69 results about "Production analysis" patented technology

The invention relates to the technical field of mine safety production analysis, and discloses a mine safety production data fusion analysis method based on multi-source perception, which comprises the following steps: step 1, carrying out real-time data acquisition on physical parameters related to safety production in a mine environment through various types of sensors arranged in the mine environment, the collected data are stored as independent data streams with timestamps, and the data of different sensors have time inconsistency according to the difference set by the sampling frequency and the clock; and step 2, carrying out time delay compensation processing on the multi-source data with the timestamps acquired in the step 1. An optimization model is established in combination with a variational method to perform time delay compensation and time synchronization processing on sensor data, the problem of time inconsistency caused by different sampling frequencies and clocks of sensors is eliminated, fusion processing of multi-source sensor data on a unified time reference is realized, and an accurate data basis with consistent time sequence is obtained.

The invention belongs to the technical field of data analysis, and particularly discloses a big data analysis and treatment system and method based on artificial intelligence, and the system comprises an equipment production analysis module, an equipment health assessment module, a maintenance trigger judgment module and a maintenance decision optimization terminal. According to the method, multi-modal data fusion modeling is performed and a dynamic threshold correction mechanism is constructed by integrating multi-source heterogeneous data such as vibration, temperature and voiceprint, so that the accuracy and adaptability of predictive maintenance are remarkably improved. A dynamic correlation analysis framework is constructed by combining equipment load and production environment disturbance factors, and upgrading from single feature anomaly detection to multi-modal linkage anomaly mode recognition is achieved; meanwhile, based on threshold self-correction and maintenance decision optimization of a real-time working condition, the problem of maintenance lag under a static rule is effectively solved, and a closed-loop intelligent maintenance treatment system is formed through cooperation of health index dynamic evaluation and resource scheduling.

The invention discloses a product production method and device based on a large model, equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting initial multi-modal data corresponding to a target production task, and obtaining target multi-modal data based on the initial multi-modal data; using different neural networks to extract feature information of different modal production data in the target multi-modal data, and fusing different feature information to obtain target multi-modal features; and generating a real-time production analysis result based on the target multi-modal features, the target multi-modal large model and a large model quantification technology, obtaining different types of target decisions corresponding to the target production task according to the production analysis result, and carrying out product production according to the target decisions. According to the method, the multi-modal production data is collected and fused, so that the production activity is guided by utilizing the multi-modal data, and the problem of low production efficiency caused by the fact that only single-modal data is analyzed for production is avoided.

A system and method for appraising film screenplays and offering predictive insights on their marketability and potential success. The artificial intelligence model and machine learning algorithms consider various production elements and generate management and production outcomes on an expedited timeline. The web services infrastructure implemented creates an enhanced ecosystem for creative development, production, and talent management by minimizing obstacles such as the inaccessibility of information and data about both national and global development trends. Moreover, the system can summarize creative data and create loglines, character breakdowns, titles, and summaries. An A.I generated a sociological analysis that provides a multi-faceted and comprehensive outlook on industry-related research, such as a cost-benefit production analysis.

The invention discloses a vehicle manufacturing supply chain adaptive optimization method and system based on data driving, and relates to the related field of supply chain management, and the method comprises the steps: calling a historical manufacturing complete cycle data set, and obtaining a real-time manufacturing data set; carrying out integrated learning, carrying out association analysis on supply chain nodes according to a learning result, and constructing an inter-chain dynamic interactive simulation network; defining a manufacturing constraint condition, activating an inter-chain dynamic interactive simulation network to carry out demand prediction, and carrying out production analysis according to a prediction result; performing simulation execution of the production scheduling strategy to perform multi-objective optimization, generating a to-be-optimized label, performing dynamic monitoring, and updating the production scheduling strategy; and synchronizing the production scheduling global optimization strategy to a vehicle manufacturing supply chain for verification, and performing adaptive optimization according to a verification result. The problems that supply chain management lacks real-time dynamics, cannot respond to changes in time and is inaccurate in scheduling are solved, and the effects of improving real-time dynamics, responding to changes in time and improving scheduling accuracy are achieved.

