210results about How to "Reduce redundancy" patented technology

The invention discloses a point cloud intelligent cutting and dynamic optimization method based on edge computing, and belongs to the technical field of intelligent traffic systems and edge computing, and the method comprises the steps: collecting three-dimensional point cloud data through a multi-line laser radar disposed on an RSU; generating a three-dimensional bounding box through a three-dimensional target detection network in an RSU edge calculation unit, generating an ROI mask, expanding a buffer region, and performing adaptive region cutting on the point cloud to form an ROI point set and a background point set; a perception-driven cutting decision is realized, and ROI extraction is converted into a self-learning process based on semantic features from fixed threshold judgment; voxelizing the non-uniform resolution point cloud, generating a unique hash index for each voxel unit, comparing a hash set of a current frame with a hash set of a previous frame, extracting newly added and disappeared point sets, and constructing a differential data packet according to a change ratio; while high timeliness is kept, data redundancy is reduced, and the point cloud updating rate and the incremental transmission efficiency are improved; and dynamic optimization of communication-calculation cooperation is realized.

The invention discloses an electroencephalogram feature recognition method for motion intention prediction in a lower limb alternate stepping process. The electroencephalogram feature recognition method comprises the following steps: acquiring an electroencephalogram signal of a subject before the subject executes an action under the requirement of a lower limb alternate stepping motion execution normal form; preprocessing the electroencephalogram signal to obtain an initial signal, performing label labeling on the initial signal according to an action type, and forming a data set by a label and the initial signal; constructing a neural network model, wherein the neural network model comprises a multi-scale grouping space-time convolution module, a self-attention mechanism module for updating external memory based on Top-k gating and a grouping causal time convolution module; and training the neural network model in a supervised manner by using the data set to obtain a prediction model for predicting the action type. According to the method provided by the invention, the movement intention of the patient can be recognized by the movement brain-computer interface before the lower limb alternate movement, the control of the rehabilitation robot on the specific lower limb movement by the brain intention is realized, and the method has an important value on the clinical application of a brain-controlled rehabilitation robot system.

This invention discloses a method, system, equipment, and storage medium for configuring the capacity of a static synchronous compensator (STATCOM) in a SLCC valve hall, belonging to the field of high-voltage direct current (HVDC) transmission technology. The method includes: analyzing the LCC commutation process based on an electromagnetic transient model of the commutation circuit using LCC capacitor reactive power compensation to determine a first analytical relationship between the amplitude of the power frequency component of the first commutation current and the capacitor reactive power compensation capacity; determining an electromagnetic transient model of the SLCC commutation circuit operating with STATCOM fundamental reactive power compensation and harmonic elimination functions based on the SLCC topology; analyzing the SLCC commutation process based on the electromagnetic transient model of the SLCC commutation circuit to determine a second analytical relationship between the amplitude of the power frequency component of the second commutation current and the fundamental reactive power compensation capacity and harmonic reactive power compensation capacity; and determining the STATCOM configuration capacity based on the first and second analytical relationships and according to capacity equivalence constraints. The technical solution provided by this invention can ensure the safety of SLCC commutation and harmonic compensation capabilities while reducing the redundancy of STATCOM capacity configuration.

This utility model relates to a cross-arranged hybrid metastructure sound barrier structure, comprising a perforated panel (1), a metastructure module (2), and an assembly base (3) arranged sequentially. The perforated panel (1) includes a cross-arranged large-aperture panel (11) and a small-aperture panel (12). The metastructure module (2) includes a cross-arranged metastructure cotton module (21) and a low-frequency module (22). The assembly base (3) includes a cross-arranged metastructure cotton fixing base (31) and a low-frequency module fixing base (32). Compared with the prior art, this utility model uses lightweight composite acoustic metastructure materials and combines a hybrid design of irregularly shaped perforated panels, metastructure modules, and an assembly base to achieve wideband sound absorption and sound insulation, significantly improving noise control capabilities. At the same time, modular assembly helps reduce overall costs and is suitable for noise reduction needs in scenarios such as rail transit and elevated bridges.

