30results about How to "Resolve lag" patented technology

The invention provides a cross-border trade supply chain risk early warning system based on digital twinning, and relates to the technical field of cross-border trade. The cross-border trade supply chain risk early warning system based on digital twinning comprises an entity layer, a data acquisition layer, a digital twinning modeling layer, a data processing layer, a risk early warning layer, a safety protection layer and an application output layer, and the entity layer is a monitoring object basis of the system and covers all key physical objects in a cross-border trade supply chain; comprising suppliers, logistics carriers, storage facilities, customs nodes and purchasers in the cross-border trade supply chain. The system integrates a multi-source data acquisition module, covers full-dimensional data of policies, logistics, finance, markets, Internet of Things perception and the like, ensures the comprehensiveness of risk factor extraction, improves the coverage of risk identification, greatly improves the accuracy and real-time performance of early warning, and solves the problem that a traditional early warning mode is inaccurate and lagged in evaluation.

The invention discloses an online absorbance detection system with a high dynamic range. The online absorbance detection system comprises a flow cell, a multi-light source assembly, a detection assembly and an electronic control unit, the multi-light-source assembly comprises at least two light sources and at least two incident optical fibers, the front ends of the incident optical fibers are arranged in the flow cell at different insertion depths, and the incident optical fibers and the emergent optical fibers form different optical path lengths; the electronic control unit is lightened by driving the light source in a time-sharing manner, and optical signals under different optical paths are synchronously acquired by using the same detection assembly. Through the structural design without a mechanical moving part, a multi-optical-path detection system is constructed by utilizing the difference of the depth of the optical fiber inserted into the flow cell, and by combining a data processing algorithm in the electronic control unit, the optimal optical path is automatically selected according to the signal intensity to calculate the absorbance. The system has the advantages of wide dynamic range, simple structure, high response speed and the like, and effectively solves the problems of limited high and low concentration detection caused by a fixed optical path and abrasion and lag caused by mechanical switching of the optical path in the prior art.

The invention discloses an information push model updating method and device, equipment, a medium and a program product, and is applied to the technical field of artificial intelligence, the method comprises the following steps: based on an obtained sample data set, constructing K training data sets, each training data set comprising at least one sample data; and using the K training data sets to carry out K rounds of iteration updating on the trained information pushing model, and in the iteration updating process, adjusting the weight of the sample data in a loss function based on the accuracy of a recommendation result prediction value of the sample data. According to the updating mode, an updating mode depending on a manual preset fixed period is completely abandoned, so that the triggering, rhythm and strength of model updating are completely determined by the change condition of data and the instant expression of the model, and the change speed of the external environment is adaptively matched. The dilemma problem that a traditional updating strategy is lagged or excessive due to uncertainty of environmental changes is fundamentally solved.

The application provides a medical question and answer method and system based on cooperative dual-source retrieval enhancement generation, which comprises the following steps: according to an input original medical question and candidate items, performing task self-adaptive query rewriting through a large language model to generate differential diagnosis keywords, entity enhanced queries and hypothetical medical abstracts; for the differential diagnosis keywords, performing an iterative network retrieval with a reflection mechanism to obtain network candidate evidence; for the entity enhanced queries and the hypothetical medical abstracts, performing a hybrid retrieval in a local medical knowledge base to obtain local candidate evidence; aggregating and deduplicating the network candidate evidence and the local candidate evidence, obtaining the aggregated heterogeneous evidence, performing deep semantic correlation scoring through a cross-encoder model, screening out a target evidence set, and splicing the original medical question as context input into a generative large language model to generate a final medical answer. The application improves the accuracy, timeliness and explainability of the medical question and answer system.

The invention discloses a high-efficiency acquisition structure and process applied to a power battery CCS temperature signal. The high-efficiency acquisition structure comprises an FPC of which the front surface is provided with a window and a copper bonding pad is exposed in the window; the NTC sensor is welded and fixed on the copper bonding pad; the aluminum reinforcing sheet is fixed on the back surface of the FPC, corresponds to a windowing position of a copper bonding pad on the front surface of the FPC, has a heat conductivity coefficient of more than or equal to 167W / (mK), is used for reinforcing a welding area on the front surface of the FPC, and is directly attached to the surface of a battery cell shell to conduct temperature, so that short-path heat conduction of the battery cell shell, the aluminum reinforcing sheet, the FPC and the NTC sensor is formed; and the temperature acquisition thermal time constant is 1-2 seconds. According to the invention, the heat conduction path is shortened through the high-heat-conduction aluminum material, the heat conduction efficiency is improved, the response speed is greatly improved, the rapid and accurate acquisition and stable transmission of the cell temperature signal are realized, and the real-time and accurate monitoring of the BMS on the battery state is ensured.

