72results about How to "Reduce computing power consumption" patented technology

The invention relates to a mobile robot dynamic obstacle real-time detection method and system based on embedded vision. The method comprises the following steps: performing motion compensation on a historical frame image, generating a motion compensation image, performing frame difference analysis on the motion compensation image and a current frame image, performing adaptive threshold segmentation in combination with local texture features, and generating a motion candidate mask; and extracting feature points in the mask region, calculating an optical flow vector and generating RGB image blocks, and performing dynamic attribute verification after fusion to obtain a verified dynamic point set. And further calculating an optical flow direction angle and a fusion speed value, constructing a motion feature vector and clustering, and generating a central position coordinate of the obstacle instance. And through rotation matrix conversion and Kalman filtering processing, a smooth position estimation value and a smooth speed estimation value are obtained, and the relative distance and the dynamic threat degree of the obstacle are calculated. According to the method, through motion compensation, filtering optimization and the like, the real-time performance, precision and motion estimation accuracy of dynamic obstacle detection in an embedded scene are improved.

The invention discloses an image transmission method, device and equipment of a remote operation system and a medium, the method is applied to a local operation end, the remote operation system comprises the local operation end and a remote operation end, and the method comprises the following steps: acquiring an initial ultrasonic image corresponding to a target object at the local operation end, performing feature extraction on the initial ultrasonic image to obtain a target ultrasonic image; determining an ultrasonic sub-image corresponding to the target ultrasonic image based on the knowledge graph, and determining an image value of the ultrasonic sub-image according to the ultrasonic image evaluation dimension; and transmitting the ultrasonic sub-image to a remote operation end according to the image value. Based on the technical scheme, the image is divided into the sub-images of different areas, the image values of the sub-images are determined, and the transmission methods corresponding to the sub-images are determined according to the image values, so that the preferential transmission of the high-value image is ensured, the overall bandwidth occupation and the system load are reduced, and the transmission efficiency is improved. And a key technical support is provided for the accuracy and safety of a remote operation.

The invention provides a channel prediction method and system based on a multi-cycle feature convolutional network. According to the method, context sensing weighted input and a dynamic multi-model pool mechanism are introduced. A terminal device obtains environmental context features in real time, dynamically calculates the weight of each time step in a historical channel state information sequence based on the features, and generates a weighted optimized input sequence. Meanwhile, the network equipment pre-trains and maintains a plurality of multi-period feature convolutional network models specific to different environments, and the network can effectively capture the complex time-varying rules in the period and during the weeks in the channel, so that the accuracy of channel state information prediction is improved. And the terminal equipment immediately selects and activates the optimal sub-model in the model pool for prediction according to the real-time context features, and locally performs increment fine adjustment. According to the invention, through management of the data input end and rapid and adaptive switching of the prediction model end, the precision of channel prediction and the adaptive capacity to environmental sudden change are effectively improved.

The invention discloses an intelligent glasses double-target speech enhancement method and system based on a multi-microphone array, terminal equipment and a medium, and relates to the technical field of speech enhancement. The method is applied to the intelligent glasses, and comprises the following steps: collecting noise-containing multichannel original time domain voice signals through a multi-microphone array of the intelligent glasses, and performing time-frequency domain conversion to obtain a frequency spectrum set; frequency domain complex spectrum amplitude-phase characteristics of each channel are extracted, a local time sequence context is introduced, and multi-channel space acoustic characteristics of amplitude-phase decoupling are constructed; and regulating and controlling the double-tone-area complex spectrum through pre-trained deep neural network modeling to obtain a target complex spectrum, and performing inverse time-frequency domain conversion and reconstruction to obtain an enhanced independent wearer voice signal and a preset direction target voice signal. According to the method, double-target voice parallel enhancement and non-target interference suppression are realized, the power consumption is low, the real-time performance is high, the method adapts to the harsh performance constraint of the end side of the intelligent glasses, and the robustness of voice enhancement in a complex noise scene is excellent.

The application discloses a same-row relationship detection method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining target video data in a target place; determining a personnel pixel area in each image frame in the target video data, and determining a target height and / or a target body shape ratio of the personnel corresponding to the personnel pixel area; determining an anonymous identity of the personnel in the image frame based on a dynamic anonymous personnel name list corresponding to the target place and the target height and / or the target body shape ratio; and judging whether a first search personnel and a second search personnel are in a same-row relationship according to the anonymous identity information of the personnel in each image frame in the target video data. The scheme can efficiently and with low algorithmic consumption detect the anonymous identity of the personnel in each image frame in the video data, so as to accurately and quickly find out whether the two search personnel are in a same-row relationship according to the anonymous identity of the personnel.

