CN117952323B - Product creation system, method, equipment and medium based on digital twin - Google Patents
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Abstract
The invention relates to the technical field of digital twinning and discloses a product creation system, a method, equipment and a medium based on digital twinning, wherein the system comprises a product demand vector generation module, a product period data acquisition module, a twinning data processing module, a product real-time feedback data extraction module and a target creation product integration module, a distributed digital twinning model of a target product is built according to the product demand vector, and product period data corresponding to the target product in product data is extracted; integrating the product cycle data into a distributed digital twin model; monitoring the performance of the distributed product digital twin model in real time according to the real-time data stream, and extracting real-time feedback data of the monitored product; and optimizing the distributed product digital twin model through product real-time feedback data, and integrating the product optimizing model with a target product to obtain the target created product. The invention can improve the accuracy of product creation.
Description
Technical Field
The invention relates to the technical field of digital twinning, in particular to a product creation system, a method, equipment and a medium based on digital twinning.
Background
Along with the development of intelligent manufacturing, the digital technology is widely applied to the fields of product design, production process optimization and the like, digital twin is widely focused and applied as an important technical means, but in order to improve the accuracy of product creation, the product performance of the product creation process needs to be accurately analyzed to create the product.
The existing product creation technology is to create a three-dimensional model of the product according to the existing software tool and perform design verification, modification and optimization. In practical application, the product creation process not only refers to an appearance model of the product, but also needs to analyze the performance of the product after the product is created, and only creates a three-dimensional model of the product, which may result in incomplete product creation, so that the accuracy of product creation is lower.
Disclosure of Invention
The invention provides a product creation system, method, equipment and medium based on digital twinning, which mainly aim to solve the problem of lower accuracy in product creation.
In order to achieve the above object, the invention provides a digital twinning-based product creation system, which comprises a product demand vector generation module, a product period data acquisition module, a twinning data processing module, a product real-time feedback data extraction module and a target creation product integration module, wherein,
The product demand vector generation module is used for acquiring product data of a target product, screening the product data according to preset product demands to obtain product key demand data, and generating a product demand vector from the product key demand data;
The product period data acquisition module is used for constructing a twin geometric model of the target product according to the geometric attributes in the product demand vector; applying physical data in the twin geometric model according to the physical attributes in the product demand vector to obtain a twin physical model of the target product; generating dynamic distributed data by using dynamic demand vectors in the product demand vectors; generating a data demand node of a preset product user according to the dynamic distributed data; deploying the twin physical model into the data demand node to obtain a distributed digital twin model of a target product, and extracting product cycle data of a full life cycle corresponding to the target product in the product data;
the twin data processing module is configured to integrate the product cycle data into the distributed digital twin model by using a preset bidirectional data integration algorithm to obtain a distributed product digital twin model, and is specifically configured to: carrying out data standardization on the product cycle data one by one to obtain product cycle standardization data; extracting product data fields of the distributed digital twin model; extracting a product cycle data field in the product cycle standardized data; and carrying out data integration according to the product data field and the product period data field by using a two-way data integration algorithm preset as follows to obtain distributed product data: Wherein/> For the distributed product data,/>For/>Product data corresponding to the individual product data fields,/>For/>Cycle phase number/>Product cycle data corresponding to the product cycle data fields,/>For/>Field quantized value of individual product data field,/>For/>Cycle phase number/>Field quantized value of individual product period data field,/>For forward integration of feedback values,/>Is a reverse integration feedback value;
generating a distributed product digital twin model according to the distributed product data;
The product real-time feedback data extraction module is used for monitoring the product performance of the distributed product digital twin model in real time according to real-time data sequences at different moments and extracting monitored product real-time feedback data;
The product integration module is created by the target, and is used for optimizing the distributed product digital twin model through a preset product automation optimization algorithm and the product real-time feedback data to obtain a product optimization model, and comprises the following steps: extracting a target feedback sequence in the real-time feedback data of the product; determining a target performance attribute of the distributed product digital twin model according to the target feedback sequence;
optimizing the target performance attribute through a preset product automation optimization algorithm to obtain a target performance optimization attribute: Wherein/> For/>Target performance optimization attribute corresponding to each product performance attribute,/>As a minimum function,/>For/>Time of day/>Item/>, in individual product categoriesThe performance attributes of the individual products are set,For/>Item/>, in individual product categoriesProduct standard performance attributes of individual products,/>Is a feedback value/>For/>Real-time product Performance at time,/>Is the standard performance of the product;
and generating a product optimization model according to the target performance optimization attribute, and integrating the product optimization model with the target product to obtain a target creation product.
Optionally, when the product demand vector generating module screens the product data according to a preset product demand to obtain product key demand data, the product demand vector generating module includes:
Carrying out data enhancement processing on the product data to obtain product enhancement data;
carrying out data source integration on the product enhancement data to obtain product integration data;
extracting product construction data in the product demand;
and screening the product integration data according to the product construction data to obtain product key demand data.
Optionally, the product demand vector generation module, when generating the product demand vector from the product key demand data, includes:
Carrying out data classification on the product key demand data to obtain the geometric attribute and the physical attribute of a target product;
Generating a static demand vector of a target product according to the geometric attribute and the physical attribute;
generating a dynamic demand vector of the target product according to the preset behavior attribute and rule attribute;
and fusing the static demand vector and the dynamic demand vector into a product demand vector.
Optionally, when the product real-time feedback data extraction module monitors the real-time product performance of the distributed product digital twin model according to real-time data sequences at different moments, the product real-time feedback data extraction module includes:
Calculating the real-time product performance of the distributed product digital twin model one by one according to the real-time data sequence, wherein the real-time product performance calculation formula is as follows: Wherein/> For/>Real-time product Performance at time,/>For/>Item/>, in individual product categoriesAttribute weight of individual product performance attributes,/>For/>Time of day/>Item/>, in individual product categoriesIndividual product Performance Properties,/>For/>Item/>, in individual product categoriesProduct standard performance attributes of individual products,/>For/>Weight of individual external factors,/>For/>Factor score of individual external factors,/>For the number of product performance attributes,/>Is the number of external factors;
optionally, the product integration module for creating a product by product integration is configured to, when the product optimization model is integrated with the target product to obtain the target created product, include:
extracting first product features in the product optimization model;
Extracting second product features of the target product;
performing dual characteristic operation on the first product characteristic and the second product characteristic to obtain a target product characteristic;
and generating a target creation product according to the target product characteristics.
