238 results about "Fuzzy clustering" patented technology

The invention discloses a gas pipe network fault positioning method based on a space-time diagram neural network, and the method employs an edge-cloud collaborative architecture, and achieves the precise detection of leakage points through simulation data generation, multi-modal perception, dynamic space-time modeling and hierarchical positioning. Firstly, simulation modeling is conducted on a pipe network, a multi-working-condition leakage data set is generated, and monitoring point layout is optimized through fuzzy clustering; a multi-modal sensing unit and a lightweight anomaly detection module are deployed at an edge end to realize coarse-grained anomaly detection and data hierarchical transmission; the cloud constructs a dynamic space-time diagram neural network, integrates a pipe network topological structure, multi-source time sequence data and physical constraints, and realizes high-precision positioning of leakage points through a hierarchical positioning strategy; and finally, realizing online evolution of the model through elastic weight solidification and hierarchical parameter updating. According to the method, the space-time diagram neural network and the physical characteristics of the pipe network are deeply fused, and the problems that space-time coupling features are difficult to extract and physical constraints are missing in complex pipe network fault positioning are solved.

A layout optimization method of water quality monitoring points based on a RF-C-SOM clustering algorithm includes: preprocessing collected water quality data to obtain preprocessed water quality data that are used as data, and using water quality categories as labels to train a random forest model to determine feature importance of water quality indicators; selecting important features based on the feature importance and model training accuracy, performing dimensionality reduction on the preprocessed water quality data to obtain dimension-reduced data; performing a fuzzy clustering on the dimension-reduced data to obtain a water quality section classification result, and based on it, determining initial weight values of a self-organizing mapping algorithm; initializing neurons and training a self-organizing mapping network model with the initial weight values; obtaining a point clustering result through the self-organizing mapping network model; and conducting a water quality index evaluation for the point clustering result before and after screening.

The invention relates to the technical field of economic resource management, and discloses an economic resource management optimization method based on intelligent decision making. According to the method, multi-source heterogeneous data, including resource stock, demand fluctuation and the like, of an economic system are collected firstly; a dynamic resource pool is divided based on multi-dimensional feature analysis, and a nonlinear optimization model is constructed to predict resource supply and demand changes so as to generate an allocation scheme; and then optimizing a distribution path by using a multi-stage decision tree, updating model parameters through an adaptive learning mechanism according to market feedback and constraint condition changes, and correcting a deployment scheme in real time. In addition, key technical details such as an elastic quota adjustment formula and a fuzzy clustering algorithm membership function are given. According to the method, complex data can be effectively integrated, resources are scientifically scheduled, supply and demand are accurately predicted, a distribution path is optimized, environmental changes are adapted, the efficiency and benefits of economic resource management are remarkably improved, and scientific and reasonable resource management decision support is provided for economic subjects.

The invention relates to the technical field of instrument multi-task optimization, in particular to an intelligent instrument multi-task real-time optimization method and system based on dynamic resource scheduling. The optimization method comprises the following steps: acquiring a target item of each task in real time through a sensor array, constructing a multi-dimensional feature vector, dividing each task into task categories by using a fuzzy clustering algorithm, and presetting an initial priority for the task categories for multi-task feature parameter acquisition and classification modeling. According to the method, the multi-dimensional feature vectors including the task urgency degree, the calculation complexity and the data interaction frequency are constructed, and the fuzzy clustering algorithm of the task dependency constraint is introduced, so that the task categories are accurately divided, the cross-category interaction overhead of the dependency task is effectively reduced, and the compatibility of a scheduling strategy is improved from the source.

The invention discloses a smelting waste gas recycling optimization system based on fuzzy control, and relates to the technical field of automatic control. The problems of low control precision and response lag caused by the fact that an existing static membership function cannot adapt to multi-scale working condition changes in real time are solved. Comprising a sensing acquisition module, a membership reconstruction module, a feature decoupling module, a coupling compensation module, a rule optimization module and an execution control module. Standardized data are acquired through sensing acquisition and filtering, an adaptive membership function is constructed by adopting incremental fuzzy clustering, and closed-loop self-correction of a multi-modal control instruction is realized by combining wavelet packet decoupling, feed-forward compensation and double-depth Q network reinforcement learning online optimization; the waste gas desulfurization efficiency, energy consumption optimization and treatment system stability of waste gas treatment can be effectively improved.

