5623 results about "Drug Package" patented technology

The present invention provides a method and intelligent agent for data collection and drug safety signal mining for PMS, which relates to the field of drug monitoring technology, including standardizing PMS data using a medical text pre-training model, and integrating drug molecular characteristics and clinical medication information to construct a multi-layer drug knowledge graph. A heterogeneous graph neural network with a time series feature extraction function is used to represent the graph, and the drug action mechanism rules are integrated into the training process to obtain a node vector representation of multi-dimensional information fusion. Based on the node vector representation, a multi-task learning framework is constructed to simultaneously complete the drug-adverse reaction association probability prediction and risk level prediction, and combine time series analysis to detect dynamic change characteristics to generate candidate signals. Finally, a hierarchical evaluation system is constructed to verify the candidate signal, and its reliability is confirmed in combination with clinical data, the target signal is output, the warning level is determined, and differentiated monitoring cycles and trigger thresholds are set to perform real-time risk warnings.

The invention belongs to the technical field of medical resource intelligent management, and relates to a hospital comprehensive monitoring analysis method and platform based on artificial intelligence, and the method comprises the following steps: generating a medicine management basic data set; generating a primary prediction signal; adjusting a prediction weight according to the conflict level and outputting a reliable demand prediction instruction; a quantitative evaluation value reflecting the medicine turnover efficiency, the immediate pressure and the cost loss is calculated, and when the quantitative evaluation value exceeds a preset safety alert threshold value, an inventory abnormity alarm signal is generated; outputting an allocation strategy instruction containing the medicine flow direction, the allocation quantity and the time efficiency; and comparing a deviation value between the reliable demand prediction instruction and the allocation strategy instruction, and when the deviation value continuously exceeds a preset deviation threshold value, triggering an update prompt of the drug safety use limit rule base. The problem that demand prediction and clinical rule verification are separated in a traditional mode is solved.

The invention provides a virtual-real fusion method for digital twinning operation of a cell drug factory in combination with the Internet of Things, which comprises the following steps: constructing geometric models of a culture medium storage tank and a bioreactor through three-dimensional modeling software, mapping and associating historical process parameters of a culture medium supply system and temperature, pH value and dissolved oxygen concentration monitored in real time with the geometric models, and obtaining a virtual-real fusion model of the culture medium storage tank and the bioreactor; constructing a digital twinborn model of the reactor; analyzing historical production data through a machine learning algorithm, extracting a quantitative relationship among key parameters of a cell metabolism rate, a glucose concentration in a culture medium, a pH value and a reactor temperature, establishing a cell growth prediction model, and predicting a growth trend and a metabolism demand of cells in a target time period; a virtual-real mapping mechanism of a culture medium supply system is integrated with an intelligent manufacturing execution system, so that the culture medium supply process and the whole pharmaceutical production process are subjected to collaborative optimization control, and the working procedures of the culture medium production process are automatically dispatched according to a production plan and cell growth requirements.

The invention discloses an intelligent detection method for medicine components and medicine quality, and belongs to the field of artificial intelligence. The method comprises the following steps: acquiring a drug sample solution and a control drug solution, and injecting the drug sample solution and the control drug solution into a high performance liquid chromatography system; determining whether operation parameters of the high performance liquid chromatography system need to be adjusted or not; determining operation parameter adjustment information, and continuously updating operation parameters of the high performance liquid chromatography analysis system until a complete drug sample chromatogram and a complete control drug chromatogram are obtained; determining medicine component information and impurity component information of the sample medicine; and determining whether the sample medicine reaches a preset medicine quality evaluation standard or not according to the medicine component information and the impurity component information, and if the preset medicine quality evaluation standard is reached, determining that the sample medicine passes quality detection. According to the scheme, the manual operation time and personal errors can be reduced, the detection efficiency and accuracy are improved, and the analysis result is more accurate and stable.

The invention relates to the technical field of medical information, in particular to a medical data conjoint analysis system based on medical knowledge graph driving. The system comprises a medical knowledge graph construction module, a medical data quality evaluation module, a medical feature engineering module and a medical knowledge driven analysis module, and heterogeneous knowledge graph modeling can be performed by integrating medical clinical guidelines, biomedical literatures, a drug database and historical medical data so as to generate a medical heterogeneous knowledge graph; obtaining new medical clinical test information and carrying out delayed contradictory learning update to generate a medical dynamic update knowledge graph; the method comprises the following steps: obtaining multi-modal medical data, carrying out quality verification evaluation and medical feature engineering analysis, carrying out knowledge path joint driving analysis at the same time, generating a decision support reasoning path corresponding to medical clinical knowledge, and outputting a corresponding medical knowledge path confidence coefficient. According to the method, fusion reasoning among cross-source data can be realized by constructing the multi-modal medical knowledge graph.