In order to solve the above problems, the present invention also provides a digital twin-based product creation method, the method comprising:
Acquiring product data of a target product, screening the product data according to preset product requirements to obtain product key requirement data, and generating a product requirement vector from the product key requirement data;
Constructing a twin geometric model of the target product according to the geometric attributes in the product demand vector; applying physical data in the twin geometric model according to the physical attributes in the product demand vector to obtain a twin physical model of the target product; generating dynamic distributed data by using dynamic demand vectors in the product demand vectors; generating a data demand node of a preset product user according to the dynamic distributed data; deploying the twin physical model into the data demand node to obtain a distributed digital twin model of a target product, and extracting product cycle data of a full life cycle corresponding to the target product in the product data;
Integrating the product cycle data into the distributed digital twin model by using a preset bidirectional data integration algorithm to obtain a distributed product digital twin model;
monitoring the performance of the distributed product digital twin model in real time according to real-time data sequences at different moments, and extracting real-time feedback data of the monitored product;
Optimizing the distributed product digital twin model through a preset product automatic optimization algorithm and the product real-time feedback data to obtain a product optimization model, and integrating the product optimization model with the target product to obtain a target creation product.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
At least one processor and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of operating the digital twinning-based product creation system described above.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one computer program that is executed by a processor in an electronic device to implement the above-mentioned method of operating a digital twin-based product creation system.
According to the embodiment of the invention, the product data is screened and processed to generate the product demand vector, so that the key demand information of the product can be more accurately captured, and a foundation is laid for the subsequent digital twin model construction; the distributed digital twin model is built, the product cycle data of the full life cycle corresponding to the target product can be extracted, and the performance and the characteristics of the product at different stages can be comprehensively understood; the digital twin model is monitored through a real-time data stream, real-time feedback data are extracted, and the digital twin model is optimized by utilizing an automatic optimization algorithm, so that the performance of a product can be timely adjusted and optimized; the product optimization model is integrated with the target product, and the target creation product which is more suitable for market demands and user expectations can be created by combining data of the digital twin model and an optimization algorithm. Therefore, the product creation system, method, equipment and medium based on digital twin can solve the problem of lower accuracy in product creation.
Drawings
FIG. 1 is a functional block diagram of a digital twinning-based product creation system provided in accordance with one embodiment of the present invention;
FIG. 2 is a flow chart of a method of operation of a digital twin based product creation system according to an embodiment of the present invention;
Fig. 3 is a schematic structural diagram of an electronic device implementing the operation method of the digital twin-based product creation system according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, the "plurality" generally includes at least two.
The words "if", as used herein, may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determination" or "when detected (stated condition or event)" or "in response to detection (stated condition or event), depending on the context.
In addition, the sequence of steps in the method embodiments described below is only an example and is not strictly limited.
In practice, a server device deployed by a digital twin-based product creation system may be made up of one or more devices. The digital twin-based product creation system described above may be implemented as: service instance, virtual machine, hardware device. For example, the digital twinning-based product creation system may be implemented as a service instance deployed on one or more devices in a cloud node. Briefly, the digital twin-based product creation system may be understood as a software deployed on a cloud node to provide a digital twin-based product creation system for each client. Or the digital twinning-based product creation system may also be implemented as a virtual machine deployed on one or more devices in a cloud node. The virtual machine is provided with application software for managing each user side. Or the product creation system based on digital twin can also be realized as a service end formed by a plurality of hardware devices of the same or different types, and one or more hardware devices are arranged for providing the product creation system based on digital twin for each user end.
In an implementation form, the digital twin-based product creation system and the user side are mutually adapted. Namely, the product creation system based on digital twinning is used as an application installed on the cloud service platform, and the user side is used as a client side for establishing communication connection with the application; or realizing a product creation system based on digital twinning as a website, and realizing the product creation system as a webpage at a user side; and then, the product creation system based on digital twinning is realized as a cloud service platform, and the user side is realized as an applet in the instant messaging application.
Referring to FIG. 1, a functional block diagram of a digital twinning-based product creation system is provided in accordance with one embodiment of the present invention.
The digital twin-based product creation system 100 of the present invention may be disposed in a cloud server, and in implementation form, may be used as one or more service devices, may be installed as an application on a cloud (e.g., a server of a mobile service operator, a server cluster, etc.), or may be developed as a website. Depending on the functions implemented, the digital twin based product creation system 100 may include a product demand vector generation module 101, a product cycle data acquisition module 102, a twin data processing module 103, a product real-time feedback data extraction module 104, and a target creation product integration module 105. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the device, capable of being executed by the processor of the device and of performing a fixed function.
In the product creation system based on digital twinning, each module can be independently realized and called with other modules. A call herein is understood to mean that a module may connect to a plurality of modules of another type and provide corresponding services to the plurality of modules to which it is connected. For example, the sharing evaluation module can call the same information acquisition module to acquire the information acquired by the information acquisition module based on the characteristics, and in the digital twin-based product creation system provided by the embodiment of the invention, the application range of the digital twin-based product creation system architecture can be adjusted by adding the module and directly calling the module without modifying the program code, so that the cluster type horizontal expansion is realized, and the purpose of rapidly and flexibly expanding the digital twin-based product creation system is achieved. In practical applications, the modules may be disposed in the same device or different devices, or may be service instances disposed in virtual devices, for example, in a cloud server.
The following description is directed to various components of a digital twin based product creation system and specific workflows, respectively, in connection with specific embodiments:
the product demand vector generation module 101 is configured to obtain product data of a target product, screen the product data according to a preset product demand, obtain product critical demand data, and generate a product demand vector from the product critical demand data.
In the embodiment of the invention, the product data includes, but is not limited to, product production process parameters, use environment parameters, such as size parameters, material properties, functional specifications, performance indexes, electrical parameters, etc. of the product, and the target product refers to an actual product to be created, such as physical equipment, electronic products, etc.
In detail, the product data of the target product may be acquired from a pre-stored storage area including, but not limited to, a database, a blockchain, etc., through a computer sentence having a data grabbing function (e.g., java sentence, python sentence, etc.).
Further, to avoid a large amount of unnecessary information interference, remove irrelevant or redundant information of the product, product data needs to be screened to ensure that the obtained data matches with preset product requirements.
In the embodiment of the invention, the product key demand data refers to data matched with the product demand at the product demand screening position based on each product, so that the key characteristics and indexes of the product can be focused more. The accuracy in the product creation process is ensured.
In the embodiment of the present invention, when the product demand vector generation module 101 performs screening on the product data according to a preset product demand to obtain product key demand data, the method includes:
Carrying out data enhancement processing on the product data to obtain product enhancement data;
carrying out data source integration on the product enhancement data to obtain product integration data;
extracting product construction data in the product demand;
and screening the product integration data according to the product construction data to obtain product key demand data.
