix objects to ensure safety and stability.

[0079] In this embodiment, the control mode describes the control method or mode of the dual-vehicle tipper under different operating states, including local system control, interlocking manual control, and fully automatic control mode.

[0080] The implementation principle and beneficial effects of this embodiment are as follows: By describing in detail the operation process of the tippler under different control modes, this invention ensures a clear understanding of the various operating methods of the dual-car tippler, providing basic data for the establishment of simulation models and performance analysis. By describing the data under different control modes in the operation process, this invention helps optimize the operation process of the dual-car tippler, improving efficiency and safety. It provides more comprehensive support for system optimization, ensuring that the dual-car tippler can operate stably and reliably in various working modes.

[0081] The method for building a simulation system model of a dual-vehicle tipper provided in this embodiment of the invention, before summarizing and outputting the first data, further includes:

[0082] The system acquires real-time environmental data of the dual-vehicle tipper in a real-world scenario by using a pre-set sensor array, and outputs equipment operation-environment comparison data by combining the operating data of the dual-vehicle tipper at the same time.

[0083] In this embodiment, the preset sensor array is a pre-set combination of sensors used to collect various data of the dual-vehicle tipper in a real-world scenario.

[0084] In this embodiment, the real-world scenario refers to the specific scene or location of the dual-car tipper in the actual operating environment, including surrounding objects, terrain, etc. For example, the scenario of the dual-car tipper loading and unloading goods on a construction site.

[0085] In this embodiment, real-time environmental data refers to various data collected in real-time by the dual-vehicle tipper in a real-world scenario through a preset sensor array, such as temperature, humidity, pressure, and location.

[0086] In this embodiment, the equipment operation-environment comparison data is obtained by comparing and analyzing the operation data of the two tippers at the same time with the real-time environmental data to obtain the correlation data between the equipment operation status and environmental factors.

[0087] The implementation principle and beneficial effects of this embodiment are as follows: This invention acquires real-time environmental data through a preset sensor array, which can more accurately simulate the operation of a dual-vehicle tippler under different environments. Comparing and analyzing the equipment operation data with environmental data helps to understand the equipment's performance under different environmental conditions. By acquiring real-time environmental data and comparing it with equipment operation data, this invention can help optimize the operating strategy of the dual-vehicle tippler, improve its ability to adapt to different environmental conditions, increase the accuracy of the simulation model, and provide a more reliable basis for system optimization.

[0088] The method for building a simulation system model of a dual-vehicle tipper provided in this embodiment of the invention includes, in step 2:

[0089] At the same time, the simulation analysis requirements of the user are obtained, and the preset simulation software is selected from the simulation tool library in combination with the user's tool selection instructions;

[0090] The first data is input into the preset simulation software for analysis and modeling to obtain the first model, and the first model that meets the preset performance conditions is output as the simulation model.

[0091] In this embodiment, the simulation analysis requirements are: the user's simulation analysis requirements for the dual-vehicle tipper, including the information and objectives the user expects to obtain from the simulation model;

[0092] In this embodiment, the tool selection instruction is as follows: according to the user's needs, select a suitable preset simulation software from the simulation tool library for subsequent data analysis and modeling work;

[0093] In this embodiment, the first model is an initial simulation model formed based on the results of the first round of analysis and modeling, which is used for subsequent performance evaluation and optimization.

[0094] In this embodiment, preset performance conditions are pre-defined conditions or indicators used to evaluate the performance of the simulation model, ensuring that the model can effectively simulate the actual situation when these conditions are met. For example, the model must maintain stability during the simulation process, and the error between the simulation results and the actual data must be within 5%.

[0095] The implementation principle and beneficial effects of this embodiment are as follows: By acquiring the user's simulation analysis requirements, selecting appropriate simulation tools, and establishing a first model based on preset performance conditions, this invention ensures that the simulation model meets user expectations and can effectively perform performance evaluation. By selecting appropriate simulation tools and setting preset performance conditions according to user needs, this invention improves the accuracy and reliability of the simulation model, ensures that the model output meets user requirements, and provides a more reliable basis for system optimization.

