224 results about "Weighted average method" patented technology

The invention provides an equipment supervision data real-time analysis method and system, and the method comprises the steps: calculating an energy value of a frequency band corresponding to a sub-band signal, determining the energy characteristics of the surface roughness and dust deposition distribution uniformity of a blade in each sub-band in a square sum form, and generating energy distribution vectors which are arranged according to a frequency band sequence; performing time-frequency analysis on the vibration signal by using the acquired real-time rotating speed information of the gas turbine, identifying a frequency component corresponding to the eccentric distance value of the rotor and the vibration frequency offset, and determining the amplitude of the frequency component; according to the classification result and the real-time rotating speed information, the comprehensive influence index of the material density change rate and the ash deposition layer thickness difference is calculated, the fault early warning level is determined through a weighted average method, and an early warning signal is generated; and according to the change trend of the early warning signal and the blade edge wear degree, a random forest algorithm is adopted to predict a development curve of the vibration frequency offset in the future target time, and real-time equipment supervision analysis data are obtained.

The invention provides a real-time deep sea subsurface buoy monitoring system, which belongs to the technical field of deep sea measurement, and comprises a control chip, a multi-parameter sensor array, a data storage device and a power supply, high-precision monitoring is realized through the following steps: recording ocean data acquired by a multi-parameter sensor; correcting the sound wave propagation model and calculating the relative position of the subsurface buoy; constructing a three-dimensional data matrix and realizing data compression; identifying abnormal data by using a matrix disorder index; processing the time sequence data by adopting a sliding window weighted average method and eliminating jump; carrying out distributed preprocessing on the multi-source heterogeneous data and carrying out parallel processing by applying a matrix partitioning technology; accumulated errors are corrected by combining historical data and applying a Kalman filtering algorithm, and the problems of environment interference and sensor drift are solved through a deep sea environment disturbance compensation network model and a self-adaptive weighted error optimization function.

The invention relates to the technical field of wind field simulation, and provides a wind field numerical simulation method based on multiple meteorological data sources. The objective of the invention is to solve the problems of large simulation error, rough terrain boundary processing and poor turbulence model parameter adaptability caused by non-uniform coverage of a single data source, insufficient precision and unscientific multi-source fusion. The method is characterized by comprising the following steps: step 1, constructing a CFD three-dimensional grid based on a geometric model of a target area; 2, collecting original data of a ground station, satellite remote sensing, numerical forecasting and the like; 3, performing standardized preprocessing (abnormal value elimination, missing interpolation, radiation / geometric correction and resampling), determining a multi-source fusion weight by combining historical data analysis, and generating comprehensive meteorological data by adopting a weighted average method; and 4, inputting the comprehensive data into a CFD-RANS model, dynamically adjusting turbulence parameters, accurately setting terrain boundary conditions, and obtaining a wind field space-time distribution result through numerical solution. According to the invention, through combination of multi-source data fusion and CFD simulation, the wind field simulation precision and stability are improved.

The invention discloses a GPS interference phased array monitoring direction-finding antenna system for a 1575 frequency band, and relates to the technical field of intelligent antennae, comprising a data integration module for integrating data of satellite ephemeris, carrier attitude and airspace coverage through a weighted average method and generating an environment parameter set, a prediction calibration module for predicting the data of the satellite ephemeris, the carrier attitude and the airspace coverage through a weighted average method and generating an environment parameter set, and a direction-finding antenna module for performing direction-finding on the environment parameter set. The phase drift prediction module is used for inputting an environment parameter set into a bidirectional LSTM network, obtaining a phase drift prediction value, carrying out nonlinear calibration on the phase drift prediction value by adopting a Kalman-particle hybrid filtering algorithm, and synchronously monitoring an IQ signal by utilizing a closed-loop feedback mechanism, and the beam generation module is used for carrying out orthogonal modulation and radio frequency up-conversion processing on the IQ signal, and outputting the processed IQ signal. According to the method, the bidirectional LSTM network is combined with the Kalman-particle hybrid filtering algorithm, so that phase drift caused by factors such as carrier vibration and the like can be accurately predicted and calibrated in real time.

