762 results about "Distribution model" patented technology

The invention discloses a health degree evaluation system and method based on a photovoltaic string, and belongs to the technical field of photovoltaic operation and maintenance intellectualization, and the method comprises the steps: firstly collecting SCADA operation data of a plurality of strings of a photovoltaic power station, including voltage, current, assembly temperature, environment irradiance and power factors; secondly, constructing a whole-station feature reference model, and generating a state vector fusing environment and load characteristics through a nonlinear projection algorithm; aiming at the target group string, extracting a health reference track, calculating a disturbance propagation factor, and identifying a health abnormal state; when the deviation degree exceeds a threshold value, dynamically screening heterogeneous reference group strings, constructing a health score distribution model, and further calculating a confidence score and a prediction residual error; if the score is low and the residual error is unstable, determining that the string is in a sub-health or hidden fault state, and generating an intervention instruction; the method has high accuracy, strong adaptability and good interpretability, and can be widely applied to intelligent diagnosis and refined operation and maintenance management of the photovoltaic power station.

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 marine surveying and mapping and submarine topography modeling, and discloses a submarine topography and landform construction method based on multi-source water depth data fusion, which comprises a multi-source data acquisition module, a data preprocessing module, a feature extraction module, a fusion algorithm matching module, a three-dimensional modeling module and an uncertainty evaluation module. A historical submarine topographic map is converted into grid data, namely, submarine topography is discretized into digital representation of regular grid units, water depths of other sources are converted into the grid data, the precision of the water depths of different sources is evaluated by adopting a superposition comparison method, and an error distribution model is established to reasonably correct and fuse the water depth data of various sources. The innovation of the method lies in that topographic feature parameters including the gradient, the curvature matrix and the bionic optimization algorithm are introduced, the defect that a single algorithm is preset in a traditional method is overcome, and the precision of coral reef and fault zone complex areas is improved.

The invention discloses a printed circuit board withstand voltage detection and evaluation method, which comprises the following steps: in the whole withstand voltage detection process, aiming at different process batches and environments, acquiring electrical signals, process parameters, environment variables and multi-modal visual data, and carrying out time sequence synchronization marking and standardized preprocessing; extracting multi-source features by using a feature coding algorithm, and establishing a multi-mode distribution model covering signal mutation, visual defects and working condition anomalies; through cross-modal feature fusion and a time sequence causal inference model, an abnormal event causal relationship graph is automatically constructed, and a voltage withstanding failure root cause chain is identified and positioned; according to the method, the efficient structured management of the detection data, the accurate traceability of the abnormal reasons and the cross-batch analysis are realized, and the capability of diagnosing and preventing the withstand voltage abnormality of the printed circuit board is remarkably improved.

The invention discloses a power equipment health evaluation method and system based on time sequence analysis and probability modeling, equipment and a medium, and relates to the technical field of power equipment state monitoring and fault prediction, and the method comprises the steps: obtaining and preprocessing multi-source operation data of power equipment, outputting a predicted value and a confidence interval of a future parameter through a time sequence prediction model, calculating a residual sequence of an actual observation value and a predicted value, fitting distribution through a probability distribution model, establishing a statistical characteristic model of a normal operation state of the equipment, performing anomaly judgment, calculating a health degree index of the equipment based on a deviation degree and a dynamic weight of a monitoring parameter and weighted accumulation, and dividing equipment state grades according to the index. Quantitative evaluation of the health state of the equipment is realized. According to the method, accurate quantification and early abnormity identification of the health state of the power equipment are realized, a reliable basis is provided for predictive maintenance, and the intelligent level and the safety guarantee capability of power grid operation and maintenance are remarkably improved.

The invention discloses a mold quality detection method and system based on image processing, and relates to the technical field of mold quality detection.The method comprises the steps that a high-precision industrial camera is installed over a mold to obtain a mold surface gray level image, and graying and smoothing are conducted on the obtained image; constructing a distribution model according to the reference distance distribution condition of each pixel point in the smoothed image, calculating a period strength degree, preliminarily screening the pixel points according to the period strength degree, and distinguishing the possibility of texture pixel points and non-texture pixel points; by using the difference between normal texture periodicity and crack randomness, through calculating the periodicity intensity degree, screening texture pixel points and non-texture pixel points, screening suspected texture pixel points, calculating the periodicity similarity, screening out a texture region and then carrying out iterative threshold segmentation, the detection problem caused by mixing of cracks and normal textures is solved; and the detection accuracy is improved.

