25 results about "Hierarchical algorithm" patented technology

The invention relates to an algorithm fusion and fault early warning system in a battery safety management platform, and aims to realize multi-dimensional risk identification and active response control in a battery operation process. The system comprises a multi-source data acquisition module, a fusion calculation engine, a fault early warning response module and an execution interface module. By collecting voltage, current, temperature, humidity, sound wave signals, electrochemical impedance spectroscopy, gas concentration and other multi-mode operation data, a fusion calculation engine completes time sequence correlation modeling, causal feature recognition and risk trend deduction on the basis of a constructed hierarchical algorithm fusion structure, and a risk response signal reflecting a safety state is generated; the fault early warning response module generates hierarchical control instructions corresponding to risk levels according to the signals, the hierarchical control instructions comprise virtual fusing, cooling scheduling, BMS refreshing and the like, the execution interface module issues the control instructions to a physical controller of the battery system, response behaviors are driven, and therefore intelligent, closed-loop and dynamic battery safety management is achieved.

The invention provides an ocean buoy data real-time acquisition method based on low earth orbit satellite communication, and belongs to the technical field of electrical digital data processing.The ocean buoy data real-time acquisition method comprises the steps that firstly, a low earth orbit satellite network communication link is built, data characteristics are analyzed through a compression conversion gain index model, and a low earth orbit satellite network communication link is established; and establishing a multi-stage compression mechanism. The system adopts an optimization equation set to dynamically adjust compression parameters, the optimization equation set comprises a resource consumption equation, a compression quality equation and a transmission efficiency equation, and self-adaptive compression of data is achieved. And meanwhile, through a hierarchical algorithm selection mechanism and a parameter optimization strategy and in combination with real-time feedback control, continuous optimization of the compression efficiency is ensured. According to the compression scheme based on multi-level optimization, the technical problem of low compression efficiency caused by poor algorithm adaptability in the ocean buoy data compression process in the prior art is effectively solved.

The invention discloses a tire production process quality data traceability system based on a multi-modal data space. The system is composed of a data layer, a processing layer and an application layer. In the data layer, multi-source data acquisition is carried out for core process links such as internal mixing, molding and vulcanization of tire production, entity recognition and relation extraction are carried out through ETL processing, and an entity triple is constructed and stored in a graph database. In the processing layer, hierarchical visualization processing and intelligent question and answer system construction are conducted on knowledge data in the graph database, a time axis hierarchical algorithm and a process step hierarchical algorithm are adopted in a hierarchical visualization engine, and the intelligent question and answer system comprises a model local deployment module, a cue word construction module and a knowledge verification module to generate structured response. Finally, the data layer and the processing layer are used for an application layer, the application layer is a Web visual interface, the application layer is used for quality tracing analysis and node query and is associated with an intelligent question and answer box of the interface, and the interface further comprises layered atlas display for visualization of the processing layer.

The invention relates to a multi-robot system global flexible preset performance control method under an unknown state and a DoS attack, and belongs to the technical field of multi-robot system control. Aiming at the problems in the prior art that the communication topology is unstable due to attack, the initial error constraint is strict, and the input saturation conflicts with the performance, the following scheme is provided: dynamically reconstructing the attacked communication topology based on a bipartite consensus layering algorithm; designing a global error constraint mechanism fusing the scale function and the tunnel type performance function; constructing a balance control input and performance boundary of the input saturation self-adaptive auxiliary system; and estimating an unknown state and compensating an observation error by adopting a state observer. The method has the technical effects that the DoS attack resistance of the system is enhanced, the global preset performance control of any initial error is realized, the contradiction between input saturation and tracking performance is dynamically coordinated, and the multi-robot collaborative operation under the unbalanced topology is supported.

The invention discloses a drug recommendation model construction method for relieving entity sparsity based on a hierarchical algorithm, and belongs to the technical field of drug recommendation, and the method comprises the steps: building a pre-training module, and obtaining the health representation of a patient; performing correlation layering; constructing a dual-attribute graph network representation stage; aggregating the drug graph representing the safety and the bipartite graph used for representing the drug disease / operation accuracy into dual-attribute representation in a graph network; recommending related medicine combinations to adapt to long-term health conditions; constructing a loss function, wherein the loss function comprises a prediction probability based on recommended drugs and cross entropy loss and DDI loss of a real label; and training the deep learning model to obtain predicted drug recommendation representation. By layering historical record data of a patient, capturing a sequence relation and forming an accurate graph structure and a safe graph structure for drugs, diseases and operations, the drug recommendation performance can be improved, and the calculation amount is reduced.