The invention provides an intelligent medicine storage management method and system based on digital twinning. The method comprises the following steps: firstly, generating a virtual storage space, embedding a binding relationship between a medicine batch number and a space coordinate, and then collecting temperature gradient distribution data, humidity fluctuation data and illumination intensity time sequence data of each medicine storage unit in a physical medicine warehouse; the method comprises the following steps: acquiring three kinds of data, mapping the three kinds of data to corresponding coordinate positions in a virtual storage space, generating a three-dimensional thermodynamic map based on temperature gradient distribution data and storage environment standard parameters associated with drug batch numbers, identifying an abnormal area, and reversely positioning a corresponding drug storage unit in a physical medicine warehouse according to the coordinate position of the abnormal area. The directional ventilation device is controlled to adjust the airflow circulation path of the medicine storage unit, a batch medicine migration path planning scheme is generated, the technical scheme provided by the invention realizes fine management and real-time monitoring of the medicine storage environment, and the safety and efficiency of medicine storage are significantly improved.

The invention relates to the technical field of image defect detection, in particular to an intelligent drug particle detection system and method based on image processing. According to the method, a high-resolution industrial camera and a polarized light source are used for dynamically shooting assembly line medicine particles so as to obtain multi-angle high-quality original images; noise suppression and microstructure enhancement are realized through multi-scale residual information enhancement and structure maintenance adaptive filtering, and the detail resolution is improved by using local contrast normalization; a multi-scale structure gradient of direction perception and region shape prior are fused, and a hierarchical contour evolution and dynamic threshold strategy is adopted to accurately segment a particle main contour and a microcrack; micro defects are identified based on a multi-scale texture sensing algorithm guided by a boundary relative potential graph, and multi-class defect classification is realized through a spatial and semantic dependency relationship between graph neural network modeling regions; and finally, a traceable quality detection report is generated. The intelligent level, the accuracy level and the automation level of medicine particle detection are improved.

The invention relates to the technical field of medicine quality detection, and particularly discloses an optical detection method and system for the content of vitamin tablets. The method comprises the following steps: driving an optical fiber probe array to carry out three-dimensional multi-angle near-infrared scanning through a multi-axis mechanical arm, and collecting reflection spectrum data; a weighted spectrum is generated through derivative spectrum analysis and double evaluation, and auxiliary material noise is corrected and separated in combination with subspace projection and a dynamic kernel function; performing spectral vector projection and regression coefficient iterative optimization through an online PLS model, and synthesizing an error coefficient based on a four-level index retrieval standard library to perform dual-channel feedback; and finally, fusing the credibility weight to output a detection result of the binding confidence. The method realizes nondestructive and high-precision detection of the vitamin tablets, has the beneficial effects of multi-dimensional data fusion, dynamic error compensation and high model adaptability, and remarkably improves the detection accuracy and reliability.

The invention relates to the technical field of medicine sales inventory management, and discloses a medicine sales inventory management system based on artificial intelligence, which comprises a multi-modal anomaly detection module, a multi-modal anomaly detection framework is constructed, and medicine sales anomaly is detected by using an isolated forest algorithm, a depth auto-encoder and a sequence anomaly detection algorithm; the causal structure learning and attribution analysis module is used for constructing a drug sales causal structure learning and attribution analysis system and analyzing abnormal reasons by adopting a NOTEARS algorithm and an SHAP attribution algorithm; the intervention strategy generation and evaluation module is used for constructing an intervention strategy generation and evaluation system and simulating different strategy effects through a causal intervention formula; and the inventory strategy optimization and execution module is used for implementing inventory strategy optimization and execution and dynamically adjusting inventory management. According to the invention, automatic detection, cause attribution and intelligent correction of sales abnormity are realized, and a closed-loop management system is formed.

