682 results about "Cloud database" patented technology

The invention relates to the technical field of agricultural data security, and discloses an agricultural data security sharing method based on federated learning, through cooperative operation of a user terminal module, a data classification module, a federated learning module, an agricultural cloud database and a security protection module, a user terminal encrypts, uploads, decrypts and feeds back agricultural data through an edge computing device; the data classification module performs multi-level label labeling on data of planting industry, animal husbandry and the like by using a differential privacy technology; the federated learning module is combined with homomorphic encryption to realize local model training and parameter aggregation; the agricultural cloud database stores encrypted knowledge assets by using an IPFS technology; the security protection module integrates block chain evidence storage and a dynamic access control mechanism, the workflow comprises data encryption uploading, privacy protection classification, federated model training, solution generation and security feedback, and by adopting a federated normal form of a data immobile model, the security protection module can effectively protect the security of the original data on the premise of ensuring that the original data is not out of the domain. And value mining and safe sharing of cross-regional agricultural data are realized.

The invention belongs to the technical field of water quality monitoring, and particularly relates to a construction method and device of a turbidity testing system based on a photoelectric detector. Optical signal data and multi-wavelength optical signal data are obtained; performing intelligent switching and spectrum matching according to the signal data, determining a target wavelength, and obtaining characteristic parameters related to turbidity by combining a scattering and transmission combined measurement principle; measuring different standard solutions according to the characteristic parameters to obtain corresponding characteristic parameters, constructing a turbidity characteristic parameter data set, and establishing a characteristic ratio turbidity calibration model according to the data set; measuring and calculating the turbidity according to the characteristic ratio turbidity calibration model in combination with a dynamic temperature compensation algorithm, and meanwhile, performing temperature compensation; and transmitting the parameter information to a cloud database for storage, performing hierarchical processing, performing regular intelligent self-inspection at the same time, and feeding back abnormal data when an abnormality occurs. Therefore, the problems of insufficient range coverage, weak automatic calibration capability, susceptibility to temperature drift and the like in the prior art are solved.

The invention belongs to the technical field of automatic driving testing, and particularly relates to an accident scene generation method based on a scene knowledge graph and considering accident causes. Comprising the following steps: step 1, modeling a scene knowledge graph; 2, modeling the scene graph time sequence prediction model; 3, modeling the time sequence causal inference model; step 4, modeling the scene graph time sequence decision generation model so as to generate an accident scene; according to the method, the accident scene database with high authenticity, high diversity and accident cause consistency can be constructed under the condition that the accident scene sample data size is limited, the number of accident scenes of the same type in the automatic driving algorithm closed-loop self-evolution cloud database is efficiently expanded, the constructed scene database is used for training the automatic driving algorithm, and the accuracy of the automatic driving algorithm is improved. The adaptive capacity of the self-driving automobile to scenes with the same type of accidents can be effectively enhanced, and safe and reliable operation of the self-driving automobile in the real world is guaranteed.

The invention discloses a big and small model cooperation-based power equipment fault knowledge graph construction method, which comprises the following steps of: preprocessing a power equipment fault document, storing image information into a cloud database, and screening high-quality text information from a big language model; summarizing different text information by using a large language model to generate an initial ontology framework; performing pre-labeling on text information by using a large language model, obtaining a training data set in combination with rule matching scripts and experts, optimizing a traditional deep learning small model based on data characteristics to obtain entities, and eliminating error entities based on a hierarchical verification strategy; utilizing a large language model to carry out relation extraction on association degrees between entities and different entity relations; and performing entity alignment by using the large language model, the SBERT model, a depth-first search algorithm and context information to form a final power equipment fault knowledge graph. According to the method, the problems of high labor cost consumption and knowledge redundancy of the knowledge graph in the existing power equipment fault knowledge graph construction process are solved.

