220 results about "Hierarchical coding" patented technology

The invention discloses a full-life-cycle carbon footprint intelligent detection system and method, belongs to the technical field of carbon emission monitoring, and aims to solve the problems of insufficient standardization of full-life-cycle carbon emission data, inaccurate abnormal link identification and delayed carbon footprint risk early warning. Carbon emission data of a production link in a full life cycle of a target product is collected and transmitted to a block chain, and standardized coding is realized through a hierarchical coding rule; detecting the collected data based on a multi-dimensional triggering rule to judge whether carbon footprint updating is triggered or not, if so, configuring an anchoring reference of a quantization range according to a production mode, and screening the data to construct a quantization data set; after the quantitative data set is corrected, the total carbon emission amount of each production link is calculated, and a carbon emission report is generated; the abnormal links are identified based on the carbon emission and proportion of the production links in the report, the abnormal source is positioned through standardized coding, historical carbon emission data are tracked to predict the emission, and carbon footprint grade change risk early warning is realized.

The invention provides an aeronautical part multi-process parameter cooperative surface treatment standardization optimization method, and relates to the technical field of aeronautical manufacturing surface engineering, and the method comprises the following steps: S1, constructing a process parameter hierarchical coding system and a correlation mapping model; s2, establishing a parameter solving method based on multi-objective collaborative optimization; s3, constructing a modular process chain; s4, establishing real-time monitoring and closed-loop control of multi-source data fusion; s5, establishing a digital simulation body and verifying the digital simulation body; and S6, constructing a whole-process quality tracing database. According to the method, dynamic collaborative optimization of multiple process parameters is realized through parameter hierarchical coding, association mapping and a cross-process-chain intelligent decision model, the parameter conflict rate is reduced, and the process decision efficiency is improved. Meanwhile, a self-adaptive optimization engine based on reinforcement learning can correct process parameters in real time, the batch consistency of key performance indexes is improved, and the process stability and the product quality are remarkably improved.

The invention relates to a building structure health state assessment method and device, and belongs to the technical field of building renovation, and the method comprises the steps: obtaining original stress time sequence data generated by dynamic strain response of a stress part of a target building under the action of a natural load or artificial excitation, building health state category labeling is carried out on the original stress time sequence data, and a multi-dimensional labeling vector with a timestamp is obtained; obtaining a health state evaluation model based on a hierarchical coding architecture of spatial-temporal feature decoupling, and training the health state evaluation model based on the clean stress time sequence data as labeled sample data in combination with unlabeled sample data to obtain a target health state evaluation model; and based on the target health state assessment model, data analysis is performed on the collected target building stress data, and the building structure health state of the target building is determined, so that the accuracy, continuity and early warning performance of building structure health state assessment can be improved.

The invention provides a video coding and compression transmission method for intelligent equipment, which belongs to the technical field of video coding and compression transmission, realizes advanced channel quality estimation by establishing a channel prediction model enhanced by a spatial transformation network, configures layered coding parameters according to a prediction result and optimizes motion search in combination with inertial measurement unit data. An optimal coding mode is selected by an application rate distortion optimization algorithm, enhancement layer sending is adjusted according to real-time feedback by adopting a dynamic transmission scheduling strategy, and differential forward error correction protection and a time-space domain error concealment mechanism are matched; the technical problem that video transmission is unstable due to violent fluctuation of channel quality when intelligent equipment moves and works in complex electromagnetic environments such as a transformer substation is solved.

The invention belongs to the technical field of crossing of satellite navigation and edge computing, and relates to a differential data broadcasting method based on edge computing, which comprises the following steps: accessing multi-source data through a dynamic data acquisition processing module, reducing noise, scheduling computing power through an edge node cooperative computing module, and fusing data to generate a differential factor; the spatio-temporal characteristic difference generation module generates adaptive difference data in combination with spatio-temporal and environmental parameters, then the intelligent broadcast resource management and control module selects channels and performs layered coding broadcast, the system health degree management module guarantees stability, the cross-domain collaborative application interface module adapts to multiple scenes, and after the system is started, all the modules operate in sequence, data are collected and processed firstly, and then the data are transmitted to the intelligent broadcast resource management and control module. And then generating factors through cooperative calculation, generating adaptive differential data, intelligently broadcasting the adaptive differential data, monitoring and maintaining the adaptive differential data by a health degree module, and finally docking each application scene through an interface module to realize high-precision positioning service.

