38 results about "Unsupervised learning algorithm" patented technology

The invention discloses a gas power plant combined heat and power generation real-time optimization method based on SIS data. The method comprises the steps of obtaining real-time SIS data of a gas power plant; based on the real-time SIS data and the historical SIS data, a dynamic safe operation boundary matched with the current operation condition is dynamically determined through an unsupervised learning algorithm, and the dynamic safe operation boundary is a variable constraint set representing the safe operation limit of gas power plant equipment and changing along with operation condition parameters; and by taking the dynamic safe operation boundary as a constraint condition of optimization solution, generating an operation control instruction for controlling the operation of the cogeneration unit. The safety boundary matched with the operation condition is dynamically identified through an unsupervised learning algorithm, and real-time optimization is performed by taking the safety boundary as a constraint, so that the conservative property of a traditional fixed boundary method is effectively overcome, and the economical efficiency of cogeneration operation is remarkably improved on the premise of ensuring the safety of a unit.

The invention discloses a big data analysis-based reduction furnace feeding method, a reduction furnace feeding device, reduction furnace feeding equipment and a computer readable storage medium, and the method comprises the following steps: carrying out data cleaning processing on historical batch data to obtain cleaned batch data; performing data feature processing on the cleaned batch data to obtain feature data; performing clustering analysis on the feature data by adopting an unsupervised learning algorithm to obtain an operation mode corresponding to each batch; training the initial prediction model based on the historical batch data of each batch and the corresponding operation mode to obtain an optimal parameter prediction model; performing prediction processing on the current operation state and the identified current operation mode based on the optimal parameter prediction model to obtain current optimized charge table data; and the current optimized charge table data are sent to a reduction furnace control device, so that the reduction furnace device carries out feeding control based on the current optimized charge table. Feeding adjustment is achieved in real time, and the feeding accuracy of the reduction furnace is improved.

The invention is suitable for the technical field of sodium ion batteries, and provides a digital twinning-based intelligent operation and maintenance method for a sodium ion battery energy management system, and the method comprises the steps: constructing and operating a virtual model corresponding to a sodium ion battery system in a cloud digital twinning platform based on collected multi-source heterogeneous operation data; the virtual model simulates the internal state and evolution trend of the sodium ion battery system by coupling the electrochemical model, the thermal model and the life model; tracking and predicting the state of charge and the state of health of the sodium ion battery system, generating a charging and discharging power instruction and a thermal management strategy through a dynamic optimization control algorithm, and issuing and executing the instruction; and performing anomaly detection by applying an unsupervised learning algorithm, and generating predictive maintenance suggestions in combination with the prediction result of the health state. By constructing digital twinborn body and multi-model coupling, multi-dimensional accurate perception and multi-target dynamic optimization of the sodium ion battery system are realized, and the full life cycle value and safety level of the energy storage system are improved.

The application belongs to the field of numerical simulation, and particularly relates to a modeling method of a shock wave light field in a hypersonic flow field, comprising the following steps: adopting an unsupervised learning algorithm to perform gradient clustering on flow field data, and determining the position of a shock wave and the inner and outer boundaries of a shock wave domain in the flow field domain; constructing a hybrid dimension numerical continuous and discrete strategy, determining the main direction of a light ray based on the strategy, performing continuous medium approximation processing on the main direction of the light ray, and performing structured grid discrete processing on the non-main direction of the light ray; based on the analogy of Fermat's principle and Maupertuis' principle, the transmission of the light ray in the refractive index field is analogized to the movement of equivalent real particles in the refractive index potential field, and the dynamic relationship between the equivalent real particles and the refractive index potential field is established; based on the dynamic relationship, the transmission path of the light ray in the shock wave is simulated, and the modeling of the shock wave light field in the hypersonic flow field is realized. The application significantly improves the efficiency and accuracy of the modeling of the shock wave light field in the hypersonic flow field.