The invention discloses an insurance clause knowledge base self-maintenance method, system and device based on a large model, and relates to the technical field of data processing. The method comprises the following steps: obtaining an associated segmentation term fragment set; carrying out vector conversion, and storing the vector into a cloud database to obtain a vectorized cloud knowledge base; after the vectorized cloud knowledge base receives a user retrieval request, performing knowledge base clause priority adjustment through a secondary retrieval mechanism to obtain an updated vector cloud knowledge base; responding to a user retrieval request based on the updated vector cloud knowledge base to obtain a clause fragment retrieval result; and inputting the Prompt construction result and the clause fragment retrieval result into a large language model constructed based on an RAG enhanced generation technology to perform retrieval enhanced generation, and obtaining a target retrieval interpretation analysis result. The technical problems that in the prior art, an insurance clause knowledge base is low in maintenance efficiency and poor in retrieval accuracy are solved, and the technical effect of improving the self-maintenance efficiency and the retrieval accuracy of the insurance clause knowledge base is achieved.

The invention relates to the field of environmental noise detection, and provides an environmental noise detection device and method, and the method comprises the following steps: collecting an environmental noise signal, and carrying out the preprocessing of the environmental noise signal, and obtaining a to-be-detected audio; adjusting a gain compensation coefficient according to the energy intensity of the to-be-tested audio to obtain a gain multi-source noise signal; separating the gain multi-source noise signal to obtain a plurality of separated noise signals, extracting key characteristics of the separated noise signals, and classifying the plurality of separated noise signals based on the key characteristics to obtain different types of classified noise signals; performing feature extraction and time series data analysis on the classified noise signals to obtain abnormal noise data, identifying the abnormal noise data to obtain a noise abnormal level, and adopting a corresponding emergency solution; the detection data and the early warning record are stored in a cloud database; a visualization result is generated based on the stored data. According to the invention, efficient detection, classification, anomaly identification and data management of environmental noise are realized.

The invention belongs to the technical field of child growth and development evaluation, and particularly relates to a child development dynamic evaluation method and system based on multi-source data fusion. According to the method, the growth and development state of the child can be preliminarily judged through the growth and development sample curve generated through fusion, the second index data of the child are obtained through the non-intrusive acquisition equipment, and the acquisition frequency is adjusted based on the initial state type, so that the comprehensiveness and accuracy of the data are ensured, unnecessary frequent acquisition is avoided, and the accuracy of the data is improved. Processing the second index data through a pre-trained convolutional neural network model to generate individual growth fingerprints, screening out historical fingerprint data with high similarity with the individual growth fingerprints from a cloud database, constructing a personalized growth corridor, providing a personalized referable trajectory range for growth and development of children, and improving the growth and development efficiency of the children. And on the basis, whether the growth and development of the children are abnormal is judged, so that the growth and development conditions of the children are accurately evaluated.

The invention discloses a vehicle fault positioning and rescue scheduling platform and method based on GPS track acquisition, and relates to the field of vehicle fault positioning and rescue scheduling, and the method comprises the steps: uploading vehicle GPS and OBD data obtained by a vehicle-mounted terminal to a cloud database through employing a mobile network technology; according to the vehicle GPS and OBD data in the cloud database, the vehicle fault type is predicted and judged, and the position coordinate information of the vehicle fault is obtained; based on an expert scoring method, processing response levels of different vehicle fault types are set; accessing a high-precision digital map API (Application Program Interface), and marking schedulable rescue points and fixed GPS (Global Positioning System) coordinate data in a digital map; and establishing a vehicle fault rescue scheduling model, and carrying out comprehensive evaluation and scheduling on surrounding schedulable rescue points. The method has the advantages of effectively predicting the state and fault of the vehicle, efficiently responding to the processing of the fault of the vehicle, and preferentially scheduling and screening the schedulable rescue points.

