249results about How to "Accurately reflect" patented technology

The embodiment of the invention discloses a malignant load identification method, device and equipment, a medium and a program product, and relates to the technical field of power load monitoring. The method comprises the following steps: performing modal decomposition on an original power utilization sequential sequence to obtain a plurality of intrinsic mode components, and reconstructing intrinsic mode components which do not belong to noise components to obtain a target power utilization sequential sequence; performing feature extraction on the target power consumption time sequence to obtain target power consumption features, and inputting the target power consumption features into a pre-trained malignant load identification model for identification to obtain an identification result; the malignant load identification model is obtained by updating model parameters of a weak learner based on a natural gradient descent method and performing training optimization. The lightweight malignant load learning model obtained through training in the scheme can be deployed and operated on the intelligent electric meter, high-quality input features are obtained through multi-mode decomposition and reconstruction, the accuracy of malignant load recognition is improved, and accurate recognition of the malignant load based on the lightweight model is achieved.

The invention belongs to the technical field of electric power measurement, and discloses a calibration method and system of an intelligent measurement switch and a multi-switch synchronous measurement method, and the method comprises the steps: obtaining voltage and current waveform data of the intelligent measurement switch under a nonlinear load condition, and extracting a frequency spectrum feature matrix of the voltage and current waveform data; constructing an amplitude-phase coupling error model representing a nonlinear incidence relation between amplitude fluctuation and phase deviation, inputting the spectrum characteristic matrix into the amplitude-phase coupling error model, and calculating an amplitude-phase coupling error vector; performing vector decomposition on the amplitude-phase coupling error vector, and separating out an in-phase component error and an orthogonal component error; and generating a calibration compensation coefficient according to the in-phase component error and the orthogonal component error, and performing vector calibration on the intelligent measurement switch, thereby improving the calibration precision of the intelligent measurement switch.

The invention belongs to the technical field of swivel bridge detection, and particularly relates to a swivel bridge horizontal alignment detection device which comprises a mounting seat fixed to the upper end of an abutment, a controller is fixedly connected to the side wall of the mounting seat, and two guide boxes are symmetrically and fixedly connected to the upper side wall of the mounting seat. After the swivel bridge construction is completed, mechanical contact detection is arranged on the abutment, compared with a detection assembly arranged between two swivel bridges which are spaced by four meters, a fixed and accurate mounting reference can be provided for the detection device by means of a stable structural foundation of the abutment, and a detection structure does not need to be arranged between the bridges which are spaced by four meters; the problems of inconvenient operation, large assembly fixing difficulty and the like caused by cross-interval installation are avoided, the bending deformation of the detection rod due to the gravity of the detection rod can be effectively avoided, and the detection precision is guaranteed from the installation reference and the detection structure.

The invention relates to the technical field of underground water simulation, in particular to an underground water numerical simulation method coupled with vegetation ecology. The method realizes bidirectional coupling between groundwater and vegetation ecology through iteration, and specifically comprises the following steps: inverting vegetation coverage based on groundwater burial depth; inversion of actual evapotranspiration is carried out based on underground water burial depth and vegetation coverage; and finally, updating the net supply amount of the groundwater model based on evapotranspiration until convergence. According to the method, a statistical function relation is established to replace a complex physical model, and drainage and overflow cell water levels are corrected in iteration to ensure calculation stability.

The embodiment of the invention provides an online reinforcement learning method and system, a storage medium and a degree product, and the method comprises the steps: controlling a robot to execute a target operation task through a strategy model, so as to obtain corresponding first training data; receiving first interaction information from a human expert to construct corresponding second training data; performing time difference loss calculation based on the first training data and the second training data to train a value evaluation model, and obtaining value evaluation information output by the value evaluation model; performing advantage calculation based on the second training data and the corresponding value evaluation information to obtain advantage information; performing fusion loss calculation based on the advantage information to train a strategy model; the fusion loss comprises advantage weighting strategy loss and near-end strategy optimization loss. According to the embodiment of the invention, the success rate of the robot on the target operation task can be improved under the conditions of lower human teaching cost and fewer environment interaction samples.

