Positioning method and device of equipment model based on digital twinning, equipment, medium and product

Abstract

The invention discloses an equipment model positioning method and device based on digital twinning, equipment, a medium and a product. The method comprises the steps of determining an equipment model corresponding to energy storage equipment based on complexity and importance of each energy storage equipment in an energy storage power station; determining a model hierarchical relationship in the virtual world according to the device hierarchical relationship of each energy storage device in the real world; carrying out model binding on each equipment model in the virtual world based on the model hierarchical relationship; and in response to the received click operation of the target equipment model, positioning the target equipment model in the virtual world. According to the method and the device, the problems of high labor cost and time cost and relatively low accuracy caused by manual binding in the prior art are solved, and automatic binding and rapid positioning of the equipment model are realized, so that the labor cost and the time cost are greatly reduced, and the positioning accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and in particular to a positioning method, device, equipment, medium and product of a device model based on digital twin. Background Art

[0002] With the continuous development and progress of electrochemical energy storage battery technology, the application of energy storage systems in power systems has also been further expanded. Energy storage power stations are used to regulate the frequency of thermal power plants and participate in system frequency regulation and peak regulation on the grid side; they have been newly applied in emergency power supply on the user side, arbitrage using the difference between peak and valley electricity prices, and compensation for the operation of new energy power stations to reduce the abandonment of solar and wind power. Digital twins are based on models and data. Through multidisciplinary coupling simulation and other methods, they can accurately map the physical entities in the real world to the mirror digital models in the virtual world, and make full use of the two-way interactive feedback and iterative operation between the two to achieve the synchronous presentation of the physical entity state in the digital space. By modeling the energy storage power station equipment, high-precision restoration of the power station scene can be achieved. Combined with big data and artificial intelligence algorithms, the full life cycle and refined management of energy storage power station equipment can be achieved.

[0003] Since the smallest equipment unit in the energy storage power station is a single battery cell, there are many devices and the industrial models are complex, the digital twin system scene model will load slowly or become stuck. During the scene construction process, the existing method is to manually input the location information and code of each model and bind them one by one, which consumes a lot of time and manpower and is prone to errors. Summary of the invention

[0004] The present invention provides a positioning method, device, equipment, medium and product for a device model based on digital twins, so as to solve the problems of high labor cost and time cost and low accuracy caused by manual binding in the prior art.

[0005] According to one aspect of the present invention, a positioning method for a device model based on a digital twin is provided, comprising:

[0006] Determine a device model corresponding to each energy storage device in the energy storage power station based on the complexity and importance of the energy storage device;

[0007] Determine the model hierarchy relationship in the virtual world according to the device hierarchy relationship of each energy storage device in the physical world;

[0008] Model binding each of the device models in the virtual world based on the model hierarchical relationship;

[0009] In response to the received click operation on the target device model, the target device model is positioned in the virtual world.

[0010] According to another aspect of the present invention, a positioning device based on a digital twin device model is provided, comprising:

[0011] A model determination module, used to determine a device model corresponding to each energy storage device in the energy storage power station based on the complexity and importance of the energy storage device;

[0012] A hierarchical relationship determination module, used to determine a model hierarchical relationship in the virtual world according to the device hierarchical relationship of each energy storage device in the physical world;

[0013] A binding module, used for model binding each of the device models in the virtual world based on the model hierarchy relationship;

[0014] The positioning module is used to position the target device model in the virtual world in response to a received click operation on the target device model.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the positioning method based on the digital twin device model described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the positioning method of a device model based on a digital twin as described in any embodiment of the present invention when executed.

[0020] According to another aspect of the present invention, a computer program product is provided, which includes a computer program, and when the computer program is executed by a processor, it implements the positioning method of the device model based on the digital twin described in any embodiment of the present invention.

[0021] The technical solution of the embodiment of the present invention obtains a device model that matches each energy storage device by lightweight processing of the high-precision industrial model according to the complexity and importance of each energy storage device; then, each device model is classified according to the device hierarchy relationship and its model hierarchy relationship in the virtual world is determined, and model binding is performed between each device model according to the model hierarchy relationship. Finally, the target device model is located in the virtual world, which solves the problems of high labor cost and time cost and low accuracy caused by manual binding in the prior art, and realizes automatic binding and rapid positioning of device models, thereby greatly reducing labor cost and time cost, and improving positioning accuracy.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 is a flow chart of a positioning method for a device model based on a digital twin provided in an embodiment of the present invention;

[0025] Figure 2 is a flow chart of another positioning method of a device model based on digital twins provided in an embodiment of the present invention;

