16521 results about "Information acquisition" patented technology

The invention relates to the technical field of twinborn model simulation, and discloses a twinborn model simulation method and system for hot working of large forgings. The method comprises the following steps: collecting and preprocessing process parameters, quality data and environment information in a multi-source manner, and obtaining hot working characteristic data; performing correlation analysis to construct a process knowledge graph; calculating the distribution of a temperature field, a stress field and an organization field by using a self-sensing multi-field coupling var value neural network; comparing and analyzing to obtain deviation data and correction parameters; adjusting a network parameter optimization prediction result; and executing multi-objective optimization calculation, and generating a whole-process technological parameter and a control instruction. According to the method, full-process multi-physics field coupling calculation from smelting, casting, forging and pressing to heat treatment can be achieved, model parameters are dynamically adjusted according to real-time production data, the optimal process scheme is generated, and therefore the manufacturing quality and efficiency of large forgings are improved.

The invention discloses a dynamic sensitive data outbound risk assessment method and system based on multi-source risk information in the technical field of data cross-border. The system is mainly composed of a multi-source risk information acquisition module, a dynamic security identifier generation module, a risk collaborative assessment engine, a dynamic weight adjustment module, a disposal range dynamic calculation module and a flexible emergency disposal module, and an acquisition-identifier-assessment-calculation-disposal full-process closed-loop architecture is formed. A full-process closed-loop processing framework of collection-identification-evaluation-calculation-disposal is innovatively proposed, and by combining a dynamic risk evaluation model, a reinforcement learning intelligent technology and a block chain evidence storage technology, the problems of insufficient dynamic nature, lack of collaboration and lack of closed-loop capability in the prior art are systematically solved; and high-precision evaluation, real-time response and traceable management and control of the cross-border data flow risk are realized.

The invention discloses a power distribution network line fault positioning detection system, and relates to the technical field of power distribution network fault detection. The system comprises a mixed information acquisition layer, a fault feature extraction layer, an intelligent diagnosis layer and a fault positioning layer. The mixed signal acquisition layer comprises a high-frequency transient wave recording unit, a power frequency measurement unit, a wireless pulse sensor and a distributed optical fiber temperature measurement unit; the fault feature extraction layer comprises a time-frequency analysis module, a preprocessing module and a three-dimensional feature vector module; the intelligent diagnosis layer comprises a convolutional attention network, a space-time diagram neural network and a transfer learning module; the fault positioning layer comprises a particle swarm module and a fuzzy reasoning module. According to the invention, data information of the cable is acquired through the mixed information acquisition layer, a video analysis window function is dynamically matched with signal characteristics, a time domain graph scale, a frequency domain resonance component and a space field intensity gradient are constructed, fault diagnosis and positioning are carried out by using the intelligent diagnosis layer, and the fault positioning detection efficiency of the power distribution network is improved.

A SQL generation method based on a large language model, comprising: generating data definition language (DDL) prompt slot information and data example slot information on the basis of a configuration operation of a user on a SQL database; receiving a natural language query request input by the user, and transcribing the natural language query request to obtain question transcribing slot information; on the basis of the question transcribing slot information, the DDL prompt slot information and the data example slot information, acquiring complete prompt information matched with a large language model; and inputting the complete prompt information into the large language model to generate an executable SQL statement matched with the natural language query request.

The present disclosure relates to the technical field of document recognition, and provides an intelligent document recognition method and apparatus, an electronic device and a storage medium. The method comprises: extracting text information and layout information from a document to be recognized; acquiring a template containing a specified field; on the basis of an industry knowledge base corresponding to the document, adding a label to the text information; and, on the basis of the layout information and the label, using a large language model to recognize text information matched with the specified field from the text information to which the label has been added, the large language model being a large language model that has been trained by using the industry knowledge base. In the embodiments of the present disclosure, document recognition results more conform to industry attributes, improving document recognition accuracy.

The invention discloses an intelligent digital operation and maintenance management system and method for a charging pile, and relates to the technical field of new energy infrastructure operation and maintenance, and the system comprises an information obtaining module which determines a delay information circulation channel, obtains real-time discussion content, processes a discussion text through employing a word segmentation technology, and carries out the operation and maintenance of the charging pile. If the confidence coefficient of the semantic feature vector is higher than a preset threshold value, the semantic feature vector is judged to be a specific question type through state space definition, classified question labels are obtained, the historical matching module obtains features associated with a historical question library, historical question records are matched, and the matching precision is evaluated through reward function design. Determining a historical solution most similar to the current problem; according to the intelligent digital operation and maintenance management system and method for the charging pile, the accuracy of problem classification and solution matching is continuously improved, and intelligentization and high efficiency of operation and maintenance management of the charging pile are achieved.