The invention discloses a real-time prediction and control method and system for the deformation state of a superplastic forming part based on digital twinning, and belongs to the technical field of intelligent manufacturing. The method aims at solving the technical problems that in the superplastic forming process, the deformation state of a part is invisible, control depends on experience, and quality is unstable due to open-loop control. The core lies in that a multi-task learning artificial intelligence prediction model taking air inflow time sequence data as input and taking pressure in a mold cavity and a part full-field deformation state as output is constructed by fusing parameterized finite element simulation and physical experiment data in an offline stage; in the online stage, the model is utilized to dynamically predict deformation states such as a strain field and a thickness field in the part according to air inflow data collected in real time, and process transparency is achieved; and a model prediction control algorithm is further combined, a prediction state is compared with an ideal path, the air inlet pressure is reversely optimized and adjusted in real time, and a closed-loop intelligent control system is formed. According to the method, perspective and active accurate control of the internal state in the black box forming process are achieved, and the quality consistency and the yield of parts can be improved.

The invention discloses a drilling pump pressure prediction method based on an artificial neural network, and the method comprises the following steps: S1, data collection; s2, data preprocessing; s3, constructing a neural network model; s4, model training and optimization; and S5, model verification and deployment. According to the method, multi-channel data fusion and time synchronization optimization are innovatively adopted, so that the data quality and consistency are improved; in combination with a deep neural network and a self-adaptive optimization strategy, the precision and generalization ability of pump pressure prediction of the drilling pump are improved; and an online updating mechanism is introduced, so that the model can be dynamically optimized according to real-time data, the defects of low prediction precision, poor adaptability and difficulty in real-time updating of a traditional method are overcome, and an efficient and reliable prediction means is provided for intelligent drilling control.

The invention provides a dam deformation prediction method and system. The method belongs to the field of water conservancy dam deformation monitoring, and comprises the following steps: carrying out clustering analysis on a plurality of measuring points of a dam by adopting a K-means algorithm, and merging the measuring points with similar characteristics into the same group to obtain a plurality of measuring point groups; wherein in the clustering analysis process, a correlation change method is selected to determine the optimal cluster number; inputting the clustered measuring point groups into a pre-established HST-M model for spatial feature fusion to obtain a high-dimensional feature matrix; carrying out feature dimension reduction on the high-dimensional feature matrix by adopting an auto-encoder; and inputting the dimensionality-reduced low-dimensional features into a ridge regression model for prediction so as to obtain a deformation prediction result of the dam measurement point. The system comprises a clustering module, a fusion module, a dimension reduction module and a prediction module. The method and the system can improve the prediction stability of dam deformation.

The invention relates to the technical field of asset management, in particular to an underground space value evaluation method fusing multi-source space big data, and the method comprises the steps: obtaining the multi-source space big data in real time, carrying out the time-space fusion processing of the obtained multi-source space big data, and generating a time-space data set of a target underground space; calculating a plurality of evaluation indexes of the target underground space based on the spatio-temporal data set, and predicting the value of the target underground space by using the plurality of evaluation indexes; and comprehensively evaluating the plurality of evaluation indexes through a machine learning model, and correcting the predicted value based on a comprehensive evaluation result to obtain a final evaluation value.

The present application relates to the technical field of dental root image processing, and particularly relates to an intelligent detection method for bone density change caused by dental root absorption area of impacted wisdom tooth. The present application constructs a coordinate system of images under different angles, obtains a gray weighted value of each pixel point in the images under each angle according to actual source image distances of the images under different angles and position distribution of preset anatomical structures, obtains a bone density change rate of each position pixel point in combination with displacement change amounts and gradient features of registered pixel points between the images under different angles, obtains a high risk degree threshold value of bone density change according to bone density change rate distribution of all position pixel points, and obtains a high risk area of bone density change of each image according to the bone density change rate of each position pixel point on each image and the high risk degree threshold value of bone density change. The present application improves the accuracy of bone density change detection by accurately analyzing the bone density change area.

