Digital-twin-based equipment screening intermediate platform and equipment simulation method

Abstract

The application provides a device screening intermediate platform and a device simulation method based on digital twinning. The device screening intermediate platform is connected with a factory end and a supplier end. The process of screening the device by the intermediate platform comprises: performing first retrieval based on a target device type; performing secondary retrieval in the first retrieval result based on expected performance parameters; calling a product twin plug-in related to the secondary retrieval result from a database, and assigning a digital twin factory copy to the product twin plug-in; replacing the target device with the product twin plug-in in the digital twin factory copy; performing simulation and emulation on the digital twin factory copy to obtain an emulation result; and outputting the emulation result to the factory end after screening or sorting based on the emulation result of the digital twin factory copy. In this way, a device type meeting the customized requirements of the factory can be quickly obtained.

Description

[0001] The original basis of the divisional application is a patent application with application number (202311752259.7), application date of December 19, 2023, and invention name of "An end-to-end production line equipment optimization system based on digital twinning". TECHNICAL FIELD

[0002] The present application relates to the field of factory production line equipment optimization and upgrading, and particularly relates to an equipment screening intermediate platform and a device simulation method based on digital twinning. BACKGROUND

[0003] The production line in a factory has a large number of constituent devices, each of which undertakes a certain job responsibility in the production line and realizes a certain function. According to the importance, the role of some devices in the production line is relatively important, and the role of some devices is relatively low. However, regardless of what kind of device, when facing the situation of device aging, approaching the use limit, and needing to be updated and replaced, the technical personnel of the factory need to carry out device optimization analysis to give the corresponding device optimization scheme. According to the current device optimization analysis mode of most factories, the technical personnel first need to investigate the basic information of the device to be optimized, such as including the current working parameters of the device; secondly, the technical personnel need to design the expected performance parameters that the optimized device should have in combination with the target that the optimized device is expected to achieve; then the technical personnel find the corresponding product device according to the designed expected performance parameters. Since most of the optimization and replacement of devices are not based on the maintenance or modification of the original device, but directly replace other models of existing products, another supplier who provides the source of these existing products is introduced. The supplier manufactures product devices that can be used for upgrading and replacement for the production line, and the factory queries and purchases suitable product devices from the supplier, which is an end-to-end transaction. The supplier generally provides product specifications and product parameters to the factory by distributing product specification books. The technical personnel of the factory compare and find the product specification books according to the designed expected performance parameters, and select the most suitable product for optimization and replacement.

[0004] The product specification is used to query the equipment, which is an inefficient way, so the prior art also establishes a networked data connection between the supplier and the factory. For example, CN112100965A discloses an electronic manufacturing industry collaborative innovation platform and a use method thereof. In the technical solution, the designer designs the required product through the design tool cloud and IP service cloud of the client, and the design file is transmitted to the background management end after being encrypted by the collaborative innovation platform. The intelligent identification module receives and decrypts the encrypted design file, automatically matches each supplier equipment that meets the product process flow and process parameters according to the decrypted design file through a deep mapping algorithm, and generates a corresponding encrypted order and sends it to the supplier end. The supplier end decrypts and generates a 3D panoramic display platform according to the product process flow, process parameters and corresponding supplier equipment recorded in the order, simulates the production scene, supplier equipment and production process through the digital twin cloud, and realizes online visualization. The technical solution establishes a visual model matched with the client through the background management platform according to the demand of the client, thereby intuitively feeding back the expected effect of the client, so as to adaptively adjust according to the model result. However, the above prior art is limited to the adjustment of the demand of the client, and cannot reflect the adjustment demand of the supplier end.

[0005] On the one hand, manufacturers will add some parameter items that they are concerned about when optimizing the expected performance parameters of equipment design, and these parameter items may not be in the product data list provided by the supplier. In fact, the supplier may not have noticed the corresponding parameters during design or factory detection, and no measurement has been made, resulting in certain difficulties in "finding existing equipment products according to the expected performance parameters of factory design", such as the system being difficult to match the equipment products with these additional parameters, or the system ignoring these additional parameters so that the matching result is difficult to meet the demand of the factory.

