61 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 building curtain walls, and discloses an industrial implementation method of a spatial special-shaped unit curtain wall. The method comprises the following steps: a design end generates a first model by using a BIM modeling tool, a production end performs production feasibility verification based on historical models, determines a first number of historical models of which the similarity with a first type unit corresponding to an unproduced first model is greater than a first threshold value, and then analyzes whether production can be performed based on the historical models; if yes, pilot production is carried out, or else, a design end is fed back for redesign. Further, the customization degree of the first type of unit bodies is evaluated, the first number is dynamically adjusted based on the customization degree, and the customization degree is set based on scores of the first information of the unit bodies. According to the method, by fully utilizing historical production data and experience, the cooperation of design and production is enhanced, the accuracy and efficiency of production feasibility judgment are improved, and the production risk and cost are reduced.

The invention discloses a risk dynamic monitoring system and method based on safety production, relates to the technical field of safety production, and solves the technical problem of low safety production risk dynamic monitoring precision caused by neglecting of mutual influence of data corresponding to production links in the prior art. According to the method, the whole production process is divided into a plurality of production links, independent analysis of safety production risks is carried out on each production link, and once a certain production link is abnormal, an abnormal reason and an abnormal point can be quickly positioned, so that workers can conveniently process in time; the safety monitoring data of each production link is reasonably predicted based on the historical record data, whether the safety production risk exists or not is judged according to the predicted safety monitoring data, the mutual influence between the production links is fully considered in the reasonable prediction process, the safety monitoring data prediction reasonability can be improved, and the safety production risk is reduced. And thus, the accuracy of safety production risk judgment in each production link is improved.

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 a dynamic visual monitoring method and system for factory production risks, and relates to the technical field of production risk monitoring, and the method comprises the steps: carrying out the risk analysis of a production region, carrying out the risk division of the production region according to a risk target attribute, a risk updating frequency and a risk recognition means, and building a production monitoring region; extracting visual parameters of the production monitoring subareas, and establishing a mapping relationship among the visual parameters, the synchronous visual rules, the import paths and the corresponding production monitoring subareas; building a visual framework; and based on an output result of the output display interface connection processing layer, performing corresponding risk image display according to the output result. The technical problem that in the prior art, factory production risk monitoring lacks accurate management and dynamic visualization of multiple risk targets, and consequently production efficiency and safety are poor is solved, real-time monitoring, dynamic updating and visual display of production risks are achieved, and the technical effect of improving production safety and efficiency is 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 invention relates to the technical field of power plant safety management, and discloses a risk quantification control method and system for intelligent safety production, and the method comprises the following steps: 1, collecting factory monitoring data, and storing the factory monitoring data in a risk database; the plant area monitoring data comprises environment parameters, equipment parameters and personnel parameters; step 2, risk quantitative analysis is carried out by the production risk quantitative model based on risk operation content selected in the risk database, risk index values are output according to regions, and then visual display is carried out by a visual unit; a plant area layout map of the power plant is loaded in the visualization unit, and the visualization unit displays risk index values on the plant area layout map in different areas; and step 3, when the risk index value exceeds a threshold value, triggering a hierarchical management and control mechanism. The power plant production risk can be accurately quantified, visual intelligent management and control can be carried out, and high real-time performance and reliability are achieved.

The invention discloses a control method and system for a civil explosive manufacturing plant based on a Lora standard, and the method comprises the steps: building a communication network with processing equipment of the civil explosive manufacturing plant according to a Lora protocol, building a digital twinborn model of the processing equipment, and obtaining the real-time data of the processing equipment from the digital twinborn model through the communication network, and when it is determined that the processing equipment has a fault according to the real-time data, carrying out production processing on the equipment based on the digital twin model. According to the invention, communication with each device of a factory is carried out through the Lora protocol, the data transmission security can be improved, the data security can be ensured, and the probability of key data leakage caused by network attacks or network faults can be reduced, so that production interruption caused by data errors can be avoided, and the production risk can be reduced; and fault monitoring is carried out in combination with the digital twin model, so that the fault processing efficiency can be improved, the overall work and production control efficiency is improved, and the production risk caused by the fault is further reduced.

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.

The present application provides a container gooseneck groove pressing production line, comprising: a feeding device, a profiling device and a discharging device arranged in sequence; the feeding device includes a stock preparation conveying mechanism, a lifting mechanism, a feeding conveying mechanism and a rotating mechanism arranged in sequence in the direction close to the profiling device; the discharging device includes a discharging conveying mechanism, a lifting and stacking mechanism and a stock storage conveying mechanism arranged in sequence in the direction away from the profiling device. Compared with the prior art, by using the container gooseneck groove pressing production line of the embodiment of the present application, the automation of gooseneck groove production can be realized, without manual participation, and the transfer between each process is automatically completed, with high automation degree and low cost. Through mechanized production, the production efficiency can be greatly improved, the workload and labor intensity of workers can be reduced, and at the same time, the production risk and production cost can also be reduced.