The invention relates to the technical field of energy storage battery control, in particular to a high-reliability low-attenuation energy storage battery management system based on intelligent control, and the system comprises a data collection module which is used for collecting battery temperature, environment temperature, SOC (state of charge), SOH (state of health) and external mechanical pressure information; the model construction module is used for constructing a battery attenuation risk dynamic coupling evaluation model and generating a battery safety and attenuation risk dynamic evaluation diagram; the self-adaptive adjustment module adopts a cross-dimension multi-stage cooperative control strategy algorithm, temperature compensation current control and SOH grading current limiting linkage are combined, and SOC dynamic charging optimization and mechanical pressure early warning are coupled; and the evaluation report module adjusts control parameters through a dynamic optimization algorithm, generates a battery health state evaluation report, synchronously uploads the battery health state evaluation report to a cloud database, and establishes a full-life-cycle battery attenuation risk file. Therefore, the problems that in the prior art, a risk assessment model is large in limitation, a control strategy lacks collaboration, and attenuation risk prevention and control lags are solved.

The invention relates to the technical field of remote monitoring, in particular to a new energy vehicle battery remote monitoring method and system based on cloud computing, and the method comprises the following steps: extracting the temperature data of a plurality of monitoring points through a vehicle-mounted temperature sensor based on the data of a new energy vehicle battery, recording the current and voltage information of a BMS (Battery Management System), and arranging the data according to a time sequence and storing the data in a cloud database to obtain battery operation monitoring data. According to the invention, by accurately collecting the operation data of the new energy vehicle battery and performing time sequence storage, data management with finer granularity is realized, the battery state is ensured to be comprehensively monitored, the abnormal temperature detection precision is improved, the quantum key generation is utilized to generate and distribute keys, the data transmission security is enhanced, and the data transmission efficiency is improved through quantum encryption and remote identity authentication. Data integrity and privacy are guaranteed, cooling and charging strategies are optimized, temperature control accuracy is improved, the service life of the battery is prolonged, energy consumption is reduced, and new energy vehicle battery management is more efficient, safer and more intelligent.

The invention discloses a riverway ecology real-time treatment method, and belongs to the field of riverway treatment, and the treatment method specifically comprises the following steps: S1, constructing a cross-platform data collection system, deploying a multi-source sensor, monitoring environmental parameters in real time, and storing the collected data in a cloud database; s2, analyzing the collected data, identifying water area pollution characteristics, evaluating the ecological health condition of a river channel and identifying main problems; according to the method, hardware resource requirements are remarkably reduced, model calculation efficiency is improved, key ecological indexes can be accurately evaluated, model prediction precision is guaranteed, and model deployment and adaptability are accelerated; the change of the ecological state of the river channel can be responded in real time, the problem of low efficiency or failure caused by a fixed strategy of a traditional treatment method is avoided, dependence on external resources is reduced, the maintenance cost is reduced, uncertainty and deviation in human decision making are reduced, and the treatment efficiency is improved.

The present invention discloses a multi-task machining process on-line monitoring method and system based on cloud-edge collaboration, comprising multi-source sensing terminals, edge computing platforms and a cloud server. Each multi-source sensing terminal uses sensing equipment to obtain multi-source heterogeneous signals such as spindle vibration signals and acoustic emission signals generated in a machining process and transmit the signals to a controller of each edge computing platform; the edge computing platform is used for periodically collecting and processing the signals, synchronously monitoring cutting vibration, tool wear, tool breakage and machining surface quality in combination with intelligent prediction models, and uploading monitoring data to the cloud server; the cloud server is used for storing the monitoring data of each edge computing platform into the cloud database, and updating the intelligent prediction models regularly with global data to improve the generalization ability of the models.

