484 results about "Distribution matrix" patented technology

The invention relates to a low-carbon park comprehensive energy system multi-agent collaborative carbon responsibility allocation method, which comprises the steps of collecting multi-source data of a park, and performing multi-energy flow optimization scheduling by taking the minimum total operation cost of a comprehensive energy system as a target; constructing a dynamic carbon flow distribution matrix by utilizing a bidirectional carbon flow dynamic correction model considering carbon flow transfer caused by an energy storage charging and discharging process; a load carbon responsibility decoupling distribution model is adopted to decouple the total load into a plurality of sub-load categories, and different carbon responsibility distribution coefficients are given to each category of sub-load basis; the method comprises the following steps: performing multi-agent collaborative optimization by taking each benefit party in a park as a game participant, taking a Nash bargaining as a collaborative optimization framework, taking maximization of utility gains of all participants as an objective function, and taking a Shapley value as a carbon responsibility distribution basis, and obtaining a carbon responsibility distribution scheme of each agent in combination with a user carbon responsibility distribution coefficient. Compared with the prior art, the method ensures fairness, efficiency and operability of a carbon responsibility allocation scheme.

The invention relates to the technical field of intelligent parking management, and discloses a parking reservation management method and system. The system obtains a parking space state data set containing a parking space number, an occupation state time sequence and a reservation request frequency through a parking management platform; performing spatio-temporal feature extraction on the set to generate a multi-dimensional feature matrix, and determining a parking space association network in combination with topology analysis; screening a candidate parking space set satisfying reservation stability and space reachability from the matrix; inputting the candidate parking space set into a pre-trained priority ranking model, and generating a parking space priority score by the model through a dynamic weight adjustment algorithm; fusing the priority score and the real-time parking space occupation data to construct a dynamic distribution matrix, and simulating a parking space distribution spatio-temporal evolution process based on a queuing theory model; and separating an optimal distribution scheme and a candidate parking space sequence from the matrix, and generating final parking space reservation confirmation information. According to the reservation parking management method and system provided by the invention, the reliability and user satisfaction of the reservation system can be improved.

The invention discloses a regulation and control system and method based on heat energy of an energy storage cabinet, and belongs to the technical field of intelligent heat management. Temperature data in the energy storage cabinet are collected, a temperature distribution matrix is constructed, and a thermal gradient vector and an overheating risk area are extracted based on a difference algorithm; further establishing a thermal power load mapping model, and dynamically generating an air circulation path optimization diagram; the opening angle of a flow deflector and the rotating speed of a fan are adjusted in combination with an optimized path, and self-adaptive regulation and control of a thermal field in the cabinet body are achieved; meanwhile, a machine learning model is trained by utilizing historical operation data, an optimal regulation and control strategy is output, and an execution layer is driven to realize collaborative response of a fan and a flow deflector; the method has the advantages of high regulation and control precision, high response speed, low energy consumption and the like, and is suitable for heat management requirements of various types of energy storage cabinets.

The invention relates to the technical field of thermal field modeling and control, and particularly provides a molecular beam epitaxy substrate temperature field control method and system based on artificial intelligence, and the method comprises the following steps: obtaining the all-region temperature data of the surface of a substrate, and the heating power and environment parameters of each region of a heating cavity; inputting the heating power and the environmental parameters into the temperature field model, and outputting a temperature distribution matrix and temperature gradient data of the surface of the substrate; predicting temperature prediction data according to the temperature distribution matrix, the heating power and the sequence data of the environmental parameters in the historical time period; and inputting a state vector formed by the temperature distribution matrix, the temperature gradient data, the heating power data and the temperature prediction data into a deep reinforcement learning model, and then outputting the heating power adjusting quantity of each area to control a temperature field. According to the invention, the problems of insufficient prediction precision and real-time performance of the substrate temperature in the prior art are solved, the influence of environmental factors and heating power on the substrate temperature is considered, and the temperature field control capability is improved.