[0096] The method for building a simulation system model of a dual-vehicle tipper provided in this embodiment of the invention includes, in step 3:

[0097] Based on the simulation analysis requirements, determine the application scenarios of the simulation model and output the scenario configuration information;

[0098] Meanwhile, based on the simulation analysis requirements, the initial operating parameters and simulation environment parameters of the dual-vehicle tipper in the simulation analysis model are determined, and the operating parameter configuration information and environment parameter configuration information are output respectively.

[0099] Summarize the scenario configuration information, runtime parameter configuration information, and environment parameter configuration information, and output the initial configuration data;

[0100] Input the initial configuration data into the simulation model and run it to obtain the simulation analysis results within the preset time period. At the same time, obtain the process data of the double-vehicle tipper during the operation of the simulation model and output the simulation analysis process data.

[0101] Based on the initial configuration data, simulation analysis results, and simulation analysis process data, and combined with the preset animation presentation components in the simulation model, the process animation of the dual-vehicle tipper during the simulation model operation is output.

[0102] Feature extraction is performed on the simulation analysis results and simulation process data. The extracted features are then matched with a preset abnormal feature library. Features that meet the preset matching conditions are summarized and output as a second feature set.

[0103] By combining the preset feature-state mapping table, the abnormal operating state of the double-car tipper corresponding to the second feature set is identified and determined, and the state analysis table is output.

[0104] The keywords in the status analysis table are identified and extracted. Combined with the preset keyword-strategy comparison table, the corresponding handling strategies and methods for each abnormal operating status in the status analysis table are selected from the strategy and method database, and the status-strategy-method comparison table is output.

[0105] Based on the state-strategy-method comparison table, the abnormal operating states of the dual-vehicle tipper are handled, and the handling process of the abnormal operating states is analyzed in combination with the preset effect analysis indicators, and the abnormal handling effect analysis results are output.

[0106] In this embodiment, the application scenario is as follows: the specific application scenario of the simulation model is determined according to the simulation analysis requirements, so as to simulate the operating behavior of the dual-vehicle tipper under specific circumstances;

[0107] In this embodiment, the scene configuration information includes a description of the scene features of the dual-vehicle tipper in the simulation model, such as terrain and obstacles.

[0108] In this embodiment, the initial operating parameters refer to the initial operating parameters set by the dual-vehicle tipper at the start of the simulation, such as speed and load.

[0109] In this embodiment, simulated environmental parameters refer to environmental parameters considered in the simulation model, such as temperature and humidity.

[0110] In this embodiment, the operating parameter configuration information describes the specific settings of the initial operating parameters of the dual-vehicle tipper, for example, speed = 2 m / s, load = 500 kg;

[0111] In this embodiment, the environmental parameter configuration information describes the specific settings of the simulated environmental parameters, such as temperature = 25°C and humidity = 60%.

[0112] In this embodiment, the initial configuration data is the initial setting data formed by summarizing the scene configuration information, the running parameter configuration information, and the environmental parameter configuration information.

[0113] In this embodiment, the preset time period refers to the preset time period during which the simulation model runs, used for simulation analysis and result evaluation.

[0114] In this embodiment, the simulation analysis results are the simulation model running results obtained within a preset time period.

[0115] In this embodiment, the simulation analysis process data includes detailed data generated by the dual-vehicle tippler during simulation operation.

[0116] In this embodiment, a preset animation presentation component is used to display the animation effects of the dual-vehicle tipper during operation in the simulation model, which can intuitively present the simulation results;

[0117] In this embodiment, a pre-defined abnormal feature library is used to match the features extracted from the simulation analysis results to identify abnormal situations, such as abnormal sounds, abnormal vibrations, etc.

[0118] In this embodiment, the preset matching conditions are the set conditions used to determine which features should be matched with features in the abnormal feature library;

[0119] In this embodiment, the second feature set is a set of features that meet preset matching conditions after feature extraction and matching, which is used to further analyze abnormal situations.

[0120] In this embodiment, a preset feature-state mapping table is used to map features to possible abnormal operating states of the double-car tipper for subsequent state analysis and processing. For example, abnormal sound frequencies are mapped to possible abnormal states, such as "mechanical failure" or "operational error".

[0121] In this embodiment, abnormal operating state refers to the abnormal state that occurs during the simulation of the dual-vehicle tipper, which may include malfunctions, incorrect operations, etc.

[0122] In this embodiment, the status analysis table records detailed information about specific abnormal operating states, including characteristics, status, and possible causes.