This invention discloses a method and system for constructing and updating a fault mode knowledge graph for motors. The method includes: constructing a motor fault domain ontology model: defining core concept class hierarchy, object attributes and semantic relationships, acquiring structured and unstructured data and defining data attributes, and setting logical constraints and axioms; dual-channel knowledge extraction: using a deep learning model to extract knowledge from unstructured data and a mapping method to extract knowledge from structured data, generating original triplet knowledge; knowledge fusion: integrating knowledge from different data sources through entity alignment, attribute fusion and conflict resolution to construct a high-quality, unambiguous knowledge graph and storing it in a graph database; and intelligent fault reasoning and dynamic updating based on the knowledge graph. This invention fuses multi-source data, more accurately identifies long and difficult entities and complex semantic relationships, and improves the accuracy and efficiency of fault troubleshooting through a dual-weighted algorithm.

The invention discloses a ship power system stability real-time evaluation system, which comprises a data acquisition module, a preprocessing module, an evaluation model module, an early warning module and a display and storage module which are connected in sequence, and is characterized in that the data acquisition module is composed of a distributed sensor array and a synchronous acquisition unit; the invention further discloses a real-time evaluation method. According to the method, by collecting multi-dimensional data in real time and constructing the dynamic evaluation model, real-time comprehensive evaluation of the stability of the ship power system and early warning of system instability are achieved. Compared with the prior art, the method has the advantages of high real-time performance, comprehensive evaluation, excellent adaptive capability, early warning and control linkage, data traceability and the like.

The invention relates to the technical field of computer data processing, provides a business-driven data asset unified management method and system, and solves the technical problems of lagging data asset discovery and low management efficiency. The method comprises the following steps: acquiring a database access log and business metadata; analyzing the database access log, and constructing a sparse tensor according to the screened triple; carrying out dimension reduction decomposition operation on the sparse tensor by utilizing an alternating least square method to obtain a user potential vector, a data potential vector and a time potential vector; taking a data potential vector corresponding to the unaccessed data in the vectors as an input vector, and inputting the input vector into the trained neural collaborative filtering model to obtain a prediction preference value of the unaccessed data; according to the method, the unaccessed data are arranged in a descending order according to the predicted preference values, and potential target data are identified based on the predicted preference values, so that unified management of the data assets is realized, and intelligent active discovery and efficient unified management of the data assets are realized.

This application relates to the field of machine tool component technology, and discloses a tailstock assembly and a machine tool. The tailstock assembly includes a tailstock component, a hook, a guide rail, and a drive assembly. When the tailstock assembly is working, the hydraulic cylinder in the drive assembly is the core power source. When locking is required, the hydraulic cylinder drives the pressure rod to move in a first direction. Because the side of the pressure rod that contacts the base is inclined, the action of the inclined surface is transmitted to the base, causing the hook connected to the base to move synchronously, gradually approaching and connecting to the guide rail on one side of the base. At this time, the tailstock assembly enters the locked state. When unlocking is required, the hydraulic cylinder drives the pressure rod to move in a second direction. The direction of the inclined surface action changes, and the base causes the hook to gradually disengage from the guide rail, switching the tailstock assembly to the unlocked state. Throughout the process, the base provides mounting support for the tailstock component, hook, guide rail, and other components, ensuring that the actions of each component are coordinated and orderly.

The invention relates to the field of sewage treatment, in particular to a dynamic calibration and multi-agent cooperative control method for an intelligent mine water dosing system, which comprises the following steps: S1, collecting multi-source data; s2, extracting a characteristic alumen ustum image; s3, standard liquid is injected into the turbidity meter measuring pool to obtain the slope for first calibration, and second calibration is carried out through a back propagation neural network soft measurement model in combination with the feeding flow and the characteristic alumen ustum image; s4, the flow, the concentration, the turbidity and the image are input into a Gaussian mixture model to judge working conditions, and the total basic dosing amount is obtained through feedforward of a time sequence convolutional network-back propagation neural network mixture model; s5, according to the turbidity and the target deviation and the ideal floc characteristics and the actual deviation, the total compensation dosage is calculated through a fuzzy proportion-integral-differential algorithm, and the total basic dosage is adjusted; and S6, constructing a 9-grid reference ratio look-up table to obtain a reference addition ratio, outputting a correction coefficient according to a hard correction rule (cost priority, extreme dose protection and oscillation suppression), and finally calculating the addition amount of the main flocculant and the coagulant aid.