The invention discloses an alloy field-oriented text and table double-track collaborative data extraction method and system. The method comprises the following steps of: obtaining an alloy related document DOI; taking the DOI as an index to obtain a full-text literature and storing the full-text literature in a JSON format; performing document screening based on an LDA topic distribution model and establishing a target topic document library; preprocessing the unstructured table, and obtaining structured table data by using a large language model; performing automatic text segment division on the JSON format text through rule matching analysis; and sorting the text segments by using a large language model to generate semi-structured text data, fusing and normalizing the text and table data, and outputting a component-process-organization-performance-strengthening mechanism alloy data chain. According to the method, through a double-track collaborative data extraction mechanism, the problems of multi-source heterogeneity, format fragments, information islands, semantic layering and the like of material subject data are effectively solved, structured treatment of literature data in the alloy field is achieved, and a unified technical path is provided for constructing a high-quality alloy database.

The invention discloses a cable-stayed bridge cross brace optimization system based on system reliability and an intelligent algorithm, which relates to the technical field of bridge engineering, and comprises a multi-source parameter input module used for collecting and processing data such as a main beam section, stay cable parameters, a strain sequence, a vibration sequence, a satellite cloud picture, a traffic monitoring flow, laser point cloud and the like; and outputting the structured design data, the monitoring data flow, the dynamic load spectrum, the probability distribution model and the construction error correction parameters. According to the invention, all-dimensional data such as design, monitoring, load, materials and construction errors are integrated through the multi-source parameter input module, and multi-level and dynamic reliability evaluation is carried out on the component, the subsystem and the system level by using the system reliability analysis module, so that the component failure probability, the system reliability index time sequence and the like can be output; therefore, the real reliability level of the cross brace of the cable-stayed bridge in the whole life cycle is reflected more accurately, and the one-sidedness of a traditional method is avoided.

The invention relates to the technical field of power system planning, and discloses a new energy grid-connectable capacity margin evaluation method based on spatio-temporal distribution characteristics, which comprises the following steps: constructing a joint probability distribution model based on nonparametric kernel density estimation and a Copula function by using historical and meteorological data of a wind and light station, and generating a random output scene reflecting the spatio-temporal complementarity of wind and light resources; constructing a multi-dimensional stability constraint set containing a generalized short circuit ratio and broadband oscillation as a safety boundary of a random output scene; establishing a grid source interaction control strategy considering energy storage adjustment and direct current power modulation by taking grid-connected capacity maximization as a target, solving an optimal installed capacity scheme, and activating the grid source interaction control strategy to perform dynamic correction when a scene is out of limit; the maximum grid-connected capacity distribution meeting the constraint is counted, and the shadow price of the constraint is calculated to identify the bottleneck. According to the method, the wind and light resource complementary benefit and the system regulation potential can be quantified, and the new energy grid-connected capacity evaluation margin is improved on the premise of guaranteeing the safety.

The invention provides a dynamic traffic scene-oriented unmanned aerial vehicle charging base station site selection and scheduling collaborative optimization method. The method comprises the following steps of: 1, establishing a dynamic traffic flow space-time distribution model and an emergency event probability model based on a GIS (Geographic Information System) platform and traffic monitoring data; step 2, constructing a multi-target dynamic optimization model; 3, solving the multi-target dynamic optimization model by adopting an improved multi-target genetic algorithm; 4, a site selection and scheduling double-layer optimization structure is constructed, and unmanned aerial vehicle task allocation and base station utilization rate balance is realized through an unmanned aerial vehicle charging task scheduling model; and 5, realizing dynamic deployment and scheduling optimization closed loop of the base station through real-time data feedback. The method has remarkable advantages in the aspects of coverage rate, energy consumption, response time delay, system stability and the like. The emergency response speed and the energy utilization efficiency are remarkably improved, the system operation and maintenance cost is reduced, and the method is suitable for scenes such as intelligent traffic and emergency communication.