[0006] In addition, on the one hand, there are differences in understanding between those skilled in the art; on the other hand, the applicant has studied a large number of literatures and patents when making the present application, but due to the limitation of space, all the details and contents are not listed in detail, which does not mean that the present application does not have these characteristics of prior art. On the contrary, the present application already has all the characteristics of prior art, and the applicant reserves the right to add relevant prior art in the background art. SUMMARY

[0007] For the optimization of factory production line equipment, the technical personnel usually designs the expected performance parameters of the optimized equipment according to the equipment to be replaced as needed, and then searches for products on the market according to the expected performance parameters. In the earlier period, the process personnel of the factory will flip through the product catalog or product specification book and other paper books sent by the supplier to find the required products. However, this method relies on the timely updating of paper books, but the inability to update in a timely manner is one of the disadvantages of paper books, which prevents the process personnel from performing timely and extensive product searches. With the popularity of electrification and networking, existing technologies have also begun to provide product query platforms using the Internet to facilitate process personnel to query the latest and most suitable products. The existing technology CN112100965A discloses an electronic manufacturing industry collaborative innovation platform and a use method thereof, which models the customer's demand in 3D to facilitate the supplier to clearly understand the customer's demand from the three-dimensional perspective, so as to facilitate the supplier to provide more goods that meet the customer's demand. However, this prior art lacks a technical design for whether the customer's demand is accepted without reservation and reflected in the 3D modeling and the product matching process of the supplier's product. Some customers have customized needs that go beyond the conventional design means. These needs do not exist in the program preset of the intermediate system or in the factory detection standard of the supplier at the beginning. In this case, how to match and solve the problem of digital twin simulation needs to be solved.

[0008] In view of the deficiencies of the prior art, the present application provides an end-to-end production line equipment optimization system based on digital twinning, which comprises: a factory end used by a factory, a supplier end used by a supplier providing products to the factory, and an intermediate platform connected with the factory end and the supplier end as an intermediate data interaction processing platform. Based on the data uploaded by the factory end, a digital twin of the factory is established in the intermediate platform. Based on the target equipment and / or target area to be optimized in the digital twin factory designated by the factory end, the intermediate platform generates a query program for all equipment in the target equipment and / or target area. After the factory end obtains the expected performance parameters of at least one target equipment, the intermediate platform automatically shares part of the parameters in the expected performance parameters to the associated equipment based on the process association between the selected equipment. In the case of determining the expected performance parameters, the intermediate platform performs a matching operation of the product twin plug-in from the database.

[0009] The present application focuses on the case that some factories have self-defined needs when optimizing equipment, for example, according to conventional design means, optimization and replacement of a certain equipment only need to provide conventional expected parameters. However, based on its own process considerations, the factory puts forward new and additional requirements for conventional equipment optimization, that is, self-defined needs (self-defined parameters). Such needs do not exist in the intermediate platform at the beginning (when the factory side uploads expected performance parameters to the intermediate platform, there is no window for input of the self-defined needs, and there is no related preset key value / digital twin simulation algorithm in the system, and the simulation algorithm is not prepared for the self-defined needs). Also, it does not exist in the supplier's out-of-box test standard (the supplier does not know the specific value of the self-defined parameter). Based on this problem, the present application provides a scheme for uploading self-defined needs (self-defined parameters) from the factory side to the intermediate platform, and based on the uploading of the self-defined needs, according to the target area that the user needs to optimize, the system can automatically share part of the expected performance parameters, especially the part containing the self-defined parameters, to the related equipment based on the process association of the equipment in the area. Thus, the factory side only needs to provide expected performance parameters containing self-defined parameters to one or several equipment, and the system can automatically fill in the expected performance parameters (including self-defined parameters) of the remaining equipment, which can significantly reduce the labor of the factory side, and also provides guarantee for the supplier side to provide complete and updated parameter data in time.

[0010] Based on the above, the supplier end can update the product twin plug-in issued by the intermediate platform after receiving the inquiry containing the custom parameters, thereby realizing the factory production line equipment optimization scheme based on the joint construction of the supplier end and the factory end. In other words, the matching mode of the present scheme is not of the type of "submitting requirements and then outputting results", but can continuously search according to the factory requirements, and even can increase the search scheme of the searchable object based on the custom requirements of the factory. The simulation process cannot perfectly simulate the entire working performance of the equipment in the production line link at the beginning, and even the pseudo calculation program will only calculate the working performance of the predicted equipment according to the conventional simulation method in the case of missing part of the parameters. Taking a pump as an example, a pump is a tool for pumping substances, especially fluid substances. In order to simulate the working performance of the pump, the material data required includes the pump head, flow, medium properties, working environment, etc. These data can be provided by the factory side or the supplier side in advance. According to these data, normal simulation and simulation can be performed. However, if the production line of the factory is special, for example, the medium has magnetism or variable fluid properties, although the simulation can also obtain results according to the conventional way, but the simulation results will be different from the actual working condition. Therefore, the present application provides a custom parameter uploading scheme to the factory end, which not only enables the supplier to optimize the test item standard of the product when it is shipped, but also upgrades the simulation algorithm of digital twin simulation, and makes the calculation result more convergent and the matching higher through the addition of conditions.

[0011] Preferably, the product twin plug-in uploaded by the supplier end to the intermediate platform is formed by digital twin, and has at least one three-dimensional model.

