139results about How to "Realize intelligent analysis" patented technology

The invention discloses a multi-modal analysis method, system and device for an industrial inspection scene and a medium. The method comprises the following steps: acquiring multi-modal data of an industrial inspection area in real time, wherein the multi-modal data comprises a video image and voice data; inputting the currently collected multi-modal data into a scene type identification model, and identifying a scene type; matching a corresponding multi-modal fusion mode based on the determined scene type; according to the matched multi-modal fusion mode, performing feature extraction on the video image by using a deep learning algorithm, performing feature extraction on the text converted from the voice data by using a natural language processing technology, and performing multi-modal fusion on the extracted features; performing joint reasoning on the multi-modal fusion features by using a deep learning algorithm, and identifying potential risks or equipment faults in the industrial inspection area; and generating an inspection report and decision based on the identified potential risk or equipment fault. According to the invention, intelligent analysis of the industrial inspection scene can be realized, and the inspection efficiency and accuracy are improved.

The invention relates to the technical field of smart communities, and discloses a smart community resident health management method based on machine learning, and the method comprises the steps: continuously collecting the health feedback data of residents, such as the physical condition change after outdoor activities, carrying out the correlation analysis of the health feedback data with the previous health data and environment data, dynamically updating and optimizing a correlation model, and obtaining the health feedback data of the residents; the prediction precision of the model and the effectiveness of suggestions are improved, the relationship between the use condition of the community public space and the resident health indexes is analyzed, key public facilities and areas influencing resident health are identified, data support and decision basis are provided for community planning and facility reconstruction, and the method is suitable for popularization and application. Community resident health and environment condition analysis reports are generated regularly, and resident health index change trends are visually displayed for reference of community managers and residents. According to the invention, intelligent analysis and application of environment and health data are realized, and technical support is provided for building a healthy community.

The invention relates to the technical field of intelligent control of transformer substations, in particular to a standardized intelligent control system of a transformer substation, which realizes real-time response to the running state of equipment by acquiring the current, voltage, power factor, temperature and humidity, operation state, insulation state and environment temperature and humidity of a power transformation unit in real time. A gradient boosting decision tree is combined with a variational auto-encoder and a self-attention mechanism, state vector features are extracted, complex abnormal states are accurately recognized, potential faults are detected in an early stage, and the monitoring efficiency and the early warning precision are greatly improved. The future power demand is accurately predicted by combining the long-short-term memory network model with historical and real-time load data, and the limitation of traditional static load management is overcome. Based on the predicted power demand, the reinforcement learning model is adopted to dynamically adjust the operation number of the power transformation units, optimal configuration of resources is achieved while the demand is met, and the overall operation efficiency of the transformer substation is remarkably improved.

The invention provides a power transmission and transformation project ecological influence space-time boundary determination method and system based on multi-source remote sensing information deep fusion interpretation, and belongs to the technical field of power transmission and transformation project management. The method comprises the steps of data acquisition, data preprocessing and multi-source data integration, multi-scale space-time analysis, ecological influence index construction, remote sensing ecological index model construction, ecological influence space-time boundary determination, dynamic monitoring and early warning system platform establishment and long-term monitoring and evaluation. By integrating satellite remote sensing, unmanned aerial vehicle aerial photography and ground monitoring data and combining an image fusion algorithm, a principal component analysis method and a random forest algorithm technology, the method is beneficial to promoting accurate evaluation of the influence of the power transmission and transformation project on the ecological environment, visualizes the space-time boundary of the ecological influence, breaks through the limitation of a traditional evaluation method on time and space coverage, and improves the evaluation efficiency of the power transmission and transformation project. And a scientific method system for analyzing and evaluating the ecological influence of the power transmission and transformation project is further improved.

The invention discloses a field monitoring method based on engineering informatization, and the method comprises the steps: collecting the tightness data of a rope in real time through a tension sensor integrated on a protection rope, and transmitting the data to a field monitoring system; the method comprises the following steps: acquiring a worker operation picture shot by video monitoring equipment deployed in a construction site, and extracting posture features such as a body inclination angle and arm extension length of a worker in the picture; determining a real-time position coordinate of the worker according to the information of the positioning tag worn by the worker, and judging whether the coordinate is in a preset dangerous area electronic fence or not; according to the distance relation between the connection points of the worker and the protection rope, the swing track of the protection rope and the movement track calculated by the position coordinate change of the worker are analyzed, and whether the worker breaks away from the protection rope illegally or not is judged; if it is judged that the worker is separated from the protection rope, the position of a connection point of the protection rope suspected to fall off is marked in the monitoring picture according to the material characteristic and the color characteristic of the protection rope.

