316 results about "Quality management" patented technology

The invention discloses a material batch full-process traceability system based on a production process, particularly relates to the field of production and manufacturing traceability, is used for solving the problems of continuity and accuracy of material batch full-process traceability, and is characterized in that batch tracking coordinates are generated by constructing a process mapping matrix and an assembly topological index; detecting circulation holes in real time and inserting placeholder marks; judging broken chain credibility based on path integrity and structural complexity; compressing traceable chain segments; executing field dynamic alignment to generate batch circulation patches; a material batch whole-process continuous tracing view is established; and furthermore, the abnormity positioning time is remarkably shortened, the real-time performance is improved, the influenced batches are accurately locked, the recall range is reduced, the production line decision-making efficiency is optimized, and efficient and reliable technical support is provided for quality management and batch tracking in the production process.

The invention relates to the technical field of building information management, in particular to a BIM (Building Information Modeling)-based prefabricated part design quality management method, which comprises the following steps of S1, establishing a prefabricated part three-dimensional information model with multi-dimensional parameters by using BIM software; S2, collecting real-time production data by using a sensor in a prefabricated part production process; s3, comparing the production data with initial design parameters in the BIM model to identify deviations exceeding a set tolerance range; s4, feeding back the identified deviation information to a production link, and adjusting operation parameters of production equipment according to the deviation condition; s5, after production is completed, whether the indexes meet the requirements or not is determined; and S6, adjusting the design and process standard of subsequent production. According to the invention, through real-time data acquisition, deviation analysis and feedback, optimization suggestion generation and closed-loop management, continuous improvement of precision and quality of a prefabricated part production process is ensured, and effective combination of design and production is realized.

The invention provides an intelligent actual measurement and quantity management method and system for a building. The method comprises the following steps: capturing environmental parameters, material circulation paths and spatial geometric information of key parts of a building in a construction process in real time to obtain a measurement data set; a distributed ledger technology is combined with a homomorphic encryption algorithm, a graph theory analysis technology is applied to evaluate a dependency relationship among operation points of a construction site, and a data log is generated; according to the data log, using an augmented reality technology to superpose a BIM model, introducing a support vector machine algorithm to carry out automatic classification and risk assessment processing on deviation data between measured data and preset standard data, and generating a rectification suggestion report; and a closed-loop quality management process is formed by continuously collecting rectification effect feedback. According to the technical scheme, high transparency and efficient quality control in the construction process are ensured.

The invention relates to the technical field of quality management, in particular to a real-time quality management method and system for a PVC pipe, and the method can be applied to quality management of the PVC pipe, especially real-time defect detection of a high-speed production line. The method comprises the following steps: firstly, acquiring a multi-modal data set of the PVC pipe in real time and preprocessing the multi-modal data set to obtain preprocessed data; secondly, constructing a deep learning model, extracting multi-scale features of the preprocessed data by using depth separable convolution and asymmetric convolution, and reducing parameters of the deep learning model; thirdly, fusing the extracted multi-scale features by using multi-modal fusion, and carrying out feature dimension reduction in combination with global mean pooling; then, training a deep learning model and evaluating the detection performance thereof; and finally, detecting and alarming the surface defects of the PVC pipe in real time. According to the method, the surface defects of the PVC pipe are detected based on the deep learning model, and the detection accuracy is ensured while the real-time performance of detection is improved.

The invention discloses a quality management control method and system based on a cosmetic production process, and relates to the technical field of cosmetic quality management.The method comprises the following steps that firstly, a conveying belt conveys a product to a detection area at a preset speed, and a visual detection system captures a high-definition image at a product sealing position through an industrial camera; establishing an ordered and searchable image data set for the captured image data, and extracting key features reflecting abnormal sealing of the product from the data set; and the extracted key features are further analyzed and processed under a detection window. According to the method, the deep learning model is introduced to intelligently evaluate the sealing light spot and heat distribution, the sealing abnormality coefficient is generated, pollution and defects are accurately distinguished, the misjudgment rate is reduced, and resource waste is reduced. Features are classified in real time, the conveying speed is dynamically adjusted, detection accuracy and production efficiency are both considered, system overload is avoided, and efficient intelligent quality control is provided for cosmetic production.

