569 results about "Manufacturing sector" patented technology

The invention relates to the technical field of intelligent manufacturing, in particular to a manufacturing industry digital twinning three-dimensional visual management and control method and system, and the method comprises the following steps: collecting manufacturing equipment operation data, converting the data into a time sequence, analyzing the trend characteristics of each dynamic subsequence in the time sequence, and generating a dynamic operation state sequence set. According to the method, the time sequence characteristics in the operation data of the manufacturing equipment are dynamically analyzed, so that the trend change of the operation state of the equipment can be accurately captured in multiple time dimensions, and potential abnormal states are deeply mined and predicted. In combination with prediction of future operation state parameters and comparison of a normal state range, an abnormal trend region and a corresponding fault level can be clearly marked, and the risk assessment capability of the equipment health state is effectively improved. And carrying out three-dimensional space mapping on the marked risk area, and accurately positioning an abnormal part and a deviation area in combination with gradient change and deviation distribution analysis of three-dimensional coordinates of equipment.

The invention provides a discrete MES-oriented intelligent production scheduling system, method and equipment and a medium, and belongs to the technical field of discrete manufacturing industry production scheduling. Data is acquired through a sensor and serves as production scheduling data; establishing a material inventory data association order ID and establishing an index; determining a process sequence constraint, a calculation equipment productivity constraint, a material supply constraint and an order priority constraint; initializing a population based on a genetic algorithm, randomly generating N groups of process sorting schemes, calculating a utilization rate index, and taking a comprehensive score as a fitness value; outputting a better solution set; the optimal solution of the genetic algorithm is used as initial pheromone distribution, high-quality path pheromones are enhanced according to the actual production effect of the completion scheme, and if the preset number of iterations is reached, the operation is stopped, and an optimized production scheduling scheme is output; and checking the production scheduling plan through a graphical interface. Through continuous optimization of the procedure sorting scheme, the equipment utilization rate is effectively improved, the total order completion time is shortened, the production resource configuration is optimized, and the production efficiency is improved.

The invention discloses a federated learning-driven cross-domain supply chain elastic inventory optimization system and a method thereof, and aims at realizing inventory data collaboration among same-level enterprises or regional nodes through transverse federated learning and ensuring data security by adopting a self-adaptive differential privacy protection mechanism. The method comprises the steps of constructing a transverse federated learning network, locally performing data preprocessing, adding differential privacy noise, iteratively training a global model based on federated deep reinforcement learning, generating a transverse inventory allocation and replenishment decision, and performing model adaptive adjustment in real time based on key performance indicators. According to the method, multi-target balance is considered, inventory configuration is dynamically optimized through a multi-target reward function, the inventory turnover rate is remarkably increased, the inventory holding cost is reduced, the service level is improved, and the method is suitable for various scenes such as retail chain, manufacturing industry distributed storage and cross-regional logistics distribution; and global optimal inventory configuration is realized on the premise of ensuring data privacy.

According to the alloy die-casting forming method and system based on intelligent parameter optimization, dynamic data streams in the die-casting process are collected in real time by introducing a sensor network, die-casting parameters are dynamically adjusted through a feedback control system, and the alloy die-casting forming method and system based on intelligent parameter optimization can automatically adapt to different alloy components and die design conditions; and it is ensured that each time of die-casting operation can be conducted under the optimal condition. According to the method, the consistency and the stability of the product quality are improved, and the production efficiency is remarkably improved. And through accurate control over the die-casting process, the quality problem caused by improper parameter setting is reduced, and high quality of each batch of products is guaranteed. The dynamic data flow is analyzed in real time, the factor fluctuation mode influencing the product quality is recognized, the die-casting parameters are adjusted in time, the stable product performance is maintained, the limitation caused by constant parameters in the prior art is overcome, and remarkable technical progress is brought to the manufacturing industry.

The invention discloses an intelligent reverse scheduling optimization system and method for the traditional manufacturing industry. The system comprises a data collection module, a work order generation module, a resource verification module, a scheduling optimization module and a monitoring adjustment module. The data collection module is used for collecting order information and processing the order information to obtain a to-be-produced work order list; the work order generation module optimizes the equipment task based on the to-be-produced work order list to generate a work order; the resource verification module is used for checking whether the equipment and the raw materials meet the requirements of the work order and generating constraint conditions; the scheduling optimization module performs priority ranking on the work orders based on constraint conditions by using a reverse production scheduling algorithm to generate an optimized production plan; the monitoring and adjusting module is used for monitoring the production process and dynamically adjusting the production plan according to the actual situation. By optimizing the production process, the efficiency can be improved and the cost can be reduced in the traditional manufacturing industry, so that the overall competitiveness is enhanced.

