CN116108717B - Traffic transportation equipment operation prediction method and device based on digital twin - Google Patents
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Abstract
The application relates to the technical field of transportation and discloses a transportation equipment operation prediction method and device based on digital twinning. The method comprises the steps of constructing a digital twin model based on topological structure data and basic environment data of target transportation equipment, importing operation data generated in a historical operation process of the target transportation equipment into the digital twin model, performing simulation calculation on the obtained target digital twin model, training the target digital twin model according to the obtained simulation operation data and the operation data to obtain a target operation state prediction model, inputting real-time state data of the target digital twin model into the target operation state prediction model to perform prediction calculation, and finally obtaining an operation state prediction result. The application realizes intelligent prediction of the running state of the transportation equipment and provides objective and effective data basis for realizing the running reliability and safety of the transportation equipment.
Description
Technical Field
The application relates to the technical field of transportation, in particular to a transportation equipment operation prediction method and device based on digital twinning.
Background
People cannot be transported in production due to population mobility and cargo transportation. With the development of traffic to a more efficient and rapid direction, the loss and injury caused by traffic accidents are also becoming larger, and the reliability and safety problems of traffic are particularly important.
In the current transportation, manual driving control is relied on under most conditions, and the manual driving control has interference of human factors and possibly has the problems of fatigue and misjudgment, so that traffic accidents are caused. While some unmanned means can avoid the interference of some human factors, various program loopholes and complex environments have poor coping ability, so that the unmanned means still cannot be effectively popularized at present.
If the traffic running state can be intelligently predicted, corresponding countermeasure is quickly taken according to the predicted state result, the occurrence rate of traffic accidents can be reduced, and loss and casualties can be reduced to the greatest extent under the condition that the traffic accidents cannot be avoided.
Meanwhile, the service life of the mechanical or electronic devices in the transportation equipment is different under different environments and different working strengths, so that relatively large potential safety hazards are easy to exist. With the continuous improvement of the functions and performances of the conventional transportation equipment, the number of parts is greatly increased, the connection complexity is greatly improved, and the possibility of sudden abnormal conditions such as equipment faults and traffic accidents is greatly increased. In a general inspection process, it is difficult to directly locate a part with potential risk. The failure of parts of the transportation equipment easily causes the damage and the out-of-control of the transportation equipment, and causes huge damage under the condition of high speed and high load.
Disclosure of Invention
The application provides a traffic transportation equipment operation prediction method and device based on digital twinning, which solve the technical problem that the operation state of traffic transportation equipment is difficult to predict in the prior art.
The first aspect of the application provides a traffic transportation equipment operation prediction method based on digital twinning, which comprises the following steps:
obtaining topological structure data and basic environment data of target transportation equipment;
constructing a corresponding digital twin device model according to the topological structure data, constructing a corresponding digital twin environment model according to the basic environment data, and correlating the digital twin device model and the digital twin environment model to obtain a digital twin model;
acquiring operation data generated in a historical operation process of the target transportation equipment;
importing the operation data into the digital twin model to obtain a target digital twin model matched with the target transportation equipment;
performing simulation calculation on the target digital twin model to obtain simulation operation data generated in the operation process of the target digital twin model;
training the target digital twin model according to the operation data and the simulation operation data to obtain a target operation state prediction model;
carrying out real-time state update on the target digital twin model to obtain corresponding real-time state data;
and inputting the real-time state data into the target running state prediction model to perform prediction calculation so as to obtain a running state prediction result of the target traffic transportation equipment.
According to one implementation manner of the first aspect of the present application, the training the target digital twin model according to the operation data and the simulation operation data includes:
comparing the operation data with the simulation operation data to obtain error data;
correcting the target digital twin model according to the error data;
constructing a sample set based on the operational data and the simulated operational data;
and training the corrected target digital twin model according to the sample set.
According to one implementation manner of the first aspect of the present application, the running state prediction result includes a fault diagnosis result, the target digital twin model generates a plurality of functional chain groups in a training process and records the functional chain groups to which each part belongs, and the functional chain groups are composed of a plurality of parts for realizing specific functions; the step of inputting the real-time state data into the target running state prediction model for prediction calculation comprises the following steps:
judging faults according to the output response of the functional chain group;
and (5) performing fault location by combining the part crossing of the functional chain group.