The invention relates to the technical field of hydropower intelligent power generation benefit optimization systems, in particular to a hydropower intelligent power generation benefit optimization system based on basin water volume analysis. The system comprises a resource analysis module, a collaborative production analysis module, an energy efficiency analysis module, a benefit analysis module, a production planning module, a system monitoring and maintenance module and a user interface and interaction module. The resource analysis module comprises a drainage basin water volume analysis unit and a drainage basin energy flow direction analysis unit; the collaborative production analysis module comprises an action instruction analysis unit, a multistage gradient collaborative analysis unit and a multi-energy complementary analysis unit; the energy efficiency analysis module comprises a unit energy efficiency analysis unit and a power generation unit energy efficiency analysis unit; the benefit analysis module comprises a water discarding and water discarding reason analysis unit and a promotion effect analysis unit; according to the system, the operation of each stage of hydropower station can be coordinated through the multi-stage gradient collaborative analysis unit, the combined dispatching and optimization of cascade power stations are realized, and the overall power generation capacity and efficiency are improved.

The invention discloses a concrete production management system and method, and belongs to the technical field of concrete production. The method comprises the following steps: 1, preliminarily analyzing raw materials; step 2, production analysis; and step 3, quality detection. Therefore, errors of manual judgment are reduced, the raw materials entering the production link meet the standard, production delay and cost increase caused by raw material problems are avoided, and the risk of production accidents caused by the fact that raw material abnormity is not treated in time is reduced. According to the method, the construction proportion model of the concrete is established, the construction proportion performance index of each abnormal production task process of the concrete is evaluated, and the construction proportion is adjusted, so that the quality stability of the concrete is effectively improved, the quality problem is solved, the performance of the concrete is optimized, and the requirements of different construction scenes are met; meanwhile, data experience is accumulated by establishing a model, and data support is provided for subsequent production.

A computer-implemented method for automated decline curve and production analysis using automated production segmentation, empirical modeling, and artificial intelligence. The method includes segmenting historical production data based on a change in a central tendency of a selected segmentation parameter to generate segmented production data. The method also includes forecasting future production data from a last production segment to a terminal decline rate according to a fitted empirical model, a trained artificial intelligence model, or any combinations thereof. The method includes forecasting exponential production data to an economic limit. Further, the method includes calculating an estimated ultimate recovery by summing the historical production data, future production data, and the exponential production data.

The invention relates to the technical field of data processing, and discloses a lean production management method and system, and the system comprises a data collection module, a data preprocessing module, a data analysis module, a data prediction module, a lean production analysis module, and a man-machine interaction module. By arranging the data analysis module and the data prediction module, production schemes of bearing products are arranged by acquiring the production condition of the bearing, a foundation is laid for subsequently acquiring a real-time optimal production scheme, and by acquiring the real-time optimal production scheme, the flexibility of value flow graph analysis can be improved, the lag phenomenon is avoided, and the production efficiency is improved. Meanwhile, according to the change of the daily production quantity of bearing products, a production scheme and improvement measures are flexibly adjusted, the product production scheme is updated in real time to continuously improve the production process and improve the product quality and the production efficiency, meanwhile, a neural network model is integrated, the nonlinear and multi-modal data modeling capability of the neural network is fully exerted, and the production efficiency is improved. And a complex data relationship is effectively captured.

The invention discloses a project commissioning analysis method and device based on a decision tree and electronic equipment, and relates to the field of financial science and technology or other related fields, and the method comprises the steps: obtaining the commissioning data of a target project from a plurality of data sources, carrying out the preprocessing of the commissioning data, and obtaining the commissioning features, the commissioning features are input into a target decision-making tree for commissioning risk assessment, a commissioning assessment result and a commissioning success probability are output, the target decision-making tree is a decision-making tree model which is pre-constructed based on historical commissioning data and historical commissioning results and subjected to pruning processing, the contribution degree of each feature in the commissioning features is calculated based on the feature values of the commissioning features, and the commissioning risk assessment result and the commissioning success probability are output; and determining a commissioning risk feature set based on the contribution degree, and generating a commissioning risk analysis report of the target project based on the commissioning evaluation result, the commissioning success probability and the commissioning risk feature set. According to the invention, the technical problem that the accuracy is low because the risk of project commissioning depends on manual judgment in the prior art is solved.

