103 results about "Analysis center" patented technology

The invention provides an intelligent fire-fighting equipment self-inspection and fault early warning system based on an AI algorithm. The intelligent fire-fighting equipment self-inspection and fault early warning system comprises a sensing layer used for integrating a multi-source heterogeneous sensing unit; the edge layer is used for deploying intelligent preprocessing nodes; the transmission layer is used for constructing a safe and reliable transmission channel; the platform layer is used for establishing an intelligent analysis center; the application layer is used for providing intelligent decision service; and the five layers of architectures work cooperatively. According to the invention, full-dimension monitoring of fire-fighting equipment is realized through a multi-physics field cooperative sensing framework of a sensing layer, and the fault detection capability is remarkably improved. The multi-source heterogeneous sensing unit integrated in the sensing layer can capture various physical quantity information such as acoustics, vibration and thermal fields at the same time, information of different physical fields is complementarily enhanced through a cross-domain feature complementation enhancement mechanism, and more comprehensive equipment state features are obtained. In particular, the sensor self-adaptive excitation technology dynamically adjusts the excitation energy of the sensor according to the environmental interference degree, and ensures that high-quality signals can be obtained under different working conditions.

The invention relates to the technical field of press monitoring, in particular to a digital twinning-based three-dimensional monitoring system for a precision servo press, which comprises a physical layer sensing module for acquiring real-time operating parameters, environment variables and workpiece processing data of the press; the dynamic twin construction module constructs a total-factor digital twin, and simulates a force-heat-deformation coupling effect by using finite element analysis and a multi-body dynamics algorithm based on physical attributes and process parameters; the intelligent analysis center identifies a potential fault mode of the press machine and locates an abnormal source through multi-physics field simulation data in combination with an improved CNN-LSTM model; the three-dimensional visual interaction unit constructs an interactive immersive three-dimensional virtual scene, renders a running state and a processing process in real time, and generates a maintenance strategy; and the self-adaptive regulation and control unit predicts the residual life of the key component and dynamically adjusts parameters according to a maintenance strategy and real-time monitoring data. Therefore, the problems of single monitoring dimension, disjunction of maintenance strategies and the like in the prior art are solved.

The invention discloses a logistics rapid distribution analysis system based on artificial intelligence, and relates to the technical field of logistics distribution analysis, in which orders, vehicle positions and traffic flow data are collected in real time, a distribution environment state is constructed, an intelligent agent is used as an independent decision unit deployed on a distribution vehicle, and the intelligent agent is used as an independent decision unit deployed on the distribution vehicle. The local planning module is used for generating a path and a resource allocation scheme, and a local scheme is shared through the communication module, so that collaborative optimization is realized, and the disadvantage that traditional static planning is difficult to deal with order changes and emergencies is solved; in the delivery process, the intelligent agent dynamically adjusts the path and task priority according to traffic and order data updated in real time, so that the loss caused by traffic congestion or order delay is remarkably reduced, and meanwhile, continuous improvement of path planning is realized through feedback adjustment; and the analysis center combines an NSGA-II algorithm to bring the distribution time, cost and task balance degree into a target function, optimizes an MADDPG strategy based on actual execution data, and improves global benefits.

The invention discloses a building facility detection data intelligent analysis and report system and method, and relates to the technical field of building structure health monitoring, and the method comprises the steps: achieving the high-precision data capture in a strong light dust raising environment through a laser radar and a dual-spectrum camera of a multi-mode anti-interference collection module; a millimeter wave radar array and an IMU inertial unit of the high-risk area strengthening module penetrate through severe working conditions to monitor key structure displacement; the credibility management engine is provided with an NFC timestamp chip and a double-chain block chain, and data judicial-level credibility and operation traceability are ensured; through humidity response gel packaging and Peltier semiconductor refrigeration of the self-maintenance sensing network, node environment self-adaption and drift suppression are achieved; and the multi-stage analysis center fuses edge-cloud computing power based on federal learning, generates a dynamic risk map and automatically outputs a compliance report. Accurate and timely reference is provided for operation and maintenance decisions, and the safety management level of building facilities is effectively improved.