The invention discloses an unmanned aerial vehicle low-altitude intelligent collaborative management system based on dynamic airspace gridding, and relates to the technical field of unmanned aerial vehicle management. Comprising the following steps: acquiring real-time environment information of a low-altitude airspace and operation state information of an unmanned aerial vehicle group; performing dynamic airspace gridding processing according to the real-time environment information to obtain a plurality of airspace grid units and airspace attribute information; according to the airspace grid units, the airspace attribute information and the operation state information of the unmanned aerial vehicle group, a cooperative flight path planning scheme is generated, and the technical key points are as follows: the information acquisition link provides comprehensive and accurate basic data, the dynamic airspace gridding processing realizes flexible adaptation of the airspace, and the cooperative path planning ensures efficient and ordered group flight. Flight control and dynamic adjustment guarantee smooth propulsion of tasks; all links support and promote each other, for example, a prediction result of the airspace state advanced judgment model can provide prospective guidance for path planning, and a deviation monitoring and adjusting mechanism can correct flight deviation in time.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing method, device, equipment and medium based on a context agreement, comprising: receiving a business demand event, extracting a demand text, and disassembling the demand text to generate a plurality of subtasks; obtaining a data source context according to the sub-tasks and task subject information, generating a corresponding data extraction path and an analysis model strategy, connecting a target data source through an access strategy, collecting original data, preprocessing the original data, and dividing the original data into training data and verification data; and training the model based on the analysis model strategy and generating a reasoning result, and completing verification output through a result verification model. According to the method, task disassembly, data access and model generation processes are driven through a model context protocol, and a knowledge graph reasoning and verification mechanism is combined, so that a closed-loop automatic analysis path is formed, and the processing efficiency and intelligent adaptation capability of cross-domain data tasks are effectively improved.

The invention relates to the technical field of building engineering automation, and discloses a building robot collaborative operation system, a BIM modeling module is used for establishing a three-dimensional model of building engineering and dividing the three-dimensional model into a plurality of operation areas; the information acquisition module is used for acquiring state information and working environment information of the robot; the central control module is used for performing task allocation and operation planning by using an ant colony algorithm; the information sharing module is used for carrying out real-time communication between the robots through wireless communication in the operation process, and sharing operation progress, state information and environment information; the operation evaluation module is used for monitoring the operation progress and state of each robot in real time, evaluating the operation effect of the robot, adjusting task allocation and planning strategies in time according to the evaluation result, optimizing the collaborative operation process of the robot, and improving the operation efficiency of the robot. According to the invention, the problems of unreasonable task allocation, low communication efficiency, difficult operation coordination and the like in the collaborative operation of the existing building robot are solved.

The invention relates to a ship safety monitoring method, system and device based on data fusion, and relates to the technical field of ship navigation safety, and the ship safety monitoring method comprises the steps: collecting multi-source heterogeneous data; preprocessing the multi-source heterogeneous data; carrying out fusion processing on the multi-source heterogeneous data based on an information entropy theory; a ship safety domain model is constructed, trajectory prediction is executed, a collision risk index is calculated, and the ship collision risk is evaluated; and performing anomaly detection on the ship equipment operation data based on manifold learning and a thermodynamic model. According to the invention, a multi-source heterogeneous data fusion technology is adopted, multi-dimensional data sources are integrated, and omnibearing ship safety situation awareness is constructed. The early warning time is prolonged to 8-10 minutes, which is far better than the early warning time of 3-5 minutes in the prior art. The system thoroughly solves the problem that subsystems in a traditional ship monitoring system operate independently and do not communicate with one another, the crew information acquisition cost is reduced, and the number of times of interactive operation is reduced by 42%.

