1543 results about "Satisfaction level" patented technology

The invention relates to the technical field of supply chains, and discloses an intelligent supply chain and logistics optimization management method and system, and the method comprises the steps: obtaining a real-time order of each supply chain station in a region, and planning a logistics distribution path; data analysis is carried out, the sales volume of orders on the current day is predicted, and the inventory of a supply chain station is predicted in combination with supply volume consumption analysis; and according to calculation of the inventory emergency degree of the supply chain station, a target scheduling station is selected, and an inventory logistics scheduling scheme is generated. According to the invention, full-chain optimization management from order obtaining to logistics distribution path planning, accurate stock prediction and reasonable stock scheduling is realized, the logistics distribution efficiency can be significantly improved, the transportation cost is reduced, refined and scientific management of the stock is realized, the stock cost and the stockout risk are accurately balanced, and the logistics distribution efficiency is improved. The capacity of the supply chain for dealing with market changes and uncertainty is enhanced, the customer satisfaction is improved, the market competitiveness is improved, and efficient, stable and sustainable operation of the supply chain system is guaranteed.

The invention relates to the technical field of business adaptation, in particular to a business adaptation degree intelligent analysis method and system combined with a customer portrait. The method comprises the following steps: firstly, collecting customer data from a plurality of data sources, then processing the collected data, extracting interest labels of customers, constructing a customer-interest label matrix, comprehensively evaluating the purchase ability and loyalty of the customers, updating the portraits of the customers according to the interest preferences of similar customers, and then collecting the business demand data of the customers. The method comprises the following steps: constructing a business demand model, quantifying the demand intensity of a customer, generating a demand score vector, matching a customer portrait with the demand score vector, calculating an adaptation degree score, generating personalized recommendation for the customer, preferentially recommending a product or service with the highest matching degree with the customer demand, and pushing related recommendation through multiple channels. And the satisfaction degree and the conversion rate of customers are improved. According to the invention, the resource utilization efficiency and the customer satisfaction are improved, so that more efficient enterprise operation and customer service are realized.

The invention discloses a material identification method based on a multi-modal large model, and particularly relates to the technical field of data processing. The method comprises the following steps: inputting image data and associated text data of advertisement materials into a multi-modal large model, and carrying out preliminary preprocessing and feature extraction; respectively extracting visual features and semantic features of the image and the text by using a convolutional neural network and a natural language processing technology, and optimizing a fusion effect of the image and text features through a multi-modal contrast learning strategy; the feature alignment condition is verified by comparing a loss function, and the recognition precision of the model is improved by combining a double-path fine adjustment mechanism and multi-label classification supervision; finally, an improved cue word mechanism is adopted to carry out accurate classification on the advertisement materials, a putting engine and a feedback mechanism are combined, the auditing effect of the advertisement materials is monitored and optimized in real time, the accuracy of advertisement compliance management is remarkably improved, auditing errors are reduced, and the customer satisfaction degree is improved.

The invention discloses a motor vehicle maintenance management service system, and relates to the technical field of motor vehicle maintenance management, and the system comprises a client information management module which builds data for storing client multi-information, supports multi-format import and export, intelligently recognizes and updates, and guarantees the information to be timely and accurate; the vehicle fault diagnosis module is provided with various interfaces and expert systems, analyzes and diagnoses faults based on big data, and gives reasons, suggestions and cost estimation; the maintenance work order management module supports work order full-process operation, automatically distributes personnel and updates the progress in real time for clients to inquire; the maintenance accessory management module is provided with an inventory database and can intelligently match accessories and provide position query navigation; and the maintenance personnel management module is used for storing personnel information and evaluating performance. According to the invention, management efficiency and accuracy can be improved, accessory management and cost are optimized, personnel quality and service quality are improved, customer experience and satisfaction are enhanced, powerful support can be provided for enterprise decision making, and intelligentization of the motor vehicle maintenance industry is promoted.

