340 results about "Conjoint analysis" patented technology

The invention belongs to the technical field of welding quality detection, and particularly relates to a visual image-based welding seam defect detection method, which comprises the steps of image acquisition, image preprocessing, data analysis, data output, defect classification and decision making, system closed-loop optimization and the like. According to the method, by synchronously collecting two-dimensional images, three-dimensional shapes and heat distribution data of metal welding seams and adopting a polarization filter and annular LED light source combination scheme, multi-dimensional conjoint analysis of physical defects and thermodynamic characteristics is achieved, basic characteristic data are extracted through primary processing, quantifiable defect coefficient indexes are generated through secondary processing, and the detection accuracy is improved. And finally, generating a comprehensive defect index through a multi-modal fusion algorithm, constructing a well-arranged intelligent analysis decision chain, and establishing a self-evolution mechanism of data acquisition-analysis decision-model iteration through real-time interaction of a detection result and an algorithm model. The system can continuously optimize the detection threshold value and the characteristic weight parameter according to the actual working condition of the production line, and the continuous improvement of the detection sensitivity is kept.

The invention provides a supply chain collaborative material management system and method, and relates to the technical field of material management, and the method comprises the steps: receiving a multi-enterprise heterogeneous material data flow, and constructing an industry knowledge graph based on the field-level semantic mapping of a dynamic ontology library; a standardized data table is generated; standardized data is injected into a distributed message queue for parallel processing, dynamic cache partitions are created according to material object labels, timestamp indexes are built in the dynamic cache partitions, a sliding window mechanism is adopted to scan data, and multi-strategy resolution is performed on conflict data in a window; performing conjoint analysis on the cleaned data to generate an enhanced analysis report, and activating a case transfer learning mechanism to update a model when the similarity between new data and historical cases exceeds a threshold value; constructing a material state transfer matrix for the abnormal data, simulating a correction operation in a digital twin environment, and triggering automatic correction; the problems of multi-enterprise heterogeneous data integration, dynamic data processing, intelligent analysis and the like are solved, and the collaboration efficiency and the management level of a supply chain are improved.

The invention relates to the technical field of electrical fault diagnosis, and discloses an electrical system fault diagnosis method based on multiple parameters. According to the method, multi-dimensional operation parameters of an electrical system are collected, wherein the multi-dimensional operation parameters comprise voltage waveform data, current waveform data and temperature distribution data; time-frequency conjoint analysis is carried out on the multi-dimensional operation parameters, and multi-scale electrical features are extracted and comprise steady-state feature components and transient feature components; based on a historical fault case library, performing mode matching on the multi-scale electrical characteristics to generate an initial fault type set; performing confidence evaluation on the fault types in the initial fault type set by using a dynamic weight distribution algorithm, and screening out high-confidence fault types; and according to the high-confidence fault type, a fault evolution path model is constructed, and the fault evolution path model is used for describing a time sequence of fault features.

The invention provides a method for reducing the multipath effect of a take-off and landing guide radar based on adaptive off-axis tracking, and the method comprises the steps: enabling the take-off and landing guide radar to transmit a signal, and transmitting the signal to a radar direction after receiving the signal; the radar receives the direct signal and the multi-path signal at the same time, and separates and establishes a spatial-temporal characteristic database of the multi-path signal; when the influence of the signal on the angle error exceeds a dynamic threshold value, starting an off-axis tracking mode; dynamically calculating the optimal off-axis angle, locking the elevation angle of the antenna, and performing closed-loop tracking of the azimuth; carrying out conjoint analysis, and identifying and eliminating false targets in real time; intelligent suppression is carried out on angle random errors; a real-time multi-path propagation model is constructed, and phase and amplitude compensation is carried out on a received signal. The method has excellent real-time performance and engineering realizability, and can meet strict requirements of aircraft precision approach guidance. The method has good expandability and adaptability, and provides reliable technical support for guaranteeing safe take-off and landing of the aircraft in severe weather and complex environments.

