52 results about "Management algorithm" patented technology

The invention relates to the technical field of oil and gas well management, in particular to an intelligent remote transmission control oil and gas well system based on the Internet of Things and multi-dimensional physical sensing feedback. Comprising a multi-dimensional sensor module; a 4G unvarnished transmission communication module and an intelligent remote decision module; the intelligent remote decision-making module is used for analyzing the collected data and generating a control strategy based on a self-adaptive gradient lifting decision-making tree algorithm; and the wide-temperature adaptive power supply module is used for providing continuous power supply for the system based on a multi-energy collaborative energy management algorithm and realizing energy optimization in an off-grid environment. By integrating multi-dimensional physical quantities such as pressure, flow, temperature and vibration, a multi-parameter coupled state space model is constructed, high-confidence real-time data input is provided for control strategy generation, limitation of traditional data processing on dimension coverage and feature extraction is broken through, and comprehensiveness and accuracy of gas well operation state evaluation are improved.

The invention discloses an ultra-low power consumption pyroelectric infrared human body detector and a networking control method thereof. The detector comprises a detection module, a control module, a power supply module, a communication module, an interaction module and a dynamic power consumption management module, human body infrared radiation is detected based on the pyroelectric effect, and ultra-low power consumption operation is achieved by dynamically switching working modes. The networking control method is based on a BLE Mesh protocol, a self-adaptive topology management algorithm is used for optimizing routing, and efficient networking with intelligent equipment is achieved. The system solves the problems of high power consumption, large networking delay, high false alarm rate and the like of a traditional detector, and is suitable for the fields of smart home, intelligent security and protection and the like.

The invention discloses a scenic spot automatic driving yacht scheduling management algorithm. The system comprises a data acquisition and processing module, a distance calculation module, a comprehensive evaluation module, a scheduling decision module, a real-time monitoring and adjusting module and the like. The data collecting and processing module accurately obtains tourist demands and yacht state data and preprocesses the tourist demands and the yacht state data. The distance calculation module constructs a water area map model, and corrects the distance from the yacht to the boarding point in combination with water flow and wind direction factors. And the comprehensive evaluation module can evaluate the yachts in multiple dimensions and exclude unseaworthy yachts. And the scheduling decision module screens candidate yachts based on the results, selects an optimal scheduling scheme by using a weighted comprehensive evaluation method, issues an instruction and feeds back the instruction to tourists. The real-time monitoring and adjusting module monitors the state of the yacht in real time, quantifies and grades according to the task emergency degree, and adopts corresponding strategies according to different grades; the system can adapt to various scenic spot environments, significantly improves the scheduling efficiency, optimizes the resource utilization, enhances the tourist experience, and guarantees the reliable operation of the automatic driving yacht system in the scenic spot.

The invention belongs to the technical field of the vertical field, and particularly relates to a bid invitation file intelligent editing system based on a vertical model, which comprises a data acquisition module, a data processing module, a knowledge graph construction module and a bid invitation file generation module. The data acquisition module acquires project information and multi-modal data of a bid invitation project; the data processing module preprocesses unstructured files in the bid invitation knowledge base by using a TextRank algorithm and a BiGRU algorithm to generate structured data with dynamic weight tags; the knowledge graph construction module constructs a bid invitation knowledge graph based on a time sequence knowledge graph, an entity life cycle management algorithm and relation dynamic reasoning; the bid invitation file generation module generates a preliminary bid invitation file through a vertical generation model, and generates a final bid invitation file through multi-dimensional verification. According to the system, intelligent and efficient compilation of the bid invitation file is realized, and the compliance, integrity and standardization degree of the file are remarkably improved.