In detail, the data enhancement processing includes performing operations such as data cleaning, deduplication, filling missing values, noise reduction, data smoothing, data conversion and the like on the product data to improve quality and usability of the data, so that enhanced product data is obtained, and further, the enhanced product data from different data sources are subjected to data integration and association to achieve consistency and integrity of the data, wherein a data model and a relationship diagram are established based on data content and formats of the different data sources, and relationships among the data are described. For example, by determining data entities, attributes and relationships, it is possible to help understand the relationships between data and establish a mapping relationship between data, so as to generate a complete data set according to the mapping relationship, thereby obtaining product integration data.
Specifically, product construction data in product demand can be extracted through a computer sentence with a data grabbing function, wherein the product construction data comprises, but is not limited to, size information, material information, process information, function information and performance information of a product, and further the product construction data is used as screening conditions to screen integrated product data, for example, product characteristics meeting requirements are screened out according to the function information, or product data meeting standards is screened out according to performance indexes, so that product critical demand data is obtained, and the product critical demand data is data closely related to the product demand and can be used for guiding decisions of product design and manufacturing.
Further, by converting the product critical demand data into vectors, data processing and analysis can be made more efficient, and by vectorizing, differences and similarities between different product demands can be measured more objectively, so that product demands can be described more accurately.
In the embodiment of the invention, the vector representation form obtained by processing and converting the product key demand data by the product demand vector is used for describing various demand characteristics of the product, and the product demand vector generally comprises a static demand vector and a dynamic demand vector, so that the demand condition of the product in a static state and in a using process is comprehensively reflected.
In the embodiment of the present invention, when the product demand vector generating module 101 generates the product demand vector from the product key demand data, the method includes:
Carrying out data classification on the product key demand data to obtain the geometric attribute and the physical attribute of a target product;
Generating a static demand vector of a target product according to the geometric attribute and the physical attribute;
generating a dynamic demand vector of the target product according to the preset behavior attribute and rule attribute;
and fusing the static demand vector and the dynamic demand vector into a product demand vector.
In detail, firstly, classifying data according to the characteristics of key requirements of a product, and dividing the data into geometric attributes and physical attributes, wherein the geometric attributes generally describe geometric characteristics such as shape, size, structure and the like of the product, and the physical attributes describe physical characteristics such as material, weight, durability and the like of the product; for the geometric attribute and the physical attribute, vectorization processing can be performed on the geometric attribute and the physical attribute respectively to generate corresponding static demand vectors, wherein the static demand vectors are used for describing demand characteristics of the product in a static state, including shapes, sizes, material attributes and the like.
Specifically, a dynamic demand vector is generated according to preset behavior attributes and rule attributes. The behavior attribute describes behavior characteristics of actions, interactions and the like of the product in the using process, represents driving and disturbance received by a response physical entity in a virtual space, and the rule attribute describes rule characteristics of constraint, specification and the like in the using process of the product, including rules of constraint, association, deduction and the like, so that a digital twin model of the product has the functions of judging, evaluating, predicting, optimizing and the like; the dynamic demand vector is used for describing demand characteristics of the product in the using process, and the static demand vector and the dynamic demand vector are fused to generate a final product demand vector, wherein the final product demand vector is the product demand vector, the product demand vector is a geometric attribute, the physical attribute, the behavior attribute and the rule attribute, and the product demand vector is the product demand vector which synthesizes the static and dynamic demand characteristics of the product and is used for comprehensively describing the demand of the target product.
Further, a digital twin model corresponding to the product can be constructed through the product demand vector, various demand characteristics of the target product, including static and dynamic demands, can be comprehensively and accurately reflected, the precision and reliability of the model can be improved, and the model is closer to an actual product.
The product cycle data acquisition module 102 is configured to construct a twin geometric model of the target product according to geometric attributes in the product demand vector; applying physical data in the twin geometric model according to the physical attributes in the product demand vector to obtain a twin physical model of the target product; generating dynamic distributed data by using dynamic demand vectors in the product demand vectors; generating a data demand node of a preset product user according to the dynamic distributed data; and deploying the twin physical model into the data demand node to obtain a distributed digital twin model of the target product, and extracting product cycle data of a full life cycle corresponding to the target product in the product data.
In the embodiment of the invention, the distributed digital twin model is that the digital twin model is deployed on nodes of different product users, so that different users can control the digital twin model corresponding to the product, and also can cooperatively work by different users, thereby realizing the omnibearing simulation and monitoring of the target product.
In detail, according to the geometric attributes in the product demand vector, a twin geometric model of the target product is constructed by utilizing Computer Aided Design (CAD) software or other modeling tools, so that the twin geometric model can accurately reflect the geometric attributes of the product such as shape, size, structure and the like, corresponding physical data including material characteristics, mechanical properties, thermal properties, loading conditions, boundary conditions and the like are applied to the twin geometric model based on the physical attributes in the product demand vector, the twin physical model of the target product can be obtained, and the behavior and performance of the product in a physical environment can be simulated.
Specifically, the dynamic demand vector in the product demand vector is converted into dynamic distributed data, dynamic characteristics of actions, interactions, constraints and the like of the product in the using process are described, and the data can comprise information such as running states, user operation behaviors, environment changes and the like of the product; according to the dynamic distributed data, generating a data demand node of a preset product user, namely describing the demand, the expected and the behavior of the user on the product, which is beneficial to simulating the performance of the product under the actual use scene and guiding the improvement and the optimization of the product; the twin physical model is deployed into a data demand node, and combines with the data demand of a user so as to provide customized product simulation and data monitoring for different users, the twin physical model is deployed into the data demand node, and the distributed digital twin model of a target product is obtained by combining with actual dynamic distributed data, so that the virtual simulation and real-time monitoring of the product under different working conditions are realized, the static attribute, physical characteristic and dynamic demand of the product can be comprehensively considered, and comprehensive support is provided for the design, test and optimization of the product.
Further, product cycle data of the full life cycle corresponding to the target product is collected through various ways such as a sensor, monitoring equipment, a production system and a supply chain system, the data are ensured to cover different stages of design, production, test, application and the like of the product, the data of the different stages are associated, a time sequence and association of the product data are established, the full life cycle data of the product can be traced back, a complete data map can be formed, and the whole process from design to retirement of the product is reflected.
Furthermore, the product period data is integrated into the distributed digital twin model, so that the full life cycle of the product can be comprehensively monitored and simulated, and the distributed digital twin model can provide more accurate data of product performance, running state and the like.
The twin data processing module 103 is configured to integrate the product cycle data into the distributed digital twin model by using a preset bidirectional data integration algorithm, so as to obtain the distributed product digital twin model.
