83 results about "Diagnosis management" patented technology

The invention discloses a ship-shore cooperation-based cabin intelligent diagnosis management system, which comprises a ship-end intelligent sensing layer, a ship-shore cooperation communication layer and a shore-based intelligent decision-making layer, and is characterized in that the ship-end intelligent sensing layer comprises a ship-end data acquisition module, and the ship-shore cooperation communication layer comprises a data transmission module, a shore-end data center and a cloud server; the shore-based intelligent decision-making layer comprises an intelligent diagnosis module, a predictive maintenance module and a ship-shore cooperation module. The invention belongs to the technical field of cabin intelligent management, and particularly provides an intelligent cabin diagnosis management system based on ship-shore cooperation and a predictive maintenance method, which can reduce the equipment failure rate, optimize the maintenance cost and prolong the equipment life cycle through real-time data acquisition, cloud intelligent analysis and dynamic maintenance decision.

The invention provides an intelligent management system for chronic diseases and common diseases of old people. The intelligent management system comprises an old people comprehensive evaluation module, a multidisciplinary collaborative diagnosis and treatment module, a multi-medication risk prediction management module, an intelligent chronic disease follow-up diagnosis management module and a health science popularization knowledge base module, the senile comprehensive assessment module inputs the personal chronic disease map of the patient into a risk factor prediction model for prediction, and outputs a comprehensive assessment result; the multidisciplinary cooperative diagnosis and treatment module is used for the first diagnosis / receiving diagnosis doctor to coordinate multidisciplinary doctors to perform cooperative diagnosis and treatment; the multi-medication risk prediction management module is used for giving comprehensive diagnosis, multi-medication management and risk prediction to the patient through collaborative diagnosis and treatment; the chronic disease intelligent follow-up diagnosis management module carries out follow-up visit tracking, health risk prediction and review prompting on the patient; and the health science knowledge base module matches and pushes corresponding health education knowledge. The accuracy and timeliness of senile chronic disease co-disease risk assessment prediction and management are improved, and a good auxiliary effect is achieved on improvement of the life quality of patients.

The invention belongs to the technical field of brain CT image diagnosis, and discloses a diagnosis management system based on brain CT image analysis. Comprising an acquisition module used for acquiring basic information, clinical information and brain CT images of a patient; the diagnosis module is used for comparing the brain CT image of the patient with a pre-constructed normal brain model to obtain abnormal brain information, and inputting the abnormal brain information into a pre-constructed brain disease diagnosis model to obtain a diagnosis result; according to the invention, tiny structural abnormality, vasculopathy and brain sulcus change in a brain CT image can be captured, and the accuracy and efficiency of brain disease diagnosis are improved; the diagnosis result of the patient can be matched to generate a disease development path, so that a doctor and the patient are helped to know the clinical stage of an illness state, and the disease development speed of the patient can be adjusted by setting the gain factor and the inhibition factor.

The invention provides an intelligent dust remover fault and health state diagnosis management method, device and equipment and a storage medium, relates to the field of dust remover management, and aims to improve the timeliness and accuracy of bag-type dust remover maintenance. The method comprises the following steps: acquiring real-time operation data of key equipment of the dust remover, wherein the real-time operation data at least comprises dust removal fan vibration data, dust removal fan rotating speed, dust removal fan current, dust removal motor vibration data, dust removal motor cooling water flow and dust removal motor current; dividing the real-time operation data to obtain first operation data and second operation data; inputting the first operation data into a trained health state evaluation model to obtain a health index; inputting the second operation data into a trained equipment fault classification model to obtain a classification result; and when the health index is lower than a first threshold value, alarm information is sent out, and fault processing information is generated according to a classification result.

The invention discloses an active diagnosis and treatment cooperation system and method based on artificial intelligence full-process dominance, and belongs to the technical field of medical artificial intelligence, and the system comprises a pre-diagnosis management module which actively carries out the medical care team distribution of a registered patient and completes chronic disease signing, performing intelligent triage after judging the disease progress and the onset risk of the signed patient, generating a pre-diagnosis examination list for the triage patient, and completing the diagnosis and treatment process of the pre-diagnosis stage; the in-diagnosis management module judges the health state of the patient and actively pushes a standardized diagnosis and treatment path to form a chronic disease prescription to be provided for a doctor, and the chronic disease prescription is pushed to the patient after being signed by the system and the doctor; and the post-diagnosis management module is used for automatically generating a dynamic tracking plan, establishing a grading follow-up visit mechanism, generating a follow-up visit scheme and automatically returning prognosis data of the patient to guide system optimization training. The whole process of chronic disease prevention and treatment is dominated through AI, the chronic disease management quality can be improved, the chronic disease prevention and treatment homogenization level is improved, and whole-process high-quality public service supply is achieved.