The invention relates to the technical field of medical decision, and discloses a medical decision support system based on a knowledge graph, and the system comprises a knowledge graph construction module which constructs an initial knowledge graph based on a medical ontology library, and the knowledge graph comprises entities and association relationships of diseases, symptoms and drugs; the data acquisition module is used for acquiring data from an electronic medical record, wearable equipment, a medical literature library and a hospital information system and normalizing the data through a standardized protocol; the dynamic knowledge updating module is used for processing normalized data through an incremental graph neural network; a multi-source knowledge fusion module; a context awareness module; a dynamic deduction module; and a decision optimization closed loop module. And triggering a preset clinical rule in real time based on the pathological state of the patient, dynamically adjusting the intensity value of the related edge in the factor graph, and persistently storing the intensity value back to the knowledge graph, so that logic adaptation and individualized experience precipitation of general medical knowledge in a special pathological state are realized, and the individualized treatment accuracy is ensured.

The invention discloses a chain drugstore purchasing scheme generation method and system based on big data, and relates to the field of purchasing management. The method comprises the following steps: firstly, collecting historical sales data, real-time inventory data and external environment data, and constructing a multi-dimensional database; performing standardization processing on the data, and constructing a medicine classification matrix through a data mining model; identifying key factors influencing drug requirements by using a causal analysis method, and establishing a requirement influence factor weight matrix; based on the weight matrix, calculating purchase parameters such as a safe inventory level, an economic purchase batch, a purchase trigger point and a purchase period; and finally, in combination with a supplier evaluation system, generating a purchasing scheme including a purchasing medicine list, a purchasing quantity, purchasing time and supplier selection. According to the method, through multi-dimensional data analysis and causal relationship mining, scientization and accuracy of purchasing decision are realized, the purchasing efficiency is improved, and the inventory cost is reduced.

The invention provides a drug identification information intelligent association method and system based on multi-source data fusion. According to the method, firstly, multi-source heterogeneous data generated in the medicine circulation process is obtained, then key information elements printed on the surface of a medicine package are recognized, meanwhile, a unique identifier bound with the medicine identity is extracted, and then a dynamic tracking link of a medicine transportation path is established; then cross-modal association matching is carried out on the key information element, the unique identifier and the dynamic tracking link to generate a dynamic mapping relation, finally the consistency of the dynamic mapping relation is verified, and the verified dynamic mapping relation is synchronously updated to a supervision terminal of a medicine circulation link. According to the technical scheme provided by the invention, the identification precision and timeliness of risks such as package tampering and path forgery are improved, the abnormal omission ratio caused by data acquisition errors or artificial tampering is reduced, and the reliability of cross-modal data collaborative verification is enhanced.

The invention relates to the technical field of medicine information management, in particular to a traceability method and system based on a medicine consumable traceability code. The method comprises the following steps: acquiring medicine purchasing information data; generating a unique code for the number of medicine purchasing information so as to generate a medicine warehousing traceability code; performing drug information association on the drug purchase information data according to the drug warehousing traceability code to generate a drug warehousing associated traceability code; storing the medicine warehousing associated traceability code in an information base to generate a medicine warehousing traceability code information base; performing scanning access mode division on the medicine warehousing traceability code, and generating a medicine dispensing scanning request mode and a medicine returning scanning request mode; and performing drug splitting tracing on the drug warehousing tracing code in a drug dispensing scanning request mode to generate drug splitting tracing data. According to the invention, through full-process closed-loop management, data association enhancement, intelligent scheduling optimization and drug return tracing improvement, the efficiency and precision of the drug consumable traceability code tracing method are improved.

The invention discloses a blockchain-based intelligent tracing and risk early warning system for medicine supply data, and relates to the technical field of data processing, and the system comprises the steps: carrying out the circulation node extraction of a medicine supply chain, and obtaining a plurality of medicine supply participants; performing distributed block deployment to obtain a medicine supply block chain; acquiring a medicine database, and acquiring a medicine supply data set; performing classification identification and mapping encryption, and uploading to the medicine supply block chain to obtain a block chain distributed account book; obtaining an access smart contract, and obtaining target drug supply data; and performing risk response early warning on the target drug based on the target drug supply data. The technical problems that in the prior art, traditional Chinese medicine supply data tracing is difficult and inaccurate, and potential risks of medicine supply are difficult to find and accurately evaluate in advance are solved, and the technical effects that accurate and rapid tracing of target medicine supply data is achieved, the potential supply risks are found in time, and effective response and early warning are carried out are achieved.