In the embodiment of the invention, the distributed product digital twin model refers to that the product period data of the target product is applied to the distributed digital twin model and is used for simulating, monitoring and optimizing the full life cycle of the distributed product.
In the embodiment of the present invention, when executing the integration of the product cycle data into the distributed digital twin model by using a preset bidirectional data integration algorithm, the twin data processing module 103 obtains the distributed product digital twin model, the method includes:
Carrying out data standardization on the product cycle data one by one to obtain product cycle standardization data;
Extracting product data fields of the distributed digital twin model;
extracting a product cycle data field in the product cycle standardized data;
And carrying out data integration according to the product data field and the product period data field by using a two-way data integration algorithm preset as follows to obtain distributed product data: Wherein/> For the distributed product data,/>For/>Product data corresponding to the individual product data fields,/>For/>Cycle phase number/>Product cycle data corresponding to the product cycle data fields,/>For/>Field quantized value of individual product data field,/>For/>Cycle phase number/>Field quantized value of individual product period data field,/>For forward integration of feedback values,/>Is a reverse integration feedback value;
And generating a distributed product digital twin model according to the distributed product data.
In detail, carrying out data standardization on the product cycle data corresponding to each cycle stage in the whole life cycle one by one, namely carrying out data classification on the product cycle data to obtain product cycle standardization data of different stages in the product cycle data, wherein if the product cycle data corresponding to the design stage comprises geometric shapes, internal structures, material characteristics, working principles, performance characteristics and the like of products, the product cycle data is subjected to data classification to obtain the product cycle standardization data, and further extracting product cycle data fields in the product cycle standardization data, and the product cycle data fields comprise but are not limited to geometric fields, performance fields and material fields; in addition, there is a need to extract product data fields, such as geometry fields, performance fields, material fields, associated with the product cycle data from the distributed digital twin model, wherein the performance fields, such as temperature, pressure, humidity, speed, etc., should be matched to corresponding fields in the product cycle data, and extract the product cycle data fields corresponding to the product data fields from the product cycle standardized data.
Specifically, according to a preset bidirectional data integration algorithm, integrating a product data field and a product period data field to obtain distributed product data, namely when the product data field and the product period data field are integrated, firstly comparing the product data field with the product period data field, feeding back a feedback value by the product data field to indicate whether the product data field is consistent with the product period data field or not, and likewise, feeding back a numerical value by the product period data field to indicate whether the product period data field is consistent with the product data field or not, and when the forward integration feedback value is equal to the reverse integration feedback value and the period field is identical to the product data field, replacing product data corresponding to the distributed digital twin model by using the product period data to obtain the latest product data in the distributed digital twin model; when the forward integration feedback value is not equal to the reverse integration feedback value or the period field is not equal to the product data field, at the moment, the update data corresponding to each field in the distributed digital twin model is set to 0, namely the original data corresponding to each field before the configuration is further configured, and then the distributed product digital twin model is regenerated according to the data size of the distributed product data, for example, the distributed product digital twin model is generated according to the updated geometric data, performance data and the like, then the distributed digital twin model can acquire the update of the product period data in real time through a bidirectional data integration algorithm, reflect the change of the actual running state of the product, and enable the distributed digital twin model to respond to the change in the life cycle of the product in time.
Further, the generated distributed product digital twin model needs to monitor the performance of the product, and the situation that the performance of the product is inconsistent with the expected performance of the model can be quickly found through real-time comparison with the digital twin model, so that measures are timely taken to adjust and correct the target product.
The product real-time feedback data extraction module 104 is configured to monitor the product performance of the distributed product digital twin model in real time according to real-time data sequences at different moments, and extract monitored product real-time feedback data.
In the embodiment of the invention, the real-time product performance monitoring means the process of real-time monitoring and evaluating various performance indexes of the product through a real-time data acquisition and processing technology and timely taking measures to adjust and optimize according to the monitoring result, so that defects and defects of the product can be timely found, thereby improving and optimizing pertinently and improving the quality and performance of the product.
In the embodiment of the present invention, when the product real-time feedback data extraction module 104 performs real-time product performance monitoring on the distributed product digital twin model according to real-time data sequences at different moments, the product real-time feedback data extraction module includes:
Calculating the real-time product performance of the distributed product digital twin model one by one according to the real-time data sequence, wherein the real-time product performance calculation formula is as follows: Wherein/> For/>Real-time product Performance at time,/>For/>Item/>, in individual product categoriesAttribute weight of individual product performance attributes,/>Is the firstTime of day/>Item/>, in individual product categoriesIndividual product Performance Properties,/>For/>Item/>, in individual product categoriesProduct standard performance attributes of individual products,/>For/>Weight of individual external factors,/>For/>Factor score of individual external factors,/>For the number of product performance attributes,/>Is the number of external factors.
In detail, a real-time data sequence of a target product is acquired from a preset real-time data stream. The data may include various performance indexes, sensor data, operation parameters and the like of the product, and the real-time data sequence refers to a product performance monitoring result obtained in the process of monitoring the performance of the product according to different moments, namely, a real-time data sequence of a target product is generated according to the corresponding relation between the different moments and the product performance.
Specifically, for calculating the real-time product performance in the performance monitoring process of the distributed product digital twin model according to the real-time data sequence at each moment, the performance attribute monitoring corresponding to each target product in the performance monitoring process of the real-time product has different attributes, and therefore, the accurate performance monitoring needs to be performed on the performance attributes of the product, such as numerous product performance attributes including temperature, pressure, strength, hardness, anti-falling degree, abrasion degree, effect, running state and the like, while products possibly existing based on product types do not need all performance monitoring, only a part of performance monitoring needs to be configured with attribute weights, the weights of the performance attributes not needing performance monitoring need to be configured to be zero, and attribute weights of different sizes need to be configured based on attributes of different importance degrees, and in addition, when performance monitoring is performed, the influence of external factors is present, so that the influence degree of the external factors on the performance monitoring process needs to be considered, wherein the external factors include but are not limited to the influence of environmental factors including temperature, humidity, air pressure and other environmental conditions on the performance of the product, supply chain factors, raw material supply in the supply chain, and the supply of parts and the like may influence the performance of the final product. The problems of raw material quality, part stability and the like can influence the product performance monitoring, and human factors are as follows: human factors such as operator skill level, whether the operating procedure is normal, etc. can also affect the stability and accuracy of the product performance. The difference of different operators may cause the difference of monitoring results, different influence weights are configured on environmental factors, supply connection factors and human factors according to different influence degrees, and then subjective scores are carried out on the environmental factors, the supply connection factors and the human factor roots to obtain factor scores of external factors, so that the real-time performance of the product population of the corresponding target products at different moments is obtained.