The invention relates to the technical field of computer-aided communication, in particular to a wireless mouse strong interference source avoiding method based on a wireless frequency hopping model, which comprises the following steps: acquiring multi-band signal characteristics by monitoring electromagnetic spectrum data of an environment where a wireless mouse is located in real time; constructing an interference source analysis model by using an improved weighted fuzzy clustering algorithm, and accurately identifying the type and intensity distribution of an interference source; according to an analysis result, dynamically generating a frequency hopping strategy by using a channel state prediction model based on reinforcement learning, and determining priority sequences of a main channel and a standby channel; when the interference on the main channel exceeds a preset threshold value, performing real-time evaluation by means of a composite quality index and switching to an optimal standby channel; meanwhile, through a closed-loop feedback mechanism, according to the difference between actual channel quality and expected channel quality, a stochastic gradient descent method is adopted to optimize frequency hopping strategy parameters, and dynamic self-adaptive adjustment of interference avoidance is achieved. Complex electromagnetic environment interference can be effectively dealt with, and the communication stability of the wireless mouse and the use experience of a user are remarkably improved.

The invention relates to the technical field of PID parameter setting, and provides a PID controller parameter adaptive adjustment method and device, electronic equipment and a medium. Multi-source heterogeneous data collected by a distributed sensor array are acquired in real time, feature quantization processing is performed on the multi-source heterogeneous data to obtain a quantization feature matrix, and working condition pattern recognition based on depth fuzzy clustering is performed on the quantization feature matrix to obtain working condition pattern vectors. Performing parameter space adaptive mapping on the working condition mode vector to obtain a PID parameter initial set and an optimized space, and performing parallel parameter optimization processing on the PID parameter initial set and the optimized space according to a hybrid optimization algorithm to obtain an optimized parameter set, and performing parameter dynamic fusion and robustness enhancement processing on the optimized parameter set to obtain a target PID parameter set. Through combination of data processing, deep learning, algorithm optimization and robustness enhancement, the precision of PID controller parameter adaptive adjustment is improved, and efficient and stable operation of a target system under different working conditions is ensured.

The invention relates to the technical field of safety evaluation of highway bridge girder erection machines, and discloses a safety evaluation method for the initial installation state of a highway bridge girder erection machine. Firstly, a multi-modal safety evaluation model including a structure layer, a dynamic parameter layer and a risk factor layer is established; dividing a dynamic parameter layer feature interval by using a fuzzy clustering algorithm, calculating a risk factor correlation coefficient by using a grey correlation analysis method, determining a dynamic parameter layer parameter objective weight by using an entropy weight method, and performing parameter fusion by combining the two to obtain a comprehensive weight; then multi-source sensing data installed by the bridge erecting machine are collected in real time and discretized; and finally calculating risk indexes layer by layer, and judging the safety level of the initial installation state of the bridge girder erection machine. The method comprehensively considers the structure, the dynamic parameters and the risk factors of the bridge girder erection machine, improves the evaluation accuracy by applying various algorithms, realizes the automatic judgment of the safety level, can effectively reduce the installation risk of the bridge girder erection machine, and guarantees the construction safety and the smooth engineering construction.

The invention discloses a circuit breaker image edge detection method fusing spatial constraint fuzzy clustering and lightweight network optimization, and the method carries out the local adaptive threshold calculation through combining spatial constraint FCM and Otsu algorithms, and optimizes the edge detection process of a Canny operator. According to the method, fuzzy classification is carried out on a circuit breaker image by adopting spatial constraint FCM to obtain a strong marginal probability graph; the circuit breaker image is subjected to block processing through local adaptive threshold calculation, a global threshold is generated through integration, and then the global threshold is input into a Canny operator for accurate edge extraction. In order to further improve the detection effect, a lightweight neural network PiDiNet is used to correct a Canny output image. According to the method, the edge detection precision of the circuit breaker image can be effectively improved, and the method is suitable for edge extraction tasks in high-noise and complex background environments.