The invention provides a fair-faced concrete forming quality control method, belongs to the technical field of fair-faced concrete, and realizes real-time monitoring of a template deformation state by establishing a component geometric dimension reference coordinate system and a template deformation monitoring network. A deep learning model based on a ResNet residual network architecture is adopted to predict dynamic lateral pressure distribution in a concrete pouring process, a deformation compensation algorithm is utilized to accurately calculate a deviation value between an actual size and a design size of a component, a concrete surface quality evaluation system is established to identify surface defects, and a defect distribution matrix is established; and a quality control feedback adjustment mechanism is executed to dynamically optimize and adjust process parameters of subsequent pouring operation, and the technical problem that the geometric dimension precision of a component is difficult to control due to the fact that the formwork deforms under the lateral pressure effect of concrete is solved.

The invention discloses an island ecological restoration effect comprehensive evaluation method, and particularly relates to the technical field of ecological environment evaluation. Obtaining a restoration area ecological basic parameter set, and constructing an original ecological characteristic matrix; calculating a restoration response curve based on the time sequence change characteristics, and determining the restoration response weight of each ecological factor; calculating a restoration net effect through a normalized difference model of parameters of a contrast area, and eliminating the influence of natural fluctuation; inputting the net effect vector into an ecological system structure coupling model, and quantifying the coupling strength between ecological factors to obtain a restoration comprehensive contribution degree; further performing space-time interpolation analysis to form a restoration effect distribution matrix and calculating a restoration vulnerability index; generating a repair optimization suggestion set according to a threshold value distribution result, and outputting a comprehensive evaluation report; according to the method, dynamic quantification and systematized diagnosis of the ecological restoration process are realized, the spatio-temporal evolution characteristics and restoration effect differences of the island ecosystem can be accurately reflected, and a scientific basis is provided for ecological restoration optimization and intelligent decision making.

The invention discloses an automatic optical prism optical axis parallelism adjusting method based on image processing, and relates to the technical field of high-end equipment optical detection. According to the scheme, a prism reflection image sequence is collected based on a calibration light source, an optical axis projection coordinate system is established, light spot geometric centroid and boundary parameters are extracted, and an optical axis direction vector matrix is constructed; calculating an optical axis included angle deviation according to the multi-frame image, and establishing an optical axis parallelism parameter model; decomposing the error distribution matrix through an iterative minimum deviation method to obtain attitude bias and system aberration components, and generating a prism attitude adjustment vector to realize closed-loop automatic adjustment; according to the invention, high-precision and self-adaptive adjustment of the optical axis parallelism can be realized under the conditions of complex illumination and device batch fluctuation, and the stability, consistency and automation level of optical system installation and adjustment are improved.

The invention belongs to the technical field of magnetic resonance imaging, and particularly discloses a magnetic resonance multi-source radio frequency shimming method, device, equipment and medium based on convolution transformation, and the method comprises the steps: obtaining a sensitivity graph of each radio frequency channel of an excitation magnetic field B1 +, and constructing an initial excitation B1 + field distribution matrix; based on the initial excitation weight of each radio frequency channel, the initial excitation B1 + field distribution matrix is transformed to a frequency domain through convolution transformation; performing filtering and main mode extraction on the initial excitation B1 + field distribution matrix to obtain a screened excitation B1 + field distribution matrix; the excitation weight is optimized in the orthogonal basis vector space; reconstructing the excitation B1 + field distribution matrix based on the optimized excitation weight to obtain a reconstructed excitation B1 + field distribution matrix; and performing shimming processing on each radio frequency channel of the excitation magnetic field B1 + according to the reconstructed excitation B1 + field distribution matrix. According to the invention, the problem of non-uniform spatial distribution of a radio frequency excitation magnetic field in magnetic resonance imaging can be solved.

The invention relates to the technical field of monitoring and detection, and discloses an intelligent early warning method and system for gaseous pollutants in the semiconductor industry, and the method comprises the steps: analyzing the molecular weight of the gaseous pollutants according to the mass-to-charge ratio in an original mass spectrum signal, and analyzing the pollutant type and concentration space-time distribution matrix of the gaseous pollutants according to the molecular weight; carrying out pollution source analysis on the gaseous pollutants according to the pollutant types and the concentration space-time distribution matrix; analyzing the concentration contribution rate of the target pollution source by using the pollutant component characteristics, and analyzing the multi-stage dynamic early warning condition of the gaseous pollutants according to the concentration contribution rate and the concentration spatial-temporal distribution matrix; when the pollutant concentration data in the concentration space-time distribution matrix exceeds a multi-stage dynamic early warning condition, triggering a graded early warning signal of gaseous pollutants in the semiconductor industrial environment; and adaptively executing a control strategy corresponding to the gaseous pollutants in the semiconductor industrial environment based on the graded early warning signal. According to the invention, the accuracy of intelligent early warning of gaseous pollutants in the semiconductor industry can be improved.

