133 results about "Time series data analysis" patented technology

The invention discloses a medical whole-course intelligent management system based on a large model, and belongs to the technical field of large models. Comprising a multi-modal data acquisition module, a privacy calculation preprocessing module, a dynamic knowledge enhancement module, a time sequence data analysis module, an intelligent decision engine module, a multidisciplinary collaboration module, a patient interaction platform module, a dynamic intervention feedback module and a system security center module. The cross-mechanism data security sharing is realized, and the compliance of sensitive information processing is also ensured; a two-channel medical knowledge base is constructed, authoritative guidelines can be synchronized, newest clinical research data can be analyzed in real time, the knowledge base is kept in the newest state all the time, and the frontier scientific basis is provided for clinical decisions; dynamic modeling and trend prediction are carried out on long-term monitoring data of a patient by adopting a hybrid neural network model, and potential health risks and development trends can be identified more accurately.

The invention relates to the technical field of exception prediction, in particular to a storage cabinet exception trend prediction system based on time series data analysis, which comprises a state monitoring module, an interval sensing module, a path reconstruction module, a symptom activation module and an evolution prediction module. According to the method, the state vectors including the temperature, the voltage, the current and the door lock state are constructed and combined with the timestamp information to form the time sequence data sequence, and the dynamic expression mode of state change is established; a jump characteristic is analyzed by using a ratio of a time interval to a state change amplitude, a short-time disturbance path and a trend evolution path are distinguished by combining a jump rate statistical index, and an evolution activation signal is identified based on trend maintenance and non-fallback characteristics. On the basis, a neural network structure with long-time dependent learning ability is introduced to capture an aperiodic thermal anomaly trend in a state sequence, and the accuracy and timeliness of anomaly recognition are improved through multi-dimensional parameter cooperative processing and path construction logic.

The invention relates to the technical field of fault monitoring, in particular to a power transmission line fault monitoring and positioning method, system and device and a medium. The method comprises the following steps: firstly, performing digital conversion on an original current signal to obtain time sequence data, and then identifying and classifying fault events through threshold detection and a feature extraction algorithm to obtain classification labels of fault types; then, based on the classification label, establishing a traveling wave propagation model and carrying out time difference calculation, carrying out clustering analysis on a plurality of wave head data in a preset time window, and judging whether the fault is a single-point fault or a multi-point fault through a clustering result; and finally, a positioning result is transmitted to the central server for comprehensive verification and alarm management, and a final fault response is formed. By introducing a time series data analysis and clustering judgment mechanism, the fault type can be accurately identified, single-point and multi-point fault conditions can be effectively distinguished, and meanwhile, the reliability of a positioning result is improved through comprehensive verification of the central server.

The invention discloses a power network topology optimization method based on time sequence data analysis, and relates to the technical field of power network topology optimization, and the method comprises the following steps: dividing a power grid into a plurality of sub-regions, and injecting disturbance signals into the sub-regions; obtaining time sequence disturbance response data, extracting oscillation characteristics, and identifying oscillation nodes; constructing an oscillation weakness evaluation model, and carrying out weakness evaluation on the oscillation nodes; correcting a phase difference matrix of the DTW based on an evaluation result, and screening a cross-regional risk propagation path; and power grid topology reconstruction is carried out according to a screening result, so that the problems of inaccurate weak node identification and low cross-regional high-risk path screening accuracy in a traditional network optimization method can be solved.

The system comprises a data acquisition layer, a data processing layer, a model layer and an application layer, the data acquisition layer is used for acquiring multivariate heterogeneous data in real time, the data processing layer is used for processing the acquired data, the model layer is used for training the processed data, and the application layer is used for applying the trained data to the data processing layer. And the application layer is used for performing dual drive by adopting a time sequence processing block and point prediction and probability prediction, the time sequence processing block is a double-branch parallel backbone network integrating a Decoder-Only Transform network and a multi-scale graph neural network, and the application layer is used for evaluating the running state of the electric energy metering device according to a predicted value and a predicted probability calculated by the model layer at a certain moment. According to the charging pile operation state prediction system based on time sequence analysis, the operation state of the electric energy metering device can be monitored more comprehensively and accurately, potential abnormity and fault hidden dangers can be found in time, early warning is carried out in advance, and economic losses caused by metering errors and equipment faults are effectively reduced.