The invention discloses a diabetic patient data management method and system based on big data. The method comprises the steps that basic information and stable blood glucose data of a patient are collected, similar cases are matched through a cloud database to obtain influence factors, and a personalized data collection scheme is generated; customizing a user terminal and establishing a data management container, adaptively acquiring blood glucose and influence factor data, calculating a change degree through a sliding window algorithm and performing early warning; and dynamically updating influence factor weights by adopting a time-varying weight model, and generating an intervention strategy according to the weights. The system comprises a cloud server, a comprehensive management terminal, a user terminal and other modules, and realizes data acquisition-analysis-intervention closed-loop management. According to the scheme, through big data analysis and the dynamic weight model, personalized accurate management of the diabetic patient is realized, the acquisition strategy and the intervention scheme can be adjusted according to individual differences, and the risk early warning accuracy and the management efficiency are improved.

The invention relates to the technical field of data processing, in particular to a cloud database data compression method based on an adaptive quantization algorithm. The method comprises the following steps of: 1, regarding to-be-compressed original data in a cloud database as a matrix, dividing the data into a plurality of non-overlapped data blocks according to predefined statistical characteristics, and calculating a mean value and a variance of each data block; 2, defining the complexity of each data block, and calculating the self-adaptive quantization step length of each data block according to the mean value and variance of each data block; step 3, performing non-uniform adaptive quantization on each data item in each data block to obtain a quantized value; and 4, establishing a probability model for a quantization result of each data block, calculating a coding length, obtaining a compression ratio, and carrying out entropy coding. The quantization step size of the data block is dynamically adjusted to be matched with the local characteristic of the data, so that the quantization error is reduced, and the data recovery precision is improved.

The invention discloses an environmental protection equipment management and data analysis system based on the Internet of Things. The environmental protection equipment management and data analysis system comprises an Internet of Things sensor group, a central data processing module and a cloud database, the operation process of the system specifically comprises the following steps that the Internet of Things sensor groups are installed on the multiple pieces of environmental protection equipment to comprehensively collect data; the Internet of Things sensor group transmits acquired data to the central data processing module; the central data processing module performs data preprocessing on the original data; performing multi-dimensional analysis and calculation on the preprocessed data to output a fault prediction report; and uploading all data to a cloud database for storage and management. The method has the following advantages and effects: the defects of the existing environmental protection equipment in the aspects of intelligence, dynamic adaptation, data processing and real-time decision making are improved, the equipment monitoring precision can be improved, the acquisition strategy can be optimized, the fault prediction capability can be enhanced, and the downtime and the maintenance cost can be remarkably reduced.

The invention relates to the technical field of intelligent closestools, in particular to an intelligent closestool with human health monitoring analysis and early warning functions, which comprises an identity recognition module, a multi-source sensing module, an internet of things module, an intelligent analysis module and a user terminal module, the intelligent analysis module performs deep integration and analysis on the multi-modal time series data in the cloud database based on a large language model, generates a personalized health portrait, identifies a potential pathological phenomenon, triggers disease early warning, and constructs a health assessment model through a model fine tuning method in combination with a medical knowledge base; and outputting a personalized health analysis report and a health intervention suggestion. The multi-source sensing module is integrated, multi-dimensional health data can be collected in real time, the problems that in the prior art, the data collection dimension is single and means are limited are solved, the complex health state of the human body is comprehensively reflected, the situation that an existing analysis method is single is broken through, and the potential value of data is fully mined; and a complex scene with individual difference and coexistence of multiple disease species can be better handled.

The invention discloses an intensive rehabilitation treatment decision method based on virtual reality, and relates to the field of medical monitoring, and the method comprises the steps: collecting user physiological state parameters in real time, creating a cloud database, and storing the user physiological state parameters collected in real time based on the cloud database; according to the method, the user physiological state parameters stored in the cloud database are traversed, the wooden barrel model is created based on the user physiological state parameters, and the standard wooden barrel model is synchronously created. The dynamic wooden barrel model is created by collecting the physiological parameters in real time, and accurate comparison with the standard model is achieved in a normalization processing and three-dimensional space alignment mode; the communication area and the risk area can be visually recognized, the abstract limitation of traditional data monitoring is broken through, and dynamic and accurate allocation of medical care resources is achieved in combination with a mobile equipment distance scheduling mechanism.

