Method and device for correcting digital twin model and computer program product

Abstract

The invention provides a digital twin model correction method and device and a computer program product. The method comprises the steps that an initial digital twinborn model is obtained, and the initial digital twinborn model is a virtual model of a fully mechanized coal mining face constructed based on the digital twinborn technology; a correction coefficient is obtained, and the correction coefficient is obtained according to the change of the coal mine fully mechanized coal mining face after the initial digital twinborn model completes the preset instruction; and correcting the initial digital twinborn model according to the correction coefficient to obtain a target digital twinborn model. According to the scheme, the problem that in the prior art, the state after the twin model responds to the completion action instruction is deviated from the state of the real coal mining environment is solved.

Description

Technical Field

[0001] The present application relates to the field of digital twin technology, and more specifically, to a method and device for correcting a digital twin model, a computer-readable storage medium, and a computer program product. Background Art

[0002] The application and development of digital twin technology in the coal mining industry is still in the stage of innovative development. Virtual visualization and remote monitoring of fully mechanized mining face equipment is the main research result at present. The continuous movement of the twin model is completed in response to action instructions based on the set motion parameters (speed, acceleration, friction). Due to the errors between parameter settings and actual conditions, network errors during instruction transmission, and the complex environment in which the actual physical equipment is located, the state of the twin model after responding to the action instructions will deviate from the state of the actual coal mining environment. Summary of the invention

[0003] The main purpose of this application is to provide a method, device, computer-readable storage medium and computer program product for correcting a digital twin model, so as to at least solve the problem in the prior art that there is a deviation between the state of the twin model after responding to the action instruction and the state of the actual coal mining environment.

[0004] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for correcting a digital twin model is provided, including: obtaining an initial digital twin model, wherein the initial digital twin model is a virtual model of a coal mine comprehensive mining working face constructed based on digital twin technology; obtaining a correction coefficient, wherein the correction coefficient is obtained according to changes in the coal mine comprehensive mining working face after the initial digital twin model completes preset instructions; and correcting the initial digital twin model according to the correction coefficient to obtain a target digital twin model.

[0005] Optionally, obtaining the correction coefficient includes: calculating a first correction coefficient according to a first formula, wherein the first correction coefficient is a coefficient for pre-correcting the data, and the first formula is:

[0006]

[0007] k p represents the first correction factor, S x1 Indicates the correction data value received after the action command stops, S x0 Indicates the last received correction data value, S d1 Indicates the data value reached in response to an action instruction.

[0008] Optionally, obtaining the correction coefficient includes: calculating a second correction coefficient according to a second formula, wherein the second correction coefficient is a coefficient for pre-correcting the virtual support in the initial digital twin model, and the virtual support corresponds to the actual support in the fully mechanized mining working face of the coal mine in one-to-one correspondence, and the second formula is:

[0009]

[0010] k n represents the second correction factor, L Md Indicates the actual distance in the corrected data, L DT Represents the distance calculated by the initial digital twin model.

[0011] Optionally, obtaining the correction coefficient includes: calculating a third correction coefficient according to a third formula, wherein the third correction coefficient is a coefficient for pre-correcting data of the n+1th knife corresponding to the virtual stent in the initial digital twin model, and the third formula is:

[0012]

[0013] represents the third correction coefficient, n represents the number of cutters corresponding to the virtual bracket, Indicates the coefficient calculated by the first correction data received within a knife. Indicates the coefficient for calculating the second correction data received within one cut.

[0014] Optionally, obtaining the correction coefficient includes: calculating a fourth correction coefficient according to a fourth formula, wherein the fourth correction coefficient is a coefficient for pre-correcting the virtual coal mining machine in the initial digital twin model, and the virtual coal mining machine corresponds to an actual coal mining machine in the fully mechanized mining face of the coal mine one by one, and the fourth formula is:

[0015]

[0016] k m represents the fourth correction coefficient, V Md Indicates the actual coal machine running speed calculated by the correction data, V DT represents the running speed of the coal machine calculated by the initial digital twin model, S Md Indicates the position in meters of the correction data, S DT represents the position of the initial digital twin model in meters, and t represents the time interval between the current revised data and the last revised data.

[0017] Optionally, obtaining the correction coefficient includes: calculating a fifth correction coefficient according to a fifth formula, wherein the fifth correction coefficient is a coefficient for pre-correcting data of the n+1th cutter corresponding to the virtual coal mining machine in the initial digital twin model, and the fifth formula is:

[0018]

[0019] represents the fifth correction coefficient, and m represents the number of cutters corresponding to the virtual coal mining machine.

[0020] Optionally, the initial digital twin model is corrected according to the correction coefficient to obtain the target digital twin model, including: obtaining a preset interval duration; when the running time of the initial digital twin model reaches the preset interval duration, the initial digital twin model is corrected according to the correction coefficient to obtain the target digital twin model.