The invention discloses a construction engineering site safety monitoring and early warning system, which comprises a high-precision sensor network, an intelligent video monitoring system, a data processing and analysis center, an early warning and decision support platform, an early warning and decision support platform, a mobile terminal application, a satellite positioning and geographic information system GIS integration and an internet of things gateway and edge computing device, the high-precision sensor network is composed of various sensors deployed at key parts of a building structure, construction equipment, a temporary support system and the like, and is used for monitoring the stress state and the deformation condition of a structure body and the operation parameters and the environment information of the equipment in real time; the data is transmitted to a data processing center through a low-power-consumption and long-distance wireless communication technology; according to the invention, a high-precision sensor network is constructed, real-time and accurate monitoring of information in multiple aspects such as key parts, construction equipment and environment of a building structure is realized, the condition of a construction site is comprehensively and accurately mastered, and monitoring dead angles and information omission are effectively avoided.

The invention discloses an information operation and maintenance monitoring system, which belongs to the technical field of information operation and maintenance monitoring and comprises a heterogeneous data acquisition layer, a streaming processing engine, an intelligent analysis center, a closed-loop control module, a visual platform and a safety protection system. Physical equipment, virtualized resources and a cloud native environment can be fully covered, the breadth and depth of data acquisition are ensured, the adaptive sampling and edge computing technology improves the efficiency and real-time performance of data acquisition, the data transmission delay and the central processing pressure are reduced, a streaming processing engine and an intelligent analysis center are combined with various algorithms and models, and the real-time performance of data acquisition is improved. Millisecond-level data processing and anomaly detection are realized, the accuracy and timeliness of fault identification are improved, the root cause positioning and fault prediction functions help operation and maintenance personnel to quickly position the root cause of a problem and formulate preventive measures, and the fault occurrence probability and the influence range are reduced.

The invention relates to the technical field of engineering structure monitoring, in particular to a structure health monitoring method based on a distributed strain dynamic test. According to the technical scheme, the method comprises the steps of arrangement of a composite sensor array, acquisition of strain data, self-adaptive data transmission, operation of a data processing and analysis center and operation of a man-machine interaction interface, and the composite sensor array composed of multiple types of distributed strain sensors is arranged at an engineering structure part. The strain sensors comprise a fiber bragg grating strain sensor, a resistance strain gauge and a piezoelectric film strain sensor. According to the invention, various types of distributed strain sensors are adopted to form a composite sensor array, and comprehensive, real-time and accurate health monitoring and damage prediction of an engineering structure are realized in combination with a self-adaptive data transmission network and an advanced data processing and analysis technology; and more effective technical support is provided for safety guarantee of civil engineering structures.

The invention discloses a geological disaster prediction system, and the system comprises a multi-source data collection module which is used for obtaining monitoring data related to a geological disaster; the data processing and analysis center is connected to the multi-source data acquisition module and is used for processing and analyzing the acquired data; and the early warning information issuing platform is connected to the data processing and analysis center and is used for issuing early warning information obtained according to a data analysis result. According to the geological disaster prediction system, the prediction accuracy and the response speed can be remarkably improved, powerful technical support can be provided for disaster prevention and reduction work, and losses caused by geological disasters are effectively reduced.

The invention relates to the technical field of coal mine safety monitoring, and particularly provides a coal mine water disaster source intelligent identification system fused with a multi-source sensor. The system adopts a hierarchical architecture with edge intelligence and center decision collaboration, comprises an edge sensing and computing node, a region fusion decision node and a ground intelligent analysis center, and realizes real-time identification and early warning of water disaster risks through local feature extraction, multi-source information fusion and global situation research and judgment. According to the invention, the network load is reduced, and the autonomous emergency capability and the overall reliability of the system during communication interruption are improved.

The invention relates to the technical field of medical information management, in particular to a medical information management system and a medical data sharing method. Comprising a heterogeneous data access module, a distributed data lake module, a block chain core layer module, a dynamic authority control module, a cross-chain Internet gateway module, a big data analysis center module, a clinical decision support module, a credible audit tracking module, a patient sovereignty portal module and a system management console module. The system has the advantages that the block chain core layer module establishes a patient main index and data hash evidence storage mechanism, a data barrier between heterogeneous medical systems is broken through in combination with the heterogeneous data access module and the cross-chain Internet gateway module for standardized protocol conversion, and the dynamic authority control module responds to an intelligent contract to execute zero-knowledge proof verification, so that the safety of the system is improved. Permission verification is completed on the premise that original privacy data does not need to be transmitted, the cross-mechanism data retrieval response time is shortened, sensitive fields are automatically filtered through a data desensitization engine, and a technical basis is provided for hierarchical diagnosis and treatment and medical conjoined cooperation.