The invention relates to the technical field of industrial production management and control, in particular to an automobile part production workshop intelligent management and control system which comprises a data acquisition module, a production management module, a quality management module, an equipment management module, a material management module and a security management module. An inventory and production capacity evaluation mechanism is introduced, the mechanism constructs a comprehensive evaluation model by integrating key parameters such as the delivery time, the order demand, the current inventory, the equipment number, the equipment availability rate, the equipment performance rate and the finished product percent of pass, and the production capacity is evaluated by comprehensively considering the equipment efficiency, the finished product quality and time factors. According to the refined evaluation method, the enterprise can more effectively predict and cope with production challenges, order delay caused by insufficient productivity or inventory shortage is avoided, in addition, the refined evaluation method further facilitates optimization of resource allocation, improvement of production efficiency and guarantee of high-quality production, meanwhile, the delivery date requirement of customers is met, and then the customer satisfaction degree and market competitiveness are enhanced.

The invention relates to the technical field of concrete quality traceability, in particular to a premixed concrete whole-process quality traceability evaluation method and device based on a block chain, and the method comprises the steps: analyzing a target premixed concrete quality management method, so as to obtain a quality management demand; determining a premixed concrete full-process monitoring chain according to quality management requirements; and constructing a premixed concrete quality management block chain framework according to the premixed concrete full-process monitoring chain so as to perform quality tracing on the premixed concrete to be supervised in a tracing stage, and generating a quality risk assessment report. Therefore, the problems that a current premixed concrete management mode still mainly depends on traditional form type management, the overall informatization degree is low, data traceability, openness and transparency, tampering resistance and the like have a large improvement space, and the quality management level is affected are solved.

The invention discloses a flexible automatic production management and control system for a new energy high-power high-frequency transformer, and relates to the field of production management and control, the flexible automatic production management and control system comprises a production demand receiving module, a production resource management module, a production planning module, a production execution module and a quality control module, an order is received through an API or EDI, and a work order is generated; collecting equipment and material information in real time, and constructing a production resource association network; carrying out production scheduling and process matching by adopting an improved deep learning model; scheduling materials, procedures and equipment based on a multi-level analysis control model; the quality is evaluated by using a two-dimensional cloud model, and data recording is performed through a distributed database, so that the traceability of the quality problem is ensured. According to the system, the production efficiency and the quality management level are effectively improved, the problem that existing rigid automatic equipment lacks flexibility is solved, and efficient and accurate production of the new energy high-power high-frequency transformer is ensured through cooperative work of a plurality of modules so as to meet different production requirements.

A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing cloud system offers a variety of industrial applications to end customers, including but not limited to MES, ERP, quality management, supply chain management, and customer relationship management (CRM). The system includes extensibility tools that allows industrial customers to customize databases, data collection templates, reporting fields, and other features of their consumed services, eliminating the need for these features to be customized by an administrator of the cloud system. Some embodiments of the manufacturing cloud system can also leverage generative artificial intelligence (AI) in connection with executing its supported services.

The invention relates to the technical field of computers, discloses a product full-process quality monitoring method and system based on big data, and aims to solve the problem of insufficient quality management refinement caused by data islands, monitoring lag, tracing difficulty and single analysis dimension in the prior art. The method comprises the following steps: constructing a full-process data acquisition system covering design, materials, production, logistics and after-sale, integrating multi-source heterogeneous data, and performing cleaning and time alignment; using an LSTM and GRU mixed model to extract time sequence features of the process parameters, and generating a high-dimensional process feature vector; and constructing a material-process-quality association knowledge graph in combination with the graph attention network, and representing the internal association of the quality influence factors of each link. According to the scheme, full-process data fusion, active quality prediction, accurate traceability and intelligent optimization are realized, and the real-time performance, the accuracy and the self-adaptive capability of quality control are remarkably improved.