The invention relates to the technical field of Internet of Things communication, discloses an Internet of Things multi-Android client state synchronization method, and solves the problems of poor state consistency, high back-end dependence, large delay and insufficient compatibility in the prior art. According to the scheme, the method comprises the steps that after a client completes initialization, MQTT connection with a rear end is established, an MQTT state message pushed by the rear end is monitored, after the message is received, equipment state information is obtained through analysis, local state cache and a UI interface are updated, meanwhile, the client serves as a distribution node, a UDP synchronization message containing the equipment state and the IP of the distribution node is established, and the UDP synchronization message is sent to the client. The UDP synchronization message is broadcasted to the local area network, other clients in the local area network receive the UDP synchronization message and then verify whether the distribution node IP is the client in the local area network, if the verification is passed, the message is analyzed, the state updating timestamp and the local cache timestamp are compared, and the local state cache and the UI interface are updated only when the state updating timestamp is larger.

Embodiments of the present application provide a check matrix generation method, device and electronic equipment. The check matrix generation method comprises: generating a plurality of reference check matrices based on target configuration parameters and error correction performance requirements; generating a plurality of mapping tables corresponding to different parameter combinations based on the target configuration parameters, constraint conditions of an LDPC encoder and constraint conditions of an LDPC decoder; adjusting the plurality of reference check matrices based on the plurality of mapping tables to obtain adjusted check matrices; and checking the adjusted check matrices, and taking an adjusted check matrix that passes the check as the target check matrix, which can shorten the check matrix generation time and reduce the computing power consumption.

The invention relates to the technical field of disaster early warning processing, in particular to a mountain torrent disaster early warning system based on deep learning and air-space-ground integrated perception. The system comprises the following modules: a space-air-ground sensing module, an edge calculation preprocessing module, a deep learning prediction module and an intelligent decision and early warning module. Wherein the air-space-ground sensing module is used for collecting multi-source data; wherein the multi-source data comprises earth surface deformation data obtained through a spaceborne synthetic aperture radar, high-precision topographic data obtained through an unmanned aerial vehicle laser radar, hydrological sensing data obtained through a ground internet-of-things distributed sensor, and meteorological numerical forecasting data obtained through a meteorological data interface. Through air-space-ground integrated perception, physical constraint deep learning and edge intelligence technologies, mountain torrent multi-source data collaborative acquisition and accurate mountain torrent disaster prediction are realized, so that the comprehensiveness, reliability and timeliness of mountain torrent disaster early warning are improved.

The invention relates to a living body detection method and device, an electronic device and a storage medium, and is applied to the field of target recognition, and the method comprises the steps: inputting a to-be-detected image collected by monocular equipment into a target lightweight network structure in a living body detection network, and obtaining a face detection frame and a living body label corresponding to the face detection frame; and the target lightweight network structure is used for acquiring target global features, including dense semantic information in the to-be-detected image, in the to-be-detected image. Cutting a human face detection frame corresponding to the target living body label containing the living body content to obtain a cut human face, and inputting the cut human face into the target texture discrimination network to obtain a living body detection result; and the target texture judgment network is used for carrying out classification judgment on the clipped face on a texture feature level. According to the method and the device, the problem that hardware cost saving and accurate face detection effect keeping cannot be taken into consideration under the condition that only one-way color image input exists in the prior art is solved.

This invention belongs to the field of power monitoring technology and discloses a brightness balancing and regional smoothing fusion algorithm for power monitoring images. The method includes: image denoising, using mean filtering and median filtering to smooth the image; image registration, performing optimal matching on two or more images obtained from different sensors, at different times, or from different angles; and image region fusion, smoothly fusing images with different angles and brightness contrasts. This method is applicable to panoramic video monitoring systems. Designed specifically for the complex lighting and high electromagnetic interference characteristics of power operation sites, this invention effectively eliminates on-site noise through adaptive preprocessing, and achieves a balance between global brightness uniformity and local detail preservation through layered brightness balancing, solving the problems of incomplete brightness balancing and detail loss in power scenarios encountered by general algorithms.