The invention particularly relates to a multi-level self-cognition system based on a large model, and relates to the technical field of large models. A neural symbol world model module; a large language model cognition core module; and a hierarchical decision planning system module. According to the method, deep integration of perception, cognition and decision making is achieved through the hierarchical fusion architecture, and compared with the prior art, the method has remarkable advantages; the multi-modal perception encoder adopts layered encoding and a cross-modal attention mechanism, so that the semantic alignment problem of multi-source perception data is effectively solved, and the understanding ability of the system to a complex scene is greatly improved; according to the neural symbol world model, the neural network and symbol reasoning are combined, the limitation of a pure neural network method in physical modeling is overcome, meanwhile, the calculation complexity of a pure symbol system is avoided, and efficient and accurate environment characterization and prediction are achieved.

The invention discloses a water conservancy project management method and system based on multi-source inspection data fusion and correlation analysis, and relates to the technical field of data processing. According to the method, hierarchical coding is performed on the inspection data in a unified acquisition period, and a three-layer envelope structure is constructed, so that time-space integrated organization of multi-source inspection information is realized, cross-source data is enabled to obtain structural consistency expression, and the accuracy and traceability of data fusion are improved; according to the method, a joint positioning point is established in a time envelope layer and a collaborative labeling identifier is distributed in a space envelope layer, so that collaborative labeling entries with synchronous semantics are formed by data of different sources, and the alignment capability and the contrastive analysis efficiency of inspection information are remarkably improved; by means of construction of a step-by-step linkage association link, continuous evolution of a water conservancy structure state can be presented on a time sequence, meanwhile, spatial-temporal characteristics can be synchronously captured in combination with generation of dynamic association paragraphs, and the recognition sensitivity of abnormal changes is enhanced.

The invention relates to the technical field of unmanned aerial vehicle image processing, and discloses a high-definition image processing and transmission method based on an unmanned aerial vehicle. The method comprises the following steps: acquiring a multi-frame high-definition image sequence obtained in the flight process of an unmanned aerial vehicle; analyzing the image sequence frame by frame, extracting key feature points and generating feature descriptors; establishing a feature matching relationship between adjacent frames, and calculating an inter-frame motion vector; constructing a global motion model according to the motion vector and performing motion compensation; performing region segmentation on the compensated image sequence to divide the image sequence into a dynamic region and a static region; performing multi-scale noise reduction on the dynamic region, and performing texture enhancement on the static region; carrying out fusion reconstruction on the processed dynamic and static regions to generate an optimized image sequence; performing layered coding on the optimized sequence according to a transmission bandwidth condition, and generating base layer and enhancement layer data; dynamically adjusting the priority of two layers of data transmission according to the network state; and sending the data to a ground receiving end through a multi-path transmission protocol.

The invention provides a virtual data transmission flow control system for an MR head-mounted display. The virtual data transmission flow control system comprises a dynamic key area identification module, a self-adaptive flow scheduling module, a low-delay transmission optimization module and a cross-layer collaborative optimization module, wherein the dynamic key region recognition module is used for recognizing a complex key region in an MR environment in real time based on a user fixation point, a head movement track and scene semantic analysis, and the complex key region comprises a high-dynamic interaction object, a visual focus region and a real-time changing virtual scene element; the dynamic key region identification module obtains user behavior data through multi-modal sensor fusion, predicts the change trend of a user concerned region in combination with a deep learning model, and outputs the priority weight of a key region; the adaptive traffic scheduling module comprises a hierarchical coding unit, a dynamic bandwidth allocation unit and a redundancy control unit; and the self-adaptive flow scheduling module is connected with the dynamic key area identification module.

The invention discloses a psychological state dynamic evaluation and early warning system based on multi-modal behavior data, which belongs to the field of medical care informatics and comprises a multi-modal behavior data hierarchical coding module, a time sequence causal atlas construction and reasoning module, a double-stage self-adaptive early warning decision module and a context awareness intervention strategy generation module. A cross-modal association mode is extracted through a double-layer coding mechanism, a time sequence graph containing a causal relationship is constructed, causal reasoning is performed, a double-stage mechanism of short-term mutation detection and long-term trend prediction is adopted to generate graded early warning, and an optimal intervention strategy is selected based on a deep Q network according to a user situation. According to the method, the accuracy, timeliness and intervention effectiveness of psychological health assessment are improved, and dynamic monitoring and early warning of the psychological state are realized.