The invention belongs to the technical field of data processing, and relates to an index data processing method, which comprises the following steps: acquiring behavior data of a target system in a running process; cleaning the behavior data to obtain target behavior data; performing feature extraction on the target operation data based on a preset extraction strategy to obtain feature data; performing index calculation processing on the feature data based on a preset atomic dimension to obtain a calculation index; performing aggregation calculation processing on the calculation index based on a preset derived dimension to obtain an application index; summarizing the calculation index and the application index to obtain a target index; and performing display processing on the target index based on a preset display interface. The invention further provides an index data processing device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the target index can be stored in a block chain. According to the method, the processing efficiency of generation processing of the index data of production analysis is improved, and the accuracy of the generated target index is ensured.

In at least one example, a method for production analysis modeling for product quality detection includes receiving a review request for a product that includes an identified failure, identifying production analysis data associated with a production process for the product, the production analysis data includes data captured during production of the product, providing the production analysis data for the product to a machine learning model for producing the product to compare the production analysis data to designated data ranges for the product, determining data from the production analysis data that are outside of the designated range identified by the machine learning model, and identifying a cause of the identified failure during the production of the product based on the determined data.

The invention relates to the technical field of copper alloys, and particularly discloses a preparation system of a copper-chromium-zirconium alloy wire for a high-speed rail, which is used for solving the problems of boiling boundary phase locking, rewetting blocking and cooling speed banding, induction of spiral hot spots, performance discretization and monitoring misjudgment in the existing production process of the copper-chromium-zirconium alloy wire. The system comprises a production processing module, a sensing module, a spraying topology module, a phase analysis module, a boundary analysis module and a production analysis module. Through uniform time base of a partition setting table, a phase coherence characteristic table and a hydrothermal boundary characteristic table and partition fineness regulation and control driven by quantitative indexes, cooling banding caused by phase locking and rewetting blocking is inhibited, spiral hot spots and parameter discreteness are reduced, a cross-partition time sequence and a set boundary are unified, set convergence and switching consistency are improved, and the method is suitable for large-scale popularization and application. Misjudgment of low-frequency pseudo fluctuation monitoring is reduced, and control vectors are optimized in combination with performance specifications of the high-speed rail overhead line system.

The invention provides an orderly power utilization regulation and control method based on reliability measurement verification and correction. According to the method, through electric power and electric quantity balance analysis, power shortage early warning grade division and orderly power utilization index distribution, the orderly power utilization indexes of a main power grid are distributed among secondary power grids based on load characteristics. Meanwhile, on the basis of accurately evaluating the capacity of the controllable resource pool of the secondary power grid and the load regulation and control cost, an orderly power utilization basic scheme is formulated; the method comprises the following steps: providing a short-time-sequence multi-sampling scene construction technology for an orderly power utilization basic scheme, carrying out power grid virtual production analysis, and calculating indexes such as orderly power utilization load loss probability and orderly power utilization load loss power; based on the indexes, the orderly power utilization scheme is executed after the orderly power utilization scheme is verified and corrected.

The present invention provides a textile fabric R&D and analysis system, relating to the field of textile production analysis technology. The system comprises an intelligent fabric comparison and processing system, a multimodal data integration and analysis system, an integrated artificial intelligence-assisted decision-making system, and an automated experimental design and adjustment system. The system includes a master database, comprising a fabric database, a fabric usage information database, and a storage database. The entire system architecture, in conjunction with the master database, enables automatic identification, processing, and allocation of R&D fabrics to complete screening. The intelligent fabric comparison and processing system automates the entire fabric processing process. The system can identify, classify, and allocate large quantities of fabric in a short period of time, significantly reducing the time and labor intensity of manual operations. The use of a robotic arm and an automated liquid handling system not only increases processing speed but also ensures operational consistency and reliability, significantly improving production efficiency and better meeting rapidly changing R&D needs.