The invention discloses an improved trajectory extraction multi-maneuvering target tracking method, which comprises the following steps of: firstly, finishing long-time and continuous trajectory extraction of various targets including derivative targets, new targets and the like by adopting a labeled nearest neighbor trajectory management algorithm; then, introducing a multi-model thought, and proposing a multi-model Gaussian mixture probability hypothesis density filtering algorithm based on a jump Markov process; based on a tagged'nearest neighbor 'trajectory management algorithm and a multi-model Gaussian mixture probability hypothesis density filtering algorithm, a tagged jump Markov Gaussian mixture probability hypothesis density filtering algorithm is designed, and effective tracking and trajectory extraction of multiple maneuvering targets in a complex environment are realized. According to the method, the application scene of the Gaussian mixture probability hypothesis density filtering algorithm is expanded, the target trajectory can be accurately and effectively extracted in the face of a multi-target tracking scene including derivative target hypothesis, and the tracking precision under the complex condition is improved.

The invention relates to the technical field of low-altitude economic meteorological safety and aircraft communication navigation monitoring service, and discloses a low-altitude flight service software and hardware platform integrated with a meteorological safety algorithm. Comprising a foundation platform (a vertical take-off and landing point / an airport and the like), a ground / satellite / and various private networks, an aircraft communication guiding and monitoring module, a low-altitude flight service software and hardware platform fused with a meteorological safety algorithm and the like. The method can effectively solve the flight safety problem of various aircrafts, provides a safe, scientific and accurate flight service early warning decision management algorithm scheme, and can be widely applied to the low-altitude economy industry. For example, project construction of low-altitude economic unmanned aerial vehicles / manned aerial vehicle foundation platforms (vertical take-off and landing points / airports and the like) and application of a low-altitude economic flight service platform in scenes such as electric power / petroleum / communication / chemical engineering / civil aviation airports / meteorological stations / forests and the like are expanded, and the market huge application value is extremely high.

The application particularly relates to a kind of asset performance analysis methods based on self-defined algorithm.The asset performance analysis method based on self-defined algorithm is through interface management algorithm factor, sets the mapping relationship of algorithm factor and collected data, and collects and stores algorithm factor data, presents algorithm factor index data in list mode;Algorithm formula is self-defined and set and legality check;Set early warning rule;If the data of current granularity has completed data collection in this period, then trigger algorithm scheduling;Algorithm calculation is carried out based on algorithm factor data;Automatic trigger early warning analysis, generate early warning message according to early warning rule, notify relevant person in charge.The asset performance analysis method based on self-defined algorithm solves the problem that analysis algorithm basic data cannot be quickly expanded, so that the expansion of basic data can be directly applied to algorithm, which can quickly realize the expansion and adjustment of algorithm, and also provides an effective means for the optimization and fine adjustment of algorithm.

The invention relates to the technical field of three-dimensional reconstruction, in particular to a rapid three-dimensional reconstruction system based on deep learning, which comprises a data layer module used for inputting multi-view image data, configuring image sizes, calculating equipment parameters and performing read-write and management on output point clouds, grids and depth maps; the algorithm layer module is used for performing calculation based on a three-dimensional reconstruction algorithm, including depth estimation, point cloud generation, global alignment and grid reconstruction; the business logic layer module is used for coordinating all the modules to execute full-process automatic reconstruction and constructing a full process from multi-view image data input to model output; the presentation layer module is used for providing an interactive interface for the user; the communication layer module is in communication connection with the data layer module, the algorithm layer module, the business logic layer module and the presentation layer module and used for completing data transmission and state synchronization among the modules, and the problems that in the prior art, equipment cost is high, real-time performance is poor, efficiency is low and precision is insufficient are solved.

The invention provides a multi-ecology user dynamic growth level management method and device based on pan-entertainment, equipment and a storage medium, user behavior data is asynchronously collected through a message queue, a user behavior feature vector containing multiple behavior type feature values is constructed, and a multi-ecology user dynamic growth level management algorithm is established based on the feature vector. User points are calculated through weighted operation of configuration weights and dynamic adjustment coefficients, historical points and monthly points are combined through the weight coefficients to obtain a comprehensive score, and balance evaluation of long-term value and short-term activeness of users is achieved. Determining a user grade through a grade mapping function according to the comprehensive score, dynamically adjusting a grade state based on upgrading and grade preservation conditions, and updating a dynamic adjustment coefficient in real time according to user grade change and contribution degrees of various behavior types to form a self-adaptive grade management closed loop; and the system can automatically learn and optimize the importance degrees of different behavior types, so that a real fair and intelligent user value evaluation system is realized.