The invention discloses a micro-seismic signal arrival time pickup method based on fuzzy clustering and variational mode decomposition. The method comprises the following steps: firstly, calculating characteristic functions of attribute characteristics such as a micro-seismic signal mean value, power and kurtosis and normalizing the characteristic functions to obtain a characteristic matrix, then primarily picking up a micro-seismic P-wave initial movement position and a first arrival moment through a fuzzy clustering algorithm, and extracting an effective time window by taking the first arrival moment as a reference point; a variational mode decomposition algorithm is utilized to decompose signals in a time window into K intrinsic mode function components, AIC function values of the components are calculated by means of an akaike information criterion algorithm, a minimum value point is picked up to serve as first arrival time, energy ratios of all the components are calculated, and final micro-seismic P-wave first arrival time is obtained through weighted calculation. The method effectively deals with the low signal-to-noise ratio environment of the underground coal mine, has higher pickup precision and reliability compared with a traditional pickup method, can provide accurate micro-seismic occurrence time and position information for mine safety early warning, and powerfully guarantees the safety production of the coal mine.

The invention discloses an intelligent water conservancy integrated big data analysis method based on a clustering algorithm, and the method comprises the following steps: S1, collecting hydrological data in an intelligent water conservancy system, and carrying out the preprocessing of the hydrological data; s2, performing data enhancement on the preprocessed hydrological data; s3, constructing and training a SimCLR self-supervised contrast learning model, and outputting embedded feature representation; s4, taking the embedded feature representation as the input of an improved fuzzy clustering model, and outputting a membership matrix and a clustering center set; s5, according to the distribution of the membership matrix and the structure of the clustering center set, identifying a hydrological characteristic region, a pollution diffusion region and an abnormal hydrological mode; and S6, inputting an identification result into the intelligent water conservancy scheduling system. According to the method, self-supervised contrast learning and an improved fuzzy clustering model are fused, complex hydrological data recognition is achieved, and the method has the advantages of being excellent in feature expression, high in clustering precision and high in application adaptability.

The invention discloses a park digital twinning modeling method based on a generative AI technology, and relates to the technical field of digital twinning modeling, and the method comprises the steps: carrying out the alignment of point cloud, image and state data, completing the preprocessing through topological adaptive filtering and multi-resolution voxelization, frequency domain harmonic fusion and hypersurface texture excitation and time delay coupling fuzzy clustering, and obtaining a digital twinning modeling result; constructing a cross-modal spine network driven by a holographic entropy film, generating a hierarchical token through graph attention, and inputting a surge tuned diffusion converter model for iterative denoising and focus decoding to obtain a three-dimensional fragment; and fusing the fragments in a voxel space by using a cross attention kernel, and adaptively updating parameters through an entropy pulse closed loop until errors converge, so as to generate a high-precision digital twinborn model. A cross-modal semantic network is constructed by constructing spectral mapping and a holographic entropy film, a three-dimensional fragment is efficiently generated in a self-adaptive surge tuned diffusion converter framework through focus fusion, and the cross-modal semantic coupling efficiency, the generative reasoning convergence speed and the multi-fragment voxel fusion continuity are improved.

The invention discloses a distributed photovoltaic optimization coordination control system and method, and relates to the technical field of distributed photovoltaic power generation, and the control system comprises a multi-dimensional data collection module, a self-adaptive cluster division module, a robust dominant node election module, a three-dimensional voltage state evaluation module, and a hierarchical coordination control execution module. According to the distributed photovoltaic optimization coordination control system and method, real-time operation data, meteorological prediction data and power grid load prediction data of each node are continuously monitored, comprehensive information support is provided for a control strategy, and when it is detected that voltage abnormity is caused by sudden change of illumination intensity in a certain area, the real-time operation data of each node, meteorological prediction data and power grid load prediction data are monitored. And immediately finishing cluster structure adjustment in an extremely short time by adopting a fuzzy clustering algorithm with constraint conditions based on the electrical distance and the voltage fluctuation trend characteristic value, and ensuring that the node voltage change characteristics in the same cluster are highly consistent.

