1701 results about "Linear regression" patented technology

The invention relates to the technical field of power monitoring, in particular to a moisture-proof environment-friendly ring main unit online monitoring system with continuous fault indication and a ring main unit, and the system comprises an electric parameter dynamic analysis module, an environment collaborative verification module, a closed-loop control execution module and a behavior path optimization module. According to the method, the current fluctuation and the power difference value are matched through dynamic time warping, the temperature rise rate and the condensation index are calculated through linear regression, multi-dimensional verification is formed through fuzzy logic judgment, operation parameters are adjusted in real time through PID control, strategy weight is optimized through a genetic algorithm, feature extraction is reversely corrected, and the fault recognition precision and the response speed are improved; closed-loop feedback is established to continuously optimize the system state, environment and electrical parameter coupling analysis is enhanced, the misjudgment probability of a single threshold value is reduced, parameters are dynamically corrected, the adjustment real-time performance and accuracy are improved, the adaptive capacity under the complex working condition is enhanced, a complete closed loop of collection, analysis and feedback is established, and the insulation degradation judgment reliability is improved.

The invention relates to the technical field of noise filtering, in particular to a communication line noise filtering system and method, and the system comprises a temperature coupling module, a voltage response module, a parameter joint debugging module and a link checking module. According to the method, inductance and capacitance node temperature data are collected and input into a piecewise linear regression model, the temperature-to-resonant frequency deviation gradient is calculated, a noise suppression bandwidth threshold value is dynamically matched in combination with a reference table, the frequency stability under temperature fluctuation is improved, Kalman filtering is used for processing voltage sampling, and a fluctuation predicted value is generated. According to the method, interval indexes are extracted based on a parameter mapping table, instant inductance and capacitance parameters are generated through least square fitting, voltage transient fluctuation self-adaption is achieved, noise power spectrum density is calculated through fast Fourier transform, a 3dB attenuation point is compared with a threshold value difference value to trigger parameter rollback, closed-loop control is formed, it is ensured that suppression bandwidth is continuously matched with a noise frequency band, and noise suppression is achieved. The bandwidth offset is reduced to be within 1.2%, and the optimization response time is shortened to 200 ms level.

The invention discloses an artificial intelligence rice water and fertilizer real-time monitoring method and system, and relates to the technical field of agricultural intelligent decision making, and the method comprises the steps: inputting a farmland feature data set into a soil thermodynamic diagram generation model, carrying out the high-resolution reconstruction of the farmland feature data set through a GAN adversarial network, and generating a whole-field high-precision soil thermodynamic diagram; based on the whole-field high-precision soil thermodynamic diagram, a collision relation between the fertilization amount and historical farming data is detected according to an FCL causal algorithm, a preliminary causal diagram is generated, and a causal diagram structure of the fertilization amount and the historical farming data is constructed by adopting a multiple linear regression method; based on a causal diagram structure, the multi-order causal effect of the fertilization amount, the soil parameters and the historical yield is analyzed through a dynamic allocation algorithm, and a water and fertilizer regulation and control strategy is formulated in combination with a multi-objective optimization algorithm. According to the method, the soil thermodynamic diagram generation model is constructed, so that the fuzzy problem of the edge of the field and the salinization area is solved, a high-fidelity soil space state substrate is provided for water and fertilizer regulation and control, and invalid irrigation is reduced.

The invention relates to the technical field of geological disaster early warning, in particular to a geological disaster early warning method and system based on dynamic data monitoring, and the method comprises the following steps: obtaining geological displacement monitoring data, setting a time window range, respectively calculating a local extreme point and a local zero point of the monitoring data in a time window, and calculating the local extreme point and the local zero point of the monitoring data in the time window; obtaining a multi-scale reference value; and based on the multi-scale reference value, calculating a comprehensive statistical magnitude, and generating a data quality evaluation parameter. According to the method, a local extreme point and a zero point are dynamically calculated by setting a time window range, a multi-scale reference value is constructed to carry out comprehensive statistic generation and data evaluation, and collaborative discrimination of piecewise linear regression and threshold comparison are executed to form a critical index; and performing characteristic quantity recursive residual spectrum analysis and matrix decomposition operation by combining the power spectral density distribution and the spectral density matrix to establish an early warning characteristic sequence, and realizing multi-stage early warning indication output based on a grading rule.

