3243 results about "Drug Package" patented technology

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 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 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 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 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 discloses a sewage treatment drug delivery control method based on reinforcement learning and particle swarm optimization, and the method achieves the precise control of turbidity and the optimization of drug cost through the dynamic adjustment of particle swarm parameters and the fusion of a reinforcement learning strategy. According to the technical scheme, the method comprises the following steps: combining a PSO algorithm with a DDPG algorithm to construct a drug control model; designing a reward function; the empirical data with the state coverage rate higher than a first preset threshold value are stored in a full-reservation empirical pool, the empirical data with the current policy access larger than a second preset threshold value are reserved in a first-in first-out mode through a policy empirical pool, and mixed sampling is carried out according to a preset proportion; training a drug control model; and inputting the current turbidity value, the drug delivery amount of the last time step, the water inlet flow, the parameters of the water quality detection camera and the future turbidity value into a trained drug control model, and outputting the current drug delivery amount. Through prediction-control closed-loop integration, a raw water turbidity prediction result is fed back to the optimizer, and the method is suitable for real-time adaptive control of a sewage treatment system.

The invention discloses a drug production safety robot inspection method, which comprises the following steps: acquiring multi-source heterogeneous data of a drug production safety state in real time, including environment sensor data, intelligent video data and equipment operation parameters; based on the multi-source heterogeneous data, constructing and dynamically updating a production safety risk assessment model; according to the production safety risk assessment model, a self-adaptive inspection task is generated and adjusted, and the task defines an inspection target, a path, a period and an execution maneuvering terminal; scheduling the maneuvering terminal to execute the inspection task, driving the multi-mode AI potential safety hazard identification engine by the returned safety state data and outputting an identification result; and an identification result is fed back to the production safety risk assessment model to form closed-loop control. The invention also discloses an intelligent inspection system for pharmaceutical production safety. According to the invention, prospective risk early warning, self-adaptive task scheduling and closed-loop self-optimization can be realized, so that higher requirements of the modern pharmaceutical industry on personnel safety, public health and compliance production can be met.

The invention discloses a drug intelligent management and prediction analysis method based on deep learning, and the method comprises the following steps: S1, collecting and normalizing drug related data, and generating unified drug data; s2, constructing an ETL process, setting a time granularity dynamic weight, and generating time sequence input; s3, constructing a drug knowledge graph, and establishing a semantic mapping relationship; s4, fusing the time sequence and the knowledge graph to extract periodic change and spatial difference features, and outputting a trend prediction result and a residual sequence; s5, mining a high-frequency drug combination rule based on an Apriori algorithm; s6, carrying out anomaly detection by using a module which is fused with an Isolation Forest and an auto-encoder; s7, integrating the results to generate a feature attribution; and S8, outputting all analysis results. According to the invention, integrated modeling of drug trend prediction, behavior mining and anomaly recognition is realized.

The invention relates to the technical field of monitoring systems, and discloses a logistics storage monitoring system based on artificial intelligence, and the system comprises a data collection module which is composed of an environment sensor array, a medicine batch identification unit and a space positioning device which are disposed on a storage three-dimensional shelf, the environment sensor array collects temperature, humidity and vibration parameters in real time, and the medicine batch identification unit stores unique codes and batch attribute data of medicines. According to the method, the dynamic association model of the medicine batch-environment sensor is established, and the spatial interpolation algorithm and the three-dimensional coordinate mapping technology are combined, so that the accurate association of the abnormal event and the medicine batch is realized. When an environment abnormity occurs in a certain area, the system can quickly position the batch of the affected medicine and generate the tracing map, and compared with a scheme in which environment data is disjointed from the position of the medicine in the prior art, the problems that the abnormity tracing efficiency is low and the high-value medicine is easy to deteriorate are solved.

The invention discloses a medicine package online detection system based on three-dimensional reconstruction, and relates to the technical field of three-dimensional reconstruction, the system is matched with a binocular industrial camera through a numerical control rotary table to collect a double-view group covering the whole surface of a medicine package and containing an overlapping surface; carrying out feature point registration by adopting a polarization fusion SIFT algorithm, and positioning a packaging area through a model fusing packaging topology priori and edge gradient constraint; generating a disparity map based on a semi-global block matching algorithm, and combining camera calibration parameters with a rotary table rotation angle for splicing to obtain a complete packaging point cloud; a three-dimensional grid model is generated through Poisson curved surface reconstruction, accurate detection of surface concave-convex defects is achieved through curvature sudden change analysis and a tolerance threshold comparison method, and finally a defect information set is output and linked with a sorting mechanism to complete qualification judgment. The system can carry out full-surface and high-precision detection on medicine packages, is suitable for multi-specification packages, and meets the quality control requirements of industrial production.