The invention belongs to the technical field of remote sensing image processing, and particularly relates to a hyperspectral lithology intelligent identification method based on fuzzy clustering, which comprises the following steps: acquiring a geological map of a research area, vectorizing the geological map of the research area, and acquiring initial lithology distribution vector data; preprocessing the hyperspectral data; performing data dimension reduction on the preprocessed hyperspectral data by using principal component analysis transformation to obtain dimension-reduced data; clustering the dimension-reduced data by adopting a spatial fuzzy C-means clustering algorithm, and segmenting a clustering result based on a neighborhood similarity criterion to obtain the clustering result; and according to the initial lithology distribution vector data, determining the lithology category with the maximum proportion in different clustered patches from the clustering result, and taking the category as the lithology category corresponding to the clustered patches to obtain an intelligent lithology identification result. According to the method, the influence of human interference factors can be effectively reduced, and the lithology identification precision and the mineralization-related geological element identification capability are improved.

The application discloses a kind of big data-based optimization analysis method and system of territorial space planning, it is related to big data and optimization analysis technical field of territorial space planning, including real-time data acquisition and dynamic preprocessing, dynamic regional division, multi-model integrated space-time prediction, multi-objective dynamic optimization planning, real-time feedback and adaptive adjustment.The big data-based optimization analysis method of territorial space planning provided in the application adopts space-time weighted fuzzy clustering method, divides the territorial space into several sub-regions according to the spatial coordinates and time attributes of data, constructs the weighted distance regular objective function between data points and regional center, and updates the membership and the position of regional center iteratively, realizes the self-adaptation and space-time smoothing of regional segmentation, effectively captures the dynamic change characteristics in region.

The invention provides a lithium ion battery safety valve opening and failure early warning method based on expansive force, and belongs to the technical field of lithium ion batteries. Battery expansive force and cycle data under different pre-tightening force conditions are collected, statistical features are extracted to construct a state feature set, health state groups are divided by adopting a fuzzy clustering algorithm, and the early warning result is obtained. Establishing a segmented nonlinear mapping model of the expansive force and the internal pressure, performing wavelet denoising and robust differential calculation on expansive force signals, and optimizing an initial expansive force derivative threshold value by analyzing time dispersion at different heating rates; a multi-scale feature fusion algorithm based on hierarchical attention aggregation is utilized to construct a state self-adaptive early warning model to correct a threshold value, and a four-stage early warning mechanism is set to monitor the opening and failure states of the safety valve. The technical problem that the opening time of the safety valve cannot be accurately predicted and self-adaptive early warning cannot be realized under different battery health states and pretightening force working conditions is solved.

The invention relates to the technical field of garbage cleaning, in particular to a self-adaptive garbage cleaning method and system based on neural fuzziness, and the method comprises the steps: constructing a multi-mode garbage image data set, and dividing the data set; fusing fuzzy clustering and deep learning features to train a target detection model; performing fuzzification processing on the image to judge the garbage category; establishing a fuzzy rule base to realize real-time decision making; training the model by adopting a hybrid optimization strategy; estimating the volume and mass of the garbage based on a three-dimensional sensing technology; a fuzzy PID controller is used for adjusting the power of the cleaning device; deploying an edge-cloud collaborative architecture update rule base; the system comprises a multi-modal data acquisition unit, a hybrid computing unit, a dynamic power control unit and a cloud management platform. The garbage classification can be accurately identified, the power of the cleaning device is optimized, energy consumption and efficiency collaboration is achieved, the system adaptability and decision real-time performance are improved, and the defects of a traditional garbage cleaning technology are effectively overcome.