The present application provides a method and system for user behavior analysis based on big data. By obtaining the query logs of target users on an Internet search service platform; extracting topic tags and long-tail query data; determining multiple behavior interest degrees based on the long-tail query data, and determining multiple query interest sub-tags based on the query interest index and all the behavior interest degrees; performing query pointing association between the query interest sub-tags and the topic tags to obtain multiple pointing association vectors, and determining behavior trend characteristics based on all the pointing association vectors; obtaining the query phrase input by the target user, performing query matching between the query phrase and the behavior trend characteristics to obtain query behavior characteristics; and uploading the query behavior characteristics to the user behavior analysis center. The above solution can accurately capture the query phrases of users based on the query behavior characteristics, and make associated classification of the search interest directions of the query phrases, so as to realize the accurate analysis of the user search behavior pattern.

The invention relates to the technical field of e-commerce, in particular to a distributed data processing system based on big data, and the system comprises an internal data collection end which is used for collecting user transaction data, commodity data and behavior logs of an e-commerce platform in real time; the external data acquisition end is used for acquiring consumer credit data and social network behavior data of the third-party platform through an API (Application Program Interface); the data classification module is used for carrying out initial classification on the collected data by adopting a multi-level label system and generating classification labels with weight coefficients; the data screening module is used for dynamically filtering the classified data according to a preset threshold condition, and the threshold condition comprises data timeliness, integrity and confidence; the data error correction module is used for correcting the labels of the misclassification data set and feeding back the corrected data to the input end of the classification module; and the plurality of distributed data analysis centers generate 16 user portrait dimensions from A1 to D4, and the analysis centers execute data modeling of the corresponding dimensions in parallel.

The invention discloses a fence piece production line monitoring device and monitoring system, and relates to the technical field of production line monitoring. Comprising a data acquisition device, an edge computing device, a feedback control device, a whole-process quality tracing module and a cloud platform data analysis center. The data acquisition device acquires multi-station multi-mode real-time data, the edge computing device performs data fusion, preprocessing and preliminary abnormality diagnosis, the cloud platform develops deep analysis and model learning, the feedback control device generates an instruction to adjust production line parameters, and the whole-process quality tracing module realizes production whole-link data binding tracing. According to the method, accurate diagnosis of production abnormity, predictive maintenance of equipment, dynamic optimization of the process and closed-loop control of quality are realized, the production efficiency and the product quality are greatly improved, and the failure shutdown loss is reduced.

The invention discloses a database autonomous protection method based on a dual-system architecture, and belongs to the technical field of database information security. The static measurement agent calculates SM3-256 hash values for all key files of a database in a trusted environment, and the SM3-256 hash values serve as static trusted references to be uploaded and safely stored to a trusted analysis center. And the dynamic measurement agent loads an eBPF program to enter a baseline learning mode. The monitoring and intelligent response during operation comprises static monitoring, dynamic monitoring and correlation analysis and response. Through the design of the dual-system architecture, the protection system and the computing system run in parallel, and the continuous protection capability of the database system is realized. Therefore, the database system has deep security protection capability, and confidentiality, integrity and availability of the database system can be effectively guaranteed when the database system is faced with known and unknown threats.

The invention discloses a transformer substation multi-parameter intelligent monitoring and linkage control system which is based on an intelligent transformer substation internet of things management and control platform, a central control module is arranged in the management and control platform, and the management and control platform comprises an intelligent power monitoring system, a linkage control system and a digital twinborn simulation engine. The intelligent power monitoring system comprises a multi-parameter sensing terminal group, an edge computing node and a cloud analysis center which are arranged in a transformer substation target area, wherein the multi-parameter sensing terminal group collects electrical parameters, environmental parameters and equipment state parameters in real time; the linkage control system comprises an instruction analysis module, a multi-stage execution mechanism and a feedback verification circuit. The linkage control system receives an early warning signal of the intelligent power monitoring system through the Internet of Things management and control platform, and drives the multi-stage execution mechanism to execute a preset linkage strategy. The transformer substation intelligent management and control system integrates multi-parameter real-time monitoring, dynamic fault early warning, digital twin simulation and hierarchical linkage control, and is suitable for unmanned operation and maintenance of an intelligent transformer substation.