The invention provides a running log anomaly detection method and system, and the method comprises the steps: obtaining historical running log data of a target system, and converting unstructured data in the historical running log data into structured data; performing feature extraction on the converted structured data by using a variational auto-encoder, and performing clustering analysis on the extracted features; aiming at each clustering result, using a Transform model to carry out time series data analysis so as to capture a long-distance dependency relationship, and carrying out modeling on time series characteristics in the cluster through a self-attention mechanism so as to identify an abnormal event; the method comprises the following steps: training an anomaly detection model consisting of a variational auto-encoder and a Transform model, and carrying out parameter optimization on the model; and processing real-time running log data of the target system by using the trained anomaly detection model to realize an automatic identification result of an abnormal event. According to the method, the potential distribution characteristics of the log data can be automatically captured, and the recognition precision of abnormal events is improved.

The invention relates to the field of environmental noise detection, and provides an environmental noise detection device and method, and the method comprises the following steps: collecting an environmental noise signal, and carrying out the preprocessing of the environmental noise signal, and obtaining a to-be-detected audio; adjusting a gain compensation coefficient according to the energy intensity of the to-be-tested audio to obtain a gain multi-source noise signal; separating the gain multi-source noise signal to obtain a plurality of separated noise signals, extracting key characteristics of the separated noise signals, and classifying the plurality of separated noise signals based on the key characteristics to obtain different types of classified noise signals; performing feature extraction and time series data analysis on the classified noise signals to obtain abnormal noise data, identifying the abnormal noise data to obtain a noise abnormal level, and adopting a corresponding emergency solution; the detection data and the early warning record are stored in a cloud database; a visualization result is generated based on the stored data. According to the invention, efficient detection, classification, anomaly identification and data management of environmental noise are realized.

The invention relates to the technical field of computers, and discloses a time series data prediction method and device based on a diffusion model, equipment and a medium, and the method can ensure the consistency of different modal data in time by performing time dimension alignment on target time series data and multi-modal evaluation data. And feature splicing is performed on the multi-modal data corresponding to each time step, so that the multi-modal information can be integrated, and the data features of each time step are enriched. And on the basis of a self-attention mechanism, weighted summation is carried out on the fused feature vectors to obtain a self-attention feature matrix, so that mutual correlation between a time sequence dependency relationship and multi-modal features in a feature sequence can be captured, and richer feature representation is generated. And diffusion simulation is carried out on the self-attention feature matrix by using the pre-training diffusion model, more accurate target prediction data is output, and the accuracy of a prediction result is improved. And the application efficiency of the diffusion model in time series data analysis scenes in the financial field and the medical field is expanded.

The invention relates to the technical field of digital marketing, in particular to a digital advertisement putting method based on big data, and the method comprises the following steps: initializing an advertisement model, importing advertisement request time sequence data, carrying out time sequence analysis, adjusting an enqueue rule and a processing rate, calculating request distribution in a differentiated time period, and generating model parameter configuration. According to the invention, through a queuing theory and time sequence data analysis, advertisement time sequence and processing efficiency optimization, time sequence analysis and parameter adjustment, advertisement timeliness is enhanced, user activity peak synchronization is ensured, user participation degree and advertisement effect are improved, and recalculation of service time and estimation of waiting time support real-time monitoring of service efficiency. Accurate matching of advertisement resources and user requirements is achieved, real-time adjustment of strategies and simulation testing rapidly adapt to market changes, the resource utilization efficiency and ROI are improved, and precision and economic benefits of advertisement putting are remarkably improved through fine management and data driving optimization.

The invention relates to the technical field of network security, in particular to a distributed network attack tracing method and system, and the method comprises the following steps: carrying out the statistics of flow transmission time delay, bandwidth occupancy rate and data packet quantity among distributed network nodes, carrying out the clustering division of each node flow feature value, and generating a node flow feature clustering value; and on the basis of the node traffic feature clustering value, constructing an inter-node traffic transmission relation graph, and calculating an inter-node link weight and an association degree. According to the method, clustering division is carried out on the flow characteristic values among the distributed network nodes, the flow transmission relation graph among the nodes is constructed, the link weight and the correlation degree are calculated, a complex network structure is converted into a topological correlation matrix capable of being quantitatively analyzed, and basic data support is provided for follow-up attack traceability. Data packet identifiers and timestamp information are extracted based on a time series data analysis method, and abnormal traffic transmission characteristics are identified and statistically analyzed, so that the attack behavior detection accuracy is improved.

