A method, device, equipment and medium for simulating operation of a virtual production device

Abstract

The application discloses a kind of virtual production equipment running simulation method, device, equipment and medium, comprising: according to the running parameter of target production equipment, the virtual production equipment corresponding to target production equipment is established;Determine the associated state between target production equipment and virtual production equipment, if the associated state is online state, then the real-time digital parameter of target production equipment is synchronized to virtual production equipment, to make virtual production equipment complete running simulation according to digital parameter;If the associated state is offline state, then according to the historical digital parameter of target production equipment, using sparse coding algorithm and compressed sensing algorithm, determine the running parameter of virtual production equipment, and control virtual production equipment to complete running simulation according to running parameter.The technical scheme of the embodiment of the application can improve the accuracy and effectiveness of virtual production equipment running simulation result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer technology, and in particular to a virtual production equipment running simulation method, device, equipment and medium. BACKGROUND

[0002] The metaverse is a virtual world built with digital technology and can interact with the real world. In order to monitor the running status of the actual production equipment, the prior art proposes a way of establishing a virtual production equipment in the metaverse and controlling the virtual production equipment to simulate the production status.

[0003] At present, digital production can enable all-round parameters of production line equipment to be monitored, and through digital parameters, a virtual mirror image of the actual production equipment can be copied in the metaverse. Therefore, when controlling the virtual production equipment to simulate the production status, the equipment production simulation in the metaverse is usually based on digital parameters.

[0004] However, in addition to generating digital parameters and information, there are some unpredictable factors such as human factors, occasional failures, and small probability situations during the running of the actual production equipment. If the equipment production simulation in the metaverse is simply based on digital parameters, there will be problems of limited information and inaccurate results. SUMMARY

[0005] The present application provides a virtual production equipment running simulation method, device, equipment and medium, which can improve the accuracy and effectiveness of the virtual production equipment running simulation results.

[0006] According to an aspect of the present application, a virtual production equipment running simulation method is provided, the method comprising:

[0007] establishing a virtual production equipment corresponding to a target production equipment according to running parameters of the target production equipment;

[0008] determining an association state between the target production equipment and the virtual production equipment, if the association state is an online state, synchronizing real-time digital parameters of the target production equipment to the virtual production equipment, so that the virtual production equipment completes running simulation according to the digital parameters;

[0009] if the association state is an offline state, determining running parameters of the virtual production equipment according to historical digital parameters of the target production equipment by using a sparse coding algorithm and a compressed sensing algorithm, and controlling the virtual production equipment to complete running simulation according to the running parameters.

[0010] According to another aspect of the present application, a virtual production equipment running simulation device is provided, the device comprising:

[0011] The device establishes a module for establishing a virtual production device corresponding to the target production device according to the running parameters of the target production device.

[0012] The online simulation module is configured to determine an association state between the target production device and the virtual production device, and if the association state is an online state, synchronize real-time digital parameters of the target production device to the virtual production device, so that the virtual production device completes the running simulation according to the digital parameters.

[0013] The offline simulation module is configured to, if the association state is an offline state, determine running parameters of the virtual production device according to historical digital parameters of the target production device by using a sparse coding algorithm and a compressed sensing algorithm, and control the virtual production device to complete the running simulation according to the running parameters.

[0014] According to another aspect of the present application, an electronic device is provided, which comprises:

[0015] at least one processor; and

[0016] a memory connected to the at least one processor in communication; wherein

[0017] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the running simulation method of the virtual production device according to any one of the embodiments of the present application.

[0018] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to implement the running simulation method of the virtual production device according to any one of the embodiments of the present application when executed by the processor.

[0019] The technical scheme provided by the embodiments of the present application can improve the accuracy and effectiveness of the running simulation result of the virtual production device by establishing a virtual production device corresponding to the target production device according to the running parameters of the target production device, determining an association state between the target production device and the virtual production device, if the association state is an online state, synchronizing real-time digital parameters of the target production device to the virtual production device, so that the virtual production device completes the running simulation according to the digital parameters, and if the association state is an offline state, determining running parameters of the virtual production device according to historical digital parameters of the target production device by using a sparse coding algorithm and a compressed sensing algorithm, and controlling the virtual production device to complete the running simulation according to the running parameters.

