42 results about "Production risk" patented technology

The invention provides a stiffening plate production risk perception and identification system based on big data analysis, and relates to the technical field of stiffening plate risk perception, and the system comprises a data collection module which is used for an industrial Internet of Things terminal to collect and preprocess multi-source data in a stiffening plate production process; the feature extraction module is used for extracting features related to the production risk of the stiffening plate; the classification module is used for dividing equipment fault sub-classifications; the risk model construction module is used for training a stiffening plate production risk analysis model under the sub-classification through the first feature set under each sub-classification; the correlation model construction module is used for training a correlation analysis model of the stiffening plate production risk; the risk prediction module is used for inputting real-time data into each trained risk analysis model and obtaining a final risk probability according to the association analysis model; the early warning module is used for triggering the early warning module when the final risk probability exceeds an early warning threshold value; and an accurate risk identification solution is provided for intelligent manufacturing of the stiffening plate.

The invention relates to the technical field of predictive maintenance and health management of industrial equipment, in particular to an intelligent electromechanical integrated monitoring system based on the industrial internet, which comprises a data acquisition module, a data processing module, a data processing module and a data processing module, and is characterized in that the data acquisition module acquires an equipment dynamic operation parameter set, inherent physical attribute parameters and historical maintenance data of target electromechanical equipment; calling preset process topology data, economic loss data and maintenance cost data; the state evaluation module iteratively calculates an accumulated degradation index representing the health condition of the equipment; the importance quantification module calculates a process importance coefficient representing the criticality of the equipment in the production line based on the process topology data; the risk calculation module generates a production risk degree which quantifies the shutdown influence; the decision generation module generates a dynamic maintenance decision instruction; according to the invention, systematic risk cognition from the individual equipment to the overall production line is realized, extensive management of all equipment is avoided, and allocation of maintenance resources is more strategic and targeted.

The invention discloses an intelligent production scheduling control method and system for a hardware manufacturing workshop, and relates to the technical field of production control, and the method comprises the steps: building a hardware manufacturing resource map, carrying out the resource dependence analysis of a hardware order set, and generating an order dependence resource map; establishing a resource adequacy evaluation sequence; performing exception analysis on the resource adequacy evaluation sequence, determining an adequacy exception factor, compensating and adjusting a hardware manufacturing resource map, establishing a first production resource adjustment space, and constructing a second production resource adjustment space through traversal optimization; global optimization is carried out on the second production resource adjustment space, a hardware production resource optimization strategy is determined, and resource optimization is carried out on the hardware manufacturing workshop. The technical problems of complex resource dependence, unbalanced supply and demand and insufficient scheduling optimization in the hardware manufacturing workshop resource scheduling process in the prior art are solved, and the technical effects of accurate perception of the resource state, dynamic optimization of the scheduling strategy and minimization of the production risk are achieved.

The invention discloses an iron and steel enterprise safety production risk early warning method and system based on multi-dimensional data, and relates to the field of data processing systems or methods specially suitable for administrative, commercial, financial, management, supervision or prediction.The method comprises the steps that a basic data score, a daily management data score, a perception data score and a video data score are determined; respectively matching corresponding weight coefficients for the basic data score, the daily management data score, the perception data score and the video data score; performing weighted summation calculation on the corresponding basic data score, the daily management data score, the perception data score and the video data score by using the weight coefficient to obtain a comprehensive risk early warning index; determining a safety risk level of the target monitoring area based on the comprehensive risk early warning index and a preset risk level threshold; and generating and sending early warning information according to the security risk level. The method is used for improving the accuracy and real-time performance of early warning of the safety production risk of the iron and steel enterprise.