This invention discloses a real-time piano timbre simulation method and system based on machine learning, relating to the field of audio signal processing technology. The method includes: data acquisition and multi-dimensional annotation, acquiring multiple types of piano audio, covering techniques and seven dynamic levels, and simultaneously acquiring information such as key presses and techniques; audio preprocessing, including pre-emphasis compensation for high frequencies, Hanning window framing, Fourier transform to frequency domain, spectral subtraction for noise reduction and normalization; multi-dimensional feature extraction, extracting static features such as MFCC and spectral parameters, dynamic features such as first- and second-order differences, and overtone structures; two-stage model training, using stacked autoencoders for dimensionality reduction; real-time parsing, filtering and converting acquired performance data into parameter sequences; timbre synthesis, where the model generates a spectrum and performs an inverse Fourier transform into a waveform; and dynamic optimization, receiving user feedback. This invention solves the problems of traditional simulation methods; the two-stage model enhances timbre coherence, dynamic control achieves low latency, and multi-scenario adaptation and feedback optimization meet specific needs.

This application relates to the field of 3D printing technology, and provides a method and system for optimizing parameters of 3D printed solid-state batteries. The method includes: acquiring multiple sensing information during the 3D printing process of a solid-state battery; sending detection signals to a shape sensing device and calculating the processing delay for determining the morphological information of the printed surface based on the detection signals; using the difference between the receiving time of the morphological information and the processing delay as the actual occurrence time of the printed surface corresponding to the morphological information in the physical world; performing time alignment of the multiple sensing information based on the actual occurrence time of the printed surface corresponding to the morphological information in the physical world; and optimizing the process parameters of the solid-state battery 3D printing process based on the time-aligned multiple sensing information. This solution can improve the efficiency of optimizing parameters for 3D printed solid-state batteries.

The application discloses a kind of rigid-flexible coupling two-way closed loop time domain dynamics calculation methods of underwater towing system, belong to the field of ocean engineering and underwater equipment technology;Method includes: establishing multi-coordinate system framework, constructs the six-degree-of-freedom rigid body motion equation of underwater vehicle, buoy and the partial differential dynamics equation of continuous elastic body of tow cable, is integrated into whole coupling model by displacement, velocity and force boundary condition.Set up two-way closed loop calculation framework: in each time step, first solve the boundary force of two rigid bodies based on the state of tow cable, and parallelly use four-order Runge-Kutta method to advance rigid body state simultaneously;Again, the updated rigid body position and velocity are used as forced boundary condition, and the tow cable is solved again using mixed difference method, to obtain the coordinated cable shape and tension distribution at the next time.This application realizes the dynamic synchronous update of rigid body motion and flexible cable deformation, and overcomes the phase lag and amplitude error of traditional weak coupling method.

The invention discloses a multi-section heating intelligent adjustment control system of a lining teflon elbow heating furnace, and belongs to the technical field of heating furnace temperature control. The system comprises a heating furnace partition sensing module, a partition temperature control execution module, an intelligent regulation and control decision module, a safety protection module and a man-machine interaction module. Multiple parameters are collected through a distributed sensing network, the position of an elbow in the furnace and operation data of the heating furnace are obtained through filtering weighting preprocessing, a temperature rise rate set value is dynamically compensated through a multi-temperature-zone collaborative scheduling unit, and then a regulation and control instruction is generated through a multi-section collaborative PID algorithm with feedforward compensation in combination with fuzzy self-adaptive correction logic. Interlocking type two-way regulation and control of heating and cooling of the Teflon-lined elbow heating furnace are achieved, and meanwhile a safety interlocking protection mechanism is matched. According to the invention, the problems of lag and overshoot of the traditional temperature control of the Teflon-lined elbow heating furnace are solved, the control precision of the sintering temperature of the Teflon-lined elbow heating furnace is improved, the sintering requirements of Teflon-lined elbows with different specifications are met, and the consistency of sintering quality is ensured.