The invention discloses a Fortran program parallel optimization method based on intelligent dependency analysis. The method comprises the following steps: firstly, constructing a parallel optimization system consisting of a loop extraction module, a loop nested relation analysis module, a semantic analysis module, an intelligent dependency analysis engine, a variable classification module and an instruction generation and injection module; the loop extraction module analyzes a nested relation and a variable action range of loops; the loop nesting relation analysis module determines a nesting relation between loops; the semantic analysis module constructs a row-level data access view; the variable classification module identifies private variables and reduction variables; the intelligent dependency analysis engine executes loop type check, I / O operation check and data dependency check; and the instruction generation and injection module generates a parallelization instruction and inserts the parallelization instruction into the source code to obtain a parallelized program source code. The method can solve the problems that an existing parallelization method is low in cyclic dependency relation recognition accuracy and safety, and parallelization errors cannot be accurately recognized.

This invention relates to a method for fusion of heterogeneous knowledge in the power industry, comprising: obtaining first triples containing entity relations from the ontology layers of at least two knowledge graphs; classifying the entity relations in the first triples into synonym relations and inclusion relations according to relation aggregation prompts by a language model; merging entity relations belonging to synonym relations; obtaining second triples containing entity attributes or relation attributes from the ontology layers of at least two knowledge graphs; classifying the attributes in the second triples into synonym attributes and non-synonymous attributes according to attribute aggregation prompts by a language model; merging entity attributes or relation attributes belonging to synonym attributes; obtaining entities from the instance layers of at least two knowledge graphs and dividing them into several data blocks; determining whether there are duplicate entities according to entity alignment prompts by a language model; and merging duplicate entities.

The invention relates to a block chain-based green power consumption and carbon asset collaborative management system, provides a carbon emission transaction, and belongs to the field of purchasing, selling or leasing transactions. An intelligent joint prediction model of a set geographic area is adopted to intelligently predict green power supply data of a future time segment and carbon emission data of each electricity consumption enterprise; the upper chain evidence storage device writes the intelligent prediction data into the block chain network; and the contract generation device is used for automatically matching and forming a carbon transaction contract of each electricity consumption enterprise based on the intelligent prediction data. According to the invention, for the technical problems that the overall level of green power consumption and carbon asset collaborative management is insufficient and the performance is low, the same intelligent joint prediction model can be adopted to complete synchronous intelligent prediction of green power generation data and carbon emission data, and then automatic matching is carried out to form each carbon transaction contract. And meanwhile, the uplink evidence storage of the key data is executed, so that the technical problem is solved.

The application discloses a kind of based on substation target detection clustering cluster waypoint automatic generation method, specifically relates to unmanned aerial vehicle inspection field, including establishing substation three-dimensional diagram, the RGB image of equipment needing to be detected is collected, the geometric center of each equipment on the plane is calculated using Graph-Detect3D, according to equipment type, orientation, the coordinate information of all equipment to be inspected target is obtained according to the geometric center of equipment, all equipment to be inspected target is numbered, the geometric center of equipment is clustered using density clustering algorithm DBSCAN, all equipment center geometric center in the same cluster is used as waypoint, and waypoint is generated according to the geometric center coordinates of each class, so the number of waypoints will not be redundant, will be just covered to each equipment, can collect the picture of all equipment of substation with least waypoint, reduce the redundancy of data collection.

The utility model discloses an aluminum material forging forming device beneficial to effective filling, which comprises an upper die holder, an upper die plate and a lower die plate which are sequentially arranged from top to bottom, a forging head is formed on the lower end face of the upper die plate, a lower die mounting groove is formed on the upper end face of the lower die plate, and an elastic forging assembly is arranged in the lower die mounting groove. By arranging the forging and pressing assembly with the elastic adjusting function, the material flowing uniformity and the filling effect in the complex workpiece forming process are effectively improved, the first forging and pressing plate and the second forging and pressing plate which are elastically arranged are matched with a dynamic abutting structure of the forging and pressing bearing block, and multi-direction synergistic acting force is formed in the pressure applying process; the metal material is guided to directionally supplement and flow towards the far end of the cavity and the narrow slit area along the preset path, the local flow resistance difference is remarkably reduced, and therefore complete filling of the cavity is achieved on the premise that the blank redundancy is reduced, the air hole defect in a workpiece is eliminated, the structural strength is improved, the material utilization rate is improved, and the production quality and efficiency are comprehensively improved.

