Digital twinning-based equipment screening intermediate platform and equipment analogue simulation method

Abstract

The invention provides an equipment screening intermediate platform based on digital twinning and an equipment analogue simulation method, the equipment screening intermediate platform is respectively connected with a factory end and a supplier end, and the process of screening equipment by the intermediate platform comprises the following steps: executing first retrieval based on a target equipment type; executing secondary retrieval in the first retrieval result based on the expected performance parameters; calling a product twinning plug-in related to the secondary retrieval result from the database, and distributing a digital twinning factory copy for the product twinning plug-in; replacing the target equipment with a product twinning plug-in in the digital twinning factory copy; performing analog simulation on the digital twin factory copy to obtain a simulation result; and based on the simulation result of the digital twinning factory copy, screening or sorting and then outputting to a factory end. Therefore, the equipment type meeting the custom requirement of the factory can be quickly obtained.

Description

[0001] The original basis of this divisional application is a patent application with application number (202311752259.7), application date December 19, 2023, and invention name “An end-to-end production line equipment optimization system based on digital twins”. Technical Field

[0002] The present invention relates to the field of factory production line equipment optimization and upgrading, and in particular to an equipment screening intermediate platform and equipment simulation method based on digital twins. Background Art

[0003] The production line in the factory has a large number of components, each of which has certain work responsibilities and functions in the production line. According to the degree of importance, some equipment plays a relatively important role in the production line, while some equipment plays a relatively low role. However, no matter what kind of equipment, when facing the situation of equipment aging, approaching the end of its service life, and needing to be replaced, the factory technicians need to perform equipment optimization analysis to provide corresponding equipment optimization solutions. According to the equipment optimization analysis mode of most factories at present, the technicians first need to investigate the basic information of the equipment to be optimized, such as the current working parameters of the equipment; secondly, the technicians need to design the expected performance parameters that the optimized equipment should have in combination with the expected goals of the optimized equipment; then the technicians find the products and equipment that meet the requirements according to the expected performance parameters of the design. Since most of the equipment optimization and replacement are not repaired or modified on the basis of the original equipment, but directly replace other models of existing products, another supplier is introduced as the source of these existing products. The supplier manufactures products and equipment that can be used for upgrading and replacement for the production line, and the factory inquires and purchases suitable products and equipment from the supplier. This is an end-to-end transaction. The supplier generally provides product catalogs and product parameters by distributing product specifications to the factory. Factory technicians will check product specifications based on the expected performance parameters of the design and select the most suitable product for optimized replacement.

[0004] Using product specifications to query equipment is an inefficient way, so there are also existing technologies that establish a networked data connection between suppliers and factories. For example, CN112100965A discloses an electronic manufacturing collaborative innovation platform and its use method. The designer in this technical solution uses the client's design tool cloud and IP service cloud to design and simulate the required products. The design file is encrypted by the collaborative innovation platform and transmitted to the backend management end; the intelligent recognition module receives and decrypts the encrypted design file, and automatically matches each supplier's equipment that meets the product process flow and process parameters through the deep mapping algorithm according to the decrypted design file, and generates a corresponding encrypted order and sends it to the supplier end; after decryption, the supplier end simulates the production scene, supplier equipment and production process through the digital twin cloud according to the product process flow, process parameters and corresponding supplier equipment recorded in the order, and generates a 3D panoramic display platform to achieve online visualization. This technical solution establishes a visualization model that matches the client's needs through the backend management platform, thereby intuitively feedbacks the client's expected effect, so as to facilitate adaptive adjustment according to the model results. However, the above-mentioned prior art is limited to the adjustment of the client's needs, and cannot reflect the adjustment needs of the supplier end.

[0005] On the one hand, when manufacturers design expected performance parameters for optimized equipment, they will add some parameter items of their own concern, and these parameter items are not necessarily in the product data list provided by the supplier. In fact, the supplier may not have detected and noticed the corresponding parameters during design or delivery, nor did they conduct measurements, resulting in certain difficulties in the work of "finding existing equipment products based on the expected performance parameters designed by the factory". For example, it is difficult for the system to match equipment products with these additional parameters, or the system ignores these additional parameters, making it difficult for the matching results to meet the needs of the factory.