This invention provides an integrated intelligent monitoring system and method for critical engineering projects, comprising a sensing layer, a transmission layer, a platform layer, and an application layer connected in sequence. It receives multiple multi-source heterogeneous monitoring data in real time, uploads this data to the platform layer, processes the data, and generates tiered early warning signals. The application layer receives these tiered early warning signals and executes risk response operations. The integrated intelligent monitoring system for critical engineering projects provided by this invention achieves multi-parameter, comprehensive engineering safety monitoring; improves the real-time performance and accuracy of monitoring data; establishes an intelligent multi-level early warning mechanism; provides intuitive data visualization; supports real-time monitoring and early warning on mobile devices; reduces manual monitoring costs; and improves monitoring efficiency.

This invention belongs to the field of data processing technology, specifically relating to a consumer data report generation system based on machine learning. This system aims to solve technical challenges such as insufficient depth in feature mining of large-scale heterogeneous consumer data and homogenization of report generation. The system includes: a multi-source heterogeneous data adaptive ingestion module, a nonlinear feature automated calculation engine, a heterogeneous behavioral semantic association graph construction unit, an incremental machine learning inference center, and a multi-dimensional logical adaptive report generation platform. Through standardized mapping, nonlinear feature extraction, and semantic graph modeling, combined with incremental model inference, the system can automatically match narrative logic according to needs and generate structured, visual reports. This invention achieves deep perception and dynamic real-time processing of massive amounts of consumer data, improving report production efficiency and business insight, and providing an efficient and adaptive solution for consumer decision-making.

The invention discloses a RESTful API intelligent application method based on a large language model, and the method comprises the following steps: automatically extracting API endpoints, parameter entities and response code information from an OpenAPI standard document, a source code annotation, HAR flow data, a WITSML standard data stream of a petroleum drilling geological design system, and an SDK document of Landmark and Schlumberger platforms through employing a BERT series model in combination with a rule template; the method comprises the following steps of: vectorizing a natural language instruction input by a user through a Sension-BERT model; calculating a comprehensive score based on three dimensions of cosine similarity of a query vector and an API description vector, a Jaccard similarity coefficient of a parameter set and a historical calling frequency; an API calling request is constructed and executed, a dynamic timeout threshold value based on a historical response time P95 quantile is set, and a backspacing strategy related to the field is configured for a key API. According to the method, end-to-end intelligent API calling is realized, multidisciplinary data islands such as geology and engineering are effectively broken through, and the automation level and service continuity of complex system integration are remarkably improved.

This application relates to the field of carbon emission monitoring technology, specifically to a method and system for intelligent carbon emission monitoring based on multi-source data fusion. The method includes: real-time acquisition of heterogeneous multi-source data and preprocessing to generate standardized data; constructing a quantitative correlation model between production conditions, environmental meteorology, emission concentration, energy metering, and carbon emissions, and extracting correlation features; dynamically updating calculation parameters based on standardized data and measured correction values, where the measured correction values ​​are obtained through collaborative calculation of emission outlet concentration, flue gas flow rate, environmental meteorology, and production load; receiving correlation features and corrected parameters at an edge computing end and performing local real-time calculations; and receiving the edge calculation results at the cloud end and performing collaborative verification and parameter optimization in conjunction with global data. This invention, through multi-source data fusion, dynamic parameter correction, and edge-cloud collaboration, solves the problems of single monitoring data, fixed factors, and delayed processing in traditional monitoring methods, achieving high-precision, real-time intelligent carbon emission monitoring.

The invention discloses a green building material product authentication method based on real-time carbon emission data, and the method comprises the following steps: S1, setting an energy consumption collection device at an energy consumption node of a green building material production line, and collecting energy consumption data in real time; s2, performing operation on the original energy consumption data and a regional dynamic emission factor library to obtain a product carbon footprint (CFP); s3, comparing the unit product carbon footprint CFPunit with a dynamic threshold value generated in real time; s4, determining whether to issue, maintain, freeze or cancel a green building material authentication certificate according to a comparison result; s5, the original emission data, the regional dynamic emission factor version number, the accounting script hash and the certificate state are written into a block chain for evidence storage; the technical scheme of the invention has the following advantages: the authentication efficiency is revolutionarily improved, and the period is greatly shortened; the calculation accuracy is remarkably improved, and errors are effectively reduced; certificate validity is dynamically managed and controlled, and standard exceeding after certificate issuing is completely eradicated.