The invention relates to the technical field of medical diagnosis systems, in particular to an early gonitis diagnosis system and method based on multi-modal deep learning, a data collection module is used for collecting and preprocessing multi-modal data including knee joint X-ray image data, MRI (Magnetic Resonance Imaging) image data and clinical evaluation data; the model construction module constructs a deep learning model by combining a convolutional neural network and a Transform architecture, realizes mapping from multi-modal data to a clinical assessment phenotype label by adopting a feature automatic learning module and a potential relationship mining module, and integrates a Grad-CAM interpretability method for understanding a model prediction basis; diagnostic data are continuously collected to be fed back to the data collection module for model optimization, the convolutional network is good at capturing local image features, the Transform architecture is good at modeling a long-range dependency relationship, and the feature extraction and relationship modeling capabilities of the model are greatly improved through combination of the convolutional network and the Transform architecture.

The invention discloses a craniocerebral injury degree diagnosis auxiliary system based on a medical image, and relates to the technical field of medical treatment, a primary judgment module can accurately judge whether a patient has the risk of interstitial encephaledema or not by analyzing the accumulation degree of interstitial fluid in white matter and quantitatively calculating a penetration anomaly coefficient Syxs, the ability of early risk screening is provided, and the risk of interstitial encephaledema can be accurately judged. Potential problems can be recognized in time before disease deterioration, a clinician is prompted to intervene rapidly, and therefore the prognosis effect of a patient is remarkably improved. When the risk of interstitial encephaledema exists, the type diagnosis module further distinguishes lesion types (such as brain contusion or other injuries) by extracting related distinguishing features in a multi-modal feature set, calculates a locking coefficient Sdxs, and provides a scientific quantitative basis for diagnosis of lesion types based on specific features of the lesions. According to the accurate type diagnosis method, diagnosis errors can be reduced, and reliable support is provided for clinical decision making.

The invention relates to a satellite internet task planning method based on intention understanding and a large model, which belongs to the field of task planning and comprises the following steps: constructing a satellite internet intention library and a satellite internet knowledge tensor library based on RAG enhanced retrieval; receiving a communication intention demand report, and performing intention acceptance according to a user communication intention; performing RAG enhanced retrieval through the accepted communication intention and the knowledge tensor database to obtain a tensor retrieval result, and performing reasoning to obtain an intention analysis result; communication intention verification is carried out according to the intention analysis result and a satellite internet intention library, if legality is met, execution is continued, and otherwise, the process is ended and abnormal warning is carried out; task planning and scheduling are carried out through a satellite internet intention library and an equipment and resource library, and a network planning parameter result is obtained; and then network parameter configuration is carried out on the application layer to realize satellite planning, station planning and terminal planning. According to the method, the total resource occupancy rate and the service capacity can be effectively improved, and meanwhile load balancing is achieved.

The invention discloses a safety early warning and alarming system based on a subway underground excavation construction environment, and relates to the technical field of early warning and alarming, the safety early warning and alarming system comprises a safety early warning platform, the safety early warning platform is in communication connection with a data acquisition module, a data transmission module, a data analysis module, an early warning analysis module, an alarm notification module and a visual report module, all the modules are connected through electric signals. By monitoring state information of key parts, performing data processing and analysis in time and capturing subtle changes which easily generate potential safety hazards in time, potential potential safety hazards can be quickly found, and once monitoring data exceed a preset alarm threshold value, the system can immediately trigger an alarm mechanism to give an alarm. Alarm information is sent to related personnel in various modes such as short messages, mails or system notifications, so that risk response time is greatly shortened, decision time is provided for managers, and occurrence risks of safety accidents are effectively reduced.