The invention discloses a progressive multi-modal medical image fusion method based on multiple scales. Relates to the technical field of multi-modal medical image fusion, in particular to a progressive multi-modal medical image fusion method based on multiple scales. Through the integrated design of multi-scale extraction, hierarchical coding, layer-by-layer fusion and adaptive modeling, efficient and accurate fusion of multi-modal medical images is realized, and reliable technical support is provided for clinical application. The method comprises the following steps: acquiring images of a mode A and a mode B, and preprocessing the images; performing multi-modal parallel coding on the preprocessed image, wherein the multi-modal parallel coding comprises four coding stages; the input of each coding stage passes through a self-adaptive visual RWKV module, an efficient cross-modal module and a progressive fusion module in sequence to obtain the output of multi-modal parallel coding; and carrying out image conversion to obtain an RGB fusion image.

A video compression system and method integrates geometric compression with cognitive understanding through a persistent cognitive machine interface. The system employs a hierarchical encoder generating multi-scale compressed representations organized within a Lorentzian manifold structure. A geometric processor maintains temporal causality through time-like geodesics and light cone constraints while organizing video content according to semantic relationships. A cognitive interface creates thought bundles as navigable submanifolds, enabling semantic access to compressed content beyond traditional temporal indexing. The system supports real-time processing through progressive refinement, streaming coarse representations immediately while adding detail in parallel. Symbolic anchors mark semantically significant points, enabling concept-based navigation through compressed video. Federated learning capabilities allow distributed systems to share geometric patterns while preserving content privacy. The architecture enables improved compression ratios while maintaining both temporal causality and semantic navigability, transforming video from sequential media into an intelligently accessible information space.

The invention discloses an artificial leather quality rapid detection method based on spectral analysis, and particularly relates to the field of quality detection.The method comprises the steps that component composition characteristics, surface and microcosmic characteristics and dynamic response characteristics of artificial leather are synchronously obtained through multi-dimensional original data acquisition, and a time-space correlation data set is formed through timestamp alignment and space calibration; constructing component analysis, surface association and dynamic response models, and eliminating space-time migration through a synchronous optimization model; after multi-dimensional features are extracted, optimized features are generated through cross-direction tensor fusion, attention enhancement and gating cycle unit time sequence fusion; and finally, quantizing the quality index based on a multi-task model, and outputting a quality grade and a traceable report in combination with a dynamic threshold value through hierarchical coding, graph neural network association enhancement and evidence fusion. The single sample collection duration is less than 3 minutes, the response time is less than 1 second, and full-dimension, rapid and accurate detection and quality traceability are realized.

The invention belongs to the technical field of image processing, and particularly relates to a Chinese painting style migration method based on multi-scale color embedding, which comprises the following steps: acquiring a content image data set and a style image data set, and constructing a Chinese painting style migration network with multi-scale color embedding, including a content-aware position encoder, a hierarchical encoder and a multi-mode decoder; inputting the content image into a content perception position encoder, and extracting through linear mapping and a Transform encoder to obtain a global semantic feature sequence of the content image; inputting the style image into a hierarchical encoder, and generating image embedding fusing content and style through a progressive decoder in combination with the global semantic feature sequence; embedding and inputting the image with the fused content and style into a multi-mode decoder, and dynamically optimizing color embedding representation; according to the method, efficient fusion of the content image and the polychrome wood print style is realized through a deep learning technology, artistic expressive force and semantic consistency are achieved, and the quality and stability of polychrome wood print style migration are improved.

The invention relates to the technical field of 5G wireless image transmission, in particular to a 5G wireless image transmission low-latency transmission method based on dynamic code rate hierarchical coding, which comprises the following steps of: coding each frame of image according to divided levels; dividing the transmission priority of each frame of image; and periodically determining whether the processing for the video is qualified based on the determined jitter influence parameter, adjusting a preset kurtosis used for dividing the hierarchy to a corresponding value when the processing for the video is determined to be abnormal, and adjusting the processing parameter for the video based on the transmission elasticity quantized value and the data retention characterization value. The transmission priority and the coding hierarchy are divided based on the actual recording condition and the transmission condition, and the coding transmission is carried out by adopting the targeted code rate, so that the video transmission efficiency is improved.