The invention discloses a method and a system for rapidly screening peanut endophytic mildew, particularly relates to the technical field of nondestructive testing of agricultural products, and is used for solving the problems of false detection and missing detection caused by low contrast and gradient boundary in an initial mildew stage and an internal diffusion sample in an existing detection method. Based on a hyperspectral imaging technology, a filtering kernel size is dynamically adjusted through conjoint analysis of frequency domain energy distribution and spatial local variance, and detail features of a gradient boundary in an image are enhanced; verifying and screening high-confidence candidate regions by adopting spectrum and spatial feature consistency, analyzing and extracting principal component features representing a mildew diffusion trend in combination with a texture direction, and dynamically splicing the principal component features with a spectrum gradient direction to construct a fusion feature vector; quantizing boundary confidence through a Bayesian probability classification model, combining with a neighborhood similarity constraint region growing algorithm to generate a mildewed mark graph with continuous space and consistent features, and introducing a dynamic threshold correction mechanism to adaptively optimize a segmentation standard; and the detection precision and the anti-interference capability of the fuzzy boundary are improved.

The invention provides an intelligent asphalt pavement segregation detection method based on multi-source data analysis, and relates to the technical field of road engineering detection.The method comprises the steps that a to-be-detected road section is determined, and multi-source data of the to-be-detected road section is obtained; preprocessing the multi-source data; constructing a double-branch neural network detection model which comprises a first branch detection model and a second branch detection model, and inputting the preprocessed optical data and laser point cloud data into the pre-trained first branch detection model to obtain a grading segregation distribution result; inputting the preprocessed thermal imaging data into a pre-trained second branch detection model to obtain a temperature segregation distribution result; performing conjoint analysis on the grading segregation distribution result and the temperature segregation distribution result to generate a comprehensive segregation index; and outputting a detection result according to the comprehensive segregation index. According to the invention, comprehensive, accurate and automatic detection of the segregation degree of the asphalt pavement can be realized, and a scientific basis is provided for pavement quality evaluation and maintenance.

The invention relates to the technical field of medical information, in particular to a medical data conjoint analysis system based on medical knowledge graph driving. The system comprises a medical knowledge graph construction module, a medical data quality evaluation module, a medical feature engineering module and a medical knowledge driven analysis module, and heterogeneous knowledge graph modeling can be performed by integrating medical clinical guidelines, biomedical literatures, a drug database and historical medical data so as to generate a medical heterogeneous knowledge graph; obtaining new medical clinical test information and carrying out delayed contradictory learning update to generate a medical dynamic update knowledge graph; the method comprises the following steps: obtaining multi-modal medical data, carrying out quality verification evaluation and medical feature engineering analysis, carrying out knowledge path joint driving analysis at the same time, generating a decision support reasoning path corresponding to medical clinical knowledge, and outputting a corresponding medical knowledge path confidence coefficient. According to the method, fusion reasoning among cross-source data can be realized by constructing the multi-modal medical knowledge graph.

The invention relates to the technical field of scene rendering, in particular to a building information model scene rendering method and system based on parallel computing, and the method comprises the following steps: based on scene data stored in a building information model, analyzing space coordinates of geometric structure nodes of a building, and associating the geometric structure nodes with material attributes; and analyzing a data partition relationship according to the illumination distribution, and generating a geometric material illumination analysis result. According to the method, deep integration of geometry, materials and illumination is realized by analyzing spatial coordinates of geometric nodes and material attributes and combining illumination partition analysis, the data organization efficiency of a complex scene and the analysis precision of an illumination path are improved, light energy in a noise area is optimized by analyzing light propagation and energy distribution in a segmented manner, refraction and reflection paths are corrected, and the accuracy of the light path analysis is improved. And through texture boundary and scene geometry conjoint analysis, texture fracture is repaired, detail expression is enhanced, efficient analysis and optimization of complex building scene data are realized, and the high-precision rendering requirement is met.

