4355 results about "Drug Package" patented technology

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 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 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 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 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.

The invention discloses a monitoring and tracing intelligent management system for toxic and anesthetic drugs, and particularly relates to the medical field, and the system comprises an equipment end module, a background management module, an intelligent medicine chest module, a function cooperation module, a drug validity period management unit, an intelligent replenishment unit, an environment monitoring unit and a multi-terminal cooperation unit. On the basis of authority linkage of biological recognition and double-person verification and in combination with whole-course encrypted storage of operation logs, the risk of false claim and misuse can be eradicated, 'person-medicine-operation 'strong-correlation tracing can be achieved, the risk of inventory overstock and shortage can be reduced, the emergency toxic and anesthetic medicine replenishment response speed is increased, the deterioration risk is reduced, and the safety and reliability of the system are improved. The method can automatically early warn and intercept expired medicine receiving, supports off-line operation and encrypted communication, can still cache records when a network is interrupted, ensures operation continuity in an emergency scene, and can realize whole-course visual tracing of medicine flow.

The invention provides a drug identification information efficient query method and system based on distributed cache. The method comprises the following steps: firstly, receiving and analyzing a drug identifier query request to generate a dynamic cache routing key, then positioning a target cache node, then matching a cache data block corresponding to a drug unique code in the target cache node, if matching succeeds, extracting a drug full life cycle record in the cache data block, and sending the drug full life cycle record to the target cache node; if the matching fails, initiating a cross-node retrieval request to the drug block chain database to return drug source data, splitting the drug source data into a core attribute set and an extended track set, and finally executing a dynamic combination operation on the core attribute set and the extended track set to generate a drug tracing result set and returning the drug tracing result set to the user terminal; according to the technical scheme provided by the invention, the query fluctuation defect of a single storage architecture is eliminated from the source, so that the whole system simultaneously meets three drug traceability core requirements of high concurrency, cross-mechanism and tamper-proofing.

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 discloses a cross-modal retrieval matching method and device for medicine information, and the method comprises the steps: obtaining a to-be-recognized target medicine box picture; performing feature extraction on the target medicine box picture by using a multi-modal medicine matching model to obtain feature representation; based on the feature representation, performing cross-modal matching by using the multi-modal drug matching model to match drug information related to the target drug box; wherein the multi-modal drug matching model is obtained by the following steps: based on an image-text pair training set of a drug field, performing field adaptation training on a pre-trained multi-modal network model to obtain the multi-modal drug matching model. The technical problem of inaccurate drug retrieval in the prior art is solved.

The invention discloses an automatic medicine vending machine based on AI inquiry. The automatic medicine vending machine comprises a machine body, an intelligent interaction system, an AI inquiry system, a medicine storage bin, a control system, a payment system and a printing device. A constant-temperature and constant-humidity system and a disinfection and air purification module are arranged in the machine body. The intelligent interaction system collects symptom information in a multi-mode mode, and the AI inquiry system achieves symptom analysis, intelligent triage and personalized medication recommendation based on a medical knowledge graph and a natural language processing technology. The medicine storage bin carries a three-dimensional matrix type medicine cabinet and an intelligent inventory system, and intelligent replenishment is carried out through an LSTM algorithm. And the control system adopts a controller and triple verification to realize high-precision medicine discharging operation. The payment system supports multiple settlement modes and block chain evidence storage data. And the printing device outputs the medication guide sheet and the anti-counterfeit label. According to the invention, AI and automation technologies are deeply integrated, the service of less than or equal to 3 minutes in the whole process of inquiry-payment-medicine dispensing is realized, and the safety and convenience of medicine use are improved.

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.