A ship lock automatic control system and method based on parallel theory, the system comprises a parallel ship lock operation management and control integrated platform, a ship lock operation process parallel control model, a ship lock operation process parallel control algorithm module, a ship lock passing process parallel control model, a ship lock passing process parallel management algorithm module, a time-based ship lock parallel management and control database, a ship lock equipment state monitoring module, a ship lock passing plan monitoring module, a passing ship state monitoring module, and a lock chamber water level monitoring module. The purpose of the present application is to solve the technical problems of high risk of ship lock operation and passing safety and low efficiency, which are caused by the fact that the existing ship lock operation and passing are analyzed by manually combining video monitoring information, voice interaction information and PLC information to obtain corresponding decisions and output instructions.

The invention discloses a large model operation method and device based on edge equipment, equipment and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the preprocessing of a to-be-operated large model, and carrying out the optimization of the to-be-operated large model after preprocessing based on the hardware characteristics of the edge equipment; determining the task priority of a to-be-executed task corresponding to the optimized model based on the resource condition of the current edge device; allocating the to-be-executed task to a pre-established model collaborative operation framework according to the task priority, and analyzing the to-be-executed task in the allocation process, so as to deploy the to-be-executed task to a load balancing node of the model collaborative operation framework according to an analysis result; and managing the related parameter data by using a preset data cache management algorithm to pre-load to-be-used data in the related parameter data into the edge device, so that the optimized model executes a corresponding to-be-executed task based on the to-be-used data through a load balancing node of the model collaborative operation framework.

This invention provides an energy management method for hybrid vehicles. First, traffic condition information and vehicle status information are simultaneously acquired and a database is generated. Then, the acquired information is used as the state space, and the reference SOC trajectory is used as the action space. Combined with a lower-level energy management algorithm, the upper-level deep reinforcement learning agent is trained until convergence. Finally, the neural network of the trained deep reinforcement learning agent is extracted as the optimal reference model for battery SOC. Based on the neural network model and combined with the lower-level energy management algorithm, the energy consumption of the entire vehicle is allocated. This invention not only solves the problem of the difficulty in determining the SOC trajectory in energy management strategies, but also improves the effectiveness, generalization, and stability of energy management strategies for new energy vehicles under actual road conditions, thus possessing many beneficial effects not found in existing technologies.

The application provides a one-to-one code marketing fund safety management method, and particularly relates to the field of marketing fund management algorithm. The application binds the fund flow identity and the state node in the block chain account book by generating a unique dynamic two-dimensional code and a master key index for each commodity, uses zero-knowledge proof to ensure that the fund data is credible and privacy-protected, and realizes automatic execution of the on-chain smart contract of fund transfer. Further, a graph neural network is introduced in combination with a time series model to construct a dynamic evolution fund path atlas for the fund transfer process, accurately identify abnormal behaviors of fund path deviating from the regular mode, effectively prevent fund reflux and abnormal aggregation, and improve the automatic detection and real-time response capability of fund safety. The application significantly enhances the safety, transparency and intelligent level of marketing fund management, realizes credible circulation of marketing funds and efficient early warning of abnormal behaviors.

The application relates to the field of retail digitization management and asset supervision, and discloses a store goods display taking and placing management algorithm based on passive RFID technology, which comprises the following steps: acquiring radio frequency characteristic flow of a target asset and a neighborhood reference label; extracting neighborhood group feature distribution and mapping a suppression operator to offset field environment noise; stripping common mode disturbance in a phase evolution sequence according to the suppression operator to extract a single body space offset; and mapping the offset to a business state machine to determine asset interaction confidence. The application realizes deep decoupling of radio frequency physical fluctuation and commercial semantic state, filters out transient physical noise through a neighborhood beacon interference suppression mechanism, ensures data consistency in asset display and transfer migration, and improves asset mapping precision of a management system without adding sensing hardware.