The invention relates to a Beidou No.3 precise point positioning method based on double-frequency combination and analysis center product optimization, and belongs to the technical field of precise point positioning, and the method comprises the following steps: S1, constructing three ionosphere-free combinations B1C-B2a, B1C-B3I and B1I-B3I by using four frequency point signals B1C, B2a, B1I and B3I; s2, through comparing a root mean square value RMS of the positioning deviation, the final positioning precision and the convergence time, respectively comparing and evaluating precision point positioning PPP performance of BDS-3 in different frequency combinations under precision products issued by a plurality of analysis centers; and S3, finding out the ionosphere-free combination and analysis center product with the optimal PPP calculation performance.

This invention discloses a method and apparatus for drill bit wear detection and remaining life prediction. The method includes: acquiring multimodal data of the drill bit; performing time-series alignment of the multimodal data using a dynamic time warping algorithm; inputting the time-series aligned multimodal data of the drill bit into an intelligent analysis center and outputting the remaining life prediction result of the target drill bit; using a visual prediction model to segment the wear region of the target drill bit; using a dynamic rating algorithm to classify the wear region; and using a digital twin simulation model, which is a multi-scale simulation system driven by digital twin simulation technology based on wear region segmentation results, classification results, real-time point cloud data of the target drill bit, and real-time sensor data, which can improve the detection accuracy, real-time performance, model generalization, and life prediction capability of drill bit wear.

The application discloses an insulation level online evaluation method applied to a vehicle-mounted metal closed device, which comprises an insulation main body, a grounding shielding layer, flexible insulation glue, a monitoring sensor, a terminal processor and a background data analysis center; the grounding shielding layer is uniformly arranged on the outer surface of the insulation main body, the local area of the insulation main body, where the monitoring sensor is arranged, is not provided with the grounding shielding layer, the shell of the monitoring sensor is annularly contacted with the grounding shielding layer after the monitoring sensor is arranged, the shell of the monitoring sensor and the grounding shielding layer completely wrap the insulation main body to form the metal closed device, the flexible insulation glue is filled between the bottom surface of the monitoring sensor and the insulation main body, the terminal processor is arranged near the insulation main body and is used with the vehicle, signal transmission is realized through wired connection between the terminal processor and the monitoring sensor, and data exchange is realized through wireless connection between the terminal processor and the background data analysis center. The metal closed grounding shielding effect and the partial discharge detection precision of the insulation main body can be considered, the monitoring principle is scientific and reasonable, the structure is simple and reliable, the shielding effect is good, the anti-interference capability is strong, the detection sensitivity is high, the cost is low, economic benefits are outstanding, and technical progress is remarkable.

The analysis system of the present invention comprises: an analysis device (200) for analyzing a sample managed by an analysis center (1200); and a server device (100) managed by a service center (1100). The server device (100) associates and stores a first reference sample ID for identifying a first reference sample with a first range based on the first reference value, the analysis device (200) analyzes the provided first reference sample without being informed of the first reference value, and sends the first reference sample ID and the analysis value of the first reference sample associated with the first reference sample ID to the server device (100). When the server device (100) determines that the analysis value of the received first reference sample does not belong to the first range corresponding to the received first reference sample ID, it outputs an abnormal signal.

The invention discloses an intelligent monitoring system and a monitoring and early warning method for an underwater composite material protection cover. The intelligent monitoring and early warning method comprises the following steps: (1) monitoring the deformation of a glass fiber reinforced plastic composite material in real time by installing a fiber bragg grating deformation monitoring sensor in a composite material structure layer of a protective cover; (2) mounting a displacement monitoring sensor on the inner side of the protective cover, paving the displacement monitoring sensor in the glass fiber reinforced plastic, and wrapping and fixing the displacement monitoring sensor with the glass fiber reinforced plastic; (3) installing a floating camera above the protective cover to carry out image monitoring in real time; (4) transmitting detection data to a signal collector and an oil-gas platform data analysis center which are arranged on the protective cover in a wired or wireless manner; and (5) setting an alarm program in an oil and gas platform data analysis center. According to the intelligent early warning method, the condition that the protection system is damaged can be effectively monitored in time, and meanwhile, the patrol pressure of the submarine robot can be reduced; in addition, an alarm mechanism and an image checking mechanism are designed, and the applicability of alarm prediction is improved.