The invention discloses an intelligent engine monitoring system, belongs to the technical field of engine monitoring, and solves the technical problem that a traditional engine monitoring system is inconvenient to expand monitoring parameters. The system comprises an electric control monitoring system of an engine, and further comprises an external mounting type sensor monitoring system, a diagnosis management platform and a mobile terminal, the external mounting type sensor monitoring system comprises an independent power supply module, a wireless receiver and a wireless intelligent sensor, the wireless receiver is installed beside an ECU of the engine and connected with the ECU through a bus, and the wireless intelligent sensor is connected with the external mounting type sensor monitoring system. The independent power supply module supplies power to the wireless receiver and the wireless intelligent sensor, detection data of the wireless intelligent sensor is transmitted to the ECU through the wireless receiver, the ECU uploads monitored parameters to the diagnosis management platform, and the diagnosis management platform sends monitored parameter information to the mobile terminal through wireless communication. Monitoring parameters can be conveniently expanded through the externally-mounted sensor monitoring system, wire harnesses and sensors of an original machine are fixed, and the circuit is simple and good in stability.

The invention discloses a children cough variant asthma diagnosis system based on a decision tree, and relates to the technical field of asthma diagnosis, the children cough variant asthma diagnosis system comprises a diagnosis management platform, the diagnosis management platform is in communication connection with a data acquisition module, a diagnosis model construction module, a diagnosis reasoning module and an intervention suggestion module, and the modules are in electric signal connection; the data acquisition module is used for collecting and preprocessing child patient clinical data including symptoms, signs, medical history and multi-dimensional examination data. By integrating the multi-dimensional examination data and the medical history, symptom change trend and family history information of the child patient, the condition of the child patient can be comprehensively and deeply analyzed, reasoning and judgment are performed by applying a branch structure of a decision tree and accurately combining various characteristics, so that subjective and empirical deviations in a traditional diagnosis method are effectively avoided, and the diagnosis accuracy is improved. The accuracy of CVA diagnosis is remarkably improved, a child patient is helped to obtain correct treatment in time, and misdiagnosis and missed diagnosis can be effectively reduced.

The present invention relates to a method for obtaining front, rear, and occlusal surface images of each tooth using a Lens-less camera and a mouthpart provided with a light source, tracking and analyzing consecutive images in a time series using a learned AI algorithm, detecting, diagnosing, managing, and predicting information, and determining whether or not the tooth is a tooth or not. Therefore, not only can the fineness, reliability and effectiveness of tooth diagnosis, treatment and management be improved, but also images of different teeth under the same conditions (imaging direction, magnification, position and the like) can be obtained regardless of the skill of a photographer, and the accuracy and effectiveness of diagnosis and treatment can be further improved. The present invention relates to a mouthpart-type oral cavity camera using a lens-free camera and a dental diagnosis and management system using the same, and can significantly reduce unnecessary time and manpower consumption caused by imaging teeth.

The invention relates to the technical field of oil and gas pipeline valve inner leakage online diagnosis, and discloses an oil and gas pipeline valve inner leakage online diagnosis method based on voiceprint analysis. According to the method, valve operation voiceprint data are obtained through a distributed interface to construct a feature database, a diagnosis framework is designed according to the database, and a multi-source voiceprint collection scheme is determined. And the voiceprint acquisition terminal acquires a multi-dimensional voiceprint signal according to the scheme, inputs the multi-dimensional voiceprint signal into a model after feature extraction to complete inner leakage level judgment and abnormal mode recognition, and outputs a diagnosis result. A diagnosis result is transmitted to the management optimization module, and diagnosis strategy adjustment is achieved through timestamp calibration, mapping table construction and the like. And a three-level evaluation sub-frame is also constructed to assist diagnosis. According to the method, accurate diagnosis and strategy optimization of oil and gas pipeline valve inner leakage are realized, and the diagnosis management level is improved.