The invention, which relates to the technical field of customer relationship management, discloses a multi-channel interactive customer relationship management system comprising a dynamic routing decision module, a multi-modal data fusion module and an intelligent feedback optimization module. The dynamic routing decision module evaluates channel load through a deep neural network, dynamically allocates client requests to an optimal node by utilizing reinforcement learning, and realizes load balancing and service continuity; the multi-modal data fusion module integrates text, voice and image data, constructs a space-time correlation graph, identifies a cross-channel behavior mode, and ensures data consistency through multi-dimensional verification; the intelligent feedback optimization module combines customer satisfaction evaluation and multi-modal sentiment analysis, optimizes a service strategy by using a genetic algorithm, and synchronizes the service strategy to a cross-channel knowledge graph to realize adaptive iteration; according to the method, the problems of unreasonable multi-channel load distribution, insufficient data fusion and consistency verification and inaccurate service strategy optimization are effectively solved, and the customer service quality and experience are improved.

The invention relates to the technical field of shopping recommendation, in particular to an intelligent shopping system with a ranking recommendation function, and the system comprises a user price sensitivity modeling module which is used for building a price sensitivity hierarchical model and extracting price sensitivity features of a user in different time periods through time sequence analysis; performing price sensitivity layering on the users by using a clustering algorithm; the commodity price feature analysis module is used for extracting the price interval, discount information, promotion frequency and historical price fluctuation data of the commodity; constructing a commodity price feature matrix, and generating a commodity price attraction factor; and the recommendation sorting decision module is used for generating a personalized recommendation score R through weighted calculation of the price sensitivity factor and the commodity price attraction factor, and carrying out commodity sorting based on the personalized recommendation score R. According to the method, the matching degree of the recommended content and the user demand is improved through multi-dimensional dynamic modeling, and the recommendation accuracy and the user satisfaction degree are improved.

The invention relates to the technical field of marketing strategies, in particular to an intelligent marketing service platform and method for financial services, and the platform comprises a user behavior analysis module, a customer classification module, a content adjustment module, a marketing strategy optimization module and a market trend prediction module. According to the method, the behaviors and preferences of the users can be accurately predicted by monitoring the behaviors of the users and identifying key interaction events of the users, customer groups can be more finely divided by classifying the customer data, more customized services are provided for different customer groups, the personalized experience of the customers is enhanced, and the user experience is improved. The marketing strategy is adjusted in real time to respond to the change of the market and consumer behaviors, so that the enterprise can keep leading in the market with intense competition, and the perspectiveness of the marketing strategy is enhanced through the dynamic prediction of the market trend, so that the enterprise can predict and adapt to the market change and keep the competitive advantage.

The invention relates to the technical field of recommendation methods, in particular to a consumer behavior-based commodity recommendation method, which comprises the following steps of: extracting behavior sequence data including clicking, browsing, searching and purchasing from a user interaction behavior log in an e-commerce platform, and carrying out sequence data slicing on the behavior sequence data through a sequence data slicing technology; dividing the long-time behavior sequence of the user into a plurality of behavior segments with a long-term dependency relationship; generating a behavior context feature vector; based on the long-term dependency feature vector and the behavior context feature vector, constructing a context-based intention speculation model, speculating the long-term shopping intention of the user, and generating an intention vector of the user behavior; and through a multi-target optimization algorithm, generating a recommendation strategy meeting various targets, and applying the generated recommendation strategy to real-time recommendation. According to the method and the device, the personalized degree of recommendation is improved, and diversified requirements of the user can be better met, so that the satisfaction degree of the user is improved.

The invention discloses a transaction platform management system based on cloud service, and relates to the technical field of cloud computing, and the system comprises the following components: a user behavior analysis module which collects the browsing track, purchase record and evaluation information of a user through an e-commerce platform, collects the attribute data and sales data of a commodity, and sends the collected data to a server; the user behavior analysis module accurately extracts key features of user activeness and commodity popularity to provide powerful support for personalized recommendation, the intelligent recommendation module generates a personalized recommendation list based on real-time behavior data and interests of the user, and the intelligent customer service module realizes personalized recommendation through natural language processing and machine learning technologies. According to the system, the user problem is quickly and accurately responded, the customer service efficiency and the user satisfaction degree are remarkably improved, the predictive maintenance module identifies potential risks and abnormities in advance through real-time data analysis, an early warning and solution scheme is provided for an administrator, and the system failure rate and the maintenance cost are effectively reduced.

