261 results about "Manufacturing data" patented technology

The invention relates to an aviation equipment manufacturing digital main line engine system supporting multi-source heterogeneous data access, and belongs to the technical field of aviation manufacturing data management. The system comprises a multi-source heterogeneous data access adaptation module, a data fusion processing module and a full-process data link construction module. The multi-source heterogeneous data access adaptation module passes through a data interface protocol and format conversion component; the data standardization and fusion processing module completes association fusion through an aviation heterogeneous data collaborative computing framework by means of an aviation manufacturing field data element standard library, a unified heterogeneous data model is generated, and the framework comprises a data fragmentation layer, a parallel node layer and a result aggregation layer; and the full-process data link construction module constructs a data link covering the full life cycle of the product based on a time sequence association algorithm and a product unique identifier mapping mechanism. The system can effectively solve the integration and management problems of multi-source heterogeneous data in aviation equipment manufacturing, and improves the data processing efficiency and the whole-process data tracing capability.

The invention relates to the technical field of chip yield prediction, in particular to a GPU chip yield prediction method and system based on multi-dimensional data fusion, and the method comprises the steps: firstly obtaining multi-dimensional data in a GPU chip manufacturing process; performing time sequence calibration and alignment on the multi-dimensional data based on a process flow, and generating high-order interaction features by using an implicit feature interaction technology; then generating a multi-dimensional abnormal association vector through yield bottleneck projection; then generating a local risk value based on the multi-dimensional abnormal association vector and the manufacturing process node, and constructing a global yield map; and finally, constructing an anomaly propagation and accumulation calculation framework, calculating along manufacturing process nodes to obtain an accumulation risk value, and obtaining a final GPU chip yield prediction value through global damage projection. According to the method, the multi-dimensional manufacturing data is subjected to time sequence fusion and feature crossing, the global yield map is constructed, and abnormal propagation and accumulation risks are calculated, so that GPU chip yield prediction is realized.

The invention provides an optical proximity correction method and system based on mask error model optimization, and a terminal, and the method comprises the steps: building a data sample set containing a plurality of process conditions through obtaining a large amount of mask manufacturing data under a specific photoetching process; and based on the sample set, constructing a physical optical sub-model and a machine learning sub-model to form a mask error model for outputting a final mask key size value. And then, according to a plurality of preset alternative weight coefficient combinations, reconstructing an objective function which introduces a mask error term, calculating corresponding objective function values, and correcting the photoetching pattern by using an OPC algorithm corresponding to each objective function value to obtain performance index data so as to determine an optimal weight coefficient combination. And finally, optimizing the OPC algorithm by using the target function of the optimal combination, thereby realizing the accurate correction of the photoetching pattern, and improving the precision and yield of semiconductor manufacturing. According to the method, the mask error model comprehensively considering physical optics and machine learning is established, and the OPC process is introduced, so that the influence of the mask error can be predicted and compensated more accurately, the critical dimension deviation and the like are reduced, and the device performance and the yield are improved. The method can adapt to different process conditions, and parameters and weight coefficients can be dynamically adjusted. And moreover, rework can be reduced, automatic adjustment is realized through closed-loop feedback control, the production stability and efficiency are improved, and the cost is reduced.

A computer-implemented method and apparatus for managing physical and digital products' customization using container format are disclosed. In some embodiments, the method comprises receiving a container file with serialized objects, deserializing these objects to form a hierarchical object structure, and interacting with a database to obtain real-time configuration data. Custom attributes of the products are defined by binding attribute / value pairs to the hierarchical structure. A visualization of the customized product is generated based on these pairs, and manufacturing instructions are determined considering the attribute / value pairs and manufacturing constraints. The apparatus comprises a memory and a processor to execute instructions for deserializing the container file, retrieving configuration data, organizing objects into a hierarchical tree, negotiating product customizations, and outputting manufacturing data. This system enables efficient and accurate production of customized products by managing data, rendering graphics, and incorporating manufacturing constraints.

The invention discloses a block chain and digital twinborn integrated production whole process credible tracing method, and belongs to the technical field of intelligent manufacturing and digital tracing. The system comprises a digital twin construction module, a block chain evidence storage module, a tracing verification module and a data acquisition module. The method comprises the following steps: creating a digital twinborn body for each product instance, and mapping material, process, equipment and quality data of the whole manufacturing process in real time; recording the twinborn key state hash value to a block chain in real time through an intelligent contract; credible tracing verification is provided based on block chain evidence storage and digital twin data; industrial Internet of Things equipment is used for collecting manufacturing data in real time to drive twin updating. According to the method, millisecond-level slice tracing in the manufacturing process is realized, the deep description capability of digital twinning and credible guarantee of the block chain are fused, the problems of insufficient data depth and low credibility of a traditional tracing system are solved, and judicial-level evidence support is provided for quality disputes.