The invention relates to the technical field of urban traffic management and carbon emission reduction accounting, and provides a bus travel carbon emission reduction accounting method fused with Beidou spatio-temporal data, which comprises the following steps: acquiring spatio-temporal trajectory data based on a Beidou satellite navigation system, and constructing a passenger-trajectory-station three-dimensional correlation model for passenger flow analysis; constructing a passenger transfer analysis system based on the passenger flow analysis result; constructing a dynamic'perception-prediction-optimization 'dynamic scheduling optimization system for passenger flow detail analysis on the basis of space-time trajectory data and a reinforcement learning algorithm; and constructing a mileage-frequency distribution model and a station efficiency evaluation model based on the spatio-temporal trajectory data, respectively generating a trajectory matching optimization strategy and a differentiated station optimization scheme, and realizing adaptive optimization of the public transportation system under dynamic demand change. Compared with a traditional static data dependence method, the method achieves the dynamic and fine-grained monitoring of the operation process of the public transportation system, and effectively improves the timeliness and integrity of data.

The invention discloses an expressway reconstruction and extension traffic diversion method, system and device and a medium, and the method comprises the steps: obtaining multi-source dynamic traffic data, and constructing a road network origin-destination matrix through data fusion and demand deduction; a traffic flow distribution result is obtained by constructing a system optimal traffic distribution model; identifying the first congestion time period, and calculating the traffic flow exceeding the residual traffic capacity in the congestion time period to obtain the traffic flow to be shunted; solving an optimal shunting path under the target of minimizing the total travel time of the system through a hybrid intelligent algorithm to obtain an optimal shunting path scheme; and verifying the optimal shunting path scheme by using microscopic traffic simulation software, updating traffic data based on a simulation result, and carrying out iterative optimization to obtain a differentiated traffic organization scheme. According to the invention, the traffic flow passing efficiency during the highway reconstruction and extension period is effectively improved.

The invention discloses a collaborative data processing method and system based on source-network-load-storage integration. The collaborative data processing method and system are used for data acquisition, processing and optimal scheduling of multi-source heterogeneous equipment. The method comprises the following steps: registering a power generation side device, an energy storage side device, a power distribution network device and a load side device, obtaining self-description information, automatically identifying a communication protocol and loading a corresponding drive; a standardized data stream is generated through protocol conversion and semantic mapping, edge nodes execute real-time feature extraction and preliminary calculation, and a cloud platform performs global state estimation, prediction analysis and optimization scheduling; establishing a time delay monitoring and task priority mechanism to realize edge and cloud dynamic collaboration; performing quality evaluation and anomaly repair on the transmission data to generate a high-quality data set; a probability distribution model is established based on data, an uncertainty weight is calculated, feature fusion and weighted dimension reduction are performed through a collaborative data processing engine, a weighted confidence matrix is formed and input into optimal scheduling, system robustness is enhanced, and the method is suitable for a power system with a high new energy proportion and large load fluctuation.

The invention relates to the technical field of power systems and automation thereof, in particular to a generative adversarial network-based source-load joint scene generation method, which comprises the following steps of: constructing a multi-source time sequence database and extracting weather, time, space and historical state driving factors; establishing a joint probability distribution model based on a vine connection function; taking a numerical weather forecast path and a date type as conditional input, constructing a generative adversarial network embedded with a physical constraint microloss function of the power system, and forming a physical information generator; performing dependent structure fidelity verification on the generated scene by using the joint probability distribution model; generator parameters are fixed, potential space vectors are optimized through a gradient ascending method to maximize power grid risk indexes, and a high-risk source-load joint scene set is generated. According to the technical scheme, accurate generation of the source-load joint scene which is physically feasible and reasonable in statistics and focuses on the high-risk working condition is realized, and the safe operation toughness and the risk early warning capability of the novel power system are remarkably improved.

The invention relates to a natural language processing technology, in particular to a fine empirical tracing and declaration verification method based on heterogeneous target fusion, which comprises the following steps of: retrieving candidate documents related to a to-be-verified declaration, and extracting candidate sentences related to the to-be-verified declaration; constructing a cascaded statement-sentence association probability distribution model and a statement-sentence set association probability distribution model; learning prediction probability distribution between the declaration and a single candidate sentence based on a declaration-sentence association probability distribution model; screening a plurality of candidate sentences with relatively high empirical values to obtain a candidate empirical set; on the basis of a statement-sentence set association probability distribution model, capturing a semantic synergistic effect between candidate sentences in the candidate empirical set, and calculating global category probability distribution of the statement to be verified and the candidate empirical set; jointly optimizing parameters of the two models by adopting a heterogeneous target; and on the basis of the two trained models, fine empirical cause tracing and declaration verification are realized. According to the invention, a visual and accurate viewpoint verification result can be provided.