The invention relates to the technical field of power distribution cabinet detection, and discloses an intelligent detection system and method for a high-low voltage power distribution cabinet, and the system comprises a data collection module, a feature extraction module, a network construction and updating module, a fault positioning module, a positioning map generation module, and a self-healing control module. The method comprises the following steps: generating a multi-physical field coupling data set by synchronously collecting electromagnetic radiation spectrum and cabinet vibration waveform, performing time-frequency domain conjoint analysis to extract fault feature vectors, constructing and dynamically updating a fault propagation network, identifying key fault sources by combining betweenness centrality and historical case matching, and generating a fault positioning map; accurate self-healing control is realized by applying reverse electromagnetic pulse and adjusting contact pressure based on the atlas; according to the invention, the problems of low positioning precision and mechanical self-healing control means during detection of the high-low voltage power distribution cabinet are solved, and the fault detection efficiency and reliability are significantly improved.

The invention discloses an intelligent teaching management system, and particularly relates to the technical field of teaching management, and the method comprises the steps: obtaining a teaching environment quality evaluation index through analyzing an audio signal, a display interface image and an environment parameter in a teaching time period; analyzing to obtain a correlation degree parameter of each class, matching a weight coefficient for each class, and performing weighted summation operation on the weight coefficient and the correlation degree parameter of each class to obtain a teaching quality evaluation index; based on the classroom interaction rate and the student activeness variance, carrying out conjoint analysis to obtain a teaching interaction quality evaluation index; jointly analyzing the teaching environment quality evaluation index, the teaching quality evaluation index and the teaching interaction quality evaluation index to obtain a teaching management quality coefficient; and the server is used for comparing the teaching management quality coefficient with a threshold value, indicating that the teaching management is abnormal when the teaching management quality coefficient is lower than the threshold value, giving an early warning to a manager, prompting to increase teaching management resources of a teaching place, and solving the problem that the existing teaching management is not intelligent and scientific enough.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides an apartment security linkage method and system based on artificial intelligence, and the method comprises the steps: enabling initial security data obtained in real time to form a security space data set matched with an apartment space according to a space-time association relation; performing conjoint analysis on the security space data set by adopting a security intelligent decision engine to generate a corresponding linkage strategy; sending the corresponding control instruction and the security control parameter to the corresponding target security equipment based on the linkage strategy; the linkage priority of the security and protection equipment is set according to the security and protection event type and the mapping relation between the security and protection equipment and the security and protection event emergency degree, and a control array containing all levels of target security and protection equipment is constructed according to the linkage priority in combination with the current security and protection event type. The control array preferentially triggers the core target security and protection equipment to respond to the indication of the linkage strategy, so that remote control and linkage response of the security and protection equipment are realized, and the intelligent level and the emergency processing capability of apartment security and protection are improved.

The invention discloses a multi-dimensional natural resource intelligent monitoring method and system based on big data analysis, and relates to the technical field of resource monitoring, and the method comprises the steps: calculating the information sharing degree between different data streams, constructing an abnormal feature credibility distribution map, and carrying out the calculation of the abnormal feature credibility distribution map; and inputting the abnormal feature credibility distribution graph and the multi-source auxiliary information into an integrated classifier, carrying out joint analysis and comprehensive research and judgment on multi-dimensional information to output a target list, constructing an event response relation graph reflecting a monitoring task priority and an execution logic relation according to the attribute features of each element in the target list, and carrying out event response analysis on the monitoring task priority and the execution logic relation. Wherein each node represents a to-be-monitored target, an edge represents a dependency relationship between tasks, and an optimal monitoring execution path is planned for various intelligent monitoring carriers by using an improved A algorithm integrated with a multi-factor cost function. Through natural resource monitoring of multi-modal data interference correction, intelligent classification and path optimization and dynamic response, the monitoring quality and the execution efficiency are effectively improved.

The embodiment of the invention discloses a feature recognition labeling method and system applied to three-dimensional tunnel modeling, and the method comprises the steps: firstly carrying out the preprocessing of an obtained tunnel construction design drawing and point cloud scanning data of a corresponding construction stage, and obtaining a three-dimensional tunnel model data set with a space coordinate alignment relation; the three-dimensional tunnel model data set comprises a multi-source data fused structural point cloud unit and a design drawing vectorized contour line unit; secondly, performing three-dimensional structure feature extraction on the data set to obtain geometric morphology features of a structure point cloud unit and structure correlation features of a contour line unit; then carrying out conjoint analysis on the two features through a deep learning network to generate a feature analysis result containing feature response distribution; and finally, based on the result, identifying and labeling a key structure region in the three-dimensional tunnel model data set, generating a feature identification and labeling result containing feature type labels and spatial position information, and providing more accurate analysis and management basis for tunnel construction.