The invention relates to a bank marketing model construction method and system based on machine learning, and the method comprises the following steps: constructing a customer multi-dimensional feature engineering system which is used for integrating customer multi-dimensional features, and extracting customer behavior period features through employing a time sequence feature coding technology; meanwhile, a graph neural network is adopted to construct customer social influence features; constructing a dynamic customer value analysis model integrated with XGBoost and establishing a dynamic attenuation function of customer life cycle value; establishing a marketing response prediction rate model of a hybrid model architecture combining LightGBM and Transform, and embedding an adversarial training sample generation mechanism; a marketing strategy generation model is constructed, the marketing strategy generation model is based on a multi-objective optimization function considering the marketing response rate, the marketing cost and the customer satisfaction, and a marketing strategy is generated through a marketing path planning algorithm of Monte Carlo tree search and the multi-objective optimization function.

The invention provides an AR intelligent shopping guide interaction method and system applied to store management, and the method comprises the steps: firstly obtaining an interaction data set of a user in a store, covering a user position movement track, a commodity attention action sequence and voice consultation content, carrying out the user intention analysis of the interaction data set, and carrying out the user intention analysis of the interaction data set; the method comprises the steps of obtaining commodity demand characteristics and interaction preference characteristics, generating AR shopping guide interaction content, including commodity three-dimensional display information and an interaction guide instruction, adaptive to a user based on the commodity demand characteristics and the interaction preference characteristics, aligning and fusing the AR shopping guide interaction content with a store physical scene space, generating an AR shopping guide interaction scene capable of being displayed in real time, and displaying the AR shopping guide interaction scene in real time. And finally, responding to user interaction operation in the AR shopping guide interaction scene, generating feedback information and updating the interaction data set to trigger interactive process iterative optimization, thereby providing personalized and immersive shopping guide experience, and improving store operation efficiency and customer satisfaction.

The invention discloses an intelligent customer service interaction method and system, and relates to the technical field of intelligent customer service interaction, and the method comprises the steps: obtaining a user request; calculating a comprehensive emotion score through multi-modal emotion analysis based on a user request; according to the comprehensive emotion score, classifying the user emotion state, according to a classification result, initializing a dialogue context, and constructing a dialogue context graph; dynamically adjusting the response mode of the intelligent customer service according to the emotional state and the user question in the dialogue context graph, and collecting the feedback information of the user; and dynamically adjusting the dialogue strategy based on the feedback information of the user and the dialogue context. According to the method, the emotion state of the user can be comprehensively captured from multiple aspects such as texts, voices and facial expressions through multi-modal emotion analysis, so that the accuracy and robustness of emotion recognition are improved, a reliable basis is provided for subsequent dialogue management and response modes, and the experience satisfaction degree and the service quality of the user are improved.

An AI-based automated pricing and inventory management system (100) for retail marketplaces, including: a data collection module configured to collect real-time data from sales transactions, competitor pricing, market trends, inventory levels, and customer behavior; an AI engine configured to process the collected data using machine learning algorithms for demand forecasting, price optimization, and customer segmentation; a price optimization module that is communicatively connected to the AI ​​machine and configured to dynamically adjust product prices based on demand patterns, competitive strategies and predefined business rules in order to maximize profitability and maintain competitiveness in the market; an inventory management module configured to predict optimal stock levels, generate replenishment alerts, and recommend inventory reduction strategies, thereby minimizing overstocking and stockouts; a dashboard interface configured to display actionable insights, pricing strategies, inventory levels, and key performance indicators in real time; an integration layer configured to interface with existing retail systems (100), including point-of-sale (POS), enterprise resource planning (ERP) and e-commerce platforms, enabling seamless operation and data exchange; the system dynamically coordinates (100) decisions on pricing and inventory management based on real-time data analysis, thereby improving operational efficiency, profitability and customer satisfaction.

The invention discloses a digital intelligent service system for enterprise services, which belongs to the field of enterprise digital services and comprises a user demand analysis module, an intelligent data adaptation module, a service resource matching module, a service efficiency evaluation module and a user-friendly interface. The user demand analysis module is used for semantic analysis and classification decision making; the intelligent data adaptation module identifies and analyzes the protocol type, and establishes an intelligent data transmission channel; the service resource matching module carries out resource pre-screening and accurate matching; the service efficiency evaluation module tracks an identifier and captures a four-dimensional index system in real time, and the dynamic evaluation unit realizes service state grading early warning through a sliding window algorithm; according to the invention, accurate demand identification is realized, the response speed and the data transmission efficiency are improved, the user satisfaction is improved, the service matching accuracy is enhanced, the resource utilization efficiency is optimized, the service capability and the customer experience are improved, and maximization of economic benefits and social values is realized.

