189 results about "Manufacturing enterprises" patented technology

The invention provides an equipment manufacturing enterprise operation risk assessment method based on a Bayesian network, and the method comprises the following steps: S1, obtaining the operation data of an equipment manufacturing enterprise, and carrying out the integration of the operation data, and obtaining a multi-source fusion data set; s2, extracting risk factors in the multi-source fusion data set; s3, adopting a recurrent neural network to identify the incidence relation between the risk factors; s4, constructing a business process model, and associating the risk factor with the business process to form a Bayesian network; and S5, based on the Bayesian network, calculating conditional value-at-risk indexes CVaR alpha of the target node under different confidence levels alpha so as to evaluate the risk level of the equipment manufacturing enterprise operation full-service process. And the operation risk cognition elasticity of an equipment manufacturing enterprise in an unstable and uncertain environment is improved.

The invention discloses a scheduling optimization method for an aviation composite material manufacturing hot-press forming process, which comprises the following steps of: 1, establishing a hot-press forming production plan scheduling objective function by taking the minimum total delay time of a workpiece as an objective according to an aviation composite material manufacturing hot-press forming production plan; 2, establishing a two-dimensional hot press molding process batch scheduling model considering a workpiece release time window and machine applicability; and step 3, taking the objective function as an optimization objective, taking the two-dimensional hot-press forming process batch scheduling model as an optimization model, and utilizing a genetic algorithm embedded with a self-adaptive domain search strategy to solve and obtain a hot-press forming production plan scheduling scheme. According to the method, the total delay time of workpieces can be remarkably shortened while the machining batch of the workpieces is minimized, the occurrence of prepreg deterioration conditions is reduced, and conditions are created for improving the hot press molding production efficiency and promoting digital and intelligent transformation and upgrading of composite material manufacturing enterprises.

The invention provides a manufacturing logistics intelligent matching recommendation method and system based on multi-modal data, and relates to the technical field of intelligent logistics, and the method comprises the steps: obtaining the logistics demand data of a manufacturing enterprise and the service capability data of a logistics supplier; constructing a multi-level feature attention network to perform feature extraction on the logistics demand data and the service capability data, and performing screening to obtain multi-modal feature representation; constructing a positive and negative sample pair based on the multi-modal feature representation, obtaining an inter-modal semantic mapping matrix through a hierarchical contrast learning mode, and projecting the multi-modal feature representation to a unified semantic space to obtain a fusion feature representation; the fusion feature representation and the historical cooperation record are constructed into a state vector, an instant reward function and a long-term reward function are constructed, and a matching degree score between the manufacturing enterprise and the logistics supplier is obtained through optimization of a deep reinforcement learning algorithm; and recommending a logistics supplier to the manufacturing enterprise based on the matching degree score.

The invention discloses an intelligent production scheduling plan scheduling collaborative optimization system, and belongs to the technical field of production scheduling optimization, and the system comprises a data collection module which is used for collecting real-time resource data of a manufacturing enterprise; the data processing module is used for processing the real-time resource data of the manufacturing enterprise; the intelligent production scheduling module is used for carrying out coordinated management on the real-time resource data of the manufacturing enterprise and generating an intelligent production scheduling plan of the manufacturing enterprise; the monitoring and early warning module is used for carrying out real-time monitoring on the whole production and manufacturing process and carrying out early warning management on abnormal production and manufacturing behaviors; and the collaborative optimization module is used for dynamically adjusting an intelligent production scheduling plan of the manufacturing enterprise, optimizing resource utilization and integrating production, inventory and sales information of the manufacturing enterprise, so that information sharing and collaborative operation of each link are realized. The problem that the production efficiency and the market competitiveness are low due to the fact that intelligent optimization of the production scheduling plan cannot be achieved in the prior art is solved. According to the invention, intelligent optimization of the production scheduling plan can be realized, and the production efficiency and the market competitiveness can be improved.