The application provides a data processing method, device, equipment and medium. Data of multiple heterogeneous systems is collected multiple times, and the data of the multiple heterogeneous systems collected each time is transmitted to an analysis system respectively. Each analysis system runs different unsupervised learning algorithms and outputs corresponding analysis results. Each analysis result includes a first interworking heterogeneous system matched by each heterogeneous system. Then, each analysis result is integrated into a classification matrix. Rows of the classification matrix include matching results of each heterogeneous system under different analysis systems, and columns of the classification matrix include matching results of each heterogeneous system under each analysis system. Secondary analysis is performed on data in each row of the classification matrix to analyze analysis results of each heterogeneous system under different analysis systems, and a second interworking heterogeneous system matched by each heterogeneous system is obtained, so that an interworking heterogeneous system can be effectively obtained.

The application provides a kind of dry reactor sound signal visual detection method, medium and system, belong to dry reactor sound signal detection technical field, include: first, the sound signal of dry reactor is collected, and pretreatment is carried out to eliminate environmental noise.Then the time-frequency analysis is carried out to the sound signal after pretreatment, and the time-frequency spectrum is obtained.Next, adopt the way of bayesian probability inference and adaptive threshold increase, and highlight the small change in time-frequency spectrum, and obtain the increased time-frequency spectrum.Subsequently, energy distribution, peak frequency and harmonic structure are extracted from the increased time-frequency spectrum, and combined into a multi-dimensional feature vector.Apply dimension reduction algorithm, map high-dimensional feature vector to two-dimensional or three-dimensional space, and obtain the second feature vector.Finally, use unsupervised learning algorithm to carry out cluster analysis on the second feature vector, and assign color or label according to the clustering result for different categories, generate visual classification image output.

The invention provides a capacity management method and device. The method comprises the following steps: determining a first feature tensor according to a preset time window, business event features and environment features; wherein the first feature tensor comprises a plurality of data dimensions; inputting the first feature tensor into a hybrid neural network model, and predicting the resource demand quantity at a specific time point in the future to obtain prediction information corresponding to the resource demand quantity at the specific time point in the future; performing abnormal data identification on the real-time data flow according to an unsupervised learning algorithm to obtain abnormal information; and determining an optimal capacity management strategy according to the prediction information and the abnormal information. Through the embodiment of the invention, the problem of low capacity management efficiency caused by the fact that the traditional CMP passively responds to resource alarm, depends on manual decision and execution and cannot predict future demands or global optimization in the related technology is solved.

The invention relates to the technical field of building nondestructive testing, and discloses a building leakage point detecting, identifying and positioning method for building engineering, which comprises the following steps: firstly, adaptively identifying a resonant frequency set of a to-be-detected building structure; then, on the basis of the resonant frequency set, normal excitation and tangential excitation are sequentially applied through a composite orthogonal bimodal excitation unit, and surface micro-motion video data of the structure are synchronously collected by a high-frame-rate optical imaging unit; thirdly, resolving the video data to generate a P-wave velocity field and an S-wave velocity field, and constructing a wave velocity ratio field sensitive to leakage characteristics; and finally, multi-dimensional features are fused to construct a high-dimensional feature vector, an unsupervised learning algorithm is utilized to calculate an abnormal score, and automatic identification and accurate positioning of the leakage point are realized. According to the method, the sensitivity and the accuracy of detection are remarkably improved by utilizing a resonance enhancement mechanism and a wave velocity ratio diagnosis basis sensitive to a water medium, and automation of a whole detection process and objectification of result interpretation are realized by introducing unsupervised learning.