The invention relates to the technical field of intelligent control systems, and discloses a multifunctional embedded intelligent cleaning control system which comprises a control execution layer, an internet-of-things transmission layer and a decision analysis layer. The control execution layer comprises an intelligent variable-frequency scraping device, a wind-water sweeping device and the like and executes sweeping and dust removal operation; the Internet of Things transmission layer realizes data interconnection and transmission through an edge computing node, a protocol conversion gateway and the like; and the decision analysis layer adopts a time sequence data analysis and convolutional neural network algorithm, dynamically optimizes the cleaning operation model, constructs a multi-dimensional linkage cleaning control strategy model, and realizes cleaning intensity adjustment, air and water parameter cooperative control and dust removal efficiency dynamic optimization. According to the system, through multi-technology fusion and collaborative optimization, the intelligent, precise and efficient level of sweeping operation is improved, the system is suitable for a conveying belt sweeping scene, the sweeping efficiency can be improved, equipment loss and dust emission can be reduced, and remarkable economic benefits and environmental protection benefits are achieved.

To monitor the state of a vehicle without being affected by the ambient environment such as in the nighttime.SOLUTION: A detector comprises: an acquisition unit that, on the basis of a reflected wave that is reflected on an object moving in the cabin from a transmitted wave transmitted toward the inside of a cabin of a vehicle, acquires time-series data of a point group indicating one or more detection points representing the position of the object moving in the cabin; an operation unit that analyzes the behavior of the point group moving with the lapse of time on the basis of the time-series data of the point group; and a determination unit that determines whether the object enters or exits from the cabin on the basis of the behavior of the point group.SELECTED DRAWING: Figure 5

The invention relates to the technical field of medical report generation, in particular to an intelligent anesthesia record sheet automatic generation system. According to the system, patient information is automatically recognized through the OCR unit, and real-time collection and structured storage of physical sign data, medication records and surgical operation are achieved in combination with a medical equipment interface and a voice recognition technology; time sequence data analysis and multi-source data fusion modeling are adopted to construct a medical event causal relationship graph so as to improve the accuracy of anomaly recognition and patient sign evaluation; based on a time axis construction unit and a structured template engine, an anesthesia record list is automatically analyzed and filled, and the manual input workload is greatly reduced; and finally, in combination with block chain evidence storage and a multi-level encryption technology, the security and non-tampering property of the anesthesia record list are ensured, the reliability of medical data management is improved, the recording, monitoring and storage efficiency of the anesthesia process is comprehensively optimized, and the medical quality and the patient safety are improved.

The invention relates to the technical field of hydrological monitoring, and discloses a regional groundwater reserve change dynamic monitoring method based on gravity satellite time series data analysis. According to the method, the multi-source data scale effect and observation noise are effectively eliminated, the physical reliability and resolution of groundwater signal extraction are improved, and the recognition precision and early warning perspectiveness of the abnormal loss trend are enhanced. A full-chain monitoring closed loop of multi-source satellite observation, high-precision physical inversion and dynamic statistical benchmark is constructed, multi-dimensional environment data is aligned, a component separation strategy based on multi-element physical mechanism constraint is established based on a water balance principle, and anomaly diagnosis is performed in combination with a self-adaptively updated long-time-sequence historical database.

The invention relates to the technical field of finance, medical health and artificial intelligence, and provides an improved Transform-based time series data analysis method, device and equipment and a medium, which can utilize an adversarial network to perform data enhancement and effectively expand a training data set. The Transform model has the ability to perceive a sequence based on adaptive position coding, the multi-head attention mechanism based on a dynamic weight adjustment strategy enables the model to improve the ability to capture key information, and multi-head parallel computing can integrate attention results of different view angles to comprehensively mine dependency relationships in data; multi-task joint training and adversarial training are performed on the model, so that the task execution capability of the model can be improved on the premise of ensuring the training effect; the attention distribution features and the intermediate layer output features are utilized to perform interpretability analysis on the time series data analysis result, the interpretability of the analysis result can be improved, and a more comprehensive data analysis result can be provided.