The invention relates to the field of power system carbon emission flow analysis, in particular to a closed-loop power grid-oriented electric energy tracking and power flow tracing coupled carbon emission flow analysis method, which is based on power flow distribution of an actual power grid and comprehensively utilizes an electric energy tracking method and a power flow tracing method. According to the invention, fine allocation and full-link physical traceability of nodes, branches and loads in a closed-loop power grid structure by carbon emission are realized. By establishing a power flow distribution matrix, a unit injection matrix, a load distribution matrix and a carbon emission intensity vector, the carbon potential of each node, the branch carbon flow rate and the load carbon flow rate are calculated in a recursive manner, an electric energy tracking method and power flow traceability are coupled, and node allocation is iteratively optimized, so that the actual flow path and spatial distribution characteristics of the carbon flow in a power grid are disclosed. The method can adapt to scenes such as complex multi-source access and a looped network structure, the scientificity and fairness of closed-loop power grid carbon emission management are improved, and theoretical and method support is provided for development of low-carbon scheduling, carbon accounting and low-carbon economy.

The invention discloses a high-frequency and high-speed wire harness test system data analysis processing method, and relates to the technical field of signal processing crossing, and the method comprises the steps: inputting a three-dimensional fusion tensor into a space-time-frequency three-dimensional convolutional neural network model, capturing time sequence features through a bidirectional LSTM, and obtaining defect features through a multi-head attention mechanism; fusing the time sequence features and the defect features to generate time sequence-defect fusion features, and performing comparative analysis on the time sequence-defect fusion features and historical defect data to obtain a risk defect area; by generating a three-dimensional fusion tensor and acquiring defect features through a multi-head attention mechanism, high-dimensional coupling of multi-modal data and adaptive focusing of key defect modes are realized; based on a three-dimensional joint feature space, a quantum coding mapping fusion method is utilized to perform row mapping combination on a time domain waveform sequence, a spatial heat distribution matrix and a frequency domain S parameter spectrum, so that collaborative coding of a time process, a spatial position and frequency change information is realized, and the joint characterization capability of a multi-source signal is improved.

The invention relates to the technical field of rail transit monitoring, and discloses a multi-sensor fusion rail transit electric passenger car bogie dynamic performance monitoring method. The method comprises the steps that multi-source heterogeneous sensing data such as bogie vibration acceleration time sequence signals, wheel-rail contact force distribution data, bearing temperature gradient data and structural strain field data are collected; analyzing the vibration signal to extract a frequency band energy feature vector, performing spatial gridding mapping on the contact force data to generate a distribution matrix, and performing time-space alignment fusion on the two to form a primary fusion feature set; positioning a bearing temperature anomaly region through a deep convolutional network, and generating a secondary fusion feature set in combination with a structural strain concentration coefficient; and inputting the two-stage feature set into a dynamic performance evaluation model, outputting a performance degradation index set of scores of axle box bearing wear degree, framework fatigue cumulant, wheel set tread damage and the like, generating a maintenance priority sequence and triggering a preventive maintenance instruction according to the performance degradation index set, and realizing accurate monitoring and efficient operation and maintenance management and control of the dynamic performance of the bogie.

The invention discloses an automatic monitoring method for an industrial solid waste treatment process, and the method comprises the steps: synchronously collecting a temperature distribution matrix, a gas concentration spectrogram, a kiln body vibration spectrum and a material rheological image in the solid waste treatment process, constructing a multi-source heterogeneous data set, and carrying out the time-space alignment preprocessing; based on a preset solid waste component feature code, calling a process parameter reference value, and performing self-adaptive correction in combination with the material rheological image features to generate a personalized reference set; fusing the multi-modal monitoring data to generate a solid waste reaction process three-dimensional state model, extracting core feature parameters, comparing the core feature parameters with a personalized reference set, and screening abnormal features; inputting the abnormal features into a pre-trained abnormal diagnosis model, and when an abnormal probability value exceeds a preset threshold value, triggering early warning and generating an abnormal identifier; according to the method and the system, intelligent monitoring and regulation of the industrial solid waste treatment process are realized, and the stability, the safety and the treatment efficiency of the treatment process are comprehensively improved.