The invention belongs to the technical field of industrial data processing, and particularly relates to a production enterprise energy consumption data prediction method based on machine learning. The method comprises the following steps: deploying an Internet of Things sensor to collect power and equipment operation data, and after three-stage preprocessing of anomaly detection, deletion repair and normalization, constructing a layered feature extraction network which comprises a time sequence feature layer, an equipment association feature layer and a modal fusion layer; the two features are respectively used for capturing power consumption features and equipment collaborative consumption features and fusing the features to generate depth feature vectors; and constructing a dynamic integrated prediction model, training three types of base models including a support vector machine and the like, screening a model entering an integrated pool by using JS divergence, and optimizing the weight through a genetic algorithm to obtain a final prediction result. The method can accurately capture the characteristics of energy consumption data, effectively improves the prediction precision, assists an enterprise in reasonably planning energy use, and reduces the cost.

The invention belongs to the technical field of intelligent manufacturing of aeronautical manufacturing enterprises, and relates to an assembly process procedure auxiliary compilation method based on a large model, which comprises the following steps: data preparation and processing: collecting and preprocessing an assembly process procedure text file; carrying out algorithm model selection and fine tuning, using an assembly process procedure text as a training data set and a test data set, and selecting different types of large models to carry out fine tuning; service publishing: deploying a plurality of large models with the best evaluation indexes as services, publishing the services, and externally providing available interface services; constructing an RAG knowledge base, vectorizing the assembly process procedure text, and constructing a vector knowledge base; intelligent question and answer: performing knowledge question and answer based on the RAG knowledge base; and assembly process procedure file generation: constructing the assembly process procedure file through text generation. According to the method, the functions of semantic retrieval intelligent question answering of the assembly technological procedure knowledge base, automatic assembly technological procedure file generation, information recording and the like are achieved.

The invention discloses an automobile hub intelligent manufacturing production line construction method and system based on digital twinning, and relates to the technical field of industrial internet and digital twinning, and the production line construction method comprises the following steps: carrying out the two-dimensional plane layout design of an automobile hub manufacturing physical production line; according to the automobile hub production system based on digital twinning, modeling is conducted on three-dimensional geometric attributes and physical attributes of a production unit of an automobile hub manufacturing physical production line, digital twinning simulation software is imported into three models of the production unit of the automobile hub manufacturing physical production line, and a digital twinning line layout is completed. The digital twin system of the intelligent automobile hub manufacturing production line is constructed by taking automobile hub machining, grinding and detecting procedures as a main line, digitalization, intelligentization, networking and visualization of automobile hub production are realized, and the production efficiency and the management and control capability of automobile hub manufacturing enterprises are improved through the functions of monitoring, prediction, analysis, diagnosis and the like, so that the production efficiency and the management and control capability of the automobile hub manufacturing enterprises are improved. And intelligent manufacturing of the automobile hub is achieved.

The invention discloses a production plan and workshop scheduling integrated optimization method of a hydraulic cylinder manufacturing enterprise. The method comprises the following steps of performing a series of assumptions on a production plan and workshop scheduling integrated model of the hydraulic cylinder manufacturing enterprise; establishing a multi-objective optimization function taking minimization of machining time as a main objective and minimization of machining cost as a secondary objective, and setting constraint conditions of the multi-objective optimization function; an improved particle swarm algorithm is adopted to solve the multi-objective optimization function, the improved particle swarm algorithm adopts double-layer coding to represent the positions of particles, the first layer of the double-layer coding is the machining sequence of each workpiece, and the second layer of the double-layer coding is machining equipment of each machining sequence; and the improved particle swarm optimization algorithm performs iterative optimization on the positions of the particles, and outputs a scheme which meets the completion time and has the minimum processing cost as an optimal production plan and workshop scheduling scheme.