The present application relates to the field of smart meter data acquisition, especially to a CPU occupancy-based meter data acquisition optimization method and system, the method comprising: obtaining the signal strength and working state of concentrator and meter nodes, establishing a data acquisition network model and calculating the load rate to reflect the network busy degree. Vector coding is used to generate initial particle swarm, each particle represents a data acquisition path, the transmission efficiency and communication quality of the path are evaluated, and the path score is obtained by weighted fusion based on the load rate. The particle with the highest score is selected as the global optimal solution to guide the particle swarm learning, the learning ratio is adjusted through the negative exponential function, the parent node vector is updated and iteratively optimized until the score no longer improves, the optimal path is output, and the data acquisition path optimization is completed. The present application dynamically adjusts the weight and optimization strategy, adapts to network changes, reduces the hop count, avoids weak signal links, and improves the transmission efficiency and stability of the data acquisition path.

This invention relates to an intelligent decision support system for rectal cancer prognosis integrating digital pathological images, belonging to the field of medical image processing technology. The system includes a data acquisition module for collecting multiple sets of sample data; a feature selection module for identifying multiple key medical imaging features of rectal cancer based on the multiple sets of sample data; a model building module for acquiring a pre-trained deep learning model and constructing a rectal cancer prognosis prediction model based on transfer learning, the pre-trained deep learning model, the multiple sets of sample data, and the multiple key medical imaging features of rectal cancer; an image acquisition module for acquiring pre- and post-operative medical images of the rectal cancer patient to be evaluated; and a recurrence prediction module for predicting the recurrence probability of the rectal cancer patient to be evaluated based on the pre- and post-operative medical images of the patient using the rectal cancer prognosis prediction model. This system has the advantage of improving the accuracy of non-invasive prediction of postoperative recurrence of rectal cancer.

The invention discloses a power distribution cabinet operation state monitoring control system, particularly relates to the technical field of power distribution cabinets, and aims to perform time weighted integration on a current sequence by using an electric heating time constant and an equivalent heat capacity, construct a short-time impact sensitivity coefficient, and combine impact duration, a current duty ratio and a temperature rise gradient to obtain a short-time impact sensitivity coefficient. A load process is divided into a short-time impact stage and a continuous load stage, so that an acceptable short-time current impact which is large in amplitude but not accumulated in heat and a dangerous overload process which is not extremely high in amplitude but continuously high in temperature can be distinguished physically; an electric heating accumulation index is constructed through an electric heating segmentation feature set, and a material allowable temperature rise curve is introduced to form a safe electric heating boundary model, so that a monitoring system can recognize a danger approaching trend in advance from the perspective of a heat accumulation risk.

This application discloses an adaptive voxelization attribute assignment and dual-model collaborative mineral exploration method and system based on multi-source data, relating to the fields of artificial intelligence and mineral exploration prediction. The method includes: dividing the target area into three-dimensional voxel grids using an adaptive grid function; assigning attribute values ​​of multi-source mineralization-prospecting information to the three-dimensional voxel grids of the target area and known mining areas, and assigning attribute values ​​of ore grade to the three-dimensional voxel grids of known mining areas; calculating contribution weights using a logistic regression model, and obtaining the first mineralization score of the target area's three-dimensional voxel grids through linear combination calculation and mapping; training a deep learning model using multi-source mineralization-prospecting information as input and ore grade as a label, and determining the second mineralization score of the target area's three-dimensional voxel grids; determining the comprehensive mineralization score of each three-dimensional voxel grid in the target area using a comprehensive scoring function, and delineating the mineral exploration target area based on the comprehensive mineralization score. This application improves the accuracy and interpretability of mineral exploration in the target area.

This application provides a temperature control method and system for vacuum wetting indium in an array field emitter, relating to the field of vacuum electronic device manufacturing technology. The method includes: obtaining the three-dimensional temperature field distribution of the surface of the field emitter using a deep convolutional neural network based on spectral radiation energy; calculating the transverse and longitudinal temperature gradients between the emitter pins based on this distribution data, and inputting this data into an evaluation model to dynamically assess the wetting uniformity; and generating multi-temperature zone heating power control commands based on the uniformity results using a proportional-integral-derivative controller, and adjusting the power output of each temperature zone to ensure the array temperature field meets the requirements for promoting uniform indium wetting. This application improves the control accuracy and reliability of indium wetting uniformity in complex three-dimensional field emitters under vacuum and high-temperature conditions.