The invention relates to a figure operation planning method and device based on satellite remote sensing, equipment and a medium. The method comprises the steps that structured processing is conducted on multi-source satellite remote sensing data, an integrated data packet is obtained, and the integrated data packet comprises a cloud catalysis potential matrix, a terrain slope matrix, an obstacle distribution matrix and a real-time airspace occupation state; analyzing the cloud catalysis potential matrix in the integrated data packet, generating an executable task set, performing task division on the task set, obtaining decomposed tasks, performing resource verification on the decomposed tasks, and generating a verified task set; and obtaining a historical risk coefficient matrix, and constructing an executable operation instruction based on the verified task set, the terrain gradient matrix, the obstacle distribution matrix and the historical risk coefficient matrix. According to the method, by means of multi-source data integration, multi-constraint task generation, instruction construction based on historical risks and real-time environments and the like, the efficiency and accuracy of figure operation planning and the safety and reliability of operation execution can be improved.

The invention provides an on-line intelligent monitoring system based on a coil, and relates to the technical field of data processing, and the system is used for calculating the comprehensive coupling degree between coil magnetic field change and current response according to monitoring data, obtaining a magnetic response correlation value, constructing a distribution matrix of coil magnetic field distribution characteristics, obtaining a coil local coupling map, and obtaining a current response correlation value. The method comprises the following steps: calculating the influence degree of temperature change on magnetic flux energy offset, obtaining a temperature drift correction value, identifying the relative offset trend of electromagnetic response and energy compensation, obtaining a phase offset, calculating the response self-stabilization degree of a coil in long-term operation, obtaining a dynamic balance factor, and identifying the dynamic characteristics of the coil in electromagnetic, temperature and space response dimensions. And judging whether the coil is in a response instability state or a normal operation state to obtain monitoring state result data. The state of the coil is monitored by identifying coupling characteristics of electrical parameters and temperature of the coil.

The invention relates to the technical field of project management, and particularly discloses a project management method and system based on an AI large model technology, and the method comprises the steps: collecting multi-dimensional project execution data, carrying out the data cleaning and feature coupling analysis of the multi-dimensional project execution data, and generating a normalized project feature tensor; inputting the task delay risk into a pre-trained time sequence prediction large model, and outputting a task delay risk probability distribution matrix; carrying out risk level label mapping based on the task delay risk probability distribution matrix, and generating a multi-level risk early warning signal; and in response to the risk event exceeding the preset threshold in the multi-level risk early warning signal, a resource redistribution scheme is generated, and a project manager not only can monitor the project progress in real time, but also can quickly adjust resource configuration when the risk occurs, thereby realizing dynamic management. The intelligent project management mode based on data driving not only improves transparency and controllability of the project, but also provides powerful technical support for successful implementation of the project.

The invention discloses a BIM (Building Information Modeling) and real-time monitoring-based cylindrical pier cast-in-place cantilever variable cross-section continuous beam bridge construction early warning system and method, and relates to the technical field of construction early warning. According to the system, alignment and noise reduction are carried out on monitoring data through BIM constrained drift correction, a curvature sequence and an acceleration sequence are obtained, an energy response index and an energy characteristic index are extracted, and a space-time distribution matrix is formed. And identifying a curvature deviation development trend by using a time sequence prediction model, and dynamically evaluating structural stress distribution and a deformation state in combination with finite element analysis. When the deviation value or the stress result exceeds a multi-level threshold value, finite element reanalysis is triggered for correction. And the decision execution module judges the real-time safety level and generates an early warning signal by integrating the monitoring characteristics and the simulation result based on a robust intelligent identification model subjected to ring loss regularization training. The method can improve the monitoring precision and robustness of the construction stage, and achieves the real-time, foresight and closed-loop management and control of the structure safety.