The invention discloses a low-voltage distribution box temperature rise abnormity identification method based on an improved fuzzy clustering algorithm. The method comprises the following steps: S1, generating a temperature-current synchronous acquisition data set according to a time sequence; s2, constructing a load-temperature difference weight matrix in combination with the current current amplitude; s3, obtaining a current membership matrix and a cluster center set; s4, calculating the membership slip amount of each measuring point according to the current membership matrix and the membership matrix at the previous moment, and generating a temperature rise trend state sequence; s5, judging whether the temperature rise trend state sequence has a measuring point which continuously slides from the normal cluster to the risk cluster or not; if yes, corresponding cluster centers in the corresponding cluster center sets are extracted and mapped to the physical coordinates according to the measuring point index table, and positioning information containing the measuring point numbers, the types of the electrical parts and the risk levels is generated. According to the invention, the fault positioning accuracy and the operation and maintenance efficiency are greatly improved.

The invention relates to the technical field of submarine cable fault detection, in particular to a submarine cable fault detection system. The method comprises the steps that an adjustable low-frequency pulse injection unit injects a low-frequency pulse excitation signal and collects a response signal generated by a submarine cable fault; the multi-source signal fusion acquisition unit acquires a reflected voltage signal, an acoustic signal and a leakage magnetic field signal, and constructs a multi-modal fusion response characteristic matrix; the intelligent broadband noise suppression unit is used for performing noise suppression processing on each modal sub-matrix to obtain a de-noised multi-modal fusion response characteristic matrix; and the intelligent fault positioning interpretation unit is used for constructing a three-dimensional signal fingerprint spectrogram, performing classification analysis on fault events based on a fuzzy clustering algorithm and a depth feature matching model to obtain a fault type, and performing calculation through a path residual fitting method to obtain a fault position. The invention realizes a detection system which does not depend on a special cable structure, can actively excite and fuse multi-source signals, and can position submarine cable faults at high precision.

The invention discloses an automatic warehousing system and a sorting method thereof, and the system comprises an execution module, a sensing module and a digital twin management module: the execution module is composed of a vertical lifting container, an autonomous mobile robot and a sorting machine; the sensing module collects cargo full life cycle data through a visual sensor, an RFID tag and a reader-writer. The digital twinborn management module constructs twinborn bodies of the cargos and the equipment, dynamically classifies the cargos based on real-time data, automatically responds to inventory abnormity and simulates and generates an optimal warehousing and sorting route. According to the sorting method, data are collected through the Internet of Things and the RFID technology, goods are classified in real time through a fuzzy clustering algorithm, the digital twinborn body serves as a training environment, a multi-device collaborative optimal sorting path is generated through a reinforcement learning model, and dynamic adjustment is conducted according to the real-time state. According to the scheme, intelligent management and efficient sorting of the warehousing system are achieved, the inventory management precision and the equipment cooperation efficiency are improved, and the system is suitable for high-frequency and high-flexibility warehousing scenes.

The invention relates to the technical field of power distribution network fault monitoring, in particular to a fault recognition method and system based on an intelligent fusion terminal, and the method comprises the steps: obtaining an instantaneous multi-dimensional electrical data set at each moment, and constructing an input sample with the current moment as an end point, inputting the input sample into the trained long-short-term memory model to calculate a first fault probability of the input sample; inputting the input sample into a trained optimal fuzzy clustering model to calculate a second fault probability of the input sample; and distributing respective weights for an output result of the long and short term memory model and an output result of the optimal fuzzy clustering model, carrying out weighted fusion on the first fault probability and the second fault probability to obtain a comprehensive fault probability of the input sample, and judging whether the power distribution network has a fault according to the comprehensive fault probability. According to the invention, through multi-source information fusion, model collaborative optimization and dynamic weight distribution, the fault identification precision and response speed of the power distribution network under complex conditions are effectively improved.