Further, the monitored real-time feedback data of the product refers to a comparison value between each performance attribute and a performance standard attribute and a total real-time product performance value, and the real-time feedback data of the product refers to a comparison value of each performance attribute at different moments, for example, the real-time feedback data of the product at the moment is that the feedback data corresponding to the performance attribute greater than the performance standard attribute is set to be 1, the feedback data corresponding to the performance attribute less than the performance standard attribute is set to be-1, and the feedback data corresponding to the performance attribute equal to the performance standard attribute is set to be 0, so that the real-time feedback data of the product corresponding to the different moments is obtained.
Furthermore, the real-time performance of the product optimization decision can be realized by optimizing the digital twin model through the real-time feedback data, and the timely feedback data can help the optimization algorithm to make adjustment faster, so that the product can keep the optimal state under the condition of continuous change, the product optimization process can be more continuous and durable by means of the real-time feedback data and the digital twin model, the real-time data can be continuously collected and the digital twin model is optimized, the product can be continuously improved, and the product is adapted to market demands and technical changes.
The target creation product integration module 105 is configured to optimize the distributed product digital twin model through a preset product automation optimization algorithm and the product real-time feedback data to obtain a product optimization model, and integrate the product optimization model with the target product to obtain a target creation product.
In the embodiment of the invention, the product optimization model refers to a product model obtained by performing performance optimization on a distributed product digital twin model, and is used for describing a model with optimal performance of a product, so that a target product with optimal performance is created.
In the embodiment of the present invention, when the target creation product integration module 105 performs optimization on the distributed product digital twin model through a preset product automation optimization algorithm and the product real-time feedback data, the method includes:
Extracting a target feedback sequence in the real-time feedback data of the product;
determining a target performance attribute of the distributed product digital twin model according to the target feedback sequence;
optimizing the target performance attribute through a preset product automation optimization algorithm to obtain a target performance optimization attribute: Wherein/> For/>Target performance optimization attribute corresponding to each product performance attribute,/>As a minimum function,/>For/>Time of day/>Item/>, in individual product categoriesThe performance attributes of the individual products are set,For/>Item/>, in individual product categoriesProduct standard performance attributes of individual products,/>Is a feedback value/>For/>Real-time product Performance at time,/>Is the standard performance of the product;
and generating a product optimization model according to the target performance optimization attribute.
In detail, the target feedback sequence refers to a value corresponding to a feedback value of 1 or-1, and further, a target performance attribute corresponding to the feedback value of 1 or-1 is extracted, if the real-time feedback data of the product is true, a value corresponding to the feedback value of 1 or-1 is extracted, if the target feedback sequence is true, and further, the latest target performance attribute of the distributed product digital twin model is determined according to the target feedback sequence, namely, the performance attribute corresponding to the feedback value of 1 or-1 is extracted as the target performance attribute.
Specifically, the target performance attribute is optimized through the product automation optimization algorithm, when the feedback value is 1 and the real-time product performance value is larger than the product standard performance value, the attribute with the smallest performance attribute value is selected as the latest target performance attribute from the feedback performance attributes corresponding to different moments, under the condition that the basic performance of the product is met, the smallest performance value is selected, the resource can be saved, when the feedback value is-1, the standard performance attribute at the moment is assigned to the product performance attribute, namely the product performance attribute is replaced by the standard performance attribute, so that the latest performance attribute is obtained, and further, the influence parameters of the performance attribute corresponding to the distributed product digital twin model are optimized according to the target performance optimization attribute, so that the product optimization model is obtained, the influence of the influence parameters of the distributed product digital twin model is determined, the influence parameters can be the product design parameters, the production process parameters, the market feedback data and the like, the influence parameters in the product performance and the feedback model are continuously monitored, and the influence parameters in the optimization model are continuously adjusted, and the continuous improvement and optimization are realized. If the product has a temperature performance A which is not as good as that of the product under a standard temperature performance B, the material of the product needs to be adjusted to the product material under the standard temperature performance B to improve the heat resistance of the product, if the product has a hardness performance C which is not as good as that of the product under the standard hardness performance D, the material of the product needs to be adjusted to the product material under the standard hardness performance D to improve the hardness performance of the product.
For example, if the product performance attribute is { a, B, C }, the target feedback sequence corresponding to the product performance attribute at different times is { -1, 1}, and the feedback value corresponding to the product performance attribute a is-1, the attribute value corresponding to the product performance attribute a is adjusted to be the product standard performance attribute value, and the feedback value corresponding to the product performance attribute B is 1, the minimum product performance attribute value is selected at all times, that is, the minimum performance value is selected under the condition of meeting the basic performance of the product, so that resources can be saved.
Further, through the integrated product optimization model, products meeting requirements can be custom designed according to specific requirements and performance indexes of target products, so that individuation and market adaptability of the products are improved, and quality and reliability of the products are improved.
In the embodiment of the invention, the target creation product refers to a target product which is created based on a product optimization model and actual requirements of the product and corresponds to the actual product.
In the embodiment of the present invention, when executing product integration between the product optimization model and the target product to obtain the target created product, the target created product integration module 105 includes:
extracting first product features in the product optimization model;
Extracting second product features of the target product;
performing dual characteristic operation on the first product characteristic and the second product characteristic to obtain a target product characteristic;
and generating a target creation product according to the target product characteristics.
In detail, the first product feature refers to product design parameters, material properties, process parameters and the like in a product optimization model, the second product feature refers to functional requirements, performance indexes, appearance designs and the like of a target product, wherein the first product feature and the second product feature can be extracted through a computer sentence with a data grabbing function, and further the first product feature and the second product feature are subjected to dual feature operation to obtain the latest target product feature, the dual feature operation refers to feature adding or deleting operation, namely, the first product feature and different features in the second product feature are fused to generate new features, such as weighted average or splicing operation of the two features to obtain a new comprehensive feature; the repeated characteristics in the first product characteristics and the second product characteristics are only reserved for one characteristic; the characteristics of the first product characteristics but the characteristics of the second product characteristics are taken as target product characteristics, and the characteristics of the first product characteristics but the characteristics of the second product characteristics are taken as target product characteristics, so that the latest target product characteristics are obtained, the complex relationship between the information of the data and the characteristics can be better expressed, the performance of the product is improved, the product is more in line with the requirements of users, and the target creation product is generated according to the target product characteristics.
Specifically, through the integration of the product optimization model and the target product characteristics, the problems in product design and development can be identified and solved more quickly, so that the process of product iteration and improvement is accelerated, and products more conforming to the requirements and preferences of users can be designed by combining the product optimization model and the target product characteristics.