According to one implementation manner of the first aspect of the present application, the operation data includes environmental history data, equipment operation data, equipment component data, equipment maintenance data and cost data;
the operation state prediction results also include health assessment results, life prediction results, and cost calculation results.
According to one manner that the first aspect of the present application can be implemented, the method further includes:
determining a target operation strategy according to the operation state prediction result;
and controlling the running state of the target transportation equipment according to the target running strategy.
According to one manner that the first aspect of the present application can be implemented, the method further includes:
and parameterizing and storing the real-time state data through the target digital twin model.
According to one manner that the first aspect of the present application can be implemented, the method further includes:
acquiring target traffic accident data of the target traffic transportation equipment; the target traffic accident data comprise operation control data before a target traffic accident and operation state data after the target traffic accident;
and carrying out deduction analysis on the target traffic accident data based on the target running state prediction model to obtain deduction analysis data about the target traffic accident process.
A second aspect of the present application provides a traffic transportation equipment operation prediction apparatus based on digital twinning, comprising:
the first acquisition module is used for acquiring topological structure data and basic environment data of the target transportation equipment;
the construction module is used for constructing a corresponding digital twin device model according to the topological structure data, constructing a corresponding digital twin environment model according to the basic environment data, and correlating the digital twin device model and the digital twin environment model to obtain a digital twin model;
the second acquisition module is used for acquiring operation data generated in the historical operation process of the target transportation equipment;
the importing module is used for importing the operation data into the digital twin model to obtain a target digital twin model matched with the target transportation equipment;
the simulation module is used for performing simulation calculation on the target digital twin model to obtain simulation operation data generated in the operation process of the target digital twin model;
the training module is used for training the target digital twin model according to the operation data and the simulation operation data to obtain a target operation state prediction model;
the updating module is used for updating the real-time state of the target digital twin model and acquiring corresponding real-time state data;
and the prediction calculation module is used for inputting the real-time state data into the target running state prediction model to perform prediction calculation so as to obtain the running state prediction result of the target traffic transportation equipment.
According to one manner in which the second aspect of the present application can be implemented, the training module includes:
the comparison unit is used for comparing the operation data with the simulation operation data to obtain error data;
the correction unit is used for correcting the target digital twin model according to the error data;
a construction unit for constructing a sample set based on the operation data and the simulation operation data;
and the training unit is used for training the corrected target digital twin model according to the sample set.
According to one implementation manner of the second aspect of the present application, the running state prediction result includes a fault diagnosis result, the target digital twin model generates a plurality of functional chain groups in a training process and records the functional chain groups to which each part belongs, and the functional chain groups are composed of a plurality of parts for realizing specific functions; the prediction calculation module includes:
the fault judging unit is used for judging faults according to the output response of the functional chain group;
and the fault positioning unit is used for performing fault positioning by combining the part crossing of the functional chain group.
According to one manner in which the second aspect of the present application can be implemented, the operational data includes environmental history data, equipment operational data, equipment component data, equipment maintenance data, and cost data;
the operation state prediction results also include health assessment results, life prediction results, and cost calculation results.
According to one manner in which the second aspect of the application can be implemented, the apparatus further comprises:
the determining module is used for determining a target operation strategy according to the operation state prediction result;
and the control module is used for controlling the running state of the target transportation equipment according to the target running strategy.
According to one manner in which the second aspect of the application can be implemented, the apparatus further comprises:
and the storage module is used for carrying out parameterization storage on the real-time state data through the target digital twin model.
According to one manner in which the second aspect of the application can be implemented, the apparatus further comprises:
the third acquisition module is used for acquiring the target traffic accident data of the target traffic transportation equipment; the target traffic accident data comprise operation control data before a target traffic accident and operation state data after the target traffic accident;
and the deduction analysis module is used for carrying out deduction analysis on the target traffic accident data based on the target running state prediction model to obtain deduction analysis data about the target traffic accident process.