The invention discloses a control method and system of an automatic dispenser, and relates to the technical field of automatic control, and the control method comprises the following steps: collecting and preprocessing a dielectric loss factor and a temperature change rate of glue, calculating the dynamic viscosity, the shear temperature rise and the phase change rate of the glue through a time-frequency conjoint analysis method, and generating a phase change parameter matrix; inputting the phase change parameter matrix into a soliton kinetic model, obtaining a soliton waveform of the colloid under a nonlinear effect and a dissipation effect, optimizing the soliton waveform in combination with real-time viscosity, generating a pulse parameter and a dispersion compensation coefficient, and forming a soliton driving instruction set; loading the soliton driving instruction set to an FPGA (Field Programmable Gate Array) controller of the electric valve, generating a high-voltage pulse waveform, executing high-precision dispensing, monitoring a jet waveform and carrying out dynamic calibration; according to the method, the waveform is optimized through the soliton kinetic model and the step-by-step Fourier method, and accurate balance between the colloid dissipation effect and the nonlinear effect is achieved.

The invention discloses a target aircraft flight trajectory correction method based on multi-source fusion navigation, and relates to the technical field of trajectory correction. According to the method, multi-source navigation information such as AI visual navigation, inertial measurement and barometric altitude can be fully fused, short-time disturbance and long-time deviation are effectively distinguished through conjoint analysis of short-time envelope and trend envelope, and the sensitivity and reliability of trajectory anomaly detection are remarkably improved; by setting a direction consistency screening and grading weight suppression mechanism, misjudgment caused by air pressure fluctuation or instantaneous noise in low-altitude flight can be suppressed, so that the false alarm rate is reduced; the method has the advantages that the method is simple and easy to implement, an envelope difference accumulated evidence and a threshold linkage triggering mechanism are utilized, early warning of trajectory deviation is achieved, the timeliness of abnormal response in a flight task is improved, stable and accurate trajectory correction can be dynamically generated in a changeable flight environment, and robustness and convergence of the correction process are guaranteed.

The invention relates to the technical field of artificial intelligence, in particular to an agent integration optimization method and system based on multi-modal interaction, and the method comprises the following steps: obtaining a time point statistical frequency difference value of a multi-modal event, comparing a trend with offset, screening out a non-linkage event, extracting a semantic comparison context, positioning a modal in-situ, and obtaining a multi-modal event time point statistical frequency difference value; and calculating the priority of a response offset sorting channel, judging interlaced and densely set interference marks, screening fast and undisturbed channel rebinding content, and obtaining an integrated optimization scheme. According to the method, abnormal modal recognition is achieved through modal frequency fluctuation and response offset trend conjoint analysis, semantic fragments are combined with context continuity and task density constraints to improve screening precision, channel response delay difference sorting is clear in priority relation, and a signal interference area is positioned through amplitude mutation and time intersection nodes. And channel path elimination and content remapping guarantee response smoothness, accurate matching between expression intention and channel response is realized, and modal collaboration consistency and task execution stability are enhanced.

The invention relates to the technical field of pipeline detection, and provides a non-contact pipeline fluid flow velocity measurement method based on a frequency wavenumber domain spatial spectrogram, and the method comprises the steps: collecting a turbulence signal in a pipeline through a piezoelectric film sensor; carrying out snapshot number segmentation on the turbulence signal to obtain a plurality of segmented turbulence time domain signals; fourier transform and narrowband signal processing are carried out on the segmented turbulence time domain signals, and a spatial spectrum corresponding to each narrowband signal component is obtained; constructing a three-dimensional frequency-wave number domain spectrogram according to the spatial spectrum function; and according to the three-dimensional frequency-wavenumber domain spectrogram, calculating the flow velocity of the pipeline fluid. According to the method, a three-dimensional spectrogram characteristic space is constructed through conjoint analysis of the frequency and the wave number, the angle, distance and frequency characteristics of the turbulence signals are decoupled through spatial spectrum estimation methods such as the MUSIC algorithm, multipath interference and noise are effectively restrained, the flow speed of fluid in a pipeline can be accurately estimated, and limitation in a traditional method is overcome.