The invention discloses an automobile working island assembly method and system, a storage medium and electronic equipment, and the method comprises the steps: installing at least one image collection device on each station of an automobile working island, and capturing the image data of workers and the working environment thereof in real time through the image collection devices; processing the image data by adopting an image processing algorithm, identifying actions and postures of workers, extracting key features, and generating feature data of worker assembly; constructing a state evaluation model based on machine learning based on pre-labeled historical feature data; and inputting feature data obtained by processing the current image into the state evaluation model for real-time evaluation, generating evaluation results of the worker state and the assembly state, sending the evaluation results and related image data to a work island assembly control terminal, and managing and controlling the assembly quality of the automobile work island. Real-time and accurate worker state monitoring is provided for automobile manufacturing enterprises, and the assembling quality and the working efficiency are improved.

The invention relates to the technical field of digital intelligent management, in particular to a digital intelligent management system and method for an unmanned aerial vehicle research, development and manufacturing enterprise, and the system comprises a knowledge management module, a data embedding processing module, an LLM center model, an AI agent, an enterprise resource planning module, a user interaction feedback module, and a security scale module. The knowledge management module is used for providing an internal knowledge base, encrypting authority management and real-time updating; the data embedding processing module is used for vector database and multi-mode embedding; the system realizes operations such as data input, storage, learning, calling, updating and the like to realize an intelligent working system, staff in a company exchange information with zero delay, and by combining intelligent auxiliary analysis of data, misjudgment caused by redundant and repeated invalid delay and information mismatching is avoided, and the working efficiency is improved. According to the method, internal information circulation of a company is effectively improved, the information island problem of each department is solved, the data secrecy capability in an enterprise is improved, and the management risk early warning capability is enhanced.

The invention discloses a discrete manufacturing enterprise product cost accounting method, and belongs to the technical field of commercial data processing, and the method comprises the steps: dividing the production process of each part in a product into a plurality of production processes; according to the inflow cost and the material picking cost of one production process, the in-process cost, the waste product cost and the completion cost of the production process are obtained through the processing motivation in the production process, and the inflow cost of the next production process is determined according to the completion cost; through circulation of the completion cost between continuous production processes, the completion cost of each part is obtained, so that the product cost is obtained. According to the method, the part production procedures are divided, cost circulation is carried out, refined cost control is achieved, cost accounting is independently carried out in each procedure, and data are accurate. By reasonably sharing the manufacturing cost and the waste cost, the cost transfer of cross-workshop circulation is simplified, and the cost accounting accuracy is improved.

The invention provides a flexible job shop scheduling method based on an improved bacterial foraging algorithm, and relates to the technical field of intelligent manufacturing. The method specifically comprises the following steps: acquiring original data of a flexible job shop, and constructing a flexible job shop scheduling optimization model; the method comprises the following steps: taking a flexible job shop scheduling scheme as an individual based on a flexible job shop scheduling optimization model, initializing parameters, and generating an initial population by adopting Logistic-Circle hybrid mapping; optimizing the initial population by adopting an improved bacterial foraging algorithm to obtain an optimal individual; and obtaining an individual position vector of the optimal individual, and converting the individual position vector of the optimal individual into a flexible job shop scheduling optimal scheme by using a conversion mechanism between the individual position vector and the scheduling scheme. According to the method, the production cycle of a manufacturing enterprise can be effectively shortened by optimizing flexible job shop scheduling.

The invention discloses a production and manufacturing enterprise cluster chain type interaction carbon emission reduction method based on dynamic electrical carbon factors, and relates to the technical field of industrial carbon emission reduction and energy management systems. According to the method, a four-layer decision-making framework of a global coordination layer, a cluster coordination layer, an enterprise execution layer and an equipment control layer is constructed, and dynamically updated electric carbon factors are introduced, so that accurate identification, carbon emission accounting and collaborative optimization of enterprise clusters on a product chain are realized. The core of the method is that carbon quota allocation, production scheduling and resource allocation are carried out among hierarchies by utilizing a multi-objective and double-layer optimization model, so that the total carbon emission of a cluster is minimized while the economic benefit is ensured. A dynamic monitoring and feedback mechanism is also established, the power carbon factor weight can be adjusted and a resource allocation plan can be triggered according to real-time data such as the proportion of clean energy of the power grid, closed-loop regulation from a macroscopic strategy to microscopic operation is formed, the overall carbon footprint of an industrial chain is effectively reduced finally, and the participation barrier of small and medium-sized enterprises is broken.