A third aspect of the present application provides a traffic transportation equipment operation prediction apparatus based on digital twinning, comprising:
a memory for storing instructions; the instructions are used for realizing the traffic transportation equipment operation prediction method based on digital twinning in the mode that any one of the above can be realized;
and the processor is used for executing the instructions in the memory.
A fourth aspect of the present application is a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method for predicting operation of a transportation device based on digital twinning as can be implemented by any one of the above.
From the above technical scheme, the application has the following advantages:
the method comprises the steps of constructing a digital twin model based on topological structure data and basic environment data of target transportation equipment, importing operation data generated in a historical operation process of the target transportation equipment into the digital twin model, performing simulation calculation on the obtained target digital twin model, training the target digital twin model according to the obtained simulation operation data and the operation data to obtain a target operation state prediction model, inputting real-time state data of the target digital twin model into the target operation state prediction model for prediction calculation, and finally obtaining an operation state prediction result; according to the application, the digital twin model is constructed, so that the digital description of the physical entity of the transportation equipment can be effectively, comprehensively and accurately realized; the simulation calculation is carried out on the obtained target digital twin model, and multi-azimuth influence factors can be comprehensively considered, so that the simulation calculation is more fit and practical; by importing historical data into the digital twin model and carrying out model training on the digital twin model, an effective model can be constructed by effectively utilizing the simulation data and the historical data, and effective matching combination of virtual and real objects is ensured; the method comprises the steps of carrying out real-time state update on the digital twin model, carrying out prediction calculation according to the state information of the digital model, enabling the digital twin model to be always in a learning and advancing state, and carrying out prediction calculation according to real-time state data, so that intelligent prediction on the running state of the transportation equipment is realized; the running state prediction result can be used for aspects such as maintenance of transportation equipment, accident analysis and the like, and an objective and effective data basis is provided for realizing the reliability and safety of the running of the transportation equipment.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the application, and that other drawings can be obtained from these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a flow chart of a method for predicting operation of a digital twin-based transportation device in accordance with an alternative embodiment of the present application;
fig. 2 is a block diagram of structural connection of a traffic transportation equipment operation prediction device based on digital twinning according to an alternative embodiment of the present application.
Reference numerals:
1-a first acquisition module; 2-building a module; 3-a second acquisition module; 4-an import module; 5-a simulation module; 6-training module; 7-updating the module; 8-a predictive computation module.
Detailed Description
The embodiment of the application provides a traffic transportation equipment operation prediction method and device based on digital twinning, which are used for solving the technical problem that the operation state of the traffic transportation equipment is difficult to predict in the prior art.
In order to make the objects, features and advantages of the present application more comprehensible, the technical solutions in the embodiments of the present application are described in detail below with reference to the accompanying drawings, and it is apparent that the embodiments described below are only some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The application provides a traffic transportation equipment operation prediction method based on digital twinning.
Referring to fig. 1, fig. 1 shows a flowchart of a traffic transportation equipment operation prediction method based on digital twinning according to an embodiment of the present application.
The embodiment of the application provides a traffic transportation equipment operation prediction method based on digital twinning, which comprises the following steps:
and step S1, obtaining topological structure data and basic environment data of the target transportation equipment.
The target transportation device in the present embodiment is a transportation device that needs to make an operation state prediction, and the type of the transportation device may be a vehicle, a ship, an airplane, or the like.
The basic environment data may include road condition data, climate data, personnel data, and cargo data, among others.
The topology data and the base environment data may be acquired based on an information acquisition component. The information acquisition component can comprise a GPS data sensor, an acceleration sensor, a gyroscope sensor, a gesture sensor, a laser ranging sensor, an environment sensor and a camera. The GPS data sensor is mainly used for collecting positioning information of the running equipment so as to determine the azimuth and the speed of the traffic equipment. The acceleration sensor is used for acquiring the running acceleration of the transportation equipment. The gyroscope sensor is used for sensing the change of acceleration of the transportation equipment and the gesture of the transportation equipment so as to further acquire the overall state of the transportation equipment. The attitude sensor can acquire data such as three-dimensional attitude and azimuth of the transportation equipment, and can accurately acquire the state of the transportation equipment in the three-dimensional space. A laser ranging sensor may be used to determine the target distance. The environment sensor is used for collecting the temperature, humidity, wind direction and the like of the environment where the transportation equipment is located. The camera can be used for acquiring image data of the transportation equipment in the running process.