The invention relates to a manufacturing process quality tracing and root cause analysis system based on a data graph, and belongs to the technical field of manufacturing process quality control. The system performs cleaning, duplicate removal and standardization processing on multi-source heterogeneous data in the whole manufacturing process through a data processing module to generate structured data; the data graph construction module fuses the manufacturing domain knowledge ontology, constructs a manufacturing process digital twin semantic model and a multi-dimensional manufacturing data graph, introduces a dynamic association weight updating mechanism and mines node potential implicit association; the quality tracing module positions map nodes based on query semantic vectors, and reconstructs an entity state change track by using a digital twin semantic model to generate a tracing link; and the root cause analysis module screens candidate abnormal factors, locates a root cause in combination with dynamic weight and multi-scene simulation verification, and generates a quality optimization scheme. According to the invention, full-link accurate tracing and root cause efficient positioning of the manufacturing process quality are realized, and the intelligence and refinement level of manufacturing quality management and control is improved.

The invention relates to the technical field of knowledge maps, in particular to a steel manufacturing knowledge map construction method based on multi-source heterogeneous data, which comprises the following steps: converting message data into a standardized semantic triple through a semantic mapping rule, and complementing missing attributes by using physical and chemical component balance logic to form a standardized entity; meanwhile, a semantic processor is used for extracting unstructured report text logic, cross-modal coupling is carried out on the unstructured report text logic and SEM image features, and a failure mechanism enhanced entity is generated. Then, full-process dynamic parameters are collected and regularized into equal-interval time sequence parameters; and finally, taking the normalized entity as an index feature and the mechanism enhanced entity as an association constraint, executing semantic fusion by utilizing a topological feature mapping engine, and constructing a dynamic weight graph structure by combining with a dynamic probability distribution correction operator iteration parameter weight. According to the method, efficient association and semantic organization of manufacturing whole-process multi-modal data are realized, and the retrieval precision and knowledge discovery capability of steel manufacturing data are greatly improved.

Provided are a method for predicting a product strain and a system thereof. A method for predicting a product strain according to one embodiment of the present disclosure is a method performed by a computing device, the method comprising the steps of: acquiring a three-dimensional shape model of a first product; preprocessing the three-dimensional shape model and generating graph data including nodes and edges; inputting an image dataset to a measurement model for predicting shape characteristics and physical property characteristics of an object, the image dataset being obtained by imaging, from multiple angles, the object manufactured on the basis of the three-dimensional shape model, and generating manufacturing data of the first product; comparing graph data and the manufacturing data for each node unit, and generating label data including strain data for each node unit; and fine-tuning a graph data-based graph node classification model by using a strain data set composed of pairs of the graph data and the label data. The step of generating the graph data may include the steps of: generating predictive precision reference information for each unit mesh included in the three-dimensional shape model by using the three-dimensional shape model of the first product and the manufacturing data of the first product; and according to the predictive precision reference information for each unit mesh, applying a first resolution to a first unit mesh that requires first predictive precision, and a second resolution to a second unit mesh that requires second predictive precision.

The invention relates to the technical field of data processing, and discloses a digital twinning-based automobile manufacturing industrial data multi-stage processing method and a digital twinning-based automobile manufacturing industrial data multi-stage processing system. The method comprises the following steps: performing grading processing on automobile manufacturing data through a five-grade grading identification mechanism to obtain a grading data set; establishing a process-equipment-material ternary coupling relation matrix according to the hierarchical data set; carrying out dynamic weight distribution on the coupling relation matrix to obtain multi-stage processing weights; dynamically reconstructing a digital twin structure according to the change of the coupling relation matrix; and carrying out collaborative decision-making on the reconstruction structure and the processing weight to generate a multi-level control instruction. According to the invention, the problem that the prior art lacks dynamic sensing and self-adaptive reconstruction capabilities for the ternary coupling relationship change of process parameters, equipment states and material attributes is solved, and the accuracy and real-time performance of tracking and responding to the complex multi-element collaborative change in the automobile manufacturing process by the digital twin system are improved.