The invention relates to the field of nuclear engineering equipment health management, in particular to a dynamic binding method of an equipment health management intelligent algorithm and a related product, and the method comprises the steps: configuring an algorithm library and a fault library; setting an algorithm binding relation; in the algorithm operation environment, according to the algorithm binding relation, measuring point data of the equipment to be analyzed are obtained, the measuring point data serve as input of an algorithm, and the algorithm is executed for reasoning calculation; outputting an equipment health state diagnosis or prediction result bound with the fault mode; through standardized management, dynamic binding and automatic operation of the intelligent algorithm, support is provided for rapid deployment and operation of the intelligent health management algorithm on a platform and efficient use of the algorithm, the burden of operation and maintenance personnel of the intelligent algorithm is relieved, and convenience is also provided for rapid iteration of the intelligent health management algorithm.

The invention discloses hemodialysis water treatment equipment with an active protection function and a control method of the hemodialysis water treatment equipment. The hemodialysis water treatment equipment comprises an RO high-pressure pump motor, an intelligent variable-frequency driver, a temperature sensing module and a controller, the RO high-pressure pump motor is used for driving the reverse osmosis membrane assembly; the intelligent variable-frequency driver is used for controlling the operation of the RO high-pressure pump motor and receiving an external control instruction; the temperature sensing module is used for collecting the temperature of the RO high-pressure pump motor; a power consumption wall management algorithm and an improved CLFormer model are configured in the controller; constructing a dynamic thermal model through a power consumption wall management algorithm, and calculating a power upper limit value; and an improved CLFormer model introduces a thermal phase guidance differential fusion mechanism to generate a thermal risk prediction value, and when an overheating trend exists, a power limit instruction is generated, so that the operation power of the motor is limited below a power upper limit value. According to the method, the dynamic thermal model and the improved CLFormer model are fused, and active power limiting protection of hemodialysis water supply is achieved.

The invention provides a safety management and control system and method for a marketing field operation platform, and relates to the technical field of power marketing operation safety management, and the system comprises a data processing module which is used for obtaining operation plan data, work order data, field operation data and environment data; the security management and control module is in communication connection with the data processing module and is used for executing a security management and control algorithm model; and the risk early warning and processing module is in communication connection with the safety management and control module and is used for generating early warning information or executing a process control instruction according to an algorithm output result. Through the key job consistency detection unit, text semantics and key fields of plans and work orders are automatically quantified and compared, and a judgment threshold value is dynamically set, so that out-of-range jobs are effectively eradicated, automatic blocking of inconsistency is realized, and the problem that manual checking of the content consistency of the plans and the work orders is unreliable is solved.

The presently disclosed technology provides methods and systems for optimally allocating power among workloads executing on a computer system through use of a power management algorithm. For example, according to the present technology a plurality of CPUs within a server can be divided into multiple groups according to application workloads. Workloads can be distributed to the CPUs as needed by a workload scheduler, and the workload scheduler can provide the CPU IDs to a power manager, enabling the power manager to optimize power settings. Each group of CPUs can be assigned an optimal power profile tailored to its respective situation.

The invention discloses a construction site personnel trajectory prediction and early warning system based on a space-time network. The construction site personnel trajectory prediction and early warning system comprises a hardware layer, a data layer, an algorithm layer and an application layer. The hardware layer collects video streams through a high-definition dome camera; the data layer performs preprocessing and labeling management on the video data; the algorithm layer adopts an ST-GCN and DETR combined training architecture to realize target detection, trajectory prediction and dangerous area delimitation; and the application layer displays early warning information through a Web end and is linked with alarm equipment. According to the invention, the dynamic interaction characteristics of the construction scene can be captured in real time, graded early warning and intervention are realized in combination with the dangerous area division and risk reasoning model, the construction site safety management efficiency is significantly improved, and the accident risk is reduced.