The invention discloses a user demand comprehensive analysis method based on online user comment data. The method comprises: calculating a user demand intensity value; calculating a user demand emotion score; calculating a user demand weight value by adopting an IGR-AHP model based on the user demand emotion score; and respectively endowing the user demand intensity value and the user demand weight value with weight coefficients by using a weighted average method, thereby calculating a user comprehensive evaluation index value, and finally carrying out priority ranking on the user demands according to the user comprehensive evaluation index value. The method has the advantages that the LDA topic model is used for text mining, and the problem of how to effectively extract user demand information from a large number of user comments is solved; a user demand emotion score is calculated by fine tuning the BERT model, and the problem that a traditional emotion dictionary analysis method is tedious in process is solved; and finally, combining and considering the strength value and the weight value of the user demand through a weighted average method, and carrying out priority ranking on the user demand.

The invention discloses a multi-dimensional drainage port overflow risk assessment system and assessment method, and belongs to the technical field of drainage port overflow risk assessment systems, and the system integrates ground monitoring and satellite remote sensing multi-source data, and collects related information of a drainage port in an omnibearing manner. The ground monitoring data provides accurate local hydrology, water quality and drainage facility operation state information, the satellite remote sensing data realizes large-range monitoring, the advantages of the ground monitoring data and the satellite remote sensing data are complementary, the data are fused through a weighted average method and a Kalman filtering method, the data accuracy and reliability are improved, and the weighted average method distributes weights according to data characteristics for preliminary fusion, so that the accuracy and reliability of the data are improved. The Kalman filtering method adapts to data dynamic change, such as water level data fusion according to a state equation and an observation equation optimization result, combines the advantages of ground high-precision monitoring and satellite wide-range coverage, accurately reflects water level change, quantifies the overflow risk of a drainage port, and provides effective support for urban water fine supervision.

The invention provides a multi-source data fusion-based high-wind disaster risk assessment method along a high-speed rail, and the method comprises the steps: obtaining the meteorological temperature distribution, the wind speed peak record, the rainfall cumulative value and the wind direction angle change of a meteorological station, carrying out the format conversion and time-space alignment of multi-source data through a preset standardization protocol, and obtaining a multi-source data format; generating a first data set with a unified spatial-temporal scale; acquiring a corresponding wind speed peak record, wind direction angle change and disaster loss degree through a similar case set in the third data set, calculating a first risk probability under the current condition by adopting a weighted average method based on historical cases, and generating a first risk value; updating a line state factor in the fourth data set through a fused probability value in a risk assessment result, adjusting a weight parameter of a wind direction matching coefficient by adopting an online learning method, and generating an optimized fifth data set; according to the continuous dynamic risk assessment sequence, the operation strategy of the high-speed rail line is adjusted in real time, and line safety is ensured.

The invention discloses a short-term photovoltaic power prediction method based on optimized decomposition and a combined neural network, belongs to the technical field of photovoltaic power generation, and aims to solve the technical problems that in the prior art, prediction precision is greatly influenced by weather changes, modal aliasing and residual error mismatching are likely to occur in signal decomposition, and the adaptability of a single deep learning model is insufficient. According to the method, meteorological factors are screened by adopting Pearson and Kendall combined correlation coefficients, so that the comprehensiveness of feature screening is improved; weather types are divided through SOM and K-means collaborative clustering, and the clustering precision is improved; an improved artificial travel mouse optimization algorithm is utilized to adaptively optimize VMD parameters, and a local optimal solution is avoided; constructing a TCN-BiGRU combined neural network model, and fusing long-term feature extraction and bidirectional time sequence modeling capabilities; a dynamic weight weighted average method is adopted to reconstruct a prediction result, the accuracy is optimized, the precision and efficiency of short-term photovoltaic power prediction are remarkably improved, and the method is suitable for a photovoltaic power generation system under complex weather conditions.