The invention provides a construction safety risk grading method and system combined with fuzzy clustering, and belongs to the technical field of building construction safety risk management.The method comprises the steps that firstly, a real-time risk feature set of a construction scene is obtained, and environmental influences, equipment operation and personnel operation features of a construction area are covered; secondly, fuzzy clustering preprocessing is conducted on the real-time risk feature set, fuzzy membership degree distribution and a feature correlation degree matrix of all risk features are obtained, a risk transmission network is constructed based on the result, nodes are risk features, edges are correlation degree parameters between the features, and risk diffusion coefficients of all the nodes are calculated through the risk transmission network; and generating a risk grade division result according to a preset grading rule, and finally outputting a construction safety grading instruction containing the risk area identifier and the corresponding management and control strategy, thereby dynamically and accurately evaluating the construction safety risk.

The invention relates to a converter transformer external interference source positioning method based on ultrasonic wave and electromagnetic wave fusion, and belongs to the field of partial discharge detection and positioning. Performing layered identification on the time-frequency structures of the two signals through a T-F fuzzy clustering method, and distinguishing internal discharge and external interference signals of the transformer; inputting the external interference signal into a dual-channel neural network for feature extraction, wherein the dual-channel neural network comprises a first channel based on CNN and a second channel based on BiLSTM; the first channel is used for extracting local frequency change characteristics of an external interference signal; the second channel is used for capturing the dependency relationship and global time sequence characteristics of the external interference signal in the time dimension; a self-attention mechanism is further introduced into the network for weighting processing; and carrying out splicing or weighted fusion on the extracted high-dimensional features through a feature fusion module, and inputting the high-dimensional features into a full-connection layer to realize three-dimensional space positioning of the partial discharge interference source.

The invention discloses a power system abnormal data clustering and vulnerability detection method based on a fuzzy mean value algorithm, and belongs to the field of power system security. Aiming at the defects of a traditional method in the aspects of data fuzziness, multi-dimensional association and dynamic adaptability, a'data preprocessing-dynamic fuzzy clustering-vulnerability diagnosis' closed-loop process is constructed. 12-dimensional feature vectors including electrical quantities, equipment states and environmental parameters are constructed, and Z-score standardization is combined with a sliding window updating mechanism to carry out data preprocessing; a feature weighted fuzzy C-means algorithm with entropy regularization is used, and a time decay factor and space-time correlation modeling are fused to realize dynamic fuzzy clustering; abnormal clusters are screened through double thresholds, and vulnerability diagnosis and scoring are completed in combination with an association rule base and a three-level vulnerability scoring model. According to the method, through verification of power grids with different voltage levels, the anomaly detection recall rate reaches 95% or above, the operation and maintenance efficiency is improved by 30%, and the accuracy and real-time performance of anomaly detection of the power system are effectively improved.

The invention provides a rolling quality parameter uncertainty quantification method based on mixed entropy-fuzzy clustering. The method comprises the following steps: S1, multi-source data fusion: integrating rolling compaction parameters, meteorological data and real-time monitoring data in an engineering construction process, and constructing a multi-dimensional feature matrix; s2, calculating mixed entropy, namely quantifying the randomness and fuzziness of parameter distribution in combination with information entropy and fuzzy entropy; s3, performing dynamic fuzzy clustering, namely, optimizing a clustering center based on an improved firefly algorithm, and dividing parameter uncertainty levels; step S4, uncertainty contribution degree analysis: quantifying the influence weight of each parameter on the rolling quality through an entropy weight-grey correlation method; according to the method, the information entropy and the fuzzy entropy are fused, and dynamic clustering and an intelligent optimization algorithm are combined, so that precise quantification and hierarchical management and control of the rolling parameter uncertainty are realized.

The invention provides a soft clustering-based positive sample expansion graph comparative learning method, which comprises the following steps of: data enhancement: performing structure enhancement and feature enhancement on an original graph to generate an enhanced view; according to the structural enhancement, edge disturbance is guided through structural similarity between nodes, key edges are reserved, and potential similar edges are supplemented; according to feature enhancement, diversified feature combinations are generated through fine-grained masks; positive sample dynamic expansion: calculating the membership degree of nodes to each category based on a fuzzy clustering algorithm, and screening a high-confidence node set; expanding a positive sample for each target node in combination with the graph structure constraint and the first-order neighborhood; and multi-task joint training: performing joint optimization on the comparison loss, the clustering uncertainty loss and the edge prediction loss, and training a graph neural network model.