[0006] In addition, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making the present invention, but due to space limitations, not all details and contents are listed in detail. However, this does not mean that the present invention does not have the characteristics of these prior arts. On the contrary, the present invention already has all the characteristics of the prior art, and the applicant reserves the right to add relevant prior art to the background technology. Summary of the invention

[0007] For the optimization of factory production line equipment, technicians usually design the expected performance parameters of the optimized equipment according to the equipment to be replaced, and then search for products on the market based on the expected performance parameters. In the early days, the factory's process personnel would look through paper books such as product catalogs or product specifications sent by suppliers to find products that meet the requirements. However, this method relies on the timely update of paper books, but the inability to update in time is one of the disadvantages of paper books, which makes it impossible for process personnel to conduct timely and extensive product searches. With the popularization of electronicization and networking, the prior art has also begun to propose the use of the Internet to provide a product query platform to facilitate process personnel to query the latest and most suitable products. Prior art CN112100965A discloses a collaborative innovation platform for the electronic manufacturing industry and its use method. The prior art is to model customer needs in 3D to facilitate suppliers to clearly understand customer needs from a three-dimensional perspective, so as to facilitate suppliers to provide products that better meet customer needs. However, the existing technology lacks technical design for whether customer needs are accepted unreservedly and reflected in the 3D modeling and supplier's product comparison process. Some customers have customized needs beyond conventional design methods. These needs do not initially exist in the program presets of the intermediate system or in the supplier's factory inspection standards. In this case, how to match and simulate digital twins is a problem that needs to be solved.

[0008] In view of the deficiencies in the prior art, the present invention provides an end-to-end production line equipment optimization system based on digital twins, comprising: a factory side, which is used by the factory, a supplier side, which is used by suppliers who provide products to the factory, and an intermediate platform, which serves as an intermediate data interaction processing platform and is respectively connected to the factory side and the supplier side for data. A digital twin of the factory is established on the intermediate platform based on the data uploaded by the factory side. Based on the target equipment and / or target area that needs to be optimized defined by the factory side in the digital twin factory, the intermediate platform generates a query program for the target equipment and / or all equipment in the target area. After the expected performance parameters for at least one target equipment are obtained from the factory side, the intermediate platform automatically shares some of the expected performance parameters with the associated equipment based on the process association between the selected equipment. When the expected performance parameters are determined, the intermediate platform performs a matching operation of the product twin plug-in from the database.

[0009] The present invention is concerned about the situation that some factories have self-defined requirements when optimizing equipment. For example, according to conventional design methods, it is only necessary to provide conventional expected parameters for the optimization and replacement of a certain equipment. However, based on its own process considerations, the factory has put forward new and additional requirements for conventional equipment optimization, namely, custom requirements (custom parameters). This requirement did not exist in the intermediate platform at the beginning (when the factory uploaded the expected performance parameters to the intermediate platform, there was no window to provide input for the custom requirement, and there was no relevant preset key value / digital twin simulation algorithm in the system, and no simulation algorithm for the custom requirement was prepared) nor did it exist in the supplier's exit inspection standard (the supplier was also unclear about the specific values ​​related to the custom parameters). Based on this problem, this solution provides a solution for uploading custom requirements (custom parameters) from the factory to the intermediate platform, and based on the upload of the custom requirements, according to the target area selected by the user that needs to be optimized, the system can automatically share some of the expected performance parameters, especially those containing custom parameters, to related equipment based on the process association of the equipment in the area. As a result, the factory only needs to provide expected performance parameters including custom parameters to one or several devices. The system can automatically fill in the expected performance parameters (including custom parameters) of the remaining devices, which can significantly reduce the labor of the factory and also provide guarantees for the supplier to provide complete updated parameter data in a timely manner.

[0010] Based on the above, after receiving the query containing the custom parameters, the supplier side can update the product twin plug-in released to the intermediate platform, so that this solution realizes the factory production line equipment optimization solution based on the joint construction of the supplier side and the factory side. In other words, the matching mode of this solution is not the type of "results will be obtained after submitting the requirements", but it can continue to search according to the factory's needs, and even increase the searchable objects based on the factory's custom requirements. The simulation process is not able to perfectly simulate the entire working performance of the equipment in the production line link at the beginning. In the case of missing some parameters, even the simulation calculation program will only calculate the predicted working performance of the equipment according to the conventional simulation method. Take the pump as an example. The pump is a tool for pumping materials, especially fluid materials. To simulate the working performance of the pump, the material data required are the pump's head, flow rate, medium properties, working environment, etc. These data can be provided in advance by the factory or supplier. According to these data, normal simulation can also be performed. However, if the factory's production line is more special, such as through the medium with magnetism, or variable fluid properties, although the conventional simulation can also obtain results, the simulation results will be different from the actual working conditions. Therefore, the present invention provides a customized parameter uploading solution to the factory side, which not only enables suppliers to optimize the inspection item standards of their products when they leave the factory, but also upgrades the simulation algorithm of digital twin simulation. By adding conditions, the calculation results are more convergent and more matching.

