17875results about How to "Improve product quality" patented technology

The invention relates to a method for preparing a lithium iron phosphate / carbon composite material of a lithium ion battery, which belongs to the technical field of lithium ion batteries. The method for preparing the lithium iron phosphate / carbon composite material of the lithium ion battery comprises the following steps of: 1) preparing a suspending graphene-dispersed aqueous solution system, namely, crushing graphite to 1 to 5 microns, adding the crushed graphite into distilled water or purified water, adding 0.1 to 5 percent of surfactant, heating with stirring the mixed solution to 180 to 250 DEG C in a sealing way, performing stirring for 2 to 6 hours and reducing the temperature; 2) crushing lithium iron phosphate to the particle size of 1 to 5 microns, adding the crushed lithium iron phosphate into the distilled water or the purified water, adding with stirring 0.01 to 1 percent of coupling agent, performing uniform stirring, adding the graphene-dispersed aqueous solution, and performing stirring and filtration; and 3) vacuum-drying solid powder obtained by the filtration, and calcinating the dried solid powder for 2 to 12 hours to obtain the graphene-coated lithium iron phosphate cathode material. The method has the advantages of simple process, high material performance, high conductivity, high bulk density, high compacted density and the like.

The invention relates to the field of new energy automobile part detection, in particular to a new energy automobile part surface defect testing method based on computer vision, and the method comprises the steps: carrying out the multi-source data collection and preprocessing, dynamically adjusting a light source, carrying out the feature fusion through a deep learning technology, and automatically extracting the multi-level features of defects. And in combination with transfer learning and reinforcement learning, intelligent defect classification, positioning and severity evaluation are realized. And through dynamic threshold adjustment and manual verification, the detection accuracy is ensured. And finally, predicting a defect trend based on historical data, analyzing reasons, and preventing and optimizing production in advance. According to the method, the detection efficiency and precision are remarkably improved, the system intelligence and adaptability are enhanced, and production optimization and quality control are promoted.

The invention provides a product quality detection method and system for an extruder, and the method comprises the steps: collecting image data of the surface and interior of a product in real time, and installing an intelligent sensor at the same time to collect multi-modal sensing data in the production process; processing the surface image data by using a convolutional neural network, identifying surface defects and internal defects in real time, and integrating the surface defects and the internal defects to obtain a final defect graph; feature extraction is carried out from the stored data and defect graphs, and real-time monitoring and anomaly detection are carried out; constructing a self-adaptive control model, and adjusting operation parameters of the extruder in real time according to an analysis result; and multiple machine learning algorithms are selected for integration, and prediction results of multiple models are fused by using a weighted average method to obtain an optimal product quality detection result. According to the invention, high-precision detection of surface and internal defects of an extruded product can be realized, and process parameters in the extrusion process can be monitored and optimized in real time, so that the consistency of product quality and the production efficiency are greatly improved.

The invention relates to the technical field of optical detection, in particular to a workpiece surface defect detection method and system based on the optical technology, and the method comprises the following steps: S1, generating a curvature model of a workpiece; s2, dynamically adjusting the irradiation angle and the light spot shape of a light source according to the curvature model in the S1; s3, adjusting the focal length of the detection equipment in real time; s4, imaging acquisition is carried out on the workpiece at the same time through multiple angles; s5, correcting image distortion caused by surface curvature change, and fusing the multi-angle images into a high-precision workpiece surface image; s6, defect features of the surface of the workpiece are extracted, defects are classified, positions are marked, and a defect detection report is generated; according to the invention, by dynamically adjusting the light source and the focal length and combining multi-view imaging and advanced image processing technologies, efficient and accurate defect detection is realized, and the product quality and the production efficiency are remarkably improved.

The invention discloses an industrial product quality detection method and system based on machine vision, and relates to the technical field of quality detection.During operation of the system, through an industrial camera and an optimized light source layout, high-resolution image acquisition is performed on electronic components in real time by adopting multi-angle shooting, a series of optimization processing is performed on the acquired images, and the quality of the electronic components is detected. The method comprises the following steps: extracting shape, texture and color features according to geometric characteristics of an electronic component to form key parameters for judging defects, identifying potential defects of the electronic component by using a deep learning model algorithm, calculating to obtain a quality evaluation index Q, and calculating the quality evaluation index Q according to calculation results of feature extraction and defect detection. And comprehensively evaluating the quality of each component, comparing the quality evaluation index Q with a first preset threshold value M and a second preset threshold value N to obtain a quality evaluation grade, feeding unqualified detection information back to a production line, and removing unqualified components.