[0021] According to another aspect of the present application, a correction device for a digital twin model is provided, including: a first acquisition unit, used to acquire an initial digital twin model, wherein the initial digital twin model is a virtual model of a coal mine comprehensive mining working face constructed based on digital twin technology; a second acquisition unit, used to acquire a correction coefficient, wherein the correction coefficient is obtained according to changes in the coal mine comprehensive mining working face after the initial digital twin model completes a preset instruction; a correction unit, used to correct the initial digital twin model according to the correction coefficient to obtain a target digital twin model.

[0022] According to another aspect of the present application, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps of any one of the methods for correcting the digital twin model.

[0023] According to another aspect of the present application, a digital twin system is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a correction method for executing any one of the digital twin models.

[0024] By applying the technical solution of the present application and using correction coefficients to perform real-time corrections on each model, the calculation results of the twin model are unified with the real-world data, which can realize the calibration of the twin model and thereby reduce the deviation between the state of the twin model after responding to the action instruction and the state of the actual coal mining environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0026] Figure 1 A hardware structure block diagram of a mobile terminal for executing a correction method for a digital twin model provided in an embodiment of the present application is shown;

[0027] Figure 2 A schematic diagram of a process for correcting a digital twin model provided according to an embodiment of the present application is shown;

[0028] Figure 3 The schematic diagram of the structure of the digital twin model self-correction system of this scheme is shown;

[0029] Figure 4 A schematic diagram of the process of correcting data is shown;

[0030] Figure 5 A schematic diagram showing the sequential relationship between execution correction and action instructions;

[0031] Figure 6 A schematic diagram showing the type of data received in this solution;

[0032] Figure 7 A structural block diagram of a digital twin model correction device provided according to an embodiment of the present application is shown.

[0033] The above drawings include the following reference numerals:

[0034] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0035] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0036] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0037] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0038] As introduced in the background technology, in the prior art, the state of the twin model after responding to the action instruction will deviate from the state of the actual coal mining environment. To solve the above problem, the embodiments of the present application provide a correction method, device, computer-readable storage medium and computer program product for a digital twin model.

[0039] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0040] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal of a method for correcting a digital twin model according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0041] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the display method of device information in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0042] In this embodiment, a method for correcting a digital twin model running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0043] Figure 2 is a flow chart of a method for correcting a digital twin model according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0044] Step S201, obtaining an initial digital twin model, wherein the initial digital twin model is a virtual model of a fully mechanized coal mine working face constructed based on digital twin technology;

[0045] Specifically, in coal mines, the initial digital twin model can be used to simulate the fully mechanized mining face of the entire coal mine production system, including the coal mining equipment inside the mine, the mine ventilation system, the mine drainage system and other aspects.

[0046] Step S202, obtaining a correction coefficient, wherein the correction coefficient is obtained according to changes in the fully mechanized mining face of the coal mine after the initial digital twin model completes the preset instructions;

[0047] Specifically, since the current digital twin model lacks the functions of fast and accurate self-calibration and self-optimization, a complete set of self-learning and corrected transparent digital twin mining scenarios has not yet been established. The existing system twin model adopts the method of direct input drive of equipment sensors, and the monitoring means of "simulating the real with the virtual and reflecting the real with the virtual" to realize the offline simulation, one-way mapping and dynamic visualization of the comprehensive mining face; or adopts the establishment of ground survey model, equipment model, construction of mining environment twin scene, generates adaptive mining template (roof and floor cutting curve, scraper straight line, etc.) to plan the mining path, and uses convolutional neural network deep learning algorithm and Pareto multi-objective optimization algorithm to mine the equipment data, ground survey data and historical data in the mining scene, train and optimize the twin model, and realize the self-learning and self-optimization of the digital twin. The former method lacks the training and optimization function of the twin model, is only a one-way data drive, lacks the ability of self-prediction and correction, but can display the operation status of the comprehensive mining face equipment in real time and accurately. The latter method has high algorithm difficulty, and needs to rely on multi-source data, and has slow results. There is inconsistency with the real physical world state during the training and optimization process.

[0048] The mechanism model in the current digital twin model is a theoretical model. The calculations in the simulation process are all based on the theoretical model, while the corresponding actual physical equipment and facilities are affected by many actual factors. The various state values ​​of the comprehensive mining face in the actual coal cutting process must be different from the theoretical calculation values. If the twin mechanism model cannot quickly respond to the difference with the actual value, it will inevitably lead to the difference between the two. Due to the accumulation of errors, the twin mechanism model will eventually deviate from the actual model, and the application value of the twin will be seriously reduced. If the method of strong correction of actual data is adopted, although the twin model can be guaranteed to be consistent with the real physical model, the twin lacks the functions of autonomous learning and autonomous calibration, resulting in the twin evolving into a digital virtual object driven by data, which does not conform to the concept of twins. At the same time, strong correction may cause the virtual model to jump or teleport, and other situations that do not conform to the real physical reality.