The invention discloses an intelligent production scheduling method and device for the equipment manufacturing industry, and relates to the technical field of production scheduling, and the method comprises the steps: building an equipment file information library, and recording the information of active equipment and standby equipment of a target production line; and constructing an equipment topology network according to the equipment file information base and the process information of the production line. Configuring a lightweight prediction model for each edge equipment node, and performing equipment health state analysis and short-term maintenance demand prediction in combination with real-time equipment operation data to obtain a preliminary evaluation state; and performing evaluation state correction through data sharing of edge equipment nodes to obtain a final evaluation result of the equipment. And according to an evaluation result and an equipment archive library, generating an equipment scheduling and maintenance decision, and realizing intelligent production scheduling. The technical problem that equipment maintenance and production scheduling are lack of flexibility and predictability in the prior art is solved, and the technical effects of improving the equipment utilization rate, optimizing production scheduling and ensuring efficient operation of a production line are achieved.

The invention discloses a reinforcement learning-based dynamic production scheduling optimization method and an ERP (Enterprise Resource Planning) integrated system, relates to the technical field of intelligent manufacturing and information technologies, and is used for solving the problem that the rapid development of the modern manufacturing industry puts forward higher requirements on production scheduling optimization. The flexibility and the adaptability are insufficient in the face of a real-time changing production environment; the intelligent level of the production process is improved through the multi-dimensional data processing capacity, the multi-dimensional quantization coding technology is adopted, dimension raising or dimension reduction processing is carried out based on the income-cost ratio of cross-domain technical parameters, and it is ensured that a calculation task is transferred to edge equipment and the PLC-level soft real-time requirement is met through edge calculation and lightweight framework deployment. On-time process completion and delay penalty are integrated, a global optimization item is combined, a meta-learning algorithm is used for dynamically adjusting the weight, and balance of delay, energy consumption and a safety target is ensured.

The invention belongs to the technical field of industrial intelligence, and relates to a deep reinforcement learning-based non-correlation parallel machine scheduling method, which comprises the following steps of: constructing a mathematical model suitable for non-correlation parallel machine scheduling; the machining time of each workpiece on each heterogeneous machine is collected, and normalization processing is carried out; initializing a genetic algorithm scheduling population and a depth Q network; constructing a deep reinforcement learning training framework; expressing a state vector by using the average fitness, the optimal fitness and the optimal individual code; operating parameters of the genetic algorithm are controlled by using the action space, and parameters of the deep Q network are updated by using a reward function; and dynamically controlling operator selection in a genetic algorithm iteration process by using the trained deep reinforcement learning model to obtain an optimal scheduling solution and realize scheduling of the non-correlation parallel machine. According to the method, a deep reinforcement learning algorithm is provided for solving similar problems in the manufacturing industry production scheduling field by analyzing a non-correlation parallel machine scheduling problem data model, and the production efficiency is improved.

The invention relates to the technical field of industrial production, in particular to a multi-target dynamic intelligent production scheduling optimization method and system for the discrete manufacturing industry, and the method comprises the steps: obtaining production feature data, such as production tasks, equipment, inventory and energy consumption, building a machine learning model through historical data, risks such as delivery delay, inventory shortage and equipment failure in the production process are predicted; constructing a task priority calculation model, comprehensively considering various risk factors, and calculating priority parameters of production tasks; dividing a plurality of optimization objectives for the production task based on the priority parameters, and creating an objective function for each objective to balance production condition conflicts; constructing a production task priority change strategy, and dynamically adjusting a priority parameter and an optimization target according to production feedback; according to the method, data driving and machine learning technologies are fully utilized, intelligent optimization and dynamic adjustment of the discrete manufacturing industry production process are realized, and the production efficiency and flexibility are improved.