The invention relates to the technical field of machine tool casting detection, and discloses an automatic detection system for surface defects of machine tool precision castings. The system comprises a surface information acquisition core module, a first defect identification core module, a second defect identification core module and a defect type fusion core module. The surface information acquisition module is used for synchronously acquiring real-time optical images and process parameter data in production aiming at the surface of the casting part, and constructing a defect diagnosis characteristic spectrum and an auxiliary text according to the real-time optical images and the process parameter data; the first defect recognition module inputs the atlas and the auxiliary text into a pre-training double-flow convolutional neural network to generate a first classification result of defect types; a second defect identification module extracts defect mechanism characteristic values from the atlas and matches the defect mechanism characteristic values with a pre-stored defect mechanism knowledge base to obtain a second classification result; and the defect type fusion module fuses the two types of results to determine a target defect type. The system solves the problems of single detection information and identification deviation in the prior art, improves the detection accuracy and real-time performance, and meets the requirements of different production scenes.

The invention discloses an intelligent construction site management method and system based on photoelectricity and a video fence, the system is realized based on the intelligent construction site management system, the system comprises a control module and an information acquisition device connected with the control module, and the information acquisition device comprises a camera device, a cable monitoring device, an environment state parameter sensor and a construction equipment intelligent switch; and the cable monitoring devices are arranged along the extending direction of the cable at intervals of preset lengths. The method comprises the following steps: regularly acquiring and preprocessing real-time data of a construction site; analyzing the video data to identify a dynamic area; cable abnormity and equipment state are evaluated; generating a motion intensity graph and a key frame candidate set; tracking a target, generating a track, and determining a responsibility subject. According to the method, comprehensive monitoring and anomaly detection of the construction site are realized through multi-source data fusion and intelligent analysis, and the management efficiency and safety are improved.

The invention relates to an ultrahigh-voltage silicon carbide start control chip protection method and system, and the method comprises the steps: obtaining the operation parameters and composite structure characteristic data of an ultrahigh-voltage silicon carbide start control chip, and carrying out the TVS-MOSFET hierarchical characteristic analysis, and obtaining the real-time state information of the chip; acquiring a grid charging curve of the ultrahigh-voltage silicon carbide starting control chip, and performing correlation analysis on the grid charging curve and the real-time state information of the chip to obtain a multi-threshold protection parameter; acquiring current change data of the ultrahigh-voltage silicon carbide starting control chip, and performing Miller platform feature analysis on the real-time state information of the chip to obtain a current abnormal trend; performing temperature control construction on a multi-threshold protection parameter and the current abnormal trend to obtain an initial control scheme; and carrying out clamping turn-off strategy construction on the initial control scheme and the potential fault area to obtain a chip start protection strategy. Optimized starting protection of the chip can be achieved through the comprehensive adjustment strategy, and therefore the service life of the chip is effectively prolonged.

The invention discloses a data model dual-drive motor life prediction system and method. The system comprises an information acquisition module, a model fusion module, a model training module and a weight adjustment module. The information acquisition module is used for acquiring state data of different motors; the model fusion module is used for effectively fusing the data driving model and the physical information model; the model training module is used for training and optimizing the fused mechanism-data dual-drive model according to the motor state data provided by the information acquisition module; the weight adjusting module plays a key adjusting role in the whole model training process. The method not only depends on a data-driven model for prediction, but also combines a physical information model to make up for the problem that a pure data-driven method lacks interpretability. Multi-physical-field factors such as environmental stress, vibration impact, thermal management and an electromagnetic field are brought into modeling, so that the performance degradation of the motor is accurately described and evaluated, and the accuracy and physical interpretability of a prediction result are enhanced.

The invention discloses a metadata real-time self-adaptive standardization system for multi-field data sharing. The metadata real-time self-adaptive standardization system comprises a metadata information layering module, an intelligent information acquisition module, a real-time self-adaptive standardization module and a visual management module, the metadata information layering module identifies sources of various metadata, and sequentially performs cleaning, classification and layering preprocessing on the metadata; the intelligent information acquisition module uniformly acquires metadata information and converts the acquired data into standardized intermediate representation; the real-time adaptive standardization module is used for realizing rule dynamic adaptation and real-time optimization in a metadata standardization process, is internally provided with a dynamic rule library, is used for storing dynamically generated metadata mapping rules and supports automatic loading, updating and semantic extension of the rules; and the visual management module realizes management, analysis and dynamic adjustment of metadata through a visual interface. The method is high in accuracy of processing unknown data, can adapt to different data scenes in real time, and is high in semantic interoperation capability.