A photovoltaic power generation associated physical quantity mining method based on historical time series data analysis, and a related apparatus. The method comprises: acquiring historical time series data of multiple dimensions during photovoltaic power generation; calculating degrees of mutual information between the historical time series data of the multiple dimensions of photovoltaic power and the photovoltaic power; selecting the historical time series data of which the degree of mutual information satisfies a set requirement to serve as data related to photovoltaic data; and using a linear discriminant analysis (LDA) method to perform feature dimension reduction on the selected historical time series data to obtain data having undergone dimension reduction processing. In the present invention, by using a maximal information coefficient (MIC) feature selection method, data most related to photovoltaic power generation is selected from original feature variables, and then by using an LDA-based feature dimension reduction method, high-dimensional data is mapped to a lower-dimensional space. By means of the MIC feature selection method and the LDA-based feature dimension reduction method, the accuracy of photovoltaic power generation prediction is effectively improved.

The invention relates to the technical field of patient diagnosis and treatment based on artificial intelligence, in particular to an infertility analysis and diagnosis and treatment auxiliary method based on artificial intelligence, which comprises the following steps: acquiring multi-source data of a patient; semantic analysis is carried out on the multi-source data of the patient, and infertility symptom feature data of the patient is recognized in combination with a machine learning algorithm; constructing a disease risk prediction model based on a deep learning algorithm, and predicting the risk probability of the infertility disease of the patient by taking the infertility symptom characteristic data of the patient as input; and based on the risk probability of the infertility disease prediction of the patient, extracting the potential cause of infertility of the patient through an association rule mining algorithm, and generating a personalized diagnosis and treatment scheme of the patient in combination with a preset medical knowledge base. Through multi-modal time series data analysis, dynamic pathogenesis mining and medical knowledge constraint verification, infertility pathogenesis accurate identification and personalized treatment scheme generation are realized, and diagnosis and treatment accuracy and clinical decision efficiency are remarkably improved.

A time series data analysis method, includes: generating a plurality of first feature vectors including feature amounts of a plurality of feature waveforms, based on distances from a plurality of first time series data sequences to the plurality of feature waveforms, the first time series data sequences belonging to a first class; generating a plurality of second feature vectors including feature amounts of the plurality of feature waveforms, based on distances from a plurality of second time series data sequences to the plurality of feature waveforms, the plurality of second time series data sequences belonging to a second class; and updating the plurality of feature waveforms, based on the plurality of first feature vectors, the plurality of second feature vectors, a performance indicator parameter related to a performance indicator for a classification model and a model parameter including weights on the plurality of feature waveforms.

The invention relates to the technical field of intelligent traffic management, and discloses a multi-modal time series data analysis-driven public traffic scheduling optimization method, which comprises the following steps of: acquiring multi-modal traffic data, performing time-space alignment preprocessing, and extracting a feature vector; fusing various feature vectors based on a cross-modal interactive learning method; and performing real-time prediction of the public traffic condition based on the multi-modal fusion feature vector to obtain a public traffic flow prediction value, and making a public traffic scheduling strategy based on the obtained public traffic flow prediction value. According to the method, multiple feature vectors are fused based on a cross-modal interactive learning method, the mutual relation between different types of data is effectively captured, the public transport scheduling strategy is formulated based on the particle swarm optimization algorithm, the global search and local search capabilities are effectively balanced, the performance of the PSO algorithm in the public transport vehicle scheduling problem can be effectively improved, and the public transport vehicle scheduling efficiency is improved. And a more efficient scheduling scheme is realized.

The invention relates to the technical field of medical data evaluation, in particular to a data evaluation and comparison system before and after posterior fossa brain tumor operation rehabilitation, and provides an accurate comparison reference for subsequent recovery prediction and progress monitoring through standardized processing of preoperative physiological data and extraction of key indexes. Through continuous monitoring and comparison of real-time physiological data, abnormal fluctuation can be found in the rehabilitation process, a treatment scheme can be adjusted in time, secondary health risks caused by recovery lag or abnormity are avoided, a hidden Markov model is adopted, a scientific basis is provided for careful division of rehabilitation stages through time series data analysis, and the method is suitable for clinical application and popularization. According to the method and the system, all rehabilitation stages are accurately identified according to the actual state of the patient, and the principal component analysis method and comprehensive evaluation of multi-dimensional data are combined, so that the precision of recovery progress monitoring is improved, challenges possibly encountered by the patient in the rehabilitation process can be identified through analysis of the recovery bottleneck, and the pertinence and effectiveness of a treatment scheme are ensured.