The invention relates to the field of numerical control machine tool cutting fluid cyclic utilization control, in particular to a numerical control machine tool cutting fluid cyclic utilization control system which comprises a machine tool equipment authentication module, a pumping module, a filtering module, a cooling module, a concentration adjusting module and a machine tool equipment scrapping module. The concentration adjusting module detects the concentration of the received standard cutting fluid, when the concentration is lower than a normal value, processing is conducted through all the devices, the pumping module monitors the cutting state of all machine tool equipment in real time and adjusts the pumping flow, and the filtering module removes impurities in the working cutting fluid according to all the devices. And the cooling module monitors and analyzes the temperature of each treatment cutting fluid according to a temperature sensor, so that the problems caused by a traditional cutting fluid use mode are solved, cyclic utilization of the cutting fluid is realized, and contribution is made to sustainable development of the manufacturing industry.

Techniques for computer science, data science, data analytics, computer software, algorithmic analysis, and networked technologies for sourcing, procurement, manufacturing, and supply chain management in small-to-medium manufacturing (“SMM”) industries that involve processes whereby implementing user input and extracted image and text from engineering drawings may be used to map manufacturing requirements to a subset of manufacturers via natural language agent programs. More specifically, natural language agent programs may be implemented to apply input and extracted image and text from engineering drawings to a large language model (“LLM”). An example method may include receiving a user input including data representing requirements to manufacture a physical structure, concatenating an engineering drawing summary data, a shoptype description, and estimated part size instruction data to combine the at least two of the engineering drawing summary data, the shoptype description, and the estimated part size instruction data to generate a subset (e.g., a list) of qualified manufacturers (e.g., SMMs).

The invention discloses a product process route generation method and device based on a bill of materials, a medium and equipment, and belongs to the technical field of computer intelligence. The method comprises the following steps: acquiring an engineering drawing of a to-be-processed product; extracting a bill of material of the to-be-processed product according to the engineering drawing of the to-be-processed product; according to the bill of material of the to-be-processed product, establishing association relationship data between parts related to the to-be-processed product; and according to the incidence relation data among the parts related to the to-be-processed product and the existing process database, a preliminary process route of the to-be-processed product is generated through operation. The device, the medium and the equipment can be used for realizing the method. By integrating the functions of engineering drawing analysis, BOM automatic generation, process database matching, process route intelligent planning and optimization and the like, full-automatic conversion from a product design drawing to a production process route is realized, and powerful support is provided for digital transformation of enterprises in the manufacturing industry.

The invention relates to the field of manufacturing industry, in particular to a self-adaptive energy consumption optimization method and system for a vacuum isothermal forging system production line. The method comprises the following steps: acquiring real-time data of production line equipment, and generating a real-time data sequence; preprocessing, denoising and standardizing the real-time data sequence to obtain clear data for feature extraction; key features are extracted by using an AI model and are trained, and a pre-trained energy efficiency prediction model is generated; and inputting the real-time data into a pre-trained model, generating an optimization decision, adjusting equipment operation parameters, and forming a feedback control system. Through real-time data acquisition and AI optimization, dynamic optimization of energy efficiency is realized, production efficiency is improved, energy consumption is reduced, system adaptability is enhanced, and green manufacturing requirements are met.

The invention provides a three-dimensional point cloud-based geometric shape and position dimension measurement method and application thereof. The method utilizes a three-dimensional scanning technology to obtain three-dimensional point cloud data of an industrial part, and processes and measures the three-dimensional point cloud data through the following steps: 1, preprocessing: removing isolated points, outliers and Gaussian noise in the point cloud through filtering; 2, geometric feature extraction: extracting geometric features with linear and arc features, and dividing the geometric features into single-cluster point clouds; 3, straightness and roundness measurement: fitting the point clouds with geometric characteristics into a straight line or a circle so as to measure straightness and roundness; and 4, arc length measurement: providing a method for sequentially searching adjacent points on the linear point cloud, calculating the sum of the distances between the adjacent points, and further measuring the arc length. The application field of the invention comprises the measurement and the quality control of the part size in the manufacturing industry. The method performs non-contact measurement based on the three-dimensional point cloud data, and has the characteristics of high efficiency, high precision and strong anti-interference performance.

The invention discloses a manufacturing industry full life cycle management system based on AI algorithm analysis, which belongs to the technical field of manufacturing industry and comprises a data acquisition module, a data processing module, a model construction module, a model optimization module, a prediction analysis module and a comprehensive management module. The problems that the manufacturing industry full life cycle management effect is poor and the manufacturing industry management requirement cannot be well met due to the fact that the manufacturing industry full life cycle cannot be monitored and managed in real time in the prior art are solved. According to the invention, the manufacturing industry full-life-cycle characteristic data is predicted and analyzed, the manufacturing industry full-life-cycle prediction analysis result based on AI algorithm analysis is determined, the manufacturing industry full-life-cycle management scheme is formulated, and the manufacturing industry full-life cycle is intelligently and comprehensively managed based on the manufacturing industry full-life-cycle management scheme. The manufacturing industry full life cycle can be monitored and managed in real time, the manufacturing industry full life cycle management effect can be improved, and the manufacturing industry management requirement can be well met.