The invention relates to the technical field of vehicle control, and discloses a vehicle starting control method and device, a vehicle and a medium, and the method comprises the following steps: in response to starting operation of the vehicle in an automatic parking state, adjusting parking release control parameters according to current road surface information and vehicle parameters; and releasing the automatic parking state according to the adjusted parking release control parameter. The parking release control parameters are dynamically adjusted according to real-time road surface information and vehicle parameters through a dynamic and self-adaptive parameter adjusting mechanism, the limitation of traditional fixed calibration is eliminated, different road conditions and vehicle states can be adapted, the vehicle starting quality in the automatic parking state is remarkably improved, and the vehicle starting speed is increased. And fixed parameters under the automatic parking function can be continuously optimized, so that the method adapts to individual manufacturing differences of different vehicles, starting abnormities such as slope sliding, dragging and vehicle channeling of the vehicles can be effectively avoided, the safety and smoothness of vehicle starting are improved, and meanwhile the user driving experience and the vehicle intelligent control level are greatly improved.

The application relates to the technical field of laser precision machining, in particular to an optical coherence tomography real-time feedback control system based on neuromorphic computing, which comprises an optical coherence tomography device and a processing module; the optical coherence tomography device comprises a collecting module and an executing module; the collecting module is used for collecting an analog voltage signal corresponding to an original optical interference signal of a biological tissue; the processing module is used for converting the analog voltage signal into a digital signal stream, inputting the digital signal stream into a neural network model, outputting a target depth position index from the neural network model, and generating a control signal according to the target depth position index; and the executing module is used for controlling the energy output of a surgical laser guided by the optical coherence tomography according to the control signal, simulating a reflex arc mechanism of a biological nervous system, constructing a bionic optical reflex arc architecture, and being low in calculation delay and small in computing power consumption. Therefore, the problems that related technologies cannot match microsecond laser pulses and cannot realize precise control are solved.

The application discloses a kind of in-service energy storage battery performance evaluation method, belong to the technical field of energy storage battery safety monitoring.Evaluation method is by analyzing the overall data characteristics of energy storage battery cluster, constructs distributed multi-scale deep learning joint statistical regression algorithm, forms experience model;Wavelet analysis fusion addition counter is constructed, according to the power unit time cumulative principle, the cumulative power difference is calculated to determine the relative output difference of single compared with the remaining battery in the same group.Simultaneously, because the current of series battery is equal, only voltage difference exists, so current value data is not used, and voltage is used for monitoring.The application fuses deep learning experience model, wavelet analysis method, and combines the difference of vehicle-mounted computing power and traditional CPU, reduces the computing power consumption, vehicle-mounted energy consumption and heat stress, improves the safety performance of energy storage battery pack, prolongs the overall calendar life of battery, improves the battery resource utilization efficiency, and reduces the life cycle cost of battery.

The invention discloses a black vehicle-oriented real-time point cloud fusion detection method, system and device, and a medium. The method comprises the following steps: step 1, obtaining point cloud data through a laser radar; 2, scanning the point cloud data, marking the continuous point cloud blocks with the reflection intensity of 0 as unlicensed car identifiers, and storing the unlicensed car identifiers in point cloud attribute positions; step 3, performing dual-thread compensation, performing clustering operation on the point cloud data set with the unlicensed car identifier by using DBSCAN, and performing imaging processing on a clustering operation result by using Point Pill to obtain a three-dimensional image model; forcibly outputting an unlicensed vehicle detection frame image according to an image processing result or improving the confidence coefficient as an unlicensed vehicle compensation confidence coefficient, and preferentially adopting a model result; when the two conditions do not belong to the above two conditions, performing weighted fusion on the operation result; and step 4, fusing operation results in the step 3 into a complete three-dimensional frame image. Therefore, the problem of missing detection is solved, and the detection rate and detection efficiency are improved.

The invention relates to the technical field of man-machine emotion interaction, and discloses a social robot multi-mode emotion analysis intelligent accompanying system, which comprises a data acquisition module used for acquiring voice, image and text input data of a user; the multi-modal emotion recognition module is used for extracting emotion features of the collected voice, image and text data and generating corresponding voice emotion vectors, and the system has five core advantages that three-modal deep fusion is combined with conflict detection and composite emotion analysis, so that the emotion recognition accuracy and anti-interference performance are greatly improved; the multi-Agent collaborative reasoning is matched with a strategy knowledge base, and the co-situation quality of the complex scene is optimized; long-term memory and short-term memory are coordinated, and continuity and individuation of long-term accompanying are The side cloud collaborative architecture reduces the computing power consumption and improves the end side adaptability; the whole process privacy compliance design ensures data security, and lays a foundation for large-scale commercial use.