The invention relates to the field of carbon emission prediction, in particular to a subway station construction stage carbon emission prediction method and system. Measuring and calculating carbon emission of the subway integrated station at different stages according to a carbon emission coefficient method, and performing data preprocessing to obtain carbon emission monitoring data; performing feature and target variable separation on the carbon emission monitoring data to obtain a feature matrix and a target variable vector; constructing a carbon emission prediction model to perform feature processing on the carbon emission monitoring data to obtain carbon emission feature data; the method comprises the following steps: constructing a carbon emission prediction integrated model based on LightGBM and XGBoost, identifying carbon emission characteristic data through the carbon emission prediction integrated model, and optimizing parameters in the LightGBM model and the XGBoost model by using grid search to obtain a carbon emission prediction amount in a subway station construction stage.

The application discloses a kind of weak steady-state energy conservation's coal-fired boiler calorific value correction method and device, the method includes the following steps: extracting weak steady-state parameter main parameter from the real-time data of unit operation, each main parameter is filtered in time detection window and weak steady-state condition is judged, when all main parameters meet weak steady-state judgment condition during detection, the mean value of energy conservation parameter under the corresponding detection number is output;Using the mean value of energy conservation parameter carries out boiler heat balance calculation, obtains the actual fuel heat value corresponding to current unit load;Actual fuel heat value Q net.ar The ratio COR of the original design fuel heat value Q design Of unit is used as coal-fired boiler calorific value correction amount, and the fuel quantity of unit is adjusted by coal-fired boiler calorific value correction amount;The application has the advantages that: avoid system repeated oscillation, improve calorific value correction precision.

The application discloses a hemodialysis sufficiency evaluation method based on sodium balance, and relates to the technical field of hemodialysis, and the method comprises the following steps: obtaining the body weight, dry body weight and serum sodium concentration of a patient before dialysis; calculating a target sodium removal amount Na_target=DeltaW* [Na]s / 0.93, and establishing a quantitative equivalent relationship between the body weight increase and sodium accumulation; obtaining or calculating an actual sodium removal amount (including two ways of convection and diffusion); calculating a sodium removal ratio R_Na=Na_actual / Na_target; and evaluating the dialysis sufficiency according to the R_Na. The application firstly changes the dialysis sufficiency evaluation from 'urea removal' to'sodium and water removal', and directly aims at the main pathological burden of kidney failure patients, i.e. sodium and water retention. The sodium removal ratio R_Na is more directly related to the volume state and cardiovascular prognosis of the patient than the traditional Kt / V index. The method only needs the body weight and serum sodium before and after dialysis to be calculated, and can be integrated into the existing dialysis information system.

This invention provides a method and device for predicting temperature changes in electronic components based on pentahedral elements. The method includes: meshing the electronic component into pentahedral elements based on its structural parameters; determining the three-dimensional transient heat conduction governing equation and initial algebraic equation for the target region; determining correction terms based on the virtual node domain defined for the pentahedral elements, the Galerkin weight function, the three-dimensional transient heat conduction governing equation, and the initial algebraic equation; correcting the initial algebraic equation based on the correction terms to obtain the target algebraic equation; determining the final algebraic equation based on the target algebraic equation and a preset time interval; solving the final algebraic equation to obtain the transient heat conduction temperature simulation result; adjusting design parameters based on the transient heat conduction temperature simulation result; and implementing optimized design of the electronic component based on the adjusted design parameters.

The application discloses an immersive neural rehabilitation training method based on virtual reality, and particularly relates to the technical field of neural rehabilitation training, and comprises the following steps: acquiring action data, tactile data, proprioceptive data and gaze data when a trainee performs a target action, performing uniform time sequence alignment on the data in a virtual reality environment, reorganizing according to a starting section, a pushing section and a reaching section of the target action, and outputting a target action response sequence; performing segmented comparison on the differences of multi-source responses of the target action under the conditions of visual weakening, tactile weakening and proprioceptive weakening to determine a perception dependency relationship, rearranging the prompt items and feedback items in the virtual reality scene according to the perception dependency relationship, and then performing recovery checking on the rearranged results.