[0012] Preferably, the expected performance parameters can include custom parameters filled by the factory end, and the item name and value of the custom parameters can be input by the factory end.

[0013] Preferably, the intermediate platform assigns at least one digital twin factory copy to each matched product twin plug-in, replaces the target equipment in the digital twin copy with the product twin plug-in, and executes simulation and simulation on the replaced digital twin factory copy. The simulation result is output to the factory end.

[0014] Preferably, in the case that the item of the expected performance parameter does not correspond or does not completely correspond to the product twin plug-in, the intermediate platform selects the non-corresponding item to generate an inquiry list and sends it to the supplier end, so that the supplier end can provide updated detection data to the intermediate platform based on the inquiry list.

[0015] Preferably, after obtaining the updated detection data, the intermediate platform updates the simulation and simulation algorithm based on this, so as to be able to output the simulation and simulation result to the factory end which is more consistent with its expected formation parameters.

[0016] Preferably, after receiving the expected performance parameters uploaded from the factory end, the intermediate platform continuously queries the matching adaptive product twin plug-ins, and after the new product twin plug-ins are updated or added at the supplier end, the updated or new product twin plug-ins can be added to the database for query matching.

[0017] Preferably, in the case of selecting the target equipment or target area that needs to be optimized at the factory end, the intermediate platform performs the first retrieval based on all equipment types in the target equipment or target area to screen the product twin plug-ins of the same type.

[0018] Preferably, when replacing the product twin plug-ins, the replacement model and the process parameters of the replacement equipment are replaced.

[0019] Preferably, the intermediate platform performs the second retrieval on the first retrieval result according to the preset rules, which can include similar use conditions, similar working performance, and process parameters of the product twin plug-ins greater than or equal to the expected performance parameters. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a schematic diagram of the system process provided by the present application;

[0021] Figure 2 is a schematic diagram of the twin factory input and output stored in the intermediate platform of the present application;

[0022] Figure 3 is a flowchart of the selection method of the present application;

[0023] Figure 4 is a schematic diagram of the data interaction of each end of the present application;

[0024] Figure 5 is a schematic diagram of the intermediate platform of the present application;

[0025] Figure 6 is a schematic diagram of the relationship between the platform modules of the present application. DETAILED DESCRIPTION

[0026] The present application will be described in detail below with reference to the accompanying drawings. Figures 1 to 6

[0027] ​A production line in a factory has a large number of constituent devices, each of which undertakes a certain work responsibility in the production line and realizes a certain function. According to the importance, the role of some devices in the production line is relatively important, and the role of some devices is relatively low. However, no matter what kind of device, when facing the situation of device aging, approaching the use limit of life, and needing to be updated, the technical personnel of the factory need to carry out device optimization analysis to give the corresponding device optimization scheme. According to the current device optimization analysis mode of most factories, the technical personnel first need to investigate the basic information of the device to be optimized, such as including the current working parameters of the device; secondly, the technical personnel need to design the expected performance parameters that the optimized device should have in combination with the target that the optimized device is expected to achieve; then the technical personnel look for the products that meet the designed expected performance parameters. Since most of the optimization and replacement of devices are not based on the repair or modification of the original device, but directly replace other models of existing products, another supplier who provides the source of these existing products is introduced. The supplier manufactures product devices that can be used for upgrading and replacement, and the factory queries and purchases suitable product devices from the supplier, which is an end-to-end transaction. The supplier provides product catalogs and product parameters in the form of distributing product specifications to the factory. The technical personnel of the factory compare and look up the product specifications according to the designed expected performance parameters, and select the most suitable product for optimization and replacement.

[0028] The way of using product specifications to query devices is an inefficient way, so the prior art has established a networked data connection between the supplier and the factory. For example, CN112100965A discloses an electronic manufacturing industry collaborative innovation platform and a use method thereof. In the technical solution, the designer designs and simulates the required product through the design tool cloud and IP service cloud of the client end, and the design file is transmitted to the background management end after being encrypted by the collaborative innovation platform; the intelligent identification module receives and decrypts the encrypted design file, automatically matches each supplier device that meets the product process flow and process parameters according to the decrypted design file through a deep mapping algorithm, and generates a corresponding encrypted order and sends it to the supplier end; the supplier end decrypts and generates a 3D panoramic display platform to realize online visualization according to the product process flow, process parameters and corresponding supplier devices recorded in the order. The technical solution establishes a visual model matched with the demand of the client end through the background management platform, thereby intuitively feeding back the expected effect of the client end, so as to make adaptive adjustments according to the model results. However, the above prior art is limited to the adjustment of the demand of the client end, and cannot reflect the adjustment demand of the supplier end.