[0020] It is to be understood that the details set forth herein do not limit the scope of the embodiments of the application to the specific embodiments described. Rather, the scope of the embodiments of the application is to be defined by the appended claims. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0022] Figure 1 is a flow chart of a virtual production equipment running simulation method according to an embodiment of the present application;

[0023] Figure 2 is a flow chart of another virtual production equipment running simulation method according to an embodiment of the present application;

[0024] Figure 3 is a flow chart of another virtual production equipment running simulation method according to an embodiment of the present application;

[0025] Figure 4 is a structural schematic diagram of a virtual production equipment running simulation device according to an embodiment of the present application;

[0026] Figure 5 is an electronic device structural schematic diagram of a virtual production equipment running simulation method according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the technical personnel in the art better understand the present application scheme, the following will combine the drawings in the embodiments of the present application, and the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and in the above drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0029] Figure 1 A flowchart of a virtual production device running simulation method provided for embodiment one of the present application, this embodiment can be applicable to the case of simulating the production status of an actual production device by a virtual production device in the metaverse, which can be executed by a virtual production device running simulation device. The virtual production device running simulation device can be realized in the form of hardware and / or software, and can be configured in an electronic device. As shown in the figure, the method comprises: Figure 1

[0030] Step 110, according to the running parameters of the target production device, a virtual production device corresponding to the target production device is established.

[0031] In this embodiment, the target production device can be an actual production device in the real world. Optionally, the running parameters can be digital parameters generated by the target production device during operation, such as device position information, motion trajectory, speed information, mechanical parameters, temperature, humidity, thermodynamic parameters, sound, time, visual image and analysis parameters thereof, electrical measurement values and analysis parameters thereof, yield, yield, quality analysis results, etc.

[0032] In this step, after obtaining the running parameters of the target production device, a virtual device model can be established in the metaverse according to the running parameters, and then the virtual device model is optimized according to the working mode of the target production device to obtain the virtual production device.

[0033] Step 120, determine the association state between the target production device and the virtual production device, if the association state is online, synchronize the real-time digital parameters of the target production device to the virtual production device, so that the virtual production device completes the running simulation according to the digital parameters.

[0034] ​In the embodiment, the target production device and the virtual production device can be connected through a wireless communication mechanism. If the target production device and the virtual production device are normally connected, that is, the association state between the target production device and the virtual production device is online, the real-time digitalized parameters of the target production device can be synchronized to the virtual production device.

[0035] In this step, after the virtual production device obtains the real-time digitalized parameters, the virtual production device can copy the running state of the target production device according to the real-time digitalized parameters, and then simulate the production status of the target production device according to the running state.

[0036] In step 130, if the association state is offline, the running parameters of the virtual production device are determined according to the historical digitalized parameters of the target production device by using a sparse coding algorithm and a compressed sensing algorithm, and the virtual production device is controlled to complete the running simulation according to the running parameters.

[0037] In the embodiment, if the connection between the target production device and the virtual production device is disconnected, that is, the association state is offline, the digitalized parameters of the target production device in the historical connection process (that is, the historical digitalized parameters) can be obtained, the historical digitalized parameters are processed by using a sparse coding algorithm and a compressed sensing algorithm, the running parameters of the virtual production device are determined according to the processing result, the running state of the target production device is copied according to the running parameters, and finally the production status of the target production device is simulated according to the running state.

[0038] In a specific embodiment, the sparse coding algorithm is an artificial intelligence algorithm that can abstract redundant information, and is commonly used for reduction and prediction under the influence of nonlinear factors such as nondeterministic polynomial time (NP) problems.