The invention provides a production maintenance and purchase collaborative scheduling method and system based on an improved dung beetle optimization algorithm, a storage medium and electronic equipment, and relates to the field of production maintenance and purchase collaborative scheduling. According to production characteristics of new energy batteries, after production resources and workpiece processing tasks are obtained, the parallel machine scheduling problem of preventive maintenance and order material purchase expected arrival time is considered at the same time, collaborative optimization is carried out on production, maintenance and purchase, and a mixed integer programming model is constructed with the goal of minimizing the maximum completion time. Besides, a dung beetle optimization algorithm combined with a VND local search algorithm is designed, the convergence speed and the search capability of the algorithm are greatly enhanced, and new energy battery manufacturing enterprises can be effectively helped to reduce the number of preventive maintenance times, so that the maintenance cost is reduced, meanwhile, more efficient production scheduling is realized, and the enterprise competitiveness is improved.

The invention relates to the field of material purchase optimization, and discloses an intelligent manufacturing enterprise multi-material joint purchase optimization method and system, which is used for solving the problem that real-time adjustment cannot be carried out during material joint purchase, and comprises the steps of obtaining historical purchase related data, carrying out prediction by using a time sequence method, obtaining the demand quantity of each material, and carrying out multi-material joint purchase optimization. Screening suppliers according to the material demands, obtaining data of the suppliers, obtaining a priority selection index according to the data of the suppliers through an optimal sequence graph method, selecting an initial supplier according to the priority selection index, obtaining the purchasing cost, the transportation cost and the inventory cost of the initial supplier, and obtaining an optimal purchasing scheme by using a mathematical optimization model. According to the method and the system, combined purchasing is carried out, historical purchasing related data and supplier data are continuously acquired in real time, and real-time purchasing adjustment is carried out, so that the accuracy of material combined purchasing is effectively improved, and the production efficiency of enterprises is improved.

The embodiment of the invention discloses a project scheduling method and device based on reinforcement learning. The method comprises the following steps: acquiring a current state vector of a target project; wherein the target project comprises engineering and service projects of a tobacco manufacturing enterprise; inputting the current state vector into a preset project scheduling model to output an execution action corresponding to the current state vector; wherein the preset project scheduling model is a convergent preset project scheduling model obtained by training by adopting a reinforcement learning algorithm and adjusting model parameters by adopting a pre-constructed project reward function; and performing dynamic project scheduling on the target project according to the execution action. By adopting the technical scheme of the embodiment of the invention, the plan and task arrangement of the target project can be dynamically adjusted according to the real-time progress of the target project, the risk factor and the external environment change, and various changes can be timely coped with, so that the project delay and failure risks are reduced, and the success rate of the project is improved.

The invention discloses a manufacturing capacity trend prediction method based on AI visual analysis, and relates to the technical field of industrial big data analysis, and the method comprises the following steps: building a cross-time scale alignment baseline, generating a phase consistency evidence set of a continuous time structure through timestamp reconstruction and sampling calibration, and obtaining a phase consistency evidence set; extracting a high-confidence time anchor point as a unified time reference; and constructing a nonlinear disturbance spectrum diagram based on the time anchor points, and carrying out hierarchical decomposition on energy mutation, slow drift and pseudo-stable fragments in a continuous time period to generate a traceable disturbance fingerprint set. Through time sequence alignment, disturbance modeling, weight repair and dynamic regulation and control mechanisms, stable convergence and trend consistency prediction of the artificial intelligence model under nonlinear disturbance are realized, the accuracy of productivity prediction and the reliability of decision making are improved, and a manufacturing enterprise is assisted to realize intelligent productivity management.