[0029] On the one hand, manufacturers add parameters they are concerned with when optimizing the expected performance parameters of equipment design. These parameters may not be in the product data list provided by the supplier. In fact, the supplier may not have considered or measured these parameters during design or manufacturing. This makes it difficult to find existing equipment products based on the factory's expected performance parameters. For example, the system may have difficulty matching equipment products with these additional parameters, or the system may ignore these additional parameters, making the matching results unsuitable for the factory's needs. Based on this, the present invention provides an end-to-end equipment optimization system based on digital twins. Figure 1 and Figure 4 The system establishes supply and demand relationships between suppliers and factories. It features a platform, known as an intermediary platform, for receiving data uploaded by both parties. This intermediary platform should consist of servers or server units with substantial computing power and a database capable of storing large amounts of data. Its computing power is primarily used to match suitable equipment products according to factory requirements and to create a digital twin factory based on the data uploaded by the factory. Specifically, a dedicated factory terminal is provided, which can be a computer, intelligent computing device, etc., allowing the factory to input and retrieve information. The factory uploads the data used to create the digital twin to the intermediary platform via the factory terminal. This data can be pre-collected within the factory using a data acquisition device, such as a laser point cloud scanner. After the digital twin factory is established on the intermediary platform, it can continuously receive information uploaded from the factory terminal, thereby updating the data within the digital twin factory. In another embodiment, the intermediate platform can provide the factory with an interface to operate the digital twin factory. The factory can share its own operational data with the digital twin factory, enabling real-time comparison and simulation of various process states within the factory, such as real-time material flow data and chemical reaction data. Figure 5As shown, the intermediate platform is configured with interfaces (which can include various interfaces such as data interfaces and graphical interfaces), computers (or CPUs) and servers (or storage databases) from the device level. The interface is used to form a communication connection with other ends and is the main carrier of data transmission. The present scheme establishes a data interaction platform trusted by both the factory end and the supplier end, so that the device automatic matching optimization and the transaction process can be promoted without the privacy information of the factory side or the supplier side being disclosed to the other party, which is secure. The computer, or the processing component with data processing capability (such as CPU), is mounted in the intermediate platform to perform a large amount of data calculation and processing tasks. The intermediate platform provided by the present scheme is responsible for the calculation and processing work with the largest amount of data, and the construction and storage of the digital twin factory can be completed in the cloud, which can greatly reduce the device building requirements of the factory side. In another embodiment, the factory end can also be responsible for the calculation and construction of the digital twin factory. The server is used to store a large amount of data. Since the present platform can be opened to multiple factory ends (corresponding to multiple users) and multiple supplier ends (corresponding to multiple suppliers), the number of digital twin factories and product twin plug-ins is large, and the amount of data will be huge, so a server or a plurality of server groups are used to store data to form a storage database. The database will be opened to the computer for easy processing of the data therein.

[0030] When it is necessary to optimize and upgrade the device, the process personnel select the device that needs to be optimized and upgraded in the digital twin factory through the factory end, which can be referred to as a target device, at which time the digital twin factory can provide the process personnel with relevant parameter data of the target device. The relevant parameter data can be the basic configuration parameters of the device that have been stored when the digital twin factory is constructed, such as size data, weight, etc., and can also be the current and / or historical working parameters of the device, such as flow, real-time power, etc. The process personnel refer to the above information and the expected process target to design the expected performance parameters and upload them to the intermediate platform. The expected performance parameters can include project names and expected values, and the project names can be completely customized by the process personnel. The present scheme focuses on the fact that some factories pay attention to some parameters that are not regular parameter projects of the device when optimizing the device. These parameters can be referred to as custom parameters. Taking a pump as an example, the regular specification table project only has the size, head, flow, etc. of the pump, but some factories hope to pay attention to the pump pipe viscosity characteristics, which are not in the pump product factory detection project or provided in the system upload list. Therefore, the present system allows the user to completely customize the parameter project name and value. In a preferred embodiment, the system is equipped with an association model that can automatically associate parameter names with similar semantics, such as flow and flux. Thus, the user-defined parameter project can be matched in a fuzzy search manner to expand the search scope.

[0031] The process of establishing a digital twin factory can be roughly divided into data collection, data processing and modeling simulation. Data collection is through on-site scanning and collection of physical equipment, structure and pipeline in the factory. Data collection usually utilizes some special sensing devices, such as three-dimensional laser point cloud collection device. By determining the original reference point, laser is emitted to the physical object in the factory to collect the reflection points of the physical object in the form of laser points. Data processing is to process the data for noise reduction, de-duplication and repair, so that the data can be used for subsequent modeling steps. During the process, certain data analysis process can be assisted, that is, to determine the mutual relationship of multiple physical scanning data in the actual factory, such as pipeline connection relationship. The modeling simulation process is to import the processed data into a mathematical modeling program, to build a virtual and real world relationship, and to construct a virtual factory which is one-to-one replication of the real factory in the virtual world. The above process is the general process of establishing a digital twin factory.