[0039] In the embodiment, by combining the nonlinear intelligent algorithm (sparse coding algorithm and compressed sensing algorithm) to copy the running state of the target production device, nonlinear prediction of the running parameters of the target production device can be realized in the case of disconnection between the virtual production device and the target production device, thereby realizing nonlinear simulation of the production status of the target production device.

[0040] The technical scheme provided by the embodiment of the present application can improve the accuracy and effectiveness of the running simulation result of the virtual production equipment by establishing the virtual production equipment corresponding to the target production equipment according to the running parameter of the target production equipment, determining the association state between the target production equipment and the virtual production equipment, synchronizing the real-time digital parameter of the target production equipment to the virtual production equipment if the association state is the online state, and enabling the virtual production equipment to complete the running simulation according to the digital parameter, and determining the running parameter of the virtual production equipment according to the historical digital parameter of the target production equipment, using the sparse coding algorithm and the compressed sensing algorithm, and controlling the virtual production equipment to complete the running simulation according to the running parameter if the association state is the offline state.

[0041] Figure 2 The flowchart of the running simulation method of the virtual production equipment provided in the second embodiment of the present application is a further refinement of the above-mentioned embodiment. As shown in the figure, the method comprises the following steps. Figure 2

[0042] Step 210: establishing the virtual production equipment corresponding to the target production equipment according to the running parameter of the target production equipment.

[0043] Step 220: determining the association state between the target production equipment and the virtual production equipment, and synchronizing the real-time digital parameter of the target production equipment to the virtual production equipment if the association state is the online state, so as to enable the virtual production equipment to complete the running simulation according to the digital parameter.

[0044] Step 230: constructing the equipment running parameter vector, the sparse feature vector and the sparse coding dictionary according to the real-time digital parameter.

[0045] In the embodiment, during the process of connecting the target production equipment and the virtual production equipment, the M (M>0) dimensional equipment running parameter vector A, the sparse parameter group (namely the M dimensional sparse feature vector B) expected to be abstractly refined, and the M*M dimensional sparse coding dictionary D can also be constructed according to the real-time digital parameter of the target production equipment.

[0046] Step 240: taking the equipment running parameter vector as the training data, and iteratively updating the sparse feature vector and the sparse coding dictionary according to the preset coding constraint condition to obtain the updated sparse feature vector and the sparse coding dictionary.

[0047] In a specific embodiment, the coding constraint condition can be shown in the following formula:

[0048]

[0049] Wherein, ρ is the preset weight value. ​

[0050] In this step, the device operating parameter vector A can be used as training data to iteratively update the sparse feature vector B and the sparse coding dictionary D by using a relaxation or greedy algorithm until the coding constraint condition is satisfied, thereby obtaining the updated sparse feature vector B and the sparse coding dictionary D.

[0051] Step 250, if the association state is the offline state, obtaining the sparse feature vector and the sparse coding dictionary corresponding to the online state.

[0052] In this step, if the connection relationship between the target production device and the virtual production device is disconnected, the sparse feature vector B and the sparse coding dictionary D generated in the online state in the above process can be obtained.

[0053] Step 260, based on the historical digital parameters of the target production device, the sparse feature vector and the sparse coding dictionary, using the sparse coding algorithm and the compressed sensing algorithm to determine the operating parameters of the virtual production device, and controlling the virtual production device to complete the operation simulation according to the operating parameters.

[0054] In this embodiment, the sparse coding algorithm and the compressed sensing algorithm can be used to process the historical digital parameters based on the sparse feature vector B and the sparse coding dictionary D obtained in step 250, and the operating parameters of the virtual production device are determined according to the processing result, then the operating state of the target production device is copied according to the operating parameters, and finally the production state of the target production device is simulated according to the operating state.

[0055] In this embodiment, by applying the sparse feature vector and the sparse coding dictionary generated in the online state to the simulation process in the offline state, the accuracy and effectiveness of the simulation result of the virtual production device can be improved.