The invention relates to the technical field of production line control, and discloses an intelligent control method and system for production line mold machining. According to the method, a pressure distribution state is determined by obtaining initial cutting parameters of a cutter and surface pressure data of a mold, mold node stress distribution is calculated by utilizing a three-dimensional finite element model, and a stress concentration position is identified. And predicting a mold deformation trend through a linear regression model according to the thermal expansion coefficient and the environment temperature data. Meanwhile, the initial cutting parameters are optimized by combining a cutting parameter optimization objective function, and the objective function comprehensively considers factors of tool wear, surface quality and residual stress. And on the basis of a die deformation prediction result, a dynamic compensation algorithm is adopted to adjust supporting structure parameters and optimize cutting parameters, and finally optimal cutting parameters and a target tool path are generated. According to the method, precise coordination control over pressure distribution and mold deformation under the complex working condition is achieved, the residual deformation risk is reduced, and the mold machining precision is improved.

The invention provides a lake water chemical oxygen demand internal and external source pollution quantitative tracing method and system, and relates to the technical field of pollution quantitative tracing. The method comprises the following steps: performing three-dimensional fluorescence spectrum analysis on water samples at different point positions of a lake to obtain fluorescence excitation / emission matrix spectrum EEMS data; performing parallel factor analysis on the EEMS data, identifying and classifying the fluorescent components of the dissolved organic matter DOM, and determining the type characteristics of each component; based on the maximum fluorescence intensity and the corresponding COD value of the fluorescence component, establishing a standardized multiple linear regression equation by adopting ridge regression analysis, and calculating the contribution percentage of each fluorescence component to the COD of the lake water body; analyzing influence paths of potential variables on DOM migration and transformation, and quantifying direct contributions and indirect contributions of different pollution sources to each fluorescent component through path coefficients; and calculating the total contribution of lake-entering rivers, bottom mud release and phytoplankton to the COD of the lake water body.

Disclosed in the present invention are a method and system for predicting a hematoma expansion event of a stroke patient. The method comprises: collecting patient data information, and performing data preprocessing; using an XG-Boost model to calculate the SHAP average value and the contribution degrees of different types of data, and extracting a feature quantity with the highest contribution degree from each type of data; performing multivariate linear regression analysis to obtain the weight of the effect of each type of data on hematoma expansion, and weighting all the feature quantities on the basis of the weight to obtain weighted feature quantities; using an extreme learning machine to predict a hematoma expansion probability; and using a northern goshawk optimization algorithm to search predicted values for the boundary of occurrence of a hematoma expansion event, and verifying the prediction accuracy. The present invention can accurately analyze and predict the expansion risk of hematoma after hemorrhagic stroke, which is crucial to early diagnosis and timely treatment.

The invention relates to the technical field of metering pumps, in particular to an external meshing gear metering pump flow calculation method based on linear regression and curve fitting, which comprises the following steps: generating an integrated data set by monitoring the flow rate, pressure, temperature and viscosity in real time, setting flow rate, pressure and temperature thresholds based on the data set, judging the flow state and recording abnormity, and calculating the flow rate of the external meshing gear metering pump. Flow velocity and viscosity are selected for flow calculation, a corrected flow value is generated, whether flow is normal or not is analyzed and determined, a flow stability evaluation result is generated, and a visual interface is created to display parameters and a calculation result. According to the method, the multiple sensors are installed at the inlet and the outlet of the metering pump, the flow speed, the pressure, the temperature and the viscosity are monitored in real time, an integrated data set provides a precise basis for flow calculation, a threshold value is set, data are monitored, it is ensured that parameters exceeding the normal range can be recorded and analyzed in time, and the process improves the accuracy of flow calculation and improves the working efficiency. And flow evaluation is optimized, and the operation safety and efficiency are improved.