[0032] As shown in Figure 2 When selecting equipment, it is necessary to first understand the working parameters of the current equipment in the factory, and determine the expected parameters of the replacement equipment in combination with the expected production target and other factors. Taking a pump as an example, the technical requirement data, design result data, rule setting and other forms of data of the existing pump need to be concerned during selection, such as type, design flow, head, motor power, etc. Finally, the relevant data of the pump are output, and all these data are structured and stored in the database, including but not limited to the following data:

[0033] 1. Technical requirement file: including the working environment of the pump, such as medium and property (viscosity, density, temperature, solid particle content of liquid, etc.); energy supply (power and power type of the pump, such as 5KW, alternating current, and voltage and frequency requirements of the power supply); design flow, head; temperature, humidity, altitude, explosion-proof requirement;

[0034] 2. Pump selection calculation table: including the pump model, flow, head, power and other parameters calculated according to the technical requirements.

[0035] 3. Pump performance curve diagram: performance curve diagram drawn according to the flow, head and other parameters of the pump, used for evaluating the working performance of the pump.

[0036] The above data can be used as expected parameters, which are uploaded to the intermediate platform from the factory end and stored in the database.

[0037] The intermediate platform associates the expected parameter information structured and stored in the database to the digital twin factory based on the master data (such as equipment location number).

[0038] The expected performance parameters are uploaded from the factory end to the intermediate platform, and the intermediate platform performs the following retrieval process: first retrieval based on the target device type; second retrieval based on the expected performance parameters within the first retrieval results; calling product twin plug-ins related to the second retrieval results from the database, assigning a digital twin factory copy to each product twin plug-in; replacing the target device with the product twin plug-in in each digital twin factory copy; performing simulation simulation for each digital twin factory copy to obtain simulation results; and outputting to the factory end after screening or sorting based on the simulation results of each digital twin factory copy.

[0039] The product twin plug-in is a digital twin model of a certain product device, which is constructed from product data uploaded by the supplier to the intermediate platform using the supplier end. When a new product is launched, the supplier uploads relevant parameters of the product, especially three-dimensional model data, to the intermediate platform, and the intermediate platform creates a digital twin model based on the digital twin algorithm. The product twin plug-in can be formed according to the smallest device unit in the factory production line. The smallest device unit can refer to the bottom layer of equipment that can be replaced individually, such as a valve part, a base part, etc. The product twin plug-in can be provided by multiple suppliers of multiple product types, and as the number of participating suppliers increases, the number and richness of product twin plug-ins stored in the system database will also increase.

[0040] The specific operation of performing the first screening is: the intermediate platform based on the input target device type, only calls the digital twin module data set conforming to the target device type from the database. Specifically, the target device type information can be included in the input expected performance parameters, or it can be automatically generated by the system after the target device is selected in advance. The target device type is a kind of key value information, through which the corresponding product twin plug-in data set can be screened from the many product twin plug-ins already stored in the database.

[0041] The specific operation of the intermediate platform for secondary retrieval is: according to the preset rules, the product twin plug-ins conforming to the rules are retrieved from the results of the first retrieval. The preset rules can be designed by humans, especially by process personnel on the factory end, and the rules that can be selected include but are not limited to: similar use conditions, product twin plug-in parameter values greater than or equal to expected performance parameters, similar working performance, appropriate cost, etc. The above rules can be selected according to specific needs, or multiple rules can be selected for combined retrieval. Taking a pump as an example, the rules that can be used include: similar use conditions; the design flow of the digital twin module is greater than the design flow of the current device to be replaced; the digital twin module has a lift greater than the current device to be replaced; the device power of the digital twin module is a specified multiple of the current device to be replaced, and the working performance is basically the same.

[0042] After the secondary search, the obtained results have approached the list of replaceable products expected by the factory. However, whether the specific performance of the product actually installed in the production line will be as expected or not is unknown to both the supplier and the factory. The problem existing in the prior art is that for some special equipment products or for some special production lines, the conventional selection matching system will also recommend products with nominal parameters meeting the expected performance parameters proposed by the factory. However, after replacing the equipment with the recommended products, the actual working performance of the equipment still fails to meet the expectations. The reason is that the expected performance parameters provided by the factory do not match the actual situation of the factory, or there is a certain difference between the product twin plug-in made according to the supplier's data and the actual product. Therefore, the present application assigns a digital twin factory copy to each product twin plug-in according to the results after the secondary search, replaces the corresponding target equipment with the product twin plug-in in each digital twin factory copy, and performs simulation to obtain the simulation results. Preferably, the digital twin factory copy can not be copied for the entire factory, but only for the process area where the target equipment is located. The process area refers to the part associated with the target equipment in the production line, such as the part belonging to the same process step. For example, in the case of the target equipment being a pump, the process area can include the upstream tank, the downstream tank, the intermediate pipeline and the pump itself. The selection of the process area can be manually specified or automatically derived by artificial intelligence according to the pre-analysis of the relevance of each production line in the digital twin factory model.