The invention discloses a ten-thousand-square-meter muddy deep foundation pit supporting structure deformation prediction method which comprises the following steps: establishing a soil-supporting interaction rheological model, simulating stress relaxation and creep effects of muddy soil in combination with a Burgers rheological model, and realizing dynamic evolution prediction of a supporting structure deformation field and an internal force field. According to the method, the particle filter algorithm is adopted to carry out denoising processing on real-time monitoring data, model parameters are updated through the Bayesian method, and the prediction precision is improved. Meanwhile, nonlinear mapping of the deformation rate and the internal force increment is established by using a random forest algorithm, and instability risk probability distribution is generated. According to the method, multi-stage collapse early warning thresholds are set, monitoring data and prediction results are fused by adopting a weighted average method, and abnormal points of a high-risk area are identified. For a high-risk area, a genetic algorithm is applied to optimize the rigidity and prestress distribution of a supporting system, effective control over deformation and internal force of a supporting structure is achieved by dynamically adjusting construction parameters, and construction safety of the deep foundation pit is ensured.

The invention discloses a high-speed rail vehicle-mounted equipment fault prediction and health management method, and relates to the technical field of high-speed rail vehicle-mounted equipment fault prediction, and the method comprises the steps: monitoring high-speed rail vehicle-mounted equipment through employing a high-precision sensor network, and obtaining an original monitoring data set; cleaning the original monitoring data set by adopting a multi-source data integration and preprocessing method to obtain a high-quality data set, and extracting key features based on the high-quality data set to obtain a feature data set; constructing a fault prediction model based on a deep learning method and the feature data set, inputting the high-quality data set to the fault prediction model, and outputting a fault early warning result; based on the fault early warning result, a decision support method is adopted to evaluate the equipment state, and a maintenance strategy suggestion is obtained; obtaining a comprehensive analysis report based on the maintenance strategy suggestion and the equipment evaluation state; environmental data and historical maintenance record data from a ground monitoring system are integrated, and the data are fused by using a weighted average method.

The invention relates to the technical field of viscosity measurement, and discloses a verification system for a viscometer, comprising: a dynamic temperature compensation module for collecting temperature data inside and outside the viscometer in real time; the embedded controller is responsible for receiving the temperature data, executing a temperature compensation process and outputting a temperature compensation coefficient and an initial viscosity value; the multi-modal data fusion calibration module is used for receiving a temperature compensation coefficient provided by the multi-modal data fusion calibration module and data from a pressure sensor, a shear rate sensor and a vibration sensor, and carrying out joint modeling on the data by utilizing a weighted average method; the invention also discloses a verification method for the viscometer, which comprises the following steps: acquiring temperature data inside and outside the viscometer through the temperature sensor, and performing temperature compensation through the embedded controller. According to the invention, the multi-modal data fusion calibration module is introduced, and data from a plurality of sensors are integrated through a weighted average method, so that the optimized viscosity value is more comprehensive and reliable.

The invention relates to the technical field of the Internet of Things, in particular to a lightweight distributed model aggregation method applied to a low-power-consumption chip of the Internet of Things, and is suitable for an Internet of Things system composed of a cloud server and a plurality of edge nodes. Each edge node learns an optimal strategy from offline data, and meanwhile, a complex teacher model is compressed into a lightweight student model through a knowledge distillation technology, so that the local calculation burden is reduced. And each edge node uploads quantized and sparse model parameters to the cloud server, and the cloud server performs federal aggregation by adopting a weighted average method to generate a global model and feeds back the global model to each terminal on the premise of ensuring data security. In order to adapt to the practical application of a low-power-consumption chip, a dynamic voltage frequency regulation technology is introduced in the hardware level, and the energy consumption and the communication overhead are effectively reduced. According to the method, the model training process of the edge nodes of the Internet of Things is optimized, and federated learning of privacy protection, efficient communication and cooperative self-adaption is realized.