The invention relates to a soil organic matter prediction method based on multi-feature fusion, and belongs to the technical field of soil organic matter prediction. The method comprises the following steps: preprocessing acquired soil data to obtain spectral features, and performing time domain reconstruction of frequency domain signals to obtain time domain data; obtaining an optimal delay time and an optimal embedding dimension based on the time domain data, and performing phase space reconstruction to obtain a phase space trajectory; chaos features are extracted based on the phase space trajectory, and the extracted chaos features, the optimal delay time and the optimal embedding dimension serve as final chaos features; obtaining a vegetation index based on the spectral feature and taking the vegetation index as an index feature; and inputting the spectral features, the final chaotic features and the index features into a constructed double-flow low-rank interaction network model to obtain a soil organic matter prediction result. The objective of the invention is to solve the technical problem of low prediction precision caused by the fact that spectral features extracted in the prior art cannot comprehensively represent complex nonlinear characteristics of soil.

A method, apparatus, device, medium, and product for security evaluation of large models are disclosed. The method includes: acquiring first test data; acquiring a first evaluation strategy from a strategy library based on the first test data; the first test data indicates the testing requirements for a first large model; the strategy library includes model evaluation strategies at different levels under a first modality; the model evaluation strategy includes the first evaluation strategy; the first modality represents the data modality that the first large model can process; generating first content according to a preset format based on the first evaluation strategy; and generating second test data based on the first content through the first model; the second test data is the first modality; inputting the second test data into the first large model to obtain output content; and determining the evaluation result based on the output content; the evaluation result is used to characterize whether the first large model has security risks. This method not only reduces code redundancy but also facilitates flexible combination of different model evaluation strategies, effectively improving cross-strategy reusability and scalability.

This invention relates to the field of image restoration technology, specifically disclosing an image restoration method and device based on discrete wavelet transform. An image restoration network, called Multi-Wavelet Attention Memory Neural Network (MWA-MNN), is designed, consisting of three sub-networks. These sub-networks include Discrete Wavelet Attention Blocks (DWABs) and Coordinate Channel Attention Blocks (CCABs) to extract spatial and channel feature information, effectively separating degraded image layers such as rain streaks from the image to obtain a clean image. The invention leverages the expressive power of wavelet transform for high-frequency information, thereby improving image restoration performance. The sub-networks also include Supervised Selective Kernel Blocks (SSKBs) for filtering effectively extracted features in each sub-network. This invention also proposes a memristor-based cross-array MWA-MNN circuit implementation scheme. Memristors are a novel type of non-volatile memory with faster read speeds and lower power consumption. Experimental results show that this method has good performance and versatility in various image restoration tasks.

The application provides a multi-domain extremely narrow pulse radar echo target fusion recognition method based on scattering center matching degree constraint and structural feature auxiliary constraint, constructs an end-to-end classification model for multi-frequency and multi-polarization fusion recognition of an extremely narrow pulse radar echo, can perform feature extraction on input multi-frequency and multi-polarization extremely narrow pulse radar echoes and fusion of frequency dimension and polarization dimension information, and then directly recognizes the fusion features, realizes end-to-end fusion recognition integration, adopts deep learning technology based on feature level fusion, can more fully utilize feature information contained in samples, makes the model make more accurate and stable decisions in the classification task. The method fully mines the correlation between multi-frequency and multi-polarization information, fuses the frequency dimension and polarization dimension information mined, utilizes rich scattering information of multi-frequency and multi-polarization extremely narrow pulse radar echoes, and achieves a relatively ideal target recognition effect.

The application provides an attack detection method and device for an electric power private network and a public network interaction node, pre-processes each piece of traffic data obtained from a node to obtain target traffic data containing feature information, classifies all the target traffic data to obtain multiple category data sets, samples and optimizes the target traffic data in each category data set to obtain an optimized category data set, selects features for each feature information in the optimized category data set by using a mutual information feature selection algorithm to obtain a feature selection result, updates the optimized category data set according to the feature selection result, reduces the redundancy of the data in the optimized category data set, obtains a more refined to-be-detected traffic data set, shortens the time required for an attack detection classification model to detect the to-be-detected traffic data set, and improves the classification accuracy of the attack detection classification model.

This utility model relates to an auxiliary device for conveying bearing rings in bearing production. The adjusting rod can slide left and right along the support member, and the guide plate can slide back and forth along the first sliding member. Combined with the positioning function of the elastic positioning component, the distance and position between the two guide plates can be flexibly adjusted to adapt to bearing rings of different diameters and thicknesses. The auxiliary device does not need to be frequently replaced, which reduces equipment costs and improves production efficiency. The adjusting rod and the first sliding member are hinged, allowing the guide plate to rotate around the hinge point to adjust the angle, thereby forming a figure-eight structure between the two guide plates. This figure-eight structure can automatically gather the bearing rings towards the centerline or a specified direction during the conveying process, preventing the bearing rings from deviating or scattering, ensuring stable conveying posture, and providing a precise positioning basis for subsequent processing, inspection and other processes.