The application discloses an evaluation method, device, equipment, storage medium and computer program product, wherein the method comprises: obtaining a performance index score of an air heat exchanger, the performance index score comprising one or more of an environmental index score, an energy efficiency index score and an air leakage index score; the energy efficiency index score is determined based on an energy efficiency measured value and an energy efficiency benchmark value, and the air leakage index score is determined based on an outdoor side outflow humidity content and an indoor side outflow humidity content; then, a weight parameter of each performance index score is obtained; finally, a performance evaluation result of the air heat exchanger is determined based on each performance index score and each weight parameter. Through the embodiment of the application, the performance of the air heat exchanger is evaluated in combination with multiple performance index scores and in combination with measured values and experimental values, so that the performance evaluation result of the air heat exchanger can be quickly and accurately determined.

The application discloses a diabetic nephropathy pathological model based on human renal tubular epithelial cell organoids and a construction method and application thereof, and belongs to the field of biomedical technology. The construction method comprises the following steps: jointly inducing the renal tubular epithelial cell organoids in a culture medium containing glucose, advanced glycation end products and tumor necrosis factor alpha to prepare the diabetic nephropathy pathological model. The diabetic nephropathy pathological model is constructed by joint induction, the model shows the core pathological phenotypes of diabetic nephropathy, including down-regulation of renal tubular epithelial cell marker expression, up-regulation of fibrosis marker expression, thickening of the basement membrane and abnormal albumin uptake function, the construction method is simple and standardized, the construction period is short, the model has good repeatability and stability, the model has wide application value in high-throughput screening of traditional Chinese medicine monomers or compound medicines with kidney protection, and provides a new platform for mechanism research and treatment strategy development of diabetic nephropathy.

The application provides a method for evaluating the risk of tuberculosis under the combined exposure of extreme weather and air pollution, comprising the steps of: obtaining meteorological data, air pollution data and tuberculosis incidence data of a target area within a target period; identifying extreme weather events based on the meteorological data, identifying air pollution events based on the air pollution data, and identifying combined exposure events based on the meteorological data and the air pollution data, wherein the combined exposure events are combinations of the extreme weather events and the air pollution events that occur within a time interval less than a preset threshold and are spatially co-located; based on the spatial co-location, constructing a regression model with the number of cases in the tuberculosis incidence data as the dependent variable and whether the combined exposure event occurs as the binary explanatory variable, and evaluating the influence of the combined exposure event on the risk of tuberculosis. The application realizes high-precision quantitative evaluation of the risk of tuberculosis under the combined exposure of extreme weather and air pollution, and provides a scientific basis for early warning and precise prevention and control.

This invention provides a method for determining the size of a pulse wind tunnel test model, comprising: determining the flow field of the nozzle and test section using numerical simulation to obtain a uniform flow field region S1 in the wind tunnel; then calibrating the uniform flow field region S2 of the nozzle and test section using a cross-shaped frame, comparing the sizes of the two uniform flow fields S1 and S2, and selecting the smaller uniform flow field region S0 as the uniform flow field region S0; scaling down the selected model under the condition of maximum test angle, with the maximum projected area M1 of the model in the direction perpendicular to the airflow occupying 60% to 65% of the cross-sectional area of ​​the uniform flow field S0; integrating the scaled-down model with the nozzle and test section into a single model, numerically simulating the entire flow process under the condition of maximum angle of attack and / or sideslip angle test angle, redetermining the model scale based on the model installation position, the shock wave position at the leading edge of the model, and the coverage of the uniform flow field, and determining the upper limit of the model length through the effective test time and the velocity of the flow field in the test section.

This invention belongs to the field of oil and gas reservoir development technology, specifically disclosing a method for establishing a high-precision stratigraphic model profile through well-seismic integration. The method comprises the following steps: S1, establishing an initial geological model; S2, calculating the stratigraphic lines of geological sub-layers; and S3, generating a new geological model. Based on seismic data and drilled vertical wells, this invention provides a high-precision stratigraphic model for horizontal wells to be drilled. It is suitable for complex models of single wells, multiple wells, and multiple stratigraphic layers, providing a basis for seismic geological guidance work during horizontal well drilling, improving the drilling rate of reservoir "boxes," and reducing drilling engineering risks.