The invention discloses a drug traceability code acquisition method, system and device based on image recognition, and a medium. The method comprises the steps of obtaining a drug package image; preprocessing the medicine packaging image, and performing edge detection, region segmentation, image correction and barcode analysis on the preprocessed medicine packaging image to obtain medicine traceability code information; obtaining prescription data, and checking the drug traceability code information according to the prescription data; and when the drug traceability code information is checked to be correct, carrying out associative storage on the drug traceability code information and the prescription data. According to the invention, drug traceability code information can be rapidly and accurately identified, and environments such as complex backgrounds, illumination changes, traceability code deformation and the like can be adapted; meanwhile, multiple drug traceability codes can be collected, the purpose of batch collection is achieved, supervision of a drug dispensing end is achieved, medication safety of patients is ensured, drug management can be perfected, and subsequent drug traceability is facilitated.

The invention provides a medical drug management system and an optimization method, and relates to the technical field of medical information management, the system comprises a medicine management platform, a medicine database and an information technology support module; wherein the medicine management platform adopts a modular design and comprises a medicine inventory management module, a patient information management module, a medicine use process management module and a report statistics module, and information interaction among the modules is realized through interfaces; the medicine database is used for storing medicine information, medicine inventory information and patient medication information; the information technology support module comprises a bar code scanning module, an RFID technology module and a cloud computing module. By integrating the advanced information technology and the medical management concept, the medical drug management system which is comprehensive in function, efficient and safe is provided, the management process is optimized, the drug use safety is improved, the management cost is reduced, and personalized drug use suggestions and decision support are provided.

The invention relates to the technical field of cold-chain medicine transportation, in particular to an intelligent monitoring and early warning system for a cold-chain medicine transportation environment. Comprising a multi-source heterogeneous data acquisition module, a time-space synchronization data fusion module, a three-dimensional thermodynamic distribution modeling module, a drug property degradation risk assessment module, a dynamic grading early warning module and a path self-optimization execution module, the multi-source heterogeneous data acquisition module is used for generating multi-source heterogeneous data; the space-time synchronization data fusion module generates an environment parameter matrix; the three-dimensional thermodynamic distribution modeling module calculates an equivalent thermal load value of each grid node; the drug property degradation risk assessment module predicts residual effective time; the dynamic grading early warning module generates a dynamic early warning signal; and the path self-optimization execution module generates optimized transportation path parameters. According to the invention, the medicine is prevented from losing efficacy due to environment change, and meanwhile, the response speed of cold-chain transportation and the medicine quality guarantee are improved.

The invention relates to the technical field of medicine purchasing, and provides a hospital easy-to-expire medicine purchasing decision optimization method, which comprises the following steps of: firstly, extracting medicine inventory, consumption records and purchasing management parameters in a hospital information system, and constructing a complete inventory state data set; then, calling a demand prediction model constructed based on a time sequence and external factors, and outputting drug prediction demand values in a plurality of periods in the future; thirdly, constructing a multi-cycle purchase optimization model according to the current inventory and the prediction demand; then, dividing a planning period into rolling windows, and gradually generating an ordering scheme meeting conditions by using a heuristic greedy and local search algorithm; then, the scheme is pushed to a purchase decision-making interface, and a formal order is submitted and generated after the scheme is confirmed by a manager; after a supplier delivers goods and stores the goods, the system updates inventory information and records scrapping and shortage data; and finally, adjusting the model through a feedback mechanism so as to improve the accuracy of subsequent prediction and decision.

The invention relates to the technical field of medical data processing and prediction, and discloses a chemotherapy adverse reaction prediction and intervention system based on big data. The system integrates an unstructured disease course text and structured inspection data of a patient, generates a time-series symptom event, and performs time window alignment and fusion on the time-series symptom event, a medication record and a physical sign monitoring stream to form a multi-dimensional time-series data block. The system is combined with an external medical knowledge base to construct a dynamic association network among symptoms, medicines and physiological indexes, and dynamically calculates the confidence coefficient of an adverse reaction mode according to real-time data. By using a predictive model of the timing attention mechanism, the system can output a continuous curve of patient risk over time. The system automatically matches and generates a personalized intervention instruction sequence containing specific measures and execution time windows according to key time points and modes when the risk curve exceeds a threshold value. According to the invention, dynamic and advanced early warning and accurate intervention of adverse reaction risks of chemotherapy are realized.