The invention relates to the technical field of wireless communication, in particular to a secure communication perception integrated system optimization method based on rate segmentation multiple access and a reconfigurable holographic surface, which comprises the following steps: constructing a secure communication perception integrated system model; constructing a multi-objective joint optimization problem; a multi-objective joint optimization problem is decomposed into three sub-problems, and solving algorithms are designed respectively; and sequentially updating the solutions of the three sub-problems in an alternate iteration mode until a convergence condition is met, outputting an optimal parameter, and carrying out multi-dimensional verification on the system performance. According to the invention, through a rate segmentation multiple access public stream-private stream layered coding mechanism, the system can intelligently convert part of signal energy into man-made interference to potential eavesdroppers, and at the same time, a high-dimensional beam forming degree of freedom provided by a reconfigurable holographic surface is utilized; accurate orientation of legal user signals and accurate control of null depth of an eavesdropping direction are realized.

The invention discloses a multi-modal remote sensing classification method and device based on spatial covariance, and belongs to the technical field of remote sensing image processing, and the method comprises the steps: obtaining multi-modal remote sensing images of the same region, and carrying out the image registration of the obtained remote sensing images; extracting spatial covariance of each modal remote sensing image so as to quantitatively represent spatial correlation characteristics of ground features in the remote sensing image; constructing a channel attention mechanism for a remote sensing image with rich spectral information; for each modal remote sensing image, multi-modal features are extracted in a layered coding mode; extracting covariance features, respectively fusing the covariance features, and finally fusing the fused multi-modal features with the spatial covariance features; carrying out convolution operation on the fused features to reduce the number of channels, and realizing classified output; an optimizer with weight attenuation is adopted, a cross entropy loss function is used as a classification error measure, and a regular term is added to control the complexity of the model. According to the invention, the ground feature classification precision in a complex scene is improved.

The invention provides an image segmentation method and device for a blood vessel image, and the method comprises the steps: carrying out the step-by-step feature extraction of a to-be-segmented blood vessel image based on N hierarchical coding modules in a coding part of a blood vessel image segmentation model, and obtaining a bottleneck layer feature image; based on a global coding module of the coding part, performing global morphological feature extraction on the bottleneck layer feature image by using a self-attention mechanism to obtain a global feature image for representing a target blood vessel in the to-be-segmented blood vessel image; performing step-by-step feature fusion and step-by-step decoding on the global feature image and coded feature images output by the N step-by-step coding modules respectively based on N step-by-step decoding modules in a decoding part of the blood vessel image segmentation model to obtain a target decoded feature image; and generating a segmentation result for the target blood vessel according to the target decoding feature image.

The invention discloses a multivariable measurement lightweight space-time redundant data construction method for intelligent driving, and belongs to the technical field of intelligent driving and multi-sensor data fusion. According to the method, firstly, multi-source sensing data acquisition and space-time synchronization are carried out, then space-time redundancy measurement and feature compression modeling are carried out, and finally, lightweight space-time data structure construction is carried out. Efficient storage and rapid retrieval are realized through indexing and hierarchical coding strategies, and real-time support is provided for subsequent fusion and fault-tolerant control. According to the method, by introducing physical consistency constraint and a space-time redundancy measurement mechanism, lightweight and high-reliability fusion of multi-source sensing data is realized, data transmission and calculation burdens are effectively reduced, and the robustness and real-time response capability of an intelligent driving system in a complex environment are improved.

The invention discloses a multi-modal three-dimensional hand grid reconstruction method and system based on topology perception Transform, and belongs to the technical field of computer vision and intelligent interaction. According to the method, a topology perception hierarchical coding denoising network containing anatomy and topology constraints is introduced in the three-dimensional hand posture and grid reconstruction process; a real hand 3D joint coordinate is subjected to perspective projection, forward noise addition and feature fusion, and then is processed by a topology perception hierarchical coding denoising network composed of a hand part hierarchical coding module, a hierarchical feature aggregation mechanism and a topology perception Transform module to obtain a joint feature containing rich structure information; and finally, two-stage hand grid vertex construction from rough to fine is realized through a joint-guided grid reconstruction module. According to the method, the precision and robustness of three-dimensional hand posture and grid reconstruction are remarkably improved.