To provide a sample inspection management system which can uniquely specify sample inspection between a plurality of inspection institutes and a plurality of clients, can correctly grasp the sample inspection, and can be also applied to pathological diagnosis management.SOLUTION: A sample inspection management system reads out unique information uniquely specifying a main body stored in storage means, wherein first sample number issuing means combines a client management number of a client and unique information of the client by SQL and issues a first sample management number, sample number issuing means combines an inspector management number of an inspector and unique information on the inspector by SQL and issues a sample management number, management number issuing means associates the first sample management number with the sample management number by SQL on a one-to-one basis, issues a management number while being uniquely associated with the numbers, and a management code issuing means issues a two-dimensional code associated with the management number, and makes the two-dimensional code stuck to a sample container, and code read-out means reads out the two-dimensional code and uniquely specifies sample inspection.SELECTED DRAWING: Figure 3

The invention discloses a pump station fault diagnosis management system which comprises a data acquisition module, a fault diagnosis module, a fault alarm module and a data management and analysis module. The data acquisition module acquires data through various sensors and preprocesses the data; the fault diagnosis module extracts characteristic parameters, and adopts an artificial intelligence algorithm to diagnose and position a fault; the fault alarm module executes a corresponding strategy according to the fault degree; the data management and analysis module uses relational and non-relational databases to store data, and can perform query, statistics, mining and prediction. The system improves the operation reliability and stability of the pump station, accurately diagnoses faults, optimizes data management and decision, provides guarantee for intelligent management of the pump station, and is suitable for the pump station fields of water conservancy, water supply and drainage and the like.

The invention belongs to the technical field of digital diagnosis, and discloses an enterprise digital diagnosis platform and a use method thereof, and the platform comprises a diagnosis management user unit which is used for carrying out the comprehensive management of a management user based on the enterprise diagnosis information; the enterprise user diagnosis unit is used for analyzing the diagnosis task of the enterprise user and providing diagnosis service and personalized support for the enterprise user according to the diagnosis task; the business authority management unit is used for defining the function access range of the enterprise user and the management user, and performing unified management on the function authority of the enterprise user and the management user based on the function access range; and the user role management unit is used for constructing a diagnosis management role and a diagnosis enterprise role. The method focuses on maintenance of global data, distribution of diagnosis tasks and comprehensive analysis, so that efficient organization and coordination of platform resources are facilitated, and stable operation of the platform and smooth development of enterprise diagnosis work are ensured.

The invention provides a vehicle diagnosis management method for a UDS server, and the method comprises the steps: enabling the UDS server to execute the steps: respectively receiving connection requests from different clients; a corresponding logic address is allocated to each client, and different logic addresses are allocated to different clients; for each client, establishing a diagnostic connection between the client and a plurality of diagnostic session instances under the corresponding logical address; and for each client, receiving and analyzing a diagnosis request sent by the client, processing the diagnosis request according to the diagnosis connection under the logic address, obtaining diagnosis response data, and feeding back the diagnosis response data to the client. The diagnosis requests of different clients are processed under different logic addresses, so that the UDS server isolates diagnosis sessions among different clients, and the processing performance of the UDS server is improved.

The invention relates to the technical field of teaching evaluation and diagnosis management, in particular to an intelligent teaching evaluation and diagnosis system and method based on multi-mode audio and video analysis. Carrying out classroom teaching event identification on the teaching state feature set extracted at the current time; in combination with an MTES technology, classroom teaching event evolution process analysis based on a time sequence model is carried out, and classroom audios and videos are dynamically segmented based on an obtained classroom teaching event evolution process analysis result. According to the method, a mode of comprehensively quantifying the index set bound with each classroom teaching event based on the MTES technology can effectively solve a modal splitting phenomenon among modal acquisition data in a teaching evaluation and diagnosis process; according to the invention, the dynamic segmentation points of the classroom audio and video are screened, locked and calibrated from multiple angles, and the accuracy of the classroom audio and video segmentation segments is ensured.

A method for managing vehicle servicing may capture at least one of a user concern / input related to a requested service or observed vehicle operational parameters. A method for managing vehicle servicing may conduct diagnosis and filtering based on the user input to identify a set of corrective actions to be performed and pre-service steps that may be required prior to service, wherein the diagnosis and filtering correspond to a suite of diagnostic services to be deployed and implemented. A method for managing vehicle servicing may transmit at least one processing result associated with the execution of the suite of diagnostic services and may obtain approval of the service request by a user and schedule the service.