The invention discloses an enterprise digital risk prevention and control system construction method and system, and relates to the technical field of data analysis systems. Multi-angle risk prevention and control analysis is lacked in the prior art. The system comprises a human risk identification unit which analyzes and calculates the daily average production capacity and the daily average attendance rate on a single production line, and evaluates the output level of each employee; the job age relevancy of the employees is analyzed, and demission risk parameters are calculated; a production cycle and a cycle production index are preset, the production index completion degree is analyzed and calculated, the minimum value in the production index completion degrees of all production lines is obtained and recorded as a theoretical production value, and human risk parameters are calculated; and the storage risk identification unit is used for acquiring the quantity of warehouse-out products in the storage products, calculating the sales ratio, constructing an ARIMA model of the warehouse-out quantity with respect to time, and analyzing and calculating to obtain a storage risk index. The technical scheme has the advantages of comprehensiveness, real-time performance, flexibility, accuracy, practicability and the like, and the risk prevention and control capability of a manufacturing enterprise can be remarkably improved.

The application provides a multi-objective robust optimization method, system, storage medium and electronic equipment for production task planning of a multi-agent collaborative platform, and relates to the technical field of production task planning of a multi-agent collaborative platform. First, according to the production task planning resources of the multi-agent collaborative platform and a single-batch overseas order, a production task planning model with a manufacturing enterprise agent as the core is constructed under the premise of determining the sea transportation time. Then, an infinity norm ball uncertain set is designed for the sea transportation time of a large ship, a robust quadratic multi-objective model for cross-regional multi-agent production task planning is constructed, and the total delivery time deviation and total delivery cost multi-objective of the single-batch overseas order are optimized. The model considers the sea transportation plan prepared in advance by a shipping company agent and the long and uncertain sea transportation time. In addition, the model is converted into an equivalent model, a two-stage hybrid heuristic algorithm is designed for solving, and a reasonable and feasible production task planning suggestion is given for the single-batch overseas order.

The invention relates to a decision recommendation system and method for preferentially processing key data of a supply chain of a manufacturing enterprise. According to the decision recommendation system and method, through a data fusion module, a priority evaluation module, a decision recommendation module and a self-adaptive feedback module, supply chain key data management realizes collection, analysis and decision; the invention discloses an optimized decision recommendation system and method for an automatic closed-loop process. The system is characterized by comprising a data fusion module, a priority evaluation module, a decision recommendation module and a self-adaptive feedback module, the data fusion module outputs structured data to the priority evaluation module, and the decision recommendation module generates a scheme according to the priority and feeds back an actual effect to the self-adaptive feedback module after execution. The self-adaptive feedback module reversely updates the priority rule; the data fusion module is composed of a data acquisition unit, a data preprocessing unit and a data storage unit, and the priority evaluation module is composed of a rule engine unit, a machine learning model unit and a dynamic weight adjustment unit.

The invention discloses a manufacturing enterprise supply chain digital transformation maturity evaluation method driven by multi-dimensional indexes. The method comprises the following steps: widely collecting internal and external multi-source data of an enterprise through a data acquisition module, storing preprocessed data in a graph structure through a knowledge graph construction module, and optimizing query efficiency; potential association and abnormal modes among multi-modal data mining indexes are fused through a semantic understanding and relation reasoning module, field adaptability parameter adjustment and optimization are performed on each key model through a model training optimization module, and a hierarchical evaluation system is established and a quantitative improvement scheme is generated through an intelligent evaluation and improvement suggestion module. And finally, a complete closed loop from data acquisition to decision optimization is formed, and accurate evaluation and continuous improvement of the digital transformation maturity of the supply chain of the manufacturing enterprise are realized. The method is characterized in that a manufacturing enterprise supply chain digital transformation maturity evaluation system driven by a multi-dimensional index is used for evaluation.