The invention discloses a supply chain production scheduling optimization method and system based on multi-source data. According to the method, supplier purchase data is extracted through a data crawler, raw material sharing is analyzed in combination with a knowledge graph, and a raw material sharing list is generated; on the basis of delivery records and product attributes, a quality stable value is predicted through linear regression, process similarity is calculated through a text similarity algorithm, and a process similarity matrix is constructed; in combination with historical sales data, a market demand prediction model is constructed by applying multiple linear regression and time sequence analysis. A complex relation among raw material sharing, process similarity and market demands is described through a graph neural network, a product association network graph is generated, an optimal balance point is found between delivery time and a resource utilization rate through Pareto optimization, and a production scheduling scheme is formed; and finally, integrating the performance score, the quality stable value, the raw material sharing list and the production scheduling scheme through an integer programming algorithm, and optimizing a supplier combination. According to the invention, the production scheduling efficiency and precision are improved, and complex business requirements are met.

The invention relates to the technical field of medical image processing, and discloses a craniofacial dynamic reconstruction method and system based on multi-modal data fusion, and the method comprises the steps: arranging a multi-modal data collection device in a target craniofacial region, and obtaining a static CT image, a static MRI image, a dynamic expression video sequence and a surface electromyogram signal; preprocessing the static CT image, the static MRI image, the dynamic expression video sequence and the surface electromyogram signal; inputting the preprocessed data into a multi-scale finite element model, and simulating a coupling relationship between muscle contraction force and skin deformation by adopting a biomechanical driving strategy to generate a dynamic craniofacial model; and fusing the geometric error and the motion consistency score of the real data by adopting a linear regression method, and outputting a comprehensive reconstruction quality index. According to the method, the problems of low craniofacial dynamic modeling accuracy and poor robustness in the prior art can be solved.

A method for carbon tracking and analysis in a city is provided. Basic information of a target city is acquired. A three-dimensional visual city model is constructed based on the basic information. The target city is divided into a plurality of sub-regions. A carbon emission monitoring plan is formulated based on regional properties of each sub-region. According to the acquired carbon emission monitoring data, a carbon emission change of each sub-region within the current preset period is analyzed by means of linear regression to obtain carbon emission change trend data of each sub-region. Carbon emission tracking is performed based on the carbon emission change trend data. A current carbon tracking route and a carbon prediction route are generated by means of a preset ant colony optimization algorithm. A system non-transitory computer-readable storage medium for implementing carbon tracking and analysis in a city and are also provided.

The invention provides a cluster reliability test method based on fault simulation, which comprises the following steps: acquiring injection parameter information, and performing fault simulation in a cluster based on the injection parameter information and a fault transfer model to obtain a complex fault scene; key performance index records are obtained in real time based on the complex fault scene; executing a reliability detection step based on a preset reliability detection model and the key performance index record, and obtaining a data analysis report; and obtaining a reliability score, an influence analysis result and a risk prediction result based on a data analysis report, a weighted scoring algorithm, an anomaly detection algorithm and linear regression analysis, and completing reliability detection of the cluster. According to the cluster reliability test method and system based on fault simulation provided by the invention, the test efficiency and the reliability detection level are greatly improved, the fault simulation is carried out in the cluster and the reliability detection model is combined, so that the accurate quantification of the reliability detection result is realized, and the reliability detection efficiency and the detection capability are improved.

Techniques are described for image processing. For example, a computing device can segment, using a first neural network, image(s) of a scene to determine respective segments for each of the image(s). The computing device can determine, using a second neural network, normal vectors for each of the image(s). The computing device can generate a graph based on each respective segment for each image, each respective normal vectors for each image, and estimated planar distances. The computing device can partition, based on the normal vectors and the estimated planar distances, the graph to determine indexes associated with Gaussian primitives. The computing device can assign, using linear regression, each descriptor of a plurality of descriptors to an index of the plurality of indexes based on a respective weight. The computing device can merge, using a Gaussian tree, Gaussian primitives of the Gaussian primitives with associated indexes that are similar to each other.

The invention relates to the technical field of electrical measurement, and discloses an electrical measurement instrument dynamic calibration error compensation method, which comprises the following steps: S1, multi-source data real-time acquisition: integrating a temperature sensor, a humidity sensor and an electromagnetic interference detection module in a measurement instrument, configuring voltage, current, frequency and other measurement channels, and carrying out multi-source data real-time acquisition at the frequency of not less than 100HZ; environmental parameters (temperature T, humidity H and electromagnetic interference intensity E) and measurement data (model characteristic parameters X1, X2,..., Xn such as voltage, current and frequency) are synchronously acquired. According to the electrical measuring instrument dynamic calibration error compensation method, through multi-source data real-time acquisition and composite error model construction, in combination with multiple linear regression and an improved BP neural network, environment and measurement signal changes are comprehensively captured, an error compensation value is accurately calculated, a measurement error can be effectively controlled within an extremely small range, and the error compensation accuracy is improved. Compared with a traditional method, the measurement precision is greatly improved.