According to the embodiment of the invention, the product data is screened and processed to generate the product demand vector, so that the key demand information of the product can be more accurately captured, and a foundation is laid for the subsequent digital twin model construction; the distributed digital twin model is built, the product cycle data of the full life cycle corresponding to the target product can be extracted, and the performance and the characteristics of the product at different stages can be comprehensively understood; the digital twin model is monitored through a real-time data stream, real-time feedback data are extracted, and the digital twin model is optimized by utilizing an automatic optimization algorithm, so that the performance of a product can be timely adjusted and optimized; the product optimization model is integrated with the target product, and the target creation product which is more suitable for market demands and user expectations can be created by combining data of the digital twin model and an optimization algorithm. Therefore, the product creation system, method, equipment and medium based on digital twin can solve the problem of lower accuracy in product creation.
Referring to fig. 2, a flow chart of an operation method of a digital twin-based product creation system according to an embodiment of the present invention is shown. In this embodiment, the method for operating the digital twin-based product creation system includes:
s1, obtaining product data of a target product, screening the product data according to preset product requirements to obtain product key requirement data, and generating a product requirement vector from the product key requirement data;
S2, constructing a twin geometric model of the target product according to geometric attributes in the product demand vector; applying physical data in the twin geometric model according to the physical attributes in the product demand vector to obtain a twin physical model of the target product; generating dynamic distributed data by using dynamic demand vectors in the product demand vectors; generating a data demand node of a preset product user according to the dynamic distributed data; deploying the twin physical model into the data demand node to obtain a distributed digital twin model of a target product, and extracting product cycle data of a full life cycle corresponding to the target product in the product data;
s3, integrating the product cycle data into the distributed digital twin model by using a preset bidirectional data integration algorithm to obtain a distributed product digital twin model;
s4, monitoring the performance of the distributed product digital twin model in real time according to real-time data sequences at different moments, and extracting real-time feedback data of the monitored product;
S5, optimizing the distributed product digital twin model through a preset product automatic optimization algorithm and the product real-time feedback data to obtain a product optimization model, and integrating the product optimization model with the target product to obtain a target creation product.
Fig. 3 is a schematic structural diagram of an electronic device implementing an operation method of a digital twin-based product creation system according to an embodiment of the present invention.
The electronic device may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as a digital twin based product creation system program.
The processor 10 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing Unit, CPU), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the apparatus, connects various parts of the entire apparatus using various interfaces and lines, and executes various functions of the apparatus and processes data by running or executing programs or modules stored in the memory 11 (for example, executing a digital twin-based product creation method program, etc.), and calling data stored in the memory 11.
The memory 11 includes at least one type of computer-readable storage medium including flash memory, a removable hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the device, such as a removable hard disk of the device. The memory 11 may also be an external storage device of the device in other embodiments, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like. Further, the memory 11 may also include both an internal storage unit and an external storage device of the device. The memory 11 may be used not only for storing application software installed in the device and various types of data, such as code of a digital twin-based product creation system program, etc., but also for temporarily storing data that has been output or is to be output.
The communication bus 12 may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
The communication interface 13 is used for communication between the above-mentioned devices and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the device and other devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the device and for displaying a visual user interface.
Fig. 3 shows only a device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 is not limiting of the device and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
For example, although not shown, the apparatus may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, etc. are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The device may also include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described in detail herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The digital twin based product creation system program stored by the memory 11 in the device is a combination of instructions that, when executed in the processor 10, may implement:
Acquiring product data of a target product, screening the product data according to preset product requirements to obtain product key requirement data, and generating a product requirement vector from the product key requirement data;
Constructing a twin geometric model of the target product according to the geometric attributes in the product demand vector; applying physical data in the twin geometric model according to the physical attributes in the product demand vector to obtain a twin physical model of the target product; generating dynamic distributed data by using dynamic demand vectors in the product demand vectors; generating a data demand node of a preset product user according to the dynamic distributed data; deploying the twin physical model into the data demand node to obtain a distributed digital twin model of a target product, and extracting product cycle data of a full life cycle corresponding to the target product in the product data;
Integrating the product cycle data into the distributed digital twin model by using a preset bidirectional data integration algorithm to obtain a distributed product digital twin model;
monitoring the performance of the distributed product digital twin model in real time according to real-time data sequences at different moments, and extracting real-time feedback data of the monitored product;
Optimizing the distributed product digital twin model through a preset product automatic optimization algorithm and the product real-time feedback data to obtain a product optimization model, and integrating the product optimization model with the target product to obtain a target creation product.
In particular, the specific implementation method of the above instructions by the processor 10 may refer to the description of the relevant steps in the corresponding embodiment of the drawings, which is not repeated herein.
Further, the device-integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer-readable storage medium. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, may implement:
Acquiring product data of a target product, screening the product data according to preset product requirements to obtain product key requirement data, and generating a product requirement vector from the product key requirement data;
Constructing a twin geometric model of the target product according to the geometric attributes in the product demand vector; applying physical data in the twin geometric model according to the physical attributes in the product demand vector to obtain a twin physical model of the target product; generating dynamic distributed data by using dynamic demand vectors in the product demand vectors; generating a data demand node of a preset product user according to the dynamic distributed data; deploying the twin physical model into the data demand node to obtain a distributed digital twin model of a target product, and extracting product cycle data of a full life cycle corresponding to the target product in the product data;
Integrating the product cycle data into the distributed digital twin model by using a preset bidirectional data integration algorithm to obtain a distributed product digital twin model;
monitoring the performance of the distributed product digital twin model in real time according to real-time data sequences at different moments, and extracting real-time feedback data of the monitored product;
Optimizing the distributed product digital twin model through a preset product automatic optimization algorithm and the product real-time feedback data to obtain a product optimization model, and integrating the product optimization model with the target product to obtain a target creation product.