The invention relates to the technical field of energy management, in particular to a multi-source heterogeneous energy load prediction method and system for a manufacturing enterprise cluster, and the method comprises the steps: collecting original energy data from a multi-source heterogeneous system in the manufacturing enterprise cluster and external environment information related to the energy load change, preprocessing the original energy data and the external environment information to obtain standardized energy time sequence data and final external environment information; based on the standardized energy time sequence data, the final external environment information and a pre-established equipment information model, constructing a triple heterogeneous atlas, and extracting structural semantic features of each node in the triple heterogeneous atlas; based on the structure semantic features, constructing a coupling sequence diagram, modeling the coupling sequence diagram, and identifying key factors; and inputting the structure semantic features and the key factors into a multi-scale prediction model, and outputting hourly load prediction curves of various energy media in a future prediction period.

The invention relates to the technical field of intelligent manufacturing workshops, in particular to an optimization method for solving group scheduling of a distributed heterogeneous flow shop, which comprises the following steps of: determining that minimization of product assembly completion time is taken as a problem solving target, and initializing parameters; initializing a solution; generating a probability random number, and judging and selecting to execute product and group operation or product and workpiece operation; and updating the optimal solution, judging whether termination time is reached or not, if yes, ending iteration, and outputting the current optimal solution. According to the application of the method, the problem of group scheduling of the distributed heterogeneous flow shop with the product assembly stage can be effectively solved, and the production efficiency of a manufacturing enterprise is improved by optimizing scheduling.

The invention relates to the technical field of artificial intelligence, and discloses an intelligent product life cycle management system and method based on a large model, a data layer is used for receiving multi-modal heterogeneous data input of different stages of a product full life cycle and is connected with a domain knowledge base; the model layer comprises a basic model and a task model, wherein the basic model is formed by fusing a large language model subjected to efficient parameter fine adjustment with a visual Transform hybrid architecture, and the task model is supported and driven by the basic model to run, and the model layer outputs a result based on the data layer; the data analysis module is used for analyzing heterogeneous data, constructing a knowledge graph for cross-source data intelligent retrieval and reasoning, and performing user demand analysis, semantic intelligent material classification, BOM full-life-cycle management, demand-driven optimization product design, supply chain risk prediction, dynamic process response and quality control and knowledge precipitation; and the application layer outputs a result based on the task model, and carries out real-time collaboration and key information visualization. According to the invention, automatic, intelligent and collaborative management of product life cycles of research, development and manufacturing enterprises is realized.

The invention discloses a material demand prediction method and system based on an iWOA-iTransform model, and the method comprises the steps: carrying out the collection and preprocessing of the time series data of material consumption in the production and manufacturing process of an equipment manufacturing enterprise, and obtaining a data set; an improved whale optimization algorithm iWOA and an iTransform model are introduced, and the improved whale optimization algorithm iWOA is used to carry out hyper-parameter optimization on the iTransform model; based on the obtained hyper-parameter optimal solution, constructing an improved iTransform model, and based on the improved iTransform model, capturing association between time sequences in the data set; and based on association between time sequences in the data set, through training an improved iTransform model, predicting the material demand, and obtaining a final material demand value.