The embodiment of the invention discloses an automatic layout and wiring method and device for a QCA circuit, equipment and a medium, and the method comprises the steps: obtaining a QCA circuit in a specified format, the QCA circuit comprising an input node, an output node and a child node; performing hierarchical processing on the QCA circuit so as to set an input node, an output node and a child node of the QCA circuit at corresponding hierarchies; setting a QCA cellular grid in each level according to a clock scheme of the QCA circuit; and carrying out layout and wiring on input nodes, output nodes and child nodes of the QCA circuit in each level of QCA cellular grid. According to the hierarchical algorithm layout provided by the embodiment of the invention, the success rate of layout and wiring on a larger circuit is higher. Meanwhile, the embodiment of the specification can provide a thought for the large-scale automatic design, which means that a traditional circuit automatic layout and wiring division scheme is also suitable for the automatic design, and can be combined with an iterative algorithm on the basis of the division method in the future to realize the large-scale automatic design.

The invention provides a point-surface additive manufacturing system and method based on dual-optical-path polarization multiplexing, and relates to the technical field of additive manufacturing, and the method comprises the steps: importing a three-dimensional model of a part, generating hierarchical data by adopting a self-adaptive hierarchical algorithm, dividing each layer of data into a contour region and a filling region, and setting corresponding parameters; the coaxiality of the patterned light spot and the point light spot is calibrated, the energy density mapping relation in different polarization states is calibrated, and the optimal energy parameter is verified and stored based on the material type; loading a filling pattern, scanning according to a planned path, monitoring the temperature of a molten pool in real time, dynamically adjusting power, switching a polarization state mode to generate a spot light spot, and scanning according to the planned path to obtain a formed part; a point light spot light path and a patterned light spot light path are integrated in the additive manufacturing system at the same time, and the printing efficiency and the printing precision of laser additive manufacturing parts are synchronously improved through efficient switching of point light spot contour scanning and patterned light spot filling scanning.

The embodiment of the invention discloses a constant value checking method and device based on a block chain, equipment and a storage medium, and the method comprises the steps: obtaining a local constant value file currently operated by a protection device, and determining a global hash value of the local constant value file; determining the Hash value of the fixed value file on the chain, and taking the Hash value as a reference Hash value; if the global hash value is inconsistent with the reference hash value, constructing a local Merkel tree, and determining a local Merkel tree root; determining a Merkel tree root of the fixed value file on the chain and taking the Merkel tree root as a reference tree root; if the local Merkel tree root is inconsistent with the reference tree root, comparing node hash values in the local Merkel tree and the on-chain Merkel tree through a recursive hierarchical algorithm, and determining difference nodes of the hash values; and determining a difference constant value item and a path between the local constant value file and the on-chain constant value file based on the difference node. Therefore, the checking efficiency can be improved, the real-time requirement of constant value adjustment can be met, the chain reaction caused by constant value errors can be effectively avoided, and the safety of a power grid is ensured.

This invention discloses a network path optimization method and system. The method includes: adjusting the adaptive weights of the heuristic function in the underlying model LPA* algorithm to intelligently change the magnitude of the heuristic function's influence with iteration; simultaneously, introducing a bias p in the K-value calculation to improve the algorithm's search efficiency; and performing unbalanced allocation of initial pheromones for the ant colony algorithm based on the initial path calculated by the underlying model. The upper-layer model applies the ACO algorithm to plan fiber optic network paths on a grid map to obtain the globally optimal network path. This invention, through the design of a hierarchical algorithm, provides the ant colony algorithm with prior information including pre-selected paths, enhancing the traditional ant colony algorithm's ability to guide pre-selected areas in path planning. This enables faster search for feasible paths, effectively solving the problems of slow search speed and susceptibility to local optima in the traditional ACO algorithm, and significantly improving the overall performance of the fiber optic network planning system.

The invention discloses a liquid and powder material measuring system and calibration method based on a transmission reflection method, and belongs to the technical field of testing. According to the system, a customized container is adopted as a clamp and is matched with the corrugated horn antenna to complete GRL calibration, a calibration end face extends to the position of the inner surface of the container, and then the dielectric constant and the magnetic conductivity of liquid and powder materials are calculated through a transmission reflection method. According to the invention, the process of removing the influence of the container through a hierarchical algorithm in the prior art can be avoided, and the calibration port directly extends to the internal surface of the container, so that the test steps are simplified, and the error rate is reduced.