The invention discloses a vehicle diagnosis management method, device and equipment and a computer program product, and relates to the technical field of vehicle diagnos.The vehicle diagnosis management method comprises the steps that a to-be-executed diagnosis function instruction is received; based on pre-generated function security level data, performing security level classification on the to-be-executed diagnosis function instruction to obtain to-be-executed function level data; and based on a pre-constructed function management interface, performing vehicle diagnosis management according to the to-be-executed function level data and the to-be-executed diagnosis function instruction, and obtaining a vehicle diagnosis management result. The function management interface sets the vehicle diagnosis mode according to the to-be-executed function level data, the permission of the USB interface for transmitting data and files is limited, the problem that an existing diagnosis equipment USB safety management method is poor in data safety is solved, and the safety of vehicle diagnosis and data transmission is improved.

The invention discloses a battery voltage difference fault diagnosis method, device and equipment and a readable storage medium, and the method comprises the steps: a cloud diagnosis platform obtains vehicle battery data, and the vehicle battery data comprises current static voltage data; calculating a difference value between the maximum charge state and the minimum charge state of each single battery in the current static voltage data to obtain a current charge state difference value; calculating the growth rate of the current charge state difference value relative to the charge state difference value before the first preset duration; and performing battery voltage difference fault diagnosis according to the current charge state difference value and the growth rate. Compared with the prior art, the method has the advantages that the cloud diagnosis platform has higher data storage and calculation capability compared with a battery management system, the cloud diagnosis platform calculates the current charge state difference value and the growth rate according to the obtained latest vehicle battery data, and further performs battery voltage difference fault diagnosis based on the current charge state difference value and the growth rate; therefore, timely and effective battery voltage difference fault diagnosis management is realized.

The invention provides an enterprise brand reputation intelligent diagnosis management system, electronic equipment and a medium, and the system comprises a data collection module which is configured to collect basic semantic data which comprises a plurality of first text vocabularies; the data processing module is configured to receive the basic semantic data and label the first text vocabulary based on a preset labeling strategy; the data processing module is further configured to extract a first entity of the basic semantic data; the analysis module is configured to receive the labels, obtain semantics of the first text vocabularies corresponding to the labels on the basis of a semantic model, determine whether the semantics are positive or negative, and analyze the labels according to the number of the first text vocabularies corresponding to the negative and on the basis of a preset model; obtaining the increment of the quantity of the basic semantic data corresponding to the negative values in a future preset time period; and the diagnosis decision module is configured to output a decision strategy based on a preset case library according to the increment and the first entity.

The present invention relates to a fault diagnosis method, system and storage medium for analyzing the rotation state of equipment, and relates to the field of equipment fault analysis, including: when the rotating equipment to be monitored is started, a vibration element set for calibrating the rotation control parameters of the equipment is obtained; the vibration element set is traversed to generate the calibration results of the monitoring points; the high-frequency vibration sensor of the calibration results of the monitoring points is activated, and a list of monitoring vibration state quantities is collected; based on the equipment rotation control parameters and the model of the rotating equipment to be monitored, the calibration results of the monitoring points are traversed to perform frequency statistics, and a list of reference vibration state quantities is obtained; a state deviation matrix is ​​constructed through the monitoring vibration state quantity list and the reference vibration state quantity list; a fault prediction probability is generated through a fault prediction model and sent to a fault diagnosis management end. The technical problem that the prior art cannot realize batch application in industry due to the manual configuration of vibration elements and monitoring points, resulting in low model building efficiency, poor migration ability of the fault diagnosis model, and inability to achieve industrial batch application is solved.

The present application relates to the technical field of dynamic evaluation of spinal morphology, and particularly relates to a child spinal morphology intelligent evaluation method and system based on big data analysis. The method comprises the following steps: S1: obtaining static data and dynamic data, dividing the static data into a high-priority evaluation data set and a basic reference data set, and dividing the dynamic data into a key change data set and an auxiliary monitoring data set; taking the complete time period between the time point when the child spinal morphology first receives medical evaluation and the time point when the system confirms that the child spinal function and morphology reach a steady state as an evaluation and diagnosis cycle, and distributing the static data and the dynamic data to the evaluation and diagnosis cycle to obtain a data-stage distribution result. The present application constructs an intelligent evaluation system, accurately tracks child spinal development, dynamically optimizes data collection and stage distribution through error analysis and similarity comparison, and realizes individualized, efficient and self-adaptive closed-loop diagnosis management.