And S2, constructing a corresponding digital twin device model according to the topological structure data, constructing a corresponding digital twin environment model according to the basic environment data, and correlating the digital twin device model and the digital twin environment model to obtain a digital twin model.
When the digital twin equipment model is built, firstly, a geometric model is built, and the assembly connection relation of all parts in the geometric model is consistent with the physical space. For convenience of description, the physical space herein refers to the real space, and the virtual space refers to the space in which the digital twin model is located. Sometimes, in order to simplify the digital twin equipment model, a certain limit is set for the digital twin equipment model in a virtual space, so that the digital twin equipment model meets a certain production operation function, and an integrated digital twin equipment model with topological characteristics is formed. According to the established geometric model, physical attributes, such as the process requirements of some parts, constraints on connecting parts, material attribute information and the like, are added to the digital twin equipment model in the virtual space, so that the digital twin equipment model is more in line with equipment in the physical space.
The digital twin environment model is constructed by considering the interrelation of the device and the environment in the physical space. The normal operation of the device has a great connection with the environment, and the simulated operation of the device in the virtual space is fully presented and cannot leave the environment. The basic environment data mainly comprise data such as personnel conditions, climate conditions, environment temperature and humidity and the like. For traffic and transportation equipment, the data of road conditions, transportation roads and surrounding environments also need to be considered when basic environment data are acquired.
In this embodiment, the digital twin device model and the digital twin environment model are correlated to obtain a digital twin model, so that the digital twin model can be completely mapped to a physical space, and thus, simulation calculation can be conveniently and comprehensively performed in subsequent science, and an accurate operation prediction model is further constructed.
And step S3, acquiring operation data generated in the historical operation process of the target transportation equipment.
As a specific embodiment, the operation data includes environmental history data, equipment operation data, equipment component data, and equipment maintenance data for comprehensively considering physical reality.
Where environmental history data refers to environmental data during operation of the device, the data being capable of characterizing changes in the environment. Excluding the influence of climate change, the influence of the device operation on the environment can be obtained based on the analysis of the environment data.
The device operation data is reaction data of various actions of the device in the operation process, and also is coordination data of parts during the actions, including responses aiming at specific inputs.
The data of the parts of the equipment generally comprise the load bearing capacity, the service life parameter, the connection strength torque and the like of the parts, and can fully reflect the physical characteristics of the parts.
The equipment repair data includes repair replacement information for specific parts, repair cycles, and repair means and costs for specific different faults. This data can be used to estimate the cost of repair while making intelligent operational predictions later.
And S4, importing the operation data into the digital twin model to obtain a target digital twin model matched with the target transportation equipment.
In the embodiment, by importing the operation data into the digital twin model, the operation data can be converted and matched from an ideal model to an actual physical object, and the accuracy and reliability of prediction calculation are improved.
And S5, performing simulation calculation on the target digital twin model to obtain simulation operation data generated in the operation process of the target digital twin model.
The content of the simulation calculation comprises load calculation, heating and heating calculation, structural stability calculation and the like which comprehensively consider parts and components, and can fully accord with the physical space. The manner of simulation calculation may be finite element analysis, aerodynamic calculation, etc. The simulation operation data are mainly used for intelligent prediction judgment in the future.
The degree of coincidence between the digital twin model and the physical space can be judged through simulation calculation, some general simulation calculation can be generally performed at the beginning, the calculation result is compared with the result of the physical space, and the digital twin model is adjusted according to the comparison result, so that the result consistent with the physical space is obtained. The calibration module can be established to calibrate the digital twin model, so that the properties and behaviors in the digital twin model are more close to physical objects in physical space.
In this embodiment, the simulation operation data is obtained by performing simulation calculation on the target digital twin model, so that the time cost of data acquisition can be reduced compared with the mode of obtaining the data based on the actual physical space experiment.