[0049] Specifically, the specific indicators or safety targets of the coal mine digital twin model that need to be corrected can be determined, such as equipment failure prediction accuracy, mine accident risk assessment, etc. According to the correction target, the correction strategy and method of the coal mine digital twin model can be formulated, including data processing, parameter adjustment, algorithm optimization, etc. The coal mine digital twin model can be corrected according to the correction strategy, and the parameters and structure can be adjusted, and the algorithm can be optimized. The corrected coal mine digital twin model can be evaluated and tested, and the safety indicators and model performance before and after the correction can be compared to determine the correction effect. The correction coefficient can be calculated based on the difference in safety indicators before and after the correction and the change in model performance to evaluate the effect of the correction and improve the safety and practicality of the coal mine digital twin model.

[0050] In addition, in the solution of the present application, the correction coefficient can be obtained according to the changes in the fully mechanized mining face of the coal mine, such as the distance of the support push slide, the speed of the coal mining machine, the distance of the scraper conveyor, etc.

[0051] Step S203: Correct the initial digital twin model according to the correction coefficient to obtain a target digital twin model.

[0052] Specifically, it is necessary to ensure the accuracy and completeness of the data input to the model, including coal mine equipment operation data, mine environment data, personnel safety data, etc. The accuracy of the model can be improved by adding sensors and improving data cleaning and processing methods. According to the actual situation and needs of the coal mine, adjust the parameters in the digital twin model, such as equipment parameters, mine condition parameters, etc., to optimize the performance and prediction accuracy of the model. The structure of the digital twin model can be adjusted to consider factors such as the correlation between coal mine equipment and the impact of environmental changes on mine safety to improve the performance and practicality of the model.

[0053] Through this embodiment, correction coefficients are used to perform real-time corrections on each model, so that the calculation results of the twin model are consistent with the real-world data, and the calibration of the twin model can be achieved, thereby reducing the deviation between the state of the twin model after responding to the action instruction and the state of the actual coal mining environment.

[0054] Specifically, Figure 3 As shown, the correction method of the digital twin model of the present solution can be applied to the platform server module of the digital twin model self-correction system, and the digital twin model self-correction system also includes a platform client module. The system can be divided into two software modules: the client and the server. The client module manages the twin model, and the server module receives sensor data and corrects the twin to accurately simulate the actual equipment operation status. At the same time, it has calibration and learning functions to achieve the purpose of unifying virtual and real. The display module is used to display data. The server module receives the real data and corrects it, and sends the report to the client module after correction.

[0055] The platform client module provides the system with model management and control management functions; the platform server module mainly provides twin simulation calculation functions, including data correction, command response, process simulation, etc. Through digital twin technology, the digital twin of the fully mechanized mining face is constructed, and the model framework of the fully mechanized mining face environment, equipment, process and other elements is established to realize the configurability and learnability of the digital twin of the fully mechanized mining face.

[0056] The platform client module mainly includes the following two functions:

[0057] Equipment management function: It provides users with the configuration function of the structural parameters, simulation models, etc. of various equipment such as coal mining machines, supports, scrapers, etc., and supports the configuration and storage of multiple models of the same type of equipment, which is convenient for flexible combination during working face configuration and simulation. Working face management is a configuration function that describes the working face environment based on the actual working face selection and combination of various equipment and coal seam information combination. The system supports the configuration of any number of working faces.

[0058] Control management function: Control management is based on the coal seam selected by the working face, three machine models and other information. After completing the initial configuration, it automatically carries out coal mining planning, simulates the coal mining process, outputs relevant operation data and three-dimensional display instructions, and forms relevant operation reports of the working face.

[0059] In the specific implementation process, obtaining the correction coefficient can be achieved through the following steps: calculating the first correction coefficient according to the first formula, wherein the first correction coefficient is a coefficient for pre-correcting the data, and the first formula is:

[0060]

[0061] k p represents the first correction coefficient mentioned above, S x1 Indicates the correction data value received after the action command stops, S x0 Indicates the last received correction data value, S d1 The above formula is only exemplary, and any variation of the formula falls within the protection scope of this application.

[0062] In this scheme, the data in the digital twin model can be corrected, and the first correction coefficient can be calculated by the first formula. The first correction coefficient is then used to correct the initial digital twin correction model. The obtained target digital twin correction model can ensure the consistency of the virtual and real phenomena, thereby further reducing the error of the data in the digital twin model.

[0063] Specifically, the twin scene is initialized using the initially sent correction data, including geological model cutting data, support posture correction data, coal mining machine posture correction data, scraper straightness data, etc., to obtain the working face equipment operating status consistent with the real physical world, and then enter the correction data loop.