The invention discloses a ship pipe section automatic blanking method and system, and belongs to the field of infield pipe processing in ship construction, and the method comprises the steps: obtaining current real-time dispatching data, historical jacking operation data and basic business data, obtaining a current task packet based on the current real-time dispatching data, the historical jacking operation data and the basic business data; based on the current task package, a dynamic planning trepanning algorithm and a preset management algorithm, obtaining a trepanning scheme, an excess material list and a trepanning processing instruction; according to the automatic discharging method and system for the ship pipe sections, full-process automation driven by real-time dispatching data and the automatic discharging processing process of the pipe sections are achieved, the automatic discharging processing process of the ship pipe sections is achieved, the automatic discharging processing process of the ship pipe sections is achieved, and the automatic discharging processing process of the ship pipe sections is achieved. And the pipe utilization rate is greatly improved.

Provided is a spatial data analysis algorithm that utilizes a Digital Elevation Model (DEM) to evaluate the collapse risk of steep slopes and to effectively identify and manage hazardous areas. By utilizing high-resolution DEM data and spatial analysis techniques, the invention provides an algorithm that can identify collapse-prone areas of steep slopes and systematically manage them. Candidate areas are defined using the DBSCAN algorithm, impact ranges are calculated, and major risk factors are analyzed. Through this process, the invention enables visualization of collapse-prone areas and proposes management priorities, thereby facilitating efficient disaster prevention and urban safety management.

Disclosed is a solar energy management method using deep reinforcement learning including receiving, by at least one processor, at least one or more pieces of energy information data for deep reinforcement learning of a deep neural network, calculating, by the at least one processor, a reward value for controlling a solar energy management algorithm constituting the deep neural network using the at least one or more pieces of energy information data and performing the deep reinforcement learning, and determining, by the at least one processor, a schedule for storing, discharging, consuming or selling solar energy by inputting the at least one or more pieces of energy information data as input data of the solar energy management algorithm.

The invention discloses a plastic bag production management algorithm and program. Compared with the prior art, the algorithm has the advantages that the algorithm is applied to various production processes of the plastic packaging bag and comprises the steps of film blowing, printing, edge folding, blanking, bag making, delivery and the like, a production process list of the product can be automatically generated by inputting product requirements, and the piece counting salaries of workers in various processes for producing the product are automatically calculated; meanwhile, data in the process sheet can be copied to a summary sheet, the order production progress can be tracked in real time through the summary sheet, comprehensive information of all orders can be directly displayed on a display interface for personnel management and use, applicability is good, and popularization is convenient.

The present application is applicable to the field of big data technology, and provides a method, device, electronic device and storage medium for processing bills, the method comprising: extracting any target bill from a database of bills to be processed; the management information of the target bill includes module identifiers of multiple preset management dimensions; selecting target submodules corresponding to the module identifiers from the submodule libraries associated with each of the management dimensions; encapsulating the target submodules of all the management dimensions to generate a target management algorithm corresponding to the target bill; importing the target bill into the target management algorithm, and outputting the bill processing result corresponding to the target bill. By adopting the above method, it is possible to realize that submodules between different management dimensions can be reused with each other, which greatly reduces the difficulty of developing the bill management algorithm and thereby improves the efficiency of bill management.

The invention relates to the technical field of intelligent manufacturing production line management, and discloses an intelligent manufacturing production line real-time optimization and scheduling management algorithm and system, and the algorithm comprises the steps: S1, obtaining the pavement image information data and cargo image information data of a carrying region; s2, dividing the pavement image information data into a plurality of image analysis units; s3, obtaining detection information data of each transfer robot; s4, analyzing each image analysis unit to obtain the road surface quality rating of each image analysis unit; obtaining a stability coefficient according to the detection information data; s5, obtaining cargo type information data; s6, according to the carrying starting point position, the carrying end point position, the road surface quality rating of each image analysis unit, the stability coefficient of each carrying robot and the cargo type information data, obtaining a path index used for evaluating the quality of each carrying path; s7, selecting the carrying path with the maximum path index as a specified carrying path; the damage risk of cargo transportation under the complex ground working condition is reduced.