The invention provides a shared vehicle scheduling method and system and related equipment. The method comprises the following steps: acquiring operation data and external environment data of a shared vehicle; performing preprocessing and time-space fusion on the operation data and the external environment data to obtain multi-source fusion data; based on the multi-source fusion data, a shared vehicle hot spot area is mined through an unsupervised learning algorithm; inputting the multi-source fusion data into a trained machine learning prediction model, and outputting the vehicle demand number of each station / region; in combination with the shared vehicle hot spot area and the vehicle demand number of each station / area, differential scheduling tasks of the pile stations and the pile-free areas are generated; carrying out cost quantitative evaluation on the differentiated scheduling tasks and obtaining a priority sequence of each scheduling task; and converting the sorted scheduling tasks into standardized instructions, and sending the standardized instructions to the scheduling terminals. According to the method, vehicle operation and environment data are taken into consideration, and pile-free differentiated scheduling is realized, so that a complex scheduling scene can be better coped with.

The invention belongs to the field of numerical simulation, and particularly relates to a modeling method for a shock wave light field in a hypersonic flow field, which comprises the following steps: carrying out gradient clustering on flow field data by adopting an unsupervised learning algorithm, and determining the position of a shock wave in a flow field domain and the inner and outer boundaries of a shock wave domain; constructing a mixed dimension numerical value continuity and discretization strategy, determining the main direction of the light based on the strategy, performing continuous medium approximation processing on the main direction of the light, and performing structured grid discretization processing on the non-main direction of the light; on the basis of the analogy of the Fermat principle and the Mopedor principle, transmission of light in a refractive index field is analogous to movement of equivalent physical particles in a refractive index potential energy field, and a dynamic relation between the equivalent physical particles and the refractive index potential energy field is established; based on the dynamic relationship, the transmission path of light in the shock wave is simulated, and modeling of the shock wave light field in the hypersonic flow field is achieved. According to the method, the modeling efficiency and precision of the shock wave light field of the hypersonic flow field are remarkably improved.

The invention discloses an intelligent fault diagnosis method and device for an energy storage battery, electronic equipment and a storage medium. The method comprises the following steps: continuously acquiring operation parameter time sequence data of each monomer in the battery pack; preprocessing the operation parameter time sequence data; extracting characteristic parameters used for representing the state of the battery from the preprocessed operation parameter time sequence data in the set time window to form a characteristic set; identifying and positioning abnormal monomers in the battery pack through an unsupervised learning algorithm based on the feature set, and performing abnormal identification on the abnormal monomers; inputting the operation parameter time sequence data of the abnormal monomer into a pre-trained lightweight fault diagnosis model to determine the fault category and the fault level of the abnormal monomer; and generating corresponding early warning and operation suggestions based on the fault category and the fault level. According to the invention, the early-stage accurate diagnosis of the fault of the energy storage battery, the stable adaptation of the extreme environment, the lightweight real-time response of the ground station and the intelligent operation and maintenance can be realized, and the flight safety and reliability are greatly improved.

This invention discloses a dynamic response analysis method for AC / DC hybrid power grids. The method first searches for a fault set in the near-field region of the DC landing point of the AC / DC hybrid power grid. Then, it uses electromechanical-electromagnetic transient simulation to analyze the faults, obtaining the system dynamic response curve corresponding to each fault in the fault set. An unsupervised learning algorithm is then used to cluster the system dynamic response curves and label the categories. Finally, a random forest model is trained using the power flow dynamic information of the fault and the fault information corresponding to the fault as model inputs, and the system dynamic response curves corresponding to the faults as model outputs. By inputting real-time fault information and power flow dynamic information into the trained random forest model, the category of the system dynamic response curve corresponding to the real-time fault can be output. This analysis method can deeply explore the correlation between power flow dynamic information and fault information and the dynamic response characteristics of power grid voltage and frequency, and quickly determine the voltage and frequency response characteristics of the power grid.