The invention provides a network risk quantification method and apparatus, a computer device and a storage medium, and belongs to the technical field of network security, and the method specifically comprises the steps of obtaining information of a plurality of risk data items of a service party; evaluating the risk data items, and determining corresponding risk values; respectively substituting the risk values into a risk rate function to obtain a single-factor risk model corresponding to each risk data item; adjusting the single-factor risk model based on the information of the industry where the customer is located and the attack type, and constructing a double-factor evaluation model for the customer; and inputting the evaluation time node into the two-factor evaluation model to obtain a risk quantification result for the customer. Through the processing scheme provided by the invention, the consequence caused by the security event is comprehensively evaluated and analyzed in combination with the influence of personnel management / consciousness on the network security event from the perspective of insurance, intelligent analysis and processing of data are realized, and the reliability and comprehensiveness of network security analysis are improved.

The invention discloses a method for automatically keeping a chassis of mobile intelligent equipment horizontal on a slope, and relates to the technical field of mobile intelligent equipment.The method comprises the following specific steps that terrain detection is conducted, specifically, the current terrain condition is detected in real time through a sensor system installed on the mobile intelligent equipment, and the terrain condition is determined; comprising slope angles, soil types and rock distribution information; by integrating the sensor system and the central processing unit, real-time monitoring and intelligent analysis of slope topography and road surface conditions are achieved, mobile intelligent equipment can rapidly sense and adapt to various complex topographies including soft soil slopes, rugged rock slopes and the like, and by automatically adjusting the level of the chassis, the slope topography and the road surface conditions can be monitored and analyzed in real time. And the stability and the operation performance of the equipment on different terrains are ensured, so that the terrain adaptability of the equipment is remarkably improved, the mobile intelligent equipment can play a role in a wider environment, and the application range and the practicability of the equipment are improved.

The invention discloses a document analysis and index construction method. The method comprises the following steps of layout detection, table image analysis and extraction, page recovery, content splitting and index construction and storage. According to layout detection, a document layout analysis model is used for carrying out area detection and classification on images, tables, titles and text elements of pdf document pages; the table image analysis extraction is to extract and process formats and contents of tables and images existing in each page of the pdf obtained based on layout detection; according to the method for analyzing the document and constructing the index, intelligent analysis is provided, texts, tables and images in the pdf document can be accurately extracted, and perfect index elements can be constructed.

An FMEA-based hydropower equipment-oriented maintenance decision-making method comprises the following steps: firstly, establishing a hydropower equipment model, and realizing unified management and sharing of information through an FMEA analysis framework and a maintenance decision-making framework; secondly, taking a risk analysis result of the FMEA as a recommendation strategy of maintenance items, and automatically generating a maintenance item list with priority ranking according to a risk priority number (RPN value); thirdly, based on the selected maintenance items, the maintenance level of the equipment is recommended, and the effectiveness and pertinence of maintenance measures are ensured; and finally, making a scheduling strategy of an equipment maintenance plan, and reasonably arranging maintenance time by considering factors such as resource optimization and operation influence. According to the method, scientificity and efficiency of maintenance decision making are improved, and safe and stable operation of the hydroelectric equipment is guaranteed.

The invention provides an intelligent financial management system and method based on a multi-modal large model, and the method comprises the following steps: obtaining a paper original voucher picture through photographing or scanning; extracting specified information in the picture through a multi-modal large model, wherein the specified information comprises a voucher type, an amount and a cause; inputting the specified information into a trained multi-mode large model, judging a financial accounting subject to which the voucher belongs based on a cue word, and generating an accounting voucher; the generated accounting voucher is submitted to financial staff for auditing, and the accounting voucher is confirmed or modified; and storing the confirmed or modified accounting voucher into a financial management background to generate a financial statement. According to the invention, the working efficiency and accuracy of financial accounting bookkeeping are greatly improved, and the level of financial management is improved.

The invention belongs to the technical field of coal mine underground mining, and discloses a working face roof periodic weighting identification method and system based on a YOLO algorithm. The method comprises the following steps: mapping data into a three-dimensional space to generate a stope working face 3D stress nephogram, and generating a uniformly distributed and visualized stope three-dimensional grid stress distribution nephogram; the high-stress area, the low-stress area and the weighting area are marked with corresponding labels; performing real-time image recognition on the high and low stress areas by adopting a YOLO algorithm, and accurately positioning a roof weighting area; through an image edge detection technology, a time range of a roof weighting area and a corresponding hydraulic support distribution range are extracted, and intelligent analysis of area pressure change is realized. According to the method, image recognition algorithms such as YOLO and the like are combined, so that a coal mine does not depend on a traditional data processing mode any more, potential errors caused by manual intervention are reduced, data processing reliability and real-time performance are greatly improved, and safety monitoring efficiency and accuracy are improved.