[0064] Specifically, the process of correcting data is as follows Figure 4As shown, each cut is a cycle, in which the support posture correction data and the coal mining machine posture correction data are received in turn, the corresponding support twin model posture and coal mining machine twin model posture are corrected and their respective correction coefficients are calculated; after completing one-cut coal cutting operation, the scraper correction data is received to correct the position relationship of the three-machine controls of the entire working face, which will affect the scraper twin model, the support twin model, and the coal mining machine twin model; finally, the drum correction data (cutting template data) is received to verify whether there is direct interference between the drum correction data and the current three-machine status of the working face.

[0065] The above correction can be made at discrete time points, that is, it can be corrected not at fixed time intervals, but in accordance with the business logic of the coal cutting cycle. The continuous movement of the twin model is completed in response to the action command on the set motion parameters (speed, acceleration, friction). Due to the error between the parameter setting and the actual, the network error during the instruction transmission process, and the complex environment of the actual physical equipment, the state of the twin model after responding to the action command will deviate from the state of the correction data given by the sensor. In order to reduce the deviation, the correction coefficient is used to correct each model in real time to make the calculation results of the twin model consistent with the real-world data.

[0066] A section of correction data and action command response sequence on the time axis Figure 5 As shown, after being corrected by n-1 correction data, the position is Sx0, and then responds to the action command and moves to the position Sd1. After the action command stops, the position where the correction data is received is Sx1.

[0067] As the model continuously receives action instructions and correction data, it will continuously obtain correction coefficients. Considering the various defects in the actual data, the correction coefficients calculated each time are filtered. According to the data characteristics of the correction coefficients, the limited average filter method is used for processing. The limited average filter combines the characteristics of "limited filter" and "recursive average filter", which can overcome occasional pulse interference and eliminate the deviation caused by pulse interference.

[0068] Each time the correction coefficient is obtained, it is first limited and then sent to a first-in-first-out queue for persistent storage. The queue is recursively averaged and filtered to obtain the average value K of the correction coefficient. Then the output data of the model is set to the original calculated data multiplied by the average correction coefficient K to obtain new data. If the data is reliable, after multiple calculations, the average correction coefficient K will tend to be stable, that is, the deviation between the movement result after the model responds to the action command and the correction data will tend to decrease.

[0069] Specifically, Figure 6 As shown, the received data includes single coal mining machine data, single support data, comprehensive mining face business data and planning input data.

[0070] The business data of fully mechanized mining face includes coal seam data, pump station data, coal mining machine data, support data, scraper data, layout data, process data, and working face data. The planning input data includes the tool path data of historical planning.

[0071] In the specific implementation process, the correction coefficient can be obtained by the following steps: calculate the second correction coefficient according to the second formula, wherein the second correction coefficient is a coefficient for pre-correcting the virtual support in the initial digital twin model, and the virtual support corresponds to the actual support in the fully mechanized mining face of the coal mine. The second formula is:

[0072]

[0073] k n represents the second correction factor mentioned above, L Md Indicates the actual distance in the corrected data, L DT The above formula is only exemplary, and any variation of the formula falls within the protection scope of this application.

[0074] In this scheme, we can take one of the brackets as an example. When the bracket correction data is received for the nth time, the second correction parameter can be calculated by the second formula based on the push-slip distance of the digital twin model and the push-slip distance in the correction data. The second correction coefficient is then used to correct the initial digital twin correction model. The target digital twin correction model obtained can ensure the consistency between the virtual and real phenomena, thereby further reducing the error of the bracket in the digital twin model.

[0075] In the specific implementation process, obtaining the correction coefficient can be achieved by the following steps: calculating the third correction coefficient according to the third formula, wherein the third correction coefficient is a coefficient for pre-correcting the data of the n+1th knife corresponding to the virtual bracket in the initial digital twin model, and the third formula is:

[0076]

[0077] represents the third correction coefficient, n represents the number of cutters corresponding to the virtual bracket, Indicates the coefficient calculated by the first correction data received within a knife. The above formula is only exemplary, and any variation of the formula falls within the protection scope of this application.

[0078] In this scheme, in each cut of the coal cutting operation, each bracket will send correction data when the pushing and moving of the bracket is completed, so there will be two correction coefficients and in each cut. In the subsequent calculation process of the digital twin model, the correction coefficient of the correction data of the n+1th cut is filtered through recursive average filtering, and the average of the most recent 6 data in the correction coefficient queue is taken to obtain the third correction coefficient. The third correction coefficient is subsequently used to correct the initial digital twin correction model, and the target digital twin correction model obtained can ensure the consistency of the virtual and real phenomena, thereby further reducing the error of the bracket in the digital twin model.

[0079] Specifically, during the process of cutting coal with the n+1th knife, the movement speed of the support will be corrected according to the correction coefficient, that is, the formula is: V' DT represents the execution speed used by the initial digital twin model, V DT Represents the execution speed of the initial digital twin model calculation.