The invention discloses a micro spring washer manufacturing process optimization method using a collaborative filtering algorithm, and the method comprises the following steps: S1, collecting and preprocessing historical manufacturing data, and constructing a tensor structure comprising manufacturing batches, process parameters and product quality indexes; s2, converting the tensor structure into a scoring matrix, and predicting an initial process parameter combination of a target batch by adopting a collaborative filtering algorithm; s3, constructing a weighted graph, introducing a label propagation network to complement scores, and determining a recommendation parameter combination; s4, constructing a multi-objective optimization model with size consistency, residual stress balance and unit energy consumption as objectives based on the recommended combination; s5, solving by adopting an improved cat swarm optimization algorithm, and outputting an optimal solution; and S6, applying the optimal solution to the manufacturing process, collecting quality result feedback, updating the scoring matrix and the graph model, and realizing a manufacturing optimization closed loop. According to the method, the tag propagation network and the improved cat swarm optimization algorithm are fused, and the manufacturing process of the miniature spring washer is optimized.

A method of overhaul is provided. During this overhaul method, a substrate is scanned using structured light to provide substrate scan data. The substrate is from a component previously installed within an engine. The substrate scan data is compared to substrate reference data to provide additive manufacturing data. Material is deposited with the substrate using an additive manufacturing device based on the substrate scan data to provide a first object. The first object is scanned using the structured light to provide first object scan data. The first object scan data is compared to first object reference data to provide machining data. The first object is machined using the machining data.

The invention discloses a metro vehicle supervision manufacturing multi-station data traceability control system, and relates to the technical field of industrial data traceability, and the system comprises a data collection module which obtains the original manufacturing data of each station of metro vehicle manufacturing; the feature extraction module is used for processing the original manufacturing data and generating behavior fingerprint vectors and station features; the identity analysis module is used for carrying out feature matching calculation and outputting a candidate component identity set and matching similarity; the logic verification module is used for carrying out logic consistency verification and determining the identity of the target component; and the traceability control module establishes a mapping relation with the original manufacturing data based on the identity of the target component, generates a unique traceability node, and writes the traceability node into a multi-station traceability data chain. According to the method, the problems of identification failure, data link breakage and affiliation errors caused by station switching or equipment sharing are solved, supervision data continuity and identity recognition accuracy are improved, and reliable support is provided for vehicle full-life-cycle data tracing.

The invention relates to the technical field of manufacturing data acquisition, and discloses a distributed manufacturing data acquisition system based on NB-IoT. The system comprises a heterogeneous node sensing layer, a narrowband signaling scheduling layer, an edge fusion processing layer, a cloud collaborative decision-making layer and a closed-loop optimization feedback layer. The heterogeneous node sensing layer obtains multi-source data through a sensor array, generates an equipment operation state thermodynamic diagram and divides region priorities; the narrowband signaling scheduling layer deploys NB-IoT nodes, transmits time division multiplexing signaling and generates a node load distribution diagram; the edge fusion processing layer synchronizes the clock, fuses the multi-source data and the load distribution map, and generates fusion confidence; the cloud collaborative decision-making layer converts the fusion confidence into an executable instruction and issues the executable instruction; and the closed-loop optimization feedback layer monitors the load change of the system, generates an optimization index and dynamically optimizes an acquisition strategy. The system can penetrate through network congestion, improves the real-time performance and accuracy of data acquisition, dynamically adapts to the change of a manufacturing environment, and is suitable for a distributed manufacturing scene.

The invention discloses a sectional type wind power blade manufacturing method and system based on intelligent assembly control. The method comprises the steps of obtaining segmented component design data, and generating assembly path planning information based on a dynamic assembly optimization model; real-time monitoring is carried out through a multi-sensor fusion technology, and an intelligent assembly control instruction is generated; the assembly precision is verified in combination with a self-adaptive deformation compensation mechanism, and a blade assembly record is generated; and storing the record to a manufacturing data system and completing manufacturing. The deformation compensation mechanism optimizes pose deviation and interface stress based on finite element analysis and real-time data, and integrates gravity and temperature influence. The system comprises a data acquisition module, an instruction generation module, a precision verification module and a storage execution module. Millimeter-level precise butt joint and bonding quality control of the large-size blades are achieved, and the assembly efficiency and reliability are improved.