The invention discloses a hierarchical Mama and multi-modal fusion unknown network threat detection method based on physical boundary perception, and relates to the technical field of network space security and artificial intelligence crossing, and the method comprises the steps: carrying out physical boundary perception preprocessing on original network traffic, and extracting byte modal features and statistical modal features; constructing a layered Mama encoder, extracting a load semantic feature of each data packet, and extracting a time sequence interaction feature between the data packets; inputting the byte modal features and the statistical modal features into a multi-modal gating fusion module to generate an anti-confusion stream representation vector; and based on the mask reconstruction pre-training model, calculating a reconstruction error of the input flow. According to the method, the physical level of a network protocol is strictly aligned, stable detection performance can still be kept, extremely high reasoning speed and low video memory occupation are kept, the limitation that a traditional closed set classifier can only recognize known attacks is broken through, and the blank of an efficient flow detection model in the field of open set detection is filled up.

The present application relates to the technical field of aquaculture, in particular to a multi-source aquaculture data low-latency anti-interference communication method and system for unmanned feeding ship, comprising the following steps: multi-source aquaculture data acquisition; edge data fusion and instruction layered coding encapsulation, real-time fusion processing through a lightweight neural network at the unmanned feeding ship end to generate decision instructions corresponding to the aquaculture event; multi-node anti-interference networking; data transmission scheduling; data reception and state feedback. By deploying a MobileNet-SSD lightweight neural network at the unmanned feeding ship end, the present application realizes edge real-time cross-modal fusion of aquaculture water environment data and feeding state data, combines layered coding design driven by aquaculture events and channel quality change rate adaptive transmission strategy, avoids data redundancy, guarantees complete transmission of key instruction information when channel quality deteriorates, and effectively solves the problem of insufficient data fusion and transmission protocol adaptability.

The application provides a human-object interaction image generation method and device based on structured semantic enhancement. The method comprises: obtaining human-object interaction triple description and boundary box conditions; based on a semantic library pre-constructed by a large language model, expanding the triple description into multi-slot structured semantic description and encoding; performing intra-slot semantic aggregation on each slot to compress into a single slot semantic vector; performing cross-slot relationship modeling on the slot semantic vector to output a global action semantic representation through self-attention fusion; performing feature modulation on the subject and object semantic representations based on the global action semantics; fusing the modulated representations with spatial position features to form a conditional representation vector, injecting the diffusion model denoising network to generate an interaction image. Through coarse-grained semantic fine-grained structured reconstruction and hierarchical coding, the application significantly improves the semantic fidelity and interaction controllability of generated images in complex interaction scenarios.

The application discloses a time capsule expression structure reservation method based on deep learning and a knowledge graph, and aims at solving the problems that the expression and organization structure of a user's sealed multi-modal content in a digital time capsule service is difficult to be identified and recorded, and the display according to the uploading time or the fixed template of the media type in the opening stage is easy to cause the distortion of the expression intention and the fragmentation of the receiving experience. The multi-modal content input in the sealing stage is normalized and time-aligned, the content representation is obtained by using multi-modal hierarchical coding, the segmentation boundary and hierarchical relationship are determined and the key weight and object information are extracted by combining span boundary detection and a pointer network, the narrative rhythm parameters are obtained by using a neural time point process modeling based on a media switching event sequence, a timing knowledge graph is fused and constructed, and the arrangement sequence and the presentation control parameters are generated under the sequence constraint and the segmentation continuous constraint by graph representation learning, and the presentation data for the display in the opening stage is generated, so that the technical effects of reserving the user's expression structure and rhythm and differentially arranging and presenting in the future are realized.

This invention provides a lightweight knowledge base construction method based on hierarchical coding. The method includes processing documents in a knowledge document library using a lightweight semantic coding model on a cloud server to generate corresponding high-dimensional semantic vectors. Each high-dimensional semantic vector is mapped to a fixed-length binary fingerprint using a pre-defined Locality Sensitive Hash (LSH) function cluster, constructing a global fingerprint index library. On a mobile terminal, fingerprint index data from the cloud server is received and stored. Responding to user-inputted query text, a query fingerprint is generated using the same lightweight semantic coding model and LSH function cluster as on the cloud. By calculating the similarity between the query fingerprint and the fingerprints in the locally stored fingerprint index data, relevant document information is retrieved, and the results are returned. The beneficial effects of this invention are that it can reduce the knowledge base storage overhead by more than an order of magnitude while keeping the accuracy loss of semantic retrieval within 5%.

