290 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 present invention relates to the field of geothermal resource development technology, specifically a geothermal well monitoring and maintenance management system and method based on the Internet of Things; the method steps are: through a denoising method combining adaptive fuzzy clustering and deep convolutional neural network, the real signal of sensor data is restored; by generating a check mark, the integrity of the collected geothermal well monitoring data is checked; combined with LSTM time series modeling and deep reinforcement learning for fault prediction and optimization, support vector machine is used for fault type identification, and maintenance strategies are formulated according to the prediction results, the appropriate maintenance type and timing are selected, and resource scheduling optimization is performed to generate a detailed maintenance plan; data and maintenance information are visualized and pushed to on-site staff in real time, and the staff performs operations according to the maintenance plan, and automatically records and updates the health status of the equipment. The present invention improves the monitoring and maintenance efficiency of geothermal wells through intelligent data processing and decision support.

The invention discloses a straw feed fermentation treatment control system and method, particularly relates to the technical field of production control, and is used for solving the problem of insufficient identification and regulation of an imbalance phenomenon in a straw fermentation pile caused by non-uniform gas diffusion in the existing fermentation process. The method comprises the following steps: acquiring environmental parameters of a fermentation heap, performing three-dimensional gas concentration field reconstruction processing, identifying a layered air mass region in combination with a fuzzy clustering algorithm, measuring heap pressure distribution and deformation characteristics, analyzing a heap body structure abnormal region, and marking a risk analysis region; through a three-dimensional tensor model and a fractal analysis model, the time dependency and the gas diffusion equilibrium degree of the gas exchange and metabolism process are evaluated respectively, and finally fermentation pile structure parameters and ventilation device operation parameters are dynamically adjusted based on a closed-loop control system; through accurate region identification and a dynamic regulation and control mechanism, the uniformity of the fermentation process and the quality consistency of the straw feed 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 distributed photovoltaic power prediction method and device, equipment, a medium and a product, and relates to the technical field of distributed photovoltaic. The method comprises the steps of obtaining a multi-source meteorological matrix; according to the historical power time sequence data, a fuzzy clustering algorithm based on a time sequence form is adopted to carry out regional division on the distributed photovoltaic power stations in the to-be-predicted region; determining an optimal network parameter of each sub-region by adopting a Bayesian method; applying the optimal network parameters to the Transform model of each sub-region to obtain a distributed photovoltaic power prediction model of each sub-region; and inputting the multi-source meteorological matrix in the future prediction time period into the distributed photovoltaic power prediction model of each region, and obtaining a distributed photovoltaic power prediction result of the region to be predicted in the future prediction time period by using the first multilayer sensor. The distributed photovoltaic power prediction method and device can be suitable for distributed photovoltaic power prediction, and the accuracy of distributed photovoltaic power prediction is improved.

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 relates to the technical field of agricultural automation and intelligent control, in particular to a weeding monitoring system and method based on an unmanned aerial vehicle, and the method comprises the steps: collecting spectral reflection data and microwave internal structure data of a target area through a multispectral camera and a microwave sensor carried by the unmanned aerial vehicle; the system generates standardized perception data through a data fusion module, and performs precise classification and recognition of weeds in combination with feature extraction, adaptive fuzzy clustering and multi-model collaborative learning technologies; based on a spatial distribution diagram of weeds, the system predicts a weed diffusion trend through a graph convolutional network, and an optimal weeding strategy is generated in combination with environmental data; and the unmanned aerial vehicle executes the weeding task through the optimized flight path, monitors the weeding effect in real time, continuously optimizes the strategy, and ensures efficient execution of the weeding task.

The invention relates to the technical field of carbon emission treatment, in particular to a building full-life-cycle carbon emission calculation method based on multiple linear regression. Comprising the following steps: acquiring carbon emission data of each stage of a building through a BI model, and extracting key characteristic variables such as a material carbon emission coefficient and energy consumption intensity by adopting a Latin hypercube sampling method and a fuzzy clustering algorithm; modeling carbon emission by using a multiple linear regression model, and constructing a carbon emission prediction model; optimizing model parameters through a differential evolution algorithm; and finally, calculating the total amount of carbon emission, analyzing key influence factors in a high-carbon emission stage through a causal reasoning algorithm, proposing an optimization strategy, generating an analysis report, and realizing accurate prediction and effective optimization of the carbon emission in the whole life cycle of the building. According to the method, the carbon emission of the whole life cycle of the building can be accurately evaluated, and powerful support can be provided for optimization decision-making of the carbon emission.