[0123] In this embodiment, a preset keyword-strategy comparison table is used to compare the keywords in the abnormal state analysis table with the corresponding handling strategies and handling methods, so that the system can quickly select the appropriate handling method based on the keywords. For example, the keyword "mechanical failure" is matched with the handling strategy "shutdown and maintenance", and the handling method is "check whether the mechanical parts are damaged".

[0124] In this embodiment, the strategy and method database stores detailed information on various handling strategies and methods, which the system can select based on a preset keyword-strategy lookup table, such as "emergency shutdown" and "replace parts".

[0125] In this embodiment, the handling strategy and handling method are as follows: the handling strategy and specific operation method for the abnormal state of the dual-vehicle tipper, so as to ensure that the system can effectively deal with various abnormal situations, such as emergency shutdown, replacement of damaged parts, recalibration, etc.

[0126] In this embodiment, the state-strategy-method lookup table is used to compare abnormal operating states with corresponding handling strategies and methods based on the information in the preset keyword-strategy lookup table. This allows the system to quickly and accurately handle abnormal states. For example, the abnormal state "electrical fault" is associated with the handling strategy "power off", and the handling method is "check whether the circuit connection is normal".

[0127] In this embodiment, preset effect analysis indicators are used to evaluate the effectiveness of handling abnormal situations, so that the system can adjust and optimize the handling methods according to the actual situation, including indicators such as handling time, cost, and safety.

[0128] In this embodiment, the results of the abnormal handling effect analysis are analyzed to evaluate the effectiveness of the handling strategies and methods adopted during the abnormal state handling process.

[0129] The implementation principle and beneficial effects of this embodiment are as follows: The application scenario and initial parameters of the simulation model are determined based on simulation analysis requirements. The simulation process is displayed using preset animation presentation components. Abnormal states are identified through feature extraction and abnormal feature library matching. Then, corresponding handling strategies and methods are selected according to a preset keyword-strategy lookup table. Finally, the handling effect is evaluated through effect analysis indicators. This invention integrates simulation analysis, animation display, abnormal identification, and handling optimization, improving the system's intelligence and handling efficiency. This invention combines simulation models and data analysis technology to achieve real-time monitoring and intelligent handling of abnormal situations in dual-vehicle tipper machines. This effectively improves the system's fault diagnosis speed and accuracy, reduces the risk of production interruption, and, through the application of effect analysis indicators, the system can continuously optimize handling strategies and methods, improving system stability and reliability, thereby achieving the goal of increasing production efficiency and reducing costs.

[0130] The method for building a simulation system model of a dual-vehicle tipper provided in this embodiment of the invention includes, in step 4:

[0131] Historical data matching the first data are selected from the historical database, and historical data with a similarity greater than the first data is output as the primary reference data. At the same time, historical data with a similarity less than the first data but greater than the second preset value is output as the secondary reference data.

[0132] The primary reference data and secondary reference data are compared and verified with the first data respectively, and the comparison and verification results are output.

[0133] Meanwhile, based on the preset optimization indicators selected from the indicator database, the comparative verification results are optimized and analyzed, and the optimization analysis results are output. The optimization analysis results include model parameter optimization, work process optimization, personnel operation optimization, control logic optimization, and environmental parameter optimization.

[0134] Based on the optimization analysis results, matching optimization strategies and methods are selected from the strategy and method database. Based on the optimization strategies and methods, the simulation model parameters, as well as the operation process, personnel operation, control logic, and environmental parameters of the double-vehicle tipper, are optimized. At the same time, optimization logs are output.

[0135] Based on the optimized logs, and combined with optimization strategies and methods, the simulation model parameters, personnel operation manual, and the operation process, control logic, and environmental parameters of the double-vehicle tipper were adjusted.

[0136] In this embodiment, the first preset degree is a threshold used to determine the similarity of historical data. Data that is greater than the threshold is regarded as primary reference data, and data that is less than the threshold but greater than the second preset degree is regarded as secondary reference data.

[0137] In this embodiment, the main reference data is historical data that has a similarity greater than a first preset degree to the first data, which is used as the main reference for comparison, verification and optimization analysis.

[0138] In this embodiment, the second preset degree is a second threshold used to determine the similarity of historical data. Data that is less than the first preset degree but greater than the second preset degree is regarded as secondary reference data.