The invention discloses a construction man-machine safety intelligent detection and early warning method based on deep learning. The method comprises the following steps: S1, introducing a wavelet convolution improved YOLOv11 target detection algorithm framework; s2, coordinate extraction of the instance segmentation module; s3, designing a monocular depth estimation network; s4, training the monocular deep network; and S5, converting the two-dimensional coordinates into the three-dimensional coordinates. According to the invention, the real distance between the worker and the equipment is obtained and real-time safety early warning is carried out; obtaining a man-machine actual distance, and judging the accurate position and posture of the machine by combining angle information of the construction machine with edge contour detection; target detection in a complex construction scene is realized by optimizing a network structure; the S2R-DepthNet depth estimation network is used for performing depth estimation on a single RGB image through a self-supervised learning method, the three-dimensional information acquisition process is simplified, and dependence on expensive hardware is eliminated.

The invention provides an intelligent agricultural environment monitoring system based on the Internet of Things, and relates to the technical field of data processing. The feature extraction and analysis module is used for performing segmented statistical analysis on the soil humidity original data according to the soil humidity original data in combination with historical soil humidity data, historical environment temperature data and preset near-period soil humidity feedback data, and automatically extracting a humidity change rate and an abnormal trend index data set under multiple time windows; the self-learning judgment module is used for dynamically adjusting the current judgment threshold value based on a self-learning algorithm, judging the soil humidity original data in combination with the humidity change rate and the abnormal trend index, and generating a judgment signal for judging whether local irrigation response is triggered or not; the response control module is used for automatically generating an irrigation equipment starting instruction; the irrigation execution module is used for executing irrigation operation according to the irrigation equipment starting instruction; according to the invention, the accuracy of agricultural environment monitoring is improved.

The invention relates to the technical field of building management, in particular to a BIM-based building construction management method and system and a medium. The method comprises the following steps: acquiring building structure data; a tower crane full-life-cycle BIM model is constructed based on the building structure data, wherein the tower crane full-life-cycle BIM model comprises three-dimensional parameterized information of a base embedded part, a tower body standard knot, a slewing mechanism and a suspension arm assembly; acquiring building construction environment data; carrying out tower crane installation optimization on the tower crane full-life-cycle BIM model by utilizing building construction environment data to obtain a tower crane installation scheme; and base stress distribution simulation is conducted on the tower crane full-life-cycle BIM model according to the tower crane installation scheme, and tower crane base stress distribution data are generated. According to the invention, by integrating the building structure and the construction environment data and combining real-time sensing monitoring and intelligent collaborative optimization, the accuracy and dispatchability of tower crane management in building construction are improved.

The invention discloses a multi-source information fusion equipment health diagnosis management system, and relates to the technical field of equipment management. Comprising an information acquisition module, a feature perception module, a processing fusion module, an equipment modeling module, an evolution prediction module, a root cause diagnosis module, a cross-domain inspection module, a learning sharing module, a state evaluation module, a collaborative decision-making module, a chain account book module and a visual decision-making module. According to the method, the perspectiveness and the sensitivity of anomaly detection are remarkably improved, noise interference and sampling deviation are reduced, fused data are more stable and have higher physical consistency, differential diagnosis and individual-level prediction are supported, the interpretability of diagnosis and the decision reliability are improved, false alarm and missing alarm are avoided, the early warning reliability is improved, and the method is suitable for popularization and application. And scientific, transparent and traceable health management is realized.

The present application relates to a multi-terminal linkage energy scheduling method and apparatus. The method comprises: acquiring multi-terminal data, wherein the multi-terminal data comprises power supply side data, power grid side data, load side data, and energy storage side data; on the basis of the acquired multi-terminal data, predicting multi-terminal electricity amount related information within a future preset time period by means of a prediction algorithm, wherein the multi-terminal electricity amount related information comprises photovoltaic power generation related information, power grid power generation related information, power consumption information of a load side device, and electricity charging and discharging related information of an energy storage device; on the basis of an energy efficiency target, acquiring energy regulation information by means of the multi-terminal electricity amount related information; and controlling the energy storage device and / or the load side device on the basis of the energy regulation information. According to the solution of the present application, by means of a multi-party linkage mechanism, a scheduling plan formed on the basis of a preset scheduling target is more suitable for actual requirements and more accurate, and the energy storage device and the load side device can be automatically controlled on the basis of the scheduling plan.