The invention discloses a processing defect detection method and system based on a semiconductor integrated circuit, and belongs to the technical field of semiconductor manufacturing data processing and defect detection, and the method comprises the steps: obtaining a high-resolution surface topography scanning matrix, an interlayer stress distribution matrix and a transient electrical response matrix of a specified process layer of a target wafer, and constructing an original feature tensor after correction; generating a multi-dimensional coupling feature matrix, and constructing a spatial adjacency topology network and a cross-layer coupling energy propagation graph model; performing energy iteration deduction on the basis of introducing a current density direction vector and a thermal diffusion coefficient parameter to obtain a potential energy gathering path; a microdefect risk coupling coefficient distribution diagram is generated through time sequence folding mapping and nonlinear coupling calculation, hidden defect characterization parameters are extracted through multi-scale feature compression and abnormal clustering analysis, and defect space positioning and process compensation parameter adjustment suggestion output are achieved; according to the invention, the subcritical microscale latent defect can be identified, and the detection sensitivity and reliability are improved.

The invention discloses an automobile part manufacturing process data processing method and system based on artificial intelligence, and relates to the technical field of data processing. The automobile part manufacturing process data processing method and system based on artificial intelligence comprises the steps of S1, collecting process record data and process characteristic data of automobile part manufacturing, preprocessing the process record data and the process characteristic data, and constructing a part manufacturing database; s2, fluctuation sensitivity judgment is carried out through the process characteristic data, and signal quality grading, enhancement parameter adjustment and noise suppression processing operation are executed according to the judgment result; s3, integrating the amplitude quantity and the information correlation quantity to construct a cross-channel causal coupling characterization quantity, and triggering the generation of a causal link; and S4, realizing aging state identification by fusing amplitude offset, an attenuation baseline and causal coupling features and combining offset grading analysis and link traceability processing. The problems of synchronous dislocation of multi-channel signals, insufficient perturbation detection precision, difficulty in causal chain reconstruction and difficulty in aging drift identification and tracking are solved.

A system may include an application server to host an application program, and the application program may include one or more modules to associate an asset with location data, manufacture data, and industry data for input into an artificial intelligence ("AI") module. Location data may include a physical location of the asset. The system may further include an AI module communicably coupled to the one or more modules, and the AI module may receive the manufacture data, the user data, and the industry data from the one or more modules. The AI module may generate a predictive maintenance schedule for the asset based on the manufacture data and the industry data. The AI module may receive the location data from the one or more modules and adjust the predictive maintenance schedule based on the location data.

The invention discloses a mechanical part cost decision support system and method driven by an industrial knowledge graph, and relates to the technical field of mechanical manufacturing cost decision, and the system comprises a multi-modal data collection module which collects multi-type data of the whole process of mechanical manufacturing and processes the multi-type data to form a unified set; the multi-modal knowledge graph construction module constructs a graph and establishes an updating mechanism; the intelligent reasoning decision module inputs new part parameters to screen an optimal scheme; the dynamic optimization adjustment module collects data in real time to update a result reversely; the decision result output module generates a visual report and supports multi-format export; according to the method, manufacturing data dispersion pain points are broken, multi-source data fusion precipitation and knowledge systematization are realized, and accurate support is provided for cost decision making; meanwhile, an optimal scheme is screened through multi-algorithm cooperation and multi-target evaluation, field data are collected in real time to dynamically optimize decisions, the crossing from static estimation to dynamic optimization is achieved, the core target is effectively balanced, and the economical efficiency and the stability are improved.

The invention provides a multidisciplinary collaborative design and engineering digitization platform for a motor system, which integrates natural language processing and large language model technologies, is connected with electrical, electromagnetic, thermal and control multidisciplinary simulation tools, realizes full-process collaborative optimization from demand input to manufacturing output, and improves the design efficiency. The platform converts a natural language into standardized parameters through an instruction analysis module, drives electrical modeling, electromagnetic simulation, thermal analysis and control algorithm design, automatically generates a two-dimensional / three-dimensional model and a corresponding processing technology file based on an optimization result, and further constructs a unified digital main line through an engineering data middle platform. Integrated management and closed-loop optimization of design, simulation and manufacturing data are achieved, meanwhile, a user can monitor a simulation result and manufacturing feasibility in real time through a graphical interface, the platform supports interdisciplinary collaborative iteration and parameter linkage closed-loop optimization, the design efficiency and manufacturing feasibility are remarkably improved, and the design cost is reduced. The method is suitable for digital and intelligent development of a motor system in the industrial and scientific research field.