This invention provides an OPC model training method, system, and terminal based on unsupervised learning. It constructs a simplified OPC training dataset by performing multi-dimensional lithographic feature extraction, feature preprocessing, clustering, classification, and hierarchical filtering on multiple lithographic mask test patterns. An OPC model is then trained based on this dataset to perform optical proximity correction on the lithographic mask patterns. This invention combines multi-dimensional lithographic characteristics to accurately cover different pattern scenarios, avoiding the omission of key samples and ensuring the accuracy of OPC model correction. It employs an unsupervised learning algorithm to lightweight sample data and classifies differentiated pattern categories through clustering, effectively improving the process coverage of the OPC model. It fully adapts to the changing lithography process requirements under advanced process nodes and requires no customized annotation rules, allowing for rapid migration to various lithography processes. It combines high precision, full coverage, and strong versatility.

This invention relates to a method for detecting foreign objects (FOOs) on airport runways based on biomimetic binocular vision. The method includes: simultaneously receiving images at least two scales from cameras with different focal lengths; performing cooperative target detection based on spatial correspondence; mapping and fusing the detection results to output supervised detection results; employing an unsupervised learning algorithm for anomaly detection and outputting unsupervised detection results; cross-validating and fusing the supervised and unsupervised detection results to generate a comprehensive FEO detection result; receiving left and right view images from a biomimetic binocular camera; performing three-dimensional spatial localization of FEOs in the comprehensive FEO detection result based on a binocular stereo vision algorithm; and fusing the comprehensive FEO detection result with the FEO location information to generate a final detection report. This method integrates multiple technological advantages, forming a complete, high-precision, and robust all-weather FEO detection solution for airport runways.

The invention discloses a predictive maintenance method and device for a brake-by-wire system, equipment and a medium, and relates to the technical field of intelligent networking, and the method comprises the steps: obtaining the operation data of the brake-by-wire system from a plurality of vehicles in a motorcade; based on a preset diagnosis rule, performing anomaly detection on the operation data of the brake-by-wire system of each vehicle in the motorcade, and determining an abnormal data fragment of each vehicle; the integrated abnormal data fragments are analyzed based on an unsupervised learning algorithm, a potential fault mode is identified, and the integrated abnormal data fragments comprise abnormal data fragments of different vehicles in the motorcade; aiming at the potential fault mode, adopting an interpretable machine learning method to generate a corresponding diagnosis rule; and sending the diagnosis rule to all the vehicles in the motorcade to update the preset diagnosis rule, thereby realizing predictive maintenance of the potential fault mode.

The invention relates to the technical field of automatic driving test, in particular to an automatic driving test scene grading method and related equipment, and the method comprises the steps: obtaining and preprocessing time sequence data of interaction behaviors of a test vehicle and a background vehicle; extracting vehicle interaction primitives representing different interaction behavior scenes by using an unsupervised learning algorithm; calculating the complexity score of each primitive by combining a gravitational model of relative motion parameters and an algorithm of aligning time sequence fluctuation; dividing the primitives into a predefined complexity level by adopting k-means clustering; and determining the overall difficulty level of the V2V scene through a preset grading function according to the proportion of each level primitive in the scene. According to the scheme, a complete and objective evaluation system from data processing to difficulty evaluation is realized, the scene difficulty evaluation accuracy is improved, and a reliable basis is provided for automatic driving test.

The invention discloses a standby power energy storage battery grading early warning method and system based on an unsupervised learning algorithm, and belongs to the technical field of batteries, and the method comprises the steps: firstly, obtaining the data of a standby power energy storage battery; secondly, based on one or more data fields, segmenting charging and discharging data fields of the energy storage battery; then, four characteristics of voltage deviation difference, voltage distribution difference, voltage variability and voltage curve similarity are obtained, and a characteristic matrix is constructed; and then clustering processing is carried out on the characteristic matrix and grading early warning is carried out on the standby power energy storage battery. Based on the constructed feature matrix, a clustering method is adopted to carry out graded safety early warning on the battery system, data support is provided for reasonable configuration and fine management of batteries, the internal evolution trend of the standby power energy storage system can be identified through data driving, and the essential relation behind the data is analyzed; in addition, through grading early warning, problem monomers can be effectively positioned, and related problem parts can be checked and corrected in a targeted manner.