[0026] Figure 3 is a schematic diagram of a configuration of a device hierarchical relationship provided by an embodiment of the present invention;

[0027] Figure 4 is a schematic diagram of configuration of device identification information provided by an embodiment of the present invention;

[0028] Figure 5 It is a structural schematic diagram of a positioning device based on a digital twin device model provided by an embodiment of the present invention;

[0029] Figure 6 It is a structural block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] In one embodiment, Figure 1 This is a flowchart of a positioning method for a device model based on a digital twin provided by an embodiment of the present invention. This embodiment is applicable to the case where each energy storage device in a real-world energy storage power station is mapped to a virtual world. The method can be executed by a positioning device based on a device model based on a digital twin. The positioning device based on a device model based on a digital twin can be implemented in the form of hardware and / or software. The positioning device based on a device model based on a digital twin can be configured in an electronic device. For example, the electronic device can be a terminal device such as a tablet computer or a computer. Figure 1 As shown, the method includes:

[0033] S110. Determine a device model corresponding to the energy storage device based on the complexity and importance of each energy storage device in the energy storage power station.

[0034] Among them, an energy storage power station refers to an energy storage facility that can store electrical energy in different forms and release it when needed. The type of energy storage power station may be an electrochemical energy storage power station. In each energy storage power station, different types and quantities of energy storage devices may be included. Exemplarily, the energy storage devices contained in each energy storage power station may include, but are not limited to, one of the following: subunits, station power systems, battery compartments, battery stacks, battery clusters, battery packs (PACK), single cells, fire protection systems, liquid cooling systems, booster all-in-ones, air conditioners, transformers, and electric energy meters.

[0035] In the embodiment, the complexity of the energy storage device refers to the complexity of the structure, composition and operation principle of the energy storage device; the importance of the energy storage device refers to the importance of the energy storage power station in the process of electric energy storage and release; the device model refers to the mirror digital model of each energy storage device in the virtual world. In the actual operation of the digital twin, the model refinement level of a device model is related to the complexity and importance of the energy storage device. For example, the higher the complexity and importance of an energy storage device, the higher the model refinement level of the corresponding device model. It can also be understood that the complexity and importance of a device model are equivalent to the complexity and importance of the corresponding energy storage device, that is, the higher the complexity and importance of the energy storage device in the energy storage power station in the real world, the higher the complexity and importance of the corresponding device model in the virtual world.

[0036] S120 , determining a model hierarchy relationship in the virtual world according to the device hierarchy relationship of each energy storage device in the physical world.

[0037] Among them, the device hierarchy relationship refers to the subordinate relationship between various energy storage devices in the physical world, and can also be represented by a parent-child relationship or a containment relationship; of course, when the parent nodes of two energy storage devices are the same, the device hierarchy relationship of the two energy storage devices is a parallel relationship. In an embodiment, the device hierarchy relationship of energy storage devices in the physical world can be represented by the device identification information of the energy storage devices, and correspondingly, the model hierarchy relationship of device models in the virtual world can also be represented by the device identification information.

[0038] S130 . Model-binding each device model in the virtual world based on the model hierarchy relationship.

[0039] In the digital twin scenario, the corresponding device model can be completely replicated in the virtual world according to the actual situation of each energy storage device in the physical world, and the device attribute information of the corresponding energy storage device can be bound to the corresponding device model according to the actual situation.

[0040] S140 . In response to the received click operation on the target device model, locate the target device model in the virtual world.

[0041] The target device model refers to the device model that the user wants to locate. In the actual display process, each target device model can be displayed in the virtual world using an icon. The click operation of the target device model can be understood as the action of quickly pressing and releasing the position of the icon using a pointer or finger through an input device such as a mouse, touchpad or touch screen. In the actual operation process, the user can send a command to the electronic device to trigger a specific operation associated with the icon corresponding to the target device model.

[0042] In an embodiment, a user can use an input device such as a mouse or a touch screen to quickly press and release an icon corresponding to a target device model on an electronic device using a pointer, so that the electronic device obtains the twin coordinate position of the target device model in the virtual world based on the click operation, determines corresponding focus parameters based on the twin coordinate position, and focuses on the target device model based on the focus parameters to achieve positioning of the target device model.

[0043] The technical solution of this embodiment obtains a device model that matches each energy storage device by lightweighting the high-precision industrial model according to the complexity and importance of each energy storage device; then, each device model is classified according to the device hierarchy relationship and its model hierarchy relationship in the virtual world is determined, and model binding is performed between each device model according to the model hierarchy relationship. Finally, the target device model is located in the virtual world, which solves the problems of high labor and time costs and low accuracy caused by manual binding in the prior art, and realizes automatic binding and rapid positioning of device models, thereby greatly reducing labor and time costs and improving positioning accuracy.