The invention discloses a multi-task collaborative robot scheduling method and system oriented to an intelligent factory, and the method comprises the steps: receiving the input data of a plurality of tasks to be executed by the intelligent factory, the input data comprising the task type, the dependency relationship between the tasks, the task priority, the resource demand of each task, and the expected completion time of each task; determining an initial task execution sequence of the tasks according to the task types, the dependency relationships and the task priorities; obtaining current resource state data of all available robots in the intelligent factory; according to the resource demand of each task, the expected completion time of each task, the initial task execution sequence and the current resource state data of all available robots, a scheduling algorithm is adopted to generate a multi-task cooperative scheduling rule; and controlling the plurality of target robots to execute the plurality of tasks according to the multi-task cooperative scheduling rule. According to the method, the resource utilization rate of the robot in the intelligent factory can be effectively improved, and the task execution efficiency is optimized.

The invention relates to the field of circuit board quality detection, in particular to an aluminum circuit board surface treatment quality detection system and method. The method comprises the following steps: acquiring a real-time thermal imaging view of the aluminum circuit board; performing multi-point sampling calculation on the real-time thermal imaging view of the aluminum circuit board, and performing temperature distribution analysis so as to generate temperature distribution data of the aluminum circuit board; performing temperature rise distribution trend reconstruction based on the temperature distribution data of the aluminum circuit board, and performing abnormal region positioning so as to obtain a coating abnormal region; carrying out multidirectional ultrasonic pulse detection on the coating abnormal area, and collecting a multi-array sound wave signal; carrying out low-pass filtering processing on the multi-array sound wave signals, then carrying out three-dimensional projection remodeling, and constructing a three-dimensional sound wave projection drawing; and acoustic wave penetrating circuit board attenuation inversion is carried out on the three-dimensional acoustic wave projection drawing, and then surface defect analysis is carried out, so that an aluminum circuit board surface defect area is obtained. The method for accurately and efficiently detecting the surface defects of the aluminum circuit board is realized.

The invention provides a real-time data monitoring system in a knitted fabric production process, and relates to the technical field of data monitoring, and the system comprises a real-time monitoring module which is used for directly collecting actual production data from equipment and the environment of a production workshop through various sensors; the data analysis module is used for comparing the initial prediction value of the prediction model with actual production data to output final prediction data and generating optimization parameters according to the final prediction data; the prediction production module is used for establishing a virtual production model according to the actual production data, and the prediction model predicts an initial prediction value of the output variable based on the input data; and the dynamic adjustment module is used for generating a dynamic deviation threshold value according to the deviation trend data and generating a corresponding adjustment strategy according to whether the deviation value falls into the deviation threshold value or not. The precision and stability of the production process are improved, and the influence of human factors is reduced.

The invention relates to the field of production fault analysis, in particular to a fault monitoring analysis method and system for lubricating oil production. The method comprises the following steps: acquiring a multi-dimensional working monitoring parameter and an omnibearing working image; performing abnormal outlier filtering processing on the multi-dimensional working monitoring parameters, and performing time sequence evolution simulation to generate time sequence state evolution data; performing continuous frame optical flow motion analysis on the omnibearing working image, performing dynamic parameter mapping based on time sequence state evolution data, and constructing a dynamic production twinborn model; obtaining a preset production standard demand log; carrying out dynamic process deviation detection based on a preset production standard demand log dynamic production twinborn model, and carrying out production fault analysis so as to obtain stage production fault data; performing potential fault traceability speculation on the staged production fault data to obtain an abnormal fault potential node; and constructing a potential fault mark twinborn model based on the abnormal fault potential nodes. According to the invention, efficient and accurate fault analysis of lubricating oil production is realized.

The invention provides a textile woven structure anomaly detection method, which comprises the following steps: acquiring real-time tension monitoring data of warps and wefts in a jacquard textile weaving process, and establishing a tension distribution model in a normal weaving state according to the real-time tension monitoring data; if the classification result is in an abnormal state, spraying a pre-prepared chemical reagent on the surface of the abnormal position of the jacquard textile, and collecting spectral data of the surface of the textile after spraying; performing feature extraction and classification on the spectral data, and obtaining a comprehensive anomaly detection result by comparing a classification result with a tension position anomaly detection result; according to the comprehensive anomaly detection result, an abnormal area is accurately positioned and marked, and a sample in the abnormal area is cut and conveyed to a sample analysis device; and carrying out microstructure analysis on the cut sample, extracting fiber arrangement characteristics, judging the abnormal degree of fiber arrangement, and obtaining a microstructure abnormality evaluation result.