The invention provides an industrial manufacturing industry-oriented carbon asset full life cycle management method and system, and the method comprises the steps: carrying out the cross-link feature fusion processing through obtaining an original carbon activity data set of an industrial manufacturing whole process, and generating carbon footprint feature data; and calling a pre-trained deep learning model to perform carbon asset state evolution analysis on the carbon footprint feature data, predicting a carbon asset evolution path, generating a carbon asset optimization strategy set based on a prediction result, including equipment energy efficiency regulation and control and a production plan recombination strategy, and pushing the carbon asset optimization strategy set to an industrial manufacturing execution system. And triggering an automatic control instruction reconstruction operation to realize automation and intelligence of carbon asset management, thereby comprehensively and accurately mastering a carbon asset state and an evolution rule, formulating a scientific and effective optimization strategy, reducing carbon emission, improving energy utilization efficiency, and improving green production capacity of an enterprise.

The invention relates to the technical field of intelligent manufacturing, in particular to a flexible intelligent manufacturing system for multi-variety small-batch production, which comprises a flexibility enhancement module, a cost efficiency improvement module, an information interaction enhancement module, a technology depth fusion module and a decision ability optimization module, rapid switching and optimization of the production process can be achieved, so that production efficiency and product quality are improved, technological parameters are intelligently adjusted according to real-time production requirements, production requirements of diversified products are met, production preparation time is effectively shortened, the production cycle is shortened, meanwhile, by means of the Internet of Things technology, the system can monitor the state of production equipment in real time, and production efficiency is improved. In addition, through data analysis and a prediction model, the system can optimize inventory management, reduce the production cost, significantly improve the production flexibility and efficiency in a multi-variety small-batch production environment, and bring significant technical effects and economic benefits to the manufacturing industry.

The invention belongs to the technical field of manufacturing and production, and particularly relates to an intelligent manufacturing and production control system, which is characterized in that production data on a production line are collected, and a database is established for the real-time data of the production line according to time nodes and station nodes for storage; carrying out instant report, trend analysis and anomaly detection on the collected data, carrying out deep analysis by comparing historical data, and identifying potential problems or optimization opportunities in production by establishing a prediction model; carrying out production scheduling based on a data analysis result, and carrying out optimal configuration on production line resources according to data analysis and production scheduling; the production strategy is optimized on the basis of the data analysis result in combination with production line production planning, actions are executed through the executor according to the control strategy, production line automatic control is achieved, and the effects of improving production efficiency, reducing cost and promoting intelligent development of the manufacturing industry are achieved.

Data from manufacturing is highly uncontextualized and siloed, requiring expert knowledge of context and substantial data pre-processing to support meaningful queries and visualizations. To address this problem, data for a number of sources in a manufacturing context can be retrieved and converted into an intermediate representation in a natural language or near-natural language form, which can in turn be ingested by a generative AI engine, along with suitable prompts by the user to summarize, analyze, and make recommendations based on the data.

The invention discloses an intelligent production line fault prediction and adaptive adjustment system and method based on artificial intelligence. In the modern manufacturing industry, a traditional production line maintenance mode is difficult to find potential faults in advance, so that shutdown loss is huge, and the problems of insufficient data utilization, poor prediction and adjustment effects and the like exist in the prior art. The system comprises a data acquisition module, a data processing module, a fault prediction module and a self-adaptive adjustment module. The data acquisition module acquires multi-modal data through various sensors and carries out edge calculation preprocessing on the multi-modal data. The data processing module cleans data by using deep learning, extracts features, and stores the features by using a block chain technology; the fault prediction module fuses multiple technologies to improve the prediction precision; and the adaptive adjustment module generates and optimizes an adjustment strategy by using reinforcement learning. The method comprises the steps of data acquisition, processing, fault prediction and adaptive adjustment. According to the method, accurate fault prediction and efficient adaptive adjustment are realized, the intelligent level of a production line is improved, the fault rate and the production cost are reduced, the enterprise competitiveness is enhanced, and the method has a wide application prospect in the field of intelligent manufacturing.