[0044] In one embodiment, Figure 2 This is a flowchart of another method for locating a device model based on digital twins provided by an embodiment of the present invention. This embodiment further illustrates the process of determining the device model, the process of determining the model hierarchy, the model binding process, and the model locating process based on the above embodiment. Figure 2 As shown, the method includes:

[0045] S210: Determine a corresponding model refinement level based on the complexity and importance of each energy storage device in the energy storage power station.

[0046] Among them, the model precision level is used to characterize the degree of simulation and accuracy of each energy storage device in the real world, and can also be understood as the degree of restoration of each energy storage device in the real world. It can be understood that the higher the model precision level corresponding to an energy storage device, the higher the simulation degree and accuracy of the energy storage device. In one example, the model precision level can be divided into four levels, namely L1, L2, L3 and L4. Among them, the model precision corresponding to level L1 is the lowest, and the basic outline of the model is retained; the model structure of level L2 is simple, simple mapping is performed, and the model is relatively rough, which is used to indicate that there is such an object but does not focus on the display effect; the model structure of level L3 is rich, the number of faces is moderate, and the mapping effect is moderate, which accounts for the majority of the digital chaotic twin scenes of energy storage power stations; the model of level L4 has a high number of faces, good mapping effect, and restores the reality, which is usually used for key models and special displays.

[0047] S220. Lightweight the pre-configured benchmark industrial model according to the model refinement level to obtain a device model that matches the energy storage device.

[0048] The benchmark industrial model refers to a model that is representative and universal. Generally speaking, the benchmark industrial model is a standard model that is widely recognized and can be used for comparison or evaluation. For example, the benchmark industrial model can be a high-precision industrial model.

[0049] Generally speaking, the equipment models of energy storage power stations are mostly high-precision industrial models. The models are complex and detailed, with more than 10 million faces and a model size of hundreds of megabytes or even several gigabytes. Loading the digital twin scene in the browser will consume a lot of network resources and time. At the same time, due to the large resources, the scene will also be stuck during use. In order to solve these problems, in the digital twin, the model can be lightweight to process the objects with high faces and invisible or no specific functions in the high-precision industrial model, and the high-precision model information in the lightweight model can be simulated by the real-time rendering engine. In the digital twin scene, the model and scene can be quickly and accurately restored.

[0050] In different industries, the classification of model precision is different, which can be divided into high-poly and low-poly. Among them, high-poly can include industrial models and art high-poly used for production and manufacturing. The number of faces and texture accuracy of this type of model are very high, and there is no need to consider the performance of real-time rendering; low-poly can include art low-poly, such as film and television low-poly, game low-poly and digital twin low-poly. This type of model uses different production processes to restore the effect of high-poly, and the real-time rendering engine simulates the high-poly information contained in the low-poly to achieve the final effect.

[0051] In the digital twin process of the energy storage power station, the benchmark industrial model can be lightweighted according to the model precision level corresponding to the energy storage equipment to obtain an equipment model that meets the model precision level.

[0052] For example, assuming that the energy storage device is an energy storage battery compartment, the energy storage battery compartment model has sufficient details and more than a thousand faces. At this time, an L4 model can be used for lightweight processing. For lightweight L4 models, you can choose the next-generation PBR production process. The production process mainly adopts two methods. Method 1 includes the following steps: Step 1: Topology low-poly: Use software for automated topology and manual production, allocate more model faces to contours or key parts, and retain secondary forms with low faces; Step 2: UV development: UV split the topological model, organize and divide the UV coordinates of the model surface, map the surface of the three-dimensional model to the 2D plane, and assign precision to the drawn texture. The key display area requires higher precision; Step 3: Baking of high- and low-poly normals and other textures: Bake the low-poly and high-poly with UV, and use baking software to record the high-poly information in the baked texture, so that the low-poly with the baked texture can show an effect close to the high-poly through computational rendering; Step 4: Texture drawing: Use drawing software or image editing software for low-poly drawing, and make customized textures with photos or software simulation materials.

[0053] Method 2 includes the following steps: Step 1: Mesh simplification: Automatically delete the model through modeling software, or manually remove redundant triangles in a more detailed manner to retain the effective outline and highly recognizable structure of the high-surface model; Step 2: UV development: The streamlined model needs to be UV split to correctly display the mapping effect; Step 3: Assign texture: Process photos or network material textures, and adjust UV to complete the final effect. If more detailed customization is required, the material image needs to be deeply edited, or it can be produced according to the texture drawing process in method 1.