The invention relates to the technical field of data analysis, and discloses a power grid operation data panoramic analysis method and system based on a cloud scene digital platform, and the method comprises the steps: obtaining first power grid operation data in real time; inputting the first power grid operation data into a hierarchical algorithm model for first judgment, second judgment and third judgment so as to obtain a capacity-increasing transformation suggestion, an automatic voltage control optimization suggestion and an equipment maintenance suggestion; and constructing a first comprehensive interface, and pushing the data wheel disc type suggestion list so as to realize panoramic analysis of the power grid operation data. According to the invention, automatic real-time acquisition and multi-dimensional mining analysis of data are realized, so that the power grid management efficiency is greatly improved, and the labor cost and time are saved; on the basis of dynamically generated capacity-increasing transformation, AVC optimization and other suggestion lists, decision scientization is promoted, and blind investment is effectively avoided; by means of equipment health degree model prediction and implementation of preventive maintenance measures, the power failure accident rate is successfully reduced, and the power supply reliability is improved.

The application discloses a kind of dynamic behavior generation method of multiple automatic driving vehicles based on H-PPO hierarchical algorithm, comprising: 1, simulating intersection vehicle intersection road condition, constructs simulation scene, and carries out dynamics modeling to automatic driving vehicle;2, according to environmental information, construct state quantity, mixed action space;3, encourage the intersection game between the driving vehicle and the measured vehicle by setting reward function;4, construct deep neural network, initialize deep reinforcement learning parameters, train deep neural network, produce for the measured vehicle intersection left turn working condition, make multiple straight automatic driving vehicles decision and action are layered output by neural network, make it have game behavior, to test the response capability of the measured vehicle to the edge scene of small probability, high risk.The application uses deep reinforcement learning and game theory to generate the game intersection scene encountered by the measured vehicle at the intersection, to provide a new way to build automatic driving scene.

The invention discloses a multi-robot control method based on a data-driven learning mechanism, and belongs to the technical field of robot interaction control. The method comprises the following steps: constructing a multi-robot system dynamic equation; robot system cooperation and pit relation communication topology is processed based on a shortest path hierarchical algorithm; designing a leader robot system matrix of a learning mechanism based on data driving; designing a distributed control protocol, and embedding the control protocol into the multi-robot system dynamic equation model; the robots interact information through a network, and progressive consistency of the multiple robots is achieved. The network is divided into different layers through a layering algorithm, and layer-by-layer information transmission is achieved; an information transmission path is optimized by using a shortest path algorithm, and efficient information transmission in unbalanced topology is ensured; through a distributed predictor of a learning system matrix, it is ensured that the state of a leader robot can be accurately estimated when the denial of service attack is active; the stable operation of the whole system is maintained, and the safety and reliability of the system are ensured.

The invention discloses a stomach soft tissue modeling and collision detection method in a virtual operation, and belongs to the field of soft tissue modeling in the virtual operation. Important indexes of whether the virtual operation system can reach the operation training effect or not comprise the real-time performance, stability and accuracy of physical modeling and collision detection. According to the method, the biomechanical characteristics of stomach soft tissue under the action of external force are analyzed, an improved deformation model based on a reset spring is provided based on a soft tissue deformation method of a mass point-spring deformation model, the problems of super-elasticity and collapse of a traditional mass point-spring model are solved, and the authenticity of soft tissue deformation is improved. Meanwhile, for a muscle layer, modeling is carried out by using a collision detection algorithm combining an axial alignment bounding box (AABB) and a spherical bounding box (Sphere); modeling of a mucous membrane layer and other soft tissues of the stomach is realized by adopting a mixed hierarchical algorithm of an AABB (Axial Alignment Burst Box) and a Sphere (Sphere); and the real-time performance and the accuracy of collision detection are considered.