The invention discloses a medicine image detection method based on an improved Canny operator, and the method comprises the steps: 1) collecting a medicine image, and carrying out the graying processing of the medicine image; 2) an improved bilateral filter is adopted to filter the medicine image after graying processing, and noise is suppressed; 3) carrying out gradient amplitude and phase extraction on the medicine image by adopting a four-direction Soble operator; 4) performing non-maximum suppression on the extracted gradient amplitude and phase by adopting improved non-maximum suppression, and accurately positioning a target edge; 5) performing adaptive threshold extraction by using Otsu improved based on gradient edge information to obtain a drug image edge contour; and 6) outputting the edge contour of the medicine image. By adjusting the weight value of the edge pixel point identified by the bilateral filter to be zero, the interference to the central pixel point is effectively reduced, so that the target edge and detail features of the image are reserved to the greatest extent.

The invention discloses an efficient medicine logistics order scheduling and distribution method and system, and relates to the technical field of medicine logistics, and the method comprises the steps: constructing a multi-modal medicine feature extraction module, and outputting an order constraint matrix; establishing an adaptive space-time distribution network according to the order constraint matrix, and performing dynamic grid division on a distribution area to generate a multi-level distribution area model; constructing a double-layer heterogeneous graph neural network, and generating a distribution path planning candidate scheme set considering drug characteristics; defining a state space and an action space, constructing a dual-target reward function, and carrying out iterative training to obtain a distribution scheduling strategy model; and aggregating multi-region distribution empirical data by adopting a federated learning framework, and periodically updating the scheduling strategy model based on a differential privacy mechanism. According to the method, the multi-modal drug feature extraction module is constructed, and the image, text and storage condition information of the drug are fused, so that the drug storage requirement and the distribution limitation condition can be identified more accurately, and the identification accuracy of the distribution constraint is improved.

The invention discloses a drug risk monitoring method based on multi-source data fusion, and relates to the technical field of data analysis. Through multi-source data fusion and processing, the problems of data isomerism and information islands are solved, a comprehensive and high-quality data basis is provided for drug risk monitoring, a weighted collaborative filtering algorithm and a Bayesian network are adopted, potential risk signals are accurately mined, real-time dynamic monitoring and early warning of drug risks are achieved, and the drug risk monitoring and early warning efficiency is improved. The timeliness and the reliability of monitoring are obviously improved; meanwhile, the real-time stream processing framework is used for carrying out incremental updating on newly-added data, dynamically adjusting a risk assessment result, and carrying out automatic early warning when a risk value exceeds a standard, so that the contradiction between a complex process and a rapid early warning demand is effectively solved, the efficiency and accuracy of drug safety supervision are improved, and powerful support is provided for guaranteeing the public drug safety.

The invention discloses a data quality governance method, and relates to the technical field of privacy calculation and data governance. According to the method, various kinds of original data are obtained, one kind of original data comes from a data source, and the data source comprises a hospital, a company and a medicine production enterprise; normalizing the original data to obtain standardized data; inputting the standardized data into a preset federal data quality evaluation model for data quality evaluation to obtain a quality evaluation result corresponding to each kind of standardized data, the quality evaluation results including an integrity evaluation result, a consistency evaluation result and a timeliness evaluation result; aggregating the quality evaluation results to obtain a global quality evaluation report; and determining a corresponding quality optimization strategy according to the global quality evaluation report, and treating data according to the quality optimization strategy. And the data quality treatment effect is improved.

The invention relates to the technical field of clinical medical data visualization, in particular to a visualization system for clinical internal medicine and a visualization method thereof. The method comprises the following steps: acquiring basic information of a clinical patient; according to the basic information of the clinical patient, performing medical treatment and medication event analysis, and performing triple entity relationship processing to generate patient medical record relationship map data; performing patient drug tolerance analysis based on the patient medical record relation map data to generate patient drug tolerance characteristic data; performing clinical physiological index risk probabilistic reasoning according to the drug tolerance characteristic data of the patient to generate individual clinical medication risk data; according to the individual clinical medication risk data, individual characteristic medicine dosage adjustment is carried out, medicine dosage curve visual dynamic display is carried out, and visual clinical medication response data is generated. According to the method, personalized and visual medication decision support is provided for clinicians through patient medication conditions and real-time physiological index analysis.