An adjustable mounting groove structure relates to the technical field of die mounting and comprises a die holder, a plurality of mounting groove groups are arranged at the edge of the die holder, and each mounting groove group comprises at least two mounting grooves; the adjustable mounting module is mounted at the position of the mounting groove group when the die holder and the sliding table are fixed; an adjustable mounting groove is formed in the first end of the adjustable mounting module, and the adjustable mounting groove is aligned with any mounting groove by adjusting the mounting position of the adjustable mounting module and the mounting groove set. According to the mounting conditions of two or more production lines, a plurality of mounting grooves are formed in the positions of the original mounting grooves in the die holder base body, so that quick-clamping chucks on different production lines are provided with the mounting grooves corresponding to the quick-clamping chucks in position, the die can be mounted corresponding to different production lines, and the requirement that when a main production line breaks down suddenly, the die holder base body cannot be damaged due to the fact that the main production line breaks down suddenly is met. The temporary-to-standby production line production demand guarantees production, after the state of the main production line is recovered, the main production line can be rapidly recovered to the main production line installation condition, and the production halt risk and economic loss caused by production line faults are reduced.

The invention relates to the technical field of centrifugal pumps, in particular to an ultra-low specific speed unit transient pressure fluctuation suppression system and method.The method comprises the steps that a segmented broken line type guide vane closing rule is optimized, and decision variables are determined; determining a unit rotating speed water head ratio item in the target function of the multi-target optimization of the guide vane closing rule; according to the method, the'segmented broken line law 'of closing of guide vanes is optimized through a multi-target genetic algorithm, the'rotating speed water head ratio' is taken as a key regulation and control index, transient pressure fluctuation and runner hydraulic thrust are balanced, and the transient pressure fluctuation and the runner hydraulic thrust are controlled. The method is suitable for controlling transition processes such as starting, stopping or sudden load change of an ultralow-specific-speed unit, vibration and noise in the operation process of equipment can be reduced, the service life of the equipment is prolonged, the production efficiency is improved, equipment failures and safety accidents caused by pressure fluctuation are avoided, the production risk is reduced, meanwhile, energy waste is reduced, and the production cost is reduced.

The application discloses a kind of based on ERP system's dynamic feeding back calculation processing method, equipment and medium, belong to enterprise informationization ERP technical field, for solving the existing feeding back calculation is prone to deviation, easily influenced by a variety of uncertain factors, it is difficult to accurate prediction and adjustment, increase the technical problem of production risk.Method includes: according to target production product, BOM table is matched and inquired and handled, and the required material information is obtained;According to the inventory result where the required material information is located, the available inventory usage of maximum production is judged, and the usage result is obtained;If the matching result is not check matching result, then according to the required material information, the product quantity of target production product is determined;If the matching result is check matching result, then the available inventory of each required material information in BOM table is circulated, and the product quantity of target production product is determined;Based on product quantity and BOM table, feeding production order information is generated.

The invention discloses a product evaluation method, device and equipment and a medium, and the method comprises the steps: obtaining a product statistical parameter, a parameter limit value, and a measurement statistical parameter of a measurement system, and the measurement system is used for carrying out the product qualification measurement; determining the production risk probability of the product and the measurement risk probability of the product measured by the measurement system based on the product statistical parameter, the parameter limit value and the measurement statistical parameter of the measurement system; and obtaining an index value corresponding to the product evaluation index based on the production risk probability and the measurement risk probability.

The present application relates to the production environment monitoring technical field, especially to the silicon steel surface magnesium oxide coating quality control system and method based on data analysis, in the magnesium oxide adhesion prediction, the shape, size matching and environmental factors of silicon steel surface pit and magnesium oxide particle are comprehensively considered, the prediction deviation caused by single factor is avoided;Coating effect analysis combines the friction collision condition and coating adhesion of the history use of silicon steel, and evaluates the dynamic risk;Coating thickness analysis accurately calculates the required coating thickness according to the coating effect threshold value;Finally, the required coating thickness is compared with the safety range, coating meets the process requirements and conforms to the actual operation range, avoids production risk from the source, and guarantees product quality and process feasibility.

