12results about How to "Good decision" patented technology

A kind of aluminum electrolysis MES system real-time interface data exception feedback and dynamic re-push method, it is related to aluminum electrolysis production informatization management technical field, including steps S1, set interface data exception data rule, according to the abnormal data rule to the real-time monitoring and abnormal perception of multi-source interface data;Step S2, set exception data level, according to exception data level, MES system receives exception data and carries out hierarchical feedback;Step S3, according to the server state exception interface data dynamic re-push condition setting, MES system according to the server load state dynamically re-pushes exception data to external system etc..The present application makes up the problem that existing aluminum electrolysis MES system and external system exist in interface data interaction process in the production process, and exception feedback lag and re-push mechanism rigidification, ensure the accuracy and reliability of interface data.

The application relates to a non-conservative intelligent automobile decision planning integrated method in a complex heterogeneous environment, which comprises the following steps: constructing a social interaction knowledge learning model offline and training; acquiring surrounding traffic participant state data and intelligent automobile state data in real time online, and obtaining an environment state after splicing processing; taking the environment state as the input of the trained social interaction knowledge learning model to obtain the predicted trajectories of all traffic participants including the intelligent automobile; updating the environment state based on the predicted trajectories; re-inputting the updated environment state into the social interaction knowledge learning model to iteratively complete intelligent automobile trajectory decision planning, wherein the planning trajectory of the intelligent automobile is the splicing result of the first point of the predicted trajectory obtained in each iteration. Compared with the prior art, the application has the advantages of considering the influence of social interaction prediction on the trajectory, decision planning integration and the like.

The invention provides a vehicle-road cloud cooperative sensing method and system based on multi-source data fusion, and relates to the technical field of intelligent traffic, and the method comprises the steps: carrying out the heterogeneous sensing through a vehicle end sensing unit, a road side sensing unit and a cloud control platform, and forming a multi-source sensing data set; feature extraction is carried out based on the multi-source perception data set, a data feature set is generated, vehicle and road cloud collaborative perception is carried out according to the data feature set, and a vehicle and road situation perception result is generated, and the vehicle and road situation perception result comprises traffic target state parameters; and risk assessment is carried out according to the traffic target state parameters, decision reasoning is carried out according to risk assessment values, and a vehicle cooperative control cooperative sensing instruction is constructed. According to the method and the device, the technical problems that in the prior art, due to data heterogeneity, multi-source data are difficult to fuse in vehicle-road collaborative perception, decision making is inaccurate in a complex traffic environment, and traffic management efficiency is further influenced are solved, the fusion precision of the multi-source data is improved, and therefore the traffic management efficiency is improved.

The present application relates to a kind of multi-point cooperative gas emergency shutdown positioning method based on gas monitoring system, belong to the field of automation control technology. Among them, the method includes: real-time acquisition multi-source alarm signal;And the analog quantity and switch quantity of the multi-source alarm signal are uniformly converted into digital processing code;Multi-source alarm signal digital processing code is analyzed in real time to obtain alarm priority and associated equipment address, and data accuracy is verified by actual combat coefficient correction model;Combining dynamic networking and cooperative decision mechanism generates shutdown path;Shutdown path is executed, and execution result is fed back to host computer monitoring platform in real time, and field alarm lamp state and broadcast system are updated simultaneously;Through host computer monitoring platform, alarm history curve, equipment state and linkage logic of shutdown operation are recorded, and manual reset interface is provided to remove shutdown state. Efficient processing and accurate response of gas leakage accident are realized.

The invention relates to the technical field of data processing, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a task file exception re-reading method, device and equipment and a medium. Generating a to-be-executed task sub-file corresponding to the to-be-executed task main file according to the task configuration parameters; reading a reading progress value and a read data volume corresponding to the to-be-executed task main file, and monitoring a task data reading state of the to-be-executed task main file according to the reading progress value and the read data volume; when the task data reading state is reading failure, reading the to-be-executed task sub-file, and executing the to-be-executed task sub-file based on a reading result of the to-be-executed task sub-file to obtain a sub-file task execution state; identifying a reading abnormal factor of the main file of the task to be executed, and extracting a file configuration parameter of the reading abnormal factor; and synchronously updating the sub-file task execution state and the file configuration parameters to the to-be-executed task main file. According to the method, the task file exception rereading efficiency can be improved.

The application discloses an energy storage power station intelligent optimization method and system based on deep coupling learning, relates to the technical field of energy storage power station optimization, and is used for solving the problems of maximum demand capture failure caused by production plan fluctuation and invalid charging and discharging loss caused thereby, extracting strong correlation characteristics of production procedures and power mutation, constructing a time-space matrix anchor peak value inducement, outputting a latent space feature vector through an encoder by using a deep coupling multi-task network, explicitly quantifying a demand refresh risk probability, introducing the peak value risk and a battery health soft measurement quantity into a target function based on an MPC-DRL rolling optimization framework, dynamically adjusting charging and discharging sensitivity, constructing a composite feedback signal by extracting extreme samples of missed or false peak values, and realizing closed-loop evolution of attention reconstruction and decision threshold prediction; the robustness of demand management under a complex production environment is improved, the power consumption cost is reduced, and the whole life cycle optimal balance of battery life and economic benefits is realized.