The invention discloses a federated learning time series data analysis method based on shuffling differential privacy, which comprises the following steps that: a central server initializes global model parameters and distributes an initial model to all federated learning clients; each client uses a local time sequence data training model, calculates a gradient, then cuts the gradient, and adds Laplacian noise; the client uploads the disturbed gradients to a shuffling device, and the shuffling device allocates random delay time to each gradient and disrupts an uploading sequence to realize privacy amplification; the central server aggregates the gradients processed by the shuffling device to generate a global gradient, and broadcasts the updated model parameters to the client; repeating the above steps to perform iterative training, and tracking privacy budget consumption in real time through a privacy accounting method until a preset privacy budget threshold value or model convergence is reached; according to the invention, privacy amplification is realized, and noise injection is reduced.

The invention discloses a remote sensing time series data analysis method and system considering geographic object space-time correlation, and belongs to the technical field of data analysis. The method comprises the following steps: acquiring and preprocessing a multi-source multi-temporal remote sensing image and auxiliary data to generate a consistent time sequence data stack; dividing the image into voxels in a three-dimensional time-space domain, carrying out adjacent tracking, identifying a geographic process object with an evolution behavior, and recording the attribute of the geographic process object; judging a space-time topological relation between geographic process objects, and generating a unified space-time relation table through composite reasoning; constructing a geographic process object space-time diagram model by taking the geographic process object as a node and the space-time relationship as an edge, and extracting and standardizing attribute features of the node and the edge; a graph convolutional network and a topology dynamic mechanism are used for joint modeling, and the importance index of each node is calculated. According to the method, closed-loop analysis from data processing, relation modeling and importance evaluation to decision support is realized, and the interpretability of remote sensing time sequence change detection and decision effectiveness are improved.

The invention relates to a data processing method for self-service borrowing service of a library. Constructing a personal behavior portrait of the user by using the historical borrowing record, borrowing duration, borrowing category and behavior characteristics including borrowing frequency and preference type of the user; historical data of user behaviors are analyzed through a time series data analysis technology, a user borrowing preference mode is mined, and periodic and seasonal changes are captured; fusing the historical borrowing data with spatio-temporal information including borrowing peak time periods and borrowing demand differences of different areas in a library, and constructing a multi-dimensional spatio-temporal data model; a multi-objective optimization algorithm is adopted, and dynamic resource allocation is carried out on the premise that the borrowing requirement is met; according to the real-time data of the borrowing system, the storage positions of the books are adjusted, the books are dynamically scheduled through the self-service borrowing terminal, and the load balance of each borrowing point of the library or the self-service borrowing machine is controlled; and modes in the borrowing history of the user are deeply analyzed.

The invention discloses a measuring result stability analysis method and system of a metering instrument, and relates to the technical field of time sequence data analysis. The method comprises the steps of measurement data flow alignment, measurement result stability baseline feature construction, three-state feature decomposition and measurement result stability analysis of the metering instrument. Extracting repeatability features, trend features and structural features of the measurement result, and constructing a measurement result stability baseline feature vector; a three-state feature decomposition method based on baseline constraint is adopted to decompose the stability change of the measurement result into a drift feature, a noise feature and a jump feature; comprehensively analyzing the stable state and the change trend of the measurement result of the metering instrument based on the baseline feature and the tri-state feature decomposition result; according to the method, the stability of the measurement result of the metering instrument can be subjected to refined and cause-distinguishable analysis under the complex working condition and the multi-state operation condition, and a reliable basis is provided for the evaluation of the operation state of the instrument and the judgment of the credibility of the measurement result.

The invention relates to a fault prediction method and system for parameter fine tuning and adaptive structure pruning based on business influence driving, and belongs to the technical field of artificial intelligence, time series data analysis and intelligent operation and maintenance. Comprising the following steps: S1, business influence data modeling: integrating multi-source operation and maintenance data and historical business fault event data, and constructing a business influence quantitative model; s2, efficient fine tuning of service influence guide parameters: loading the pre-training fault prediction model, and performing efficient fine tuning of the parameters on the basis of service influence signals; s3, business influence driven adaptive structure pruning; S4, dynamic reasoning and alarm generation: realizing a dynamic reasoning mechanism of a pruned fault prediction model, and generating a structured and business value oriented fault prediction alarm according to a reasoning result; and S5, performing closed-loop optimization and continuous evolution. According to the invention, the configuration accuracy of the operation and maintenance resources is improved.