The invention discloses a power storage performance attenuation and variation detection method for an energy storage battery, and relates to the field of energy storage batteries. The method comprises the following steps: deploying a terahertz time-domain spectroscopy system and a microphone array on the surface of the energy storage battery, based on a terahertz time-domain spectroscopy system and a microphone array, acquiring a three-dimensional imaging map and a voiceprint signal of the energy storage battery at the beginning stage of charging the energy storage battery each time; creating a cloud database, and storing the three-dimensional imaging map and the voiceprint signal of the energy storage battery by using the cloud database; according to the method, terahertz time-domain spectrum three-dimensional imaging and voiceprint signal bimodal data are fused, battery global information acquisition is realized through distributed array scanning and matrix deployment, and a multi-dimensional attenuation evaluation model is constructed by combining dynamic storage and comparative analysis of a cloud database, so that the accuracy of battery attenuation evaluation is improved. A dynamic weight adjustment mechanism and ontology state information correction are introduced, so that the attenuation index estimation precision is remarkably improved.

The invention relates to the technical field of carbon emission management, in particular to an enterprise energy collection and carbon emission intelligent management system based on the Internet of Things, which comprises a data collection module, a server, a feature recognition analysis module, an energy consumption analysis module, a carbon emission analysis module, a carbon quota distribution module, a carbon quota transaction module and a cloud database. According to the method, the comprehensive carbon shadow value is calculated by monitoring the basic characteristics and the energy consumption information of the enterprise factory, meanwhile, the carbon emission detection points are arranged in the enterprise factory, the carbon emission data of each layer are collected, the carbon emission evaluation coefficient is calculated by combining the comprehensive carbon shadow value, and the carbon emission evaluation coefficient is compared with the carbon emission state grading table; the method determines the carbon emission state grade of the enterprise, distributes the carbon quota according to the carbon emission state grade, tracks the current carbon emission evaluation coefficient in real time, and automatically triggers the carbon quota transaction instruction when the current carbon emission evaluation coefficient exceeds the preset threshold value, thereby achieving the flexible management and optimization of the carbon quota, and improving the environmental protection enthusiasm and management benefits.

The invention relates to the technical field of intelligent battery replacement recommendation, and discloses an intelligent battery replacement recommendation method and system based on battery historical data, and the method comprises the steps: a vehicle central control system carries out the vehicle identity recognition of a battery replacement request signal triggered by a user, and obtains the parameter information of a current vehicle; querying battery historical matching data corresponding to the parameter information from a cloud database, and calculating the suitability score of each battery model and the current vehicle; the battery replacement cabinet control system carries out multi-target collaborative recommendation decision making on the suitability score and the inventory battery to obtain an optimal recommended battery identifier; the battery replacement cabinet opens the corresponding bin door to execute the battery replacement process according to the optimal recommended battery identifier, and a battery replacement operation record is generated. The accuracy of battery suitability evaluation is improved, the performance association relationship between different battery models can be found, and the automation and intelligence level of the whole battery replacement process is improved.

The invention relates to the field of intelligent greenhouse management, and particularly discloses an intelligent greenhouse management system based on computer vision and deep learning, and the system comprises a data collection module which obtains environment data and soil data in a greenhouse in real time; the image acquisition module is used for acquiring crop growth images; the core controller preprocesses the data acquired by the data acquisition module and the image acquisition module and uploads the data to the cloud database; the computer vision and deep learning analysis module is used for identifying crop types and pest and disease damage conditions based on the crop images; the water-fertilizer control unit comprises an intelligent control module, a water-fertilizer all-in-one machine and a drip irrigation system; the user interaction module provides remote monitoring and control functions for a user; according to the system and the method, the condition of economic loss caused by the fact that crop diseases and insect pests cannot be correctly treated due to insufficient experience in traditional agriculture is overcome, environmental visualization is realized, a farmer can conveniently monitor the greenhouse in real time, precise irrigation and fertilization are realized, the agricultural production efficiency is improved, and resource waste is reduced.