The utility model discloses a mobile energy storage charging station belongs to charging station technical field, including container, liquid cooling device, multiple battery group and flat -bed car, the inside of container is provided with a plurality of battery holder, and the both sides of container rotatably are provided with side box door, and battery holder can pass through side box door and set up in the outside of container, and liquid cooling device fixedly sets up at the top of each battery holder, and each battery group sets up on battery holder, and sets up below liquid cooling device, and container detachably sets up on flat -bed car, this mobile energy storage charging station places multiple battery group on battery holder, and battery holder sets up in container, and two containers set up on movable flat -bed car, realizes nimble disposition, dynamic power supply, reduces fixed station redundancy, expands charging station power supply radius range, eliminates power supply blind area, satisfies charging station temporary expansion, temporary construction site, heavy truck emergency power supply demand.

The invention provides a power data transmission method and system based on cloud edge collaboration. The method comprises the following steps: firstly, acquiring first state data of power equipment and second state data of a corresponding supporting structure; performing correlation analysis based on the determined deformation parameter of the support structure and the waveform change of the first state data, and transmitting a risk level signal generated by analysis, the first state data and the second state data to a cloud end; comparing the second state data with a pre-stored safety range to obtain a comparison result; generating an acquisition control instruction based on a comparison result and issuing the acquisition control instruction to an electric power station side; and finally, adjusting the acquisition frequency according to the acquisition control instruction, and executing data acquisition and transmission according to the adjusted acquisition frequency. According to the technical scheme provided by the invention, the adaptive adjustment of the data acquisition frequency along with the equipment operation risk level is realized, and the transmission efficiency and the monitoring accuracy are also considered.

This invention discloses a design method for parallel current sharing of BOOST multiphase interleaved silicon carbide single transistors. It identifies key parasitic parameters by building a dual-pulse simulation platform, completes device layout in conjunction with the overall system structure, uses PCB symmetry and copper layer stacking to control parasitic parameter consistency, and forms a design closed loop through dual-pulse and steady-state thermal testing. This effectively solves the problem of poor current sharing in parallel silicon carbide single transistors. Furthermore, this method ensures balanced current distribution during switching transients and conduction steady states, eliminates current sharing failures caused by parasitic parameter mismatch, fully utilizes the performance of silicon carbide single transistors without requiring amplification device margins, flexibly adapts to the overall system layout, improves space utilization, optimizes system efficiency, reduces design costs and redundancy, and achieves a reliable, efficient, and highly adaptable design for parallel current sharing of BOOST multiphase interleaved silicon carbide single transistors.

The invention discloses a cache management method and device, equipment, a storage medium and a product, and the method comprises the steps: responding to a cache access, and obtaining a hit condition of the cache access; adjusting global leapfrogging parameters according to the hit condition; and according to the hit condition and the global leapfrogging parameter, adjusting the item level of the data item. According to the embodiment of the invention, the adjustment step length of the item level of the data item is reduced during hit, it is ensured that the hot data partition stores the high-frequency accessed data item, the occasionally accessed data item is prevented from mistakenly entering the hot area, the hot data partition entering speed of the sudden hot data item can be reduced, and the hot data partition entering efficiency is improved. The concerned data items are prevented from being quickly deleted; when the new inserted data is not hit, a large entry level is configured for the new inserted data, the new inserted data is placed in a cold data area, storage of high-frequency access data is prevented from being affected, negative feedback closed-loop control over a cache area is achieved, cold and hot data are effectively distinguished, the problem of access delay caused by the fact that the cold and hot data are difficult to distinguish is solved, and the access efficiency is improved. And the response speed and the cache stability are dynamically balanced.