The invention provides an electrocardiosignal reconstruction method based on hybrid optimization and multi-modal feature fusion, and the method comprises the steps: obtaining a 12-lead electrocardiosignal, processing the 12-lead electrocardiosignal through a linear regression model, a genetic algorithm and a simulated annealing algorithm in sequence to obtain an optimal three-lead electrocardiosignal, and reconstructing the optimal three-lead electrocardiosignal according to the optimal three-lead electrocardiosignal. The optimal three-lead electrocardiosignal is subjected to one-dimensional convolution layer and maximum pooling processing in sequence to obtain time domain features, the time domain features, the frequency domain features and expert features are subjected to multi-modal feature fusion to obtain fusion features, and the fusion features are input into a Transform mechanism to be processed to obtain a reconstructed electrocardiosignal. The method has remarkable effects in long-range time sequence modeling and local waveform detail optimization. The traditional CNN / RNN is limited by the problem of local receptive field or gradient disappearance, the global rhythm and the local form are difficult to balance, the QRS is adopted to perceive a Transform architecture, and a multi-head self-attention and nonlinear feed-forward network is combined, so that the width error of a QRS wave group is smaller.

According to the unmanned aerial vehicle multi-source quantitative remote sensing rape growth state evaluation method, rape is divided into a seedling stage, a flowering stage and a legume stage according to growth nodes, ground hyperspectral and multi-spectral data are synchronously obtained, resolution is increased and decreased, the data are converted into all-dimensional, multi-scale and multi-temporal data, a growth state factor inversion model of the rape is established, and the rape growth state is evaluated. Evaluating the contribution of different growth state factors to the growth vigor of the rape, and evaluating the growth state of the rape based on comprehensive evaluation indexes of leaf area index, overground biomass and chlorophyll content. The method comprises the following steps: establishing a model based on multi-layer linear regression and partial least square optimization models of three types of growth factors LAI, AGB and CC, multi-mode prediction based on entropy aggregation and hierarchical analysis of entropy loading expert knowledge in combination with expert knowledge and agronomic knowledge, and setting a model with an optimal verification index in different growth periods as a growth state evaluation model in the period. The rape growth state evaluation period is short, the pertinence is strong, and the accuracy is good.

The invention discloses a wrist joint spasm assessment method and a wrist joint spasm assessment device based on multi-source data fusion. The rotating speed data, the rotating angular speed data and the angle change data of the wrist joint, the pressure change data of the whole palm, the acceleration change data of the palm, the torque change data of the wrist joint and the electromyographic change data of related muscles in the movement process are measured, and scoring is carried out in combination with an expert system. The evaluation of wrist spasm is realized by combining a multiple linear regression algorithm with a differential evolution algorithm; and based on the multi-source data fusion, establishing an evaluation model by using a Bi-LSTM network so as to realize spasm evaluation. The wrist serves as a spasm detection part, and compared with traditional spasm detection equipment, a more convenient data acquisition mode is achieved while the evaluation precision is guaranteed.

The invention discloses a comprehensive evaluation method for drought resistance of white birch germplasm resources in the seedling stage, and belongs to the technical field of drought resistance evaluation. Under different gradient drought stress and normal conditions, 14 drought resistance indexes of 90 white birch half-sib families are measured, and the drought resistance is comprehensively evaluated by combining membership function analysis, principal component analysis and clustering analysis. A comprehensive drought resistance evaluation value (D value) is obtained according to a membership function, comprehensive clustering analysis is carried out according to the D value of each family, 90 families are divided into four categories at the Euclidean distance of 1.5, and BWY32 and BWY29 are screened out as drought-resistant families. A stepwise linear regression analysis method is utilized to respectively establish linear regression equations under drought stress 5d and 10d, and four important drought resistance indexes are screened out. Compared with the previous research, the method fully considers the difference of the sensitivity degree of each selected index to drought stress and the contribution rate of each index to drought resistance evaluation, and lays a good foundation for simplification and high efficiency of the drought resistance evaluation of the white birch.

