608 results about "Support vector machine" patented technology

The invention discloses a rotating machine fault diagnosis method, which comprises the following steps of: acquiring vibration, temperature, acoustic emission and current signals at key parts of a rotating machine, and extracting characteristic parameters such as time domain and frequency domain after preprocessing such as filtering and noise reduction; and inputting the characteristic parameters into machine learning models such as a support vector machine, combining deep learning models such as a convolutional neural network and a long-short-term memory network, performing comparative analysis by using a digital twin model, and fusing diagnosis results to output fault types, positions, severity and maintenance suggestions. The method overcomes single diagnosis limitation, multi-source signal complementation, multi-model collaboration, accurate fault diagnosis and diagnosis reliability improvement, provides a scientific basis for equipment maintenance, and is of great significance for guaranteeing safe operation of rotating machinery, reducing maintenance cost and promoting industrial intelligent development.

The invention provides a sewage treatment plant terminal effluent carbon source quality detection and evaluation method, which realizes full-flow dynamic evaluation and regulation of carbon source quality through on-line monitoring and intelligent algorithm fusion. According to the method, an online water quality full-spectrum detector is used for collecting original spectrum data flow, and after preprocessing such as variational mode decomposition denoising and mutual information feature selection, organic matter content quantification, variation trend analysis and anomaly detection are completed in combination with algorithms such as a support vector machine and an autoregressive moving average model. An entropy weight method is introduced to dynamically adjust the weight of the evaluation model, model parameters are optimized based on a gradient descent algorithm, a process adjustment instruction is generated through reinforcement learning and fuzzy logic, and an automatic system is linked to execute regulation and control. According to the method, the problems of hysteresis and singleness of traditional offline analysis are solved, multi-dimensional real-time evaluation, abnormal quick response and process dynamic optimization of the quality of the carbon source are realized, the sewage treatment efficiency and the effluent quality stability are improved, and a technical support is provided for continuous standard reaching of the quality of the carbon source.

The invention discloses a burn and damaged skin recovery degree evaluation method based on skin image recognition, and the method comprises the steps: obtaining a visible light image and an infrared thermal imaging image of a skin burn patient, carrying out the preprocessing, and fusing into a skin burn image; performing feature extraction, recognition and segmentation to obtain a burn area image; constructing a static feature set based on the burn feature parameters, constructing a three-dimensional model of the burn wound based on the burn area image and the depth image, and constructing a dynamic feature set; and inputting the extracted static and dynamic features into the optimized deep learning-support vector machine fusion model for training, carrying out final classification on a recovery stage, and calculating the time required for complete healing. The method provided by the invention not only can effectively capture the static feature information of the burn area, but also can dynamically monitor the recovery change trend of the wound in real time, accurately predict the time required for complete healing, and can significantly improve the accuracy and personalized assistance of burn diagnosis and treatment.

The invention discloses a mechatronics product detection system based on an artificial intelligence image recognition technology, and relates to the technical field of artificial intelligence and mechatronics, and the mechatronics product detection system comprises an image acquisition module, a preprocessing module, a feature extraction module, a defect recognition module, a data management module, a result output module and a system control module, the preprocessing module has the functions of adaptive zooming and intelligent obstacle avoidance, and processes images by using a deep learning algorithm and a quantum filtering technology; a deep convolutional neural network is used for feature extraction, SVM and CNN are combined for defect recognition, a meta-learning mechanism is introduced, a distributed database and federated learning are adopted for data management, AR navigation and gesture interaction is used for result output, and a block chain intelligent contract is used for system control to deploy resources. The method has obvious advantages and high detection precision, and can accurately identify tiny and hidden defects; efficiency is greatly improved, and edge and cloud computing are combined to reduce processing time; operation is convenient, VR, AR and voice interaction are used, and the maintenance efficiency is improved.