This invention discloses a visualized intelligent scheduling system for SMT production lines, relating to the field of precision electronics. It solves the problem of poor scheduling performance in existing intelligent scheduling systems for production lines. The system includes a data acquisition module: analyzing production demand for target orders to be executed, obtaining the quantity of products in the target order, performing historical production quantitative analysis on the production line executing the order based on the quantity of products in the target order, and acquiring production line scheduling data based on the analysis results; a production line matching module: matching production lines for target orders to be executed, obtaining target-suitable production lines; a production analysis module: monitoring the production of sample production units included in the target-suitable production line, obtaining the overall efficiency of the unit equipment based on the monitoring results, and obtaining production scheduling analysis data; and a production scheduling module: performing real-time production scheduling for the product production units on the target-suitable production line. This invention can improve the scheduling efficiency of intelligent scheduling systems for production lines.

The invention provides a method and equipment for calculating a productivity curve of a fractured-vuggy gas reservoir and a storable medium, and belongs to the technical field of gas reservoir development. The method comprises the steps that a well testing model of a well to be analyzed is established, Laplace transformation solving is conducted, and a Laplace space bottom hole pressure solution function is obtained; on the basis of the Laplacian space bottom hole pressure solution function, a numerical inversion technology is used for solving a real space bottom hole pressure solution; substituting the real space bottom hole pressure solution into a bottom hole pressure calculation formula under the four-stage pressure to obtain a fracture-vuggy gas reservoir productivity calculation result, and fitting to obtain a fracture-vuggy gas reservoir productivity curve; the invention further provides corresponding equipment and a storable medium. Compared with the prior art, the method for calculating the productivity curve of the fractured-vuggy gas reservoir provided by the invention is suitable for production analysis, prediction and dynamic data inversion in the actual production process of the fractured-vuggy gas reservoir; practice can be effectively guided, and economic benefits are improved.

The invention relates to the technical field of medical assistance, in particular to a predelivery monitoring device, system and method, and the method comprises the steps: collecting the physical sign information of a puerpera and the physical sign information of a fetus, and carrying out the preprocessing; a production analysis model is constructed and adopted, and the uterine contraction condition of the puerpera is analyzed and predicted according to the physical sign information of the puerpera; according to the predicted uterine contraction condition, the production time is analyzed and predicted; performing comparative analysis according to the predicted uterine contraction condition and the actually collected uterine contraction condition in the preset time period, and optimizing the production analysis model according to an analysis result; according to the predicted parturition time of the puerpera, suggestion information is generated and pushed; whether the puerpera sign information and the fetus sign information are abnormal or not is judged, and if yes, early warning is conducted. According to the scheme, the state of the puerpera can be analyzed according to the monitored physical sign information, the parturition time is predicted, the information utilization rate is improved, the puerpera and medical staff are assisted in knowing the condition of the puerpera, corresponding measures can be conveniently taken in time, and parturition safety of the puerpera is guaranteed.

The invention relates to the field of shale gas well yield analysis, and particularly discloses a shale gas well yield linear and nonlinear hybrid automatic classification and prediction method, which comprises the following steps of: selecting geological and engineering factors as characteristic variables, establishing a linear and nonlinear hybrid model, and determining an influence relationship of each variable by minimizing a fitting error; unsupervised automatic classification is carried out on the samples until the number of residual samples is insufficient; and calculating the minimum value of the distance from the prediction sample to each classification, and predicting the yield according to the classification rule. According to the method, unsupervised automatic classification is realized, the prediction problem of unknown classification is solved, the yield prediction precision is improved, the geological complexity is fit, and the method can be expanded and applied.

The invention discloses a carbonate rock electric pump well full-process multi-dimensional production analysis adjustment method and system, and the method comprises the steps: obtaining electric pump well related data including geological data, oil reservoir data and the like, and forming an integrated electric pump well data frame; a mechanism diagnosis logic sub-model, a current and voltage diagnosis sub-model, a pump parameter diagnosis sub-model, a pressure yield diagnosis sub-model and a vibration diagnosis sub-model are electrically constructed, and the sub-models are fused into a comprehensive diagnosis model; inputting data of the electric pump well integrated database into a comprehensive diagnosis model, and judging whether the electric pump well breaks down or not; if the electric pump well breaks down, fault generation reason analysis is conducted according to the fault type of the electric pump well in combination with the recognition logic table, and follow-up measure optimization is conducted according to different fault reasons and the fault type of the electric pump well. According to the method, the fault type of the electric pump can be accurately identified in combination with geological reservoir related information, the fault generation reason of the electric pump can be accurately analyzed, and production optimization adjustment of an electric pump well can be further guided.