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

[0012] Preferably, the expected performance parameters can include custom parameters filled in autonomously by the factory side, and the item names and values ​​of the custom parameters can be independently input by the factory side.

[0013] Preferably, the intermediate platform assigns at least one digital twin factory copy to each matched product twin plug-in, replaces the target device in the digital twin copy with the product twin plug-in, performs simulation on the replaced digital twin factory copy, and outputs the simulation results to the factory end.

[0014] Preferably, when the items of expected performance parameters do not correspond or do not fully correspond to the product twin plug-in, the intermediate platform selects the non-corresponding items to generate an inquiry list and sends it to the supplier side, so that the supplier side can provide updated detection data to the intermediate platform based on the inquiry list.

[0015] Preferably, after acquiring updated detection data, the intermediate platform updates the simulation algorithm based on the updated detection data, so that simulation results that are more in line with expected formation parameters can be output to the factory end.

[0016] Preferably, after receiving the expected performance parameters uploaded by the factory, the intermediate platform continuously queries and matches the adapted product twin plug-in. After the supplier updates or adds a new product twin plug-in, the updated or new product twin plug-in can be added to the database for query and matching.

[0017] Preferably, when the factory side selects the target device or target area to be optimized, the intermediate platform performs a first retrieval based on all device types in the target device or target area to filter out product twin plug-ins that match the type.

[0018] Preferably, when replacing the product twin plug-in, the model is replaced as well as the process parameters of the equipment.

[0019] Preferably, the intermediate platform performs a secondary search for the first search results according to preset rules, and the preset rules may include similar usage conditions, similar working performance, and process parameters of the product twin plug-in being greater than or equal to expected performance parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the system flow provided by the present invention;

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

[0022] Figure 3 It is a schematic flow chart of the selection method of the present invention;

[0023] Figure 4 It is a schematic diagram of data interaction between various terminals of the present invention;

[0024] Figure 5 It is a schematic diagram of the composition of the intermediate platform of the present invention;

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

[0026] The following is combined with Figures 1 to 6 Provide detailed explanation.

[0027] The production line in the factory has a large number of components, each of which has certain work responsibilities and functions in the production line. According to the degree of importance, some equipment plays a relatively important role in the production line, while some equipment plays a relatively low role. However, no matter what kind of equipment, when facing the situation of equipment aging, approaching the end of its service life, and needing to be replaced, the factory technicians need to perform equipment optimization analysis to provide corresponding equipment optimization solutions. According to the equipment optimization analysis mode of most factories at present, the technicians first need to investigate the basic information of the equipment to be optimized, such as the current working parameters of the equipment; secondly, the technicians need to design the expected performance parameters that the optimized equipment should have in combination with the expected goals of the optimized equipment; then the technicians find the products and equipment that meet the requirements according to the expected performance parameters of the design. Since most of the equipment optimization and replacement are not repaired or modified on the basis of the original equipment, but directly replace other models of existing products, another supplier is introduced as the source of these existing products. The supplier manufactures products and equipment that can be used for upgrading and replacement for the production line, and the factory inquires and purchases suitable products and equipment from the supplier. This is an end-to-end transaction. The supplier generally provides product catalogs and product parameters by distributing product specifications to the factory. Factory technicians will check product specifications based on the expected performance parameters of the design and select the most suitable product for optimized replacement.