The invention discloses a boiler air pre-heater and a full-load air leakage online monitoring system and method for a tail flue of the boiler air pre-heater, and belongs to the technical field of heat energy engineering. The system comprises a total cross-section automatic blowback stable inspection system, a data preprocessing module, a full-load air leakage rate calculation module, a sealing characteristic analysis module and a closed-loop maintenance and optimization module. The method comprises the following steps: continuously acquiring key section oxygen parameters in a boiler full-load interval through a patrol system, and after temperature and dust compensation pretreatment, inputting an air leakage rate model covering full load to calculate a real-time air leakage rate; generating an air leakage rate-load characteristic curve based on the multi-load air leakage rate data, and analyzing the load adaptation characteristic of the sealing system; according to the real-time air leakage rate and the characteristic analysis result, closed-loop operation of air leakage early warning, maintenance guidance and induced draft fan operation correction is achieved. According to the method, the problems of low operation energy efficiency and high energy consumption caused by insufficient air leakage dynamic capture capability and lack of full-load monitoring are solved.

This invention discloses a behavior monitoring and companionship system based on visual recognition and voice interaction. The system includes an image acquisition module that collects user image data; a behavior recognition module that analyzes the monitored person's behavior based on human posture key point detection, eye direction recognition, and hand movements, and uses multimodal feature fusion to identify abnormal behavior using a threshold discrimination algorithm; an anomaly detection and response module that executes response operations, performs voice broadcasting, and uploads data to the cloud; an intelligent voice assistant module that interacts with the user via voice; a dynamic visual feedback and auxiliary lighting module that generates dynamic facial expressions to provide visual psychological compensation for the user; and a remote terminal for remote monitoring. By improving the depth of recognition and adaptability to complex learning behaviors, the system achieves accurate identification and proactive intervention in children's learning states, thereby comprehensively improving children's learning concentration and the level of intelligent supervision of family education.

The embodiment of the present application relates to the technical field of battery pack dehumidification, and discloses a dehumidification device, a battery pack assembly, a dehumidification method and a vehicle, the dehumidification device comprising: a flow guide cover, a condensation fin, a gas conveying device and a water storage device; the flow guide cover is formed with an airflow channel, and the flow guide cover is further formed with an air inlet and an air outlet which are in communication with the airflow channel; the water storage device is arranged at the air outlet; the condensation fin is arranged in the airflow channel, and the condensation fin is formed with a flow guide groove, the flow guide groove is arranged along the extension direction of the airflow channel, and the flow guide groove is used for guiding the condensate water on the condensation fin to flow to the water storage device; and the gas conveying device is suitable for conveying gas from the air inlet to the air outlet. By using the technical scheme of the present application, the condensate water in the battery pack assembly can be removed.

This application provides a data processing method, electronic device, medium, and product; wherein the method includes: acquiring performance information and operational information of a database during business operations; the operational information is used to indicate information associated with the operational status of each data table in the database; determining maintenance information of the database based on the operational information; the maintenance information is used to indicate a first index to be deleted and / or a second index to be added in the database; deleting the first index and / or adding the second index based on the performance information; through this application, timely maintenance of the database can be achieved, while reducing the performance impact of index maintenance on the database.

The invention discloses a multi-loop power supply abnormity cooperative regulation and control system for an oil storage station, and belongs to the technical field of industrial power supply and distribution safety and intellectualization. The system adopts a'cloud-side-end 'distributed intelligent architecture, and comprises a field sensing and execution layer, a regional cooperative control layer and a global optimization decision-making layer. Electrical and non-electrical quantity information is collected in real time through a multi-source heterogeneous data fusion sensing network; based on a dynamic topology and multi-agent cooperation mechanism, rapid abnormity diagnosis and flexible isolation are realized; fault clearing and seamless power supply switching are realized by using a software-defined flexible switch; based on a digital twinning and global optimization engine, a collaborative recovery strategy is generated and verified. The system has self-learning and ductile evolution capabilities, and can be continuously optimized from historical events. According to the invention, the crossing from the static hysteresis response to the dynamic cooperative active regulation is realized, and the safety, reliability and recovery capability of the power supply system of the oil storage station are remarkably improved.