The invention discloses a ship power grid transient electromagnetic interference grading evaluation method and system, relates to the technical field of ship power grid safety evaluation, realizes scientific grading evaluation of ship power grid transient peak interference, and provides a reliable basis for stable operation and equipment protection of a ship power grid. Comprising the following steps: measuring a transient peak sensitive threshold value of equipment by adopting a peak signal transduction sensitivity test; performing classification and importance coefficient assignment on the equipment according to the importance degree of the equipment; and calculating a weighted average sensitive threshold value of load equipment at the rear end of the distribution box by adopting a weighted average method. At the output end of a power distribution box of a ship power grid cabin, a transient peak conducted emission detection device is used for monitoring and collecting power grid transient peak conducted emission data in real time, and a power grid transient peak interference measured value is recognized from the power grid transient peak conducted emission data. Comparing the power grid transient peak interference measured value with the weighted average sensitive threshold value, and calculating a difference value between the two values; and according to a difference value result, carrying out ship power grid transient peak interference grading evaluation.

The invention belongs to the field of time synchronization and data processing, and discloses a satellite and Rowland timing data fusion method and related device.Firstly, original data are intercepted and subjected to mean value removal to eliminate baseline offset, and a frequency domain matrix is reconstructed by combining frequency domain conversion with singular value decomposition; according to the method, periodic term interference signals in a frequency domain are accurately recognized and filtered out through a dynamic threshold strategy, then effective components are reserved through time domain conversion, finally, a noise covariance matrix and observation model parameters are dynamically adjusted based on an adaptive Kalman filtering algorithm, and dynamic weight fusion of double-source data is achieved. By the adoption of the method, the defects that a traditional weighted average method is insensitive in fixed weight, Kalman filtering parameters are rigid and global interference suppression of wavelet transformation is insufficient are effectively overcome, the suppression capacity for non-stationary noise and periodic interference is remarkably improved, fused data have the high precision of a satellite system and the anti-interference characteristic of a Rowland system, and the method is suitable for being applied to the field of satellite communication. And finally, high-reliability and high-stability time synchronization performance is realized.

The invention relates to the field of image processing, in particular to a raw material quality detection method for a foreign fiber machine, and the method comprises the steps: obtaining raw material surface images at a preset angle and a preset wave band, respectively forming an image set A and an image set B, and carrying out the preprocessing of the two sets; calculating a first abnormal degree of each pixel point by using the reflection characteristic of the image set A, obtaining a fluctuation degree through the gradient value change of the image set B, and calculating a second abnormal degree; fusing the two abnormal degrees by adopting a weighted average method to obtain a comprehensive abnormal index, comparing the comprehensive abnormal index with an abnormal threshold value, determining abnormal pixel points, constructing an abnormal region, and evaluating the quality of the raw material surface image according to the characteristic parameters of the abnormal region. According to the method, the multi-angle reflection characteristic and the multi-band gradient value change are combined, the included angle weight and the band weight are introduced, the transparent or semitransparent foreign fibers are effectively recognized, and the detection accuracy and adaptability are improved.

The invention provides a mechanical arm imitation learning method and device based on DMP, and belongs to the technical field of robots. The method comprises the steps that a trajectory is fitted through a motion primitive method; according to the teaching or simulation track, the weight of the motion primitive is obtained through a least square method; a new trajectory regeneration method is provided, and new trajectories with the same geometric features are generated by setting a new starting point, a new ending point or a new scaling coefficient; a new track passing point strategy is provided, so that the generated new track can pass through a specific key point; applying a Gaussian process to generate a high-order smooth trajectory passing through a specific point; generating a high-order smooth curve meeting the geometric characteristics by using a weighted average method; substituting the generated new trajectory into the new DMP model, and establishing a dynamic system model; and according to the dynamic system model, the mechanical arm joints are driven through the joint speed track, and speed control is achieved. According to the method, a large amount of similar industrial programming work can be reduced, and the flexibility and efficiency of trajectory planning and control are improved.

The invention provides a real-time monitoring method and system for a power transformer, and relates to the technical field of intelligent monitoring, and the method comprises the steps: carrying out the noise removal and time synchronization of original data through wavelet transform, and carrying out the recognition and elimination of abnormal data through an isolation forest algorithm, and obtaining the preprocessed data; key features are extracted from the preprocessed data, a weighted average method is adopted for fusion, and a comprehensive state index is generated; based on the comprehensive state index, using a fault classification model constructed based on a deep neural network to identify a fault type, using a long short-term memory network to analyze and predict the future state of the transformer, and calculating a transformer health score according to the comprehensive state index and the future state of the transformer; and according to the transformer health score, setting multi-level alarm thresholds for real-time alarm, and generating a maintenance suggestion in combination with the fault type. According to the invention, the accuracy of fault early warning is improved.