The invention discloses a multi-mode continuous transportation supply and demand matching degree evaluation method for a comprehensive transportation junction, and the method comprises the steps: carrying out the collection, cleaning, standardization and time-space alignment of multi-source passenger flow and transport capacity data of inter-city traffic and an intra-city continuous mode, and constructing a unified data basis; according to operation characteristics of different connection modes, a differential supply and demand measurement model is established, and accurate mapping of multi-mode passenger flow and transport capacity is realized through a total passenger flow-connection mode distribution matrix; and introducing a cloud model theory, and carrying out weighted fusion on matching degree indexes of each continuing mode to describe fuzziness and uncertainty in a supply-demand relationship, so as to realize quantitative evaluation of the multi-mode transport capacity coordination level of the comprehensive hub. Through differential supply and demand modeling and cloud model fusion, the supply and demand matching state of multi-mode continuous transportation of the comprehensive passenger transport hub can be accurately reflected, and a scientific decision basis is provided for operation scheduling and resource optimization of a hub management department.

The invention relates to the technical field of e-commerce logistics and supply chain management, and discloses a multi-channel e-commerce platform order processing optimization system and method, and the method comprises the steps: correcting physical inventory data through a Lagrange dual variable fed back in a previous period, calculating a virtual inventory, and receiving a discrete order flow; constructing a space-time hypergraph model of the to-be-processed order according to the commodity homogeneous attribute and the spatial neighborhood attribute; performing constraint optimization solution on the hypergraph model based on a Lagrangian relaxation algorithm, and generating an optimal order allocation matrix meeting productivity constraints and a Lagrangian dual variable of a current period; and analyzing the distribution matrix to generate an operation instruction, issuing the operation instruction to the physical warehouse, and feeding back the dual variables to the inventory calculation module. According to the method, collaborative operation benefits are mined through the space-time hypergraph, and a negative feedback closed loop with physical capacity pointing to front-end sales is constructed by using dual variables, so that performance cost optimization and flow adaptive control are realized.

The invention relates to the technical field of product design, in particular to a household appliance appearance defect optimization product design method based on image discrimination, which comprises the following steps: acquiring a household appliance appearance image, calculating a gradient direction, constructing a distribution matrix, analyzing consistency to generate a defect edge image, extracting high-frequency feature scores to identify defect areas, and clustering point cloud to output defect clusters. According to the method, by calculating the gradient direction numerical value of each pixel point and constructing the gradient direction distribution matrix, the processing capacity of the directivity difference of the household appliance surface texture is effectively improved, the defect point cloud is accurately divided through the density-based noise application spatial clustering algorithm, the defect point cloud is accurately divided, and the defect point cloud is accurately divided. According to the method, the space positioning accuracy of the defect area is improved, the precision of optimization design is ensured, curved surface reconstruction is performed in combination with the least square method, the appearance design of the household appliance is accurately corrected, and the production efficiency and the design quality are optimized.

The invention discloses a PINN-based large numerical aperture photoetching objective lens vector diffraction field calculation method, which comprises the following steps: obtaining a three-dimensional vector diffraction light field of a large NA photoetching objective lens through simulation, reconstructing the three-dimensional vector diffraction light field at a sampling point position, and generating corresponding complex electric field distribution matrix data; performing physical purification on the complex electric field distribution matrix data to construct a corresponding data set; constructing a corresponding Helmholtz equation as a constraint equation based on the light field tight focusing characteristic of the large NA photoetching objective lens; constructing an initial model, and training the initial model by using the data set in combination with the constraint equation to obtain a PINN model for predicting complex electric field data; and inputting space coordinates of a to-be-predicted target into the PINN model to obtain complex electric field data. According to the method provided by the invention, the calculation efficiency of the vector diffraction field can be remarkably improved on the premise of keeping relatively high simulation precision, and the method is suitable for rapid modeling and analysis of the three-dimensional vector light field of the large-numerical-aperture photoetching object mirror.