The invention relates to an electric power information questioning system based on reference knowledge graph dynamic expansion. The system comprises a reference knowledge graph construction module, a multi-modal data fusion module, a questioning inertia library generation module and a user questioning analysis module. And deep upgrading is carried out through multi-source data fusion. And the multi-modal data fusion module dynamically weights different modal data through an attention mechanism, and realizes image-text-operation behavior conjoint analysis in combination with cross-modal transfer learning, so that the equipment fault identification efficiency is shortened from manual processing. Simulation fault data of novel equipment is generated through a data enhancement algorithm, a knowledge graph and a question inertia library are automatically updated in combination with incremental learning, a closed-loop evolutionary mechanism of data acquisition-evaluation-enhancement-application is formed, the adaptation period of a new technology is shortened, and the efficiency is improved. And the knowledge graph data quality is continuously optimized through a three-dimensional evaluation system (integrity, accuracy and consistency).

The invention provides an operation optimization method and system based on electric power data conjoint analysis, and the method comprises the steps: obtaining synchronous sampling data and power grid topological parameters of a wide-area electric power system; performing equipment state evaluation, broadband resonance traceability and harmonic responsibility quantification on the synchronous sampling data and the power grid topological parameters by using a conjoint analysis engine, and generating a power grid control strategy; and carrying out operation optimization on the power equipment in the wide-area power system based on the power grid control strategy. According to the method, conjoint analysis is carried out on equipment state evaluation, broadband resonance traceability and harmonic responsibility quantification of the wide-area power system, traditional static spectrum analysis is upgraded to dynamic behavior perception, the power grid analysis depth and accuracy can be improved, the high-frequency harmonic analysis requirement can be met through broadband resonance traceability, and the method is suitable for large-scale popularization and application. Intelligent analysis can be realized for different application scenes, the waveform analysis capability and the frequency response range are improved, and the analysis effect is improved.

The invention relates to a forklift examination anti-cheating method and system based on artificial intelligence and a quantification method. The anti-cheating method for the forklift examination comprises the following steps: S1, acquiring a limb action video V (t) of an examinee during the examination and pressure data P (t) of a forklift driving system; s2, extracting a human body skeleton graph Gt at the t-th moment from the V (t); extracting the Gt to obtain a spatial structure feature set St, and extracting a time sequence dynamic feature set Dt of the St; and fusing the St and Dt features to obtain a pose feature Zt. Visual sense and hydraulic sensing data are fused, association analysis is carried out on examinee actions and vehicle responses, and abnormities which are difficult to find through a single video or sensing means are detected. Through conjoint analysis of images and pressure data, hidden cheating means such as remote control and dangerous violation operation can be identified, and three-dimensional monitoring of the examination process is realized.

The invention discloses a power distribution area fluctuation load early warning method and system, and relates to the technical field of power distribution network intelligent monitoring and control. The method comprises the following steps: constructing a digital twinborn model synchronous with an operation state, carrying out time domain and frequency domain conjoint analysis on a digital twinborn data set, extracting a power amplitude change rate and a phase jump index, and generating a load fluctuation vector; determining an adaptive threshold based on the statistical characteristics of the load fluctuation vector and historical load distribution, and generating a primary early warning signal when the fluctuation exceeds the limit; using a distributed collaborative analysis algorithm to identify a dominant load source and an imbalance category; performing correlation analysis on the identification result of the dominant load source and the primary early warning signal to generate comprehensive early warning information; and the comprehensive early warning information is output to a power distribution area monitoring platform, so that dynamic visualization and intelligent regulation and control are realized. According to the method, real-time identification and active early warning of the fluctuation load of the power distribution area can be realized, and the safety and the intelligent level of system operation are improved.