This invention relates to the field of social network analysis technology and proposes a social network sentiment evolution model that integrates user influence and activity. To address the shortcomings of existing technologies, such as inaccurate simulations and inflexible predictions in current network sentiment analysis and sentiment evolution processes, this invention provides a social network sentiment evolution model that integrates user influence and activity. The model analysis method involves: collecting text sentiment data from target users; collecting user influence data from each user; collecting the activity level of the target social network at time t; collecting the forgetting probability of each user at time t as a measure of sentiment evolution capability; establishing a dynamic model for the sentiment evolution of the social network; optimizing the evolution probability of the information propagation model based on text sentiment; and analyzing the evolution process of the dynamic model for the sentiment evolution of the social network based on the optimized information propagation model. This model is suitable for application in social network analysis.

This invention relates to a method for modeling the capillary evolution of humidity-related adhesion forces under micrometer-scale probes. This method establishes a physical relationship between ambient relative humidity and Kelvin radius, and calculates the Kelvin radius in conjunction with the initial volume of the liquid bridge, thereby constructing a parameterized model of capillary forces that characterizes the change in liquid bridge volume with ambient humidity. The influence of the liquid film on the solid surface is considered during model construction, enabling the model to more realistically reflect the formation and evolution of liquid bridges in actual micro / nano interfaces. As the two surfaces gradually move away, the relationship between the curvature of the liquid bridge interface and the Kelvin radius is established under the condition that the liquid bridge volume remains constant, thus achieving a continuous description of the evolution of capillary and adhesion forces during the separation process between the micrometer-scale probe and the sample interface. Furthermore, experimental measurements and model verification of micrometer-scale interface forces are conducted using a self-developed fiber Bragg grating probe.

This application relates to a method and system for recommending urban park locations based on crowd activity preferences. The method includes: responding to a park location recommendation instruction by detecting user-input information; and obtaining a recommendation result based on a knowledge graph and a trained park location recommendation model, whereby the recommendation result includes at least one location. The knowledge graph is constructed based on a park location feature dataset and a crowd activity preference dataset; the park location recommendation model is trained using a deep learning model based on the knowledge graph; and the park location feature dataset includes multi-dimensional sensory environment data and spatial structure data of all locations within the park. This method enables personalized recommendations for park locations.

The application discloses an interactive form design method and system based on a language large model, and belongs to the technical field of human-computer interaction and form automatic generation. In view of the problems that a traditional form design tool is tedious to operate, highly depends on manual experience, and an existing method based on a large model has high training cost, weak interaction ability and difficulty in real-time collaboration, the following scheme is used for improvement: defining a JSON Schema specification of a form and constructing a visual form designer, integrating a language large model interaction entrance in an interface, dynamically assembling a request containing user instructions, a JSON Schema specification and existing form data according to whether the form is empty, and submitting the request to the language large model; receiving a response in a streaming manner, and real-time analyzing and progressively updating a form designer preview interface, so that a user can directly generate or modify a form through natural language instructions, and multi-round interaction and manual subsequent adjustment are supported. The method is mainly used for reducing a form design threshold, improving design efficiency and intelligent collaboration level.

The invention relates to the technical field of calcification evaluation, in particular to a human tissue calcification evaluation method based on a CT image, which comprises the following steps: S1, personalized calcification focus definition: determining an observation range on the CT image according to a to-be-evaluated tissue, and setting a CT value and a lower limit of a continuous area according to tissue characteristics; s2, pixel point screening: screening calcification focus pixel points according to the personalized definition; s3, calculating a calcification score: calculating the calcification score by using a proper method according to the screened calcification focus pixel points, wherein the method comprises a mean area method, a layered accumulation method and a CT value summation method; s4, calcification score correction: correcting the calcification score according to the number of CT layers; and S5, other related calculations: calculating the total calcification area, the total volume, the total mass and the Agatston score. According to the method, the CT value threshold value and the continuous area lower limit of the calcification focus are set in a personalized mode, different tissue characteristics and individual differences can be reflected more accurately, and therefore the accuracy of calcification evaluation is improved.