The invention discloses a water quality degradation early warning method and system for a water supply pipe network in a low-pressure area. The method comprises the following steps: S1, collecting topological structure data and pressure, flow, residual chlorine concentration and turbidity data of an urban water supply pipe network, and preprocessing the topological structure data and the pressure, flow, residual chlorine concentration and turbidity data; s2, constructing an initial node feature matrix, a pipe network node graph model, an adjacent matrix and a low-pressure area identification matrix; sequentially extracting a first pipe network spatial feature, a second pipe network spatial feature and a water flow stagnation time sequence feature, and fusing to obtain a space-time coupling feature; s3, extracting space-time coupling characteristics of the low-voltage area, and calculating to obtain a water age distribution matrix of each node of the low-voltage area; s4, residual chlorine attenuation characteristics are constructed, and then a low-pressure area residual chlorine concentration prediction sequence and a low-pressure area water quality degradation risk matrix are calculated; and S5, performing graded early warning. According to the method, the problems of inaccurate water age distribution inversion and inaccurate water quality risk prediction caused by residual chlorine attenuation prediction of the low-pressure area of the water supply network in a traditional method can be solved.

The invention discloses a space-time collaborative optimization-based virtual power plant adjustable capability evaluation method and system. The method comprises the following steps of S10, obtaining operation parameters of distributed energy resources in a virtual power plant; s20, performing space-time analysis on the operation parameters, extracting space distribution characteristics and time change characteristics of the distributed energy resources, and generating a space-time distribution matrix; s30, constructing a joint scheduling model of the virtual power plant based on the spatial-temporal distribution matrix, and obtaining an adjustable capability evaluation result of the virtual power plant by solving the joint scheduling model; and S40, generating a regulation and control instruction according to the adjustable capability evaluation result, and sending the regulation and control instruction to the distributed energy resources to realize collaborative optimization scheduling. According to the method, the operation parameters of the time dimension and the space dimension of the distributed energy resources are jointly scheduled, and the dynamic data of the virtual power plant are acquired in real time for analysis and evaluation, so that the regulation and control process is more comprehensive, and the evaluation result of the adjustable capability of the virtual power plant is optimized.

The invention discloses a lithium ion battery pole piece uniformity detection method and system, a product and a storage medium, and relates to the field of analyzing materials by measuring physical properties of the materials. The method comprises the following steps: controlling a mechanical grinding device to remove a surface layer material of a pole piece so as to expose a to-be-detected interface; scanning an interface by using a distance measuring sensor to obtain height data and calculating a height distribution matrix; moving the detection probe to a target position, and synchronously adjusting the vertical height of the probe according to the height value in the matrix; controlling the spectrograph to collect spectral signal data; extracting the characteristic peak intensity values of the active substance and the binder, and calculating the ratio of the two to obtain the component distribution value of the target position. By implementing the technical scheme provided by the invention, the accuracy of uniformity detection of the lithium ion battery pole piece is improved.

The invention provides an abnormal user detection method and system based on topology awareness and hub node guidance. The method comprises the steps of obtaining graph structure data; fusing the topological relation in the graph structure data and the initial features of the nodes by using a topological adaptive label evolution mechanism, and predicting and optimizing labels of unlabeled nodes to generate an optimized label distribution matrix; based on the node initial features, taking the optimized label distribution matrix as a supervision signal, and generating semantic hub node features through a double-layer multi-scale contrast learning mechanism; dynamically screening neighbors of each target node by adopting a strategy gradient driven neighbor aggregation mechanism based on semantic hub node features; performing message passing through a multilayer graph neural network based on the screened neighbor set, and fusing node representations of different layers by using an attention mechanism to obtain a final node representation; and inputting the final node representation into a classifier, and outputting an abnormal user detection result.

The invention provides a method and a device for measuring the solid-phase deposition amount of crude oil under high-temperature and high-pressure conditions, and relates to the technical field of oil-gas field development. Under the reservoir temperature and pressure of a target oil reservoir, ground degassed crude oil, associated gas and an on-site solid-phase sediment sample are mixed and dissolved; an in-situ crude oil sample capable of truly reflecting in-situ crude oil components is prepared; then, reducing the pressure to a target pressure point in a stepped manner at a constant temperature, and delimiting a deposition monitoring area; spectral image data of the area is obtained, spectral reflection reference rate is analyzed and calculated pixel by pixel, and a spectral distribution matrix is constructed; judging whether the solid-phase deposition reaches balance or not by calculating a space consistency index and a time convergence index of the matrix; finally, the solid-phase deposition amount is calculated after deposition equilibrium; according to the method, the problem of solid-phase deposition measurement distortion under the ultrahigh-pressure oil reservoir condition is solved, and interference-free, real-time monitoring and quantitative analysis in the deposition process are achieved.