The invention discloses an industrial data acquisition and management method based on the Internet of Things, and relates to the technical field of data management, and the method comprises the following steps: carrying out the demand analysis of an industrial environment and a production process, and determining to-be-monitored industrial equipment indexes in the industrial environment and the production process, namely an equipment performance index and a production performance index; according to the industrial data acquisition and management method based on the Internet of Things, equipment performance indexes and production performance indexes are monitored in real time, so that the reliability of equipment is remarkably improved, the accidental downtime is shortened, and through comprehensive analysis of an equipment state evaluation index, a fault analysis evaluation index and a production performance evaluation index, the reliability of the equipment is improved. The method can predict possible faults and performance reduction of the equipment, so as to take preventive maintenance measures in advance, not only can reduce production interruption caused by equipment faults, but also can improve the efficiency and effect of maintenance work by optimizing a maintenance plan.

The invention relates to the technical field of building information management, in particular to a BIM (Building Information Modeling)-based prefabricated part design quality management method, which comprises the following steps of S1, establishing a prefabricated part three-dimensional information model with multi-dimensional parameters by using BIM software; S2, collecting real-time production data by using a sensor in a prefabricated part production process; s3, comparing the production data with initial design parameters in the BIM model to identify deviations exceeding a set tolerance range; s4, feeding back the identified deviation information to a production link, and adjusting operation parameters of production equipment according to the deviation condition; s5, after production is completed, whether the indexes meet the requirements or not is determined; and S6, adjusting the design and process standard of subsequent production. According to the invention, through real-time data acquisition, deviation analysis and feedback, optimization suggestion generation and closed-loop management, continuous improvement of precision and quality of a prefabricated part production process is ensured, and effective combination of design and production is realized.

The invention relates to the technical field of cable production automation control, and discloses a control method, system and device of cable extrusion equipment and the extrusion equipment, the method comprises the steps that sensor parameters are acquired, and the sensor parameters comprise the cable extrusion temperature and the extrusion speed; performing time delay correction on the sensor parameter to obtain a corrected parameter value; inputting the correction parameter value into a pre-trained extrusion speed and extrusion temperature dynamic compensation model to generate an adjustment instruction; and sending the adjustment instruction to equipment to adjust the heating temperature and the extrusion speed of the cable. Compared with the prior art, the method has the following effects that the heating temperature and the extrusion speed of the cable can be accurately adjusted in real time through combined use of time delay correction and an extrusion speed and extrusion temperature dynamic compensation model, and the waiting time and the adjustment time in the production process are reduced, so that the control precision and the production efficiency of cable extrusion equipment are improved.

The invention relates to the technical field of quality traceability management, in particular to an organic fertilizer quality safety traceability management method based on multi-source data. The method comprises the following steps: collecting organic fertilizer source data in real time from links of organic fertilizer production, circulation, storage and use, performing standardization processing, and performing production batch synchronous set processing and traceability association connection file establishment to generate an organic fertilizer multi-source data traceability association connection file; and carrying out production batch tracing extraction processing on the organic fertilizer multi-source data traceability association connection file, carrying out organic fertilizer quality index evaluation analysis and quality index error limitation analysis, carrying out organic fertilizer quality safety abnormity traceability processing and abnormity decision support processing, and generating an organic fertilizer quality safety abnormity problem decision support report. Therefore, the corresponding organic fertilizer quality safety abnormity traceability problem management work is executed. The scientificity and accuracy of organic fertilizer quality safety management can be improved.

The invention discloses a mold parameter adjustment method and system based on a multi-objective optimization algorithm, and relates to the technical field of mold manufacturing. The method comprises the following steps: calling a production database, carrying out production state division, and establishing a production state division result; executing process data acquisition, and synchronizing a process data acquisition result to a data processing center through an MQTT protocol; matching a process data acquisition result with a production state division result by using a data processing center, and constructing a real-time dynamic weight; updating the multi-objective evaluation function by using the real-time dynamic weight, and establishing a configuration optimization priority; adjusting and optimizing the mold parameters according to the multi-objective evaluation function and the optimizing priority, and optimizing the mold parameters based on an adjusting and optimizing result. The technical problems that in the prior art, the die production efficiency is low, and the quality is unstable are solved, the die parameters are adjusted through the multi-objective optimization algorithm and the real-time dynamic weight, and the technical effect of improving the die production efficiency and the manufacturing quality is achieved.