The invention discloses a health degree assessment method for a measurement laboratory. The health degree assessment method comprises the following steps: S1, acquiring historical operation data collected from a plurality of heterogeneous data sources of the measurement laboratory; the heterogeneous data source comprises an equipment sensor reading, an environment monitoring system log and an operation record, continuously preprocessing historical normal operation data, constructing the historical normal operation data into a training data set, and training a health benchmark model by adopting an unsupervised learning algorithm; s2, collecting real-time operation data, and preprocessing the real-time operation data to form a feature vector containing multi-dimensional features; inputting the feature vector into a trained health reference model to obtain a deviation degree, and mapping the deviation degree into a real-time health degree index; and S3, according to the real-time health degree index, obtaining a health state evaluation result and early warning information of the measurement laboratory. According to the invention, by integrating the equipment sensor, the environment monitoring system and the operation record, the overall health state of the laboratory is systematically evaluated.

This invention discloses a wheel and axle sound detection system based on DBSCAN clustering and unsupervised learning. The system comprises a wheel and axle audio acquisition module, a DBSCAN clustering module, an unsupervised learning module, and a state evaluation module. The wheel and axle audio acquisition module collects and samples audio signals generated by wheel and axle rolling on the track. The DBSCAN clustering module performs DBSCAN clustering on the audio signal amplitude. The unsupervised learning module extracts features from the clustered audio data based on the distribution patterns of subway wheel and axles using an unsupervised learning algorithm, removing pseudo-signals formed by subway trackside noise. The state evaluation module evaluates the state of the wheel and axle audio signals after removing pseudo-signals and issues corresponding fault warning information based on the evaluation results. This invention is applicable to wheel and axle state detection in subway and other rail transit fields, improving subway operational safety.

The invention discloses a chronic pain rehabilitation training system, chip and device and a computer readable storage medium, and belongs to the technical field of digital therapy. The technical problem to be solved is to provide a personalized comprehensive chronic pain intervention scheme integrating CBT, metronome therapy and attention transfer technologies, and the technical scheme is as follows: a chronic pain rehabilitation training system comprises the following modules: an information acquisition module, a risk prediction module, a classification module and an adjustment module, the training tasks comprise CBT (Cathode Beam Transfer), metrorphism treatment and attention transfer; the construction method of the chronic pain rehabilitation training model comprises the steps of collecting training set information, wherein the training set information comprises physiological dimension information, psychological dimension information, cognitive dimension information, social function dimension information and lifestyle dimension information; abnormal values of training set information are eliminated, and then dimension reduction optimization is carried out to obtain multi-dimensional data; through K-Means clustering of an unsupervised learning algorithm, cluster data values are obtained, and three types are set: a psychological dominant type, a health maintenance type and a physiology dominant type.

The invention provides an AI Agent-driven cross-department collaborative cost dynamic management and control method and system, and belongs to the technical field of cost management and control, and the method comprises the steps: receiving a standardized data flow obtained through the conversion of an edge AI Agent deployed in a business system of each department based on a preset semantic model; inputting the standardized data stream into a global cost calculation model for accounting to obtain a cost estimation result; performing clustering analysis and anomaly detection on a cost estimation result by adopting an unsupervised learning algorithm to obtain a potential cost anomaly mode and a cost behavior baseline; when the cost estimation result reaches an early warning threshold rule, triggering a corresponding early warning signal; calling a causal analysis model to perform attribution analysis on the cost anomaly triggering the early warning to obtain an attribution result; classifying the cost anomaly as controllable cost anomaly or uncontrollable cost anomaly according to the attribution result; and according to a cost anomaly classification result, selecting and executing a corresponding hierarchical regulation and control mechanism for regulation and control to obtain a target management and control result.