The invention relates to the technical field of industrial equipment monitoring and management, in particular to an industrial equipment fault diagnosis system based on multi-class time series data analysis, which comprises a data acquisition unit used for acquiring operation data of multi-class industrial equipment, the operation data comprising a plurality of to-be-trained operation time series data and a plurality of to-be-diagnosed operation time series data; the model training unit is used for analyzing a plurality of data types corresponding to the to-be-trained operation time sequence data and transmitting the to-be-trained operation time sequence data corresponding to each data type to a pre-constructed initial fault diagnosis model for training to obtain a target fault diagnosis model corresponding to each data type; and the fault diagnosis unit is used for analyzing a plurality of data types corresponding to the to-be-diagnosed operation time sequence data and transmitting the to-be-diagnosed operation time sequence data to the corresponding target fault diagnosis model according to the data types. According to the invention, when the industrial equipment is monitored and managed, the efficiency and accuracy of fault diagnosis are improved through analysis of multiple types of time series data.

The invention discloses a frequency converter abnormal state detection method based on time series data analysis, which belongs to the technical field of electric power, and comprises the steps of: finally generating a structured abnormal event through acquisition, preprocessing, feature extraction, anomaly detection, anomaly level judgment and trend analysis of data of a multi-source sensor; accurate prediction and early warning of the operation state of a complex system are achieved, the technical problems of high real-time performance, low data redundancy and multi-source feature fusion of abnormal monitoring of the frequency converter are solved, redundant data are reduced, the data volume is reduced, the communication bandwidth pressure is reduced, it is ensured that high-frequency, low-frequency and event data are aligned on the same time axis, and the accuracy of abnormal monitoring of the frequency converter is improved. Abnormal data are transmitted preferentially, abnormal information is guaranteed to be complete and reliable, suspicious anomalies are screened rapidly, local and remote alarms are generated according to the abnormal levels, an abnormal database and a periodic summary report are stored, and operation and maintenance and remote monitoring are facilitated.

The invention discloses a nuclear power material purchase demand prediction method and system based on a time convolutional network, and relates to the technical field of time series data analysis, and the method comprises the steps: collecting a data set, carrying out the preprocessing of the data set, classifying the data set, setting a representation mode of the input data set, and constructing a time convolutional neural network model. A data set is divided, model training is performed by using the divided data set, time sequence data is input into a trained model, and a material purchasing demand prediction value is output through multi-layer convolution, residual connection and full connection layer processing. According to the method, the time sequence data features are deeply learned through expansion convolution and residual connection, the ability of capturing the complex time dependency relationship by the model is improved, gradient flow in a deep network is promoted through introduction of the residual connection, the gradient disappearance problem is avoided, and the training efficiency and the learning ability of the complex features of the model are improved.

The invention provides a full-period follow-up visit patient risk dynamic management method based on time series data analysis. The method comprises the steps of performing standardization processing on multi-source follow-up visit data of a patient; data acquisition is dynamically optimized through an adaptive strategy based on reinforcement learning, and multi-scale time sequence features are extracted; performing dynamic risk assessment by using the attention mechanism enhanced double-layer LSTM model, and outputting a risk probability; and calculating a comprehensive risk index by integrating the risk probability, the change trend and the volatility, and realizing dynamic risk layering and automatic intervention based on a rule engine. According to the method, a traditional fixed acquisition mode is converted into a dynamic optimization process capable of responding to patient risks, equipment states and system loads in real time, on the premise that high-risk patient monitoring is guaranteed, system resource consumption is remarkably reduced, and long-term optimal balance of data quality, system performance and resource overhead is achieved.

The invention discloses a non-contact screening system for motion coordination based on computer vision, which relates to the field of computer vision, and comprises the following steps: acquiring a human body motion image through a non-contact camera, extracting human body key point coordinates frame by frame by using a deep learning algorithm after noise reduction and distortion correction, and constructing a time sequence data set; performing smooth jitter removal and interpolation processing to form standardized data; calculating track, rhythm and stability multi-dimensional motion coordination quantitative characteristics based on the data, and performing comparative analysis with a personal historical database to identify a motion function degradation trend; and based on age groups and scenes, customizing screening parameters, coordinating workflows of the components, and realizing full-process automatic screening. The method has the advantages that invasive and cross infection risks are avoided through non-contact acquisition, accurate and objective assessment of the exercise coordination is realized by means of deep learning and time series data analysis, exercise function degradation can be early warned, and an efficient and reliable scheme is provided for various exercise coordination screening scenes.