[0056] The technical scheme provided by the embodiment of the present application comprises the following steps: a virtual production device corresponding to a target production device is established according to the running parameters of the target production device; if the association state is an online state, real-time digital parameters of the target production device are synchronized to the virtual production device, so that the virtual production device completes running simulation according to the digital parameters; a device running parameter vector, a sparse feature vector and a sparse coding dictionary are constructed according to the real-time digital parameters; the device running parameter vector is taken as training data, and the sparse feature vector and the sparse coding dictionary are iteratively updated according to coding constraint conditions; if the association state is an offline state, the sparse feature vector and the sparse coding dictionary corresponding to the online state are acquired, the running parameters of the virtual production device are determined by using a sparse coding algorithm and a compressed sensing algorithm according to historical digital parameters of the target production device, the sparse feature vector and the sparse coding dictionary, and the virtual production device is controlled to complete running simulation according to the running parameters, so that the accuracy and effectiveness of the running simulation result of the virtual production device can be improved.

[0057] Figure 3 The flowchart of another virtual production device running simulation method provided for the third embodiment of the present application is a further refinement of the above-mentioned embodiments. As shown in the figure, the method comprises the following steps: Figure 3

[0058] Step 310: a virtual production device corresponding to a target production device is established according to the running parameters of the target production device.

[0059] Step 320: the association state between the target production device and the virtual production device is determined, and if the association state is an online state, real-time digital parameters of the target production device are synchronized to the virtual production device, so that the virtual production device completes running simulation according to the digital parameters.

[0060] Step 330: if the association state is an offline state, the sparse feature vector B and the sparse coding dictionary D corresponding to the online state are acquired.

[0061] Step 340: a target running parameter vector is constructed according to historical digital parameters of the target production device, and an initial compressed vector corresponding to the target running parameter vector is determined.

[0062] In this step, optionally, an M-dimensional target running parameter vector A can be constructed according to the historical digital parameters, and the target running parameter vector A is compressed to obtain an N-dimensional initial compressed vector S.

[0063] Step 350: the sparse coding dictionary is taken as a sparse basis matrix, the initial compressed vector is adjusted, and then a target sparse feature vector and a target compressed vector are determined according to the sparse basis matrix, the sparse feature vector and a preset compression constraint condition.​

[0064] In one embodiment of the present application, the sparse coding dictionary is taken as a sparse basis matrix, and the initial compressed vector is adjusted, and then the target sparse feature vector and the target compressed vector are determined according to the sparse basis matrix, the sparse feature vector, and the preset compression constraint condition, including: calculating a plurality of adjusted compressed vectors according to the sparse basis matrix, the sparse feature vector, and the preset compression constraint condition; recovering the plurality of adjusted compressed vectors according to the preset observation matrix to obtain a compressed vector group; and determining the target sparse feature vector and the target compressed vector according to the sparse basis matrix, the sparse feature vector, the compressed vector group, and the preset compression constraint condition.

[0065] In one specific embodiment, the sparse coding dictionary D can be taken as a sparse basis matrix for a compression sensing algorithm, the initial compressed vector S is fine-tuned, for example, the vector element value is fine-tuned or is adjusted in displacement, and then a plurality of adjusted compressed vectors S are calculated according to the compression constraint condition of the following formula:

[0066]

[0067] Wherein, W is a preset M*N-dimensional observation matrix. Specifically, W can be a highly random white noise matrix.

[0068] In the present embodiment, after the plurality of adjusted compressed vectors S are calculated, the plurality of compressed vectors S can be recovered according to the observation matrix W to obtain a compressed vector group including the plurality of compressed vectors, and then the optimal sparse feature vector B (i.e., the target sparse feature vector) and the optimal compressed vector S (i.e., the target compressed vector) are determined according to the sparse basis matrix D, the compressed vector group, and the above compression constraint condition.

[0069] Step 360, determining the running parameters of the virtual production equipment according to the target sparse feature vector and the target compressed vector, and controlling the virtual production equipment to complete the running simulation according to the running parameters.