[0028] Using product specifications to query equipment is an inefficient way, so there are also existing technologies that establish a networked data connection between suppliers and factories. For example, CN112100965A discloses an electronic manufacturing collaborative innovation platform and its use method. The designer in this technical solution uses the client's design tool cloud and IP service cloud to design and simulate the required products. The design file is encrypted by the collaborative innovation platform and transmitted to the backend management end; the intelligent recognition module receives and decrypts the encrypted design file, and automatically matches each supplier's equipment that meets the product process flow and process parameters through the deep mapping algorithm according to the decrypted design file, and generates a corresponding encrypted order and sends it to the supplier end; after decryption, the supplier end simulates the production scene, supplier equipment and production process through the digital twin cloud according to the product process flow, process parameters and corresponding supplier equipment recorded in the order, and generates a 3D panoramic display platform to achieve online visualization. This technical solution establishes a visual model that matches the client's needs through the backend management platform, thereby intuitively feedbacks the client's expected effect, so as to facilitate adaptive adjustment according to the model results. However, the above-mentioned prior art is limited to the adjustment of the client's needs, and cannot reflect the adjustment needs of the supplier end.

[0029] On the one hand, when manufacturers design expected performance parameters for optimized equipment, they will add some parameter items of their own concern, and these parameter items are not necessarily in the product data list provided by the supplier. In fact, the supplier may not have detected and noticed the corresponding parameters during design or delivery, nor did they measure them, resulting in certain difficulties in the work of "finding existing equipment products based on the expected performance parameters designed by the factory". For example, it is difficult for the system to match equipment products with these additional parameters, or the system ignores these additional parameters, making it difficult for the matching results to meet the needs of the factory. Based on this, the present invention provides an end-to-end equipment optimization system based on digital twins. According to Figure 1 and Figure 4 , the system is used to establish the supply and demand relationship between suppliers and factories. The system has a platform for receiving data uploaded by both suppliers and factories, which can be called an intermediate platform. The intermediate platform should be composed of servers or server units with considerable computing power, and have a database capable of storing a large amount of data. The computing power of the intermediate platform is mainly used to match suitable equipment products according to the requirements of the factory, and to establish a digital twin factory based on the data uploaded by the factory. Specifically, a factory end dedicated to the factory is allocated, and the port can be a computer, an intelligent computing device, etc. The factory can input information to the factory end and obtain information from the factory end. The factory uploads the data used to make the digital twin to the intermediate platform through the factory end. The data used to make the digital twin can be scanned and collected in advance by the collection device in the factory, for example, by using a laser point cloud scanning device. After the digital twin factory is established on the intermediate platform, the information uploaded by the factory end can be continuously received to update the data in the digital twin factory. In another embodiment, the intermediate platform can open the interface of operating the digital twin factory to the factory side, and the factory can share the operation and maintenance data in its own factory with the digital twin factory, so that the various process states in the simulated factory can be compared in real time in the digital twin factory, such as real-time material flow data, chemical reaction data, etc. Figure 5As shown, the intermediate platform is configured with an interface (which may include multiple interfaces such as data interface and graphic interface), a computer (or CPU) and a server (or storage database) at the device level. The interface is used to form a communication connection with other terminals and is the main carrier for transmitting data. This solution establishes a data interaction platform that is trusted by both the factory side and the supplier side, so that the automatic matching optimization of equipment and the transaction process can be promoted without revealing the privacy information of the factory side or the supplier side to the other party, which is safe. Computers, or processing components with data processing capabilities (such as CPUs), are installed in the intermediate platform to perform a large number of data calculation and processing tasks. The intermediate platform provided by this solution is responsible for the calculation and processing work with the largest amount of data. The construction and storage of the digital twin factory can be completed in the cloud, which can greatly reduce the equipment construction requirements of the factory side. In another embodiment, the factory side 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 this platform can be open to multiple factory sides (corresponding to multiple users) and multiple supplier sides (corresponding to multiple suppliers), there are many digital twin factories and product twin plug-ins, and the amount of data will be huge. Therefore, the data is stored in the form of a server or multiple server groups to form a storage database. The database will be open to computers to facilitate their processing of the data.