The invention provides a data circulation optimization method and system under AI combined trusted data space, and relates to the technical field of data communication, and the method comprises the steps: determining a data circulation demand, carrying out the intelligent matching of a data provider and a data user, and generating an optimal matching result; a trusted data space is constructed, data flow path optimization is carried out, and an optimal data flow path is generated; establishing a unified supervision platform, performing data circulation risk monitoring on each node, and outputting a risk index of each node; and when the risk index of each node is greater than a preset risk index, generating risk reminding information. According to the method and the device, the technical problem that the supervision demand of current data circulation is difficult to meet due to the problems of supervision blind areas, response lag and the like in the prior art can be solved, and the technical effects of effectively improving the safety, the efficiency and the intelligent level in the data circulation process and reducing potential risk events are achieved.

The invention provides a wind power operation and maintenance fault diagnosis method and system, and belongs to the technical field of wind power operation and maintenance. The method comprises the following steps: constructing a wind power operation and maintenance knowledge base based on multi-source historical operation data of target wind power equipment and operation knowledge experience data of the target wind power equipment; training based on a wind power operation and maintenance knowledge base to obtain a fault diagnosis model; wherein the fault diagnosis model comprises a plurality of feature extraction combination layers used for extracting data features of corresponding layers in the multi-source historical operation data of the target wind power equipment; inputting the multi-source real-time operation data, collected in real time, of the target wind power equipment into the fault diagnosis model, so as to extract the data features of the corresponding layers in the multi-source real-time operation data of the target wind power equipment based on each feature extraction combination layer, and extracting the multi-source real-time operation data of the target wind power equipment based on the data features of each layer; and determining a wind power operation and maintenance fault diagnosis result corresponding to the target wind power equipment. Intelligent operation and maintenance management of the wind power equipment is realized, the operation and maintenance efficiency and quality are improved, and the operation and maintenance cost is reduced.

The invention, which relates to the technical field of tobacco primary processing equipment parameter adjustment, discloses a big-data-based primary processing equipment parameter adaptive adjustment system comprising a humidity sensing module, a humidity intelligent analysis module, a data correlation evaluation module, a data urgency degree analysis module, a data priority processing module and a feedback and optimization module. The data correlation evaluation module is used for evaluating the correlation between data needing to be processed by the system and the tobacco leaf cutting speed under the condition that the humidity increase degree of the tobacco leaves to be cut needs to reduce the cutting speed; according to the evaluation result, data needing to be processed by the system is divided into necessary data and unnecessary data; and the data urgency degree analysis module is used for evaluating the urgency degree of each piece of data in the necessary data in the cutting process of the tobacco leaves to be cut. According to the invention, the cutting problem caused by sudden humidity change can be effectively avoided, and the problems of sensor network communication delay and long processing time of mass data transmission are solved.

The invention provides a one-hospital multi-area logistics human resource configuration method and system, and the method achieves the intelligent analysis of human resource configuration demands and constraint conditions in different business scenes through the construction of a hospital logistics scene knowledge graph and a configuration demand template, and the utilization of the language understanding capability and multi-modal information processing capability of # imgabs0 #. And a set of dynamic resource allocation mechanism is designed in combination with a multi-objective optimization algorithm, so that an optimal manpower allocation scheme meeting multi-region collaboration and dynamic business requirements can be adaptively generated. Theoretical analysis and experimental results show that the method can significantly improve the intelligent level and management efficiency of hospital logistics human resource allocation, and provides powerful support for one-hospital multi-region high-quality operation of a hospital.

The invention discloses an intelligent software and hardware system for fusion calculation of an air attitude and height, and relates to the technical field of intelligent monitoring and control, the system comprises a hardware part and a software part, the hardware part comprises a high-precision sensor group, a microprocessor, an alarm device and an execution mechanism, the data of the high-precision sensor group are fused, and the data of the high-precision sensor group are acquired; according to the invention, a high-precision sensor group and an advanced attitude resolving algorithm are combined to realize real-time and high-precision resolving of the aerial attitude and absolute height of the equipment, the high-precision sensor group can capture accurate motion information of the equipment, and the advanced algorithm ensures effective fusion and accurate resolving of the information. Therefore, the stability and reliability of the system in various complex environments are improved, the ability of the system to deal with emergencies and carry out high-precision positioning is greatly enhanced, and the system can continuously optimize the resolving process through real-time monitoring and feedback adjustment of resolving results, so that the resolving efficiency is improved. And the accuracy and the stability of attitude and height data are further improved.