[0054] Comparing the above two methods for L4 level models, method 1 has the best effect and highest efficiency for models with ultra-high polygon counts; method 2 is efficient for models with low polygon counts, but the effect is not as good as method 1.

[0055] For example, assuming that the energy storage device is a transformer and a booster, the corresponding device model is not a high-poly model, has a moderate number of faces, and has few details. For such energy storage devices that do not have special display requirements, they can be processed according to the L3 model through face reduction optimization. The L3 model lightweight processing process includes the following steps:

[0056] Step 1: Mesh simplification. Automatically delete the model through modeling software, or manually remove redundant triangles in a more detailed manner to retain the effective outline and highly recognizable structure of the high-surface model;

[0057] Step 2: UV development. The topological model needs to be UV split to allocate precision for the drawn texture, and the key display area requires higher precision.

[0058] Step 3: Assign textures. Process photos or online textures and adjust UV to achieve the final effect. If more detailed customization is required, deep editing of the material images is required.

[0059] In energy storage power stations, for energy storage equipment that does not require a high degree of restoration, such as fire-fighting equipment, high-voltage cabinets, and low-voltage cabinets, L2-level models can be used to generate corresponding equipment models. Of course, generally speaking, L1-level models and L2-level models are BIM models or manually made simple models. In the digital twin scene of the energy storage power station, L1-level models and L2-level models may not be used, that is, there is no need for lightweight processing, only UV processing to assign materials.

[0060] S230: Determine device identification information of each energy storage device based on a pre-configured identification configuration rule and a device hierarchy relationship of each energy storage device in the physical world.

[0061] The device identification information refers to a set of data used to uniquely identify and distinguish each energy storage device or device model. The device identification information can be represented by letters and numbers, and the device identification information is unique and can distinguish different types of energy storage devices and device models. Of course, in order to ensure accurate identification between the same energy storage device and its corresponding device model, the same energy storage device and its corresponding device model are represented by the same device identification information.

[0062] The identification configuration rule refers to the model encoding rule corresponding to each type of device model. It can be understood that the identification configuration rules corresponding to each type of device model or energy storage device are the same. For example, when multiple energy storage devices are battery clusters, their corresponding identification configuration rules are the same.

[0063] Figure 3 FIG. 1 is a schematic diagram of a configuration of a device hierarchical relationship provided by an embodiment of the present invention. Figure 3As shown, it is assumed that the energy storage equipment includes: subunit, station power system, battery compartment, battery stack, battery cluster, PACK, single cell, fire protection system, liquid cooling system, booster, power conversion system (PCS), input / output (I / O) module, uninterruptible power supply (UPS), air conditioner, transformer and energy meter. Each energy storage device is identified by two capital letters, and the identification is unique to distinguish different types of energy storage devices. For example, the subunit is abbreviated as SU, the battery compartment is abbreviated as BT, the battery stack is abbreviated as BS, the battery cluster is abbreviated as BC, the PACK is abbreviated as BP, the single cell is abbreviated as CE, the fire protection system is abbreviated as FS, the liquid cooling system is abbreviated as LC, the PCS is abbreviated as PC, the booster is abbreviated as ST, the I / O module is abbreviated as MD, the air conditioner is abbreviated as AC, the transformer is abbreviated as TS, the energy meter is abbreviated as EM, the station power system is abbreviated as SP, and the UPS is abbreviated as UP.

[0064] According to the hierarchical division and classification of the equipment models corresponding to the energy storage equipment, and in comparison with the physical scene of the energy storage power station, each equipment model is encoded to obtain the corresponding identification configuration rules to ensure that all energy storage equipment has unique equipment identification information (SN for short). At the same time, the equipment hierarchy relationship and model hierarchy relationship can be parsed through the encoding. The encoding is encoded layer by layer from top to bottom according to the equipment hierarchy and connected by "_". The encoding of each layer of energy storage equipment consists of a device identification and a serial number. In this way, the subordination and relative position of the energy storage equipment, as well as the subordination and relative position between equipment models can be parsed through the equipment encoding. For example, Figure 4 FIG. 1 is a schematic diagram of a configuration of device identification information provided by an embodiment of the present invention. Figure 4As shown, assuming that the device identification information corresponding to the energy storage power station is ZJ01, the power station contains 11 subunits and 1 station power system, the device identification information corresponding to the subunit is ZJ01_SU01 to ZJ01_SU11, the device identification information corresponding to the station power system is ZJ01_SP01, the subunit contains two battery compartments, the device identification information corresponding to the battery compartment under subunit 1 is ZJ01_SU01_BT01 and ZJ01_SU01_BT02, the battery compartment contains two battery stacks, the device identification information corresponding to the two battery stacks under battery compartment 1 is ZJ01_SU01_BT01_BS01 and ZJ01_SU01_BT01_BS02, according to this rule, the battery cluster, PACK and single cell are encoded layer by layer to obtain the corresponding device identification information, wherein the single cell is the smallest unit, and the device identification information corresponding to a single cell is ZJ01_SU01_BT01_BS01_BC01_BP01_CE01.