The invention discloses a casting density optimization system and method based on cooperative regulation and control of molding sand moisture and clay blue absorption amount, and relates to the technical field of casting control. According to the method, a self-cleaning wear-resistant alloy probe is used for synchronously collecting a molding sand high-frequency eddy current signal and a multi-spectrum near-infrared signal in the sand mixing process; inputting a pre-training support vector machine regression signal fusion model to obtain a real-time moisture content and a clay blue absorption amount; a preset density target threshold value is combined, a density-molding sand parameter association database is inquired to determine a parameter target interval, a regulation and control instruction is generated after a deviation value is calculated, and a water adding device and a clay conveying device are controlled to adjust the use amount; the density of the casting after regulation and control is detected, if the density does not reach the standard, model output weight iteration optimization is corrected until the density reaches the standard, the system comprises a detection module, a signal processing module, a regulation and control execution module, a density detection and correction module and a central control module, closed-loop control is achieved, and the density stability of the casting is improved.

The invention relates to the technical field of fault prediction and health management, in particular to a primary and secondary fusion complete ring main unit fault diagnosis method and system. Comprising the following steps: acquiring three-phase instantaneous voltage and current signals in real time, and converting the signals into digital transient data; performing time window preprocessing on the digital transient data, and executing wavelet packet decomposition to generate a transient feature vector; calculating and analyzing the transient feature vector through a transient zero-sequence power direction method and a support vector machine model to generate a local diagnosis result; when the local diagnosis result is that cooperative positioning needs to be started, a transient current similarity coefficient and a transient waveform intensity difference coefficient are calculated based on the digital transient data, and a comprehensive positioning result is generated; determining a fault line according to the comprehensive positioning result, and generating a remote control command; and executing a fault isolation operation based on the remote control command, and executing a PHM process. According to the method, the double local transient diagnosis and the cross-terminal cooperative positioning are deeply fused, so that the high-precision determination of the boundary of the fault section is realized.

The invention discloses a railway LEU-transponder transmission system lightning stroke fault risk assessment method, and the method comprises the steps: determining a lightning current invasion path through building a lightning stroke transient electromagnetic disturbance calculation model of an electromagnetic sensitive equipment port, and calculating the voltage and current at the port when incoming waves invade. And building an electromagnetic sensitivity test experiment platform of the railway signal system based on the lightning current invasion path and the voltage and current at the port. And performing label category prediction on the to-be-predicted sample point through each support vector machine model in the target model set, and voting each prediction result. According to the invention, a complex transmission relation between lightning transient electromagnetic disturbance and equipment failure risk is established through deep learning. The fault risk of the signal system under the lightning transient electromagnetic disturbance action is predicted through transient electromagnetic effect experimental data of real equipment of the railway signal system, and powerful support is provided for improving the electromagnetic sensitivity of the signal system and guaranteeing stable operation of a railway.

The invention discloses a water quality safety monitoring and early warning method based on big data, and relates to the technical field of monitoring and early warning. Setting sampling points according to areas, pasting scene labels, and arranging sensors to obtain water quality information; establishing a water quality evaluation model based on a support vector machine improved model, inputting the water quality information into the water quality evaluation model, outputting to obtain a water quality category, and dividing the sampling points into normal sampling points and key sampling points according to the water quality category; based on the normal point data, using LSTM to predict water quality safety and performing graded early warning; based on the key point data, positioning a pollution source by using space-time Kriging interpolation, and simulating a pollution diffusion path by using multivariable collaborative interpolation; and combining normal point early warning and key point diffusion simulation to obtain a water quality safety monitoring early warning result. According to the method, a global risk grading report is generated by fusing a normal sampling point early warning result and a key sampling point diffusion path, and emergency response and long-term treatment strategies are matched.

The invention relates to the technical field, and discloses a power distribution network multi-time scale fault scene deduction method comprising the following steps: obtaining meteorological and power grid operation data, establishing a time sequence fault tree model, and analyzing the trigger probability of multi-line disconnection and rainstorm short circuit faults in a target time period; extracting cross-level fault propagation features, analyzing a dynamic coupling relationship among multi-line disconnection, transformer substation flooding and cascading trip, forming a fault feature mode set, and identifying a single fault and a cascading fault in combination with time sequence analysis; historical fault data are analyzed, time sequence features and topological features of single and cascading faults are extracted, if a single fault propagation path is in a single level, a support vector machine is used for being combined with the features to judge fault types, and a classification result is output; and performing anomaly detection and confidence evaluation on a fault classification result, outputting a fault evolution path and risk evaluation, and analyzing the contribution degree of cascading trip to a large-area power failure risk in combination with historical blackout data to obtain power failure risk probability distribution.

