31results about How to "Good decision" patented technology

The invention provides a multi-core heterogeneous system-oriented startup deployment and cooperative processing method, which comprises the following steps of: when the system runs, obtaining startup codes after a master core and a slave core are electrified, respectively obtaining mirror image files of Linux and RTOS (Real Time Operating System) on the basis of synchronous running and storage partition, and running the mirror image files on the master core and the slave core after moving the mirror image files; and after the heterogeneous operating system is loaded, generating an interrupt signal based on inter-core interrupt and a corresponding register through a CLINT module in the main core, and realizing data communication between the main core and each slave core based on the interrupt signal and the shared memory. By means of the method, after the system is powered on, the multiple cores can synchronously run the starting codes, and time delay and uncertainty possibly caused by one-by-one starting of the single cores are avoided; communication and data transmission among multiple cores are realized through interruption and memory sharing, the synchronism and consistency of data are ensured, and misjudgment and decision errors caused by data delay and inconsistency are avoided, so that the real-time performance of the system is remarkably improved.

The invention provides a cross-regional air conditioner regulation and control strategy modeling method and a cross-regional air conditioner regulation and control method and system, and is applied to the technical field of intelligent regulation and control of air conditioners. The method comprises the steps that clustering processing is conducted on air conditioner load data of users in all regions, and a clustering result is obtained; constructing a regional cold supply system simulation model corresponding to the clustering cluster based on the users in the same clustering cluster in the clustering result; the operation safety of an actual regional cooling system is considered, modeling is carried out on the regulation and control process of the simulation model based on the Markov decision process, and an optimization strategy model is constructed by taking expected return maximization of a value function corresponding to an operation strategy of the simulation model as a target; and training an optimization strategy model based on simulation operation data of the simulation model. According to the method, the problems that in the process that the DCS participates in power grid load regulation and control, a trial-and-error training-based model is prone to system damage in practical application, and single-model regulation and control cannot guarantee the load regulation and control effect of a whole power grid are solved.

The invention relates to the technical field of internet of things equipment power consumption optimization methods, in particular to an internet of things equipment power consumption self-adaptive optimization method and system based on edge computing, and the method comprises the following steps: firstly, collecting historical operation data of different edge computing equipment, including but not limited to resource utilization rate, power consumption and network bandwidth; then, learning the historical operation data by using an improved multi-target deep reinforcement learning algorithm to obtain an equipment model; thirdly, predicting the optimal power value of the equipment through the equipment model; then, comparing the optimal power value with the actual operation power value of the current equipment, and when the optimal power value is inconsistent with the actual operation power value, sending an optimization instruction to the edge computing equipment; and finally, adjusting the parameter value of the edge computing device according to the optimization instruction to realize self-adaptive optimization of the power consumption of the device, so that the power consumption can be obviously reduced under various load conditions, and the task completion rate and the system response speed can be improved at the same time.

The invention relates to the field of production scheduling and resource optimization, in particular to a flexible job shop scheduling method based on deep reinforcement learning, which is characterized by comprising the following steps: 1) constructing a flexible shop scheduling environment; (2) a flexible workshop scheduling environment is abstracted to be Disjunction Graph, and the flexible workshop scheduling environment is abstracted to be Disjunction Graph; 3) extracting state features; 4) coding the state features; 5) executing a decision based on the encoded features to obtain scheduling probability distribution; and 6) generating a scheduling plan according to the probability distribution. According to the invention, through multi-level feature extraction and fusion, accurate perception of a production environment is realized, and the accuracy and robustness of a scheduling decision are effectively improved; by utilizing a deep reinforcement learning framework, the dynamic change of the production environment can be adaptively learned, the scheduling strategy is continuously optimized, and the generalization ability is relatively high; and feature representation is enhanced through an attention mechanism, so that the model can still quickly find a global optimal solution when facing a large-scale and high-complexity FJSP problem.