[0065] S240: Determine the model affiliation and relative position of the device model corresponding to the energy storage device in the virtual world based on the device identification information.

[0066] Among them, model subordination refers to the parent-child relationship, inclusion relationship or parallel relationship between various device models; the relative position can be understood as the arrangement and layout of various device models between parent nodes, as well as the inclusion situation. In an embodiment, it can be determined whether two device models are in a subordinate relationship or a parallel relationship based on the device identification information. For example, the device identification information of a subunit is ZJ01_SU01, and the device identification information of a battery compartment is ZJ01_SU01_BT01. At this time, it can be determined that the model subordination relationship between the subunit and the battery compartment is a parent-child relationship based on the two device identification information, and the relative position between the subunit and the battery compartment is that the battery compartment is located in the subunit.

[0067] S250: Obtain initial device attribute information of each energy storage device in the energy storage power station.

[0068] Among them, the initial device attribute information is used to characterize the characteristics of each energy storage device. In one example, the device attribute information includes: device identification information, device name, device media information, physical coordinate position and internal component information. Among them, the device identification information is unique; the device name can be used to record and describe the energy storage device; the device media information refers to photos, videos and other information of the energy storage device; the physical coordinate position refers to the real coordinate position of each energy storage device in the physical world, which can be characterized by longitude and latitude; the internal component information refers to the components contained in each energy storage device, and the corresponding device identification information, which is used for data transmission. In an embodiment, sensor monitoring, communication protocol reading and special data analysis software can be used to collect data from all battery compartments, battery stacks, battery clusters, PACKs, battery cells, booster integrated machines, station power systems and other equipment in the energy storage power station.

[0069] S260: Perform data preprocessing on the initial device attribute information to obtain corresponding target device attribute information.

[0070] Among them, data preprocessing may include data cleaning and data sorting operations. In one embodiment, the initial device attribute information is preprocessed to obtain the corresponding target device attribute information, including: classifying the device identification information, device media information, internal component information and physical coordinate position of each energy storage device in the energy storage power station; using the device identification information of each energy storage device as a key, and the corresponding device media information, internal component information and physical coordinate position as a value to store as the corresponding target device attribute information. In an embodiment, the initial device attribute information is formulated according to the program requirements, and the unique device identification information of each energy storage device is uniformly managed, and the device identification information is used as a key; then other data is classified, such as the device name, device media information, physical coordinate position and internal component information are uniformly stored locally as values, and the program key is used as the primary key as the retrieval key to save; the physical coordinate position is uniformly sorted for use in the subsequent reproduction of the scene; the internal component information of the device is sorted to form table data; and the device media information of the energy storage device, such as photos, videos, etc., is uniformly sorted to assist in modeling.

[0071] S270 , laying out each device model in the virtual world according to the model hierarchy relationship, and binding the target device attribute information with the corresponding device model.

[0072] After completing the 3D modeling of the virtual world, the devices in the virtual world are completely replicated according to the situation in the physical world, and the data of the corresponding energy storage device is bound in the 3D scene (virtual world) according to the real situation. In one embodiment, each device model is modeled in the virtual world according to the model hierarchy relationship, and the target device attribute information is bound to the corresponding device model, including: determining the twin coordinate position of the corresponding device model in the virtual world according to the physical coordinate position of each energy storage device in the physical world and the model hierarchy relationship; configuring the target device attribute information corresponding to the device model in the twin information configuration option in the information configuration panel of each device model; binding the twin coordinate position of the device model and the twin information configuration option, and model binding the subordinate relationship between each device model. In the virtual world, find the corresponding device position of the energy storage device corresponding to each device model in the physical world; open the information configuration panel of the energy storage device and find the twin information configuration option; fill in the corresponding device identification information on the twin information configuration option; and fill in other required device attribute information in the twin information column; perform secondary development based on the configuration file and all related data saved and exported from the scenario.