The invention provides a feature extraction method for an electroacoustic background interference signal of operation power transformation equipment, which belongs to the technical field of power transformation equipment, and comprises the following steps: acquiring an electroacoustic signal, preprocessing the electroacoustic signal, and generating a controlled interference signal at the same time; and performing time-frequency analysis on the preprocessed electroacoustic signal and the controlled interference signal, and optimizing the interference signal parameter to enable the interference signal parameter to be highly similar to the electroacoustic signal. And constructing a wavelet basis function library based on the optimized controlled interference signal, and performing wavelet packet decomposition on the electroacoustic signal to obtain a plurality of frequency band sub-signals. A self-adaptive threshold model is established by using a controlled interference signal, and soft threshold denoising processing is performed on sub-signals. Time-frequency features of the denoised sub-signals and the controlled interference signals are extracted, a feature mapping relation is established, and an initial feature set is obtained; and performing nonlinear dimensionality reduction on the initial feature set by adopting principal component analysis to obtain a dimensionality-reduced feature set. And an improved support vector machine model is adopted to evaluate the importance of dimension reduction features, and an optimal feature set is selected as an electroacoustic background interference signal feature.

The invention discloses a high-precision electrical calibration method and system for a wedge flowmeter. The method comprises the following steps: step 1, data acquisition and preprocessing; step 2, dynamic working condition identification; 3, updating the self-adaptive model; 4, multi-source data fusion calibration is carried out; and 5, performing closed-loop verification and compensation. According to the invention, multiple types of signals are collected in real time through the multi-dimensional sensor array and de-noised to construct a data set, so that comprehensive and accurate data collection is realized, and a good basis is provided for calibration; carrying out dynamic working condition identification by utilizing a support vector machine, triggering model updating, and endowing calibration with dynamic self-adaptive capability; model parameters are updated by adopting a recursive least square method, compensation items are established, and the characterization capability of the model is enhanced; a calibration coefficient is generated by means of a data fusion algorithm, and the calibration precision is improved; through verification and compensation of a digital twinborn model, traditional defects are effectively overcome, model transplantation errors are reduced, the measurement precision of the wedge flowmeter is improved under extreme working conditions, and production stability and product quality are guaranteed.

The invention discloses an optical fiber adapter control method and system based on artificial intelligence. The method comprises the following steps: acquiring multi-dimensional data acquired in real time by a multipoint sensor network deployed in an optical communication environment; according to the multi-dimensional data, a filtering algorithm is adopted to remove noise interference, and a cleaned environment state data set is obtained; inputting the environment state data set into a pre-established support vector machine model, and generating an abnormal state identifier and a corresponding environment parameter value if detecting that the temperature data or the vibration data exceed a preset threshold range; predicting a control parameter adjustment scheme of the optical fiber adapter by adopting a deep neural network algorithm according to the abnormal state identifier and the environmental parameter value, and generating a control signal set containing an adjustment instruction; and transmitting the control signal set to an automatic execution module, executing mechanical displacement and optical path calibration operation according to the adjustment instruction, and obtaining adjusted optical signal transmission state data. The reliability, the stability and the operation and maintenance efficiency of the optical communication system are remarkably improved.