In the several embodiments provided by the present invention, it should be understood that the disclosed media, systems, and methods may be implemented in other ways. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the foregoing description, and all changes which come within the meaning and range of equivalency of the scope of the invention are therefore intended to be embraced therein.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Multiple units or systems as set forth in the system claims may also be implemented by means of one unit or system in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (7)
1. A product creation system based on digital twinning is characterized by comprising a product demand vector generation module, a product period data acquisition module, a twinning data processing module, a product real-time feedback data extraction module and a target creation product integration module, wherein,
The product demand vector generation module is used for acquiring product data of a target product, screening the product data according to preset product demands to obtain product key demand data, and generating a product demand vector from the product key demand data;
The product period data acquisition module is used for constructing a twin geometric model of the target product according to the geometric attributes in the product demand vector; applying physical data in the twin geometric model according to the physical attributes in the product demand vector to obtain a twin physical model of the target product; generating dynamic distributed data by using dynamic demand vectors in the product demand vectors; generating a data demand node of a preset product user according to the dynamic distributed data; deploying the twin physical model into the data demand nodes to obtain a distributed digital twin model of a target product, and extracting product cycle data of a full life cycle corresponding to the target product in the product data, wherein the distributed digital twin model refers to deploying the digital twin model on nodes of different product users, and controlling the digital twin model corresponding to the product through the different nodes;
The twin data processing module is configured to integrate the product cycle data into the distributed digital twin model by using a preset bidirectional data integration algorithm to obtain a distributed product digital twin model, and includes: carrying out data standardization on the product cycle data one by one to obtain product cycle standardization data; extracting product data fields of the distributed digital twin model; extracting a product cycle data field in the product cycle standardized data; the method comprises the steps of carrying out data integration according to a product data field and a product period data field by using a preset bidirectional data integration algorithm to obtain distributed product data, wherein the preset bidirectional integration algorithm is to integrate the product data field and the product period data field to obtain the distributed product data, namely when the product data field and the product period data field are subjected to data integration, firstly comparing the product data field with the product period data field, feeding back a feedback value by the product data field to indicate whether the product data field is consistent with the product period data field or not, and likewise feeding back a numerical value by the product period data field to indicate whether the product period data field is consistent with the product data field or not, and when the forward integration feedback value is equal to the reverse integration feedback value and the period field is identical to the product data field, replacing product data corresponding to a distributed digital twin model by using the product period data to obtain the latest product data in the distributed digital twin model; when the forward integration feedback value is not equal to the reverse integration feedback value or the period field is not equal to the product data field, at this time, the update data corresponding to each field in the distributed digital twin model is set to 0, that is, the original data corresponding to each field before is further configured, and further, the distributed product digital twin model is regenerated according to the data size of the distributed product data, for example, the distributed product digital twin model is generated according to the updated geometric data, performance data and the like, then the distributed digital twin model can acquire the update of the product period data in real time through a bidirectional data integration algorithm, and reflect the change of the actual running state of the product, so that the distributed digital twin model can respond to the change in the life cycle of the product in time, that is: Wherein/> For the distributed product data,/>For/>Product data corresponding to the individual product data fields,/>For/>Cycle phase number/>Product cycle data corresponding to the product cycle data fields,/>For/>Field quantized value of individual product data field,/>For/>Cycle phase number/>Field quantized value of individual product period data field,/>For forward integration of feedback values,/>Is a reverse integration feedback value;
generating a distributed product digital twin model according to the distributed product data;
The product real-time feedback data extraction module is used for monitoring the product performance of the distributed product digital twin model in real time according to real-time data sequences at different moments and extracting monitored product real-time feedback data;
The product integration module is created by the target, and is used for optimizing the distributed product digital twin model through a preset product automation optimization algorithm and the product real-time feedback data to obtain a product optimization model, and comprises the following steps: extracting a target feedback sequence in the real-time feedback data of the product; determining a target performance attribute of the distributed product digital twin model according to the target feedback sequence; optimizing the target performance attribute through a preset product automation optimization algorithm to obtain a target performance optimization attribute, wherein the target feedback sequence refers to a value corresponding to a feedback value of 1 or-1, further extracting the target performance attribute corresponding to the feedback value of 1 or-1, determining the latest target performance attribute of the distributed product digital twin model according to the target feedback sequence, namely extracting the performance attribute corresponding to the feedback value of 1 or-1 as the target performance attribute, optimizing the target performance attribute through the product automation optimization algorithm to obtain the optimized target performance attribute, selecting the attribute with the smallest performance attribute value as the latest target performance attribute in the feedback performance attribute corresponding to different moments when the feedback value is 1 and the real-time product performance value is larger than the product standard performance value in the product automation optimization algorithm, selecting the smallest performance attribute under the condition of meeting the basic performance of the product, saving resources, assigning the standard performance attribute at the moment to the product performance attribute as the product performance attribute when the feedback value is-1, namely utilizing the standard performance attribute to replace the product performance attribute, further optimizing the product performance attribute according to the target performance attribute, optimizing the product model according to the target performance attribute, and optimizing the product parameter corresponding to the product model, Wherein/>For/>Target performance optimization attribute corresponding to each product performance attribute,/>As a minimum function,/>For/>Time of day/>Item/>, in individual product categoriesIndividual product Performance Properties,/>For/>Item/>, in individual product categoriesProduct standard performance attributes of individual products,/>Is a feedback value/>For/>Real-time product Performance at time,/>Is the standard performance of the product;
Generating a product optimization model according to the target performance optimization attribute, and extracting first product features in the product optimization model; extracting second product features of the target product; performing weighted average or splicing operation on the two features to obtain new comprehensive features; retaining only one of the repeated features in the first product feature and the second product feature; taking the characteristics which are not included in the first product characteristics but are not included in the second product characteristics as target product characteristics, and taking the characteristics which are not included in the first product characteristics but are included in the second product characteristics as target product characteristics; and generating a target creation product according to the target product characteristics, wherein the first product characteristics refer to product design parameters, material properties and process parameters in a product optimization model, and the second product characteristics refer to functional requirements, performance indexes and appearance designs of the target product.
2. The digital twin based product creation system of claim 1, wherein the product demand vector generation module, when performing filtering of the product data according to preset product demands to obtain product critical demand data, comprises:
Carrying out data enhancement processing on the product data to obtain product enhancement data, wherein the data enhancement processing comprises the operations of data cleaning, duplication removal, missing value filling, noise reduction, data smoothing and data conversion on the product data;
Carrying out data source integration on the product enhanced data to obtain product integrated data, wherein the data source integration is to establish a data model and a relationship diagram based on the data content and the formats of different data sources, describe the relationship between the data, understand the relationship between the data by determining the data entity, the attribute and the relationship, establish the mapping relationship between the data, and generate a complete data set according to the mapping relationship to obtain the product integrated data;
extracting product construction data in the product demand;
and screening the product integration data according to the product construction data to obtain product key demand data.
3. The digital twinning-based product creation system of claim 1, wherein the product demand vector generation module, when executing the generation of the product demand vector from the product critical demand data, comprises:
Carrying out data classification on the product key demand data to obtain the geometric attribute and the physical attribute of a target product;
Generating a static demand vector of a target product according to the geometric attribute and the physical attribute;
generating a dynamic demand vector of the target product according to the preset behavior attribute and rule attribute;
and fusing the static demand vector and the dynamic demand vector into a product demand vector.