The invention discloses an image registration system based on multi-algorithm fusion and adaptive optimization. The system comprises an image acquisition module used for synchronously acquiring visible light and infrared images; the cross-modal feature matching module preprocesses the infrared image, extracts feature points of the bimodal image by adopting an improved ORB algorithm, and generates high-confidence matching point pairs through feature matching and geometric constraint; the multi-algorithm registration module inputs the matching point pairs into a hierarchical algorithm framework, and outputs an optimal spatial transformation matrix based on dynamic weight evaluation and a multi-target optimization mechanism; and the dynamic optimization and incremental learning module monitors the registration performance in real time and triggers adaptive updating of the model. According to the method, the problems of unstable feature matching and poor environmental adaptability in visible light and infrared image registration are solved, and the registration precision, efficiency and system robustness are remarkably improved.

The present disclosure provides a system that searches for and finds orbital debris that can be a hazard to satellites and the like, for which the debris is sunlit. The system includes a ground-based telescope pointed at the sky; a detector array that detects images of the sunlit debris crossing the field of the detector; and a processing system that computes tracks from the detector data using a hierarchical algorithm, which builds longer tracks from previously computed shorter tracks, determines whether the computed tracks correspond to valid debris detections, and converts the track computations into debris brightness and debris orbital trajectory.

The application provides a kind of ocean buoy data real-time acquisition method based on low-orbit satellite communication, belongs to the technical field of electric digital data processing, the ocean buoy data real-time acquisition method provided by the application first constructs low-orbit satellite network communication link, data characteristics are analyzed by compression conversion gain index model, and multi-stage compression mechanism is established.System uses optimization equation set to dynamically adjust compression parameters, including resource consumption equation, compression quality equation and transmission efficiency equation, adaptive compression of data is realized.At the same time, through hierarchical algorithm selection mechanism and parameter optimization strategy, combined with real-time feedback control, the continuous optimization of compression efficiency is ensured.This compression scheme based on multi-level optimization effectively solves the technical problems of low compression efficiency caused by poor algorithm adaptability in the compression process of ocean buoy data in the prior art.

The application provides a driving blind area detection and early warning method and device, a storage medium and a vehicle. The method comprises the following steps: performing sound source identification on a plurality of first sound signals of a vehicle by using a voiceprint recognition hierarchical algorithm to obtain at least one sound source and a sound source type of the sound source; determining a three-dimensional space coordinate of the sound source relative to the vehicle according to a collection time of the plurality of first sound signals; performing data fusion on the sound source type corresponding to at least one sound source, the three-dimensional space coordinate and other sensor data of the vehicle to obtain environment perception information, wherein the environment perception information comprises target information of at least one target around the vehicle; determining that the target is a potential collision risk according to the environment perception information, and controlling the vehicle to perform a warning operation, so that the cost and technical complexity of driving blind area detection can be reduced.

This invention relates to an algorithm fusion and fault early warning system in a battery safety management platform, aiming to achieve multi-dimensional risk identification and proactive response control during battery operation. The system includes a multi-source data acquisition module, a fusion computing engine, a fault early warning and response module, and an execution interface module. By acquiring multi-modal operating data such as voltage, current, temperature, humidity, acoustic signals, electrochemical impedance spectroscopy, and gas concentration, the fusion computing engine, based on a constructed hierarchical algorithm fusion structure, completes time-series correlation modeling, causal feature identification, and risk trend inference, generating risk response signals reflecting the safety status. The fault early warning and response module generates graded control instructions corresponding to the risk level based on the signals, including virtual fuse breaking, cooling scheduling, and BMS refresh, etc., and the execution interface module sends the control instructions to the physical controller of the battery system to drive the response behavior, thereby realizing intelligent, closed-loop, and dynamic battery safety management.

The invention belongs to the field of information security, and discloses a sensitive data classification and classification protection method and device and electronic equipment, and the method comprises the steps: carrying out the classification processing of to-be-processed data through employing a preset sensitive data classification algorithm; inputting the to-be-processed data and the sensitivity level into a preset desensitization algorithm selection model to obtain a selection result; and performing desensitization processing on the to-be-processed data by using a desensitization algorithm corresponding to the selection result. Through the preset sensitive data grading algorithm, the sensitivity of the to-be-processed data can be accurately identified and graded, and the objectivity and consistency of data classification are ensured. The model is selected by using the desensitization algorithm, and the most suitable desensitization algorithm is automatically selected according to the sensitivity level of the data, so that the error of manual intervention is reduced, and the flexibility and adaptability of desensitization processing are also improved. Finally, by applying the selected desensitization algorithm, sensitive data are effectively protected, and a solid foundation is provided for safety management of railway data.