[0080] In the specific implementation process, obtaining the correction coefficient can be achieved through the following steps: calculating the fourth correction coefficient according to the fourth formula, wherein the fourth correction coefficient is a coefficient for pre-correcting the virtual coal mining machine in the initial digital twin model, and the virtual coal mining machine corresponds to the actual coal mining machine in the fully mechanized mining face of the coal mine. The fourth formula is:

[0081]

[0082] k m represents the fourth correction coefficient, V Md Indicates the actual coal machine running speed calculated by the correction data, V DT The above initial digital twin model calculates the simulated coal machine running speed, S Md Indicates the position in meters of the correction data, S DT represents the position of the initial digital twin model in meters, and t represents the time interval between the current correction data and the last correction data. The above formula is only exemplary, and any variation of the formula falls within the protection scope of this application.

[0083] In this scheme, the position of the coal mining machine can be used as an example. The coal mining machine is only calculated in the local coordinate system of the comprehensive mining face, and the speed is used as the correction object, and the left and right arm angles are the same. Assuming that when the nth knife receives the correction data of the coal mining machine, based on the position of the digital twin model and the position in the correction data, since the actual movement time of the coal mining machine is the same as the movement time in the digital twin model, the fourth correction coefficient can be calculated by the fourth formula. The fourth correction coefficient is subsequently used to correct the initial digital twin correction model, and the target digital twin correction model obtained can ensure the consistency of the virtual and real phenomena, thereby further reducing the error of the coal mining machine in the digital twin model.

[0084] In the specific implementation process, obtaining the correction coefficient can be achieved by the following steps: calculating the fifth correction coefficient according to the fifth formula, wherein the fifth correction coefficient is a coefficient for pre-correcting the data of the n+1th cutter corresponding to the virtual coal mining machine in the initial digital twin model, and the fifth formula is:

[0085]

[0086] represents the fifth correction coefficient, and m represents the number of cutters corresponding to the virtual coal mining machine. The above formula is only exemplary, and any variation of the formula falls within the protection scope of this application.

[0087] In this scheme, each time the coal mining machine receives correction data, it calculates the current correction coefficient, and sends it to the left and right correction coefficient queues of the coal mining machine after filtering. It also uses recursive average filtering to take the average of the last 6 data to obtain the fifth correction coefficient. The fifth correction coefficient is subsequently used to correct the initial digital twin correction model. The target digital twin correction model obtained can ensure the consistency between the virtual and real phenomena, thereby further reducing the error of the coal mining machine in the digital twin model.

[0088] Specifically, the parameter in the digital twin model of the coal mining machine, namely the maximum moving speed, is corrected, that is, the formula is: In the subsequent calculation process, the position value of the digital twin model of the coal mining machine is used for calculation.

[0089] In addition, in addition to correcting the digital twin model data, supports and coal mining machines, this solution can also correct other equipment in the coal mine, such as ventilation equipment, transportation equipment, lifting equipment, etc.

[0090] The digital twin model can be used to analyze the operating status of equipment, predict the life of equipment, and perform maintenance and replacement in advance to avoid production interruptions due to equipment failure.

[0091] In some embodiments, the initial digital twin model is corrected according to the correction coefficient to obtain the target digital twin model, which can be specifically achieved through the following steps: obtaining a preset interval duration; when the running time of the initial digital twin model reaches the preset interval duration, correcting the initial digital twin model according to the correction coefficient to obtain the target digital twin model.

[0092] In this solution, data and environment may change over time, causing the model to become outdated or invalid. Therefore, each time the running time of the initial digital twin model reaches the preset interval, the initial digital twin model can be corrected. This way, the parameters and structure of the model can be updated in time, further improving the practicality of the digital twin model.

[0093] In summary, the main process of this solution is: it is necessary to collect relevant data in the coal mine production process, including coal mine equipment operation data, production efficiency data, safety data, etc. Clean the collected data, including data deduplication, data filling, outlier processing, etc., to ensure data quality. Preprocess the cleaned data, including feature selection, feature transformation, etc., to facilitate subsequent modeling and analysis. Use the original data to establish a coal mine digital twin model, and model the coal mine production process through data analysis, model building and other steps. Evaluate the established original digital twin model to test the accuracy and reliability of the model. According to the evaluation results and actual conditions, modify the digital twin model, add new features, adjust model parameters, etc. Verify the modified digital twin model to verify the effect of the model in actual production. Apply the modified digital twin model to the coal mine production process to monitor and optimize the production process and improve production efficiency and safety.

[0094] In summary, this solution uses a combination of real data and digital twin model calculation data for self-calibration, which is not only close to the real physical world, but also meets the twin calculation logic, and can quickly realize the twin model calibration to ensure the consistency of virtual and real phenomena. The ratio of real data to twin data is calculated as the parameter coefficient of the digital twin model. The operation logic is simple and easy to understand, and easy to implement. Using sensor data in the real world for correction calculations is more accurate than the results obtained by existing solutions, and has a better role in guiding production. It is widely adapted to multiple types of working surfaces, and the system has good self-learning and correction capabilities.