The invention discloses a comprehensive energy coordinated optimization method and system based on language model analysis, and relates to the technical field of energy management and control, and the method comprises the steps: analyzing historical equipment load, weather, operation conditions and economic index data through a large language model, and forming a historical effective information segment group for describing an energy system; constructing a historical information characteristic parameter library; obtaining a historical information characteristic parameter subset through primary and secondary equivalent classification; constructing a reference characteristic parameter equivalent range interval library; the real-time information characteristic parameter group is retrieved and analyzed by using the database, a corresponding interval is matched, and the energy system is deployed according to a disposal strategy; according to the technical scheme, comprehensive integration, intelligent analysis and real-time optimization of energy system data are realized, and the intelligence and refinement level of energy management is effectively improved.

The invention discloses an edge real-time monitoring and abnormal behavior analysis method based on a deep learning large model, and the method comprises the steps: monitoring and collecting a multi-dimensional behavior data set of edge equipment in real time, and generating multi-modal aggregation data of the multi-dimensional behavior data set; generating a behavior descriptor of each data point in the multi-dimensional behavior data set according to the multi-modal aggregated data; calculating a behavior similarity index based on the behavior descriptor; according to the multi-dimensional behavior data set, predicting a predicted abnormal score of each data point by using an abnormal score prediction model based on deep learning; and according to the anomaly prediction score and the behavior similarity index, evaluating the anomaly degree of each data point, and determining an abnormal behavior data point according to the anomaly degree. According to the embodiment of the invention, the method can provide a reliable basis for the recognition and response of abnormal behaviors through the combination of a deep learning technology and multi-modal data analysis, and achieves the comprehensive monitoring and intelligent analysis of the side equipment.

The invention discloses an online marketing coupon and promotion activity data tracking and optimizing system, and belongs to the technical field of marketing data processing, and the system comprises a data storage module which is used for storing data related to promotion activities; the data acquisition module is used for acquiring consumption record information of the system; the range setting module is used for analyzing data related to promotion activities to obtain a time interval and an activity effect threshold value; the promotion analysis module is used for analyzing the newly generated consumption records with cards and coupons according to the time interval and the activity effect threshold value to obtain an evaluation result of the promotion activity effect; and the optimization early warning module is used for carrying out early warning on the user according to the evaluation result so as to adjust the promotion strategy. According to the method, key indexes such as participation conditions, conversion effects and the like of cards, coupons and sales promotion activities are tracked by adopting an advanced data processing algorithm, the effects of different sales promotion strategies can be accurately evaluated, and real-time acquisition, efficient storage, intelligent analysis and dynamic optimization of online marketing data are realized.

The invention relates to the technical field of hydroelectric generator regulation and control, in particular to a hydroelectric generator dynamic regulation and control method and system based on environment change, which improve the operation efficiency and stability of the hydroelectric generator in a complex and changeable environment and enhance the safety and economy. The method comprises the following steps: according to a set acquisition frequency, continuously acquiring environment data information of a place where the hydroelectric generator is located; performing feature extraction on the environment data information acquired each time by using a preset trained key feature extraction model to obtain an environment key feature vector; performing fluctuation analysis on a plurality of continuous environment key feature vectors to obtain an environment fluctuation index; comparing the environmental fluctuation index with a preset fluctuation threshold value; and in response to the environment fluctuation index exceeding a preset fluctuation threshold value, inputting the latest acquired environment key feature vector into the hydroelectric generator environment influence analysis model to obtain a hydroelectric generator environment influence index.

The invention provides a combustible metal dust spontaneous combustion early warning system used in an industrial dust removal facility, relates to the technical field of industrial safety monitoring, and aims to comprehensively monitor multiple parameters such as temperature, carbon monoxide concentration, hydrogen concentration, smoke concentration and the like and perform intelligent analysis by using a deep learning technology. A deep neural network model based on spatio-temporal feature fusion is adopted in the deep learning module, dynamic motion information is extracted by introducing an optical flow method, intelligent analysis and risk prediction of field images are achieved, the system can capture tiny changes in the initial stage of spontaneous combustion in time, the spontaneous combustion risk is accurately pre-judged through multi-parameter correlation analysis, and the risk prediction efficiency is improved. And alarm signals of different levels are automatically triggered according to the risk levels, so that the accuracy and timeliness of early warning are remarkably improved, and the occurrence rate of spontaneous combustion accidents of combustible metal dust is effectively reduced.