The present invention relates to a planar transport system (128) for a plant (100) for processing pharmaceutical containers (102), comprising a frame (106) that can be erected on a mounting surface (104), the planar transport system comprising drive units (134) configured to interact electromagnetically with transport movers (130) for picking up the containers (102), and a support device (142) comprising or forming a housing (148) in which the drive units (134) are received, wherein the transport movers (130) are arranged outside the housing (148) for contactless movement relative to the housing (148) in a transport plane, wherein the support device (142) comprises at least one support area (150) for support on and, in particular, on the frame (106) and at least one cantilevered area (152).which adjoins at least one support area (150) and on which the housing (148) is positioned at a distance from the frame (106), forming a gap (158). The invention also relates to a system (100) for processing containers (102).

The invention relates to an intelligent purchasing method and system for medicine consumables. The method comprises the following steps: acquiring historical medicine purchasing data, patient treatment data and epidemiological data of a hospital; adopting a drug demand prediction model to predict and determine first drug purchase data according to the hospital historical drug purchase data, the patient treatment data and the epidemiological data; and determining a medicine purchasing adjustment adaptability coefficient, and adjusting the first medicine purchasing data according to the medicine purchasing adjustment adaptability coefficient to obtain second medicine purchasing data. According to the technical scheme, the adaptability coefficient is adjusted by determining the drug purchase. And the system or service logic can better fit the actual scene change. The coefficients change dynamically, errors caused by fixed values are reduced, medicine purchasing prediction is more accurate, and data accuracy is improved.

The invention provides a drug temperature and humidity digital monitoring and quality tracing system based on a full life cycle, and relates to the technical field of digital monitoring, and the system comprises the steps: firstly collecting the temperature and humidity data of the whole link of drug production, storage, transportation to terminal sales, and obtaining a standardized data set through the verification and packaging of an industrial standard library; performing secondary screening through primary screening in combination with a dynamic temperature and humidity screening model to obtain a precise abnormal data set; associating drug full-cycle multi-class information, and constructing a temperature and humidity-drug state mapping relation based on a time sequence association algorithm to form a full-dimension association data set; based on the data set, combining with a quality risk assessment model for early warning, realizing full-period traceability through a reverse traceability algorithm, and generating an assessment result and a traceability link; and finally, relevant data are converted into a dynamic temperature and humidity monitoring curve, a quality risk early warning report and a full-life-cycle tracing atlas, and an interactive report is assembled, so that the temperature and humidity state of the medicine can be checked conveniently.

The invention discloses a drug full life cycle risk monitoring and early warning method based on multi-source data fusion, and relates to the technical field of data analysis, and the method comprises the steps: constructing a time sequence heterogeneous graph, calculating the similarity between drugs and side effect pairs in the time sequence heterogeneous graph through a comparative learning algorithm, and generating a candidate signal list; constructing a data set, and identifying hybrid factors associated with drug selection and side effect risks in the data set by using a separation characterization learning algorithm to obtain an average causal effect value; a space-time risk thermodynamic diagram is drawn, risks in the whole life cycle of the medicine are graded based on risk distribution characteristics in the space-time risk thermodynamic diagram, and a corresponding early warning strategy is triggered; and converting the early warning strategy into a risk assessment report in a standard format by using a preset report template. By accurately calculating the individual and average causal effect value, the causal relationship between the drug and the side effect of the drug is quantified, and a solid foundation is laid for deep evaluation of drug safety.

The invention belongs to the technical field of image processing, and particularly relates to a medicine production line remote monitoring method based on image processing, which comprises the following steps of: calculating a reflection intensity score according to a relative deviation and a gradient magnitude between a gray value of a pixel point in a bubble cap region and the overall gray of the region, and further determining a gray validity weight; initializing tablet center estimation, calculating an edge quality score in combination with a gray validity weight and an included angle between a gradient direction and a radial direction, and optimizing a tablet center position through an iteration gravity center; dividing a fan-shaped region by taking the optimized center as an original point, screening a reliable region and a region to be complemented according to the final edge quality score, and determining a complementation value by using the quality score interpolation of the reliable regions on the two sides; and synthesizing the gradient magnitude, the final edge score and the complementation value to construct an edge strength graph, and carrying out tablet defect detection according to a Hough circle detection result of the edge strength graph. The interference of light reflection and tablet deviation is eliminated, and the detection accuracy is improved.