[0095] The embodiment of the present application also provides a correction device for a digital twin model. It should be noted that the correction device for the digital twin model of the embodiment of the present application can be used to execute the correction method for the digital twin model provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0096] The following introduces the correction device of the digital twin model provided in the embodiment of the present application.

[0097] Figure 7is a structural block diagram of a correction device for a digital twin model according to an embodiment of the present application. Figure 7 As shown, the device comprises:

[0098] A first acquisition unit 10 is used to acquire an initial digital twin model, wherein the initial digital twin model is a virtual model of a fully mechanized coal mine working face constructed based on digital twin technology;

[0099] A second acquisition unit 20 is used to acquire a correction coefficient, wherein the correction coefficient is obtained according to changes in the fully mechanized mining face of the coal mine after the initial digital twin model completes the preset instructions;

[0100] The correction unit 30 is used to correct the initial digital twin model according to the correction coefficient to obtain a target digital twin model.

[0101] Through this embodiment, correction coefficients are used to perform real-time corrections on each model, so that the calculation results of the twin model are consistent with the real-world data, and the calibration of the twin model can be achieved, thereby reducing the deviation between the state of the twin model after responding to the action instruction and the state of the actual coal mining environment.

[0102] In a specific implementation process, the second acquisition unit includes a first calculation module, which is used to calculate a first correction coefficient according to a first formula, wherein the first correction coefficient is a coefficient for pre-correcting the data, and the first formula is:

[0103]

[0104] k p represents the first correction coefficient mentioned above, S x1 Indicates the correction data value received after the action command stops, S x0 Indicates the last received correction data value, S d1 The above formula is only exemplary, and any variation of the formula falls within the protection scope of this application.

[0105] In this scheme, the data in the digital twin model can be corrected, and the first correction coefficient can be calculated by the first formula. The first correction coefficient is then used to correct the initial digital twin correction model. The obtained target digital twin correction model can ensure the consistency of the virtual and real phenomena, thereby further reducing the error of the data in the digital twin model.

[0106] In the specific implementation process, the second acquisition unit includes a second calculation module, which is used to calculate the second correction coefficient according to the second formula, wherein the second correction coefficient is a coefficient for pre-correcting the virtual support in the initial digital twin model, and the virtual support corresponds to the actual support in the fully mechanized mining working face of the coal mine. The second formula is:

[0107]

[0108] k n represents the second correction factor mentioned above, L Md Indicates the actual distance in the corrected data, L DT The above formula is only exemplary, and any variation of the formula falls within the protection scope of this application.

[0109] In this scheme, we can take one of the brackets as an example. When the bracket correction data is received for the nth time, the second correction parameter can be calculated by the second formula based on the push-slip distance of the digital twin model and the push-slip distance in the correction data. The second correction coefficient is then used to correct the initial digital twin correction model. The target digital twin correction model obtained can ensure the consistency between the virtual and real phenomena, thereby further reducing the error of the bracket in the digital twin model.

[0110] In the specific implementation process, the second acquisition unit includes a third calculation module, which is used to calculate a third correction coefficient according to a third formula, wherein the third correction coefficient is a coefficient for pre-correcting the data of the n+1th knife corresponding to the virtual bracket in the initial digital twin model, and the third formula is:

[0111]

[0112] represents the third correction coefficient, n represents the number of cutters corresponding to the virtual bracket, Indicates the coefficient calculated by the first correction data received within a knife. The above formula is only exemplary, and any variation of the formula falls within the protection scope of this application.

[0113] In this scheme, in each cut of the coal cutting operation, each bracket will send correction data when the pushing and moving of the bracket is completed, so there will be two correction coefficients and in each cut. In the subsequent calculation process of the digital twin model, the correction coefficient of the correction data of the n+1th cut is filtered through recursive average filtering, and the average of the most recent 6 data in the correction coefficient queue is taken to obtain the third correction coefficient. The third correction coefficient is subsequently used to correct the initial digital twin correction model, and the target digital twin correction model obtained can ensure the consistency of the virtual and real phenomena, thereby further reducing the error of the bracket in the digital twin model.

[0114] In the specific implementation process, the second acquisition unit includes a fourth calculation module, which is used to calculate a fourth correction coefficient according to a fourth formula, wherein the fourth correction coefficient is a coefficient for pre-correcting the virtual coal mining machine in the initial digital twin model, and the virtual coal mining machine corresponds to the actual coal mining machine in the fully mechanized mining face of the coal mine. The fourth formula is:

[0115]

[0116] k m represents the fourth correction coefficient, V Md Indicates the actual coal machine running speed calculated by the correction data, V DT The above initial digital twin model calculates the simulated coal machine running speed, S Md Indicates the position in meters of the correction data, S DT represents the position of the initial digital twin model in meters, and t represents the time interval between the current correction data and the last correction data. The above formula is only exemplary, and any variation of the formula falls within the protection scope of this application.