The invention discloses a manufacturing industry ERP dynamic resource optimization configuration system based on reinforcement learning, and relates to the technical field of manufacturing industry ERP resource configuration, and the system comprises an ERP state data perception module which is in butt joint with a manufacturing industry ERP system, collects multi-source heterogeneous data in real time, fuses the multi-source heterogeneous data in real time by using a Kalman filtering technology, detects an abnormal value, and sends the abnormal value to the ERP state data perception module; an improved Robust Scaling method is adopted for standardization processing, and initial data of a unified structure are output and transmitted to the feature extraction and integration processing representation module; multi-source heterogeneous data are collected in real time through the ERP state data sensing module, fusion is carried out through the Kalman filtering technology, dynamic changes in the production environment can be captured in time, strategies can be continuously adjusted according to the real-time dynamic change data through the reinforcement learning method, and the real-time dynamic change data can be obtained. And outputting an optimal resource allocation action adapting to a dynamic environment, so as to greatly improve the adaptability of the system to a complex production environment.

The invention provides a full-life-cycle forward and reverse quality traceability system and method for discrete manufacturing industry products, and the system comprises a configuration module which is used for configuring traceability code rules and multi-dimensional traceability conditions; the interaction module is used for data interaction between systems so as to track process data of the whole life cycle of each discrete manufacturing industry product in real time; the binding module is used for generating a production bar code according to the traceability code rule, and binding the production bar code as a unique traceability identification code of each discrete manufacturing industry product in each production stage; the scanning module is used for scanning a production bar code of each discrete manufacturing industry product, associating process data related to each production stage with the production bar code and storing the process data in a system database; the traceability module is used for providing a one-key traceability function and allowing the whole-process data of the whole life cycle of each discrete manufacturing industry product to be inquired according to a traceability condition combination so as to realize the forward and reverse quality traceability of the whole life cycle; and the display module is used for displaying the whole-process data.

Disclosed in the present invention are an engineering design preamble information generation method, an information display method and a drawing examination method. In the information display method, an information display plug-in that is pre-loaded in engineering design software is called to display preamble information related to the current drawing file and intelligent information related to a target object, such that a system can automatically display related intelligent information along with a design action during the process of a user designing the drawing file, thereby implementing follow-up information display; and the user is reminded of related design specifications at any time, thereby omitting the workload caused by a designer manually searching for the intelligent information, further solving the problem of various quality incidents occurring frequently due to incomplete searching, incorrect searching, etc., caused by the designer manually searching for the intelligent information, significantly improving the design research and development efficiency, and having positive economic benefits and social benefits for automatic design and subsequent automatic production in the manufacturing industry, the building industry and the engineering industry.

The invention is suitable for the technical field of production quality detection, and provides an intelligent detection method and system based on an aluminum alloy production line, and the method comprises the steps: firstly responding to a gradienter starting instruction, obtaining real-shot image information through an industrial camera, and then carrying out the image detection processing of the real-shot image information according to an image detection algorithm, according to the method, laser datum line information and workpiece edge contour line information are determined, then multiple pieces of actual measurement distance information are effectively generated according to the laser datum line information and the workpiece edge contour line information, and finally detection result information is accurately generated according to the multiple pieces of actual measurement distance information. According to the invention, the flatness of multiple positions of the aluminum alloy workpiece can be rapidly and accurately detected, the detection efficiency is remarkably improved, and the production requirements of the modern aluminum processing and manufacturing industry for efficient and high-precision detection are met.

State of the art systems used for industrial plant monitoring have the disadvantage that they fail to correctly assess reason for dip in performance of the plant and in turn trigger appropriate corrective measures. The disclosure herein generally relates to industrial plant monitoring, and, more particularly, to a system and method for development and deployment of self-organizing cyber-physical systems for manufacturing industries. The system monitors and collects data with respect to various parameters, from the industrial plant. If any performance dip is detected, the system determines corresponding cause, and also triggers one or more corrective actions to improve performance of the plant and different plant components to a desired performance level.