The invention discloses a credit marketing intelligent recommendation system. According to the system, for the existing credit marketing problem, a customer portrait is constructed through multi-channel data acquisition, cleaning and preprocessing, and personalized credit product recommendation is carried out based on content and collaborative filtering. And training a recommendation model by using a neural network model and a reinforcement learning algorithm, updating recommendation in real time or at regular intervals according to real-time conditions and feedback of customers, establishing a system evaluation effect including recommendation accuracy, customer satisfaction and business indexes, and optimizing model parameters, improving the algorithm and updating data according to evaluation. The method can improve the customer satisfaction and the business conversion rate, reduces the risk, adapts to the market change, and effectively solves the defects of the existing credit marketing channel and strategy.

The present invention relates to the field of data analysis technology, and specifically to a customer relationship management data analysis method and system based on artificial intelligence, comprising the following steps: step one, collecting customer data; step two, storing the data; step three, analyzing and mining the data; step four, building a customer portrait; step five, providing intelligent decision support for customer relationship management based on the customer portrait and data analysis results; the customer relationship management data analysis method and system based on artificial intelligence of the present invention can significantly improve the customer relationship management level of an enterprise. Through artificial intelligence deep learning, the system can automatically analyze a customer's massive conversation records, social media interactions, and transaction data, quickly identify customer needs and preferences, and then generate personalized customer portraits and behavior analysis reports, to help enterprises accurately locate potential customer groups, optimize service strategies and marketing plans, and thus improve customer satisfaction and loyalty.

The invention relates to the technical field of supply chain management, and discloses a commodity supply chain management method based on the Internet of Things. According to the commodity supply chain management method based on the Internet of Things, errors caused by manual data entry are avoided through time sequence analysis and data filtering processing of the cloud analysis management platform, enterprises can accurately predict future market demands, and the inventory turnover rate, the optimal inventory level and the transportation and storage cost are dynamically calculated, so that the inventory management is optimized, and the enterprise management efficiency is improved. In addition, according to the method, by comprehensively evaluating the delivery timeliness rate of suppliers and the quality defect rate of goods, it is ensured that enterprises can select reliable suppliers, the stability and efficiency of a supply chain are improved, the real-time logistics monitoring and dispatching capacity enables goods to be quickly and efficiently delivered to clients, and the efficiency is improved. The customer satisfaction is comprehensively improved, and the competitiveness and market adaptability of enterprises are promoted.

The invention relates to the technical field of logistics order delivery management, in particular to a cross-border e-commerce logistics order management system based on big data, which comprises a data acquisition processing unit, an order clustering delivery unit and a delivery execution feedback unit, the data acquisition and processing unit collects orders, geographic information and real-time traffic data from a cross-border e-commerce platform and performs cleaning preprocessing, repeated orders are removed by using a Hash algorithm, the order clustering and delivery unit clusters the orders by using a density peak clustering algorithm, and the clustering effect is optimized by combining the order emergency degree and the customer loyalty, so that the service quality of the cross-border e-commerce platform is improved. A genetic algorithm is adopted to optimize the delivery sequence, the crossover and mutation probability is dynamically adjusted, a fitness function is calculated by considering the delivery distance, time cost and regional limitation penalty factors, a delivery execution feedback unit sends an optimized route to a delivery vehicle, client feedback evaluation data is collected, a delivery strategy is dynamically adjusted by means of an upper confidence bound algorithm, and the delivery efficiency is improved. And the delivery efficiency and the customer satisfaction are improved.

The invention discloses an industrial marketing management method, system and equipment based on digital twinning and a medium, belongs to the technical field of big data processing and analysis, and aims to solve the technical problem of how to collect and process various data in a cigarette marketing management process in real time, find market changes in time, improve marketing efficiency and flexibility and improve marketing efficiency and flexibility. According to the technical scheme, the method comprises the following steps: data acquisition and data cleaning: collecting full-life-cycle data of an industrial product; wherein the full life cycle data of the industrial product comprises information of each stage of design, production, test and sales; performing data modeling: performing accurate three-dimensional modeling on the industrial product by utilizing three-dimensional modeling software, constructing a virtual model of the product based on a physical law and engineering knowledge, and simulating the running state and performance of the product under various working conditions through a simulation technology; virtualizing a production scene; building a virtual product model; and simulating a virtual marketing scene.