The invention discloses an industrial park pollution source rapid identification method based on fuzzy clustering, and the method comprises the following steps: S1, collecting pollution data in an industrial park, and carrying out the preprocessing of the pollution data; s2, identifying the pollution data by adopting a fuzzy clustering algorithm combining a possibility C mean value and a fuzzy rough C mean value; s3, constructing a pollution diffusion model, calculating a diffusion path of pollutants, and performing correction in combination with an identification result; s4, dynamically adjusting parameters of the possibility C mean value and the fuzzy rough C mean value by adopting an improved differential evolution algorithm; s5, when abnormal fluctuation occurs in the pollution data, dynamically adjusting the membership degree, and correcting the pollution source identification result; and S6, outputting an optimized pollution source identification result, and generating a pollution traceability analysis report. According to the industrial park pollution source identification method, the possibility C mean value algorithm and the fuzzy rough C mean value algorithm are combined, the improved differential evolution algorithm is used for optimizing parameters, and the industrial park pollution source identification accuracy, the anti-interference performance and the self-adaptive optimization capacity are improved.

The invention provides an intelligent cabinet heat dissipation control method and system, and belongs to the technical field of cabinet heat dissipation control. According to the method, data items all have acquisition time and cabinet area identifiers; the method comprises the following steps: dividing a cabinet hot area by using fuzzy clustering based on a historical sample, dynamically modeling by combining an adaptive PID control algorithm, optimizing control parameters through a particle swarm optimization algorithm, adjusting a hot area control model weight coefficient according to real-time feedback, generating a hot area-equipment association map, and constructing an adaptive heat dissipation control network; predicting a temperature trend through a decision tree model by using a correlation map and real-time operation characteristics generated by the network, and generating a fan rotating speed adjusting strategy, an air conditioner power distribution scheme and a multi-stage heat dissipation control strategy according to the temperature trend; inputting a heat dissipation control strategy and an execution result as feedback into the self-adaptive heat dissipation control network, and dynamically optimizing a correlation map and control parameter modeling; and multi-cycle feedback closed-loop optimization control is realized through multi-time feedback and parameter adjustment.

The invention discloses a high arch dam operation modal parameter automatic identification method and system based on discharge excitation. The method comprises the following steps: 1) obtaining a vibration displacement response signal: determining a penalty factor and an optimal decomposition layer number based on an adaptive multivariate variational mode decomposition algorithm to obtain an optimal IMF component of each sensor channel signal, and performing IMF component screening and signal reconstruction through a frequency domain cross correlation coefficient to realize adaptive noise reduction of the signal; 2) establishing a Monte Carlo three-dimensional stability diagram based on a random subspace recognition algorithm driven by a covariance matrix; and 3) modal parameter automatic identification based on an intelligent clustering algorithm. According to the method, the noise is suppressed by automatically optimizing the modal component reconstruction signal of the multi-sensor vibration signal; a Monde-Carlo three-dimensional stability diagram is established in combination with a Monde-Carlo theory and a covariance driven random subspace method to determine a model order, and automatic interpretation of the stability diagram is realized by applying improved fuzzy clustering, so that operation modal parameters of the high arch dam are accurately identified.

The invention provides a multivariate data-based printing machine top project approval design method, which relates to the technical field of printing equipment, and comprises the following steps of: acquiring printing machine operation data comprising printing quality parameters, equipment working condition parameters and environment parameters; performing feature analysis and parameter extraction through an adaptive fuzzy clustering algorithm, dynamically adjusting a sample classification membership degree, identifying a key parameter combination influencing the printing quality, and constructing a correlation matrix; constructing a dynamic Bayesian probability network model based on a parameter mapping relation, constructing a core network structure through an information entropy value and information gain, and establishing probability association between a printing quality parameter and an equipment working condition parameter; and determining the optimal parameter configuration of the function modules of the printing machine by utilizing a bidirectional constraint dynamic programming algorithm and combining the coupling weight and the state transition equation among the function modules, and generating a top design scheme.