The invention relates to the technical field of hyper-spectral satellite remote sensing, in particular to a hyper-spectral-VOCs underway combined tracing method based on deep learning, which screens abnormal concentration through underway monitoring, extracts multiple indexes to judge pollution phase change, identifies boundaries in combination with remote sensing images, extracts pixel identification components and calculates probability to screen a tracing emission source. According to the method, space-time linkage screening is carried out on VOCs concentration threshold exceeding behaviors by constructing a concentration abnormal response task flow, a high-sensitivity mark of pollution response is effectively formed, and on the basis of conjoint analysis of three groups of indexes of concentration time sequence difference, space standard deviation and local fluctuation ratio, by means of standardization processing and jump trend identification means, the VOCs concentration threshold exceeding behaviors are identified, and the VOCs concentration threshold exceeding behaviors are identified. And multi-dimensional dynamic discrimination of the pollution phase change phenomenon is realized, so that the response capability of sudden or hidden emission events is enhanced. The boundary of a suspected pollution area in a remote sensing image is finely depicted through a gray gradient change trend, and the stability of the boundary is judged in combination with time sequence displacement and local gradient density change, so that artifact disturbance factors are effectively eliminated.

The invention relates to the technical field of reservoir prediction, in particular to a high-resolution reservoir prediction method under seismic full-wave information mining. The method comprises the following steps: acquiring seismic full-wave information data; performing frequency domain conjoint analysis on the seismic full-wave information data to generate a multi-scale full-wave feature tensor; performing seismic full-wave signal multi-scale decoupling on the multi-scale full-wave feature tensor to generate seismic multi-wave field separation data; implicit reservoir feature mapping is carried out on the seismic multi-wave-field separation data to generate initial reservoir feature mapping data; performing nonlinear correlation analysis on the initial reservoir feature mapping data to generate high-order reservoir feature data; and performing inversion linkage optimization on the high-order reservoir feature data and the seismic multi-wave-field separation data to generate reservoir dynamic response optimization data. According to the method, through multi-wave-field information extraction, high-resolution reservoir detail display, dynamic reservoir change prediction and confidence evaluation, the accuracy and resolution of reservoir prediction are improved.

The invention provides a remote maintenance and intelligent inspection early warning management method and system for a weight meter, and relates to the technical field of intelligent measurement and monitoring, and the method comprises the steps: arranging a multi-source sensor array on a weighing mechanism to collect detection data, carrying out the three-domain conjoint analysis through an edge calculation unit, and extracting multi-dimensional coupling features; the remote monitoring platform establishes a multi-scale damage evolution prediction model based on deep learning to realize the prediction of microscopic crack propagation and macroscopic fatigue damage; an intelligent material is adopted to realize self-adaptive active compensation, and prediction-compensation-evaluation-optimization closed-loop control is formed. The operation reliability and the metering precision of the weight meter can be improved, and the service life of equipment is prolonged.

The invention discloses a regulation and control method and system for gradient resource utilization of rural domestic tail water, and the requirements of reuse water in different seasons are met through regulation and control, that is, the quality of outlet water in the irrigation season meets the farmland water quality standard, and the quality standard of miscellaneous use of tail water or ecological water supplement is met in the non-irrigation season. According to the method, the state of tail water is monitored in real time through a sensor matrix, meanwhile, organic matter (or chemical oxygen demand) and ammonia nitrogen, total nitrogen, total phosphorus and microorganism effluent quality requirements input by a user are obtained, and time sequence semantic features of the state of the tail water are mined by adopting a neural network technology based on deep learning; and carrying out conjoint analysis on organic matters, ammonia nitrogen, total nitrogen and total phosphorus effluent to obtain associated characterization of multi-aspect tail water treatment requirements.