The invention relates to the technical field of topological optimization, and discloses a thermal coupling multi-scale parallel topological optimization method for additive manufacturing, and the method comprises the steps: dividing a macroscopic design domain into a plurality of macroscopic sub-design domains through a fuzzy C clustering method, and mapping the information of the macroscopic sub-design domains on the microcosmic level as the constraint condition of microcosmic optimization; performing equivalent static conversion on the transient temperature load, and introducing an equivalent thermal load into the multi-scale topological optimization model; macroscopic and microscopic thermal coupling finite element analysis is carried out according to the thermal coupling control equation, and a displacement vector matrix is solved by using an HHT-alpha method; sensitivity analysis is carried out on macro and micro objective functions and constraint conditions, and a moving asymptote MMA method is used as a solver for macro and micro design variable updating; and filling the microstructure into the macroscopic configuration to obtain an overall configuration, judging a convergence condition, and if the condition is met, outputting an optimal material distribution structure and an optimal microscopic configuration. The method can better reflect the real working state of the workpiece.

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 improvement method and device for farmland crop irrigation water productivity. The method comprises the steps that soil physical indexes and nutrient indexes are obtained; performing management area division on the farmland by using a fuzzy clustering method based on the soil physical indexes and the nutrient indexes; sorting the contribution rate of each soil index to the crop irrigation water productivity in each management partition by using a partial least square discriminant analysis method to obtain a driving factor of the crop irrigation water productivity; using a boundary line analysis method to determine a regulation threshold value of a crop irrigation water productivity driving factor in each management area; calculating actual stage water consumption of crops in each management area, establishing an irrigation system optimization model, and performing multi-objective optimization solution on the irrigation system optimization model by applying a genetic algorithm to obtain an optimized irrigation system; and combining the regulation and control threshold value of the crop irrigation water productivity driving factor with the optimized irrigation system to obtain a farmland distributed crop management scheme, so that the crop irrigation water productivity is 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 invention discloses a news event prediction method based on time smooth constraint depth evolution clustering. The method comprises the following steps: constructing an entity map of a news event; performing information aggregation on the entity graph by using a relationship-aware graph convolutional neural network; in the deep evolution clustering module, fuzzy clustering is utilized to obtain a soft membership matrix of an entity to clusters, and the clusters of adjacent timestamps are aligned and fused to construct a cluster graph; in the cluster graph information transmission module, information aggregation is carried out by using implicit correlation between cluster pairs so as to update cluster representation, entity representation and relation representation; combining the updated entity representation and relationship representation with the initial entity representation and relationship representation by using a time residual gate to serve as a next timestamp input; inputting the entity representation and the relation representation updated by different timestamps into a time-dependent encoder based on an attention mechanism to obtain a comprehensive entity representation and a relation representation; and predicting through the news event model based on convolution. According to the method, time sequence evolution of high-order correlation between entities can be effectively modeled.

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 relates to a user interaction behavior prediction method and device, computer equipment, a storage medium and a computer program product. Relates to the technical field of big data. The method comprises the following steps: acquiring historical interaction behavior data of a user; performing fuzzy clustering processing on the historical interaction behavior data to obtain category distribution information corresponding to each data point in the historical interaction behavior data; generating an interest file of the user according to the category distribution information, and obtaining a user portrait of the user based on the interest file; and inputting the user portrait into the trained user behavior prediction model to obtain an interaction behavior prediction result of the user. By adopting the method, the data analysis efficiency of the social media user interaction behaviors can be improved.

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 provides a freshwater fish pond water temperature monitoring method, system, medium and equipment, and relates to the technical field of freshwater fish pond cultivation, a fish pond water temperature data set is formed based on water temperature data of different levels of a fish pond water body and data such as meteorological factors and water quality parameters, and the data set is preprocessed; according to the method, a preprocessed data set is utilized, meteorological factors of the day and a set time period are screened out to serve as forecasting factors, statistical methods such as multiple regression, stepwise regression and fuzzy clustering are adopted, and freshwater fish pond water temperature forecasting models under different weather types such as a low-temperature and low-illumination type, a rapid temperature and pressure reduction type and a high-temperature and stuffiness type are constructed.

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.