The invention discloses an automatic fault positioning method for a fan flange fastening bolt. The method comprises the following specific implementation steps of: firstly, uniformly mounting ultrasonic sensors at a flange connecting part, and carrying out multi-point synchronous data acquisition; the method comprises the steps that axial stress data are preprocessed through a quartile method, the axial stress data of units of the same model in the initial operation state are determined as test data, the test data and real-time collected data are combined, an axial stress threshold value is set and optimized through a 3-sigma criterion and a dynamic adjustment coefficient, and preliminary abnormal state judgment is achieved. The method comprises the following steps: constructing a mechanical model based on the stress condition of a flange fastening bolt, obtaining the axial stress distribution characteristics of a bolt group under different working conditions by adopting a theoretical calculation method, optimizing an axial stress distribution model by utilizing test data, and introducing a multi-modal matrix for characterization so as to formulate a fault evaluation criterion. And finally, extracting stress distribution characteristics of the unmonitored bolts through a weight average method, and realizing fault positioning according to a fault evaluation criterion.

The invention provides a freight freight prediction method and system based on multi-dimensional feature fusion, and the method comprises the steps: constructing a multi-dimensional fusion feature based on an interaction relation between a multi-dimensional waybill feature in waybill data and a multi-dimensional external influence factor in external influence data; and obtaining a plurality of prediction sub-models based on multi-dimensional fusion feature training. And combining the plurality of prediction sub-models by adopting a weighted average method to obtain a prediction model. And under the condition that the query request is obtained, performing semantic analysis on the query request to obtain standardized query information, and inputting the query information into the prediction model to obtain freight freight prediction information corresponding to the query information. According to the scheme, the influence of external factors on freight prediction is introduced, the freight prediction accuracy is remarkably improved, the multiple prediction sub-models are combined according to the weight to obtain the prediction model, and the adaptability in different scenes is improved. The complete query information is obtained by performing semantic analysis on the query request, so that accurate execution of prediction can be guided, and the use experience of a user is improved.

The invention provides a dry-type transformer health state assessment method and system based on an improved fuzzy analytic hierarchy process and an abrupt change series method, and belongs to the technical field of transformer state assessment. A health state assessment index system is established, and a health state comment set is constructed according to the health state deterioration degree of a transformer; dynamically adjusting the weight, and determining the index weight of each layer; calculating the membership degree of each index, and combining the membership degree with the weight matrix to obtain a total fuzzy evaluation matrix; calculating to obtain a system total mutation membership function value; and determining the health state grade of the dry-type transformer according to the maximum membership degree principle by adopting a weighted average method and integrating the evaluation results of the two. According to the method, the interaction and synergistic effect between indexes are considered, and the defects of existing single-angle evaluation are overcome; the method considers the influence of operation time, dynamically adjusts the weight, accurately reflects the influence degree of each index in different operation stages on the health state of the transformer, and achieves the accurate and comprehensive evaluation of the health state of the dry-type transformer.

The invention provides an unequal-precision point cloud data fusion method, and the method comprises the steps: enabling the point cloud data of different sources to have internal consistency after the preprocessing of unequal precision; benchmark conversion is carried out on point cloud data of different sources, so that the point cloud data have a unified plane and elevation system; setting a certain grid size according to the overall range of the point cloud data, and performing gridding on all the data; for each piece of grid data, finding out a grid corresponding to the grid data in different source point cloud data, finding out a plane distance of each point in the grid in the corresponding grid, and calculating an average value of height differences among the points as a system error of the different source point cloud data at the grid; weighing is carried out according to different source point cloud data precision, and a grid area data system error is calculated through a weighted average method.