The invention relates to an AI management and control method and system based on multi-source heterogeneous data analysis. The method comprises the following steps: performing cleaning and format standardization processing on multi-source heterogeneous data in a production full link; performing feature extraction on the standardized data, and extracting an equipment operation feature vector and a production efficiency feature vector related to a production state; inputting the feature vectors into a production analysis model based on machine learning for fusion analysis, and generating a production state evaluation result; performing optimization decision processing according to the production state evaluation result and the real-time production plan to obtain a production adjustment instruction; finally, generating a control command according to the production adjustment instruction and sending the control command to a production execution system; according to the scheme, intelligent control of the production process can be realized, so that the production efficiency and the product quality are effectively improved.

The present application relates to the technical field of pressure sensitive adhesive production monitoring, and particularly relates to a pressure sensitive adhesive online production analysis method and system based on data monitoring. The method comprises the following steps: deploying a coating data monitoring network on a coating production line; synchronously obtaining coating real-time monitoring data containing coating thickness data and coating pressure data online through the coating data monitoring network; performing horizontal and vertical decoupling analysis and pressure disturbance feature based on the coating thickness data in the coating real-time monitoring data, then determining the defect source type, and generating corresponding vertical control instructions, horizontal control instructions or step-by-step cooperative control instructions according to the defect source type, so as to realize real-time automatic control of the horizontal and vertical thickness of the wide-width pressure sensitive adhesive coating. The present application realizes accurate determination of the defect source through online correlation diagnosis of the coating thickness and process pressure data, so as to complete real-time regulation of the horizontal and vertical thickness.

The invention discloses an unconventional oil reservoir oil well development problem analysis method and device, and relates to the technical field of oil-gas field development, and the method comprises the steps: obtaining basic parameter data and fracturing construction parameter data of an unconventional oil reservoir oil well development process; determining a production analysis curve; determining the end time of the crack linear flow stage; the effective artificial fracture half length is determined; determining a well control radius; the reservoir permeability within the well control range is determined; simulating an oil well development model; determining simulation basic parameter data; determining a simulated production analysis curve according to the simulated basic parameter data; according to the production analysis curve and the simulated production analysis curve, the oil well development effect is analyzed, and an analysis result is determined. According to the method, the oil well development problem can be comprehensively, accurately and dynamically analyzed through the production data, whether the oil well production dynamic state is reasonable or not is analyzed, the input cost of problem analysis is reduced, and the oil well recovery rate can be effectively improved.

The invention provides a real-time data analysis method for an aluminum shell stamping production line, and belongs to the technical field of production analysis, and the method comprises the steps: capturing the current flow velocity of a material from a convex die fillet through optical flow velocity detection, synchronously collecting stress distribution, and obtaining the wall thickness of a flange region and the flow resistance at a draw bead; a yield strength real-time value and a male die fillet radius set value are extracted in combination with a material database, and a flow state data set is obtained through integration; according to the wall thickness of the flange area in the flow state data set and the flow resistance at the draw bead, the material accumulation amount is evaluated, the yield strength real-time value is fused to adjust the accumulation evaluation weight, and the accumulation state of the flange area is recognized; determining a coordinate range and a risk level identifier of the tension fracture risk area; whether the area proportion of the risk area exceeds an allowable limit value or not is judged through comparative analysis of the coordinate range of the tension crack risk area and a preset forming safety threshold value, and therefore it is determined that the overall forming state is overrun forming or safe forming.