The invention discloses a project quality management monitoring system, which belongs to the field of project monitoring and comprises a data acquisition and integration module used for acquiring and primarily processing data information of a construction site; the BIM model deviation monitoring module is used for constructing an accurate building engineering BIM model and carrying out effective deviation monitoring; and the quality analysis module is used for analyzing the data stored in the data storage module, and analyzing whether the construction material quality parameters meet the standard or not and whether the personnel working hour utilization efficiency, the equipment operation state and the construction progress meet the plan or not in the construction process. According to the invention, by integrating a plurality of modules, automatic acquisition, integration, analysis and early warning of construction site data are realized, the efficiency and effect of project quality management are improved, the cost is reduced, the project quality is improved, and the management level of the construction industry is effectively improved.

The invention relates to the technical field of data processing, and provides a surface quality management method for a stator core mold. The method comprises the following steps: performing surface quality detection on a target stator core mold to obtain a quality deviation coefficient; performing multi-dimensional verification based on the deviation coefficient, and outputting a plurality of quality management layers; constructing a teacher model according to hierarchical association; obtaining a superior distillation result through the knowledge distillation channel and the branch optimization model; therefore, a student model is constructed, and stator core mold surface quality management is optimized. The technical problems that a traditional detection method often depends on artificial experience and visual inspection, only the roughness and smoothness of the surface of the mold are concerned, the actual quality of the surface of the mold is difficult to accurately evaluate, and the accuracy and stability of quality management are low are solved. Through comprehensive strategy of multiple links such as the knowledge distillation channel and the quality management teacher model, the technical effects of improving the surface quality of the mold and improving the accuracy and efficiency of quality management are achieved.

The invention provides a product quality sampling inspection method and system based on an industrial internet of things, and a medium, and relates to the technical field of product quality management. The method comprises the following steps: acquiring model data, production data and equipment data, and determining a first preset condition, a first sampling parameter, a second preset condition and a second sampling parameter; obtaining production characteristic data, and responding to the condition that the production characteristic data meets a first preset condition: generating a first sampling inspection instruction based on the first sampling parameter, and obtaining a first quality inspection result; in response to the historical sampling inspection result and / or the historical model data meeting a second preset condition, generating a second sampling inspection instruction based on a second sampling parameter, and obtaining a second quality inspection result; and adjusting a first preset condition, a first sampling parameter, a second preset condition and a second sampling parameter based on the first quality inspection result and the second quality inspection result. According to the invention, the condition and / or strength of sampling inspection can be intelligently adjusted, and the quality inspection efficiency is improved.

The invention discloses an integrated intelligent production management and control system which comprises a basic data management module, a product process quality management module, a production management module, a material management module, an inspection management module, a quality management module, an equipment management module, an energy management module and a safety management module. The invention relates to the technical field of production management, and discloses an integrated intelligent production management and control system, which carries out custom configuration on various basic data through a basic data management module, and guarantees data exchange and sharing; the product process quality management module runs through the whole crystal production process, improves the production efficiency and the management level, forms a knowledge base and provides scientific data support; the production management module establishes a production management system integrating multiple functions, and the field cooperation and management and control capability is improved; and the material management module realizes full-process material management and control and mobile visual tracking.

The invention discloses a demand tracing method based on an OSLC standard. The method comprises the following steps of S1, developing a demand software adapter; s2, developing a modeling software adapter; s3, designing a client application to obtain and display data; s4, establishing a traceability relationship between the demand and the model; step S5, realizing query and visual display of the traceability relationship; and S6, demand change influence analysis is realized. According to the method, the OSLC standard is fully utilized, seamless integration of heterogeneous tools can be realized without large-scale transformation through data standardization of the demand software and the modeling software, the universality and interoperability between the tools are greatly improved, the implementation cost is effectively reduced, the bidirectional tracing capability between the demand and the model is provided, and the method is suitable for popularization and application. The demand integrity and the model consistency are ensured, and the quality management capability in the system development process is remarkably improved.