The invention discloses a data management system and method based on a large model, and relates to the technical field of big data analysis, and the method comprises the steps: setting a data monitoring management period, enabling the system to collect the stock change information of various types of medicines in a hospital, analyzing the historical warehouse-out data of a department in combination with a time sequence model, and precisely measuring and calculating a first warehouse-out predicted value; and comparing the daily average dose with a standard limited daily dose, deeply mining compatible drug data associated data in an auxiliary drug use scene, analyzing a compatible drug data ex-warehouse trend, and accurately mastering a collaborative consumption rule. By tracking ex-warehouse proportion changes of target drug data and compatible drug data in a historical period, predicting a proportion trend, predicting ex-warehouse quantity in combination with the compatible drug data, dynamically calculating an auxiliary drug use demand, and deeply deconstructing inventory change data, prospective pre-judgment of the inventory change data is realized, hospitals are assisted to optimize an inventory structure, and the workload of the hospitals is reduced. And the accuracy and response efficiency of inventory data management are improved.

The invention discloses an improved defect detection method for a medicine bubble cap plate by using YOLOv12, and relates to the technical field of medicine packaging detection, and the method comprises the steps: S1, taking a defect data set of the medicine bubble cap plate, carrying out data enhancement processing on the data set, expanding the scale of the data set, and improving the generalization ability of a model; s2, images of the data set are input into an improved YOLOv12 model, the model adaptively changes the weight of each convolution kernel through a dynamic convolution module, and the features of the images are efficiently extracted; and S3, inputting the original features into a partial convolution module, performing convolution operation on a subset of an input channel to realize model distillation, and combining point-by-point convolution to realize cross-channel feature recombination. According to the improved defect detection method, a DynamicConv reconstruction network basic unit is introduced to replace a conventional convolutional layer of an original model, and in order to further lighten the model, partial convolution is used to replace an A2C2f module at the tail end of a backbone network, so that an effective solution is provided for real-time detection of medicine packaging quality control.

The utility model relates to an integrally-torn display packaging box, and belongs to the technical field of medicine packaging. The packaging box comprises a box body and a front face built-in layer, the section of the box body is rectangular, the box body is closed, a containing cavity is formed in the box body and used for containing contents, the box body comprises a rectangular box front face, two tearing lines are arranged at preset positions of the box front face, the two tearing lines are horizontally arranged at intervals, and an easy-to-tear strip is arranged between the two tearing lines; the front built-in layer is matched with the box front, the front built-in layer is connected with the inner side of the box front, the front built-in layer and the box front are arranged in a stacked mode, an image-text area is arranged on the first side, close to the box front, of the front built-in layer, the image-text area is used for displaying information of contents and is used for being arranged corresponding to the easy-to-tear strip, and the easy-to-tear strip can cover the outer side of the image-text area. And the image-text area can be exposed after the easy-to-tear tape is torn. The display effect of the packaging box can be improved.

The invention belongs to the technical field of medical informatization, and particularly relates to a medical insurance medication rationality interception system and method based on deep reinforcement learning and an FP-Growth algorithm, and the method comprises the following steps: S1, receiving electronic prescription data transmitted by a hospital HIS system in real time; s2, according to original diagnosis information in the prescription, performing standardization processing to obtain a diagnosis set; according to the original medicine information in the prescription, cleaning and integrating to obtain a medicine set; s3, based on an FP-Growth association rule algorithm, performing diagnosis-drug association analysis and drug-drug association analysis on the diagnosis set and the drug set, and identifying a low-association prescription according to a preset condition and marking the low-association prescription as a suspicious prescription; s4, starting deep reinforcement learning analysis on the suspicious prescription through DRL decision optimization, and outputting a prescription reinforcement learning feature set; and S5, outputting a prescription qualitative analysis result and a corresponding confidence score according to the prescription reinforcement learning feature set.

The invention discloses an RWS-based post-market drug effect tracking platform, which comprises a data acquisition layer, a data processing layer and a result analysis layer, and is characterized in that the data acquisition layer is used for acquiring RWS data from different sources, the data processing layer is used for preprocessing the RWS data from different sources, constructing an original data chain based on a block chain technology, and analyzing the result of the original data chain; writing the preprocessed RWS data of different sources into an original data chain for storage; and the result analysis layer is used for acquiring the preprocessed RWS data through the original data chain, analyzing the preprocessed RWS data, constructing a drug tracking knowledge graph, and comprehensively analyzing the drug tracking knowledge graph to obtain an indication distribution result, an adverse reaction monitoring result, a treatment cost distribution result and a curative effect analysis result.

A container system with a recordable device having a substantially unaltered packaging profile when compared to standard commercially available prescription containers. The container includes a bottle having a cylindrical body having a length greater than its width. A cap is provided with an inclined spiral or helical land and stops to permit it to be affixed to the container. The cap is equipped with a snap fit ring or a fitting on its underside. A recording device is configured with a land or snap ring removably engageable with the snap fit ring on the underside of the cap. The cap has an aperture to accommodate a recording switch on the recording device. The cap is provided with a speaker switch aperture to permit the user to activate the message on the recording device to hear about the contents of the bottle.