The application relates to the technical field of intelligent diagnosis of power supply systems and optical communication cross technology, and discloses a power supply system health diagnosis method based on adaptive multi-dimensional ripple feature fusion, which comprises the following steps: short-time coarse sampling of a power supply ripple signal, dynamic adjustment of sampling parameters and window function types according to the coarse sampling result; extraction of frequency domain features, time domain features and time-frequency domain features to form a multi-dimensional feature vector; dynamic updating of a fault threshold value through Bayesian estimation based on historical normal feature samples in a fault feature sample library; adoption of weighted D-S evidence fusion of the fault confidence of the multi-dimensional feature vector based on the fault threshold value, and determination of a system state based on the fault confidence to output a diagnosis result; and hierarchical fault tracing when the system state is an abnormal state. The application realizes adaptive sampling and feature fusion, reduces the fault misjudgment rate, shortens the tracing time consumption, has low computing power consumption, and is suitable for real-time health diagnosis of a data center optical module power supply system.

The invention provides a fault diagnosis method and device based on causal state space prompt, a medium and electronic equipment, and relates to the field of data processing, and the method comprises the steps: mapping each IT operation and maintenance event into an event feature vector; inputting the online state space model according to a time sequence, and outputting a potential hidden state corresponding to each time step; in response to the received IT operation and maintenance diagnosis request, mapping the potential hidden state corresponding to the current time step into a plurality of discrete state lexical elements; and splicing the problem description corresponding to the IT operation and maintenance service interruption, the IT operation and maintenance event occurring in the target time window and the plurality of discrete state lexical elements into a target prompt, and inputting the target prompt into the frozen large language model to obtain a fault diagnosis result. The method not only has the advantage of natural language reasoning of the large language model, but also avoids the illusion problem caused by limited context windows and missing of key causal information, and meanwhile, the use mode of freezing the large language model greatly reduces the training cost and the computing power consumption.

This application discloses a method, system, device, and computer medium for processing vehicle lateral speed, relating to the field of vehicle control technology. The method involves obtaining a sampling time interval for the vehicle lateral speed, generated based on computational and noise limitations for lateral control; obtaining an update time interval for the vehicle lateral speed, where the update time interval is less than the sampling time interval; determining the update moment for the vehicle lateral speed based on the update time interval; and generating the latest vehicle lateral speed according to the sampling time interval when the current moment matches the update moment, so as to perform lateral control based on the latest vehicle lateral speed. The lateral speeds generated in two consecutive iterations are independent, resulting in smooth and continuous lateral speeds. This allows for higher-frequency updates of the vehicle lateral speed, reducing computational consumption and noise impact, ensuring accurate lateral control of the vehicle even under computationally limited conditions.

This disclosure provides a method, apparatus, device, medium, and vehicle for predicting traffic light switching points, relating to the field of data processing technology, specifically big data, intelligent transportation, and cloud computing technologies. It can be applied to navigation, vehicle-to-everything (V2X) technology, smart cockpit, and autonomous driving technology. The method includes: acquiring target intersection image data; performing image recognition on the target intersection image data to obtain a target traffic light identification result; and sending the target traffic light identification result to a server. The server calculates the switching point information of the target traffic light based on the target traffic light identification result and the target traffic light's switching pattern information. This disclosure reduces the time and cost of traffic light switching point prediction, as well as the computational power consumption of the server, thereby improving the accuracy and efficiency of traffic light switching point prediction.

The invention provides a three-dimensional reconstruction method and device, a medium and electronic equipment. The method comprises the following steps: firstly, acquiring an original two-dimensional image containing a to-be-reconstructed main body, then identifying the original two-dimensional image to determine a pixel position corresponding to the to-be-reconstructed main body contained in the original two-dimensional image, and further performing differential coding processing on the original two-dimensional image based on the pixel position to obtain a coded image, a pixel region corresponding to a to-be-reconstructed main body is coded by adopting a first coding parameter with higher coding quality, and other pixel regions are coded by adopting a second coding parameter with lower coding quality, so that the image quality of the to-be-reconstructed main body is preferentially ensured on the premise of controlling the occupation of computing resources and network transmission bandwidth of an acquisition end, and the image quality of the to-be-reconstructed main body is improved. And finally, the encoded image is sent to a server, and the server completes three-dimensional reconstruction of the to-be-reconstructed main body according to the encoded image, so that the overall efficiency and precision of three-dimensional reconstruction are effectively improved.