A production risk analysis device (100) comprises: a risk scenario pattern acquisition unit (112) that, on the basis of product-related information (121) including at least one of product specifications and production process information, uses a language model (see a language model server (200)) to acquire a risk scenario pattern pertaining to the production of the product; a causal model generation unit (113) that extracts causal relationships between factors and results included in the risk scenario pattern and generates a causal model (130); and a detailed risk scenario acquisition unit (114) that, on the basis of the product-related information (121) and the causal model (130), uses the language model to acquire a detailed risk scenario pertaining to the production of the product.

The invention relates to an intelligent production scheduling system architecture integrated with deep reinforcement learning and time sequence prediction, which realizes production scheduling strategy autonomous learning, production risk pre-judgment and man-machine collaborative optimization through collaborative linkage of six layers of architectures. The architecture is composed of a data acquisition and fusion layer, a micro-service business core layer, a deep learning engine layer, a production scheduling execution layer, a visual decision-making layer and a system guarantee layer, and multi-source production data is gathered on the basis of the data acquisition and fusion layer; a deep learning engine layer is taken as a core, and production scheduling decision and risk prediction are carried out; the production scheduling execution layer is used as a bridge, and the AI decision is converted into a production instruction; man-machine cooperation is realized through a visual decision-making layer; and finally, a feedback data driving model in the deep learning engine layer is continuously optimized to form a self-evolved intelligent closed loop, the micro-service business core layer provides a necessary business data standardization function, and the system guarantee layer ensures stable operation of the AI service. The method is suitable for the technical field of manufacturing industry production scheduling.

The invention relates to application of a digital twinning technology in the aspect of risk monitoring of chemical production equipment, and discloses a real-time risk monitoring and early warning system for isocyanate production equipment, the classification number is G06Q or G06F, and the keyword is digital twinning. The system comprises a sensor data acquisition module, a data preprocessing module, a digital twinborn simulation module, a risk assessment module, an early warning decision module and an intervention control module. According to the method, the equipment state is predicted through the digital twinborn model, the risk index is calculated, the early warning is generated, and pre-intervention is realized; the method is characterized in that the digital twinning technology is integrated, excessive dependence on sensor precision is reduced, the problems of uncertainty and hysteresis of early warning of an existing system are solved, the technical effect of improving early warning accuracy, timeliness and safety is achieved, and the method is suitable for chemical production risk monitoring.

According to the safety production risk conduction analysis method and device, the storage medium and the computer equipment provided by the invention, when the target operation task is received, the system obtains the multi-source data related to the task, including the personnel qualification data, the operation plan data, the tool data, the work ticket data and the environment data. And then, a target risk conduction model corresponding to the target operation task can be determined, and after the multi-source data is converted into standardized evidence, the overall risk level and risk value of the target operation task and a risk conduction path are reasoned based on the standardized evidence through the target risk conduction model. And finally, the risk levels, the risk values and the risk conduction paths can be visually displayed, and real-time early warning is performed according to the risk values and the conduction paths, so that a data island is broken, deep fusion and correlation analysis of multi-source data such as people, plans, tools, tickets and environments are realized, and systematic risks are actively reduced.

The invention relates to the technical field of production monitoring, in particular to an industrial safety production monitoring method and system based on AI, and the method comprises the following steps: collecting an omnibearing monitoring video stream of a production environment, carrying out the synchronous calibration, and constructing a synchronous monitoring video stream; performing risk labeling on the synchronous monitoring video stream, and constructing a production environment topology risk field; personnel area matching detection is carried out based on the production environment topology risk field, and a position safety early warning signal is generated; performing action safety logic verification based on the synchronous monitoring video stream, and marking an abnormal operation behavior event; and performing intelligent production monitoring decision based on the position safety early warning signal and the abnormal operation behavior event. According to the invention, the safety early warning efficiency and accuracy of precision industrial production are improved, the production safety is improved, and the generation risk is effectively reduced.