[0043] The detailed rules of the secondary search can be formulated by the factory, for example, the intermediate platform can open a rule formulation window to the factory end, and the factory end can select, for example, economic priority (products with lower prices are arranged in front), quality priority (products that can completely cover the expected performance parameters and have larger differences are arranged in front), or more complex rules can be formulated.

[0044] In the simulation of the digital twin factory copy, the product twin plug-in is replaced with the selected target device (or a plurality of corresponding product twin plug-ins are replaced with corresponding devices in the selected target area), and the replacement includes replacement of the model and replacement of the process parameters. Based on the replaced digital twin factory copy, simulation calculation is performed according to the process operation logic. The process operation logic refers to a set of pre-stored algorithms that can simulate the changing state of materials in the digital twin factory production line from the basic scientific level such as physics, chemistry and biology. For example, the algorithm can include a fluid flow model, a pipe constraint model, and a pump actuation power model. The process operation logic algorithm can be pre-made by a person in cooperation with a computer according to the specific characteristics of the factory production line, combined with a large amount of prior basic physical and chemical knowledge, and a relatively complete process operation logic algorithm can be obtained. After replacing the product twin plug-in, some calculation parameters in the process operation logic are updated, and new simulation results can be output based on the update of the calculation parameters. The simulation results are one of the outputs of the system and are displayed to the users of the factory. The factory personnel can decide which product to purchase according to the simulation results.

[0045] The DCS real-time monitoring system can be used to associate the plug-in with the digital twin factory. The system can compare the expected performance data with the simulation parameters of the real-time simulation to form a warning result.

[0046] Preferably, the intermediate platform or the factory end can have a material management system. The material management system is used to manage the devices already stored in the factory, such as inventory devices. In the case that the factory end can select, the intermediate platform preferentially selects the devices already stored in the material management system based on the simulation results recommended to the factory end or the intermediate platform in the first and second retrievals, so as to optimize the use of inventory and reduce costs.

[0047] As shown in Figure 3 The application also provides a complete end-to-end production line device optimization method based on digital twinning. The method can include the following contents: establishing a digital twin factory with a digital twin of an entity factory; retrieving a product twin plug-in matched with the selected device (or the device in the selected area) based on the formed expected performance parameters; assigning a digital twin factory copy to each product twin plug-in, and performing simulation based on the copy, wherein the product twin plug-in is replaced with the selected device; and outputting the simulation results to the factory personnel for selection.

[0048] Preferably, according to the above, the present scheme finds the field of optimizing the use of network platforms to query equipment, although it can be more convenient for factory technicians to access more products provided by suppliers, it can also achieve a certain degree of automated query of products that may meet the requirements. But no matter from the aspect of whether the product can actually meet the production demand after installation, or from the aspect of the special needs of the factory itself, the present scheme believes that the custom needs of the factory (customer) also need to be concerned, that is, the parameters of the supplier's product have not been tested before leaving the factory. Therefore, in the case of uploading the expected performance parameters of the factory end containing custom parameters, first, the relevant parameter key values are matched based on semantic analysis, if there is a match, the key value is searched in the database, if there is no match, an inquiry list is formed and sent to the supplier end. The supplier end receiving the inquiry list can test the parameter items recorded on the inquiry list, so as to obtain the answer to the custom demand. Subsequently, the new data obtained by the test is uploaded by the supplier end to the intermediate platform to form a new product twin plug-in or update the original product twin plug-in, so that the product twin plug-in can be inserted into the ongoing optimization simulation selection process for the target equipment. The above optimization selection process is continuously carried out in the intermediate platform, in other words, the present retrieval is not of the type of "submitting requirements and then getting results", but can continuously retrieve according to the factory demand, and even can increase the retrieval scheme of the retrievable object based on the custom demand of the factory. The simulation process cannot perfectly simulate the entire work performance of the equipment in the production line at the beginning, in the case of missing part of the parameters, even the pseudo calculation program will only calculate the predicted work performance of the equipment according to the conventional simulation method. Taking a pump as an example, a pump is a tool for pumping substances, especially fluid substances. To simulate the work performance of the pump, the required data includes the pump head, flow, medium properties, working environment, etc. These data can be provided by the factory or the supplier in advance. According to these data, normal simulation and simulation can be carried out. However, if the production line of the factory is special, for example, the medium has magnetism or variable fluid properties, the simulation result will be different from the actual working condition although the simulation result can be obtained according to the conventional way. Therefore, the present application provides a custom parameter uploading scheme to the factory end, which not only enables the supplier to optimize the test item standard of its product before leaving the factory, but also upgrades the simulation algorithm of digital twin simulation, so that the calculation result is more convergent and the matching is higher. Preferably, the present system can also record the custom parameters, the corresponding target equipment type and the feedback data of the supplier after receiving the inquiry list, which can be used as a system template for other similar equipment replacement.In the case of other factory users uploading similar requirements (preferably, the upstream and downstream data linkage integration function of the digital twin factory can be used to autonomously infer), whether to add relevant custom parameters is automatically recommended to the factory, so that the factory has a better starting point when making design decisions. Preferably, the products of suppliers whose feedback inquiry list speed and quality (which can be evaluated by the factory side) are higher can be displayed preferentially when forming the recommended list for the factory end. This can encourage more suppliers to actively understand the needs of the factory, develop and provide more types of products that better meet the needs, and promote product updates and iterations.