The invention discloses an unmanned aerial vehicle patrol management method and system based on a new energy station, and relates to the technical field of unmanned aerial vehicle monitoring, and the method comprises the steps: calling a wind power plant SCADA system to obtain the topological information of a wind power plant, and synchronously building an electromagnetic interference distribution diagram through an unmanned aerial vehicle sensor in combination with Gaussian regression; sound pressure measurement is combined with a CFD inversion model to simulate a fan wake flow vortex core to construct a wake flow three-dimensional thermodynamic diagram; calculating a three-dimensional path cost diagram based on the electromagnetic interference distribution diagram and the wake flow three-dimensional thermodynamic diagram, and planning an unmanned aerial vehicle inspection path through a path planning algorithm in combination with unmanned aerial vehicle dynamics constraints; fan operation data are collected and processed through an airborne PDV system of the unmanned aerial vehicle, and fan operation faults are recognized based on frequency spectrum transformation and a support vector machine model. According to the method, the safety and the executive performance of the unmanned aerial vehicle routing inspection path in the complex space environment of the wind power plant are improved, and the efficient perception and the accurate diagnosis of the operation state of the fan are enhanced.

The invention relates to the technical field of intelligent welding and welding processes, in particular to a double-wire welding process parameter prediction method based on a machine learning method. The method comprises the following steps that welding parameters and weld joint morphology parameter data are obtained through a welding process test, the data are preprocessed, and a data set is established; constructing a back propagation neural network model based on the data set, constructing a support vector machine model, and training the back propagation neural network model and the support vector machine model by using the data set; evaluating the trained back propagation neural network model and the support vector machine model, and selecting an optimal model as a model for actual industrial application; and inputting parameters required by a weld bead to be welded into the selected model to obtain double-wire welding process parameters. According to the design, the welding process parameters are predicted through the method based on machine learning, so that the quality stability and efficiency of double-wire welding are improved.

The invention provides a trajectory prediction method and device based on behavior intention of an unmanned aerial vehicle, a device and a medium, and the method comprises the steps: employing a random forest model to screen multi-source state information of the unmanned aerial vehicle, obtaining flight state key features, and completing feature importance evaluation and precise screening; building a multi-classification maneuvering intention recognition model by adopting a support vector machine based on the flight state key features, and obtaining probability distribution of various maneuvering actions of the unmanned aerial vehicle based on the multi-classification maneuvering intention recognition model; a neural network trajectory prediction model of a coding-decoding structure with a bidirectional gating circulation unit is constructed, probability distribution of various maneuvering actions of the unmanned aerial vehicle is introduced, the probability distribution is used as key semantic constraints to be embedded into a trajectory prediction algorithm, a technical link of multi-link collaborative optimization is formed, and a prediction result of a future trajectory of the unmanned aerial vehicle is obtained. And high-precision prediction of the track of the unmanned aerial vehicle in a complex environment is effectively realized.

The invention provides an optical cable fault positioning method based on OTDR signal characteristic adaptive denoising and event identification, and the method comprises the steps: classifying modal components after OTDR signal adaptive noise decomposition based on a preset entropy threshold value, and obtaining a noise dominant component, a mixed component and a signal dominant component; singular value difference spectrum abrupt change point detection is carried out on the noise dominant component, and residual useful signals are extracted; verifying the entropy value of the mixed component, and reconstructing the component meeting the entropy threshold value, the signal dominant component and the residual useful signal into a de-noised signal; synchronously optimizing hyper-parameters and feature selection subsets of the support vector machine by adopting a swarm intelligence algorithm, wherein search parameters of the algorithm are dynamically updated according to an exponential decay mechanism; and outputting fault point space position information based on the optimized support vector machine model.

The invention relates to the technical field of defect detection, and discloses a method for detecting defects of live cable equipment by using a high-frequency current detection method, which comprises the following steps of: exciting the live cable equipment to generate a high-frequency current signal, and judging the optimal frequency of the high-frequency current signal by adopting the minimum signal propagation loss; the method comprises the following steps: acquiring a current signal on cable equipment through a high-frequency current sensor, and performing multi-scale time-frequency analysis on the signal through wavelet packet transformation; carrying out noise reduction processing on the converted signal to remove an interference signal and extract a partial discharge signal; positioning the partial discharge signal based on the Bayesian theorem, and judging the position of a partial discharge source; and classifying the partial discharge signals by adopting a support vector machine. The excitation frequency of the high-frequency current signal enables the signal transmission loss to be minimized and the extraction efficiency of the partial discharge signal to be improved, and the signal transmission efficiency is improved in a complex cable environment, so that the defect detection precision and sensitivity are enhanced.