The invention relates to the technical field of mobile edge computing, in particular to a mobile edge computing server deployment and management method based on revenue maximization, and the method comprises the following steps: S1, constructing an MEC system model and a revenue model, and outputting deployment decision variables; s2, based on the deployment decision variable, optimizing by adopting a three-layer efficient deployment and management algorithm, and outputting a static optimal decision set; when the method is used, the server deployment problem is converted into the optimization problem of income maximization by considering all components, including construction operation cost, income loss, service profit and the like, of the total income of the MEC system, the total income of the MEC system can be effectively improved through the provided EDM algorithm, the system profitability can be improved, and the method is suitable for popularization and application. The three sub-algorithms in the EDM algorithm cooperate with each other to respectively determine the optimal wholesale computing resource unit, the base station computing resource allocation and the MEC server deployment position, so that efficient utilization and reasonable allocation of computing resources are conveniently realized, and waste of the resources is favorably avoided.

The invention relates to beam management using adaptive learning. Certain aspects of the present disclosure provide techniques for beam management using adaptive learning. Certain aspects provide a method that can be performed by a node, such as a user equipment (UE) or a base station (BS). The node uses adaptive learning to determine one or more beams to be used for the beam management procedure. The node performs a beam management procedure using the determined one or more beams. In some aspects, the node uses an adaptive reinforcement learning algorithm to select a beam for measurements in the beam discovery procedure. The node may adapt the beam management algorithm based on feedback associated with beam selection, such as based on throughput achieved using beam pairing determined during the beam management procedure.

This application provides a generalized verification and evaluation method and platform for fault prediction and health management. The method includes: S1, constructing an evaluation index system based on requirements and industry standards; S2, constructing a fault model based on fault mechanisms and data-driven approaches, and generating a fault simulation mathematical model; S3, using the fault simulation mathematical model to generate simulated fault data, which, together with normal working data and actual operating condition data, constitutes the original dataset; S4, designing and generating test case templates based on the evaluation index system and actual requirements, and adapting the test cases; S5, adjusting the original dataset according to the test cases to generate a verification data stream; S6, inputting the verification data stream into the algorithm or prototype for functional verification; S7, analyzing the verification process and results based on the evaluation index system, and generating an evaluation report and charts. This solution can achieve comprehensive, reliable, and efficient verification and evaluation of fault prediction and health management algorithms and prototypes.

This invention relates to the technical field of radiotherapy information management, and more specifically, to an intelligent management system and method for the entire radiotherapy process. The system includes shelves, a mobile device, a transport device, a docking station, a cleaning mechanism, a radiotherapy information system program, and a radiotherapy storage management system program. The radiotherapy information system program is used to create patient files, and the radiotherapy storage management system program is used to manage and automatically transport radiotherapy molds. By incorporating the mobile device, transport device, docking station, cleaning mechanism, a radiotherapy information system program capable of creating patient files and sending outbound instructions, and a radiotherapy storage management system program with lifecycle management algorithms, order priority algorithms, and storage location allocation algorithms, this invention enables the entire process from admission and file creation, mold storage and retrieval, treatment execution to final cleaning, without requiring the patient to handle the molds personally.

The invention relates to the technical field of communication information, and particularly provides a domestic data encryption and decryption implementation method and device, a linux operating system is adopted, the device comprises a main control module and an encryption and decryption module, the main control module comprises a CPU, an internal memory, a hard disk and a bridge piece, the CPU is connected with the internal memory and is connected with the bridge piece through PCIE, the bridge piece is connected with the hard disk, a VGA and a USB are led out, and the VGA and the USB are connected with the main control module. The user operation is convenient; the encryption and decryption module comprises four FPGA (Field Programmable Gate Array) chips and two management CPU (Central Processing Unit) chips, a management CPU 1 loads a program for a business algorithm FPGA1 and a management algorithm FPGA3, a management CPU 2 loads a program for a black area FPG4, and a red area FPGA2 is electrified and externally hung with a flash loading program; the internal network 1 and the internal network 2 are led out of the red area FPGA A2 for data interaction, and the external network 1 and the external network 2 are led out of the black area FPGA 4 for data interaction. Compared with the prior art, the real-time data encryption and decryption function can be realized, the data security can be improved, and the method has important significance on aerospace, enterprise data security and the like.