The invention provides a multi-dimensional data fusion analysis method and system, and the method comprises the steps: collecting multi-source manufacturing information of a manufacturing execution system, carrying out the time sequence alignment of the multi-source manufacturing information through employing a production technology constraint, carrying out the attribute reduction of a multi-dimensional manufacturing data set obtained through alignment, and obtaining a core attribute set, and calculating the importance value of each manufacturing sub-task based on the core attribute set, and generating a task priority sequence. Based on the task priority sequence, candidate resources corresponding to the priority subtasks are extracted from the multi-dimensional manufacturing data set, and the multi-dimensional matching degree of the candidate resources is calculated. And performing weighted fusion on the multi-dimensional matching degree through historical execution data to obtain a resource matching matrix, and performing resource conflict resolution according to the resource matching matrix and the task constraint relationship to generate a resource allocation scheme. According to the method, accurate resource allocation is realized through time sequence alignment and multi-dimensional matching degree calculation, so that the resource utilization rate and the production scheduling efficiency are improved.

The invention discloses a manufacturing data intelligent detection method based on time sequence analysis, and relates to the technical field of machine learning, and the method comprises the steps: building an equipment standardized operation array and an equipment historical operation array, carrying out the difference operation, obtaining the deviation operation parameters of equipment, building a deviation operation matrix, and carrying out the characteristic decomposition, obtaining a plurality of equipment abnormal characteristics; the method comprises the steps of determining a manufacturing and production event under historical manufacturing data, analyzing associated factors influencing equipment abnormity according to the manufacturing and production event under the historical manufacturing data and a plurality of equipment abnormity characteristics, establishing an intelligent detection model based on the plurality of equipment abnormity characteristics and abnormity influence factors, and evaluating the health state of the equipment. And judging whether the health state of the equipment is within an ideal range, if so, judging that no abnormity exists, and if not, judging that abnormity exists. The method has the advantages that the maintenance plan is optimized, the maintenance cost is reduced, the service life of equipment is prolonged, and the equipment management level is improved.

Methods, computer systems, and computer-readable memory media for determining a warp function. An as-designed watertight spline model of an object is received. A point cloud and the as-designed watertight spline model are used to construct a model of the object. The point cloud is obtained from a physical or virtual (simulated) inspection and / or manufacturing process. A warp function is determined based on a difference between the as-designed watertight spline model and the constructed model. The warp function is a continuous function quantifying differences between the as-designed model and the constructed model. As-preprocessed instructions for a simulation or analysis process of the object are determined based on metadata of the as-designed watertight spline model and the warp function. The simulation or analysis process is performed on the object according to the as-preprocessed instructions to produce as-simulated data, and the as-simulated data is stored in a non-transitory computer-readable memory medium.

A system and method for collecting real time manufacturing data from a factory having multiple machines therein using internet-of-things (IoT) connectivity, comprising: at least one gateway comprising at least one server, the at least one gateway receiving data from the multiple machines; at least one machine communications module (MCM) that applies to the received data at least factory, enterprise, and analytics applications, and which outputs command and control information from the applications to the at least one gateway to directly control operations of the multiple machines; wherein the gateway provides security isolation on the machine side of the gateway, and encryption, authentication, and authorization security on the MCM side of the gateway; and wherein the applications process and normalize data from at least a plurality of types and generations of the multiple machines.

To support a user in monitoring of manufacturing data.SOLUTION: A manufacturing data analysis device is provided with an acquisition unit, an analysis unit, and an output data generation unit. The acquisition unit acquires, from manufacturing data related to a plurality of products, in a first acquisition condition, first manufacturing data including a manufacturing condition data group including one or more pieces of manufacturing condition data related to values indicating manufacturing conditions for the products and a quality data group including one or more pieces of quality data related to values indicating quality of each of the products. The analysis unit analyzes the first manufacturing data to calculate a degree of influence of the first manufacturing condition data included in the manufacturing condition data group on the respective pieces of quality data included in the quality data group. The output data generation unit generates output data including details related to at least one of the first manufacturing condition data, one or more pieces of quality data provided, by the first manufacturing condition data, with the degree of influence satisfying a first determination condition, and the degree of influence satisfying the first determination condition.SELECTED DRAWING: Figure 1

The present disclosure presents systems and methods for creating and recording digital cyber-physical passports during a manufacturing process. One such system or method is adapted to supply manufacturing data for a particular physical part instance being manufactured by the manufacturing machine; track a progress of the manufactured part instance and store manufacturing data supplied by the one or more monitoring devices and / or the manufacturing machine in a data store; generate digital cyber-physical passports for each completed phase of manufacturing for the particular part instance, wherein the cyber-physical passport contains proof data associated with the physical part instance, wherein the proof data indicates a compliance status of the particular part instance with one or more tolerance requirements, structural requirements, functional requirements, manufacturing requirements, or compatibility requirements; and record individual cyber-physical passports on a cyber-physical passport-linked ledger on a distributed ledger technology platform during the manufacturing process of the part instance.