The invention discloses a magmatic rock mineral analysis method and system based on geological images, and relates to the technical field of mineral analysis. A magmatic rock mineral analysis system based on geological images comprises a magmatic rock feature analysis module and a magmatic rock comprehensive analysis module. According to the method, the magmatic rock mineral image and the magmatic rock geological auxiliary data are fused, so that the mineralization evolution history and the future mineralization potential of the magmatic rock can be disclosed; high-precision automatic recognition and classification of minerals in magmatic rocks are achieved through the mineral classification model, manual intervention is reduced, and the recognition efficiency and accuracy are improved; the extracted mineral crystallization characteristics are integrated with the oxygen fugacity characteristics, so that the conjoint analysis of the physical structure and the chemical property of the mineral is realized, and more comprehensive data support is provided for subsequent geological evolution research and mineral product prediction.

The invention discloses a fast coupling tensor decomposition algorithm suitable for conjoint analysis of brain imaging data. According to the method, a multi-dimensional tensor joint decomposition model is constructed by adopting a CP model. By introducing low-rank approximation and replacing large-size tensor data with a small-size factor matrix, the calculation complexity of the algorithm is remarkably reduced. In the decomposition process, the coupling constraint is utilized to enable different tested brain imaging data to realize consistency and coordination on a shared factor matrix, so that effective conjoint analysis is carried out. And gradually updating the factor matrix through an APG iterative optimization algorithm, and minimizing a reconstruction error, thereby efficiently separating and extracting common and personalized characteristics in the imaging data of the multiple tested brains. The fast coupling tensor decomposition algorithm provides an efficient and reliable method for conjoint analysis of multi-tested brain imaging data. Through the algorithm, meaningful neural activity modes can be rapidly extracted from complex brain imaging data, and powerful support is provided for neuroscience research and clinical application.

The invention relates to the technical field of cell data analysis, and discloses a single-cell transcriptome data and text description conjoint analysis method based on a multi-modal language model, which comprises the following steps: acquiring a single-cell RNA sequencing expression matrix and a corresponding cell text description, preprocessing the single-cell RNA sequencing expression matrix and the corresponding cell text description, and analyzing the single-cell transcriptome data and the corresponding cell text description; according to the method, a multi-modal data set is constructed, deep fusion of gene expression data and text knowledge is realized by constructing a double-model and cross-modal projection module, limitation of a single mode is avoided, a gene expression value and an index sequence are reserved during preprocessing, a rough coding mode is changed, and the cell type identification accuracy is improved; based on a pre-training strategy of comparative learning, matching learning and a cross-modal projection module, fine-grained cross-modal information interaction and sharing are realized, and cross-modal task effects of text generation cells or cell generation texts and the like are optimized.

The invention discloses a refrigerator load temperature control system, a refrigerator load temperature control method and a temperature controller, relates to the technical field of refrigerator load temperature control, and solves the problems that the temperature control load of a refrigerator load cannot be coordinated and controlled, and temperature parameters set by a user cannot be accurately achieved. The time-frequency domain conjoint analysis mechanism carries out feature analysis on the temperature output signal to obtain temperature feature data, the particle swarm optimization PID control algorithm analyzes the temperature feature data and set temperature parameters to generate a PID control instruction, and the load optimization scheduling control model realizes temperature control load coordination control on the refrigerator load. According to the refrigerator load temperature control system and method, the refrigerator load temperature control load coordination control is achieved, and the temperature parameters set by the user are accurately achieved.

The invention discloses an environmental risk step-by-step assessment method in a multi-medium and multi-scale complex scene, and belongs to the technical field of environmental risk assessment. The method comprises the following steps: obtaining the heavy metal concentration of the waste residue through a leaching experiment, and determining the toxicity grade by using a single pollution index method and a potential ecological risk index method; dividing volume grades based on quantiles, dividing local diffusion risk grades by using an inverse distance weighted interpolation method and the like, dividing remote receptor sensitivity grades according to a surrounding land and water source construction model, and generating priority control grades by integrating the grades; and finally, fusing multi-source data to construct a spatio-temporal conjoint analysis model verification result. Therefore, by integrating pollution source toxicity, volume, diffusion risk and receptor sensitivity to carry out risk step-by-step assessment, a multi-source data fusion and spatio-temporal conjoint analysis method is proposed for the first time, and the problems of single assessment dimension, insufficient data fusion, lack of dynamic verification and the like of an existing assessment method in a multi-medium and multi-scale scene are effectively solved.