4. The digital twinning-based product creation system of claim 1, wherein the product real-time feedback data extraction module, when performing real-time product performance monitoring of the distributed product digital twinning model according to real-time data sequences at different times, comprises:
Calculating the real-time product performance of the distributed product digital twin model one by one according to the real-time data sequence, wherein the real-time product performance calculation formula is as follows: Wherein/> For/>Real-time product Performance at time,/>For/>Item/>, in individual product categoriesAttribute weight of individual product performance attributes,/>For/>Time of day/>Item/>, in individual product categoriesIndividual product Performance Properties,/>For/>Item/>, in individual product categoriesProduct standard performance attributes of individual products,/>For/>Weight of individual external factors,/>For/>Factor score of individual external factors,/>For the number of product performance attributes,/>Is the number of external factors.
5. A method of operation of a digital twinning-based product creation system, for implementing a digital twinning-based product creation system as recited in any one of claims 1-4, the method comprising:
Acquiring product data of a target product, screening the product data according to preset product requirements to obtain product key requirement data, and generating a product requirement vector from the product key requirement data;
constructing a twin geometric model of the target product according to the geometric attributes in the product demand vector; applying physical data in the twin geometric model according to the physical attributes in the product demand vector to obtain a twin physical model of the target product; generating dynamic distributed data by using dynamic demand vectors in the product demand vectors; generating a data demand node of a preset product user according to the dynamic distributed data; deploying the twin physical model into the data demand nodes to obtain a distributed digital twin model of a target product, and extracting product cycle data of a full life cycle corresponding to the target product in the product data, wherein the distributed digital twin model refers to deploying the digital twin model on nodes of different product users, and controlling the digital twin model corresponding to the product through the different nodes;
integrating the product cycle data into the distributed digital twin model by using a preset bidirectional data integration algorithm to obtain the distributed product digital twin model, wherein the distributed product digital twin model is specifically used for: carrying out data standardization on the product cycle data one by one to obtain product cycle standardization data; extracting product data fields of the distributed digital twin model; extracting a product cycle data field in the product cycle standardized data; the method comprises the steps of carrying out data integration according to a product data field and a product period data field by using a preset bidirectional data integration algorithm to obtain distributed product data, wherein the preset bidirectional integration algorithm is to integrate the product data field and the product period data field to obtain the distributed product data, namely when the product data field and the product period data field are subjected to data integration, firstly comparing the product data field with the product period data field, feeding back a feedback value by the product data field to indicate whether the product data field is consistent with the product period data field or not, and likewise feeding back a numerical value by the product period data field to indicate whether the product period data field is consistent with the product data field or not, and when the forward integration feedback value is equal to the reverse integration feedback value and the period field is identical to the product data field, replacing product data corresponding to a distributed digital twin model by using the product period data to obtain the latest product data in the distributed digital twin model; when the forward integration feedback value is not equal to the reverse integration feedback value or the period field is not equal to the product data field, at this time, the update data corresponding to each field in the distributed digital twin model is set to 0, that is, the original data corresponding to each field before is further configured, and further, the distributed product digital twin model is regenerated according to the data size of the distributed product data, for example, the distributed product digital twin model is generated according to the updated geometric data, performance data and the like, then the distributed digital twin model can acquire the update of the product period data in real time through a bidirectional data integration algorithm, and reflect the change of the actual running state of the product, so that the distributed digital twin model can respond to the change in the life cycle of the product in time, that is: Wherein/> For the distributed product data,/>For/>Product data corresponding to the individual product data fields,/>For/>Cycle phase number/>Product cycle data corresponding to the product cycle data fields,/>For/>Field quantized value of individual product data field,/>For/>Cycle phase number/>Field quantized value of individual product period data field,/>For forward integration of feedback values,/>Is a reverse integration feedback value;
generating a distributed product digital twin model according to the distributed product data;
monitoring the performance of the distributed product digital twin model in real time according to real-time data sequences at different moments, and extracting real-time feedback data of the monitored product;
Optimizing the distributed product digital twin model through a preset product automation optimization algorithm and the product real-time feedback data to obtain a product optimization model, wherein the method comprises the following steps: extracting a target feedback sequence in the real-time feedback data of the product; determining a target performance attribute of the distributed product digital twin model according to the target feedback sequence; optimizing the target performance attribute through a preset product automation optimization algorithm to obtain a target performance optimization attribute, wherein the target feedback sequence refers to a value corresponding to a feedback value of 1 or-1, further extracting the target performance attribute corresponding to the feedback value of 1 or-1, determining the latest target performance attribute of the distributed product digital twin model according to the target feedback sequence, namely extracting the performance attribute corresponding to the feedback value of 1 or-1 as the target performance attribute, optimizing the target performance attribute through the product automation optimization algorithm to obtain the optimized target performance attribute, selecting the attribute with the smallest performance attribute value as the latest target performance attribute in the feedback performance attribute corresponding to different moments when the feedback value is 1 and the real-time product performance value is larger than the product standard performance value in the product automation optimization algorithm, selecting the smallest performance attribute under the condition of meeting the basic performance of the product, saving resources, assigning the standard performance attribute at the moment to the product performance attribute as the product performance attribute when the feedback value is-1, namely utilizing the standard performance attribute to replace the product performance attribute, further optimizing the product performance attribute according to the target performance attribute, optimizing the product model according to the target performance attribute, and optimizing the product parameter corresponding to the product model, Wherein/>For/>Target performance optimization attributes corresponding to the individual product performance attributes,As a minimum function,/>For/>Time of day/>Item/>, in individual product categoriesIndividual product Performance Properties,/>For/>Item/>, in individual product categoriesProduct standard performance attributes of individual products,/>Is a feedback value/>For/>Real-time product Performance at time,/>Is the standard performance of the product;
Generating a product optimization model according to the target performance optimization attribute, and extracting first product features in the product optimization model; extracting second product features of the target product; performing weighted average or splicing operation on the two features to obtain new comprehensive features; retaining only one of the repeated features in the first product feature and the second product feature; taking the characteristics which are not included in the first product characteristics but are not included in the second product characteristics as target product characteristics, and taking the characteristics which are not included in the first product characteristics but are included in the second product characteristics as target product characteristics; and generating a target creation product according to the target product characteristics, wherein the first product characteristics refer to product design parameters, material properties and process parameters in a product optimization model, and the second product characteristics refer to functional requirements, performance indexes and appearance designs of the target product.
6. An electronic device, the electronic device comprising:
at least one processor and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of operation of the digital twinning-based product creation system of claim 5.
7. A computer readable storage medium storing a computer program, which when executed by a processor implements a method of operating a digital twin based product creation system as defined in claim 5.
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