[0070] In one embodiment of the present application, determining the running parameters of the virtual production equipment according to the target sparse feature vector and the target compressed vector includes: transforming the target sparse feature vector and the target compressed vector according to a preset observation matrix, and determining the running parameters of the virtual production equipment according to the transformation result.

[0071] In one embodiment of the embodiment of the application, the step 340 of determining the initial compression vector corresponding to the target operation parameter vector comprises: judging whether the time corresponding to the offline state is the initial time in the offline process; if yes, compressing the target operation parameter vector according to the preset observation matrix to obtain the initial compression vector; and if no, obtaining the target compression vector corresponding to the last offline time and taking the target compression vector as the initial compression vector corresponding to the current time.

[0072] The advantage of such arrangement is that if the time corresponding to the current offline state is not the initial time in the offline process, the determination time of the virtual production equipment operation parameter can be saved and the simulation efficiency of the virtual production equipment can be improved by taking the target compression vector corresponding to the last offline time as the initial compression vector corresponding to the current time.

[0073] In one specific embodiment, after the target sparse feature vector B is determined through the step 350, the target sparse feature vector B can be applied to the vector updating process in the online state to determine the optimized sparse feature vector according to the target sparse feature vector B.

[0074] The technical scheme provided by the embodiment of the application can improve the accuracy and effectiveness of the simulation result of the virtual production equipment by establishing the virtual production equipment corresponding to the target production equipment according to the operation parameter of the target production equipment, synchronizing the real-time digital parameter of the target production equipment to the virtual production equipment if the association state is the online state to enable the virtual production equipment to complete the operation simulation according to the digital parameter, obtaining the sparse feature vector and the sparse coding dictionary in the online state if the association state is the offline state, constructing the target operation parameter vector according to the historical digital parameter of the target production equipment, determining the initial compression vector corresponding to the target operation parameter vector, taking the sparse coding dictionary as the sparse basis matrix, adjusting the initial compression vector, then determining the target sparse feature vector and the target compression vector according to the sparse basis matrix, the sparse feature vector and the compression constraint condition, determining the operation parameter of the virtual production equipment according to the target sparse feature vector and the target compression vector, and controlling the virtual production equipment to complete the operation simulation according to the operation parameter.

[0075] Figure 4 A structural schematic diagram of a virtual production equipment operation simulation device provided by the fourth embodiment of the application is shown in FIG. 4. The device is applied in an electronic device. As shown in FIG. 4, the device comprises a device establishing module 410, an online simulation module 420 and an offline simulation module 430. Figure 4

[0076] The device establishing module 410 is configured to establish the virtual production equipment corresponding to the target production equipment according to the operation parameter of the target production equipment. ​

[0077] an online simulation module 420, configured to determine an association state between the target production device and the virtual production device, and if the association state is an online state, synchronize real-time digitized parameters of the target production device to the virtual production device, so that the virtual production device completes the running simulation according to the digitized parameters;

[0078] an offline simulation module 430, configured to if the association state is an offline state, determine running parameters of the virtual production device according to historical digitized parameters of the target production device, and control the virtual production device to complete the running simulation according to the running parameters.

[0079] The technical scheme provided by the embodiment of the present application can improve the accuracy and effectiveness of the running simulation result of the virtual production device.

[0080] In the above embodiment, the online simulation module 420 comprises:

[0081] a sparse parameter construction unit, configured to construct a device running parameter vector, a sparse feature vector and a sparse coding dictionary according to the real-time digitized parameters;

[0082] an iterative updating unit, configured to take the device running parameter vector as training data, and perform iterative updating on the sparse feature vector and the sparse coding dictionary according to a preset coding constraint condition, to obtain an updated sparse feature vector and an updated sparse coding dictionary.