The invention provides a method for detecting a gas production state after thermal runaway of a battery, which comprises the following steps: placing a lithium battery in a closed anti-explosion tank, heating to trigger runaway, constructing a coupling matrix through pressure and temperature data acquired by a dynamic pressure compensation model, and correcting pressure distribution by using a nonlinear thermodynamic equation; analyzing the concentration of gas components by adopting a Fourier transform infrared spectrometer, and calculating the total gas production amount by combining multiple regression analysis; and quantizing energy dissipation based on a second law of thermodynamics to generate an energy release characteristic matrix, mapping the energy release characteristic matrix to a high-dimensional space to generate a risk index, and finally predicting thermal runaway probability distribution through a support vector machine. According to the method, high-precision measurement and analysis of the gas production rate in the thermal runaway process of the battery are realized through dynamic pressure compensation, multi-dimensional gas component analysis and nonlinear thermodynamic modeling, and the testing efficiency and the early warning capability of the battery are improved.

The invention provides a method for identifying forest tree species by using laser point cloud data, and the method comprises the following steps: collecting three-dimensional laser point cloud data of a forest region, setting an elevation threshold, filtering ground points, and extracting a point cloud sample object; respectively extracting VFH, CVFH and ESF feature descriptors from the sample point cloud, and constructing three types of geometric feature vectors; performing supervised classification on the features by adopting a random forest and a support vector machine learning classifier; the output of each classifier is fused through strategies such as weighted voting, an average method or a stacking method, and a final tree species identification result is obtained; according to the method, three types of global or semi-global feature descriptors of VFH, CVFH and ESF are extracted for a point cloud sample object of a single tree, feature modeling is carried out on tree species from three dimensions of spatial attitude, local scale structure and global shape distribution, the advantage of real restoration of a target structure by using point cloud data is utilized, and the feature modeling efficiency is improved. And the problem of projection distortion of image features under multiple view angles is avoided.

The invention relates to the technical field of medical informatization and intelligent management, and particularly discloses a special management medicine intelligent shift change management system and method for a hospital pharmacy, and the system obtains the medicine information of the shift change shift of the pharmacy through a data collection module, and carries out the structural arrangement and standardization processing of the data. The medicine inventory and operation records of the current shift are recorded, and the abnormal operation recognition module recognizes potential operation anomalies through a single-class support vector machine model and calculates abnormal operation feature values; the drug difference identification module adopts a principal component analysis method to analyze inventory difference changes, calculates drug difference feature values, constructs comprehensive feature vectors, uses a K-means clustering algorithm to assess the safety degree of shift change management and outputs risk coefficients, and the dynamic intelligent shift change management module formulates corresponding strategies according to risk levels. The system also introduces an adaptive threshold adjustment mechanism, calculates the time sequence similarity of historical and current shift change characteristics through a dynamic time warping algorithm, and dynamically adjusts a risk threshold.

The invention discloses a method for detecting the nitrogen content of organ tissues of different varieties of oilseed rapes based on visible near-infrared spectroscopy, which comprises the following steps: firstly, collecting oilseed rape leaf, shell, stalk and root system samples under the conditions of multiple varieties and multiple nitrogen fertilizer levels, and acquiring spectral data within the range of 430-2500nm by using a visible near-infrared spectroscopy; and a Kjeldahl method is synchronously adopted to measure the real nitrogen content as a reference value. Preprocessing the spectral data, including de-noising, standard normal variable transformation, multivariate scatter correction and derivative processing, so as to weaken the influence of scattering and baseline drift; characteristic wavelengths related to the nitrogen content are screened through stepwise regression, variable projection importance, competitive self-adaptive reweighted sampling, a continuous projection algorithm and other methods, and a random forest model, a support vector machine model, a partial least squares discriminant analysis model, a partial least squares regression model, a support vector regression model, an XGBoost model and other models are combined. And respectively constructing a classification identification model and a regression prediction model.