[0073] S280 . In response to the received click operation on the target device model, the corresponding device identification information is searched in the target device attribute information of the target device model obtained in the virtual world.

[0074] The click operation of the device model in the virtual world is monitored, and the target device attribute information of the energy storage device is obtained in the click operation; the device twin information is found from the target device attribute information; and the device identification information is found from the twin information.

[0075] S290. Searching for the twin coordinate position of the target device model in the virtual world based on the device identification information.

[0076] The twin coordinate position refers to the coordinate position of the device model in the virtual world. In an embodiment, the device identification information can be used to find the device model in the virtual world; then, the twin coordinate position of the device model in the 3D scene is obtained.

[0077] S2100 , positioning the target device model based on focusing parameters determined based on the twin coordinate positions.

[0078] Among them, the focus parameters refer to the configuration parameters required to focus on the target device model. For example, the focus parameters may include but are not limited to: the world coordinates of the camera, the pitch angle, rotation angle and wide angle of the camera, etc. In an embodiment, the parameters required for the camera to focus on this device model are calculated according to the twin coordinate position of the target device model, including the world coordinates of the camera, the pitch angle, rotation angle and wide angle of the camera, etc.; then the camera is configured based on these parameters, and a slow-motion flight animation is added to make the camera move slowly to the specified position and focus on the target device model.

[0079] The technical solution of this embodiment, based on the above embodiment, adopts a model lightweighting method to lightweight objects with a high number of faces and invisible or without specific functions in the high-precision model, so as to obtain a device model that matches the complexity and importance of the energy storage device; and, classifies the energy storage devices according to the device hierarchy relationship, formulates unified identification configuration rules, and realizes automatic binding and rapid positioning of device models.

[0080] In one example, assuming that in a digital twin scenario of a certain energy storage power station, the above-mentioned digital twin-based device model positioning method is used to lightweight the energy storage power station equipment model, achieving rapid binding and positioning of a total of approximately 110,000 device models.

[0081] According to the industrial equipment model of the energy storage power station and the requirements of digital twins, the battery compartment, battery compartment fire protection system, battery compartment liquid cooling system, battery cluster, PACK and single cell of the energy storage power station are lightweighted at the L4 level, and the booster, PCS, air conditioner, electric energy meter, station power system and other equipment are lightweighted at the L3 level to obtain the equipment model used in the digital twin.

[0082] Use Figure 3 The energy storage power station equipment model hierarchy and identification are shown. In the embodiment, the equipment model of the energy storage power station includes 11 sub-units, 21 energy storage battery compartments, 11 booster integrated machines, 42 battery stacks, 42 PCSs, 252 battery clusters, 2016 PACKs and about 100,000 battery cells. All equipment in the power station is encoded in combination with the equipment identification information and identification configuration rules. The equipment identification information corresponding to the sub-units is ZJ01_SU01 to ZJ01_SU11, and the equipment identification information corresponding to the station power system is ZJ01_SP01, of which 10 sub-units contain two battery compartments and 4 PCSs, 1 sub-unit contains 1 battery compartment and 2 PCSs, each battery compartment contains 12 battery clusters, each battery cluster contains 8 PACKs, and each PACK contains 52 single battery cells. The encoding method is as follows: Figure 4 .

[0083] According to the implementation steps of rapid cell binding and positioning, first collect the unique device identification information of all energy storage devices, and collect information such as longitude and latitude, altitude and model name according to CAD drawings to complete data preprocessing. According to the actual scene equipment location, place 42 battery compartment models, 11 booster models and a station power system model at the corresponding position in the virtual world, fill in the device attribute information of each model, and complete data binding.

[0084] In the energy storage battery compartment, such as Figure 3 The device level division includes battery stacks, battery clusters, PACKs and single cells. After selecting a battery compartment, first obtain the device identification information of the battery compartment, such as ZJ01_SU03_BT01. When clicking the next level battery stack, the program will automatically add "_" and the next level code on the basis of the battery compartment code, and obtain other device attribute information of the energy storage device by querying the local database. For example, the device identification information corresponding to the battery stack is ZJ01_SU03_BT01_BS01. Click the device model layer by layer to obtain the next level device code and information, and realize automatic binding through the program. Similarly, if the device code is obtained, by parsing the device code, such as ZJ01_SU03_BT01, the parsing result is power station>subunit 3>battery compartment 1>, the twin coordinate position of the device in the twin scene is obtained through the database, and the focusing parameters required for the camera to focus on this device are calculated according to the position of the twin coordinate position, including the camera's world coordinates, the camera's pitch angle, rotation angle and wide angle, etc., set the camera parameters, add a slow-motion flight animation, make the camera move slowly to the specified position, focus on the device model, and realize rapid positioning of the energy storage device in the virtual world.