The invention discloses a zero-carbon park land-air integrated inspection system, and the system comprises a function module which comprises an air inspection unit, a land inspection unit, a fixed sensing unit, an abnormality warning unit, an energy consumption management and cooperation unit, and a coordination control unit; the network transmission layer is used for transmitting inspection data in multiple aspects to a cloud database for storage through a 4G / 5G / wide area network, a LoPa gateway and a transmission protocol; and the sensing layer is used for monitoring the daily operation state in the park in real time through a camera, a fixed sensor, a positioning base station, an unmanned aerial vehicle and a land robot. According to the park inspection system, multiple terminal devices such as the land robot, the unmanned aerial vehicle and the fixed sensor are integrated, a multi-dimensional inspection network is constructed, all-dimensional inspection without dead corners in the park can be achieved, and the technical problem that an existing inspection method cannot meet the inspection standard of the zero-carbon park is solved.

The present disclosure provides techniques and solutions for more efficiently managing computing resources used for cloud database instances. Operational data for shared cloud database instances and dedicated database instances can be monitored, including on a continuous basis. The operational data can be compared with one or more thresholds. If the operational data indicates that an entity assigned to a dedicated cloud database instance is underutilizing its assigned resources, it can be migrated to become a tenant of a shared cloud database instance. If the resources of a shared cloud database instance are overutilized, a tenant can be migrated to a dedicated cloud database instance. Migration can include copying data from the original cloud database instance to a migration target cloud database instance. After migration, computing resources can be released that were assigned to the dedicated cloud database instance or to a particular tenant of the shared cloud database instance.

The invention relates to the technical field of science and technology management and information, in particular to a scientific and technological achievement feasibility analysis system which comprises a central processing unit and further comprises a data input module used for inputting data samples of scientific and technological achievements into a sample database; the data analysis module is used for importing the key technical key points in the data sample into a cloud database for analysis; and the feasibility evaluation module is used for carrying out feasibility evaluation on a result obtained after preliminary analysis of the data analysis module. According to the method, the achievement conversion rate can be comprehensively and accurately analyzed and evaluated from technical feasibility, economic feasibility, market feasibility and operation feasibility, the reliability and accuracy of scientific and technological achievement feasibility analysis are improved, the scientific and technological achievement conversion rate and the industry development speed are increased, and the scientific and technological achievement conversion period and the scientific and technological achievement conversion number are stably increased; and the industry is helped to make an accurate emergency strategy.

The invention is suitable for the technical field of cloud databases, and provides a cloud database operation and maintenance management method and system, electronic equipment and a storage medium. The cloud database operation and maintenance management method comprises the following steps: detecting instance nodes according to early warning information and operation state data; determining a target repair strategy from a first repair strategy library; determining a target fault type corresponding to the target operation state data; determining a target repair strategy from a second repair strategy library; determining a target repair strategy according to the target joint state data and a strategy recommendation model; and repairing the target instance node according to the determined target repairing strategy. According to the method, the abnormal instance node can be detected in time according to the early-warning potential fault information, the fault discovery time is effectively shortened, in addition, the applicable repair strategy is autonomously determined through the synergistic effect of multiple modes, the abnormal instance node in the cloud database is efficiently and accurately repaired, and stable operation of the cloud database is ensured.