[0083] The offline simulation module 430 comprises:

[0084] a sparse parameter acquisition unit, configured to acquire a corresponding sparse feature vector and a corresponding sparse coding dictionary under the online state;

[0085] a running parameter determination unit, configured to determine running parameters of the virtual production device according to the historical digitized parameters of the target production device, the sparse feature vector and the sparse coding dictionary, by using a sparse coding algorithm and a compressed sensing algorithm;

[0086] A parameter vector construction unit is configured to construct a target operation parameter vector according to historical digital parameters of a target production device, and determine an initial compression vector corresponding to the target operation parameter vector;

[0087] A target vector determination unit is configured to take the sparse coding dictionary as a sparse base matrix, adjust the initial compression vector, and then determine a target sparse feature vector and a target compression vector according to the sparse base matrix, the sparse feature vector, and a preset compression constraint condition;

[0088] A target vector processing unit is configured to determine operation parameters of a virtual production device according to the target sparse feature vector and the target compression vector.

[0089] A time point judgment unit is configured to judge whether a time point corresponding to the offline state is an initial time point in an offline process, and if so, compress the target operation parameter vector to obtain an initial compression vector according to a preset observation matrix, and if not, obtain a target compression vector corresponding to a previous offline time point, and take the target compression vector as an initial compression vector corresponding to a current time point.

[0090] A compression vector calculation unit is configured to calculate a plurality of adjusted compression vectors according to the sparse base matrix, the sparse feature vector, and the preset compression constraint condition.

[0091] A compression vector recovery unit is configured to recover the plurality of adjusted compression vectors according to a preset observation matrix to obtain a compression vector group.

[0092] A vector group processing unit is configured to determine a target sparse feature vector and a target compression vector according to the sparse base matrix, the sparse feature vector, the compression vector group, and the preset compression constraint condition.

[0093] A vector transformation unit is configured to transform the target sparse feature vector and the target compression vector according to a preset observation matrix, and determine operation parameters of a virtual production device according to a transformation result.

[0094] The above device can perform the method provided by all the foregoing embodiments of the present application, and has corresponding function modules and beneficial effects for performing the above method. Technical details not described in detail in the embodiments of the present application can be referred to the method provided by all the foregoing embodiments of the present application.

[0095] Figure 5 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0096] AsFigure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., communicatively connected to the at least one processor 11, where the memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0097] Various components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, a speaker, etc., a storage unit 18, such as a magnetic disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0098] The processor 11 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the method of running simulation of a virtual production device.

[0099] In some embodiments, the method of running simulation of a virtual production device can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method of running simulation of a virtual production device described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the method of running simulation of a virtual production device by any other appropriate means, such as by means of firmware.

[0100] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a load programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0101] Computer programs used to implement the processes of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, can cause instructions defined in the flow charts and / or block diagrams to be implemented. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a standalone software package and partially on a remote machine or entirely on a remote machine or server.

[0102] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0103] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0104] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0105] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0106] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in sequence, or executed in a different order, as long as the desired results of the present disclosure are achieved, and the present disclosure is not limited herein.

[0107] The specific embodiments described above are not intended to be limiting, and persons skilled in the art will appreciate that various modifications, combinations, sub-combinations and alternatives can be made to the specific embodiments without departing from the spirit and principles of the disclosure. Accordingly, the disclosure is not limited to the specific embodiments described above, but only by the scope of the appended claims.

Claims

1. A method of running simulation of a virtual production plant, characterized by, The method comprises: According to the running parameters of the target production equipment, a virtual production equipment corresponding to the target production equipment is established; The association state between the target production equipment and the virtual production equipment is determined, if the association state is an online state, the real-time digital parameters of the target production equipment are synchronized to the virtual production equipment, so that the virtual production equipment completes the running simulation according to the digital parameters; If the association state is an offline state, the running parameters of the virtual production equipment are determined according to the historical digital parameters of the target production equipment by using a sparse coding algorithm and a compressed sensing algorithm, and the virtual production equipment is controlled to complete the running simulation according to the running parameters; Wherein, according to the historical digital parameters of the target production equipment, the running parameters of the virtual production equipment are determined by using a sparse coding algorithm and a compressed sensing algorithm, comprising: Obtaining the sparse feature vector and the sparse coding dictionary corresponding to the online state; According to the historical digital parameters of the target production equipment, a target running parameter vector is constructed, and an initial compression vector corresponding to the target running parameter vector is determined; The sparse coding dictionary is taken as a sparse basis matrix, and the initial compression vector is adjusted, and then the target sparse feature vector and the target compression vector are determined according to the sparse basis matrix, the sparse feature vector and the preset compression constraint condition; According to the target sparse feature vector and the target compression vector, the running parameters of the virtual production equipment are determined.