[0117] In this scheme, the position of the coal mining machine can be used as an example. The coal mining machine is only calculated in the local coordinate system of the comprehensive mining face, and the speed is used as the correction object, and the left and right arm angles are the same. Assuming that when the nth knife receives the correction data of the coal mining machine, based on the position of the digital twin model and the position in the correction data, since the actual movement time of the coal mining machine is the same as the movement time in the digital twin model, the fourth correction coefficient can be calculated by the fourth formula. The fourth correction coefficient is subsequently used to correct the initial digital twin correction model, and the target digital twin correction model obtained can ensure the consistency of the virtual and real phenomena, thereby further reducing the error of the coal mining machine in the digital twin model.

[0118] In the specific implementation process, the second acquisition unit includes a fifth calculation module, which is used to calculate a fifth correction coefficient according to a fifth formula, wherein the fifth correction coefficient is a coefficient for pre-correcting the data of the n+1th knife corresponding to the virtual coal mining machine in the initial digital twin model, and the fifth formula is:

[0119]

[0120] represents the fifth correction coefficient, and m represents the number of cutters corresponding to the virtual coal mining machine. The above formula is only exemplary, and any variation of the formula falls within the protection scope of this application.

[0121] In this scheme, each time the coal mining machine receives correction data, it calculates the current correction coefficient, and sends it to the left and right correction coefficient queues of the coal mining machine after filtering. It also uses recursive average filtering to take the average of the last 6 data to obtain the fifth correction coefficient. The fifth correction coefficient is subsequently used to correct the initial digital twin correction model. The target digital twin correction model obtained can ensure the consistency between the virtual and real phenomena, thereby further reducing the error of the coal mining machine in the digital twin model.

[0122] In some embodiments, the correction unit includes an acquisition module and a correction module. The acquisition module is used to obtain a preset interval duration; the correction module is used to correct the above-mentioned initial digital twin model according to the above-mentioned correction coefficient when the running time of the above-mentioned initial digital twin model reaches the above-mentioned preset interval duration, so as to obtain the above-mentioned target digital twin model.

[0123] In this solution, data and environment may change over time, causing the model to become outdated or invalid. Therefore, each time the running time of the initial digital twin model reaches the preset interval, the initial digital twin model can be corrected. This way, the parameters and structure of the model can be updated in time, further improving the practicality of the digital twin model.

[0124] The correction device of the digital twin model includes a processor and a memory. The first acquisition unit, the second acquisition unit and the correction unit are stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions. The modules are all located in the same processor; or, the modules are located in different processors in any combination.

[0125] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the kernel parameters can be adjusted to solve the problem in the prior art that the state of the twin model after responding to the action instruction is different from the state of the actual coal mining environment.

[0126] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0127] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the method for correcting the digital twin model.

[0128] Specifically, the correction method of the digital twin model includes:

[0129] Step S201, obtaining an initial digital twin model, wherein the initial digital twin model is a virtual model of a fully mechanized coal mine working face constructed based on digital twin technology;

[0130] Step S202, obtaining a correction coefficient, wherein the correction coefficient is obtained according to changes in the fully mechanized mining face of the coal mine after the initial digital twin model completes the preset instructions;

[0131] Step S203: Correct the initial digital twin model according to the correction coefficient to obtain a target digital twin model.

[0132] An embodiment of the present invention provides a processor, which is used to run a program, wherein the method for correcting the digital twin model is executed when the program is running.

[0133] Specifically, the correction method of the digital twin model includes:

[0134] Step S201, obtaining an initial digital twin model, wherein the initial digital twin model is a virtual model of a fully mechanized coal mine working face constructed based on digital twin technology;

[0135] Step S202, obtaining a correction coefficient, wherein the correction coefficient is obtained according to changes in the fully mechanized mining face of the coal mine after the initial digital twin model completes the preset instructions;

[0136] Step S203: Correct the initial digital twin model according to the correction coefficient to obtain a target digital twin model.

[0137] An embodiment of the present invention provides a device, comprising a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, at least the correction step of the digital twin model is implemented:

[0138] Specifically, the correction method of the digital twin model includes:

[0139] Step S201, obtaining an initial digital twin model, wherein the initial digital twin model is a virtual model of a fully mechanized coal mine working face constructed based on digital twin technology;

[0140] Step S202, obtaining a correction coefficient, wherein the correction coefficient is obtained according to changes in the fully mechanized mining face of the coal mine after the initial digital twin model completes the preset instructions;

[0141] Step S203: Correct the initial digital twin model according to the correction coefficient to obtain a target digital twin model.