The invention discloses a diagnosis processing method for a driving chip, and the method comprises the following steps: enabling the driving chip to enter different diagnosis state machines according to the driving state of the driving chip after the driving chip is powered on; the diagnosis state machine performs sub-state switching in the diagnosis state machine according to the fault content and the fault frequency; and the sub-state in the diagnosis state machine determines the fault state of the drive through the numerical value of the fault register and the reset frequency of the drive chip. In order to ensure the safety of the vehicle in any scene, the driving chip self-diagnosis faults in different driving states are monitored in an independent state, fault confirmation and recovery of the driving assembly are managed through temporary faults, confirmation faults and permanent faults, and compared with an existing diagnosis management method, the safety damage of the vehicle can be avoided, and the safety of the vehicle can be ensured. And the normal use of the vehicle can be ensured to the greatest extent.

The present application relates to the technical field of teaching evaluation diagnosis management, in particular to an intelligent teaching evaluation and diagnosis system and method based on multi-modal audio and video analysis, wherein the audio and video dynamic segmentation module combines the teaching state feature data in the historical data to perform classroom teaching event identification on the teaching state feature set extracted at the current time; and combines the MTES technology to perform time sequence model-based classroom teaching event evolution process analysis, and based on the obtained classroom teaching event evolution process analysis result, the classroom audio and video are dynamically segmented. The comprehensive quantification mode of the index set bound to each classroom teaching event based on the MTES technology can effectively solve the modal fragmentation phenomenon between the multi-modal collected data in the teaching evaluation and diagnosis process; the dynamic segmentation points of the classroom audio and video are screened, locked and calibrated from multiple angles, so that the accuracy of the classroom audio and video segmentation segments is ensured.

The application relates to the technical field of internet medical treatment and artificial intelligence, and discloses an internet hospital whole-process intelligent diagnosis and treatment service management system based on artificial intelligence. The method collects multi-source digital phenotype data through a double-granularity aggregation strategy, generates continuous representation of patient behavior state by using a time sequence representation learning model based on a mask autoencoder, separates a disease activity score and a compliance probability score through a causally decoupled double-branch network, calculates a crisis emergency degree score by using a mental and psychological crisis early warning network and triggers a fuse mechanism, realizes post-diagnosis management path switching, emergency resource matching and crisis handover information package generation, and solves the problems of insufficient time resolution, data loss vulnerability, cause coupling confusion and response channel loss in mental and psychological department post-diagnosis acute crisis detection and emergency response.

The present invention discloses a vehicle remote diagnosis management method, which subscribes to first fault diagnosis information and obtains second fault diagnosis information based on a bus through a first master node, and publishes the first fault diagnosis information and the second fault diagnosis information; based on a data distribution service, subscribes to the first fault diagnosis information and the second fault diagnosis information published by the first master node through a second master node; and sends the first fault diagnosis information and the second fault diagnosis information subscribed by the first master node and / or the second master node to a terminal device through the first master node and / or the second master node. The present invention combines the network system of the vehicle to diagnose various traditional faults on the vehicle, as well as diagnose abnormalities of various embedded controller hardware, systems, and applications, completes the complete vehicle diagnosis of traditional diagnosis and vehicle applications, and the remote PC terminal and the cloud vehicle diagnosis management, thereby improving the ability to diagnose vehicle faults.

The invention relates to the technical field of syphilis detection, in particular to a syphilis disease prediction method and device, equipment and a medium. The method comprises the following steps: extracting syphilis feature vectors as syphilis input data by using CIA, TPPA, RPR, WB and MTPPA monitoring data in local clinical data, constructing a training set with syphilis output data, participating in machine learning of a clinical data end to obtain a disease prediction model, and predicting the syphilis disease risk according to the model, so that the prediction accuracy can be improved, and the syphilis disease risk can be predicted. The error caused by single detection means is effectively reduced, the clinical environment adaptability difference of diagnosis management is reduced, and accurate diagnosis of syphilis is facilitated.