This invention relates to a beamforming and resource allocation method based on communication-aware joint optimization, applied to hierarchical coding communication scenarios. The method includes the following steps: Step 1, initializing the beamforming matrix and rate allocation vector; Step 2, solving the perception optimization subproblem based on the current beamforming matrix and rate allocation vector to obtain a new beamforming matrix; Step 3, solving the communication optimization subproblem based on the current beamforming matrix and rate allocation vector to obtain a new rate allocation vector, and determining whether convergence has occurred. If not, proceed to Step 2; if yes, proceed to Step 4; Step 4, solving the communication-aware joint optimization problem based on the current beamforming matrix and rate allocation vector to obtain the final beamforming matrix and the final rate allocation vector, thus realizing beamforming and resource allocation. Compared with existing technologies, this invention has advantages such as large capacity, high speed, and low complexity.

The present document relates to a method of layered encoding of a frame of a compressed higher-order Ambisonics, HOA, representation of a sound or sound field. The compressed HOA representation comprises a plurality of transport signals. The method comprises assigning the plurality of transport signals to a plurality of hierarchical layers, the plurality of layers including a base layer and one or more hierarchical enhancement layers, generating, for each layer, a respective HOA extension payload including side information for parametrically enhancing a reconstructed HOA representation obtainable from the transport signals assigned to the respective layer and any layers lower than the respective layer, assigning the generated HOA extension payloads to their respective layers, and signaling the generated HOA extension payloads in an output bitstream. The present document further relates to a method of decoding a frame of a compressed HOA representation of a sound or sound field, an encoder and a decoder for layered coding of a compressed HOA representation, and a data structure representing a frame of a compressed HOA representation of a sound or sound field.

The application provides an aviation part multi-process parameter collaborative surface treatment standardization optimization method, and relates to the technical field of aviation manufacturing surface engineering, which comprises the following steps: S1, constructing a process parameter hierarchical coding system and a correlation mapping model; S2, establishing a parameter solving method based on multi-objective collaborative optimization; S3, constructing a modular process chain; S4, establishing real-time monitoring and closed-loop control of multi-source data fusion; S5, establishing a digital simulation body and verifying; S6, constructing a full-process quality traceability database. Through parameter hierarchical coding and correlation mapping and cross-process chain intelligent decision-making model, the application realizes dynamic collaborative optimization of multi-process parameters, reduces parameter conflict rate, and improves process decision-making efficiency. At the same time, the adaptive optimization engine based on reinforcement learning can correct process parameters in real time, improve batch consistency of key performance indicators, and significantly improve process stability and product quality.

The invention discloses an unmanned station intelligent inspection system supporting multi-device cooperation, and the system comprises a device sensing layer which is used for the data collection of inspection devices; the equipment abstraction layer comprises a plurality of adapters; the edge calculation layer is used for carrying out data analysis; the cloud service layer comprises: a hierarchical coding system for performing multi-level identification on a service entity by using a pCode of a segmented structure; the data management service module is used for associating data acquired by a plurality of inspection devices through pCode; the task scheduling module is used for matching inspection equipment to execute an inspection task according to the inspection equipment capability description, and establishing a linkage rule through pCode to realize cross-equipment-type collaborative operation; an algorithm micro-service cluster, wherein each algorithm is deployed in an independent service form and is associated with routing inspection task configuration; and the data management service module is also used for configuring an associated algorithm according to the inspection equipment and the inspection task to complete multi-source inspection data set retrieval and defect identification. The inspection system provided by the invention is light in weight, extensible and capable of arranging services.

The present application relates to the technical field of digital circuit control, and particularly relates to a contact state monitoring and control method of a direct current contactor. The method comprises the following steps: monitoring the contact state of the direct current contactor and collecting the driving code signal of the direct current contactor, and extracting the characteristic code after filtering; performing spiral hierarchical identification on the characteristic code, analyzing the hierarchical information to distinguish the driving instruction, and obtaining the characteristic code identification result; distributing the characteristic code identification result to the corresponding driving circuit; outputting the rated driving voltage to the corresponding direct current contactor coil according to the characteristic code identification result, driving the coil to attract the armature, and closing the contact of the direct current contactor. The present application realizes fine management and control and abnormal processing of the direct current contactor by combining filtering and spiral hierarchical coding identification, and improves the instruction execution accuracy and the control whole-process stability.