The invention relates to the technical field of geological informatization, in particular to an artificial intelligence modeling analysis method for a hydrate pilot production data set, which comprises the following steps of: acquiring logging data, lithology data, stratum physical property parameters and natural gas hydrate production dynamic data; screening, cleaning, complementing, de-noising and standardizing are carried out in sequence to obtain an artificial intelligence modeling data set; and establishing a stratum lithology machine learning recognition model, a stratum physical property machine learning recognition model and a natural gas hydrate artificial intelligence historical fitting model through a support vector machine SVM, a random forest RF and a neural network DNN. According to the method, a serial modeling architecture of lithology identification, physical property prediction and production history fitting is created, and the prediction output of the upstream model is used as the optimization input of the downstream model, so that the downstream production prediction model can learn physical property parameters which are recalculated based on machine learning and have higher precision; and the accuracy of final production prediction is improved from the data source.

The invention relates to the field of new energy power generation control, in particular to a new energy power generation grid-connected protocol conversion control method. According to the method, the voltage, frequency and communication state of a grid-connected point are monitored in real time, and a support vector machine model is combined to judge whether a system is in a grid loss state; after network loss, the controller is switched to an isolated network operation protocol, a collaborative inertia regulation and control strategy is executed, output of each power generation unit and the energy storage system is dynamically distributed, and frequency and voltage stabilization is achieved; further adjusting the output of each new energy unit based on a linear quadratic Gaussian control algorithm to realize active power balance under the isolated network; after the power supply of the power grid is recovered, the frequency, voltage and phase synchronization of the power generation system and the power grid is realized by adopting self-adaptive fuzzy control through a set synchronization threshold judgment condition; and finally, combining a soft switching-on technology to realize safe grid connection and switching to a grid connection control protocol. The method has the advantages of quick response, high control precision, stable grid connection process and the like, and is suitable for various new energy microgrid scenes.

The invention discloses a wind power blade state monitoring and fault early warning system based on a wavelet clustering algorithm, and relates to the field of wind power blade fault diagnosis. According to the system, blade operation data are synchronously acquired through a vibration sensor and an acoustic emission sensor, time domain, frequency domain and time-frequency domain features are fused by adopting a multi-domain joint feature extraction method, unsupervised clustering analysis is performed on feature vectors by utilizing an improved wavelet clustering algorithm for automatically acquiring local clustering information, and a damage mode is automatically identified. Fault classification is realized by establishing a support vector machine diagnosis model, an acoustic emission event rate fluctuation verification mechanism based on blade rotation periodicity is innovatively introduced, and the authenticity of damage is verified through mutual verification of vibration and acoustic emission signals. According to the method, the problems of insufficient feature extraction, poor clustering effect and high false alarm rate of a traditional method are solved, accurate identification and early warning of multiple damage modes are realized, and the accuracy of fault identification is effectively improved.

The invention relates to the technical field of information, and discloses a real-time early warning method and system for double-crane hoisting, and the method comprises the steps: obtaining the operation condition data of a plurality of hoisting devices in real time, and carrying out the standardized packaging, and obtaining heterogeneous processing data; on the basis of the heterogeneous processing data, constructing a scene simulation model through a digital twinning technology so as to output collaborative parameters of the hoisting equipment in various business scenes; inputting the collaborative parameters and the operation condition data into a pre-constructed double-layer early warning model for processing, and outputting an early warning result; wherein the double-layer early warning model comprises a hybrid anomaly detection model integrating an isolated forest and a single-class support vector machine and an LSTM network model introducing an attention mechanism; and real-time and accurate early warning of double-crane lifting is realized through a digital twinborn technology and a machine learning algorithm.

The invention relates to the technical field of machine learning, and discloses a concrete filled steel tube member lateral impact deflection prediction method based on machine learning, comprising: generating a lateral impact sample set; respectively training a support vector machine model, a random forest model and an extreme gradient lifting model based on the lateral impact sample set, and screening an optimal model; inputting the characteristic parameters of each lateral impact sample into the optimal model, and performing performance analysis on the optimal model by adopting an interpretable method to obtain optimal characteristic parameters; constructing a relational expression between the maximum deflection and the optimal characteristic parameters, selecting a basic quantity, and after converting the relational expression into a dimensionless relational expression based on a theorem, performing fitting by adopting a symbolic regression method of genetic coding to generate a fitted maximum deflection prediction formula of the component under lateral impact so as to obtain the optimal maximum deflection; according to the method, the maximum deflection prediction precision of the component is improved.