The application discloses a kind of based on efficient reinforcement fine tuning and rule check multi-modal industrial intelligent detection method and system, method includes: S1.to industrial detection data is divided, and reference sample library and industrial acceptance rule base are constructed;S2.in frozen multi-modal base model, introduce interlayer collaborative adjustment module, and build feature space;S3.in feature space, extract locatable abnormal evidence, and output structured detection result that can be directly accessed in industrial acceptance rule base and check;S4.structurized detection result is input to industrial acceptance rule base and is checked, obtains industrial rule check result;S5.based on industrial rule check result, the related parameter of interlayer collaborative adjustment module is reinforced fine tuning, obtains multi-modal detection model;S6.based on multi-modal detection model, the abnormal detection and industrial acceptance determination of sample to be detected on production line are carried out, obtains final detection result.

A medical event extraction method based on a generative model comprises the following steps: 1) obtaining electronic medical record text data in a database, and pre-setting entity types and event templates to be extracted; 2) in the electronic medical record text, entities and events in the medical record are marked according to the set template through a marking tool; 3) a transformer model is used as a text encoder to convert the text into a vector representation; 4) an entity is marked using a pointer network; 4) an event type is classified using a pooling layer and a linear layer; 5) a generative transformer is used to generate an event information sequence; and 6) arguments in the event information sequence are extracted, and event information is summarized. The unstructured data in the medical electronic medical record is extracted by using the joint extraction algorithm, so that structured event information is obtained, and strong help is provided for subsequent disease analysis of patients.

This invention discloses a high-precision RTK positioning method based on an improved adaptive genetic algorithm-based satellite selection strategy, belonging to the field of satellite navigation and positioning technology. A subset of satellites with a specified number of satellites is selected to minimize the loss of the Position Accuracy Factor (PDOP) and satellite geometric volume. The satellite selection problem is transformed into a combinatorial optimization problem. A fitness function is designed with the reciprocal of PDOP and geometric volume as its core, and adaptive crossover and mutation probabilities are introduced. The genetic operation parameters are dynamically adjusted according to the population evolutionary state to balance global search and local convergence capabilities. Iterative evolution is performed under the condition of satisfying subset size constraints. When the convergence condition is met, the subset of satellites with locally optimal PDOP values ​​is output for subsequent RTK ambiguity resolution and positioning. This invention can quickly converge to a near-globally optimal satellite geometry configuration in polynomial time while also considering satellite quality, significantly reducing computational complexity and improving the real-time performance and reliability of RTK positioning.

This invention relates to the field of industrial manufacturing information management and data processing technology, specifically to a method and system for integrating and querying piecework data related to departmental relationships through tree-like node linkage. The method includes: constructing a hash-dependent directed acyclic graph (DAG) corresponding to the topological structure; obtaining the current composite fingerprint of the target root node; retrieving the current composite fingerprint from a cache library; if the retrieval is successful, directly returning the pre-stored aggregation result mapped in the cache library; otherwise, traversing downwards along the hash-dependent DAG, determining if the fingerprint has not changed and directly calling the pre-stored aggregation result; for sub-branches without a composite fingerprint mapping in the cache library, determining if the fingerprint has changed and performing real-time data aggregation calculation to generate a new aggregation result; establishing a mapping relationship and writing it into the cache library for subsequent query calls. This invention ensures millisecond-level real-time response of piecework cost data in scenarios with frequent engineering changes, eliminating data lag between design changes and financial accounting.

This invention relates to the field of IoT communication technology for lubrication equipment, and discloses a method and system for switching control of IoT edge communication modes for lubrication equipment. Edge nodes collect real-time communication environment data, equipment operation data, and business data requirements. Based on this multi-dimensional data, a multi-dimensional switching decision model is constructed. Physical dimension differences are eliminated through standardization processing, and a comprehensive score for each supported communication mode is dynamically calculated. Dynamic monitoring is performed based on the comprehensive score and trigger conditions including preset difference parameters. When the conditions are met, the target communication mode is selected and seamless switching is performed. When a device fault is detected, a fault adaptation evaluation value is calculated, and a forced switch to a low-latency communication link is initiated. This invention achieves deep coupling between the underlying communication state and mechanical operating conditions, thereby possessing edge emergency takeover and overflow prevention buffering capabilities under network-wide interruption, ensuring equipment operational safety.