The invention discloses a hydropower engineering electromechanical equipment installation detail control method based on a three-dimensional laser scanning technology, and the method comprises the steps: S1, setting and determining the instrument parameters of a three-dimensional laser scanner, and collecting the point cloud data of a target region through the three-dimensional laser scanner; s2, remotely reading the point cloud data through a computer, uploading the point cloud data to a cloud database in a file stream form, and storing the point cloud data; s3, performing iterative fitting on the plane model by using a random sample consensus algorithm RANSAC and removing inner points, fitting a target ball model by multiplexing the RANSAC algorithm in the residual point cloud data after the plane model is removed, and screening target ball parameters with the maximum number of inner points; s4, constructing a combined reference model based on the electromechanical equipment to be installed and the virtual target ball, and performing coordinate deviation value comparison with the actually measured target ball model to obtain a deviation value; and S5, generating an installation detail adjustment scheme according to the coordinate deviation value, and sending the adjustment scheme to a mobile client of the field installation personnel.

The present invention relates to a smart mattress that primarily uses non-contact sensors to measure the user's physiological data. Through a non-contact sensing method, it is capable of detecting REM sleep stages, monitoring heart rhythm, and measuring vital information such as respiration, as well as recording and storing physiological data such as bed-leaving movements and sleeping position. By connecting to a mobile device or a computer via a control box, all the recorded information can be displayed as a screen output or uploaded to a cloud database for storage. This enables long-term measurement of the user's physiological data or real-time monitoring, thus contributing to targeted patient care.

The invention discloses a swimming pool water quality management system and method based on intelligent regulation and control of residual chlorine concentration, and belongs to the technical field of swimming pool water quality management. According to the system, water quality parameters are monitored in real time at the frequency of one second through residual chlorine, pH, turbidity and water temperature sensors, after filtering processing is conducted through a data acquisition module, the adding amount is dynamically calculated through an intelligent adding algorithm in combination with a preset residual chlorine target range, and then accurate adding is conducted through a high-precision metering pump. Meanwhile, the water quality comprehensive management unit automatically adjusts the pH value to a preset range, and a flocculating agent is regularly added to reduce the content of organic matters. When the residual chlorine concentration is lower than 0.3 mg / L or higher than 1.0 mg / L, early warning is triggered, and all monitoring data, adding records and early warning information are synchronously stored in a local server and a cloud database; water quality real-time monitoring and dynamic accurate regulation can be realized, and safety and stability of water quality of the swimming pool are guaranteed.

The invention relates to a medical data screening system and method based on AI auxiliary interrogation, the screening system at least comprises a client APP, an AI client and a cloud which are mutually connected, compared with the prior art, the system fully combines the advantages of AI, cloud storage, a user intelligent terminal and an offline diagnosis and treatment end, and realizes data acquisition, classification, fusion and interaction; the upstream of the AI client side can allow a patient to input basic data and image data, the downstream of the AI client side can call a cloud database, pathological data can be accurately screened through feature comparison, and pathological similarity suggestions are provided; in the high-incidence period of infectious diseases, the screening system or the screening method can be used for shunting hospitals, limited medical resources are fully allocated, and cross infection is avoided; by means of big data screening and online and offline diagnosis and treatment combination, the pathology screening accuracy is improved, accurate diagnosis and treatment suggestions can be given, and the medical diagnosis service experience is improved.

The invention discloses a control method and device of a cancel-after-verification system and a storage medium, and belongs to the technical field of cancel-after-verification systems. The method comprises the steps that after an offline cancel-after-verification action is triggered, a cancel-after-verification task is generated based on cancel-after-verification information and first cancel-after-verification card information in a local database, the cancel-after-verification task is decomposed into at least two cancel-after-verification subtasks, time sequence information of the cancel-after-verification subtasks is constructed based on execution time corresponding to the cancel-after-verification subtasks, and after it is detected that a network is recovered, the cancel-after-verification task is executed. And based on the time sequence information, executing a replay action of the cancel-after-verification task on the cloud database so as to synchronize the first cancel-after-verification card information and second cancel-after-verification card information in the cloud database. According to the method, the verification states of the local database and the cloud database are synchronized step by step according to the original time sequence of the offline operation, so that the continuity of the verification service is ensured, the problem of state conflict or data inconsistency possibly caused by direct data coverage is avoided, and the reliability and user experience of the verification system in a complex network environment are improved.