[0030] When the equipment needs to be optimized and upgraded, the process personnel use the factory end to select the equipment that needs to be optimized and upgraded in the digital twin factory, which can be called the target equipment. At this time, the digital twin factory can provide the process personnel with the parameter data related to the target equipment. The relevant parameter data can be the basic configuration parameters of the equipment that have been stored when the digital twin factory is built, such as size data, weight, etc., or the current and / or historical working parameters of the equipment, such as flow rate, real-time power, etc. The process personnel refer to the above information and the expected process goals to design the expected performance parameters and upload them to the intermediate platform. The expected performance parameters can include project names and expected values. The project names can be completely customized by the process personnel. This solution pays attention to some factories that will pay attention to some parameters that do not belong to the conventional parameter items of the equipment when optimizing the equipment. These parameters can be called custom parameters. Taking the pump as an example, the conventional specification table items only include the size, head, flow rate, etc. of the pump, while some factories want to pay attention to the viscosity characteristics of the pump tube, which are not included in the factory inspection items of the pump product, nor are they provided in the upload list of the system. Therefore, this system allows users to completely customize the parameter item names and values. 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, so that user-defined parameter items can be matched in a fuzzy search manner to expand the search surface.

[0031] The process of establishing a digital twin factory can be roughly divided into data collection, data processing and modeling simulation. Data collection is to scan and collect the physical equipment, structures and pipelines of the factory on site. Data collection usually uses some special sensing equipment, such as using a three-dimensional laser point cloud acquisition device to determine the original reference point, emit lasers to the physical objects in the factory, and collect the various reflection points of the physical objects in the form of laser points. Data processing is to reduce noise, remove duplicates, repair and other processing on the data so that the data can be used for subsequent modeling steps. The process can also be assisted by a certain data analysis process, that is, to determine the relationship between multiple physical scan data in the actual factory, such as pipeline connection relationship. The modeling simulation process refers to importing the processed data into the mathematical modeling program, and by establishing the physical and chemical relationship between the virtual and real worlds, a virtual factory that is a one-to-one copy of the real factory is constructed in the virtual world. The above process is the general process of establishing a digital twin factory.

[0032] like Figure 2 As shown, when selecting equipment, you need to first understand the working parameters of the current equipment in the factory, and determine the expected parameters of a replacement equipment in combination with various factors such as the expected production goals. Taking the pump as an example, when selecting, you need to pay attention to the technical requirements data, design results data, rule settings and other forms of data of the existing pump, such as type, design flow, head, motor power, etc., and finally output the relevant data of the pump, and store all of this data in a structured manner in the database, including but not limited to the following data:

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

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

[0035] 3. Pump performance curve: A performance curve drawn based on the pump's flow rate, head and other parameters, used to evaluate the pump's working performance.

[0036] The data of the above points can be used as expected parameters, uploaded to the intermediate platform through the factory side, and stored in the database.

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

[0038] The expected performance parameters are uploaded from the factory side to the intermediate platform, and the intermediate platform executes the following retrieval process: perform the first retrieval based on the target device type; perform the second retrieval based on the expected performance parameters within the first retrieval results; retrieve the product twin plug-in related to the second retrieval results from the database, and assign a digital twin factory copy to each twin plug-in; replace the target device with the product twin plug-in in each digital twin factory copy; perform simulation for each digital twin factory copy to obtain simulation results; based on the simulation results of each digital twin factory copy, filter or sort them and output them to the factory side.

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

[0040] The specific operation of performing the first screening is as follows: based on the input target device type, the intermediate platform only retrieves the digital twin module data set that meets 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 the early stage. The target device type is a query key value information, and the key value query can filter out the corresponding product twin plug-in data set from the many product twin plug-ins that have been stored in the database.

[0041] The specific operation of the secondary search on the intermediate platform is: according to the preset rules, retrieve the product twin plug-in that meets the rules from the results of the first search. The preset rules can be designed manually, especially by process personnel on the factory side, and the selectable rules include but are not limited to: similar conditions of use, product twin plug-in parameter values ​​greater than or equal to expected performance parameters, similar working performance, appropriate cost, etc. One of the above rules can be selected according to specific needs, or multiple rules can be selected for combined search. Taking the pump as an example, the adopted rules may include: similar conditions of use; the design flow rate of the digital twin module is greater than the design flow rate of the current equipment to be replaced; the head of the digital twin module is greater than the head of the current equipment to be replaced; the equipment power of the digital twin module is a specified number of multiples of the current equipment to be replaced, and the working performance is basically the same.