[0049] Preferably, the system can also provide optimized equipment query selection for a selected part of the production line. By the factory side defining the target area of the production line in the digital twin factory model, the intermediate platform automatically finds all the equipment in the target area, and the user uploads the expected performance parameters to the intermediate platform. The expected performance parameters are different for each device in the target area, and only some of the expected performance parameters of the devices can be given. Based on the process association between the devices in the target area in the digital twin factory, the intermediate platform automatically shares some of the expected performance parameters (especially including custom parameters) to all associated devices in the target area. And match the appropriate product twin plug-in for each device according to the updated expected performance parameters. The process association can be the relationship between the devices on the production line, and the most common relationship is the upstream and downstream relationship. Basically, all devices have an upstream and a downstream, and through the upstream and downstream relationship, multiple devices can be associated. For example, the A tank body is connected to the B tank body through a pipeline, which is an upstream and downstream relationship, including the A tank body, the pipeline, and the B tank body. They also have certain association in terms of parameter relationship. The present scheme believes that the expected performance parameters are also related between the devices with process association, for example, the pump requires magnetic resistance, and its upstream and downstream storage tanks also have magnetic resistance requirements. Therefore, the present scheme can automatically fill the expected performance parameter requirements of the devices of the associated production line based on the association, thereby significantly reducing the labor cost of user input. More importantly, by updating the special parameter requirements of each associated device to the supplier, the simulation results can be more organically and smoothly combined within the production line part defined by the factory, significantly reducing the probability of problems after optimization and replacement.

[0050] As Figure 6As shown, preferably, the factory end provided by the present solution can obtain data in the factory's existing data asset management library in the form of accessing the factory database. At present, many factories have their own data management centers, and a large amount of production data, construction data, operation and maintenance data and the like in the factory will be collected to the data management center, and some of the privacy contents cannot be shared. Based on the database connection protocol in the factory end, the present solution configures data extraction monitoring means, extracts only the data related to the equipment to be optimized, and basically extracts only the existing attribute data of the equipment, without involving the data collected in the production process (i.e. production data) or operation and maintenance data. In the optional case, the factory personnel opens access to the above-mentioned production data or operation and maintenance data, and the present solution has considerable security. In addition, the intermediate platform in the present solution will independently show the interface of the digital twin (including the digital twin factory and the product twin module), and respectively establish a private connection with the corresponding factory end and supplier end. The ordinary data interface can transmit the result data of whether the simulation matches or not, or the low-fidelity three-dimensional model data (according to the need to hide or blur part of the model parameters or model shape). On the one hand, this can reduce the congestion caused by data parallelism, and on the other hand, it avoids irrelevant personnel from accessing the fine model and causing important information leakage (for example, the data interface data can be shared to all ports using the intermediate platform to facilitate personnel to understand and select, while the copier cannot accurately obtain the accurate model of the product twin module to copy the product, thereby protecting the interests of the supplier).

[0051] Preferably, the intermediate platform can be built in the form of pure software on the factory end and / or the supplier end, or can be built in the cloud.

[0052] It should be noted that the above specific embodiments are exemplary, and those skilled in the art can think of various solutions under the inspiration of the disclosure of the present application, and these solutions also belong to the disclosed scope of the present application and fall within the protection scope of the present application. Those skilled in the art should understand that the present application specification and its drawings are illustrative and not constitute a limitation on the claims. The protection scope of the present application is defined by the claims and their equivalents. The present application specification contains multiple inventive concepts, such as "preferably", "according to a preferred embodiment" or "optionally", which means that the corresponding paragraph discloses an independent concept, and the applicant reserves the right to file a divisional application according to each inventive concept. Throughout the text, the features introduced by "preferably" are only optional ways, and should not be understood as necessarily set, therefore the applicant reserves the right to abandon or delete the relevant preferred features at any time.