The invention discloses an efficient vertical machining center BOM building method, and belongs to the technical field of machine manufacturing. The method comprises the following steps that S1, a BOM frame is built based on the modular design concept; s2, carrying out association design by utilizing three-dimensional design software; s3, establishing a BOM change management mechanism; and S4, introducing a related research and development product management informatization system. According to the method, the building efficiency is improved, a large amount of manual input work is avoided, and the BOM building time is greatly shortened; the accuracy is improved, human errors are reduced, and the accuracy and integrity of BOM information are ensured; the method adapts to design changes, can quickly respond to the design changes, timely updates BOM information, and effectively reduces production risks and cost increase caused by the design changes. Data management is facilitated, efficient management and utilization of BOM information are achieved, and data query and analysis of links such as production plan making, purchase management and cost control are facilitated.

This technology provides a way to quickly assess the manufacturing risk of steel materials based on past manufacturing records. [Solution] A risk determination device comprising a control unit that receives specification information of an inquiry, including the specification conditions and specified values ​​of steel materials, aggregates past manufacturing results stored in a manufacturing database, compares the specification information with the past manufacturing results, determines the manufacturing risk, and outputs the determined manufacturing risk.

The present application relates to a rolling production line adaptive control method and computer equipment, by acquiring the multi-modal original production data of the rolling production line, extracting the multi-modal production physical characteristics for identifying the roll eccentricity, surface defects or temperature abnormal production risk, and performing semantic fusion on the multi-modal production physical characteristics to generate a joint semantic vector, and then acquiring the production risk data based on the joint semantic vector, and finally adaptively adjusting the rolling parameters according to the preset control strategy and the production risk data. Compared with the extensive control mode in the prior art which depends on the preset rolling procedure or a single sensor, the present application can realize real-time perception of various production risks such as roll eccentricity, surface defects and temperature abnormalities from multiple dimensions, accurately understand the internal relationship between different modal information through semantic fusion, and realize adaptive adjustment of rolling parameters based on the fused risk data, so as to realize accurate adaptive control of the rolling production line based on process information.

This invention relates to the field of database construction technology, specifically disclosing a database construction and management method for multi-source data. The method includes the following steps: S1: acquiring and preprocessing multi-source data; S2: establishing an agricultural and rural spatiotemporal database; S3: establishing a first construction management analysis model; S4: establishing a second construction management analysis model; S5: evaluating the construction management quality; and S6: establishing a construction management in-depth analysis model. This invention acquires and preprocesses multi-source data to construct a spatiotemporal database, and evaluates the construction management quality by establishing a first and a second construction management analysis model. This invention achieves the integration and unified management of multi-source agricultural and rural data, significantly improving data quality and application value, providing comprehensive and accurate data support for agricultural analysis, and helping to improve agricultural production efficiency, reduce production risks, and promote sustainable agricultural development.

The invention relates to the technical field of punch forming, in particular to a method for quantitatively judging the shaking risk of a material strip in a progressive die, which comprises the following steps: calculating the first-order inherent frequency of a part of the material strip through finite element modal analysis, and dividing the shaking risk according to a safety threshold value; whether a material lifting device needs to be additionally arranged on a work station of the progressive die and which position of the work station is additionally provided with the material lifting device are quantitatively judged, so that the shaking risk of a material belt on the progressive die is reduced, redundant design is avoided, the work station larger than a safety threshold value is not additionally provided with the material lifting device, and the machining and manufacturing cost of the progressive die is reduced. A material lifting device is designed in a work station with high shaking risk, the shaking problem is restrained from the design source of the progressive die, and the stability of machining and forming of the material belt is improved; the progressive die can be designed in a standardized mode, manual experience design is replaced, the progressive die design period is shortened, the production risk is reduced, and the progressive die quality and the production quality are improved.

The application discloses a distributed multi-sensor cooperative industrial automatic inspection device positioning method and system, and relates to the technical field of industrial automation measurement.The method comprises the following steps: analyzing heterogeneous sensing information by a first edge fault analysis unit and outputting fault risk, positioning M downstream risk correlation units.Sending a cooperative diagnosis request, receiving the returned sensing sequence, conducting risk transmission analysis, and generating a risk probability distribution heat map.According to the heat map, the production risk sub-area is framed, the inspection priority is calculated, the inspection path is constructed, and the industrial automatic inspection device is driven to conduct fault risk verification.The application solves the technical problems of the prior art, such as the dependence of the traditional industrial equipment monitoring method on a single sensor or manual inspection, low fault recognition accuracy, and low inspection efficiency, and achieves the technical effect of improving the accuracy of fault detection and the efficiency of inspection through distributed multi-sensor cooperation and edge computing, combined with dynamic risk analysis and inspection path optimization.