The invention relates to the field of edge data transmission, in particular to a data processing system and method based on big data, and the system comprises a node monitoring module which is used for determining whether to carry out node processing analysis for each edge processing node or not according to a transmission fluctuation index and a supervision fluctuation index; the analysis and evaluation module is used for determining a processing and analysis mode according to the execution interference index and the execution duration index; the production analysis module is used for determining a change analysis strategy according to the branch change generalization coefficient and determining whether each initial analysis node is in a third node risk state or not according to the change reference interference parameter; the interference analysis module is used for determining a data redundancy coefficient according to the estimated key proportion index and the topological estimated key index; and the execution optimization module is used for determining whether to perform dynamic execution analysis on the execution analysis node according to the node execution duration coefficient. According to the invention, the transmission efficiency of big data real-time transmission in the industrial production process is improved.

The invention discloses a data processing method and device, computer equipment, a computer product and a storage medium. The method comprises the following steps: receiving M financial feature data of M data parties; wherein the financial feature data is obtained by desensitizing feature data related to financial activities of the target object; then, based on the M pieces of financial feature data, determining a financial black production analysis result; the financial black property analysis result corresponds to a financial black property analysis task participated by the M data parties, the financial black property analysis result is used for representing whether the financial activity of the target object has the risk of financial black property or not, and the financial black property analysis result is based on the financial black property feature data. Processing the financial black production analysis task through a multi-party security computing method; according to the embodiment of the invention, the financial black production analysis result is determined through multi-party security calculation, so that the financial black production analysis is more accurate while the security of the data in the data processing process is ensured.

The quality of natural products such as beer, bread, wine, etc. is determined by many parameters: process parameters that may or may not be changed, external parameters, and parameters of the raw materials. With complex processes and many process steps, it is very difficult to relate the parameters to the quality, if possible. Data on process control, values ​​of product and process related parameters, as well as values ​​that can be interpreted as indicators of quality, substances and their amounts found in the product (either final or intermediate), other parameters, or a combination thereof. Between the values ​​obtained for the parameters, relationships can be determined that can be used for process optimization. This can be performed for one or more process steps, one or more parameters in the process, and one or more quality parameters.

The present invention relates to the technical field of reservoir physical properties, and specifically discloses the application of an improved variable production analysis method in evaluating reservoir physical properties. The method comprises the following steps: first, simplifying the change in production in actual production into a step-by-step change, and calculating the bottom hole flow pressure at each moment according to the superposition principle; second, treating each production section as a homogeneous reservoir, solving the bottom hole flow pressure equation obtained at each moment, obtaining the change in reservoir permeability, and establishing a minimum objective function for each flow pressure; finally, recording the permeability when the minimum M is achieved at each production rate, and obtaining the average permeability of the pressure-affected area at that production rate. The method can analyze the continuous changes in reservoir parameters around the production well, is simple, and has accurate analysis results. It is applicable to both homogeneous reservoirs and heterogeneous reservoirs for averaging processing, and can be extended to production analysis of other reservoir types, with great application prospects.

An integrated autonomous operation system that holistically renders the operation in digital form at multiple scales, including reservoir, surface infrastructure, workflows, processes, and the real asset. The system provides an end-to-end digital twin connecting subsurface to production. A subsurface model identifies and monitors water-producing zones for strategic decisions. The models use intelligent Al to provide optimum water injection setpoints. The models provide data to systems that automatically control the chokes and valves to meet the setpoints, thus achieving fully integrated, autonomous operations.

The application relates to the field of visual monitoring, in particular to an intelligent welding system based on machine vision, which comprises a production analysis module used for determining a production line monitoring strategy; a node monitoring module used for determining an interference analysis mode in response to an analysis setting condition; an interference analysis module used for determining a node adjustment strategy based on an abnormal reflection parameter and an abnormal reflection distribution index in response to a reflection analysis condition, and determining whether to adjust a light source distribution index based on the abnormal reflection parameter and an edge abnormal parameter in response to a quality analysis condition; a monitoring evaluation module used for determining whether to update a monitoring parameter for a monitoring execution node in response to a difference analysis condition; and a difference compensation module connected with the monitoring evaluation module and used for determining a difference compensation mode corresponding to each difference analysis node in response to a compensation setting condition. The application improves the accuracy of quality inspection results of target welding pieces.