The invention discloses a multi-dimensional distribution offset adaptive threshold detection method and system, and the method comprises the steps: obtaining real-time data, extracting a multi-dimensional statistical feature, and obtaining a feature vector; based on historical normal data, using an expectation maximization algorithm to train a Gaussian mixture model, and determining parameters to obtain a normal distribution model; inputting the feature vector into the model, and calculating a probability value of the feature vector belonging to normal distribution as a first offset judgment index; based on the real-time data distribution of a plurality of detection objects in the same group, the distribution difference of any two objects is calculated by using a Wasserstein distance, and the similarity between the objects is obtained; and constructing a similarity network and calculating connectivity as a second offset judgment index. Setting a fixed-length sliding window, dynamically updating two indexes in the window, and obtaining a first self-adaptive threshold value and a second self-adaptive threshold value; and when any index is lower than a corresponding threshold value, determining distribution offset and giving an alarm, and updating model parameters in real time by using an incremental expectation maximization algorithm. According to the invention, accurate detection and intelligent analysis of data distribution offset are realized.

The invention relates to the technical field of AI vision, in particular to a sesame seed candy forming defect real-time detection method and device based on AI vision. The method comprises the following steps: respectively collecting multimode images of qualified sesame seed candies, generating a qualified characteristic fingerprint set, and calculating sugar body light transmission uniformity and sesame adhesion density as a domain parameter set; constructing a probability distribution model, and setting an anomaly judgment threshold value and a domain parameter early warning threshold value; collecting a multi-mode image of the to-be-detected sesame seed candy in real time, extracting a to-be-detected feature fingerprint, and calculating the light-transmitting uniformity of a to-be-detected candy body and the sesame adhesion density; calculating a comprehensive abnormal score, and judging a defect; and capturing a low-confidence sample based on the comprehensive anomaly score, obtaining an artificial correction feedback sample, and updating a probability distribution model, an anomaly judgment threshold value and a domain parameter early warning threshold value by utilizing the feedback sample through online incremental learning. According to the invention, the detection cost of a high-yield production line can be reduced, and the model deployment period is shortened.

The invention discloses an artificial intelligence-based agricultural waste carbon emission reduction scenario simulation method and system, and the method comprises the steps: obtaining multi-source geographic space data of a target region, uniformly fusing the multi-source geographic space data into a grid system with a preset resolution, and carrying out the calculation of the multi-source geographic space data based on a space weighted distribution model, the statistical yield of provincial agricultural products is distributed to each grid of a grid system, and the theoretical resource quantity and the high-precision spatial distribution diagram of the agricultural wastes are obtained in combination with a grass-grain ratio database. Method for determining carbon emission and carbon sink quantitative parameters of agricultural wastes under various utilization paths through dynamic accounting model, simulating carbon emission reduction potential and spatial distribution under different resource utilization scenes based on artificial intelligence model according to carbon emission and carbon sink quantitative parameters of agricultural wastes under various utilization paths, and planting front-end crops and structure and space layout is optimized. A quantifiable and visual scientific decision-making tool is provided for regional low-carbon agricultural planning, and the accuracy and feasibility of an emission reduction strategy are remarkably improved.

The invention belongs to the technical field of smart park management, and discloses an Internet of Things data fusion method for comprehensive management of a city-level smart park. The method comprises the following steps: constructing an odor spatial distribution model corresponding to each park, and carrying out chemical component analysis to obtain park odor characteristics of each park; comparing the park odor characteristics with a predefined normal odor baseline, and dynamically identifying abnormal odor characteristics corresponding to each park; determining a potential risk event corresponding to each park based on the abnormal odor characteristics; based on the odor spatial distribution model and the abnormal odor characteristics, reversely positioning the position of an abnormal source, and generating an odor source positioning map corresponding to each park; according to the potential risk event and the smell source positioning map, a targeted emergency response strategy is automatically generated and executed; according to the invention, a closed-loop management system from monitoring and early warning to emergency response can be formed, and the environmental risk prevention and control capability and the emergency response speed of the park are improved.