The invention belongs to the technical field of railway multi-source data fusion, and particularly relates to a railway infrastructure multi-source data fusion system and method. According to the method, the time features of each reference data source are analyzed, the data sources with the centralized features and the dispersive features can be accurately recognized, the corresponding fusion rules are formulated accordingly, the scientificity and reasonability of the data fusion process are ensured, abnormal fusion nodes can be found and processed in time, and the fusion efficiency is improved. In addition, the abnormal fusion nodes can be dynamically adjusted according to the fusion deviation value, the continuity and stability of the data fusion process are ensured, the deviation in the data fusion process can be found and corrected in time through real-time monitoring and backtracking analysis, and the data fusion accuracy is improved. Therefore, decision errors caused by data fusion errors are avoided, and accurate and timely data support is provided for maintenance and management of railway infrastructures.

The present invention belongs to the technical field of vehicle positioning, and specifically discloses a vehicle positioning method based on multi-view and multi-scale feature fusion. A multi-view multi-scale pyramid fusion neural network is proposed in the method of the present invention. At the input level of the network, a multi-channel range image RIV projected by a lidar point cloud and a BEV representation generated by a normal distribution transformation are used to complement each other at global and local scales, thereby improving the overall understanding of the scene. Multi-view input effectively alleviates the limitations of a single view and improves the robustness and anti-interference of the model. At the network architecture level, a self-attention mechanism using Mamba is proposed. The present invention can not only make full use of the complementary correlations between multiple views, but also encode and integrate contextual information across multi-scale features. The global descriptor finally generated is feature-rich, thereby improving the accuracy of vehicle positioning.

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 present invention discloses an industrial Internet information interaction method and system based on industrial algorithms, and relates to the technical field of industrial Internet information interaction. Through the blockchain recording module, data cannot be tampered with during the process of collection, processing and storage, ensuring the authenticity and transparency of information; this improvement enhances the trust basis of all parties and avoids the generation of information islands. The real-time data analysis module integrated in the system can quickly analyze the data standard set and calculate the equipment efficiency coefficient and energy consumption efficiency coefficient. This real-time feedback mechanism enables problems in the production process to be quickly identified and solved, improving production efficiency and resource utilization. The smart contract module automatically triggers contract execution according to real-time data, realizes the automation of information interaction, and reduces the need for human intervention. By automatically issuing alarms and adjusting production parameters, the system can more flexibly respond to equipment failures and excessive energy consumption problems, reducing production risks.

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 invention discloses a cloud-edge collaborative resource scheduling method, system, electronic device and readable medium, the method comprising: establishing energy consumption models for IT equipment, power supply equipment and cooling equipment in the cloud-edge collaborative system respectively; setting the energy consumption optimization target of the cloud-edge collaborative system based on the energy consumption model; and using a reinforcement learning algorithm to generate resource scheduling decisions based on the current resource status, task request and energy consumption of the cloud-edge collaborative system to minimize the energy consumption optimization target. The cloud-edge collaborative resource scheduling method, system, electronic device and readable medium provided by the present invention respond to low-carbon operation and reduce energy consumption in a cloud-edge collaborative resource scheduling scenario for computing-network integration, while taking into account the energy consumption of IT equipment and infrastructure of edge clusters and data centers, and performing multi-dimensional joint optimization.

The invention is suitable for the technical field of model construction, and provides a construction system for a power grid health degree evaluation model, and the system comprises a data quality management module which carries out the cleaning, denoising, difference and anomaly detection of data, and processing of the data; the data integration and consistency management module is used for performing format consistency processing on the multi-source data and integrating the data; the self-adaptability and flexibility enhancing module is used for setting configurable algorithms and model parameters to automatically adjust and optimize the model; the security and privacy protection module is used for matching a power grid data processing standard through an encryption technology, data desensitization, access control and security audit, and ensuring the security of the data in the processes of acquisition, transmission, storage and processing; and the continuous maintenance and update management module is used for regularly updating the model and the algorithm by implementing an automatic monitoring and feedback mechanism so as to ensure that the model performance is continuously optimized and adapts to new technology and data optimization. Misjudgment caused by a data quality problem is avoided, and the accuracy of the evaluation model is improved.