The application provides a kind of power grid time series data anomaly detection method and system, comprising: based on the waveform data of current, voltage and frequency in the power grid collected to form original time series data set;Through unsupervised learning algorithm, original time series data set is enhanced and noise is suppressed to generate clean time series data set;Using anomaly detection model, the reconstruction error and prediction error corresponding to each data point in clean time series data set are calculated, the reconstruction error and prediction error corresponding to each data point are weighted and summed to obtain the error value of each data point;The error value of each data point is compared with dynamic threshold respectively to obtain abnormal mark sequence;According to abnormal mark sequence, structured anomaly report is generated and output to power grid monitoring platform;Through unsupervised learning algorithm, the characteristics of data are enhanced and noise is suppressed, the problem that the false alarm rate of anomaly detection model caused by abnormal event in power grid time series data is high is solved, and the reliability of power grid safety supervision is improved.

The application provides an AI Agent driven cross-department collaboration cost dynamic management and control method and system, belonging to the technical field of cost management and control. The method comprises the following steps: receiving standardized data streams obtained by converting preset semantic models based on edge AI Agents deployed in department business systems; inputting the standardized data streams into a global cost calculation model for accounting to obtain cost estimation results; using an unsupervised learning algorithm to perform cluster analysis and anomaly detection on the cost estimation results to obtain potential cost anomaly patterns and cost behavior baselines; when the cost estimation results reach a warning threshold rule, triggering a corresponding warning signal; calling a causal analysis model to perform attribution analysis on the cost anomaly triggering the warning to obtain an attribution result; classifying the cost anomaly into controllable cost anomaly or uncontrollable cost anomaly according to the attribution result; selecting and executing a corresponding hierarchical regulation mechanism for regulation according to the result of the cost anomaly classification to obtain a target management and control result.

The application discloses a crystallization abnormality recognition method based on unsupervised learning, relates to the technical field of crystallization abnormality recognition, and comprises the following steps: setting a plurality of positions, acquiring crystallization video stream data, frame extraction and analysis to obtain multi-position crystallization frame images; each image is cut into a plurality of subblocks; an unsupervised learning algorithm is used to recognize the crystallization main body area of each subblock; feature extraction is performed on the crystallization main body area to obtain crystallization main body features; the similarity between the crystallization main body features of each subblock is compared, and the minimum similarity is taken as the overall similarity; the overall similarity is compared with an adaptive threshold value to determine whether there is crystallization abnormality. The application realizes all-around abnormality recognition by acquiring crystallization video stream data through multiple positions, and combines the unsupervised learning algorithm, feature extraction, similarity comparison and adaptive threshold value, so that the comprehensiveness and robustness of crystallization abnormality recognition are improved.

The invention provides a two-stage production date defect detection method and system. The two-stage production date defect detection method comprises the steps that S1, data are collected and marked; preprocessing and enhancing the marked data, dividing the preprocessed and enhanced data set, and training a supervised learning model by adopting the divided data set; and carrying out model deployment and application after completion. S2, extracting features by adopting an improved unsupervised learning algorithm, storing the features in a feature library, then sampling the feature library, and reserving representative features; and finally, calculating a picture score through a nearest neighbor retrieval distance, and generating an abnormal segmentation thermodynamic diagram in combination with a pixel-level abnormal score to realize defect detection. Wherein the supervised learning algorithm is used for executing data collection and annotation, data preprocessing and enhancement, model training and evaluation and model deployment and application; the unsupervised learning algorithm is used for executing feature extraction and feature library construction, sub-sampling optimization and anomaly detection and positioning.

The invention provides a coal mine data communication method and device, electronic equipment and a storage medium, and relates to the technical field of communication, and data localization routing is realized by constructing a coal mine underground dedicated communication network and deploying a user plane function at an underground edge node by adopting a control plane and user plane separation architecture. Based on the real-time operation state of a network, in combination with a software defined network technology, a micro-service architecture and a service traffic prediction model, dynamic allocation and pre-allocation of network resources are performed, and a multi-layer security protection mechanism including unsupervised learning algorithm traffic anomaly detection and distributed account book technology log tamper-proof storage is deployed. And the data processing unit is integrated at the underground edge node to perform target detection on the video data to extract the key frame, so that the problems of non-localization of data routing, static network resource allocation and no pre-allocation capability caused by lack of a coal mine underground special communication architecture in the prior art can be solved.