The invention relates to the field of medical treatment, and discloses a method for treating chronic insomnia by percutaneous magnetic stimulation of stellate ganglion block, which comprises the following steps: preprocessing: determining the initial position of stellate ganglion, tissue initial parameters and initial stimulation parameters through multi-modal image positioning and initialization to obtain reference data; dynamic sensing: collecting neck tissue displacement data, temperature field data and electromyographic signals in real time, and calculating spatial position variation of stellate ganglions and time-varying parameters of tissue electromagnetic characteristics; and modeling optimization: constructing a time-varying electromagnetic field distribution model based on the reference data, the spatial position variable quantity and the time-varying parameters. The method comprises the following steps: acquiring neck tissue displacement, a temperature field and an electromyographic signal in real time through reference data, capturing a stellate ganglion space position change and tissue electromagnetic property time-varying rule, constructing a time-varying electromagnetic field distribution model, and solving target stimulation parameters adaptive to a real-time tissue state through an optimal control algorithm.

The invention belongs to the technical field of uncertainty probability characterization analysis, and discloses a thermoplastic composite material leading edge structure design allowable value probability characterization method and system under a small sample condition, and the method comprises the steps: defining a plurality of candidate probability distribution models; fitting each model based on the original sample data and calculating an AIC value and a BIC value; a dynamic weight factor alpha is calculated according to the sample size n, and then a hybrid information criterion HIC value is calculated; generating a plurality of sample sets through Bootstrap self-service sampling, recalculating the HIC value on each sample set, and counting the selected optimal frequency of each model; and determining an optimal probability distribution model according to the frequency, wherein the optimal probability distribution model is used for representing a design allowable value. According to the method, the dynamic weight factor alpha is introduced, AIC and BIC criteria are effectively unified, optimal balance between prediction precision and model complexity is achieved under the condition of small samples, and engineering practicability and robustness are remarkably improved.

The invention relates to the technical field of three-dimensional reconstruction and rendering, and particularly discloses an underwater long-sight-distance adaptive three-dimensional reconstruction and rendering method based on a Gaussian variable-distance field, and the method comprises the steps: carrying out the initialization point cloud generation based on a motion recovery structure algorithm for RGB image data obtained by underwater image collection equipment; points on the initialized point cloud are combined with a three-dimensional Gaussian distribution model, a three-dimensional Gaussian primitive is defined, and parameter initialization about information such as a Gaussian geometric center, a three-dimensional covariance matrix, opacity and color is carried out on the three-dimensional Gaussian primitive; the method comprises the following steps: establishing a Gaussian cross-space transformation model, performing transformation from a Cartesian coordinate space to a homogeneous coordinate space on a three-dimensional Gaussian primitive, establishing a Gaussian full-parameter space mapping model, mapping color information of the three-dimensional Gaussian primitive after cross-space transformation into a spherical harmonic function, and spatial adaptive three-dimensional reconstruction and rendering in an underwater long-sight-distance environment are realized.

The invention relates to an industrial supply chain big data inventory dynamic optimization method, and belongs to the field of industrial supply chain management. Comprising the steps of supply chain multi-dimensional data acquisition and preprocessing, dynamic inventory demand prediction based on a time sequence interactive feature extraction (SIFM) algorithm, inventory safety line intelligent regulation and control in combination with credit and response factors, and real-time dynamic inventory adjustment and optimization based on an inventory circulation active early warning and adjustment and optimization algorithm (PIAW). And continuous adaptive optimization based on incremental distributed feedback learning is carried out. According to the method, the inventory transfer and replenishment scheme can be automatically decided under the emergencies such as order sudden increase or logistics abnormity, and the inventory distribution model is dynamically optimized. Through multi-source data real-time integration and intelligent algorithm cooperation, optimal distribution and efficient and stable operation of supply chain inventory are realized, and the supply chain response speed and the inventory turnover efficiency are greatly improved.

The invention particularly relates to an airborne SAR distance compressed domain sea clutter suppression method based on generalized gamma distribution, and the method comprises the steps: carrying out the range pulse compression processing of an echo signal received by an airborne SAR, and extracting a pure sea clutter sample from compressed data through employing a non-local sampling strategy; then, a generalized gamma distribution model is quickly fitted for the clutter samples by using an analytical method based on a second type logarithmic cumulant, and a self-adaptive detection threshold is solved through an incomplete gamma function based on the model and a set false alarm probability. And finally, according to the threshold value, carrying out judgment and filtering on distance compressed domain data, suppressing clutter regions lower than the threshold value, and meanwhile, retaining potential target signals, thereby realizing efficient and steady clutter suppression under complex sea conditions.