[0042] After the second search, the results obtained are close to the list of replaceable products expected by the factory. However, it is unclear to both the supplier and the factory whether the specific performance of the product after it is actually installed on the production line will be as expected. The problem in the prior art is that for some special equipment products, or for some special production lines, the conventional selection and matching system will also recommend products whose nominal parameters meet the expected performance parameters proposed by the factory. However, after purchasing the product equipment according to the recommendation for replacement, the actual working performance of the equipment still fails to meet expectations. On the one hand, the reason is that the expected performance parameters provided by the factory do not match the actual situation of its factory, or the product twin plug-in produced according to the data provided by the supplier is different from the actual product. Therefore, after the second search, the present invention allocates a digital twin factory copy to each product twin plug-in according to the result, and replaces the corresponding target device with the product twin plug-in in each digital twin factory copy, and performs simulation to obtain the simulation result. Preferably, the digital twin factory copy can not be copied for the entire factory range, but only for the process area where the target device 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 link. Taking the case where the target equipment is a pump as an example, the process area can include the upstream tank, downstream tank, intermediate pipeline and the pump itself. The selection of the process area can be manually specified or automatically derived by artificial intelligence based on the pre-analysis of the correlation between each production line in the digital twin factory model.

[0043] The detailed rules for the secondary search can be formulated by the factory. For example, the intermediate platform can open a rule-making window to the factory, and the factory can choose, for example, economic priority (products with lower prices are ranked first), quality priority (products that can fully cover the expected performance parameters and have a larger difference are ranked first), 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 multiple corresponding product twin plug-ins are replaced with the corresponding devices in the selected target area), and the replacement includes the replacement of the model and the 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 production line of the digital twin factory from the basic scientific level such as physics, chemistry and biology. Taking the passage of fluid through a pipeline as an example, the algorithm may include a fluid flow model, a pipeline constraint model, a pump actuation power model, etc. The process operation logic algorithm can be pre-made by manual cooperation with a computer according to the specific characteristics of the factory's production line. Combined with a large amount of prior basic physics, chemistry and biology knowledge, a relatively complete process operation logic algorithm can be obtained. After replacing the product twin plug-in, it is equivalent to updating some calculation parameters in the process operation logic. Based on the update of the calculation parameters, a new simulation result can be output. The simulation result is one of the outputs of this system and is displayed to the users at the factory end. Factory personnel can decide which product to buy based on the simulation results.

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

[0046] Preferably, the intermediate platform or the factory side may have a material management system. The material management system is used to manage the equipment already in the factory, such as inventory equipment. When the factory side can select, the intermediate platform recommends to the factory side based on the simulation results or the intermediate platform during the first and second retrieval, the equipment already stored in the material management system is preferred, so as to optimize the use of inventory and reduce costs.

[0047] like Figure 3 As shown, the present invention also provides a matching end-to-end production line equipment optimization method based on digital twins. The method may include the following contents: establishing a digital twin factory that is a digital twin of the physical factory; retrieving a product twin plug-in that matches the selected equipment (or equipment in the selected area) based on the expected performance parameters formed; allocating a digital twin factory copy to each product twin plug-in, and performing simulation based on the copy, wherein the product twin plug-in replaces the selected equipment; and outputting the simulation results to the factory personnel for selection.