The invention relates to the technical field of tobacco finance, and discloses a tobacco finance data risk analysis system, which comprises an authority management module, a data source collection module, a data processing module, a data analysis module and a risk assessment module. According to the tobacco financial data risk analysis system, an authorizer can directly obtain tobacco financial data after key input and fingerprint identification success, and a third-party authorizer needs to send a success signal to a confirmation window after third-party key and third-party fingerprint information identification success; the financial data source can be obtained only after the user confirms authorization, the safety and privacy of the financial data are effectively guaranteed, only the authorized user can access the financial data, data leakage or malicious tampering is prevented, manual intervention can be reduced by automatically collecting and processing data, the working efficiency is improved, human errors are reduced, and the user experience is improved. An enterprise is helped to identify and evaluate potential risks in real time, so that preventive measures are taken, and risk influences are reduced.

The invention relates to the technical field of computer vision, in particular to an out-of-distribution detection method and system based on abnormal activation and multi-source information fusion and a computer readable storage medium, and the method comprises the steps: 1) calculating class related items based on an image classification probability outputted by a classifier, and obtaining class related information in a probability space; 2) calculating class irrelevant items based on the depth features of the input image sample extracted by the feature extractor so as to obtain class irrelevant information in the feature space; and 3) calculating an abnormal factor based on network activation, namely depth features. And calculating out-of-distribution scores based on the class-related items, the class-independent items and the abnormal factors, and distinguishing out-of-distribution samples and in-distribution samples by using the out-of-distribution scores. According to the method, various information of the feature space and the probability space is fused to calculate the distribution external score, so that the limitation of single-source information is overcome, and the complementary advantages of various types of information are fully exerted.

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 present invention relates to a method, a device, a program product and a storage medium for ensuring data consistency. The method comprises: receiving data to be compared sent by a RocketMQ message queue with a delay, querying first summary information of the received data to be compared; obtaining second summary information from Redis; comparing the first summary information with the second summary information to see whether they are consistent, and if they are consistent, obtaining third summary information from an Elasticsearch server; comparing the first summary information with the third summary information to see whether they are consistent, and if they are consistent, deleting the second summary information in Redis, otherwise judging whether the comparison count is greater than a predetermined value; if it is greater than the predetermined value, the comparison fails, deleting the second summary information in Redis, and saving the business data that failed the comparison to a database, otherwise synchronizing the business data corresponding to the first summary information to the Elasticsearch server again, and sending the data to be compared to the message queue at the same time, and performing the comparison after a delay.

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 invention belongs to the field of large electronic tags, and particularly relates to an electronic tag reading and displaying method and reading equipment based on commodity dynamic information, and the method comprises the steps: capturing the electronic tag information of a commodity selected by a user through a radio frequency reading device, and rapidly recognizing and obtaining the unique identifier of the commodity; secondly, in combination with the commodity identifier and the past search record of the user, an entity-relation extraction algorithm, a knowledge graph and a graph database are utilized to trace back in a commodity dynamic association information base and construct an association information chain of the selected commodity, and the chain not only covers direct related commodities, but also updates the latest information containing the associated commodities in real time; and finally, displaying the latest commodity information of the selected commodity and the related commodity through a set display terminal. According to the method, the dynamic display and recommendation efficiency of the commodity information is remarkably improved, and real-time shopping experience is provided for consumers.

The present invention belongs to the technical field of vehicle environment perception, and specifically discloses a vehicle environment perception method based on multi-view and multi-scale feature fusion. This method aims at the problem of information loss in the projection process of the projection-based point cloud segmentation method, and builds a vehicle environment perception network based on multi-view and multi-scale feature fusion. In terms of network input, the present invention uses multi-layer distance view representation and bird's-eye view feature dual encoding to design a multi-level multi-projection fusion deep learning network, which effectively improves the information extraction accuracy of targets in point clouds with prominent vertical information and long distances, and reduces the information loss of separated projections. In terms of network structure, the present invention proposes a multi-view fusion module, which combines MFB and MMViT to calculate the high-order correlation between BEV features and RV features, and deeply fuses multi-view features. The present invention is conducive to avoiding losses incurred during the projection process and improving the vehicle environment perception capability.