The invention belongs to the technical field of image processing, and particularly relates to an insulator size deviation analysis method and system based on point cloud data, and the method comprises the steps: obtaining an insulator point cloud, carrying out the slicing processing, and calculating the form distortion index of each slice; based on a gravity torque distribution model, the distortion index is normalized into a gravity impedance coefficient, and the gravity compliance confidence coefficient is determined according to the difference value of the impedance coefficients of the adjacent slices; and driving a standard CAD model skeleton to perform weighted deformation by using the confidence coefficient, generating a deformation reference model, and registering the deformation reference model with the point cloud to obtain the dimensional deviation. According to the method, natural falling and structural defects of the insulator caused by gravity can be distinguished, gravity bending interference is eliminated by constructing the self-adaptive reference model, and high-precision detection of abnormal conditions such as dirt accumulation, icing or damage of the insulator is achieved.

The invention relates to the technical field of radiation environment monitoring, and discloses a dose calculation method and system based on radiation environment monitoring data. The method comprises the following steps: acquiring radiation source data, calculating initial radiation field distribution based on the data, obtaining an initial dose deposition value of each space point in a monitoring area, analyzing interaction among particles, and determining an energy redistribution coefficient of each space point; adjusting the initial dose deposition value point by point by using the coefficient to obtain corrected three-dimensional dose concentration field data, calculating a gradient vector between spatial points, and generating an optimized gradient distribution diagram; extracting a boundary point set with gradient change exceeding a threshold value, constructing a contour surface grid to form a three-dimensional contour surface structure, obtaining a time sequence radiation source data iteration updating coefficient, and obtaining a concentration field time sequence evolution sequence; the high-concentration area is identified and marked based on the sequence, the high-concentration area and the three-dimensional contour surface structure are fused, a visual distribution model reflecting the dose concentration is generated, and the accuracy of radiation dose calculation is improved.

The invention provides a pipeline leakage positioning method, device and equipment and a storage medium, and belongs to the field of intelligent detection.The pipeline leakage positioning method comprises the steps that concentration data and a pipeline image are synchronously obtained when a detection probe conducts detection along a to-be-detected pipeline; performing image matching on the pipeline image and a reference image to determine spatial position data of the detection probe in a three-dimensional space, the reference image being an image obtained by photographing the pipeline by using known spatial position data in the three-dimensional space; determining a concentration distribution model of the tracer gas on the to-be-detected pipeline based on the concentration data and the spatial position data at different moments; and determining one or more leakage point positions on the pipeline based on the area with the concentration value reaching a preset condition in the concentration distribution model. In the process, by establishing a concentration distribution model, the problem that in the prior art, due to gas diffusion, a pain point is difficult to accurately position is greatly solved, and the repair efficiency and the detection reliability are remarkably improved.

The invention belongs to the field of industrial energy optimization, and discloses a steel plate production-energy collaborative optimization method. The method comprises the following steps: firstly, determining various constraint conditions and optimization targets in a cold rolled steel coil production process; secondly, a rule-driven rolling mill distribution model is established to serve as a first-layer optimization model, contract requirements are matched with the production capacity of different rolling mills, and therefore contracts are distributed to all the rolling mills; and finally, solving the lower-layer time scheduling model, carrying out rolling calculation on the demand electric charge, and carrying out production scheduling on the contract by taking the minimization of the demand electric charge as a target and combining actual production requirements. According to the method, the actual working condition of the cold rolling mill can be well adapted while the maximum power demand of the factory is optimized.

The invention belongs to the technical field of information technology, and discloses a moxibustion temperature control method and system based on multi-sensor fusion, and the method comprises the steps: obtaining a biopotential signal and position offset data from an acupoint region through a sensor, processing the biopotential signal through a classification algorithm, and obtaining a classified potential distribution mode; on the basis of the classified potential distribution modes, heat conduction influence data between adjacent acupoints are obtained, the position offset data are integrated through an information fusion algorithm, fused physiological state indexes are determined, and the problem of measurement distortion caused by acupoint movement and equipment offset is solved through multi-source data fusion and dynamic error compensation; and meanwhile, the system constructs an accurate potential distribution model by utilizing a spatial calibration and classification algorithm, and temperature control accuracy reaches a millimeter level in combination with real-time position tracking, so that the integrity and reliability of data are remarkably improved.