The invention discloses a purchase management decision support system driven by an AI large model, and particularly relates to the technical field of intelligent purchase management. A data acquisition module obtains historical and current purchase, market price, inventory, sales and customer feedback data of commodities; the clustering analysis module divides purchasing groups based on inventory states and market prices; the self-portrait construction module generates a comprehensive self-portrait through client feedback data; the market analysis module analyzes sales data and predicts future demand changes; the optimization adjustment module optimizes the purchase quantity in combination with demand prediction, customer preferences and purchase group data, so that the accuracy and efficiency of purchase decision making are improved; through intelligent data processing and model prediction, the limitation of dependence on experience and static rules in traditional purchase management is avoided, fluctuation of market demands can be responded in real time, the purchase quantity is ensured to be matched with the market demands, inventory overstock and shortage phenomena are avoided, and customer satisfaction and market competitiveness are improved.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to an intelligent customer service method and system based on a large model, and the method comprises the steps: generating a unique session ID based on a user identity, and associating a historical context; multi-modal input of texts, voices, images and the like is carried out, and data standardization is carried out through a unified interface; realizing mixed intention recognition by combining a rule engine and a large model; constructing a dynamic customer portrait through multiple rounds of dialogue records, historical work orders and business data; according to the question emergency degree and customer value grading response, analyzing customer emotion in real time, adjusting a reply style, and outputting an answer; the manual operation process of the traditional customer service is converted into an AI automatic closed loop; the method has the beneficial effects that customer intention deep understanding, multi-mode interaction, emotion perception and service process automation are realized through a large language model (LLM) technology, and the service efficiency and the user satisfaction are remarkably improved.

The invention relates to the technical field of computer vision, in particular to an improved 4S store passenger flow statistical algorithm based on pedestrian re-identification, which comprises the following steps of acquiring video data of a 4S store entrance in real time based on image acquisition equipment, performing standardization processing on the video data in combination with an input requirement of video processing, and generating a video standardization processing result. According to the method, the image quality and the data processing continuity under the condition of high passenger flow are ensured by collecting the video data in real time and performing standardization processing, the accuracy of target identification and tracking is improved by combining the adjustment of the size and the position of the target detection frame, the motion path of the target is analyzed, and the mark information is added; the trend of a plurality of customers is accurately tracked and compared with feature vectors stored in a database, repeated targets are effectively distinguished, passenger flow statistics is carried out, in-out behavior analysis is carried out in combination with store layout parameters, a storefront is helped to adjust human resource allocation, and the operation efficiency and customer satisfaction are improved.

The invention discloses a method, a system and equipment for simulating a customer approval result based on historical data learning. The method comprises the steps of user data collection and data preprocessing; constructing and training a model; model evaluation: performing prediction accuracy evaluation on the trained model by using an independent test data set, and if an evaluation result is not good, adjusting and optimizing model parameters until the evaluation result meets requirements; adjusting a marketing strategy according to a simulation result; applying the strategy to a marketing operation scene, and applying the adjusted marketing strategy to actual marketing operation; effect monitoring and iterative optimization are carried out, feedback and iterative optimization are carried out on the model, the marketing strategy or the whole process according to the monitoring result, and the prediction accuracy and the marketing effect are continuously improved. According to the method, intelligent simulation and prediction of a complex approval process are realized, the accuracy and reliability of an approval result are greatly improved, a more personalized marketing strategy and service scheme are formulated, and the marketing effect and customer satisfaction are remarkably improved.

The invention discloses a multi-modal-based digital intelligent customer service system. Data compensation is carried out on initial multi-modal problem data by adopting a dynamic adaptive compensation mechanism through a multi-modal input module. And carrying out dynamic weight distribution on the target multi-modal problem data by adopting a cross-modal attention mechanism through a multi-modal comparison learning module. And performing intention recognition on the target multi-modal semantic vector through an intention recognition module according to the historical interaction data and a preset dynamic knowledge graph. And inputting the user intention data into the target reinforcement learning reply model through a reply generation module to construct reply data. And performing potential problem prediction by adopting the target multi-modal semantic vector, the historical interaction sequence corresponding to the user and the time data through a potential problem prediction module. And by utilizing the dynamic updating capability of the knowledge graph, the context sensitive identification of the user problem is realized, and the identification accuracy is improved. And the potential problem prediction module turns from passive response to active service, so that the interaction efficiency and the user satisfaction are remarkably improved.