The present application relates to the field of artificial intelligence and data processing technology, specifically to a temperature control sensor data completion method based on a generative adversarial network, comprising obtaining a time series data matrix of a temperature control sensor containing missing data; constructing an adversarial model containing a generator and a discriminator; inputting the data matrix into a forward topological hard embedding layer, calculating the time and space partial differential physical residual and embedding the neuron transmission equation to output a latent feature matrix; inputting the latent matrix into an active feature selection module, eliminating redundant vectors and generating a synthetic sequence matrix; inputting the synthetic sequence and the real sequence into the discriminator to obtain the confidence, calculating the composite loss of the adversarial, reconstruction and residual constraints; using a gradient penalty mechanism to iterate the network parameters until convergence, and finally outputting the target temperature time series to complete the matrix. The present application effectively solves the problem of missing sensor data by introducing partial differential physical residual constraints and active feature selection, improving the adversarial network interpolation accuracy and the reliability of the time series sequence.

The invention relates to the technical field of design identification, in particular to an assembly type bathroom modular construction management method and system based on the BIM technology, and the system comprises a data collection module which is used for obtaining a standard interface data set in an assembly type bathroom BIM design model and three-dimensional laser scanning point cloud data of a reserved plate descending area of a construction site, the standard interface data set comprises geometric coordinates of a bathroom chassis interface and physical attribute parameters of a connecting piece; the registration module is used for mapping the three-dimensional laser scanning point cloud data into a coordinate system of the BIM design model through an iterative nearest point algorithm, and calculating a spatial deviation vector of the bathroom chassis interface projected to a field reserved pipe orifice; and the risk index calculation module is used for calculating a stress state risk index of the connecting piece under the flexible fitting path based on the spatial deviation vector and the physical attribute parameters. According to the invention, through the stress simulation module, the hidden dangers of leakage and fracture caused by long-term stress concentration in the blind connection area of the assembly type bathroom are avoided.

The application discloses a coal industry digital transformation maturity evaluation data management method and system, relates to the coal industry digital transformation maturity evaluation data management field, generates an event identifier containing a preset cause-effect chain for each event by responding to original event data collected in an industrial field, and actively pushes the event identifier to an associated receiving device to trigger a preset action, so that intelligent identification and response of event data are realized. More importantly, in the process of constructing an event cause-effect diagram, the method can intelligently detect conflicts between physical time stamps in original event data and cause-effect logic indicated by the preset cause-effect chain. The application can significantly improve the authenticity and reliability of coal industry digital transformation maturity evaluation data, provide solid data support for enterprise strategic decision-making, avoid incorrect decisions caused by data deviation, and has remarkable excellent technical effects.

This invention discloses an automated control method and operation and maintenance system for the coordinated remediation of purification and replenishment in small water bodies, relating to the field of water environment governance and automated control technology. The method includes: acquiring data on escaping gas concentration, ambient temperature and humidity, and conventional vital signs through an environmental sensing array; compensating and correcting the escaping gas concentration data based on ambient temperature and humidity; performing trend analysis on the corrected escaping gas concentration data, generating an early sign signal of anaerobic decomposition in sediment when the concentration continues to rise and exceeds a low concentration threshold; retrieving historical trend data for correlation judgment, generating a sediment nutrient release status signal when it conforms to preset anaerobic decomposition evolution characteristics; executing coordinated intervention in response to this signal; continuously acquiring updated data and evaluating the effect after intervention, and dynamically adjusting execution parameters. This invention achieves early warning and multi-strategy coordinated remediation of anaerobic decomposition in sediment of small water bodies.

This invention relates to the field of heterogeneous computing resource scheduling technology, and discloses a method for collaborative scheduling and optimization of heterogeneous resources in an algorithm training platform. The method includes: real-time acquisition of telemetry parameters within an intelligent chip, and calculation of a scheduling entropy inversely proportional to the matching degree between the task and the chip architecture; when the scheduling entropy continuously exceeds a preset threshold, the computing task is migrated to heterogeneous chips of different types. This invention achieves spontaneous coupling between the task and the hardware architecture through scheduling entropy, avoiding architectural mismatch caused by resource abstraction in existing technologies. Its scheduling decisions no longer rely on complex performance predictions. Simultaneously, combined with multi-level verification and control mechanisms, it effectively improves the robustness and operational reliability of the entire scheduling system.

The utility model relates to the technical field of new energy automobile battery management systems, in particular to a new energy automobile BMS temperature monitoring parameter acquisition device. The system comprises a composite monitoring unit, a working condition identification unit and a filtering optimization unit. According to the utility model, the composite monitoring unit uses a surface contact type temperature sensor, an internal penetration type temperature sensor and a non-contact type temperature sensor to carry out multi-dimensional acquisition on battery temperature data, and the working condition identification unit uses a support vector machine to establish an optimal classification hyperplane to classify characteristic data of different working conditions. The current operation condition of the battery is judged, the size of a filtering window is dynamically adjusted according to the working condition, the filtering optimization unit carries out multi-stage amplification and filtering processing on collected temperature data through an amplification filtering circuit, the problem that a traditional single sensor cannot comprehensively reflect the thermal state of the battery is solved, and the integrity and accuracy of the temperature data are improved.