[0048] Preferably, according to the above, this solution finds that in the field of optimizing solutions for querying equipment using a network platform, although it can more conveniently enable factory technicians to access more products provided by suppliers, it can also realize automatic query of products and equipment that may meet the requirements to a certain extent. However, whether it is from the perspective of whether the product can actually meet the production needs after installation, or from the perspective of the special needs of the factory itself, this solution believes that the customized needs of the factory (customer) also need to be paid attention to, that is, the parameters of the supplier's products that have not been tested before leaving the factory. Therefore, when the expected performance parameters uploaded by the factory end contain customized parameters, the system first matches the relevant parameter key values ​​based on semantic analysis. If there is a match, it is searched in the database according to the key value. If there is no match, a query list is formed and sent to the supplier end. The supplier end here can be a supplier end that has been screened, such as a supplier end that meets the target device type after the above-mentioned first query. After receiving the query list, the supplier end can specifically test the parameter items recorded on the query list, so as to obtain the answer to the customized needs. Subsequently, the new data obtained from the test is uploaded to the intermediate platform by the supplier 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 device. The above optimization selection process is carried out continuously on the intermediate platform. In other words, this search is not the type of "results after submitting the requirements", but can be continuously searched according to the factory's needs, and can even reversely increase the search scheme of the searchable objects based on the factory's custom requirements. The simulation process is not able to perfectly simulate the entire working performance of the equipment in the production line at the beginning. In the case of missing some parameters, even the simulation calculation program will only calculate the working performance of the predicted equipment according to conventional simulation methods. Taking the pump as an example, the pump is a tool for pumping materials, especially fluid materials. To simulate the working performance of the pump, the material data required is the pump's head, flow rate, medium properties, working environment, etc. These data can be provided in advance by the factory or supplier. According to these data, normal simulation can also be carried out. However, if the factory's production line is more special, for example, the medium is magnetic, or has variable fluid properties, then although the simulation results can be obtained in the conventional way, there will be differences between the simulation results and the actual working conditions. Therefore, the present invention provides a custom parameter upload solution to the factory side, which not only enables suppliers to optimize the inspection item standards of their own products when they leave the factory, but also upgrades the simulation algorithm of digital twin simulation, and makes the calculation results more convergent and more matching by adding conditions. Preferably, the system can also record custom parameters, corresponding target equipment types, and data fed back by suppliers after receiving the inquiry list, which can be used as a system template when other similar equipment is replaced.When other factory users upload similar requirements (preferably, the upstream and downstream data link integration function of the digital twin factory can be used for autonomous inference), the factory is automatically recommended whether to add relevant custom parameters, so that the factory has a better starting point when making design decisions. Preferably, the products of suppliers with higher feedback inquiry list speed and quality (which can be evaluated by the factory) can be displayed first when the recommendation list is finally formed to the factory side. This can encourage more suppliers to actively understand factory needs, develop and provide more types of products that better meet needs, and promote product updates and iterations.

[0049] Preferably, the system can also provide optimized equipment query selection for a selected part of a production line. The target area of ​​the production line is delineated in the digital twin factory model by the factory end, and the intermediate platform automatically searches for all 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 the expected performance parameters of some 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 parameters (especially custom parameters) in the expected performance 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 refer to the relationship between multiple devices on the production line. The most common relationship is the upstream and downstream relationship. Basically all devices have an upstream and a downstream. Multiple devices can be associated through the upstream and downstream relationship. For example, the connection between tank A and tank B through a pipeline is an upstream and downstream relationship, including three components: tank A, pipeline and tank B. Then they are also related in parameter relationship. This solution assumes that the expected performance parameters are also interoperable between process-related equipment. For example, if the pump requires anti-magnetism, then the upstream and downstream tanks must also have anti-magnetic requirements. Therefore, this solution can automatically fill in the expected performance parameter requirements of the equipment in the associated production line based on the correlation, thereby significantly reducing the labor cost of user input. More importantly, by updating the special parameter requirements of each associated equipment to the supplier, the retrieval simulation results can be more organically and smoothly combined within the production line section designated by the factory, significantly reducing the probability of problems after optimization replacement.

[0050] like Figure 6As shown, preferably, the factory end provided by this solution can obtain the data in the factory's existing data asset management library in the form of access to 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, etc. in the factory will be summarized in the data management center, and some of the privacy content cannot be shared. This solution is based on the database connection protocol in the factory end, configures data extraction and monitoring means, and only extracts data related to the equipment to be optimized, and basically, only extracts the existing attribute data of the equipment, without involving data collected during the production process (i.e., production data) or operation and maintenance data. In the case of optional, the factory personnel open access to the above production data or operation and maintenance data, and this solution has considerable security. In addition, the intermediate platform of this solution will display the interface of the digital twin (including the digital twin factory and product twin module) to the outside independently (i.e., an independent graphical interface), and establish private connections with the corresponding factory end and supplier end respectively. The ordinary data interface can transmit the result data of whether the simulation matches, or the three-dimensional model data with low precision (hiding or blurring the parameters or model shapes of some models as needed). On the one hand, this can reduce the congestion caused by data parallelism, and on the other hand, it can prevent irrelevant personnel from accessing sophisticated models and causing important information leakage (for example, data from the data interface can be shared to all ports using the intermediate platform to facilitate personnel understanding and selection, and plagiarists cannot accurately know the precise model of the product twin module to imitate the product, thereby protecting the interests of suppliers).