A medication dispensing system is provided with several cooperating components which help to accommodate the responsible handling of medications. These systems include a user interface to receive input from the user and to present appropriate information / notices / alarms. Further, physical controls and multiple sensors are provided to detect tampering, misuse, and attempts to inappropriately access medications contained within the dispensing system. This misuse can thus be detected, and appropriate notifications can be provided, where appropriate. Further, by adding network communications capabilities, notices can be widely communicated to others, and operation of the medication dispensing system can be controlled and / or monitored remotely.

The invention relates to the technical field of medical products, in particular to a medical product market prediction and supply chain collaborative management system, which comprises a health risk identification module, a risk trend analysis module, a demand sorting and grading module, a supply chain matching module and a distribution path optimization module. According to the method, through synchronous analysis of spatial data, the risk pattern of population mobility and disease distribution in each geographic area can be dynamically displayed, multi-dimensional accurate identification for high-risk areas of the mobility population is realized, fluctuation of market demands and potential public health pressure are actively identified based on a trend linkage triggering mechanism and in combination with a periodic change index, and the risk of population mobility and disease distribution in each geographic area is accurately identified. The early warning response speed is improved, multiple parameters such as drug use demand urgency levels, drug delivery timeliness and spatial distances are comprehensively considered, a demand sorting and resource allocation global system is constructed, supply and demand connection is promoted to be more scientific and reasonable, resource flows between medical institutions and storage nodes are matched, and multi-point inventory and priority demands are combined, so that the efficiency of early warning is improved. And the overall resource utilization rate is improved.

A system and method is described for printing a label with an RFID tag. The system includes an RFID reader that queries a first RFID tag coupled to a first medicinal container that includes a medication. In response, the system receives a first unique identifier and uses the first unique identifier to determine a status of the medication, associate the first medicinal container with a medical provider and print a second label that includes a second RFID tag for a second medicinal container.

The invention relates to a medicine whole-process intelligent industrial control management method and system, and the system comprises a control server which is connected with a message management server, an equipment management server, a collection and detection server, and an auxiliary equipment management server. The message management server is connected with the control server and the dispensing management server; a message subscription mechanism is adopted, so that the control server and the dispensing management server decouple and asynchronously cooperate; the auxiliary equipment management server is used for controlling the medicine decocting equipment to decoct medicines under the control of the control server; and the acquisition and detection server is used for controlling the detection device to carry out detection under the control of the control server. According to the technical scheme, each server is clear in division of labor and efficient in cooperation, the message management server achieves cooperative decoupling, and the method is suitable for a full-process efficient medicine circulation scene in a hospital.

The invention belongs to the technical field of medicine inventory management, and discloses a Western medicine intelligent inventory optimization and automatic replenishment decision-making method and a Western medicine intelligent inventory optimization and automatic replenishment decision-making system. Comprising the steps of collecting environment data, inventory information and historical inventory consumption data of inventory drugs, and integrating the environment data and the inventory information to obtain a health degree index of each drug; based on the drug health degree index and the consumption prediction result, generating a replenishment decision matrix, and outputting replenishment opportunity, replenishment amount and replenishment priority; in combination with an operation audit result and inventory area division, differential inventory management scheme configuration is realized; and after external information is obtained, a replenishment strategy is dynamically adjusted, and a target inventory optimization decision is formed. According to the method, accurate evaluation of the inventory state and intelligent decision of replenishment response are realized, and the safety, efficiency and refinement level of medicine inventory management are improved.

The invention relates to the technical field of information security and risk management, in particular to a drug inspection traceability system and method based on a block chain, and the method comprises the steps: collecting a plurality of trust loss factors, and carrying out the calculation to obtain a supervision trust loss index; calculating a system failure probability by combining the supervision trust loss index and a preset failure window duration; determining a system auditing mode; when the system auditing mode is a trust repair priority mode, executing a risk hedging process, including: selecting an optimal checking path meeting a lowest acceptable accuracy threshold; when the auditing mode of the system is recovered from the trust repair priority mode to the conventional zero-tolerance auditing mode, executing a strategy self-optimization process which comprises the steps of evaluating an execution effect in the trust repair priority mode, generating a strategy evaluation result, iteratively adjusting a preset weight, activating a threshold value and recovering the threshold value; according to the invention, the conversion from passive recording to active risk pre-judgment is realized, and the initiative and foresight of risk management and control are greatly improved.