The invention discloses an automatic soft soil foundation settlement monitoring system based on multi-source data fusion, and relates to the technical field of soft soil foundation monitoring, the system comprises an information acquisition module, a fusion processing module, a settlement prediction module and an intelligent monitoring module; the information acquisition module is used for acquiring foundation settlement sensing data and inputting the acquired data into the fusion processing module; the fusion processing module is used for preprocessing and integrating the collected data; the settlement prediction module is used for soft soil foundation settlement prediction; the intelligent monitoring module comprises a self-adaptive processing module and an interaction alarm module, the self-adaptive processing module is used for generating an optimization strategy, and the interaction alarm module is used for carrying out user interaction and multi-mode abnormal alarm reminding. An early warning response window is provided for engineering personnel, and the occurrence rate of sudden settlement accidents is reduced.

The invention provides an interaction method, system and device and a medium, and relates to the technical field of man-machine interaction, and the method comprises the steps: obtaining an interaction process of a main control agent and a target user in a current round of interaction process according to the intention information and state information of the target user in the current round of interaction process; according to the interaction process, controlling an execution agent to perform response operation on current input information to obtain a response operation result of the current input information; the current input information is information input by the target user in a current round of interaction process; and outputting response information corresponding to the current input information according to the response operation result. According to the method, the interaction process and the dialogue content are flexibly adjusted according to the real-time intention and the real-time state of the user, and the fluency, naturalness and adaptability of interaction, the efficiency and real-time performance of interaction processing and the user experience feeling are remarkably improved.

The invention belongs to the field of electricity consumption information acquisition, and provides an anomaly detection method and system for redundancy elimination perception multi-dimensional electricity consumption data fusion, and the method comprises the steps: obtaining historical electricity consumption data, carrying out the preprocessing, and obtaining a time sequence feature vector of the historical electricity consumption data through a trained convolution recursion auto-encoder; based on the preprocessed historical power consumption data, a space mapping function is adopted to obtain a basic feature vector; based on the preprocessed historical power consumption data, obtaining an incidence relation feature vector by adopting a trained graph convolutional neural network model; fusing the time sequence feature vector, the basic feature vector and the incidence relation feature vector by adopting a multi-head attention mechanism to obtain a fused representation vector; filtering redundant information in the fusion representation vector by using the trained redundancy elimination fusion model to obtain a comprehensive representation vector of the time sequence power consumption data; and performing anomaly detection according to the comprehensive representation vector of the time sequence power consumption data to obtain a historical power consumption data evaluation result.

The invention relates to the technical field of natural language processing, in particular to an intelligent search engine system and method based on NLP and vector hybrid retrieval, and the system comprises a query analysis module which is used for receiving a query statement input by a user and obtaining structured query information based on the query statement; the symbiotic index module comprises a sparse extension index unit which is used for constructing a sparse inverted index table based on keywords in the structured query information; the vectorization hypergraph index unit is used for forming a hypergraph index based on the business data; the recall arrangement module is used for acquiring a candidate object set according to the structured query information; the constraint rearrangement module is used for calculating a joint priority score between query and candidate objects based on the candidate object set, and obtaining a sorting result under constraint conditions of meeting a supplier proportion, a category proportion and a price interval; and the evidence generation module is used for generating evidence information based on the sorting result and outputting the evidence information to the user interface.

The invention discloses an energy consumption optimization management system and method, and particularly relates to the field of energy management, and the system comprises an intelligent information collection terminal collection module, an energy data preprocessing module, an intelligent prediction module, a multi-target optimization scheduling module, a real-time feedback module, an abnormity alarm module, and a scheduling scheme output module. According to the energy consumption optimization management system and the method thereof, data are acquired through the intelligent information acquisition terminal acquisition module by means of various intelligent information acquisition terminals, so that efficient management of energy consumption is realized; dynamic prediction, optimal scheduling and efficient management of energy consumption are realized through the intelligent prediction module, and the accuracy of energy consumption trend prediction is improved. And a multi-target optimization scheduling scheme is obtained by applying a multi-target optimization algorithm through the multi-target optimization scheduling module, so that the scientificity and rationality of energy scheduling are improved, and energy waste caused by unreasonable scheduling is reduced.