[0142] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0143] A computer program product includes a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for correcting the digital twin model in each embodiment of the present application are implemented:

[0144] Specifically, the correction method of the digital twin model includes:

[0145] Step S201, obtaining an initial digital twin model, wherein the initial digital twin model is a virtual model of a fully mechanized coal mine working face constructed based on digital twin technology;

[0146] Step S202, obtaining a correction coefficient, wherein the correction coefficient is obtained according to changes in the fully mechanized mining face of the coal mine after the initial digital twin model completes the preset instructions;

[0147] Step S203: Correct the initial digital twin model according to the correction coefficient to obtain a target digital twin model.

[0148] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0149] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0150] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0151] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0152] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0153] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0154] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0155] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0156] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0157] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0158] 1) The correction method of the digital twin model of the present application uses a correction coefficient to perform real-time correction on each model, so as to unify the calculation results of the twin model with the real-world data, thereby realizing the calibration of the twin model, and thereby reducing the deviation between the state of the twin model after responding to the action instruction and the state of the actual coal mining environment.

[0159] 2) The correction device of the digital twin model of the present application uses a correction coefficient to perform real-time correction on each model, thereby unifying the calculation results of the twin model with the real-world data, and can realize the calibration of the twin model, thereby reducing the deviation between the state of the twin model after responding to the action instruction and the state of the actual coal mining environment.

[0160] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for correcting a digital twin model, characterized in that: include: Acquire an initial digital twin model, wherein the initial digital twin model is a virtual model of a fully mechanized coal mine working face constructed based on digital twin technology; Obtaining a correction coefficient, wherein the correction coefficient is obtained according to changes in the fully mechanized mining face of the coal mine after the initial digital twin model completes a preset instruction; The initial digital twin model is corrected according to the correction coefficient to obtain a target digital twin model.

2. The method according to claim 1, characterized in that Get correction factors, including: The first correction coefficient is calculated according to the first formula, wherein the first correction coefficient is a coefficient for pre-correcting the data, and the first formula is: k p represents the first correction factor, S x1 Indicates the correction data value received after the action command stops, S x0 Indicates the last received correction data value, S d1 Indicates the data value reached in response to an action instruction.

3. The method according to claim 1, characterized in that Get correction factors, including: The second correction coefficient is calculated according to the second formula, wherein the second correction coefficient is a coefficient for pre-correcting the virtual support in the initial digital twin model, and the virtual support corresponds to the actual support in the fully mechanized mining face of the coal mine one by one. The second formula is: k n represents the second correction factor, L Md Indicates the actual distance in the corrected data, L DT Represents the distance calculated by the initial digital twin model.

4. The method according to claim 3, characterized in that Get correction factors, including: The third correction coefficient is calculated according to the third formula, wherein the third correction coefficient is a coefficient for pre-correcting the data of the n+1th knife corresponding to the virtual stent in the initial digital twin model, and the third formula is: represents the third correction coefficient, n represents the number of cutters corresponding to the virtual bracket, Indicates the coefficient calculated by the first correction data received within a knife. Indicates the coefficient for calculating the second correction data received within one cut.

5. The method according to claim 1, characterized in that Get correction factors, including: The fourth correction coefficient is calculated according to the fourth formula, wherein the fourth correction coefficient is a coefficient for pre-correcting the virtual coal mining machine in the initial digital twin model, and the virtual coal mining machine corresponds one to one to the actual coal mining machine in the fully mechanized mining working face of the coal mine. The fourth formula is: k m represents the fourth correction coefficient, V Md Indicates the actual coal machine running speed calculated by the correction data, V DT represents the running speed of the coal machine calculated by the initial digital twin model, S Md Indicates the position in the correction data, meters, S DT represents the position of the initial digital twin model in meters, and t represents the time interval between the current revised data and the last revised data.

6. The method according to claim 5, characterized in that Get correction factors, including: The fifth correction coefficient is calculated according to the fifth formula, wherein the fifth correction coefficient is a coefficient for pre-correcting the data of the n+1th cutter corresponding to the virtual coal mining machine in the initial digital twin model, and the fifth formula is: represents the fifth correction coefficient, and m represents the number of cutters corresponding to the virtual coal mining machine.

7. The method according to any one of claims 1 to 6, characterized in that: The initial digital twin model is corrected according to the correction coefficient to obtain a target digital twin model, including: Get the preset interval duration; When the running time of the initial digital twin model reaches the preset interval duration, the initial digital twin model is corrected according to the correction coefficient to obtain the target digital twin model.

8. A correction device for a digital twin model, characterized in that: include: A first acquisition unit is used to acquire an initial digital twin model, wherein the initial digital twin model is a virtual model of a fully mechanized coal mine working face constructed based on digital twin technology; A second acquisition unit is used to acquire a correction coefficient, wherein the correction coefficient is obtained according to changes in the fully mechanized mining face of the coal mine after the initial digital twin model completes a preset instruction; A correction unit is used to correct the initial digital twin model according to the correction coefficient to obtain a target digital twin model.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute the method for correcting the digital twin model described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for correcting the digital twin model described in any one of claims 1 to 7 are implemented.

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