The invention relates to the technical field of artificial intelligence, in particular to an AI digital human-based customer service method and system, and the method comprises the steps: receiving a voice stream, a visual stream and auxiliary sensor data inputted by a user, achieving the time-space alignment of multi-modal data through a cross-modal feature synchronization engine, and fusing the multi-modal data into a unified fusion data packet; based on the fused data packet, extracting an emotion intensity value and an emotion type code in real time, generating a dynamic emotion label, and extracting service demand characteristics to generate a demand vector; inputting the dynamic emotion label and the demand vector into a strategy decision tree, and generating a strategy identifier containing an emotion pacifying coefficient and a response emergency degree; the AI digital human is driven to generate the multi-modal response based on the strategy identifier, deep perception and flexible response to the user state can be achieved, the interaction naturalness and the user satisfaction of a digital human service system are effectively improved, and the method is suitable for multiple intelligent service scenes such as online customer service and intelligent terminal assistants.

The invention discloses a service scheduling system based on artificial intelligence, and belongs to the technical field of service scheduling. Comprising a data acquisition layer, an artificial intelligence engine layer, a scheduling execution layer, a monitoring management layer and a function module. The data acquisition layer acquires and preprocesses data related to insurance services; the artificial intelligence engine layer integrates natural language processing and machine learning technologies, and identifies and analyzes customer demands; the scheduling execution layer realizes access of customer service staff through a scheduling algorithm according to customer requirements and customer service staff information; and the monitoring management layer monitors and optimizes the service scheduling process. According to the invention, customer demands are intelligently analyzed, appropriate professional service personnel are accurately matched, and zero-delay docking between demands and services is realized; service personnel are intelligently dispatched, the waiting time of clients is greatly shortened, and the satisfaction degree is improved; tasks are deployed according to the insurance business rhythm, the potential of service personnel is mined, idleness is reduced, and the overall service capacity is improved.

The invention relates to an intelligent customer service self-learning method and system, and the method comprises the steps: collecting the interaction data of a user and an intelligent customer service, and forming a multi-dimensional data set; based on a PID (Proportion Integration Differentiation) controller, processing the performance indexes in the multi-dimensional data set, calculating a current error signal, and generating a corresponding control instruction according to the current error signal so as to adjust the response behavior of the intelligent customer service system in real time; evaluating the system performance data adjusted by the control instruction to obtain evaluation feedback; and according to the evaluation feedback, dynamically adjusting the parameters of the PID controller through a self-adaptive control strategy, and feeding back the adjusted parameters to the PID controller in the step S2. According to the invention, by introducing a closed-loop feedback mechanism based on the PID controller and a parameter adaptive optimization strategy, real-time regulation and control of the response behavior of the intelligent customer service system are realized, and the stability, the response speed and the user satisfaction of the system are remarkably improved.

The invention discloses an unmanned aerial vehicle take-off and landing site selection method for urban end logistics, and relates to the technical field of facility site selection, and the method comprises the following steps: S1, obtaining a building contour of a region to be subjected to site selection, and constructing a customer buffer region according to the building contour; s2, based on the customer buffer area, performing noise analysis and safety analysis on the unmanned aerial vehicle, and constructing an overall restricted area; and S3, based on the overall restricted area, constructing a target function taking the lowest cost as an optimization target, a target function taking the highest satisfaction as an optimization target and a plurality of constraint conditions, and obtaining a site selection layout result. According to the method, the applicability of the grid map method in the complex urban environment is verified, and a new thought is provided for the layout of the urban logistics unmanned aerial vehicle take-off and landing points.

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 discloses a workshop task allocation method and system based on multiple dimensions, and relates to the technical field of production control management. Comprising the steps of obtaining order data and then performing preprocessing; based on the preprocessed order data, an advanced machine learning algorithm is adopted to construct a decision tree model, and the processing complexity of each order is identified; determining a task allocation scheme by using an optimization algorithm according to the identified order processing complexity and the current state of the production line; monitoring the actual operation condition of each production line in real time, starting a dynamic adjustment mechanism when the work load of a certain production line is unbalanced, and re-evaluating and adjusting task distribution; through intelligent task allocation and resource optimization, the production efficiency and customer satisfaction are improved, and meanwhile, dynamic adjustment and intelligent management of the production process are realized.