The invention relates to satellite autonomous task planning, and discloses a satellite autonomous task planning method based on a dynamic planning algorithm, which comprises the following steps: a) constructing a satellite task multi-dimensional description model, the model comprises a task type parameter, a time window constraint parameter, an energy consumption parameter, a load working mode parameter and a task priority initial parameter, and the task priority initial parameter is obtained through weighted calculation of three indexes including a task emergency degree, a scientific value weight and a ground instruction priority; and b) on-satellite real-time state data based on satellites. The method is high in dynamic adaptability, constraints are updated within 10 seconds, and dynamic changes such as resources and equipment faults on a satellite are adapted; the planning efficiency is high, the time consumed by incremental replanning is shortened by 60% +, the local backtracking depth is smaller than or equal to 3, and conflicts are rapidly resolved; and the task income is excellent: the hierarchical algorithm realizes priority dynamic adjustment and sequence optimization, and the core task completion rate and the resource utilization rate are remarkably improved.

The invention belongs to the technical field of smart home, and particularly relates to a method for automatically adjusting temperature and humidity of a central control screen, which comprises the following steps of: S1, pre-integrating a temperature sensor and a humidity sensor with a resistance effect in the central control screen so as to collect environment temperature and humidity information in real time; s2, pre-integrating a hierarchical algorithm model in the central control screen, wherein the model comprises a sensing layer, a planning layer and a control layer; the central control screen can sense the change of temperature and humidity in the environment in real time by adding a temperature sensor and a humidity sensor, analyzes the collected temperature and humidity information and decides whether to adjust the temperature and humidity according to the analysis result in combination with the realization of hierarchical algorithm model codes. According to the scheme, the purposes of autonomous monitoring and autonomous control are achieved, the intelligent degree is greatly improved, manual control operation of a user is reduced, convenience is greatly improved, and the experience feeling of the user is improved.

The invention belongs to the technical field of medical data processing, and particularly relates to an algorithm library matching system and a difference calculation method for a complex chronic common disease scene, and the system comprises a hierarchical algorithm library construction module which is used for constructing a common disease algorithm library and realizing the structured storage of an algorithm module; meanwhile, relying on a real-time updating interface, newest clinical evidence and diagnosis and treatment practice data are accessed, and algorithm rules of all levels are dynamically optimized; and the multi-dimensional matching module is used for completing algorithm matching by utilizing three-stage matching and ensuring that a matching result covers disease generality, interaction characteristics and individual differences. According to the method, the hierarchical algorithm library of the three-level architecture of the base layer, the interaction layer and the individual layer is constructed, and the multi-dimensional matching module is combined with the extraction of the disease interaction characteristics and the accurate matching of the interaction rule module, so that the interaction relationship among multiple diseases is effectively incorporated, and the diagnosis and treatment scheme conflict caused by the matching of a single disease algorithm is avoided.

The invention relates to the technical field of geotechnical engineering construction, in particular to a rock mass grading method based on a K-means clustering grading algorithm. According to the method, construction data including but not limited to exploration drilling parameters, geophysical exploration data, foundation bearing capacity parameters and rock geological parameters are integrated, and a multi-dimensional rock feature data set is constructed; and performing unsupervised learning by adopting a K-means clustering algorithm, automatically mining an internal rule of data, and realizing intelligent identification of rock mass grades. Through fusion of machine learning and traditional engineering geological experience, subjective experience dependence and fixed weight limitation in manual grading are broken through, and a data-driven objective grading system is established. Practical engineering verification shows that the method has remarkable advantages in the aspect of multi-source heterogeneous geological data integration, the rock mass grading accuracy rate exceeds 85%, and a reliable scientific basis is provided for dynamic optimization of blasting parameters.

The invention discloses a method for constructing an annotation data set based on an optimized Bayesian hierarchical algorithm, and the method comprises the following steps: S1, obtaining an original annotation data set composed of annotation samples, the annotation samples comprising a sample unique identifier, an annotation personnel identifier and an annotation label list; performing data preprocessing and label expansion on the labeled samples to enable each labeled sample-label pair to form an independent record, and performing unified mapping processing on the labels; according to the invention, hierarchical processing is carried out on annotation data through hierarchical Bayesian confidence calculation, and classification processing of annotation samples of different scales is realized; and meanwhile, multi-label identification is realized through dynamic judgment of the dispersion degree of label ticket distribution, so that the precision and robustness of subsequent model training are remarkably improved.