The application provides an electronic product production risk assessment method and system based on artificial intelligence, relates to the field of risk assessment, and solves the technical problems of fuzzy cause-effect relationship and insufficient response to distribution deviation during electronic product production risk assessment. The method first extracts material, process, equipment and environment heterogeneous elements of the production process, constructs an MPEE standard data set and a heterogeneous cause-effect diagram; collects real-time MPEE data through a fixed time sliding window to construct a sliding window data set, detects the distribution deviation degree of the data set from the standard data set by using a kernel function, sets a threshold value in combination with process stability and locates the deviation nodes, realizes dynamic updating of the heterogeneous cause-effect diagram, inputs the real-time data into a risk assessment model trained by a deep neural network to obtain a risk result, reversely traverses a cause-effect path based on the risk level and the heterogeneous cause-effect diagram, completes root cause positioning and impact degree quantification, and finally matches a process knowledge base strategy and selects an optimal execution strategy.

The invention discloses an intelligent operation management method and platform for the whole process of industrial production, and relates to the technical field of operation management, and the method comprises the steps: obtaining corresponding equipment state data flow, production state data flow and material state data flow, performing coupling hidden danger prediction under multi-node fault tracing on the current production scheme to establish an equipment associated production hidden danger space; introducing a production risk identification network to carry out risk-driven process environment coupling hidden danger prediction on the current production scheme, and establishing a process environment coupling hidden danger space; carrying out material support adaptation associated hidden danger prediction on the current production scheme, and building a material support associated hidden danger space; and according to the plurality of hidden danger spaces, collaboratively optimizing the current production scheme, obtaining a production optimization strategy, encrypting and sending to a management cockpit of the thermal power plant. The technical problem of inaccurate production scheme optimization in the prior art is solved, and the technical effects of realizing accurate prediction of production hidden dangers and improving the overall optimization effect of the production scheme are achieved.

A computer-implemented method for testing a risk model trained to output a risk determination for open banking transactions that includes: unit testing the risk model implemented in an application programming interface (API) platform; performing manual and automated feature validation of the risk model by comparing feature outputs from the risk model to expected feature output values; performing functional validation of the risk model according to an accuracy metric; benchmark testing the risk model, implemented in the API platform, using simulated scenario test data sets; load testing the risk model; after performing the foregoing, deploying the risk model as a production risk model with a web portal in a production environment; and dog-food testing the production risk model using a plurality of simulated scenario test data sets configured to simulate real production usage.

The invention discloses a production risk monitoring and early warning system and method, and relates to the technical field of safety production monitoring in the steel production industry. The system comprises a data acquisition module used for acquiring key index data of production equipment in real time; the data processing module is used for carrying out data cleaning, standardization processing and data storage on the collected key index data; the algorithm model module is used for carrying out risk analysis and anomaly detection based on a rule engine and a machine learning model to obtain a detection result; the early warning management module is used for triggering graded and classified early warning according to the detection result and pushing early warning information to the terminal; and the visual display module is used for generating map point location early warning based on the early warning information and providing a real-time monitoring board. According to the production risk monitoring and early warning system provided by the invention, risk assessment and early warning information is provided through a data processing technology, and map point location early warning is generated based on the early warning information, so that the risk management and control informatization and intelligence level of a production management system is improved.