[0085] In one embodiment, Figure 5 is a structural schematic diagram of a positioning device based on a digital twin device model provided by an embodiment of the present invention. Figure 5 As shown, the device includes: a model determination module 510, a hierarchical relationship determination module 520, a binding module 530 and a positioning module 540.

[0086] A model determination module 510, for determining a device model corresponding to an energy storage device based on the complexity and importance of each energy storage device in the energy storage power station;

[0087] A hierarchical relationship determination module 520, configured to determine a model hierarchical relationship in the virtual world according to a device hierarchical relationship of each energy storage device in the physical world;

[0088] A binding module 530 is used to perform model binding on each device model in the virtual world based on the model hierarchy relationship;

[0089] The positioning module 540 is used to position the target device model in the virtual world in response to a received click operation on the target device model.

[0090] In one embodiment, the model determination module 510 includes:

[0091] A precision level determination unit, used to determine a corresponding model precision level based on the complexity and importance of each energy storage device in the energy storage power station;

[0092] The device model determination unit is used to perform lightweight processing on the pre-configured benchmark industrial model according to the model precision level to obtain a device model that matches the energy storage device.

[0093] In one embodiment, the model hierarchical relationship is represented by model subordination and relative position; the hierarchical relationship determination module 520 includes:

[0094] An identification information determination unit, used to determine the device identification information of each energy storage device based on a pre-configured identification configuration rule and a device hierarchical relationship of each energy storage device in the physical world;

[0095] The relationship determination unit is used to determine the model affiliation and relative position of the device model corresponding to the energy storage device in the virtual world based on the device identification information.

[0096] In one embodiment, the binding module 530 includes:

[0097] An attribute information acquisition unit, used to acquire initial device attribute information of each energy storage device in the energy storage power station;

[0098] An information preprocessing unit, used for performing data preprocessing on the initial device attribute information to obtain corresponding target device attribute information;

[0099] The model binding unit is used to lay out each device model in the virtual world according to the model hierarchy relationship, and to bind the target device attribute information with the corresponding device model.

[0100] In one embodiment, the device attribute information includes: device identification information, device name, device media information, physical coordinate position and internal component information; the information preprocessing unit includes:

[0101] The classification subunit is used to classify the device identification information, device media information, internal component information and physical coordinate position of each energy storage device in the energy storage power station;

[0102] The storage subunit is used to store the device identification information of each energy storage device as a key, and the corresponding device media information, internal component information and physical coordinate position as a value, as the corresponding target device attribute information.

[0103] In one embodiment, the model binding unit includes:

[0104] A coordinate position subunit, used to determine the twin coordinate position of the corresponding device model in the virtual world according to the physical coordinate position of each energy storage device in the physical world and the model hierarchy relationship;

[0105] An information configuration subunit, used to configure the target device attribute information corresponding to the device model in the twin information configuration option in the information configuration panel of each device model;

[0106] The binding subunit is used to bind the twin coordinate position and twin information configuration options of the device model, and to model the subordinate relationship between each device model.

[0107] In one embodiment, the positioning module 540 includes:

[0108] an identification information searching unit, configured to search for corresponding device identification information in target device attribute information of the target device model obtained in the virtual world in response to a received click operation on the target device model;

[0109] A coordinate position search unit, used to search for the twin coordinate position of the target device model in the virtual world based on the device identification information;

[0110] A positioning unit is used to position the target device model based on the focusing parameters determined by the twin coordinate positions.

[0111] The positioning device of the device model based on digital twin provided in the embodiment of the present invention can execute the positioning method of the device model based on digital twin provided in any embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method.

[0112] In one embodiment, Figure 6 is a structural block diagram of an electronic device provided by an embodiment of the present invention, such as Figure 6As shown, a schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. 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 invention described and / or required herein.

[0113] like Figure 6 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. 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.

[0114] A number of 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, speakers, etc.; a storage unit 18, such as a 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.

[0115] The processor 11 may be a variety of general and / or special 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 dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a positioning method based on a device model of a digital twin.

[0116] In some embodiments, the positioning method of the device model based on the digital twin may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on 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 positioning method of the device model based on the digital twin described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the positioning method of the device model based on the digital twin by any other appropriate means (e.g., by means of firmware).