The invention discloses a battery production defect inspection method, system, medium and equipment, and relates to the technical field of battery defect inspection. The method comprises the following steps: acquiring a video stream corresponding to each battery production line, and extracting a key frame sequence corresponding to each video stream; performing fusion analysis on spatial features and time sequence features of the key frame sequences to obtain abnormal state features; performing multi-stage screening on each key frame sequence based on the abnormal state features to obtain a target key frame with defects; performing space-time mapping on the target key frame, and determining an associated station corresponding to the abnormal production line; and according to the process parameters of the associated stations, carrying out grading evaluation on the abnormal state features, and generating abnormal early warning information. By implementing the technical scheme provided by the invention, misjudgment or missed judgment of abnormal conditions on the battery production line can be effectively avoided, so that the production quality of battery products is improved.

The invention relates to the technical field of yarn detection, and particularly discloses a yarn quality detection system, method and equipment. Shooting parameter information is adjusted based on yarn quality standards and external configuration information, and a high-speed camera corresponding to the shooting parameter information is used for collecting original image data of yarn in the production process in real time; processing the original image data to obtain yarn feature information, wherein the yarn feature information comprises yarn material, yarn color, yarn thickness, fiber uniformity, hairiness and knot faults; classifying defects on the surface of the yarn based on the yarn feature information to obtain yarn defect categories; and a defect abnormal area on the enhanced image is identified based on the yarn defect category, statistical analysis is performed on the defect abnormal area to generate a quality detection report, and the quality detection report is transmitted to the control module and the display alarm module.

The invention belongs to the technical field of fermentation, and discloses a cloud computing-based fermentation tank fermentation process management and control service system and a cloud computing-based fermentation tank fermentation process management and control service method. Comprising the steps that an equipment end collects fermentation parameters in real time in the fermentation process and sends the fermentation parameters to a cloud computing platform; the fermentation parameters comprise fermentation characteristic parameters and fermentation control parameters; the cloud computing platform analyzes the fermentation characteristic parameters to obtain optimal fermentation control parameters; the cloud computing platform identifies deviation parameters in the fermentation control parameters collected in real time; the cloud computing platform judges and generates an early warning instruction or an adjusting instruction according to the deviation parameter; if the adjusting instruction is generated, the cloud computing platform calculates the adjusting quantity corresponding to each deviation parameter and sends the adjusting quantity to the corresponding equipment end; intelligent monitoring and control in the fermentation process are achieved through the cloud computing platform, the fermentation quality and yield can be effectively controlled, the fermentation efficiency is improved, and therefore the automation level and stability of the fermentation process are improved.

The invention relates to the technical field of plastic production, in particular to an intelligent processing method and system for plastic mold process parameters. The method comprises the following steps: carrying out sensor network deployment on a plastic mold to obtain a mold parameter sensing network; operating parameters of the plastic mold are collected in real time through the mold parameter sensing network, and mold operating state data flow is obtained; performing process state feature extraction on the mold operation state data stream to obtain mold process state feature data; performing spatio-temporal data hierarchical analysis on the mold process state feature data to obtain mold state multi-scale feature data; and performing layered thermal field dynamic modeling on the plastic mold based on the mold state multi-scale characteristic data to obtain a mold thermal field evolution model. The key process parameters of the mold can be identified, and the stability and reliability of the mold performance are enhanced.

The invention discloses a pipe production task management method and system, and belongs to the technical field of pipe production, and in the pipe production task management method and system, factors such as order priority, equipment state and raw material supply are analyzed through an intelligent algorithm, and the production schedule is automatically optimized, so that production resources are utilized more reasonably and efficiently. Resource waste can be avoided, idle time in the production process is shortened, and therefore production efficiency and the productivity utilization rate are improved. The urgency degree and the delivery date of the order are analyzed through an intelligent algorithm, and the enterprise can preferentially arrange the production of the urgent order to ensure that the customer demand is met in time. Meanwhile, production tasks are reasonably distributed according to the running state of the equipment and the raw material supply condition, and the use efficiency of the equipment and the utilization rate of raw materials are improved. The dynamic adjustment mechanism monitors the production progress in real time and automatically adjusts the production plan according to the actual situation, so that the system can rapidly adjust the production plan and distribute the tasks to other devices, and continuity and on-time delivery of the production tasks are ensured.