The invention belongs to the technical field of power distribution network operation and control, and particularly relates to a power distribution network topology switching path generation method and system based on loop matching, and the method comprises the steps: accurately obtaining a to-be-closed branch and a to-be-disconnected branch of a topology network through the structural difference between an initial topology structure and a target topology structure; the perfect matching between each to-be-closed branch and each to-be-disconnected branch is obtained by constructing the operation bipartite graph, and the complex topology constraint relationship between the branches can be converted into the matching and sorting problem in the graph theory, so that the computing power consumption in the topology switching path acquisition process can be obviously reduced; therefore, the generation efficiency of the topology switching path can be improved; through the timing constraint graph and topology connectivity verification, the non-executable topology switching paths can be screened out, so that the feasible topology switching paths can be accurately reserved. Therefore, the method and the system provided by the invention can efficiently and accurately generate the feasible topology switching path.

The embodiment of the present application relates to the technical field of block chain, and discloses a consensus method of block chain, which comprises the following steps: according to the mixed asset conditions of each node in the current round of account selection, randomly generating a random number list of the current round for each node; the mixed asset conditions comprise asset number and node number; one or more random numbers are included in the random number list of the current round; according to the random number generation range, randomly generating a target random number of the current round; matching the target random number of the current round with the random number list of each node to determine the node of the current round of account; and performing the current round of account through the node of the current round of account. Through the above method, the embodiment of the present application effectively reduces the power consumption of the consensus algorithm, and solves the problem of excessive rights of the node with large currency holding amount.

This invention provides a method and system for road condition data acquisition and analysis based on a commercial vehicle connected driving recorder. The invention utilizes a connected driving recorder, a mandatory standard feature in commercial vehicles, to acquire road images in real time via an image acquisition module and perform preprocessing such as backlight compensation, distortion correction, noise reduction, and effective area cropping. An image recognition module employs a lightweight deep learning and rule-based algorithm to locally identify congestion and traffic accidents. A positioning module acquires latitude and longitude, vehicle speed, and timestamps via GPS / BeiDou dual-mode, achieving precise spatiotemporal binding through timestamp alignment and interpolation compensation. A 4G communication module uploads data using a strategy prioritizing event triggering and supplementing with periodic heartbeats. A data monitoring platform verifies the data, performs 3D deduplication, and then visualizes it on an electronic map. This invention requires no additional hardware, is low-cost, has wide coverage, and strong real-time performance, enabling automatic and precise monitoring of road conditions across the entire area, and is suitable for traffic management and logistics scheduling.

The application relates to the technical field of traffic control and discloses an intelligent road recognition system based on deep learning, wherein an image acquisition device acquires a road surface image sequence and inputs the road surface image sequence into a double-branch decoupling neural network of a roadside edge computing node; a structural feature extraction path extracts structural feature vectors of road boundaries and lane markings through a spatial attention mechanism; an abnormal feature extraction path extracts abnormal feature vectors of crack, pit and water accumulation areas through a frequency domain filtering convolution layer; the edge computing node compares the abnormal feature vectors with a preset road damage level mapping table to generate road damage state features; and a roadside traffic control signal machine determines a speed limit threshold according to the state features and generates a speed limit control signal. The application completes recognition and control locally at the roadside, eliminates delay caused by back-and-forth transmission between the roadside and the cloud, realizes instant synchronization of damage recognition and speed limit instructions, and separates structural and defect feature expressions through decoupling processing.

The invention relates to a battery data encryption communication system and method, and belongs to the technical field of encryption communication, the battery data encryption communication system comprises a server, a battery equipment end and a mobile terminal, the server is used for configuring a pre-shared key for the battery equipment end and the mobile terminal; the battery equipment end and the mobile terminal are used for performing identity verification based on the pre-shared key, setting a random number counter value after the identity verification is passed, encrypting data to be transmitted through the random number counter value and the pre-shared key, and generating an encrypted data packet containing an authentication label; and decrypting the encrypted data packet through the shared key, and determining the legality and integrity of the encrypted data packet through the random number counter value and the authentication tag. The battery data encryption communication provided by the invention can meet the core requirements of safety, real-time performance, equipment adaptability and operation and maintenance convenience.