The invention discloses a multi-source data fusion-based dangerous chemical accident dynamic event map construction method, relates to the technical field of event map construction, and applies static mapping of a dangerous chemical event map to a time dimension to describe dynamic, sudden and timely characteristics of dangerous chemical accident occurrence. Based on a large number of accident investigation report texts, a Word2vec training word vector technology is applied, and through information extraction, knowledge fusion, ontology construction and dynamic reasoning of event knowledge, a dangerous chemical accident affair graph is systematically constructed. And a ResNet method is adopted in the knowledge fusion process, so that the equalization problem of a sample data set is solved. And general event generalization is carried out by combining causal relationship feature extraction of the hazardous chemical accidents, and a relatively complete hazardous chemical accident affair map is further constructed.

The invention discloses a coal mine unplanned power failure intelligent identification and alarm method and system, and relates to the technical field of coal mine monitoring, and the method comprises the following steps: collecting the power data of a coal mine, and generating a circuit curve; and analyzing the circuit curve, determining abnormal information of the unplanned power failure, and giving an alarm prompt. According to the invention, omen information of unplanned power failure is accurately identified, an alarm is given in time, processing is carried out in time, the coal mine production process is prevented from being influenced, safety risks caused by sudden power failure are eliminated, comprehensive monitoring and intelligent analysis of a coal mine power system are realized, the safety and stability of the power system are improved, and the power system safety and reliability are improved. And a powerful guarantee is provided for safe production of a coal mine.

The invention discloses a navigation mark fault intelligent identification system based on AI artificial intelligence in the technical field of image identification. The navigation mark fault intelligent identification system comprises a camera, a platform layer, an algorithm layer and a hardware layer used for configuring the system. The platform layer comprises the following modules: an AI middle station which is used as a basic platform of the system and is responsible for data input, processing, task management, computing power scheduling, alarm processing and data processing and storage; the data input processing service module is responsible for receiving data from different cameras and providing support for video stream access of the different cameras and camera control access; the task management module is used for automatically scheduling inspection tasks according to a preset inspection plan and monitoring task execution conditions; the method is scientific and reasonable, and realizes real-time monitoring, automatic identification and intelligent analysis of the state of the navigation mark through the AI technology, thereby improving the efficiency and accuracy of navigation mark patrol inspection, reducing the cost of manpower and material resources, and improving the navigation safety guarantee level.

The invention discloses an intelligent gene detection database management system, and aims to solve the technical problem that the current gene detection database management cannot efficiently and accurately process and analyze gene data, so that powerful support cannot be provided for precise medical treatment and personalized health management. Comprising a genome data storage unit, a gene information data management unit, a gene detection and analysis unit and a gene pool big data system, the genome data storage unit is used for storing and processing genome map data; and the gene information data management unit is used for collecting, storing, maintaining, analyzing and sharing the gene information of the organism. By integrating an advanced data storage technology, an efficient data analysis algorithm, an automatic report management process and strict data security measures, intelligent analysis and prediction of data are realized, and the accuracy and reliability of an experimental result are improved.

The invention discloses a blood vessel blood flow velocity measurement method based on optical flow method area tracking, which relates to the technical field of blood flow velocity measurement, and comprises the following steps: extracting multiple frames of blood vessel images from a blood vessel video, and determining an ROI area in each frame of blood vessel image according to a set ROI area division rule; performing movement estimation on the two adjacent frames of blood vessel images through a DIS optical flow method to obtain blood flow data of the two adjacent frames of blood vessel images; performing area tracking on the blood flow data through affine transformation to obtain affine transformation parameters of each frame of blood vessel image; inputting the radiation transformation parameters into a preset movement amount prediction model, predicting and obtaining the movement amounts of the ROI regions of the multiple groups of adjacent frames of blood vessel images, and performing speed estimation according to the multiple groups of movement amounts so as to output the blood flow speed; motion estimation is carried out on two adjacent frames of blood vessel images through the DIS optical flow method, the response capacity of the system to dynamic blood flow changes in a complex blood vessel structure is improved, and therefore accurate measurement under different blood vessel conditions is ensured.