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 discloses a pre-warning method for furnace-front iron loss and profit. Comprising the following steps of: acquiring basic data, namely acquiring furnace top charging related data at a second level, acquiring molten iron weighing data at a minute level, acquiring multi-source data, including blast furnace related temperature and other data and fusing the basic data, arranging a redundant channel, processing the acquired data and judging a furnace top charging signal, judging a material type signal by using fuzzy logic and the like, and calculating an iron profit and loss amount; comprising the steps of calculating the furnace top charging minute theoretical yield and the like, updating the accumulated iron profit and loss amount, pre-warning the furnace front iron profit and loss amount and the like. The method has many advantages, can monitor the iron profit and loss in real time, and improves the timeliness and accuracy of production management; multi-source data acquisition enables furnace condition monitoring to be more comprehensive, and redundant channels guarantee data transmission stability. And fuzzy logic is used for judging material type signals to improve the iron loss and profit calculation accuracy, and an early warning mechanism is further arranged to avoid production risks.

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 present invention discloses a method for building an innovative platform for a manufacturing unit based on a digital twin industrial robot. The present invention relates to the field of digital twin industrial robots. A method for building an innovative platform for a manufacturing unit based on a digital twin industrial robot comprises the following steps: collecting a set of building parameters, generating a building plan according to the set of building parameters, entering manual intervention, combining the manual intervention with the building plan to generate a building scheme; receiving building construction parameters, comparing the building construction parameters with the building scheme, and obtaining a set of correction data. By collecting building parameters and generating a building scheme in combination with manual intervention, and then comparing the building construction parameters with the building scheme in real time to obtain correction data, the accuracy of the manufacturing unit construction is ensured. This process not only improves the construction efficiency, but also effectively reduces the production risks caused by construction deviations, thereby improving the overall performance of the manufacturing unit.

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 invention provides a rationality evaluation method and system based on a hot rolling strip steel rolling plan provided by the embodiment of the invention, and belongs to the field of metallurgical process formulation and evaluation. The method comprises the steps that a rolling simulation model is established based on an obtained rolling plan and corresponding product information, and a simulation production result is obtained through calculation of the simulation model and serves as a judgment basis of simulation production; a historical production result recorded in the past is obtained to serve as a judgment basis of historical production; respectively calculating a simulated production risk coefficient and a historical production risk coefficient, then carrying out weighted summation on the simulated production risk coefficient and the historical risk coefficient according to the plate shape consideration weight, and calculating a plate shape problem risk coefficient of the current steel coil; and after traversing all the steel coils, presetting an evaluation standard, arranging an evaluation result and an identifier according to the risk coefficient and the evaluation standard, and outputting the evaluation result as a risk evaluation result by adopting the identifier. According to the method, the reliability of a big data analysis result is improved, and the reliability of a rationality evaluation result is ensured.

The present application relates to the technical field of building curtain walls, and discloses an industrialized implementation method for spatial special-shaped unit curtain walls. The method includes: the design side uses a BIM modeling tool to generate a first model; the production side performs production feasibility verification based on historical models; determines a first number of historical models whose similarity to the first type of unit corresponding to the first model that has not been produced is greater than a first threshold; and then analyzes whether production is possible based on these historical models; if so, trial production is performed; otherwise, feedback is given to the design side for redesign. Furthermore, the degree of customization of the first type of unit is evaluated, and the first number is dynamically adjusted based on its level; the degree of customization is set based on the score of the first information of the unit. This method strengthens the coordination between design and production by making full use of historical production data and experience, improves the accuracy and efficiency of production feasibility judgment, and reduces production risks and costs.

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.

The invention discloses a distributed multi-sensor cooperative industrial automatic inspection device positioning method and system, and relates to the technical field of industrial automatic measurement, and the method comprises the steps: analyzing heterogeneous perception information through a first edge fault analysis unit, outputting a fault risk, and positioning M downstream risk association units; and sending a collaborative diagnosis request, receiving a returned sensing sequence, carrying out risk conduction analysis, and generating a risk probability distribution thermodynamic diagram. And framing a production risk sub-region according to the thermodynamic diagram, calculating an inspection priority, constructing an inspection path, and driving the industrial automatic inspection device to perform fault risk verification. The technical problems that in the prior art, a traditional industrial equipment monitoring method mostly depends on a single sensor or manual inspection, the fault recognition accuracy is low, and the inspection efficiency is low are solved, and through distributed multi-sensor cooperation and edge calculation, dynamic risk analysis and inspection path optimization are combined, so that the inspection efficiency of the industrial equipment is improved. And the accuracy of fault detection and the inspection efficiency are improved.

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.