The invention discloses a supply chain quality collaboration method and system based on a block chain, and relates to the technical field of block chain and supply chain quality management, and the method comprises the steps: collecting quality events according to a batch quality collaboration baseline table, generating a credible collection proof package and a to-be-verified quality event queue, executing dynamic endorsement verification, and writing the dynamic endorsement verification into a quality event account book, outputting a batch state table and an under-chain evidence pointer; performing rapid detection and judgment based on the batch state table and the under-chain evidence pointer, generating a pre-release report and establishing a pre-release mapping table; and triggering a difference correction contract according to the pre-release mapping table, updating the batch state table, generating a collaborative work order, pushing a quality collaborative strategy instruction, and outputting a quality collaborative closed-loop record. According to the method, the risk feedback analysis result table is generated by auditing and evaluating the quality collaborative closed-loop record, the risk layering strategy table is revised, and the batch quality collaborative baseline table is updated, so that the key field constraint is continuously calibrated, and the traceability and consistency of collaborative processing are improved.

A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing cloud system offers a variety of industrial applications to end customers, including but not limited to MES, ERP, quality management, supply chain management, and customer relationship management (CRM). The system includes extensibility tools that allows industrial customers to customize databases, data collection templates, reporting fields, and other features of their consumed services, eliminating the need for these features to be customized by an administrator of the cloud system. Some embodiments of the manufacturing cloud system can also leverage generative artificial intelligence (AI) in connection with executing its supported services.

The present invention relates to the field of intelligent manufacturing and discloses a PCBA product quality management and optimization system and a data transmission method to achieve secure storage and targeted sharing of quality management data. The method includes: a cloud server receives and parses a request sent by a client, performs a hash calculation based on the parsed information of the request, and searches the cloud storage resources for ciphertext data A with the hash calculation result as the file name; if so, when forwarding the request to an edge server deployed locally on the requested party, the request is accompanied by a mark indicating that the target quality management data has been uploaded to the cloud; the ciphertext data A is then sent to the edge server; after receiving the request, the edge server performs waiting processing on the ciphertext data A according to the mark information, and after receiving the ciphertext data A, the ciphertext data A is decrypted and then encrypted with the public key of the requested party to obtain ciphertext data B, which is then forwarded to the client via the cloud server to finally decrypt and output the target quality management data.

The invention relates to a titanium alloy smelting quality management method and system. The method comprises the steps that real-time monitoring data in the titanium alloy smelting process are collected; classifying and storing the real-time monitoring data to obtain historical process data; performing equipment abnormity early warning based on the equipment operation data and a preset first early warning rule, and generating first early warning information; obtaining a process capability index based on the process control node data, performing process data deviation early warning based on the obtained process capability index and a preset second early warning rule, and generating second early warning information; performing product manufacturing and detection early warning based on the ingot casting product manufacturing and detection data and a preset third early warning rule, and generating third early warning information; and displaying and processing the first early warning information, the second early warning information and the third early warning information in a man-machine interaction mode. Effective guidance is provided for quality management of titanium alloy smelting, and potential problems can be found and eliminated in time.

The invention discloses a full-process management and control method and system applied to a tea processing production line, and the method comprises the steps: obtaining production full-process monitoring information, and carrying out the multi-modal feature fusion, and obtaining the multi-modal production full-process monitoring fusion features; establishing a production quality knowledge graph, performing production quality evaluation on the current production line, judging whether the processing quality is unqualified or not, and performing processing technology optimization; analyzing the operation health condition and the residual life of the production equipment of the current production line by using the multi-modal production full-process monitoring fusion features, and generating an operation health condition index and residual life prediction information; and whether the current production line needs processing supervision is analyzed based on the operation health condition index and the residual life prediction information, and a production line management and control scheme is formulated to manage and control the processing production line. The operation condition and health of processing equipment are concerned while tea processing quality management and control are considered, equipment management and control and production quality management and control are achieved, and the comprehensiveness and intelligence of tea processing are improved.

A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing cloud system offers a variety of industrial applications to end customers, including but not limited to MES, ERP, quality management, supply chain management, and customer relationship management (CRM). The system includes extensibility tools that allows industrial customers to customize databases, data collection templates, reporting fields, and other features of their consumed services, eliminating the need for these features to be customized by an administrator of the cloud system. Some embodiments of the manufacturing cloud system can also leverage generative artificial intelligence (AI) in connection with executing its supported services.