And step S6, training the target digital twin model according to the operation data and the simulation operation data to obtain a target operation state prediction model.
In one implementation, the training the target digital twin model according to the operational data and the simulated operational data includes:
comparing the operation data with the simulation operation data to obtain error data;
correcting the target digital twin model according to the error data;
constructing a sample set based on the operational data and the simulated operational data;
and training the corrected target digital twin model according to the sample set.
In the embodiment, the target digital twin model is corrected in the model training process, so that the digital twin model can be ensured to be consistent with the functional characteristics of equipment and environment in the physical space, and the training accuracy is improved.
In model training, an intelligent prediction model which is efficient and applicable can be obtained based on a method of combining a judgment model and a generation model. Here, evolution calculation is performed on the basis of the operation data and the simulation data on the basis of a proper model to obtain proper model parameters. The judgment model can be one or a combination of a plurality of linear regression, logarithmic regression, linear discriminant analysis, support vector machine, boosting, conditional random field, neural network and the like. The generative model may be a collection of one or more models, such as a gaussian mixture model, a hidden markov model, a naive bayes model, and the like.
The generated target running state prediction model can give out the prediction of the running state of the equipment according to the input state, and can usually give out health evaluation results, fault diagnosis results and life prediction results, so that related personnel can be helped to find problems in advance and take measures to cope with the problems, and loss and casualties are reduced or reduced.
It should be noted that, the process of generating the prediction model based on the method of combining the judgment model and the generation model may refer to a corresponding process in the prior art, and in this embodiment, the method is not limited thereto.
And S7, updating the real-time state of the target digital twin model to acquire corresponding real-time state data.
The real-time state generally includes a device state, an environment state, and an input-output state, among others. The device state and the environment state belong to states evolving over time; and the input-output state refers to a response state, i.e., a response to a specific input. The updating of the device state and the environmental state can cause the digital twin model to change with the physical space object. And the input-output state includes the output of the device at a particular input and the output of the environment.
And S8, inputting the real-time state data into the target running state prediction model to perform prediction calculation, and obtaining a running state prediction result of the target transportation equipment.
In one implementation manner, the running state prediction result includes a fault diagnosis result, the target digital twin model generates a plurality of functional chain groups in the training process and records the functional chain groups to which each part belongs, and the functional chain groups are composed of a plurality of parts for realizing specific functions; the step of inputting the real-time state data into the target running state prediction model for prediction calculation comprises the following steps:
judging faults according to the output response of the functional chain group;
and (5) performing fault location by combining the part crossing of the functional chain group.
Wherein, the function chain group is the combination of parts for realizing a certain function, and the cross connection of the parts exists in the function chain group. For example, assume that the first function chain set and the second function chain set are composed of several parts, which have the intersections of the target parts. The operation condition of the target part can be determined through the input and output of the first functional chain group and the second functional chain group, so that the rapid determination and positioning of faults are realized.
Taking the target transportation equipment as an automobile as an example, the functional chain group for realizing the buffer and stable operation of the automobile consists of parts such as a tire, a rim, a spring, a shock absorber, a stabilizer, a lower arm and the like, and the functional component for realizing the steering of the automobile consists of the tire, the rim, the lower arm, a steering gear, a steering wheel and the like, and the parts are crossed. The automobile steering instability (output) can be found through rotating the steering wheel (input), and the automobile can be positioned on the crossed part through bumpy road (input) and bumpy road (output), so that the rapid positioning of the fault part is realized.
In one implementation, the operational data further includes cost data; the operation state prediction result further includes a cost calculation result.
The cost data may include cost data information such as operation cost, part cost, and labor cost.
In one implementation, the method further comprises:
determining a target operation strategy according to the operation state prediction result;
and controlling the running state of the target transportation equipment according to the target running strategy.
As a specific embodiment, a list of the correspondence between the operation state prediction result and the target operation policy may be constructed, and the target operation policy may be determined based on the list.
As another specific implementation manner, the operation state prediction result may be fed back to a preset user terminal, and the fed back operation policy is taken as the target operation policy.