[0139] In this embodiment, secondary reference data refers to historical data that has a similarity to the first data that is less than the first preset degree but greater than the second preset degree, and is used as auxiliary reference data for comparison verification and optimization analysis.

[0140] In this embodiment, the comparison verification results are used to compare the differences between the main reference data and the secondary reference data and the first data, in order to verify the accuracy and reliability of the simulation analysis data.

[0141] In this embodiment, preset optimization indicators are selected from the indicator database for optimization analysis, used to evaluate and compare the verification results and make optimization decisions.

[0142] In this embodiment, the optimization analysis results include model parameter optimization, workflow optimization, personnel operation optimization, control logic optimization, and environmental parameter optimization, which are used to guide the optimization direction and decision-making of the system.

[0143] In this embodiment, model parameter optimization involves adjusting and optimizing various parameters in the simulation model to improve the model's accuracy and simulation effect. For example, adjusting parameters such as the speed and load of the double-vehicle tipper can better simulate the actual situation.

[0144] In this embodiment, the operation process is optimized: the operation process of the double-car tipper is optimized, including task allocation, work sequence and other aspects, in order to improve operation efficiency and reduce resource waste. For example, the loading and unloading process of the double-car tipper is optimized to reduce waiting time.

[0145] In this embodiment, personnel operation optimization involves optimizing the operating methods and procedures for personnel operating the dual-vehicle tippler to improve work efficiency and safety. For example, training personnel on best practices for using the dual-vehicle tippler can reduce operational errors.

[0146] In this embodiment, the control logic is optimized: the control system logic of the dual-vehicle tippler is optimized, including the optimization of the automatic control system and the manual control system, in order to improve the system's response speed and stability. For example, the feedback mechanism of the control system is optimized to adjust the operating status of the dual-vehicle tippler more quickly.

[0147] In this embodiment, environmental parameter optimization involves optimizing various parameters in the operating environment of the dual-car tippler, including factors such as temperature, humidity, and light, to improve the performance and stability of the dual-car tippler. For example, adjusting the temperature and humidity of the workplace can ensure the normal operation of the dual-car tippler.

[0148] In this embodiment, the optimization strategy and method are: specific optimization strategies and methods selected and applied based on the optimization analysis results, used to optimize model parameters, work processes, personnel operations, control logic, and environmental parameters;

[0149] In this embodiment, the optimization log is a log that records the adjustments and optimization measures made during the optimization process, used to track the optimization effect and compare the effects of different optimization schemes.

[0150] In this embodiment, the personnel operation manual is a manual that details how personnel should operate the double-car tipper, including operating procedures, safety precautions, and other content, and is used to guide personnel to operate the double-car tipper correctly.

[0151] The implementation principle and beneficial effects of this embodiment are as follows: This invention achieves comprehensive optimization of the simulation system model of a double-car tipper by matching and comparing historical data, combined with preset optimization indicators for optimization analysis. Through historical data analysis and optimization processes, the operating parameters and procedures of the simulation model and the double-car tipper are continuously adjusted and optimized to adapt to different working environments and improve the overall operating effect of the system. This invention improves the accuracy and reliability of the system by selecting matching optimization strategies and methods to adjust and optimize model parameters, work processes, personnel operations, control logic, and environmental parameters, thereby optimizing the work processes and personnel operations, enhancing the optimization of control logic and environmental parameters, and ultimately improving the operating efficiency and production quality of the double-car tipper, achieving continuous optimization and improvement of the system.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for building a simulation system model of a double car dumper, characterized in that, The application relates to a double-vehicle turnover machine simulation method, which comprises the following steps: Step 1: obtaining vehicle type data of a double-vehicle turnover machine through a preset scanning method, simultaneously obtaining structure data and operation principle data corresponding to the vehicle type data in a device database, and outputting first data; Step 2: selecting a preset simulation software in a simulation tool library, analyzing and modeling the first data, and outputting a simulation model; Step 3: configuring simulation operation parameters of the double-vehicle turnover machine in the simulation model, obtaining configuration parameters, and running the simulation model based on the configuration parameters to output simulation analysis data; Step 4: obtaining actual operation data matched with the configuration parameters in a historical database, comparing and verifying the actual operation data with the simulation analysis data, and optimizing the simulation model based on a comparison and analysis result.