2. The method of claim 1, wherein, At the same time of synchronizing the real-time digital parameters of the target production equipment to the virtual production equipment to make the virtual production equipment complete the running simulation according to the digital parameters, it also comprises: According to the real-time digital parameters, the equipment running parameter vector, the sparse feature vector and the sparse coding dictionary are constructed; The equipment running parameter vector is taken as the training data, the sparse feature vector and the sparse coding dictionary are iteratively updated according to the preset coding constraint condition, and the updated sparse feature vector and the sparse coding dictionary are obtained.

3. The method of claim 1, wherein, Determination of the initial compression vector corresponding to the target running parameter vector comprises: Judging whether the moment corresponding to the offline state is the initial moment in the offline process; If yes, the target running parameter vector is compressed according to the preset observation matrix to obtain the initial compression vector; If not, the target compression vector corresponding to the last offline moment is obtained, and the target compression vector is taken as the initial compression vector corresponding to the current moment.

4. The method of claim 1, wherein, The sparse coding dictionary is taken as a sparse basis matrix, and the initial compression vector is adjusted, and then the target sparse feature vector and the target compression vector are determined according to the sparse basis matrix, the sparse feature vector and the preset compression constraint condition, comprising: According to the sparse basis matrix, the sparse feature vector and the preset compression constraint condition, a plurality of adjusted compression vectors are calculated; The plurality of adjusted compression vectors are recovered according to the preset observation matrix to obtain a compression vector group; According to the sparse basis matrix, the sparse feature vector, the compression vector group and the preset compression constraint condition, the target sparse feature vector and the target compression vector are determined.

5. The method of claim 1, wherein, According to the target sparse feature vector and the target compression vector, a running parameter of the virtual production device is determined, comprising: The target sparse feature vector and the target compression vector are transformed according to a preset observation matrix, and a running parameter of the virtual production device is determined according to a transformation result.

6. A virtual production equipment operation simulation device, characterized in that, The device comprises: A device establishment module is configured to establish a virtual production device corresponding to a target production device according to a running parameter of the target production device; An online simulation module is configured to determine an association state between the target production device and the virtual production device, and if the association state is an online state, to synchronize a real-time digital parameter of the target production device to the virtual production device, so that the virtual production device completes running simulation according to the digital parameter; An offline simulation module is configured to, if the association state is an offline state, determine a running parameter of the virtual production device according to a historical digital parameter of the target production device, and control the virtual production device to complete running simulation according to the running parameter, by using a sparse coding algorithm and a compression sensing algorithm; The offline simulation module comprises: A sparse parameter acquisition unit is configured to acquire a sparse feature vector and a sparse coding dictionary corresponding to the online state; A parameter vector construction unit is configured to construct a target running parameter vector according to the historical digital parameter of the target production device, and determine an initial compression vector corresponding to the target running parameter vector; A target vector determination unit is configured to take the sparse coding dictionary as a sparse basis matrix, adjust the initial compression vector, and then determine a target sparse feature vector and a target compression vector according to the sparse basis matrix, the sparse feature vector, and a preset compression constraint condition; A target vector processing unit is configured to determine a running parameter of the virtual production device according to the target sparse feature vector and the target compression vector.

7. An electronic device, comprising: The device comprises: At least one processor; and A memory connected in communication with the at least one processor; wherein The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the running simulation method of the virtual production device according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to implement the running simulation method of the virtual production device according to any one of claims 1-5 when executed.

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