Claims

1. A device screening intermediate platform based on digital twins, characterized in that, The intermediate platform is connected to both the factory and the supplier. The factory is used to upload data for creating digital twins to the intermediate platform. The supplier side is used by suppliers to upload digital twin plugins for their products and equipment to the middleware platform. The process for screening equipment on the intermediate platform includes: Perform an initial search based on the target device type; A second search is performed within the initial search results based on the expected performance parameters. Retrieve product twin plugins related to the secondary search results from the database, and assign digital twin factory copies to the product twin plugins; In the digital twin factory replica, the target device is replaced with a product twin plug-in; A simulation is performed on the digital twin factory replica to obtain simulation results; Based on the simulation results of the digital twin factory replica, the results are filtered or sorted and then output to the factory. Specifically, based on the target area of ​​the production line defined by the factory in the digital twin factory model, the intermediate platform automatically finds all the equipment in the target area. Based on the process association between the equipment in the target area in the digital twin factory, it automatically shares a portion of the expected performance parameters uploaded by the user to all the associated equipment in the target area, thereby matching a suitable product twin plugin for each equipment according to the updated expected performance parameters.

2. The device screening intermediate platform based on digital twins according to claim 1, characterized in that, The process of the intermediate platform performing the initial retrieval includes: Based on the input target device type, retrieve only the digital twin module dataset that matches the target device type from the database; The target device type is query key-value information, and the intermediate platform uses key-value queries to filter out the corresponding product twin plugin dataset from a large number of product twin plugins already stored in the database.

3. The device screening intermediate platform based on digital twins according to claim 2, characterized in that, The process of the intermediate platform performing secondary retrieval includes: According to preset rules, retrieve product twin plugins that match the rules from the results of the initial search; The preset rules include: usage conditions, product twin plugin parameter values ​​being greater than or equal to expected performance parameters, working performance, and cost.

4. The device screening intermediate platform based on digital twins according to any one of claims 1 to 3, characterized in that, When a supplier uploads product data to an intermediate platform using the supplier's terminal, the intermediate platform creates a product twin plugin based on the digital twin algorithm using the model data and parameters.

5. The device screening intermediate platform based on digital twins according to any one of claims 1 to 3, characterized in that, The intermediate platform recommends equipment types to the factory based on simulation results; Alternatively, the intermediate platform may prioritize selecting devices already stored in the materials management system during the initial and secondary searches.

6. The device screening intermediate platform based on digital twins according to any one of claims 1 to 3, characterized in that, If the expected performance parameters uploaded by the factory include custom parameters, the intermediate platform matches relevant parameter keys based on semantic analysis. If a match is found, the platform searches the database using that key; otherwise, it generates a query list and sends it to the supplier. The intermediate platform receives test data corresponding to the parameter items recorded in the test query list through the supplier, in order to form new product twin plugins or update existing product twin plugins; and / or The intermediate platform receives the answer information corresponding to the customized requirements through the supplier.

7. A device simulation method based on digital twins, characterized in that, The method includes: Retrieve product twin plugins related to the search results of the intermediate platform from the database, and assign digital twin factory copies to the product twin plugins; In the digital twin factory replica, the target device is replaced with a product twin plug-in; Based on the replaced digital twin factory copy, simulation calculations are performed according to the process operation logic; the process operation logic is a pre-stored algorithm that simulates the changing state of materials in the digital twin factory production line from a basic scientific perspective. Specifically, based on the target area of ​​the production line defined by the factory in the digital twin factory model, the intermediate platform automatically finds all the equipment in the target area. Based on the process association between the equipment in the target area in the digital twin factory, it automatically shares a portion of the expected performance parameters uploaded by the user to all the associated equipment in the target area, thereby matching a suitable product twin plugin for each equipment according to the updated expected performance parameters.

8. The equipment simulation method according to claim 7, characterized in that, The search results from the intermediate platform are obtained after performing the initial search and the second search. The process of the intermediate platform performing the initial retrieval includes: Based on the input target device type, retrieve only the digital twin module dataset that matches the target device type from the database; The target device type is query key-value information, and the intermediate platform uses key-value queries to filter out the corresponding product twin plugin dataset from a large number of product twin plugins already stored in the database.

9. The equipment simulation method according to claim 8, characterized in that, The process of the intermediate platform performing secondary retrieval includes: According to preset rules, retrieve product twin plugins that match the rules from the results of the initial search; The preset rules include: usage conditions, product twin plugin parameter values ​​being greater than or equal to expected performance parameters, working performance, and cost.

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