The invention discloses a model prediction control method and system for a totem-pole PFC circuit, and belongs to the technical field of totem-pole PFC circuits, and the method comprises the steps: monitoring the input voltage of an AC power supply, the bus voltage of the totem-pole PFC circuit, and load demand information in real time; determining that the totem-pole PFC circuit works in a boost mode or a voltage-multiplying mode and driving signal distribution logic of each switch tube in the bridge circuit; calculating an optimal modulation ratio based on a model prediction control algorithm according to an error between the target bus voltage and the currently detected bus voltage; generating a first SPWM signal with a corresponding duty ratio and a complementary second SPWM signal according to the optimal modulation ratio; and distributing the first SPWM signal and the second SPWM signal to corresponding switch tubes to realize control of the bus voltage. According to the method, the SPWM duty ratio is dynamically adjusted through model prediction control, and rapid, accurate and stable control over the totem pole PFC bus voltage is achieved.

The application relates to the technical field of shield slag measurement, and discloses a real-time measurement method and system for the slump degree and water content of shield slag, the method comprising the following steps: a sensing assembly acquires resistance information of shield slag; a signal processing unit receives environmental data of a shield slag belt conveyor, and processes the environmental data and the resistance information to obtain standardized multidimensional data; a data conversion module performs data shunting on resistance impedance digital values and belt speed digital values in the standardized multidimensional data, performs inverse discrete Fourier transform on the frequency domain components to obtain a reflection coefficient and an attenuation coefficient, calculates a dielectric constant according to the reflection coefficient and the attenuation coefficient, calculates the water content of the slag according to the dielectric constant, inputs time domain amplitude and the belt speed digital values into a nonlinear regression model, and outputs a slag slump degree value; and a display terminal displays the slag slump degree value and the water content of the slag. The application can solve the data lag problem and improve the accuracy and reliability of the slag state data.

The invention relates to the technical field of water treatment environmental protection equipment, and discloses a micro-nano bubble strengthening based efficient flotation water treatment device which comprises a material box, a bubble generation assembly, a central stirring assembly and a bubble scraping assembly. The control system comprises a data perception module, a fluid analysis module, a phase decision-making module and an execution cooperation module, and the system collects the instantaneous active power of a main motor in real time to calculate the load drop rate and quantify the bubble saturation of an impeller cavity. Meanwhile, differentiating the dissolved oxygen data to obtain the average oxygen consumption rate. When the load drop rate reaches a threshold value, the system controls the frequency converter and the circulating pump to be switched from a high-pressure suction phase to a low-shear diffusion phase, and bubbles are prevented from being damaged by strong shear; and the dosage of the compensation medicament is fed forward according to the oxygen consumption rate. Through self-adaptive monitoring of load characteristics and double-phase time-sharing decoupling control, the problems that micro-nano bubbles are prone to coalescence and fracture and dosing lags behind are solved, and the air flotation efficiency and the load fluctuation resistance are improved under the condition that an intrusive sensor is not additionally arranged.

The invention discloses a customer asset intelligent management system based on a hierarchical dynamic driving model, which relates to the technical field of business data processing and comprises a unified data middle table, a hierarchical dynamic driving engine, a strategy driving unit, a feedback optimization unit, a loss early warning and intervention unit and a quantitative stripping decision unit. According to the invention, dynamic and automatic customer asset management is realized, and the system can automatically execute a precise strategy for intervention, upgrading or stripping, so that the overall value and operation efficiency of customer assets are remarkably improved, and the management efficiency of the customer assets is improved.

This utility model discloses a battery health detection circuit and detection terminal, including: a voltage detection module, a load module, a control module, and a load switching module; the control module is electrically connected to the load module through the load switching module; the load module is electrically connected to the control module through the voltage detection module; the positive terminal of the load is electrically connected to the positive terminal of the battery. Beneficial effects: Through dynamic load switching and voltage difference detection, the battery health status can be predicted in advance, avoiding the interruption of backup power function due to sudden battery failure, and ensuring that the equipment can still work normally when the mains power is lost.

The invention provides a drivable area ODD platform device based on cloud end-vehicle end cooperation. The drivable area ODD platform device based on cloud-vehicle end cooperation comprises a cloud core layer, a vehicle end execution layer and a tool supporting layer which operate cooperatively. The cloud core layer is used for storage, processing, version management and issuing of non-ODD area data; the vehicle end execution layer is integrated on an intelligent driving vehicle-mounted system and is used for data receiving, real-time calculation, mode switching and early warning prompting; the tool supporting layer provides operation, maintenance and verification tools for the cloud core layer and the vehicle end execution layer. The drivable area ODD platform device based on cloud end-vehicle end collaboration has the advantage of realizing full-process closed-loop management of non-ODD area data from acquisition, processing and updating to execution and feedback by constructing a three-layer collaboration architecture.