The invention discloses an IPv6 stateless address dynamic planning method and system based on an elastic prefix, particularly relates to the field of network communication, is used for solving the problems of prefix rigidity and service semantic deficiency in IPv6 stateless address configuration of a railway network, and is implemented by extracting basic prefix information and compressing a service level identifier to generate an elastic sub-prefix bit string. The prefix is ensured to be flexibly embedded into multi-level semantics; fusing the MAC address of the host to construct a temporary interface ID, and enabling the address to have deep association between equipment and service; splicing candidate addresses and broadcasting verification information to collect conflict feedback to realize real-time conflict detection; based on feedback calculation correlation strength and a variation rate index, driving a bit flipping optimization prefix and an interface ID through a support vector machine decision, and forming an iterative adjustment closed loop; and finally, repeatedly verifying and confirming a final address and feeding back the final address to the semantic pool, or rolling back and expanding the prefix so as to solve stubborn conflicts, thereby overcoming the prefix rigidity and semantic deficiency of the traditional mechanism.

The present invention belongs to the field of multi-energy complementary coordinated dispatching. Disclosed is a meteorological-downscaling-coupled dispatching method for the power generation and consumption of a hydro-wind-solar system. The method comprises: using a support vector machine regression algorithm to identify different hydrometeorological variable data, and establishing a statistical relationship between observation data and a meteorological factor to implement high-resolution spatial downscaling; using a wind-solar power-generation empirical formula to calculate a wind-solar output process; and introducing a series of time-series peak regulation modes to determine a regulated peak and a hydro-wind-solar power consumption linkage equation, thereby avoiding overestimation of energy consumption caused by neglecting climate change impacts and short-term power generation rules. By means of the analysis of engineering examples consisting of Yunnan Lancang River Basin and surrounding wind-solar power stations thereof, the result shows that the present invention can effectively reduce hydrometeorological downscaling errors, and more accurately describe hydro-wind-solar energy power generation rules by means of a hydro-wind-solar power consumption linkage equation, thereby making the dispatching result show better accuracy and reliability.

The invention discloses an urban dangerous case medical demand dynamic prediction and resource scheduling method and system, and relates to the technical field of scheduling optimization. Comprising the steps of obtaining current data and historical dangerous case data of a target area, adopting a support vector machine regression method to construct an urban casualty population prediction model, performing training based on the historical dangerous case data to obtain the trained urban casualty population prediction model, inputting the current data to obtain the number of current casualties, and predicting the number of the current casualties. The method comprises the following steps: determining health rescue strength and medical material requirements, decomposing the rescue strength to form a plurality of rescue units, determining a rescue priority based on current data, distributing the rescue units according to the rescue priority, associating the rescue units with the medical material requirements, constructing a rescue process, obtaining a real-time rescue result, and dynamically adjusting the rescue priority. According to the invention, accurate prediction of urban dangerous case medical demands and efficient scheduling of medical resources can be realized, and the response speed and rescue effect of urban emergency medical rescue are improved.

The invention discloses an intelligent ore mining control method and system based on machine learning, and relates to the technical field of mining intelligentization, and the method comprises the steps: building a mining area geologic model based on a convolutional neural network model and a support vector machine model, inputting an ore body geologic feature map into the mining area geologic model, and generating a geologic analysis report; fusing the ore body geologic feature map and the geologic analysis report through a Bayesian updating method to obtain a geomechanical map; through a data fusion technology and a real-time analysis algorithm, a geomechanical map and mining area real-time monitoring data are combined, ore mining risk factors are analyzed, and an ore mining strategy is dynamically adjusted to generate an optimized ore mining instruction. The mining area geological environment data is converted into high-quality graph database nodes and edges, and key geological features are identified by using a complex network analysis technology, so that the ore extraction efficiency, safety and scientific decision-making capability are remarkably improved.