The application discloses an intelligent internal audit method and system based on deep learning and unsupervised learning, belongs to the technical field of intelligent audit, realizes comprehensive coverage monitoring on business activities by acquiring historical multi-source business data of an enterprise, fundamentally changes the audit mode depending on sampling, improves audit efficiency and reduces audit cost, then uses an unsupervised learning algorithm to learn the historical multi-source business data, thereby constructs a behavior baseline model, uses the behavior baseline model to detect data acquired in real time, thereby improves the accuracy and foresight of abnormal data identification, does not need an auditor to sort out original multi-source heterogeneous data collected by a business system, and adaptively configures the structure of the model according to different time; when the business mode of the enterprise, accounting standards or fraud means change, the rules and the model can be directly reconfigured, the application is convenient to maintain, has dynamic adaptability, ensures the standardization and consistency of audit work, and is convenient to apply and promote.

The invention discloses an intelligent anti-crawler device and method based on streaming computing and unsupervised learning, and belongs to the technical field of computers.The device comprises a data collecting and buffering module used for collecting user access logs in real time and buffering log data to a message queue; the real-time processing and intelligent detection module is used for consuming the log data in the message queue and carrying out anomaly detection to obtain an anomaly detection result; and the intelligent decision module is used for generating a disposal instruction according to the anomaly detection result. Streaming calculation, feature engineering, unsupervised learning, a dynamic strategy engine and a micro-service gateway are organically fused to form a highly automatic and adaptive closed-loop anti-crawler device, and a real-time intelligent detection module based on unsupervised learning integrates and applies various unsupervised learning algorithms to a streaming data environment. Real-time and accurate discovery of unknown behaviors is realized, and dependence on labeled data is eliminated.

The invention discloses an image retrieval method based on polynomial access control, and the method comprises the following steps: carrying out the image feature extraction of an image data set through employing a convolutional neural network model, and obtaining an image feature vector; performing clustering operation on the image features by using a K-means algorithm according to the similarity of the image features; constructing a role polynomial to configure a polynomial access control strategy of each image; and expanding the corresponding image feature vector by using the coefficient of each image polynomial to obtain an access control index tree. According to the method, the clustering operation of the image features is completed by applying the unsupervised learning K-means algorithm, and the method focuses on related categories during retrieval, so that the retrieval efficiency is improved; according to the method, the range of data which can be accessed by individuals is accurately defined through polynomial setting, and the security and confidentiality of image data are ensured; according to the method, a polynomial access control strategy is combined with an index tree structure, so that the access speed of a ciphertext domain is increased while the image retrieval security is ensured.

This application relates to the field of non-destructive testing technology for buildings, and discloses a method for detecting, identifying, and locating leak points in buildings for construction engineering. The method includes: first, adaptively identifying the set of resonant frequencies of the building structure to be tested; then, based on this set of resonant frequencies, applying normal and tangential excitations sequentially through a composite orthogonal dual-modal excitation unit, and simultaneously acquiring surface micro-motion video data of the structure by a high frame rate optical imaging unit; next, decomposing the video data to generate P-wave velocity fields and S-wave velocity fields, and constructing a wave velocity ratio field sensitive to leakage characteristics; finally, fusing multi-dimensional features to construct a high-dimensional feature vector, and using an unsupervised learning algorithm to calculate anomaly scores, thereby achieving automated identification and precise location of leak points. This invention utilizes a resonance enhancement mechanism and a wave velocity ratio diagnostic basis sensitive to water media, significantly improving the sensitivity and accuracy of detection, and achieving automation of the entire detection process and objectification of result interpretation through the introduction of unsupervised learning.