2. The method of claim 1, wherein, The vehicle type data of the double-vehicle turnover machine is obtained through a preset scanning method, which comprises the following steps: A preset scanning method for scanning the double-vehicle turnover machine is selected in a method database, and the identification information and vehicle type characteristics of the double-vehicle turnover machine are scanned based on the preset scanning method, and vehicle type data is output after comprehensive analysis.

3. The method of claim 1, wherein: The structure data and operation principle data corresponding to the vehicle type data are obtained in the device database, which comprises the following steps: The external and internal structure data of the double-vehicle turnover machine corresponding to the vehicle type data are selected in the device database to output structure data, and the operation flow and operation logic of the double-vehicle turnover machine matched with the vehicle type data are obtained to output operation principle data.

4. The method of claim 3, wherein, The operation flow comprises flow data of a turnover machine re-adjustment, an air conditioner, a car pulling table, a body and a wheel clamping device under various control modes.

5. The method of claim 1, wherein, Before the first data is output, the following steps are further included: Real-time environment data of the double-vehicle turnover machine in a real scene is obtained through a preset sensor array, and device operation-environment comparison data is output by combining the running data of the double-vehicle turnover machine at the same time.

6. The method of claim 1, wherein, In step 2, the following steps are included: Meanwhile, the simulation analysis requirement of a user is obtained, and a preset simulation software is selected in the simulation tool library by combining the tool selection instruction of the user; The first data is input into the preset simulation software for analysis and modeling to obtain a first model, and the first model meeting a preset performance condition is output as a simulation model.

7. The method of claim 6, wherein, In step 3, the following steps are included: Based on the simulation analysis requirement, the application scene of the simulation model is determined to output scene configuration information; Meanwhile, based on the simulation analysis requirement, the initial operation parameters and simulation environment parameters of the double-vehicle turnover machine in the simulation analysis model are determined to respectively output operation parameter configuration information and environment parameter configuration information; The scene configuration information, operation parameter configuration information and environment parameter configuration information are summarized to output initial configuration data; The initial configuration data is input into the simulation model and is run to obtain simulation analysis results in a preset period, and process data of the double-vehicle turnover machine in the running process of the simulation model is obtained to output simulation analysis process data; Based on the initial configuration data, simulation analysis results and simulation analysis process data, and by combining a preset animation presentation component in the simulation model, process animation of the double-vehicle turnover machine in the running process of the simulation model is output. Feature extraction is performed on the simulation analysis result and simulation process data, and the extracted features are matched with a preset abnormal feature library, and features meeting a preset matching condition are output as a second feature set; A state analysis table is output by identifying and determining abnormal running states of the double-car roll-over machine corresponding to the second feature set in combination with a preset feature-state mapping table; Keywords in the state analysis table are identified and extracted, and a state-strategy-method mapping table is output by selecting a disposal strategy and disposal method corresponding to each abnormal running state in the state analysis table in combination with a preset keyword-strategy mapping table in a strategy method database; Based on the state-strategy-method mapping table, each abnormal running state of the double-car roll-over machine is disposed, and an abnormal disposal effect analysis result is output by performing effect analysis on the disposal process of the abnormal running state in combination with a preset effect analysis index.

8. The method of claim 1, wherein: In step 4, the following steps are included: In the historical database, historical data matching the first data are selected, and historical data having a similarity greater than a first preset degree to the first data are output as main reference data, and historical data having a similarity less than the first preset degree and greater than a second preset degree to the first data are output as secondary reference data; The main reference data and the secondary reference data are compared and verified with the first data, and a comparison and verification result is output; Meanwhile, the comparison and verification result is optimized and analyzed in combination with a preset optimization index selected in an index database, and an optimization analysis result is output, wherein the optimization analysis result includes model parameter optimization, operation flow optimization, personnel operation optimization, control logic optimization, and environmental parameter optimization; Based on the optimization analysis result, a matched optimization strategy and method are selected in a strategy method database, and the simulation model parameters and the operation flow, personnel operation, control logic, and environmental parameters of the double-car roll-over machine are optimized based on the optimization strategy and method, and an optimization log is output; Based on the optimization log, the simulation model parameters, personnel operation manual, and the operation flow, control logic, and environmental parameters of the double-car roll-over machine are adjusted in combination with the optimization strategy and method.

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