The invention discloses a milling chatter diagnosis method based on multi-step differential entropy, and belongs to the technical field of mechanical system fault diagnos.The method comprises the operation steps that the type and the installation position of a sensor are selected, and chatter diagnosis parameters are determined; acquiring synchronous vibration data X; the difference step number mu is set to be equal to 1, the embedded dimension is m, the time delay is tau, and the following calculation is repeated until the difference step number mu is larger than the maximum difference step number mu T; reconstructing a phase space P [mu] of the synchronous vibration data X; processing the phase space P [mu] according to a difference rule to obtain a phase space P '[mu]; calculating the cosine similarity between each two adjacent rows of data in the phase space P 'mu; the interval [-1, 1] is divided into epsilon intervals at equal intervals, probability distribution of the obtained cosine similarity in each interval is counted, and mu step difference entropy is calculated; making the difference step number mu = mu + 1 and the embedded dimension m = m0 + (mu-1), and returning to continue to calculate the mu step difference entropy; determining a flutter type according to a flutter diagnosis criterion; the method can effectively overcome the inherent defects of traditional entropy, remarkably improve the accuracy, timeliness and reliability of flutter diagnosis, provide solid technical support for flutter research and recognition in the machining process, practically improve the machining efficiency, reduce the influence of flutter on the machining quality and reduce the production cost. Besides, the method is highly in line with the trend of intelligent development of the manufacturing industry, has wide application prospects, and is expected to promote technical innovation and progress of the machining industry.

The invention relates to the technical field of automobile accessories, and discloses a manufacturing lean production management system based on transfer card work reporting, and the system comprises a data collection module which is used for collecting production data in real time through scanning a code transfer card; the production plan and circulation card configuration module is used for making a production plan and generating a circulation card matched with the production plan; the production process tracking module is used for dynamically monitoring the production state of the transfer card and the use condition of equipment; and the quality inspection and batch tracing module is used for binding the quality inspection data with the circulation card to realize quality problem tracing. By scanning a code transfer card, collecting production data in real time, and updating the state and task progress of each station in the production process to the system in real time, a manager can master the current production situation at any time, find potential problems in time and quickly take countermeasures, the response speed of sudden problems and the production efficiency are effectively improved, and the production efficiency is improved. And the production lean degree is improved.

The invention relates to the technical field of information retrieval, in particular to a part three-dimensional model retrieval method and system based on feature extraction. The part three-dimensional model retrieval method comprises the following steps that a manufacturing industry part design drawing is obtained; geometric structure feature extraction and material feature extraction are carried out according to a manufacturing industry part design drawing, and geometric structure data and material data are obtained; constructing a part three-dimensional model based on the geometric structure data and the material data; performing process processing feature extraction according to a manufacturing industry part design drawing to obtain part process processing data; and performing process treatment, including surface treatment, heat treatment and welding treatment, on the part three-dimensional model according to the part process treatment data to obtain surface treatment data, heat treatment data and welding treatment data. Based on the information retrieval technology, the part three-dimensional model retrieval precision and the assembly reliability are improved, and the part design optimization rate and the production quality control efficiency are remarkably improved.

The invention relates to the technical field of industrial master machine multi-axis coordination, and particularly discloses an industrial master machine multi-axis coordination method, which comprises the following steps of: selecting thermomagnetic sheets with thermomagnetic coupling characteristics, respectively embedding the thermomagnetic sheets into key parts of corresponding motion axes of a multi-axis machine tool, and simultaneously installing a high-precision magnetic field sensor in a main shaft of the multi-axis machine tool, the receiving module is used for receiving a magnetic field vector of each motion axis in each monitoring time point; collecting the temperature of each motion shaft in each monitoring time point, and calculating the thermal deformation amount of each motion shaft corresponding to the next time point; therefore, each motion shaft is dynamically judged and analyzed, and finally, the position compensation of each motion shaft corresponding to the next time point is realized, so that the machining precision and efficiency of the multi-shaft machine tool are improved, and the reliability, maintainability and intelligent level of the system are enhanced. The application of the technology has important practical significance and development potential in the modern manufacturing industry, and is a necessary means for realizing efficient, precise and intelligent manufacturing.

The invention discloses a production and manufacturing industry enterprise qualification evaluation model construction method, which comprises the following steps: data preprocessing: carrying out standardization processing on risk characteristics; adopting PCA-based risk score extraction to generate a comprehensive risk score; customizing a weight loss function, and setting a risk grading weight; a loss function is trained and optimized by adopting a LightGBM model, and a weighted loss function after optimization is self-defined; and obtaining an enterprise qualification evaluation model according to the optimized weighted loss function training model. The prediction effect of the model is improved, and the stability of the model in a real business scene is remarkably enhanced.