The invention relates to a high-order analysis power flow and carbon flow tracking method based on full-pure embedding, and the method comprises the specific steps: introducing a complex embedding parameter s, constructing a node voltage vector, an injection power vector and a tracking matrix in a power grid power flow equation into an analysis power series and an analysis power series of the complex embedding parameter s based on the analysis continuation characteristic of full-pure embedding, solving a node voltage initial value when the power grid is in a basic operation state by adopting a Newton-Raphson method; calculating a zero-order line active power vector based on the node voltage initial value, and constructing a zero-order distribution matrix representing a power distribution relation in a basic operation state of the power grid; solving a zero-order power flow tracking matrix based on the zero-order line active power vector and the zero-order power distribution matrix; calculating node indirect total carbon emission based on the zero-order power flow tracking matrix; and replacing the power distribution matrix, the node outflow power and the power flow direction matrix in the zero-order power distribution matrix with power series expansion of corresponding variables.

The invention discloses a method for automatically inverting seismic slip distribution based on deep learning. The method comprises the following steps: collecting random slip distribution corresponding to different seismic events and synthesized three-component seismic HR-GNSS waveform data, and preprocessing to obtain a sample group; building a SlipNet neural network based on a Transform-CNN model, and carrying out the training through the sample group; and inputting to-be-processed three-component seismic HR-GNSS waveform data into the trained SlipNet neural network, and generating a corresponding inverted seismic fracture area binary distribution matrix and an inverted seismic fracture slippage distribution matrix. The method solves the problem that a traditional seismic slip inversion method is low in calculation efficiency and lacks physical constraints due to single dependence on seismic waves or deformation data, and is difficult to meet the requirements of complex seismic source characterization. The method can be applied to three-component seismic HR-GNSS waveform data of large earthquakes of different types and different magnitudes.

The invention belongs to the technical field of rotating machinery signal processing, and discloses a multi-shafting rotating machinery rotating speed estimation method based on a displacement vibration signal, and the method comprises the steps: collecting the displacement vibration signal, and carrying out the short-time Fourier transform to obtain a time-frequency distribution matrix; screening out a safety harmonic set of each shafting based on the estimated rotating speed range; calculating a definition evaluation value of each safety harmonic wave and selecting an optimal harmonic wave as a target tracking harmonic wave; constructing a cost function fusing amplitude and frequency abrupt change penalty, and performing ridge line tracking in the local time-frequency matrix after noise reduction to obtain a preliminary estimation time-frequency curve; and constructing a demodulation phase function based on the curve, performing generalized demodulation and narrow-band filtering on an original signal, extracting a pure harmonic component, and finally reconstructing a final rotating speed curve of a corresponding shaft system through instantaneous phase differential. According to the method, high-precision and anti-interference decoupling and estimation of the rotating speed of each shafting under a strong interference background are realized.

The invention discloses a filter element self-adaptive temperature regulation and control method based on a dynamic environment, particularly relates to the field of temperature regulation and control, and aims to solve the problem that an existing filter element is insufficient in deformation compensation under complex working conditions. A time sequence temperature distribution matrix is obtained by constructing a multi-point temperature monitoring network on the surface of the filter element, a heat flow density distribution diagram is generated by combining material heat conduction characteristics, the heat flow convergence acceleration rate is calculated through continuous time sequence heat flow changes, and deformation risk indexes of all areas are obtained by combining material thermal expansion coefficients. And the warping or ripple trend can be identified in advance. And judging a deformation mode according to the time sequence change of the risk index and the temperature gradient, respectively generating a warping compensation temperature instruction or a ripple suppression temperature instruction, and forming closed-loop regulation and control by continuously updating the temperature distribution and the risk index. According to the method, refined temperature regulation and active anti-deformation control of the filter element are achieved, the stability of the filter element is remarkably improved, and the service life of the filter element is remarkably prolonged.