The invention discloses a rock-fill dam deformation stability assessment method based on multi-parameter analysis, and relates to the technical field of hydraulic engineering, the assessment steps are as follows: S1, data of a rock-fill dam is collected based on a monitoring point, the data comprises dam body deformation data and dam body contour data, and the data is preprocessed to form a data set; s2, the dam body deformation data is converted into a deformation time sequence according to the time sequence, the fractal dimension of the deformation time sequence is calculated through a box counting method, and the mapping relation between the fractal dimension and the rock-fill dam deformation stability is established in combination with historical data statistical analysis; and S3, the dam body contour data are imported into three-dimensional modeling software, and a rock-fill dam three-dimensional geometric model is constructed. According to the method, a comprehensive evaluation system is constructed, correlation analysis and verification are performed on fractal dimensions and displacement strain indexes, an evaluation value is obtained based on a weighted average method, stability levels are intelligently divided, and a visual decision basis is provided for water conservancy project management.

The invention discloses a pulmonary nodule detection system and method based on federated learning and a target detection algorithm, and relates to the technical field of medical image detection and auxiliary diagnosis, the system comprises a plurality of medical institution clients and a cloud central server, the central server initializes to generate a global model, and deploys the global model to each client; each client uses a local CT data set to train a global model, model parameters are updated through training until loss is minimized so as to complete training, a local model is generated, and the updated model parameters are uploaded to the central server; wherein the total loss of the local model is the dynamic weighted sum of various losses; the central server adopts a dynamic weighted average method to aggregate the updated model parameters of all the clients, generates an updated global model, then deploys the updated global model, and gradually optimizes the global model through multiple times of global iteration updating; and each client identifies the to-be-detected CT image by using the locally deployed optimized global model, and outputs a more accurate pulmonary nodule identification result.

The invention discloses an equipment fault diagnosis method and system based on FrFT and information entropy quantization, and the method comprises the steps: obtaining a real-time preprocessed mechanical vibration signal of electrical equipment, and carrying out the fractional Fourier transform feature extraction, and obtaining feature data; performing information entropy quantification processing according to the feature data to obtain corresponding information entropy data; according to the feature data and the corresponding information entropy data, feature fusion is carried out through a weighted average method, and fused features are obtained; according to the fusion features, dimension reduction is carried out based on a principal component analysis method, and final extraction features are obtained; and inputting the final extracted features into a pre-trained equipment fault diagnosis model to obtain an equipment fault diagnosis result. According to the method, multi-scale time-frequency features can be extracted from complex mechanical vibration signals by using fractional Fourier transform, complexity information of the signals is extracted in combination with an information entropy quantization technology, and finally, fault diagnosis is performed on dimensionality reduction features, so that the accuracy and reliability of fault diagnosis are effectively improved.

The invention discloses an urban garden carbon sink visual simulation and optimization system based on digital twinning, and belongs to the technical field of urban garden carbon sink management. The method solves the problems that in the prior art, data collection is not comprehensive, an optimization strategy lacks scientific basis and the like, feature extraction is conducted on multi-source data by introducing the random forest model, different weights are given to the complex relation between the features in combination with the weighted average method, and the multi-source data are obtained. Features having significant influence on carbon sink potential evaluation can be identified more accurately; by setting a score threshold value and comparison verification, when the feature importance score is lower than the score threshold value, removing or grouping the non-significant features to form new comprehensive features, and retraining the random forest model; therefore, the accuracy of urban garden carbon sink potential evaluation is improved, decision makers are helped to know the current carbon sink situation of urban gardens more visually, a more effective optimization strategy is formulated, and the ecological benefits and environmental quality of the urban gardens are improved.

The invention discloses a multi-information fusion tunnel advanced geological forecast water disaster hidden danger judgment method, relates to the technical field of advanced geological forecast, and can collect various tunnel advanced geological forecast method information when implementing advanced geological forecast work. An information fusion scheme formula is realized based on a dynamic weighted average method, various information weight scores are quantified, the weighted average method is to endow each parameter or index with a weight, and then the value of each parameter or index is subjected to weighted average to obtain a comprehensive value; the problems of scattered reference information, much man-made interference, inaccurate judgment and the like of water disaster hidden danger judgment in tunnel advanced geological forecast work by means of geophysical prospecting, drilling and the like are solved, and spatial position characteristics of water-rich region hidden dangers such as karst caves and fractures in front of a tunnel face can be more accurately judged. And the development probability of the water disaster hidden danger in front of the tunnel is quantified, reference is provided for tunnel excavation construction, and the safety risk hidden danger of tunnel excavation construction is reduced.