The invention relates to the technical field of non-woven fabric production monitoring, and particularly discloses an intelligent monitoring method and system for a high-performance fiber non-woven fabric production line. The method mainly comprises the steps of obtaining a data set of a non-woven fabric production line; the fiber uniformity in the fiber image is calculated, whether the fiber uniformity belongs to a preset interval or not is judged, if not, the fiber putting speed, tension, temperature and humidity of the module area are input into a prediction model, and the fiber prediction uniformity in the specified time in the future is obtained; judging whether the fiber prediction uniformity belongs to a preset interval or not; and adjusting the fiber feeding speed and tension of the module area based on the analysis processing result. According to the method, the change trend of the fiber uniformity can be quickly identified and predicted, potential parameter anomalies can be identified in advance, production management personnel can take preventive measures before problems occur, meanwhile, the calculation amount can be effectively reduced, adjustment measures can be implemented more quickly, and therefore the production efficiency and the product quality are improved.

The invention provides a lamination bottling control method and system integrating an image and a photoelectric sensor, and the method comprises the steps: obtaining signals collected by a photoelectric sensor array, a pressure sensor and a vibration sensor, carrying out the filtering and time alignment processing, and obtaining a multi-source synchronous data matrix; performing preprocessing and feature extraction on the image acquired by the visual camera to obtain visual reference data; based on the multi-source synchronous data matrix and the visual reference data, the vibration, track and speed states of the equipment are evaluated, and a mechanism state vector is obtained; analyzing the separation, posture and stacking state of the tablets to obtain a tablet state vector; generating and optimizing speed control, time sequence control and synchronous control instructions according to the mechanism state vector and the tablet state vector; the control effect is evaluated in real time, and parameter optimization is carried out. And through multi-sensor data fusion and multi-level control strategy optimization, the control precision and stability of the bottling process are improved.

The invention relates to the technical field of flow anomaly identification, and discloses a flow anomaly identification method for a numerical control machine tool protocol, which comprises the following steps of: 1, multi-source data acquisition: acquiring various types of data from a numerical control machine tool and a control system thereof by arranging a data acquisition module, the data sources include network protocol flow data, machine tool sensor data, operation log data and equipment environment data, and the data formats of the data sources are kept uniform; the collected original data is subjected to standardization processing, the problems of data dimension inconsistency, noise interference and missing values are solved, and alignment of the data on a time axis is guaranteed through a synchronization algorithm; through a multi-source data acquisition and high-precision sensor system, the real-time monitoring of the flow of each key fluid system of the numerical control machine tool is realized. And by combining a plurality of feature extraction methods, the traffic anomaly can be effectively identified.

The invention provides a mold injection control method and device for electric toothbrush plastic molding, and relates to the technical field of plastic injection molding.The method comprises the steps that geometric data of an electric toothbrush and physical performance data of a plastic part are obtained, and an electric toothbrush mold model is initialized; initializing particle swarm parameters, performing material flow path optimization, and performing iteration to obtain an optimized mold model; injection molding simulation is conducted, and mold injection molding performance influence parameters are obtained; constructing an injection molding control strategy library, and performing matching retrieval to obtain a matched injection molding control strategy; selecting interval division is conducted, and a mold injection molding control strategy parameter interval is obtained; constructing a fitness function; global optimization is carried out, target mold injection control strategy parameters are determined, and mold injection control over electric toothbrush plastic forming is carried out. The technical problems that in the prior art, injection molding parameter optimization is always started after mold manufacturing is completed, and control parameters are adjusted through trial and error, so that cost is high, and efficiency is low are solved.

The invention discloses a thermal deformation prediction and optimization method and system in metal plastic composite product processing, and the method comprises the steps: building a composite product geometric model according to the material characteristics and preset boundary conditions of a composite product; according to the geometric model of the composite product, a finite element analysis method is used, a preset time step length and a nonlinear solver are selected for coupling solving of a thermal field and a mechanical field; the change data of the temperature field and the stress field along with time and thermomechanical circulation are analyzed through simulation, accumulated deformation and interface stress data of the material in circulation of the preset number of times are monitored, and a simulation prediction result is generated; and according to the simulation prediction result, the heating and cooling rate and the mold loading path are optimized. The thermal deformation prediction and optimization method in metal plastic composite product processing can be used for solving based on multi-physics field coupling in combination with a nonlinear finite element.