The invention discloses an intelligent project implementation management system and method based on multi-source perception fusion, and aims to solve the problems of information isolated island, response lag, experience dependence, extensive management and control and the like in an engineering project. According to the system, multi-source data acquisition is carried out through an Internet of Things sensor, a video monitor, a mobile terminal, a BIM / GIS, a project management platform and the like, and after space-time alignment and standardization processing are carried out, a unified situation awareness view is constructed by adopting a hierarchical fusion strategy; performing real-time intelligent evaluation and prediction on progress, quality, safety, cost and resources based on the fused data; a rectification, scheduling or early warning instruction is automatically generated, and a sensing-analysis-decision-execution closed loop is realized by pushing and linking a field execution system through multiple channels; according to the method, the safety prevention and control, quality control and progress performance capabilities are remarkably improved, the accident rate and cost are reduced, and project management is promoted to be transformed and upgraded to a refined, active and self-evolution direction.

The invention discloses a semantic routing query question and answer method, system and device and a medium, and the method comprises the steps: determining a person who may participate in project quality management, and obtaining a portrait of the person who may participate in project quality management; extracting a sub-knowledge base selected by semantic routing from a quality safety knowledge base in the engineering field according to the portraits of the personnel possibly participating in the engineering quality management; obtaining a to-be-queried statement, and according to the to-be-queried statement, screening out the most relevant K knowledge fragments from the sub-knowledge bases selected by the semantic routing; and inputting the most relevant K knowledge fragments into the large language model to obtain an answer of the to-be-queried statement, the method, the system, the equipment and the medium can realize question and answer query, and meanwhile, the redundancy of feedback information and the leakage of sensitive information are not easily caused.

The invention relates to the technical field of gas appliance quality control, and discloses a quality data analysis management system based on gas appliance production and after-sales service. A data acquisition module of the system acquires a process parameter time sequence from a distributed production terminal, and acquires a running state alarm event flow from a user service terminal in real time; the feature analysis module performs abnormal fluctuation compensation on a process parameter time sequence in a quality information fusion analysis mode, generates a multi-dimensional quality defect feature spectrum and determines a process tracing period; the risk conduction module obtains part circulation path information and analyzes defect correlation characteristics to obtain quality coupling data, and the production batch quality safety situation level is determined in combination with an alarm event flow; and the regulation and control execution module intervenes the full-life-cycle dynamic quality management and control blind area according to the process tracing cycle and the quality safety situation level. The system realizes integrated management of production and after-sales service quality data, and improves the effectiveness of quality management and control.

The invention provides a quality management and control method for a supply chain fusing a cloud platform and an industrial internet, and belongs to the technical field of the internet, and the method comprises the steps: deploying a collection network for a corresponding supply link according to the supply mode of each supply link of the supply chain through employing the industrial internet technology, and transmitting the collection data of each supply link to the cloud platform; constructing a new standard set according to the current supply demand of each supply link and the mapping relationship between the acquisition network and the preset risk item under the corresponding supply link; analyzing the collected data of the corresponding supply link according to the new standard set, constructing a quality chain of the corresponding supply link, and comparing and analyzing the quality chain with a standard chain to determine a quality problem; and based on the quality problem of each supply link, determining a management and control optimization strategy of the corresponding supply link for optimization deployment, and meanwhile, based on the cloud platform, feeding back the quality problem to a management end of each supply link for reminding. And comprehensive and real-time intelligent management and control of all supply links on the supply chain are ensured.