The present application relates to the technical field of medical equipment and smart alarm clocks, and discloses a smart auxiliary medication reminder method, system, device and medium, wherein the method includes: obtaining a target scheme for a target drug object; determining whether the current time is within the reminder time period of the target scheme, and obtaining a first result; if the first result is yes, obtaining the detection data of the target space corresponding to the target drug object, and determining whether a human body exists according to the detection data, and obtaining a second result; if the second result is yes, performing a medication reminder according to a preset first multi-level reminder strategy; if the second result is no, performing a medication reminder according to a preset second multi-level reminder strategy. This effectively solves the problem in the prior art that only ringing reminders based on the set time often cannot ensure that the information is accurately transmitted to the user in actual applications, and greatly improves the effectiveness of medication reminders.

The invention belongs to the technical field of medical informatization, and provides an anesthesia medication management system with biological recognition, comprising: a biological recognition module verifies the identities of medical staff and patients through a vein recognition technology; the multi-source data acquisition module acquires vital sign data and equipment parameters of a patient in real time to obtain multi-source data; the intelligent drug management module authorizes to store and take drugs according to the biological recognition result and generates an electronic anesthesia prescription; the processing decision module receives the multi-source data and generates a medicine adjusting instruction through a preset rule and an algorithm in combination with an electronic anesthesia prescription; the execution control module carries out secondary confirmation on the medicine adjusting instruction through biological recognition and then sends the medicine adjusting instruction to anesthetic infusion equipment to be executed; according to the method, through the electroencephalogram double-frequency index, the entropy index and the hemodynamic parameters, in combination with the anesthesia depth evaluation result and the personalized drug metabolism and effect model, the medication scheme is dynamically adjusted, the optimal infusion rate is generated, the anesthesia depth control is more accurate, and the fluctuation range is narrowed.

The invention discloses a liquid medicine foreign matter detection method and system based on machine vision, and the method comprises the steps: obtaining multiband spectral data of a liquid medicine sample, and carrying out the normalization processing to generate a liquid medicine background standard spectral feature template; comparing the refractive index spectral characteristics of the template and the foreign matter-containing liquid medicine, and recording the surface roughness scattering mode and polarized light reflection angle change data of a foreign matter area; calculating a spectrum peak displacement quantized value according to the polarized light data, and extracting foreign matter edge spectrum gradient information in combination with a threshold value; calculating a transparency spectral attenuation coefficient based on the edge gradient information, and obtaining spectral contrast enhancement factors of different foreign matters in combination with the spectral response characteristic data; and if the enhancement factor does not reach the standard, adjusting the spectrum correction parameter and recalculating, fusing the scattering mode and the multi-angle incident spectrum response result to carry out consistency verification, and obtaining a final identification result containing the foreign matter material type and the danger level. The method can improve the drug detection accuracy and guarantee the drug quality safety.

The invention relates to the technical field of medical data integration, in particular to a block chain and AI-based medical whole industry data asset integration system and method. Comprising a data acquisition layer, a block chain right confirmation layer, a federal learning analysis layer, a data asset transaction platform and a supply chain traceability optimization module. The data acquisition layer comprises an edge computing node, a data cleaning engine and a desensitization algorithm are built in the edge computing node, and the edge computing node is used for carrying out localized cleaning and desensitization processing on hospital HIS system data, medicine RFID data, medicine research and development experiment data and medical image data; the block chain right confirmation layer constructs a medical data asset account book based on an alliance chain architecture, the asset account book comprises a data hash record, a contributor identity identifier and a use authorization log, and an integrated intelligent contract module is used for dynamic right management; according to the invention, medical data islands can be broken, and safe integration and efficient utilization of cross-domain data are realized.

The invention provides a medicine raw material label consistency comparison method, system and device and a medium, and belongs to the technical field of medicine raw material label identification. The edge server searches a local template library according to the template index based on the comparison picture, and obtains a template tag picture and a feature vector file; extracting a feature vector of a comparison label picture by using a reconstructed Resnet18 network, calculating the similarity between the feature vector of the template label picture and the feature vector of the comparison label picture, and obtaining a label consistency result of the template picture and the comparison picture; and carrying out result visualization display on the label consistency result. Through a traditional image processing algorithm and a deep learning network feature extraction technology, the verification workload is reduced, and the verification efficiency is improved. The feature vectors are extracted by using the reconstructed Resnet18 network, and the consistency of the tag styles can be accurately judged in combination with an SIFT key point matching algorithm.