The invention discloses an intelligent seismic intensity analysis system based on bidirectional dynamic visual identification, and relates to the field of intelligent seismic intensity analysis, and the system comprises an intelligent analysis center which is in communication connection with a data collection module, a data processing module, a data analysis module and a seismic intensity evaluation module. The method comprises the following steps: collecting video recording data of a building when an earthquake occurs, processing the collected video recording data of the building to obtain a building height change curve graph and a building height change broken line graph, and analyzing the obtained building height change curve graph and the building height change broken line graph to obtain a building height change curve graph and a building height change broken line graph; according to the method, the seismic intensity evaluation coefficient is obtained, and the seismic intensity is evaluated according to the obtained seismic intensity evaluation coefficient, so that the accuracy of seismic intensity evaluation is further improved, and the damage degree of the earthquake to the building is accurately explained.

The invention provides a voice interactive health monitoring system. The voice interactive health monitoring system comprises a voice interaction module, a physiological data acquisition module, a data analysis center and an early warning feedback module, the voice interaction module is connected with at least one wearable medical device and used for receiving and analyzing a natural language instruction of a user, generating a device operation instruction according to the natural language instruction, sending the device operation instruction to the corresponding wearable medical device and feeding back an operation result to the user; the physiological data acquisition module is used for acquiring physiological index data of a user from at least one wearable medical device and sending the physiological index data to the data analysis center; the data analysis center is used for performing dynamic analysis and anomaly detection on the received physiological index data of the user based on a machine learning algorithm, and sending an analysis and detection result to the early warning feedback module; and the early warning feedback module is used for sending graded early warning information to the user or the medical staff according to the received analysis and detection result. According to the invention, the health monitoring service can be provided for the user in a simpler and more convenient manner.

1. The name of the product of this design: Graphical user interface of tumor gene mutation analysis system for electronic equipment. 2. Purpose of this design product: This design product uses an electronic device as a carrier to operate a tumor gene mutation analysis system through the electronic device and to perform interactive operations on the tumor gene mutation analysis system. 3. The design highlights of this appearance design product lie in the interface content of the graphical user interface on the product screen. 4. The picture or photo that best illustrates the design points: Interface change state diagram 1. 5. If the electronic device is an existing product, the rear view is omitted; if the electronic device is an existing product, the left view is omitted; if the electronic device is an existing product, the right view is omitted; if the electronic device is an existing product, the top view is omitted; if the electronic device is an existing product, the bottom view is omitted. 6. Purpose of the graphical user interface: used to run the tumor gene mutation analysis system and improve the work efficiency of business personnel. Main view: Run the program software to enter the main view; Interface change state diagram 1: Enter the user login information in the main view and click the login button to enter the interface change state diagram 1; Interface change state diagram 2: Click the sample management button in the interface change state diagram 1 to enter the interface change state diagram 2; Interface change state diagram 3: Click the experiment management button in the interface change state diagram 2 to enter the interface change state diagram 3; Interface change state diagram 4: Click the analysis center button in the interface change state diagram 3, and click the batch analysis button in the pop-up sub-option to enter the interface change state diagram 4; Interface change state diagram 5: Click the sample analysis button in the interface change state diagram 4 to enter the interface change state diagram 5; Interface change state diagram 6: Click the report center button in the interface change state diagram 5 to enter the pop-up : Click the Report Management button in the sub-option to enter the interface change state diagram 6; Interface change state diagram 7: Click the Report Template button in the interface change state diagram 6 to enter the interface change state diagram 7; Interface change state diagram 8: Click the Statistics Center button in the interface change state diagram 7, and click the TAT Statistics Duration button in the pop-up sub-option to enter the interface change state diagram 8; Interface change state diagram 9: Click the System Configuration button in the interface change state diagram 8, and click the Analysis Process button in the pop-up sub-option to enter the interface change state diagram 9; Interface change state diagram 10: Click the Analysis Tools button in the interface change state diagram 9 to enter the interface change state diagram 10; Interface change state diagram 11: Click the List View button in the interface change state diagram 10 to enter the interface change state diagram 11.

The invention provides a water quality monitoring system based on the Internet of Things, and the system comprises a sensor network which is used for obtaining related parameter data of a target water area, and transmitting the parameter data to a data processing and analysis center in real time through an Internet of Things communication module; the data processing and analysis center is used for processing and analyzing the parameter data to obtain prediction data and sending the prediction data to the user interaction platform; and the user interaction platform is used for visually displaying the prediction data. According to the invention, real-time monitoring of water quality parameters can be realized, and timeliness and accuracy of monitoring can be effectively improved.