The present invention discloses a method for mapping cyberspace assets of an electric power monitoring system, which belongs to the technical field of equipment mapping, and includes: constructing a cyberspace asset model of the electric power monitoring system based on cyberspace modeling demand information of various assets in the electric power monitoring system and asset operation parameter information; constructing a cyberspace asset mapping framework of the electric power monitoring system based on the cyberspace asset model and cyberspace mapping parameter information; mapping the actual network access behaviors between various assets based on the cyberspace asset mapping framework and cyberspace mapping demand information, and obtaining an optimal network access relationship diagram between various assets. The present invention can more comprehensively map the actual network access behaviors between various assets, reduce the data error of network asset mapping in the electric power monitoring system, and significantly improve the network asset mapping effect in the electric power monitoring system.

Provided are a communication method and apparatus, and a computer readable storage medium, the communication method comprising: reporting a first resource index and a second resource index, the first resource index indicating a preferred beam in beams generating cross-link interference, and the second resource index indicating a preferred beam for data transmission. The invention provides a scheme for reducing cross-link interference between terminal equipment when the terminal equipment receives downlink data in a sub-band full duplex scene.

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 invention relates to the technical field of three-dimensional map simulation, in particular to a three-dimensional simulation drilling system, method and equipment and a medium. The system comprises a three-dimensional map building module which is used for building simulation drilling geographic information according to acquired three-dimensional map data of a simulation drilling position; the three-dimensional scene management module is used for managing a scene simulation information set; the simulation drilling editing module is used for performing user-defined editing on the simulation drilling geographic information and the scene simulation information set according to drilling process contents; the free configuration module is used for carrying out free combination according to the simulation scenes edited in the simulation drilling editing module to obtain simulation drilling tasks; and the simulation drill execution module is used for performing simulation drill according to the simulation drill task of the free configuration module to realize three-dimensional simulation drill simulation. According to the system, the simulation drilling process and simulation drilling content self-definition can be realized.

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.

The invention relates to the technical field of Tibetan medicine information arrangement, and discloses a Tibetan medicine informatization platform which comprises a medicine data cleaning module and a Tibetan medicine inheritance auxiliary module. The Tibetan medicine knowledge base module is used for integrating and storing classical literatures, disease diagnosis and treatment schemes and medicinal material information of Tibetan medicine; the scientific research and innovation module is used for tracking and managing scientific research projects related to Tibetan medicine, deeply mining and analyzing Tibetan medicine data and providing a communication and cooperation platform for scientific research personnel; the Tibetan medicine inheritance auxiliary module is used for managing Tibetan medicine inheritance data, planning an inheritance mode and way and evaluating the actual situation of inheritance work. According to the method, a more accurate medication scheme is provided for a patient by analyzing the flavor of the Tibetan medicine, the result can provide a basis for personalized treatment according to the individual difference of the patient, different patients possibly have different responses to different flavor, and the effect of the Tibetan medicine combination most suitable for the patient can be found through analysis.

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.

A user data processing system, method, and apparatus are provided, and are applied to the field of artificial intelligence. A first data processing device in the system generates a first intermediate result, and sends a third intermediate result to a second data processing device in the system. The third intermediate result is obtained from the first intermediate result based on a parameter of a first machine learning model and target historical user data obtained by the first data processing device, and an identifier of the target historical user data is the same as an identifier of historical user data of the second data processing device. The first data processing device further receives a second intermediate result sent by the second data processing device, and updates the parameter of the first machine learning model based on the first intermediate result and the second intermediate result. The second data processing device further updates a parameter of a second machine learning model based on the received third intermediate result and the second intermediate result. The solutions can improve prediction accuracy of the machine learning models in the system.