[0117] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0118] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0119] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0120] To provide interaction with a user, the systems and techniques described herein may 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 trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0121] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may 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.

[0122] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may 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 difficult management and weak business scalability in traditional physical hosts and VPS services.

[0123] An embodiment of the present invention also provides a computer program product, including a computer program, which, when executed by a processor, can implement a positioning method for a device model based on a digital twin as provided in any embodiment of the present application.

[0124] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present application, and the programming language includes an object-oriented programming language, such as Java, Smalltalk, C++, and also includes a conventional procedural programming language, such as "C" language or similar programming language. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0125] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0126] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A positioning method for device models based on digital twins, It is characterized in that include: Determine a device model corresponding to each energy storage device in the energy storage power station based on the complexity and importance of the energy storage device; Determine the model hierarchical relationship in the virtual world according to the device hierarchical relationship of each energy storage device in the physical world; Model binding each of the device models in the virtual world based on the model hierarchical relationship; In response to the received click operation on the target device model, the target device model is positioned in the virtual world.

2. The method according to claim 1, It is characterized in that The determining of the device model corresponding to each energy storage device in the energy storage power station based on the complexity and importance of the energy storage device includes: Determine the corresponding model refinement level based on the complexity and importance of each energy storage device in the energy storage power station; The pre-configured benchmark industrial model is lightweighted according to the model refinement level to obtain a device model that matches the energy storage device.

3. The method according to claim 1, It is characterized in that The model hierarchical relationship is characterized by model subordination and relative position; the model hierarchical relationship in the virtual world is determined according to the device hierarchical relationship of each energy storage device in the physical world, including: Determine device identification information of each of the energy storage devices based on a preconfigured identification configuration rule and a device hierarchical relationship of each of the energy storage devices in the physical world; The model affiliation and relative position of the device model corresponding to the energy storage device in the virtual world are determined based on the device identification information.

4. The method according to claim 1, It is characterized in that Model binding each of the device models in the virtual world based on the model hierarchy relationship includes: Obtaining initial device attribute information of each energy storage device in the energy storage power station; Performing data preprocessing on the initial device attribute information to obtain corresponding target device attribute information; Each of the device models is laid out in the virtual world according to the model hierarchy, and the target device attribute information is bound to the corresponding device model.

5. The method according to claim 4, It is characterized in that The device attribute information includes: device identification information, device name, device media information, physical coordinate position and internal component information; the data preprocessing of the initial device attribute information to obtain the corresponding target device attribute information includes: Classify the device identification information, device media information, internal component information and physical coordinate position of each energy storage device in the energy storage power station; The device identification information of each energy storage device is used as a key, and the corresponding device media information, internal component information and physical coordinate position are stored as values ​​as corresponding target device attribute information.

6. The method according to claim 4, It is characterized in that The step of laying out each device model in the virtual world according to the model hierarchy relationship, and binding the target device attribute information with the corresponding device model, includes: Determine the twin coordinate position of the corresponding device model in the virtual world according to the physical coordinate position of each energy storage device in the physical world and the model hierarchical relationship; Configuring the target device attribute information corresponding to the device model in the twin information configuration option in the information configuration panel of each device model; The twin coordinate position of the device model and the twin information configuration options are bound, and the subordinate relationship between each of the device models is model-bound.

7. The method according to claim 1, It is characterized in that In response to a received click operation on the target device model, positioning the target device model in the virtual world includes: In response to the received click operation of the target device model, searching for corresponding device identification information in the target device attribute information of the target device model acquired in the virtual world; Finding the twin coordinate position of the target device model in the virtual world based on the device identification information; The target device model is positioned based on focusing parameters determined based on the twin coordinate positions.

8. A positioning device based on a digital twin equipment model, It is characterized in that include: A model determination module, used to determine a device model corresponding to each energy storage device in the energy storage power station based on the complexity and importance of the energy storage device; A hierarchical relationship determination module, used to determine a model hierarchical relationship in the virtual world according to the device hierarchical relationship of each energy storage device in the physical world; A binding module, used for model binding each of the device models in the virtual world based on the model hierarchy relationship; The positioning module is used to position the target device model in the virtual world in response to a received click operation on the target device model.

9. An electronic device, It is characterized in that The electronic device comprises: at least one processor; and a memory communicatively connected to 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 so that the at least one processor can execute the positioning method of the device model based on the digital twin according to any one of claims 1 to 7.

10. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the positioning method of a digital twin-based device model according to any one of claims 1 to 7 when executed.

11. A computer program product, It is characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the positioning method of the device model based on the digital twin according to any one of claims 1 to 7.

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