When determining the target operation strategy, the protection priority can be set, and the limiting condition determined by the operation strategy is constructed according to the set protection priority. For example, the personal safety of the staff may be set as the first factor, so that the safety of the staff is protected as a constraint when determining the target operation policy.
Corresponding limiting conditions can also be set according to the limitation of the equipment or the environment, and the best measures which can be taken by the target are determined as target operation strategies aiming at the limiting conditions. If the brake is found to be damaged during the running of the equipment, the running input is limited at the moment, and the equipment can be processed in a way of realizing forced stopping by utilizing the friction and collision between rigid components such as a bumper, a carriage and the like of the automobile and natural obstacles on the roadside.
In this embodiment, a target operation policy is obtained according to an operation state prediction result, so that an operation state of the target transportation device is controlled, operators can be effectively reduced, mechanized and intelligent operation management is realized, and intelligent transportation can be realized when unmanned is matched.
In one implementation, the method further comprises:
and parameterizing and storing the real-time state data through the target digital twin model.
The parameterized storage can effectively save the storage space, and the digital twin model can be efficiently utilized to intuitively describe the physical entity of the transportation equipment by leading the parameters into the digital twin model.
With respect to the target operating state prediction model that has been established, in the case of input (operating control) and output (operating state) determinations, some possible processes may be determined. The accident process may be calculated based on the device status and the environmental status at the instant immediately before the car accident. Based on this, in one implementation, the method further comprises:
acquiring target traffic accident data of the target traffic transportation equipment; the target traffic accident data comprise operation control data before a target traffic accident and operation state data after the target traffic accident;
and carrying out deduction analysis on the target traffic accident data based on the target running state prediction model to obtain deduction analysis data about the target traffic accident process.
Further, the data acquired during the execution of the method may be received and saved using an existing information recording system. As a specific embodiment, the information recording system is a black box.
In traffic, particularly in the case of air traffic, there are significant injuries and losses in case of traffic accidents. When a traffic accident occurs, the information of the traffic accident is generally obtained by analyzing the data stored in the black box in the prior art, and the mode is relatively one-sided, so that the specific reason of the traffic accident is difficult to find. However, in the application, the final state of the traffic accident and the data in the black box can be imported into the target running state prediction model by storing the state information when the traffic accident occurs, and the traffic accident process is calculated by using the target running state prediction model, so that the accident process is reproduced in a concrete image, and the concrete reason thereof is obtained quickly and accurately.
According to the embodiment of the application, the digital twin model is constructed, so that the digital description of the physical entity of the transportation equipment can be effectively, comprehensively and accurately realized; the simulation calculation is carried out on the obtained target digital twin model, and multi-azimuth influence factors can be comprehensively considered, so that the simulation calculation is more fit and practical; by importing historical data into the digital twin model and carrying out model training on the digital twin model, an effective model can be constructed by effectively utilizing the simulation data and the historical data, and effective matching combination of virtual and real objects is ensured; the method comprises the steps of carrying out real-time state update on the digital twin model, carrying out prediction calculation according to the state information of the digital model, enabling the digital twin model to be always in a learning and advancing state, and carrying out prediction calculation according to real-time state data, so that intelligent prediction on the running state of the transportation equipment is realized; the running state prediction result can be used for aspects such as maintenance of transportation equipment, accident analysis and the like, and an objective and effective data basis is provided for realizing the reliability and safety of the running of the transportation equipment.
The application also provides a traffic and transportation equipment operation prediction device based on digital twinning, which can be used for executing the traffic and transportation equipment operation prediction method based on digital twinning according to any one of the embodiments of the application.
Referring to fig. 2, fig. 2 shows a block diagram of structural connection of a traffic transportation equipment operation prediction device based on digital twinning according to an embodiment of the present application.