The invention discloses a data acquisition and evaluation method for a digital twin network, and designs a data acquisition and evaluation system for the digital twin network. Digital twin network management software, a virtual switch, virtual equipment, a network mirror image data acquisition and analysis terminal and a network mirror image data evaluation and analysis center are designed in a digital twin network data acquisition and evaluation system, and system composition, functions and processes are explained. According to the method, a safe, controllable and efficient research environment is realized by constructing a digital twin network environment, a brand new mode is provided for network data acquisition and evaluation, and the defects of actual physical network data acquisition and evaluation can be overcome.

The invention discloses an AI-driven natural resource data checking and cascading updating method and device based on a rule engine, and the method comprises the steps: decoupling a process into four technical components: 1) a field operator library which provides an atomized checking and updating function; the rule engine serves as a configuration and analysis center of a configuration rule; 3) an AI auxiliary module which assists a user in generating a process framework and rule parameters; and 4) a process executor which is responsible for driving the process, scheduling operators and managing transactions. According to the method, an AI auxiliary module assists a user in defining a process template and generating rule parameters, and the rule parameters are written into a rule engine; and the process executor starts the process and starts a transaction, requests rule configuration from the rule engine according to steps, calls the operator library to execute operation, runs through the process of'checking-updating-re-checking ', and ensures the atomicity of cascade updating through a transaction mechanism. According to the invention, decoupling of execution and logic is realized, and the definition threshold of business rules is obviously reduced.

The invention discloses a chain type anti-intrusion intelligent early warning system for a highway reconstruction and extension construction area. The chain type anti-intrusion intelligent early warning system comprises a sensor network which is responsible for collecting multi-dimensional data in real time; the data processing and analysis center is used for storing and processing the collected data through a cloud computing platform, determining which features are crucial to the early warning effect in combination with the actual situation and domain knowledge of the highway reconstruction and extension construction area, and automatically searching an optimal feature subset by adopting a recursive feature elimination method to reduce the risk of key feature omission; the intelligent early warning and decision support module is used for simulating a construction area scene, generating synthetic data, training and optimizing an early warning model according to the synthetic data, triggering an early warning mechanism through the early warning model based on an analysis result, sending early warning information to a driver, construction personnel and related departments, and providing emergency disposal suggestions at the same time; and the communication and Internet of Things module is used for realizing real-time data transmission between the sensor network and the cloud computing platform as well as between the sensor network and the terminal equipment by using a communication technology.

The invention discloses an interactive response system based on artificial intelligence in the technical field of artificial intelligence, and the system comprises an intelligent analysis center, and input information is firstly processed to extract interaction features, including recognition of key elements inputted by a user and relationships thereof. Based on the characteristics, the system uses an advanced algorithm to carry out data analysis, and aims to find records or model output with the highest matching degree from a database. And generating accurate and related interaction feedback according to the optimal matching result. Interaction results are detected and analyzed in real time through connected sensor data, output content is optimized through environment, position information and the like, information is searched and displayed through intelligent analysis, if the information is internal data, the face and pupils of interaction personnel are analyzed through the sensor data, and the interaction effect is improved. After the analysis is completed, a visual report is generated and displayed after the external environment is confirmed through the sensor data, and if the data is the external data, the visual report is generated and displayed after the external environment is confirmed through the sensor data.

The invention relates to the technical field of data processing, and discloses a vehicle diagnosis function construction method based on multi-source data and intelligent analysis and related equipment, and the method comprises the steps: collecting original diagnosis data for a target vehicle diagnosis function from at least two mutually independent compliance data sources; standardizing the original diagnostic data to generate a unified standardized diagnostic data description file; the standardized diagnosis data description file is input into an intelligent analysis center for reasoning and verification, and verified diagnosis function logic is obtained; and according to the verified diagnosis function logic, automatically generating a diagnosis function framework for integrated use of the diagnosis platform. According to the invention, compliance and efficient development of the vehicle diagnosis function can be realized, and the problems of low development efficiency and insufficient law compliance in the prior art are solved.

The application discloses an Excel data analysis and extraction method, which comprises the following steps: receiving an Excel uploading request sent by a client by a server, performing eigenvalue calculation and data extraction on the uploaded Excel as a task and adding the task to a task queue, executing the task to be executed in the task queue by a data extraction and analysis center in the server, saving the extracted data, and feeding back the execution result to the task queue to provide the client with the execution result when the client queries the execution result. The technical effect of the application is to provide a method for analyzing and extracting Excel data, and the application can automatically identify the Excel structure to perform data extraction and conversion, greatly reducing the conversion cost and improving the conversion efficiency.