The invention discloses a production monitoring system and method applied to electronic detonator assembly, and relates to the technical field of electronic detonator assembly. The method comprises the steps of resistance value collection, capacitance value measurement, chip data verification and sealing performance and welding firmness detection. And the first ratio is calculated and compared with a preset evaluation value, and the single-point assembly condition is determined. And generating a parameter distribution curve by adopting a moving average method, and counting the number of alarm condition items to determine the assembly abnormality level. And calculating a second difference absolute value according to the acquisition period and the parameter variation, and determining a monitoring point position mark type. And dividing production line areas according to mark types, calculating an actual assembly evaluation value by adopting a weighted average method, comparing the actual assembly evaluation value with a preset standard, and judging the operation mode of the assembly equipment. According to the method, the electronic detonator assembly process can be comprehensively monitored, assembly abnormity can be found in time, and the production efficiency and the product quality are improved. By adjusting the operation mode of the equipment, the assembly process can be optimized, and the product is ensured to meet the standard.

The invention relates to the technical field of photovoltaic power generation and load prediction, and discloses a gateway electric quantity prediction method and system for a virtual power plant, and the method comprises the steps: carrying out the preprocessing of obtained historical multi-source weather, photovoltaic output and load power data; based on the preprocessed multi-source weather forecast data and the corresponding historical measured data, training a preset weather deviation correction model to correct irradiance and temperature in future weather forecast; fusing the corrected data through a weighted average method to construct meteorological input data; performing feature derivation processing on the meteorological input data and screening features strongly related to photovoltaic output and net load electricity consumption; a dynamic weighted integration model is further adopted to predict the photovoltaic power generation capacity and the net load power consumption; and determining the gateway electric quantity according to the difference value between the photovoltaic generating capacity predicted value and the net load electricity consumption predicted value. According to the method provided by the invention, the data quality, the meteorological input precision and the model prediction performance can be improved, and the generalization ability and the interpretability of the model are enhanced.

The invention discloses an algorithm model configuration method based on territorial space planning current situation evaluation indexes, and relates to the technical field of territorial space planning, and the method comprises the steps: obtaining data based on a territorial space planning data API interface; establishing a task scheduling engine based on a preset plan, and dynamically adjusting task priorities; establishing a process engine based on a preset plan, and visualizing the process; calling an optimized space operation algorithm, calculating the preprocessed data, and generating a land space planning current situation evaluation index; and analyzing the deviation between the planning implementation effect and the expected target, and providing improvement suggestions. According to the method, the multi-dimensional territorial space planning data is obtained through the API, the weighted average method is adopted to fuse the data, the accuracy of the evaluation model is improved, redundancy is removed and missing data is filled up through the space data quality evaluation algorithm, the data reliability is ensured, the task scheduling engine dynamically adjusts the priority, resource allocation is optimized, and the efficiency is improved.

The invention discloses a detection and monitoring system for fault early warning in use of an insulating sling, relates to the technical field of electrical safety and intelligent monitoring, and is used for solving the problem that an internal progressive deterioration trend cannot be captured. A microwave resonance signal in the sling is obtained, the resonant frequency offset of each layer is extracted based on spectral decomposition, and a humidity distribution result is inversed and fed back in combination with a dielectric property database, and is used for setting a monitoring period and collecting operation data; a key area is screened based on a humidity diffusion coefficient, an evaluation area of which the risk level needs to be improved is identified by using a weighted average method, and the risk weight is calculated and the early warning time is set through a nonlinear model in combination with the physical attribute and the diffusion coefficient of the area, so that the early identification and dynamic early warning of the hidden fault of the sling are realized, and the safety and the monitoring precision are effectively improved. And the sudden failure risk is reduced.