The embodiment of the application provides a traffic transportation equipment operation prediction device based on digital twinning, which comprises:
the first acquisition module 1 is used for acquiring topological structure data and basic environment data of the target transportation equipment;
the construction module 2 is used for constructing a corresponding digital twin device model according to the topological structure data, constructing a corresponding digital twin environment model according to the basic environment data, and correlating the digital twin device model and the digital twin environment model to obtain a digital twin model;
a second obtaining module 3, configured to obtain operation data generated in a historical operation process of the target transportation device;
the importing module 4 is used for importing the operation data into the digital twin model to obtain a target digital twin model matched with the target transportation equipment;
the simulation module 5 is used for performing simulation calculation on the target digital twin model to obtain simulation operation data generated in the operation process of the target digital twin model;
the training module 6 is used for training the target digital twin model according to the operation data and the simulation operation data to obtain a target operation state prediction model;
the updating module 7 is used for updating the real-time state of the target digital twin model and acquiring corresponding real-time state data;
and the prediction calculation module 8 is used for inputting the real-time state data into the target running state prediction model to perform prediction calculation so as to obtain the running state prediction result of the target traffic and transportation equipment.
In one possible implementation, the training module 6 comprises:
the comparison unit is used for comparing the operation data with the simulation operation data to obtain error data;
the correction unit is used for correcting the target digital twin model according to the error data;
a construction unit for constructing a sample set based on the operation data and the simulation operation data;
and the training unit is used for training the corrected target digital twin model according to the sample set.
In one implementation manner, the running state prediction result includes a fault diagnosis result, the target digital twin model generates a plurality of functional chain groups in the training process and records the functional chain groups to which each part belongs, and the functional chain groups are composed of a plurality of parts for realizing specific functions; the prediction calculation module 8 includes:
the fault judging unit is used for judging faults according to the output response of the functional chain group;
and the fault positioning unit is used for performing fault positioning by combining the part crossing of the functional chain group.
In one implementation, the operational data includes environmental history data, equipment operational data, equipment parts data, equipment maintenance data, and cost data;
the operation state prediction results also include health assessment results, life prediction results, and cost calculation results.
In one implementation, the apparatus further comprises:
the determining module is used for determining a target operation strategy according to the operation state prediction result;
and the control module is used for controlling the running state of the target transportation equipment according to the target running strategy.
In one implementation, the apparatus further comprises:
and the storage module is used for carrying out parameterization storage on the real-time state data through the target digital twin model.
In one implementation, the apparatus further comprises:
the third acquisition module is used for acquiring the target traffic accident data of the target traffic transportation equipment; the target traffic accident data comprise operation control data before a target traffic accident and operation state data after the target traffic accident;
and the deduction analysis module is used for carrying out deduction analysis on the target traffic accident data based on the target running state prediction model to obtain deduction analysis data about the target traffic accident process.
The application also provides a traffic transportation equipment operation prediction device based on digital twinning, which comprises:
a memory for storing instructions; the instructions are used for implementing the traffic transportation equipment operation prediction method based on digital twinning according to any one of the embodiments;
and the processor is used for executing the instructions in the memory.
The application also provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program realizes the traffic transportation equipment operation prediction method based on digital twin according to any one of the embodiments when being executed by a processor.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described apparatus and module may refer to corresponding procedures in the foregoing method embodiments, and specific beneficial effects of the above-described apparatus and module may refer to corresponding beneficial effects in the foregoing method embodiments, which are not described herein again.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or components may be combined or integrated into another apparatus, or some features may be omitted or not performed.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in software functional modules.
The integrated modules, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (9)
1. A digital twinning-based traffic transportation equipment operation prediction method, comprising:
obtaining topological structure data and basic environment data of target transportation equipment; the basic environment data comprise road condition data, climate data, personnel data and goods data;
constructing a corresponding digital twin device model according to the topological structure data, constructing a corresponding digital twin environment model according to the basic environment data, and correlating the digital twin device model and the digital twin environment model to obtain a digital twin model;
acquiring operation data generated in a historical operation process of the target transportation equipment;
importing the operation data into the digital twin model to obtain a target digital twin model matched with the target transportation equipment;
performing simulation calculation on the target digital twin model to obtain simulation operation data generated in the operation process of the target digital twin model;
training the target digital twin model according to the operation data and the simulation operation data to obtain a target operation state prediction model;
carrying out real-time state update on the target digital twin model to obtain corresponding real-time state data;
inputting the real-time state data into the target running state prediction model for prediction calculation to obtain a running state prediction result of the target traffic transportation equipment;
the running state prediction result comprises a fault diagnosis result, the target digital twin model generates a plurality of functional chain groups in the training process and records the functional chain groups of the parts, and the functional chain groups are composed of a plurality of parts for realizing specific functions; the step of inputting the real-time state data into the target running state prediction model for prediction calculation comprises the following steps:
judging faults according to the output response of the functional chain group;
and (5) performing fault location by combining the part crossing of the functional chain group.