The present invention discloses a method and system for judging elderly chronic diseases based on data analysis, which belongs to the technical field of elderly chronic diseases. The method includes: collecting real-time data of elderly patients and determining characteristic data of elderly patients after processing; training and optimizing a model for judging elderly chronic diseases, analyzing and judging the characteristic data of elderly patients, predicting the risk of elderly chronic diseases, determining the results of judgment of elderly chronic diseases, and performing early warning and health management on elderly patients. The present invention solves the problem that the existing system cannot judge elderly chronic diseases and timely manage health, resulting in poor diagnosis and management effects of elderly chronic diseases. The present invention constructs a model for judging elderly chronic diseases that predicts the risk of elderly chronic diseases and judges elderly chronic diseases, analyzes and judges the characteristic data of elderly patients, predicts the risk of elderly chronic diseases, and determines the results of judgment of elderly chronic diseases. It can effectively judge elderly chronic diseases and timely manage health and treat them, thereby improving the diagnosis and management effects of elderly chronic diseases.

Eleven percent of all men will develop prostate cancer (PC). In the US, 3 million men are living with the disease, 192,000 new cases are expected, and 33,000 men will die from the illness this year. PC is the second leading cause of cancer (CA) deaths in men worldwide, the diagnosis of PC relies on a combination of tests and procedures, including the digital rectal exam (DRE), magnetic resonance imaging (MRI), ultrasound, and multiple tissue biopsies, each with escalating costs and risks. In 2018, the cost of PC care in the US was $15.3 billion dollars, of which $5.7 billion was spent on diagnosis and $8.3 billion on management. There is a critical need for an early, accurate, reliable and cancer specific diagnostic for PC. The technology as described herein relates to compositions and methods for diagnosing and staging of cancer in a subject.

The invention relates to the technical field of medical informatization management, in particular to a medical unit patient full-process management system based on a block chain, which comprises a data aggregation module, a grading decision module and a feedback synchronization module. According to the method, original physiological parameters of home wearable equipment are periodically retrieved, a clinical rehabilitation scale stored in diagnosis is matched to align a rehabilitation time sequence of a patient, and stage dynamic physiological data blocks are output; injecting the data into a block chain multi-stage physiological sign analysis model, quantitatively evaluating a clinical rehabilitation deviation value of a patient, and dynamically triggering a limited retrieval permission of a health file according to the clinical rehabilitation deviation value; executing clinical early warning according to the rehabilitation deviation value, receiving follow-up intervention suggestions, packaging the suggestions into encrypted receipts, synchronizing the receipts to the originating mechanism, and updating the post-diagnosis management state. According to the invention, closed-loop management of medical data from acquisition, early warning, authorization to feedback is realized, the technical problems of controlled opening of private data and dynamic verification of rehabilitation reference in a distributed environment are solved, and the real-time performance and accuracy of medical intervention behaviors are ensured.

The invention discloses an intelligent medical system based on big data, which comprises a data collection module, a diagnostic analysis module and an evaluation management module, and is characterized in that the data collection module is used for collecting facial features of doctors, disease information uploaded by users and medical record data of the users; the diagnosis management module is used for analyzing the fatigue degree of a doctor, adjusting and analyzing whether medicines used by a user interact or not, the evaluation management module is used for reminding the user to take the medicines on time, the user evaluates the doctor, and the data collection module, the diagnosis management module and the evaluation management module are in communication connection with one another. The data collection module comprises a visual module, an information input module and a medical record collection module, the visual module is used for collecting facial features of a doctor, the information input module is used for inputting focus information into the system by a user, and the system has the advantages that the safety of medicine use is improved, and the fatigue degree of the diagnosis doctor is reduced.

The application discloses a kind of diagnostic data subscription method, device and storage medium and electronic device, which comprises: based on diagnostic configuration table, first diagnostic configuration information is issued to kernel diagnosis management center, and second diagnostic configuration information is issued to application diagnosis management center;First unit diagnostic data reported by kernel diagnosis management center based on first diagnostic configuration information is obtained, and second unit diagnostic data reported by application diagnosis management center based on second diagnostic configuration information is obtained;In the case where the target unit diagnostic data of electronic control unit is obtained based on first unit diagnostic data and second unit diagnostic data, according to diagnostic configuration table, target unit diagnostic data is forwarded to at least one other electronic control unit.The application solves the technical problem that the acquisition efficiency of diagnostic data in the prior art is low.