The invention discloses a large model plan scheduling and simulation rehearsal agent platform, belongs to the field of advanced planning and scheduling, belongs to the field of intelligent scheduling, and solves the problem of intelligence of an advanced planning and scheduling system, the problem of association between the advanced planning and scheduling system and multiple systems and the problem of advanced simulation rehearsal of plan scheduling. The problem of efficient application of a large model under different resource scene schedules is solved, a large model LLMs and AI model set is designed and fused, automatic and intelligent advanced planning and scheduling are achieved, full unmanned chemical plant application scene scheduling is supported, highly structured resource data is designed, manufacturing enterprise resources and rules can be understood, and the method is suitable for large-scale industrial production. Accurate quantitative scheduling is achieved in an enhanced tool mode, large model training of purchase plans, inventory plans, production scheduling and order delivery plans is achieved in an auxiliary enhanced training and tool enhancement mode, the system is associated with a robot and mechanical arm system, and direct cluster scheduling, AI simulation preview scheduling results and ERP system associated viewing are achieved.

A task-level cooperative optimization dispatching method supporting multi-cloud disconnection disaster recovery is provided. Since cooperative dispatching is required for different resource requirements in the abnormal stage of a network, a cooperative computing system is formed by nodes of a production shop, production tasks are transmitted and processed between the nodes according to technological requirements, and task-level cooperative optimization dispatching is conducted through a CoopDispatch method in case of disconnection to ensure continuous production. The CoopDispatch method has the characteristic of high dependence on specific resources for manufacturing tasks. Data transmission, storage and calculation are achieved through a LocalAny mechanism, which can reduce task delay, improve the cooperation efficiency among the tasks and satisfy the needs of manufacturing enterprises for disconnection disaster recovery in the multi-cloud mode.

The utility model discloses a kind of worm processing comprehensive machine tools, including bed, the upper surface both sides of bed are fixedly installed with two symmetrical distribution primary bearing seat, one number screw rod is rotatably connected between two primary bearing seats, the outer thread of one number screw rod is connected with horizontal slide, the output shaft of one number motor is fixedly connected to one end of one number screw rod, symmetrically distributed secondary bearing seat is fixedly installed in the front and back two ends of horizontal slide, two secondary bearing seats are rotatably connected with two number screw rod, two number screw rod and one number screw rod are perpendicularly arranged between each other, the outer thread of two number screw rod is connected with longitudinal slide, the output shaft of two number motor is fixedly connected to one end of two number screw rod, equipment seat is fixedly installed on the upper surface of longitudinal slide, the machine tool of the utility model whole structure is simple, floor area is small, manufacturing difficulty is low, effectively reduce the enterprise's early purchase and later use cost, with good use economy, suitable for small and micro manufacturing enterprises development use.

The invention provides an industrial manufacturing enterprise-based energy measurement management method and related equipment, which can be used for analyzing and managing various energy consumption of a large industrial manufacturing enterprise. The method comprises the steps that N energy metering points corresponding to a target manufacturing enterprise are determined, N is an integer larger than or equal to 1, and the energy metering points correspond to energy consumption equipment in the target manufacturing enterprise; periodically acquiring encrypted energy data of each energy metering point in the N energy metering points; determining a key corresponding to each energy metering point in the N energy metering points; decrypting the encrypted energy data of each energy metering point based on the key to obtain decrypted energy data corresponding to each energy metering point; and analyzing the decrypted energy data corresponding to each energy metering point to obtain energy analysis information of each energy metering point in the current period, and outputting the energy analysis information in the current period.

This application discloses a method and system for information-based diagnosis of production batches in pharmaceutical manufacturing enterprises, belonging to the interdisciplinary field of artificial intelligence and pharmaceutical information technology. The method includes: unifying the time base of multi-source equipment; collecting equipment operating status, process parameters, and event data to construct a multi-dimensional array; establishing process constraints in the dimensions of product, process, and operational equipment flow; using a binning method to logically combine equipment status parameters into different operating status categories; training a random forest model using labeled historical batch data to obtain a batch diagnosis model; inputting the start time of the batch to be diagnosed into the model to derive the equipment operating time period, matching it with the operating status category, and statistically analyzing the classified operating time of each piece of equipment. This application solves the problem of statistical efficiency and validity in calculating the net operating time of each piece of equipment within a batch in pharmaceutical enterprises.