The invention relates to the technical field of equipment monitoring analysis, in particular to an intelligent monitoring and upgrading optimization system for dairy beverage production equipment, which comprises a raw material fluctuation monitoring and identifying module for monitoring and identifying the quality fluctuation of raw materials in the dairy beverage production process in real time and automatically detecting the quality fluctuation data of the raw materials; the production process self-adaptive adjustment module is used for adjusting production process parameters in real time through a self-adaptive algorithm on the basis of the raw material quality fluctuation data so as to compensate the influence caused by raw material fluctuation; and the production process feedback and optimization module is used for feeding back the execution effect of the production process according to the product quality data collected in real time in the production process in combination with the adjustment of the production process parameters, and adjusting and optimizing the production process parameters based on the execution effect. According to the invention, product quality fluctuation caused by unstable quality of raw materials in the production process is avoided, so that the production stability and the product consistency are improved.

The invention relates to the technical field of production scheduling of manufacturing workshops, in particular to a distributed intelligent production scheduling decision-making system based on cloud computing. The system is composed of a cloud computing platform, a data acquisition module, a data processing module, an intelligent production scheduling engine and a distributed execution control module. The problems of performance bottleneck, data acquisition limitation, lack of flexibility and adaptability of a production scheduling scheme and the like in a traditional production scheduling system are solved, intelligent distribution and scheduling of production tasks can be realized through fusion application of advanced technologies such as cloud computing, big data and artificial intelligence, the production efficiency and the product quality are improved, and the production cost is reduced. And greater economic benefits and social benefits are created for enterprises.

The invention relates to the field of circuit board processing, and discloses a printed circuit board splitting device, which comprises a workbench, the top end of the workbench is fixedly provided with a protective cover, and the outer wall of the protective cover is connected with an opening and closing door through a hinge; the movable cleaning assembly is arranged on the workbench and used for cleaning dust on the surface of the circuit board while moving the circuit board; the supporting and collecting assembly is arranged on the workbench and used for limiting the circuit board and collecting impurities generated during machining; the processing assembly is arranged on the workbench and used for cutting and splitting the circuit board; the auxiliary assembly is arranged on the workbench and used for assisting the machining assembly; and the placing assembly is arranged on the workbench and is used for placing the unprocessed circuit board and the processed circuit board. And through cooperation of internal structures of the movable cleaning assembly, the effect that residual dust and chippings on the surface of the circuit board are effectively cleaned through the cleaning device while the circuit board is accurately moved is achieved.

The invention relates to the technical field of metal processing and casting, and particularly discloses a residual stress control system and method based on metal casting molding, and the system collects simulation process parameters of a metal casting molding process and basic parameters of a to-be-cast assembly, and analyzes a residual stress risk assessment value of the metal casting molding process through big data. Potential casting problems can be recognized and solved from the source, powerful data support is provided for follow-up casting optimization work, the actual effect brought by process optimization is quantified by evaluating the process optimization index of the metal casting forming process after the process optimization set of the metal casting forming process is executed, and the process optimization efficiency is improved. A basis is provided for subsequent adjustment and improvement, the technology optimization set of the metal casting forming process is adjusted, the technology adjustment index of the metal casting forming process is judged, and finally adjustment feedback is conducted on intelligent control over the residual stress of the metal casting forming process, so that the metal casting forming process meets the actual casting requirement.

The invention discloses a traditional Chinese medicine clinical research scheme generation method and system, and relates to the field of medicine. The method comprises the following steps: aiming at traditional Chinese medicine clinical research, constructing a knowledge graph covering evidence-based evidence elements PICO; establishing a traditional Chinese medicine cue word project and an element template based on evidence-based evidence elements PICO covered by the knowledge graph; identifying element information selected in the element template by adopting a traditional Chinese medicine cue word project, and generating a cue word feature vector; retrieving data matched with the cue word feature vector from the knowledge graph by adopting a trained language model; when the feature in the cue word feature vector cannot retrieve the matched data from the knowledge graph, automatically generating the matched data by using the trained language model; and integrating the matching data automatically generated by the trained language model and the matching data retrieved from the knowledge graph to obtain a traditional Chinese medicine clinical research scheme. The preparation efficiency and quality of the traditional Chinese medicine clinical research scheme can be effectively improved.