This invention discloses an industrial safety production risk perception method and system based on an LLM (Limited Least Mechanism) agent, relating to the field of industrial data processing technology. The method includes: during tank leak repair, predicting the set of hazardous gas concentrations in area A within a repair time window based on historical hazardous gas concentration monitoring records; acquiring images of protective equipment worn by personnel going to area A for repair within the preset repair time window, performing wear feature recognition to obtain the fit, and obtaining the filter canister model and effective pressure difference accumulation duration; using an abnormal risk perception engine trained on an LLM agent, deriving the predicted probability of abnormal protection risk based on the predicted hazardous gas concentration set, fit, filter canister model, and effective pressure difference accumulation duration; and issuing a protective risk warning to personnel if the risk exceeds a preset threshold. This invention effectively improves the accuracy and reliability of industrial safety production risk perception.

The application is suitable for the technical field of fault early warning, and provides a multi-device cooperative scheduling and fault early warning method and system in an industrial internet scene, which comprises the following steps: acquiring real-time operation data of devices and acquiring device early warning influence degree information; comparing the overall health degree with a health degree threshold value, adjusting the in-process cache according to a load balancing strategy, and reevaluating the overall health degree of the process according to the adjustment result; comparing the overall health degree of the production line with an overall health degree threshold value of the production line, marking the key path node closest to the fault device and having a physical isolation function, and giving a hierarchical suggestion according to the key path node; comparing the production risk index with a production risk index threshold value, acquiring the production risk index in real time, and sending a feedback inhibition instruction according to the change trend, which improves the safety, continuity and intelligent level of multi-device cooperative production in the industrial internet scene.

The invention discloses a packaging production risk early warning system fused with time sequence modeling, and the system comprises the following modules: a data collection and preprocessing module which is used for collecting multi-modal perception data and carrying out the preprocessing of the multi-modal perception data; the time alignment module is used for performing time alignment by using an improved Soft-DTW algorithm; the technological process mapping module is used for generating a risk causal map according to the standard production process of the packaged product and the multi-modal unified data sample set; the anomaly judgment module is used for carrying out anomaly identification on the collected real-time multi-modal sensing data based on an index rule base; the risk scoring module is used for calculating an accumulated abnormal score of each batch of products based on the abnormal judgment result set; and the risk diffusion and response module is used for calling the risk causal atlas to track the risk path of the downstream process and generating a risk diffusion response list. According to the invention, multi-modal sensing and time sequence modeling are fused, and the intelligent risk early warning system for the packaging production line is constructed.

The application provides a continuous delivery version production control method and device, relates to the version control technical field, and can be used in the financial field or other technical fields. The method comprises the following steps: determining a target environment of version production; the target environment comprises a gray environment or an official environment; determining a version sequence corresponding to the target environment according to a production date; executing a version production control process corresponding to the version sequence, and completing version production process instantiation. The device executes the above method. The continuous delivery version production control method and device provided in the embodiment of the application can reduce manual operation, improve the degree of automation, and then improve the efficiency, and effectively control the version production risk.

A computer-implemented method for testing a risk model trained to output a risk determination for open banking transactions that includes: unit testing the risk model implemented in an application programming interface (API) platform; performing manual and automated feature validation of the risk model by comparing feature outputs from the risk model to expected feature output values; performing functional validation of the risk model according to an accuracy metric; benchmark testing the risk model, implemented in the API platform, using simulated scenario test data sets; load testing the risk model; after performing the foregoing, deploying the risk model as a production risk model with a web portal in a production environment; and dog-food testing the production risk model using a plurality of simulated scenario test data sets configured to simulate real production usage.

The invention discloses a power grid safety production risk analysis method, system and device, and relates to the technical field of power grid safety risk assessment, and the method comprises the steps: constructing a personnel electric shock accident tree comprising a top event, an intermediate event and basic events, and calculating the structural importance of each basic event through a Boolean algebraic method; carrying out N-K model mapping on basic events of the accident tree according to four types of risk factors of man-made, equipment, environment and management, and quantitatively calculating risk coupling effect values from a single factor to four factors by adopting an interaction information theory; and finally, determining key risk factors and a coupling relationship thereof according to the coupling effect value, and formulating targeted accident prevention measures. According to the method, the key risk path under the multi-factor coupling effect can be effectively identified, a scientific decision basis is provided for power grid safety production management, and the practical value is high.