The invention belongs to the technical field of burn wound analysis. According to the skin burn analysis method and system based on contrast agent blood perfusion, firstly, a fluorescence sequence after a contrast agent is injected is obtained, and a preprocessing sequence is generated through area cutting and down-sampling; a fluorescence intensity time curve of each pixel point is extracted, peak time is determined, and statistics is carried out to construct a peak time histogram; performing smooth denoising, extreme value detection and weak peak removal on the histogram, and adaptively selecting a threshold value for distinguishing normal and burnt tissues; generating an initial mask by using the threshold value, and performing connected domain analysis and morphological post-processing to obtain a target burn area mask; and finally, generating a pseudo-color image in combination with peak time. According to the method, the time dynamic characteristic of blood perfusion is fully utilized, the burn area can be accurately segmented and the microcirculation disturbance degree can be visually presented without depending on skin textures or a large amount of training data, and the objectivity and accuracy of burn depth evaluation are remarkably improved.

The application relates to the technical field of distributed system data synchronization, and discloses a data synchronization processing method and system based on message driving, a terminal and a medium. In a producer service end, a data synchronization message is generated, a version identifier is injected into the data synchronization message before the data synchronization message is sent to a message middleware, and then the data synchronization message is published into a preset topic or queue through a client of the message middleware; in a consumer service end, the data synchronization message is received from the message middleware, the data synchronization message is intercepted before a business processing logic is executed, the version identifier is parsed and extracted; a corresponding target data source is determined based on a unique identifier of a data table, a current maximum timestamp of the synchronized data is queried from the target data source, the current maximum timestamp is compared with a data version timestamp, and according to a result, it is decided whether the data synchronization message is passed to the business processing logic or an automatic retry mechanism is triggered. The application improves the reliability, data consistency and operation efficiency of a distributed system.

The invention relates to the technical field of battery containers, in particular to a container type control room and energy storage battery cabin combined heat management system and method.The system comprises a refrigerant loop and a secondary refrigerant loop which can conduct heat transfer, and the refrigerant loop is connected to the inner side and the outer side of a control room in series to adjust the temperature and humidity of the control room; the secondary refrigerant loop is connected with the battery cluster in the battery cabin and is used for adjusting the temperature of the battery cluster; the refrigerant loop comprises an indoor unit and an outdoor unit which are connected in series, the indoor unit is arranged in the control room, the outdoor unit is arranged outside the control room, and an outdoor unit assembly can exchange heat with the secondary refrigerant loop; the battery compartment and the control chamber are arranged in the container. The mechanism is compact, the battery cabin heat management loop and the control room heat management loop are fused, energy cross-region transfer can be achieved, and cold or heat can be flexibly distributed according to the real-time temperature control requirements of the battery and the control room.

A non-intrusive multi-load load monitoring method and system based on shared time-series coding and load-specific refinement is disclosed, relating to the fields of smart grid, non-intrusive load monitoring, and energy data analysis. This method addresses the lack of existing non-intrusive multi-load load monitoring methods that can simultaneously address shared modeling, load difference representation, training stability, and engineering deployment feasibility. The process involves generating a fixed-length input window; after the input window is compressed to form latent space temporal features, multiple shared time-series coding modules extract the shared time-series representations for the corresponding multiple loads; these shared time-series representations are then fed into multiple independent load-specific refinement heads, which generate power predictions and state predictions for the target loads; residual gating is applied to the power predictions of the target loads using the state predictions, yielding the final decomposition results for the multiple target loads.

The application discloses a human activity recognition method based on multi-scale group information distillation and refinement network, relates to the technical field of artificial intelligence and edge computing, and comprises the following steps: constructing an input tensor based on measurement data of an inertial measurement unit; inputting a trained activity type recognition model; and calculating, which comprises the following steps: processing the input tensor through a multi-scale feature extraction module to obtain multi-scale fusion features; processing the input features through a group information distillation module in a group information distillation stage to obtain residual enhancement features; processing the residual enhancement features through a feature aggregation module to obtain aggregated features; processing the aggregated features through a feature integration module to obtain a global feature vector; and processing the global feature vector through a classification module to obtain an activity type classification result. The method provided by the application can significantly improve the recognition accuracy under the premise that the parameter quantity and the calculation cost are controlled, can be directly deployed on a smart phone, and can provide an efficient and reliable solution for real-time medical monitoring and motion analysis.

This application relates to the field of bathroom equipment technology, and discloses a liquid filling assembly and a smart toilet. The liquid filling assembly includes a pipe body, a liquid filling element, and a snap-fit ​​assembly. The pipe body has an input end and an output end arranged along its axial direction. The liquid filling element is slidably disposed in the inner cavity of the pipe body along its axial direction, and the liquid filling element is provided with a liquid filling channel adapted to connect the input end and the output end. The snap-fit ​​assembly is located in the inner cavity of the pipe body and is disposed at the end of the liquid filling element near the output end. This application can shorten the length of the liquid filling assembly and reduce the space occupied.