The invention discloses an information acquisition and transmission system of track control equipment, which relates to the technical field of track traffic intelligent monitoring and comprises an intelligent sensing terminal, an edge intelligent analysis node, a safety communication module, a central control platform, a digital twin diagnosis center and a predictive maintenance engine. According to the invention, the intelligent sensing terminal integrates various sensors, adopts a permalloy shielding cavity to isolate external electromagnetic interference, is equipped with a three-stage lightning protection circuit to realize microsecond-stage lightning surge protection, can stably collect parameters of track control equipment in a severe environment, is internally provided with a lightweight LSTM model, carries out preliminary filtering analysis on original data, and is used for monitoring the parameters of the track control equipment. The device operation state can be dynamically identified, the sampling strategy can be adjusted, the basic sampling frequency is maintained in the stable state to reduce power consumption, the sampling frequency is improved by 10 times and the sensor gain is enhanced in the early warning or emergency state, all analysis decisions are completed at the local millisecond level without depending on the cloud, and the real-time performance and accuracy of data processing are effectively improved.

The invention discloses a power grid visual diagnosis system based on multi-source data fusion, and relates to the field of power grid diagnosis systems. The system comprises an information acquisition module, an electrical fault alarm module, a non-electrical quantity fault alarm module, a fault discrimination module, a communication module and a fault recording module. Firstly, information of a power grid is collected through the information collection module, then the information is transmitted to the electrical fault alarm module and the non-electrical fault alarm module for monitoring, corresponding actions are made when abnormity occurs, then the information is transmitted to the fault judgment module, input information is analyzed, and the fault type is obtained; meanwhile, the fault recording module records and broadcasts the power grid; the communication module collects information of other modules in the system and exchanges information with the monitoring system. According to the method, the abnormal information of the power grid is monitored and alarmed in combination with the multi-source data, and the fault type is judged through the data model, so that the omnibearing monitoring of the power grid is realized, and data support is provided for a subsequent maintenance plan.

The invention relates to a big data-based passenger-roll transport demand prediction and ship intelligent scheduling method and system. The method comprises the steps of obtaining multi-source shipping data for a target area; inputting the multi-source shipping data into the spatial-temporal feature mining model, and predicting passenger rolling transportation demand information of the target area; acquiring ship real-time position, passenger carrying capacity, energy consumption data and port real-time operation state in the target area, and dynamically generating an optimal scheduling scheme by adopting a shipping scheduling model in combination with the predicted passenger transport demand information; the shipping scheduling model is obtained by interacting a decision scheduling model with an intelligent agent corresponding to the passenger roller transportation system and performing iterative training by adopting a reinforcement learning algorithm; and converting the optimal scheduling scheme into visual information, pushing the visual information to operation terminals of the ship and port workers so as to start corresponding shipping scheduling operation, and monitoring the execution effect of the shipping scheduling operation in real time.

The invention specifically relates to a power transmission line fault diagnosis and visual early warning system. The system comprises the following parts: line data acquisition; fault analysis; image information acquisition, image analysis, meteorological analysis, comprehensive processing, state monitoring and an early warning module. According to the invention, multi-dimensional data such as a current value of a power transmission line, a leakage current value of an insulator, a lightning stroke condition and image information acquired by an unmanned aerial vehicle are comprehensively and deeply analyzed and processed to obtain a line state value, an image state value and a meteorological influence value, and an early warning value is comprehensively obtained. According to the invention, the fault hidden danger of the power transmission line can be accurately diagnosed, the accuracy and timeliness of fault diagnosis are improved, and the risk and potential loss of fault occurrence are effectively reduced.

The present invention provides a data processing method for a heart rate monitoring system, and belongs to the technical field of data processing. The present invention effectively solves the problems that heart rate monitoring systems have low accuracy in acquiring raw heart rate data of patients, have difficulty in removing heart rate interference factors, and require precise heart rate value data and analysis results. The present invention is used in a heart rate monitoring system, and the heart rate monitoring system comprises: a power supply apparatus, an information acquisition apparatus, an information conditioning module, a data fusion unit, a data analysis module, an anomaly detection alarm module, a display unit, and a storage unit. The present invention comprises the following steps: S1, information acquisition; S2, information conditioning; S3, data fusion; and S4, data analysis. The present invention can ensure that the heart rate monitoring system can accurately and reliably monitor and interpret heart rate data during data processing, thereby providing more reliable support for clinical practice.