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

[0052] It should be noted that the above-mentioned specific embodiments are exemplary, and those skilled in the art can come up with various solutions inspired by the disclosure of the present invention, and these solutions also belong to the disclosure scope of the present invention and fall within the protection scope of the present invention. Those skilled in the art should understand that the present invention specification and its drawings are illustrative and do not constitute a limitation of the claims. The scope of protection of the present invention is defined by the claims and their equivalents. The present invention specification contains multiple inventive concepts, such as "preferably", "according to a preferred embodiment" or "optionally", all of which indicate that the corresponding paragraph discloses an independent concept, and the applicant reserves the right to file a divisional application based on each inventive concept. Throughout the text, the feature guided by "preferably" is only an optional method and should not be understood as a must-have setting. 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 the factory side and the supplier side respectively, and the factory side is used for the factory to upload the data used to make the digital twin to the intermediate platform; The supplier side is used for suppliers to upload the digital twin plug-in of product equipment to the intermediate platform. The process of screening equipment on the intermediate platform includes: Perform a first search based on the target device type; performing a secondary search within the results of the first search based on expected performance parameters; Retrieving a product twin plug-in related to the secondary search result from the database, and allocating 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 on the digital twin factory replica to obtain simulation results; The simulation results of the digital twin factory replica are filtered or sorted and then output to the factory end.

2. The device screening intermediate platform based on digital twin according to claim 1 is characterized in that: The process of the intermediate platform performing the first search includes: Based on the input target device type, only the digital twin module data set that meets the target device type is retrieved from the database; Among them, the target device type is the query key value information, and the intermediate platform filters out the corresponding product twin plug-in data set from the numerous product twin plug-ins stored in the database through key value query.

3. The device screening intermediate platform based on digital twin according to claim 2 is characterized in that: The process of the intermediate platform performing secondary retrieval includes: Retrieve product twin plug-ins that meet the rules from the results of the first search according to the preset rules; The preset rules include, but are not limited to: similar usage conditions, product twin plug-in parameter values ​​greater than or equal to expected performance parameters, similar working performance, and appropriate cost.

4. The device screening intermediate platform based on digital twin according to any one of claims 1 to 3, characterized in that: When the supplier uses the supplier end to upload product data to the intermediate platform, the intermediate platform creates the model data and parameters as a product twin plug-in based on the digital twin algorithm.

5. The device screening intermediate platform based on digital twin according to any one of claims 1 to 4, characterized in that: The intermediate platform recommends the equipment type to the factory based on the simulation results; Alternatively, the intermediate platform gives priority to devices that have been stored in the material management system during the first and second searches.

6. The device screening intermediate platform based on digital twin according to any one of claims 1 to 4, characterized in that: In the case where the expected performance parameters uploaded by the factory end include custom parameters, the intermediate platform matches the relevant parameter key values ​​based on semantic analysis. If there is a match, it searches in the database according to the key value. If there is no match, it forms an inquiry list and sends it to the supplier end. The intermediate platform receives test data corresponding to the parameter items recorded in the test inquiry list through the supplier end to form a new product twin plug-in or update the original product twin plug-in; and / or The intermediate platform receives answer information corresponding to the customized requirements through the supplier side.

7. The device screening intermediate platform based on digital twin according to any one of claims 1 to 4, characterized in that: According to the target area of ​​the production line demarcated by the factory in the digital twin factory model, the intermediate platform automatically searches for all equipment in the target area, and based on the process association between the equipment in the target area in the digital twin factory, automatically shares part of the expected performance parameters uploaded by the user to all associated equipment in the target area, thereby matching each device with a suitable product twin plug-in according to the updated expected performance parameters.

8. A device simulation method based on digital twins, characterized in that: The method comprises: Retrieving a product twin plug-in related to the search result of the intermediate platform from the database, and allocating 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; Based on the replaced copy of the digital twin factory, 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 production line of the digital twin factory from a basic scientific level.

9. The device simulation method according to claim 8, characterized in that: The search results of the intermediate platform are obtained after performing the first search and the second search; The process of the intermediate platform performing the first search includes: Based on the input target device type, only the digital twin module data set that meets the target device type is retrieved from the database; Among them, the target device type is the query key value information, and the intermediate platform filters out the corresponding product twin plug-in data set from the numerous product twin plug-ins stored in the database through key value query.

10. The device simulation method according to claim 9, characterized in that: The process of the intermediate platform performing secondary retrieval includes: Retrieve product twin plug-ins that meet the rules from the results of the first search according to the preset rules; The preset rules include but are not limited to: similar usage conditions, product twin plug-in parameter values ​​greater than or equal to expected performance parameters, similar working performance and appropriate cost.

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