This invention provides a nonlinear time-series estimation method for the state of health (SOH) of lithium batteries, comprising: collecting feature data during the charge-discharge cycle of the lithium battery and generating a multi-channel standardized time-series dataset; extracting features and compressing dimensions of the data through an encoder; reconstructing and denoising the data through a decoder to output sequence features; mining the global correlation between feature sequences to obtain a context vector; further obtaining the SOH estimation result and synchronously updating the parameters. This method effectively suppresses model overfitting while introducing an improved KL divergence to prevent posterior collapse, achieving high-precision and robust estimation of the SOH of lithium batteries.

The invention relates to a laser heat treatment control method and device based on visual recognition and application. A laser heat treatment strategy is established; the method comprises the following steps: acquiring a three-dimensional point cloud of a workpiece to be subjected to heat treatment, performing mesh segmentation on the point cloud on a reference projection surface to obtain a plurality of point cloud combinations taking a grid as a unit, and collecting associated information of each point cloud combination; performing heat treatment on the to-be-heat-treated workpiece according to a laser heat treatment strategy, obtaining a dynamic feedback signal, adjusting output power, and realizing heat treatment performance consistency and uniformity control; the equipment comprises a base provided with a position transfer unit, a scanning modeling unit and a heat treatment unit are sequentially arranged on the position transfer unit in the translation direction, a control end controls the position transfer unit to translate and obtains data output by the scanning modeling unit, and after the method is adopted, the heat treatment unit is controlled to execute heat treatment operation; the heat treatment application is realized based on equipment. The quenching efficiency is improved, the quenching integrity and comprehensiveness are ensured, and high-quality and repeatable production can be achieved.

The application discloses a kind of industrial flue gas carbon monitoring early warning method and system based on fractal dimension, it is related to environmental protection field, comprising: based on continuous carbon monitoring data, first fractal dimension of t time and t-1 time is obtained using box covering algorithm respectively, second fractal dimension of t time and t-1 time is obtained using sphere covering algorithm respectively;First fractal dimension change rate is calculated based on the first fractal dimension of t time and t-1 time;Second fractal dimension change rate is calculated based on the second fractal dimension of t time and t-1 time;Determine the change rate correlation coefficient of first fractal dimension change rate and second fractal dimension change rate;Fractal dimension comprehensive change rate is calculated based on first fractal dimension change rate, second fractal dimension change rate and change rate correlation coefficient;When the absolute value of fractal dimension comprehensive change rate is greater than or equal to early warning critical value, generate industrial flue gas carbon emission abnormal alarm signal.The application realizes the effective monitoring of industrial flue gas carbon emission.

The invention belongs to the technical field of power system network security and intelligent power grid operation monitoring, and discloses a power grid penetration level coupling alarm association method, system and device and a medium, and the method comprises the steps: obtaining a suspicious range of an alarm log based on a monitoring host and a time label; constructing an alarm association graph, and performing extraction processing in a local adjacent sub-graph mode to obtain an attack path; constructing a natural language processing mechanism in a classified manner to obtain an attack database; matching and identifying by using a graph matching algorithm with label classification to obtain a network information layer attack event; carrying out delay sampling on abnormal data, carrying out state aggregation by utilizing a prior fault knowledge base, constructing an initial state sequence, and carrying out compression and merging to obtain a target state sequence; and carrying out hierarchical coupling analysis on the network information layer attack event and the target state sequence to obtain an attack event sequence after alarm association. According to the invention, cross-level and multi-stage abnormal alarm association of power grid penetration attacks is realized, and a complete link can be identified and tracked.

The embodiment of the invention provides a vehicle control device and system and a vehicle, and relates to the technical field of vehicles. The vehicle control device at least comprises an alternating current-direct current converter, a first control module, a second control module and a power battery module, wherein the first control module is connected with the AC / DC converter and the power battery module through a target relay to form a current conversion loop; and the second control module is connected with the AC / DC converter and the power battery module to form a motor driving loop. The device can realize decoupling of the power supply module and the electric drive module through the target relay, so that the power supply module and the electric drive module share a part of power tubes, the number of power devices is reduced, and the hardware cost is reduced.