The invention discloses a staged quality detection method and system for SMT (Surface Mount Technology) semi-finished products, and particularly relates to the technical field of electronic manufacturing quality management.The staged quality detection method comprises the following steps: performing quality detection on each stage of a production line, recording detection results and forming a detection result set, identifying early signs of production rhythm imbalance by using time interval distribution analysis; if an imbalance sign exists, further calculating a defect frequency fluctuation index and a production line dynamic disorder index, and quantifying dynamic fluctuation and systematicness problems of an unqualified phenomenon; inputting the two indexes into a pre-trained machine learning model, and distinguishing whether the quality state of the production line is high quality or low quality through model output; a low-quality production line is optimized according to a preset adjustment strategy; the method can monitor the quality state of the production line in real time, accurately distinguish accidental problems from non-accidental problems, improve the quality management efficiency, guarantee the stable operation of the production line, and reduce the proportion of unqualified products.

The invention discloses a product image-text and document association identifier generation method fusing a PLM coding rule, relates to the technical field of product data management, and is used for solving the problem that an image-text and document association identifier is not clear. According to the method, document information vectors are constructed by fusing PLM coding rules and collecting document metadata of design drawings, process documents, quality management and bill-of-material systems, an image-text document structure mapping chain is generated through a structure mapping judgment model, a unique association identifier of the image-text document is generated in combination with a preset template, and the unique association identifier of the image-text document is obtained. And real-time updating is carried out during structure replacement, hierarchical reconstruction or configuration change, cross-system serial number semantic alignment is realized through a heterogeneous coding analysis rule base, heterogeneous naming conflicts are solved, identifiers are written into a product data management platform, an index relationship is established, hierarchical retrieval, version switching and historical backtracking are supported, and the product data management efficiency is improved. And the consistency and cooperation efficiency of product research and development and quality control are improved.

The invention relates to the field of intelligent manufacturing, and discloses a PCBA (Printed Circuit Board Assembly) product quality management and optimization system and a data transmission method, so as to realize safe storage and directional sharing of quality management data. The method comprises the following steps: a cloud server receives and analyzes a request sent by a client, performs Hash calculation based on analysis information of the request, and searches whether ciphertext data A taking a Hash calculation result as a file name exists in cloud storage resources or not; if yes, when the request is forwarded to an edge server deployed in the local of the requested party, carrying a mark that the target quality management data is clouded; sending the ciphertext data A to an edge server; and after receiving the request, the edge server executes waiting processing of the ciphertext data A according to the mark information, decrypts the ciphertext data A after receiving the ciphertext data A, encrypts the ciphertext data A with the public key of the requested party to obtain ciphertext data B, and forwards the ciphertext data B to the client through the cloud server to finally decrypt and output target quality management data.

The present invention relates to the technical field of engineering safety and quality management. In order to solve the problem of low management efficiency of the management system in the prior art, a project safety and quality supervision system is provided, comprising a receiving module for receiving an inspection request; a creating module for generating an inspection notice according to the received inspection request; an inspection module for sending the inspection notice to a corresponding inspection end and receiving uploaded inspection information; a rectification module for sending the inspection information to a corresponding rectification end and receiving feedback rectification information; a verification module for sending the rectification information to the inspection end and receiving feedback verification information; a determination module for determining the verification information and obtaining a determination result; when the determination result of the rectification module is not passed, the rectification module sends the verification information to the rectification end; and a storage module for archiving and storing the corresponding inspection request, inspection notice, inspection information, rectification information and verification information when the determination result is passed.

The present invention relates to the technical field of quality management, and specifically provides a quality management method based on standard data. Specifically, it includes: constructing a dynamic standard database; collecting production data in real time; adopting a hybrid anomaly detection and dynamic interpolation strategy to handle data missing and noise, generating a standardized matching code based on hash and polynomial ring operations to ensure data consistency; determining quality deviations based on a dynamic threshold model and fuzzy logic, quantifying the deviation amount by combining the weighted Euclidean distance, realizing hierarchical early warning through a membership function, and triggering response measures according to the early warning level; using a scoring model and non-linear prediction to evaluate the effectiveness of measures and screening optimization plans; a closed-loop feedback mechanism continuously updates model parameters and the knowledge base. Through dynamic data integration, hybrid detection algorithms and matching code design, the present invention effectively solves the problems of inflexible static thresholds, single anomaly detection, and poor data consistency in traditional quality management, and significantly improves the detection accuracy and system robustness.