2. The digital twinning-based transportation device operation prediction method of claim 1, wherein the training the target digital twinning model from the operation data and the simulated operation data comprises:
comparing the operation data with the simulation operation data to obtain error data;
correcting the target digital twin model according to the error data;
constructing a sample set based on the operational data and the simulated operational data;
and training the corrected target digital twin model according to the sample set.
3. The digital twinning-based transportation device operation prediction method of claim 1, wherein the operation data comprises environmental history data, device operation data, device part data, device maintenance data, and cost data;
the operation state prediction results also include health assessment results, life prediction results, and cost calculation results.
4. The digital twinning-based transportation device operation prediction method of claim 1, further comprising:
determining a target operation strategy according to the operation state prediction result;
and controlling the running state of the target transportation equipment according to the target running strategy.
5. The digital twinning-based transportation device operation prediction method of claim 1, further comprising:
and parameterizing and storing the real-time state data through the target digital twin model.
6. The digital twinning-based transportation device operation prediction method of claim 1, further comprising:
acquiring target traffic accident data of the target traffic transportation equipment; the target traffic accident data comprise operation control data before a target traffic accident and operation state data after the target traffic accident;
and carrying out deduction analysis on the target traffic accident data based on the target running state prediction model to obtain deduction analysis data about the target traffic accident process.
7. A digital twinning-based traffic transportation equipment operation prediction device, comprising:
the first acquisition module is used for acquiring topological structure data and basic environment data of the target transportation equipment; the basic environment data comprise road condition data, climate data, personnel data and goods data;
the construction module is used for constructing a corresponding digital twin device model according to the topological structure data, constructing a corresponding digital twin environment model according to the basic environment data, and correlating the digital twin device model and the digital twin environment model to obtain a digital twin model;
the second acquisition module is used for acquiring operation data generated in the historical operation process of the target transportation equipment;
the importing module is used for importing the operation data into the digital twin model to obtain a target digital twin model matched with the target transportation equipment;
the simulation module is used for performing simulation calculation on the target digital twin model to obtain simulation operation data generated in the operation process of the target digital twin model;
the training module is used for training the target digital twin model according to the operation data and the simulation operation data to obtain a target operation state prediction model;
the updating module is used for updating the real-time state of the target digital twin model and acquiring corresponding real-time state data;
the prediction calculation module is used for inputting the real-time state data into the target running state prediction model to perform prediction calculation so as to obtain a running state prediction result of the target traffic transportation equipment;
the running state prediction result comprises a fault diagnosis result, the target digital twin model generates a plurality of functional chain groups in the training process and records the functional chain groups of the parts, and the functional chain groups are composed of a plurality of parts for realizing specific functions; the prediction calculation module includes:
the fault judging unit is used for judging faults according to the output response of the functional chain group;
and the fault positioning unit is used for performing fault positioning by combining the part crossing of the functional chain group.
8. A digital twinning-based traffic transportation equipment operation prediction device, comprising:
a memory for storing instructions; wherein the instructions are for implementing a digital twinning-based transportation device operation prediction method as claimed in any one of claims 1 to 6;
and the processor is used for executing the instructions in the memory.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the digital twinning based transportation device operation prediction method according to any of claims 1-6.
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| CN117252099B (en) * | 2023-09-21 | 2024-06-18 | 中山大学 | Damage monitoring method, system, equipment and medium based on digital twinning |
| CN117111997B (en) * | 2023-10-08 | 2024-03-22 | 西安大合智能科技有限公司 | FPGA configuration file remote upgrading and updating method |
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