Industrial Internet of Things for automatic executing product manufacturing based on a task is provided. The Industrial Internet of Things is configured to generate a product manufacturing task and a first instruction corresponding to the product manufacturing task, determine manufacturing process information based on the first instruction, decompose the manufacturing process information and generate sub-process manufacturing data, compose a set of manufacturing data based on the sub-process manufacturing data and the process execution time, perform a manufacturing feasibility analysis based on the set of manufacturing data, in response to a determination that an analysis result of the manufacturing feasibility analysis is feasible, perform the product manufacturing corresponding to sub-process based on the sub-process manufacturing data.

The invention discloses a method for complementing data in a cigarette manufacturing process based on a GAN (Germanic Analysis)-Transformer (Germanic Analysis-Transformer). The method comprises the following steps: acquiring multi-modal time sequence data in a cigarette manufacturing process, performing preliminary interpolation to obtain a multi-modal original tensor, and generating a missing mask at the same time; performing feature extraction on the multi-modal original tensor, and embedding process prior to obtain sparse perception unified representation; inputting the sparse perception unified representation and the missing mask into the generative adversarial network to obtain a preliminary completion sample; and inputting the preliminary completion sample and the multi-modal original tensor into a mixed Transform model in parallel for fine calibration, and obtaining a corrected completion sequence. Through cooperation of multi-modal deep fusion, physical knowledge embedding, uncertainty quantification and online reinforcement learning, the problem of data missing is solved, and data support is provided for a credible, reliable and evolvable cigarette manufacturing data intelligent maintenance system.

The invention provides a reactor core coolant flow failure probability calculation method and system and a readable medium, and relates to the technical field of reactors. The failure probability calculation method comprises the following steps: acquiring manufacturing standard deviation data of at least part of real fuel plates in a reactor core; obtaining average value data corresponding to at least part of real fuel plates; according to the manufacturing standard deviation data and the average value data, randomly generating simulated manufacturing data of the plurality of simulated fuel plates according to normal distribution; according to all the simulation manufacturing data corresponding to each simulation fuel assembly, simulation flow values of the simulation fuel assemblies are obtained through calculation, and according to all the simulation flow values, a distribution type and at least one target standard deviation are determined; and obtaining a flow acceptance limit value and a minimum flow value of the real fuel assembly, and calculating a failure probability value corresponding to the at least one target standard deviation according to the distribution type, the minimum flow value, the flow acceptance limit value and the at least one target standard deviation.

The present application relates to the technical field of die stamping manufacturing, and discloses a die stamping manufacturing cost intelligent prediction method. The method first acquires a multi-dimensional manufacturing data set composed of material characteristic parameters, process machining parameters and equipment operation parameters. Then, based on the material characteristic parameters, key process features are extracted, a process influence topology graph is generated according to the process machining parameters, and the equipment operation parameters are processed to obtain an equipment state evolution trend. Then, these data are input into a multi-factor evaluation model to generate a cost prediction requirement vector, and finally, a self-adaptive prediction strategy graph is constructed through a dynamic programming algorithm to output a manufacturing cost prediction scheme. The present application comprehensively considers multi-dimensional data, can effectively process data dynamic changes, improves the accuracy and reliability of die stamping manufacturing cost prediction, and helps enterprises to reasonably control costs, optimize production decisions and improve market competitiveness.

The invention discloses a design method for automatically generating a battery pack based on big data intelligent multi-dimensional constraint and a generator, and relates to the technical field of battery pack design. According to the design method for automatically generating the battery pack based on the big data intelligent multi-dimensional constraint, a user demand parameter set of the to-be-designed battery pack and a material library stored in a database are input into a pre-trained constraint screening model for comprehensive analysis, and the user demand parameter set of the to-be-designed battery pack and the material library stored in the database are selected within the limited range of material combination; generating one or more initial design schemes meeting user requirements; and based on the electrical data and the manufacturing data of each initial design scheme, analyzing the electrical performance index and the battery pack manufacturing difficulty factor of the corresponding initial design scheme, and generating a comprehensive evaluation optimization index so as to screen the target battery pack design scheme. And the technical requirements of users on the battery pack are met, the feasibility of the manufacturing process is met, and the deliverability of the battery pack product is also realized.