The invention relates to the technical field of grounding resistance detection, in particular to a power transmission line tower grounding resistance monitoring system and a use method, and the system comprises a signal collection and correction module which collects and updates various parameters of each tower in real time, builds a mathematical model of environment parameters and grounding resistance values, and obtains the real-time environment parameters and the grounding resistance values after obtaining the real-time environment parameters; automatically correcting the measured resistance value by using the mathematical model; the fault early warning module is used for collecting historical monitoring data and calculating an abnormal risk probability in combination with real-time abnormal data; the fault positioning module is used for realizing accurate positioning of a fault tower through a time difference positioning method; and the remote control module is linked with the meteorological early warning platform, starts high-frequency monitoring before thunderstorm, and dynamically adjusts a ground resistance threshold value. The measurement value is automatically corrected through the linear regression model, interference of environmental fluctuation on resistance measurement can be effectively eliminated, and errors of traditional static measurement are reduced.

The invention provides a farmland yield prediction method and system based on soil parameter inversion, and the method specifically comprises the steps: collecting the soil profile information and earth surface three-dimensional information of a target farmland through a ground penetrating radar and a laser radar, and carrying out the precise inversion of the water content and porosity of soil based on a water-porosity inversion model, therefore, a high-precision three-dimensional soil distribution model is constructed, crop growth monitoring data is combined, a farmland yield prediction model is established, the relationship among soil structure characteristics, crop growth indexes and meteorological factors is quantified, and high-precision farmland yield prediction is realized. According to the method, through accurate modeling of the double-pore structure and optimization of the multiple linear regression prediction model, yield prediction is more accurate and reliable, and the method is particularly suitable for farmland management under different soil types and meteorological conditions.

The invention relates to an equipment residual life prediction method based on a physical-data model, and the method comprises the steps: building a degradation model based on a physical degradation mechanism and a standard Wiener process model; obtaining fixed parameters in the degradation model by using a maximum likelihood estimation method in combination with a nonlinear regression method; acquiring an equipment state observation value in real time, and updating random parameters in the degradation model by using a weight optimization particle filter algorithm; and utilizing the degradation model, the fixed parameters in the degradation model and the random parameters in the degradation model to obtain a probability density function of the residual life of the equipment under a random failure threshold value, and performing numerical integration on the probability density function of the residual life of the equipment to obtain an expected value of the residual life of the equipment, and outputting the expected value as the residual life of the equipment. Compared with the prior art, high-precision and high-reliability residual life prediction is realized by fusing a physical mechanism and a data driving method.

A smart shopping cart identifies items using cameras and sensors. The cart captures images of items within its storage area and applies machine-learning models, such as a barcode detection model, an OCR model, and an image embedding model, to generate identifier predictions. These predictions are processed using an efficient selection algorithm, which may involve majority voting, weighted voting, or linear regression, to select the most accurate identifier. The cart updates its display and user interface with the identified item. The process may be performed primarily by the CPU to enhance computational efficiency, avoiding the latency associated with GPU data transfer. Additional techniques, such as circular buffers and frame skipping, are employed to further optimize resource usage.

The invention relates to the technical field of marine ecological environment monitoring and data analysis, and particularly discloses a sea area phytoplankton biodiversity index prediction method and system based on multi-model integration and feature engineering. After preprocessing, constructing four groups of nonlinear interaction characteristics of a temperature-salt relationship, oxygen-salt balance, chlorophyll chemical oxygen demand coupling and a nitrogen-phosphorus ratio based on environmental factors, combining station characteristics with basic environment and interaction characteristics, carrying out variance threshold screening, inputting a characteristic set into a multi-model integration framework containing models such as linear regression and gradient lifting, and carrying out multi-model integration; training and tuning according to a time sequence segmentation strategy, selecting model output according to a decision coefficient, using a result if the decision coefficient of the support vector regression model is within a preset range, and otherwise, taking a gradient lifting and extreme gradient lifting tree model to predict a mean value. And the prediction accuracy and the model generalization, stability and reliability are improved.

The invention provides a regional ecological environment quality evaluation method and system based on remote sensing data, and relates to the technical field of environment remote sensing and ecological monitoring, and the method comprises the steps: extracting red light and near-infrared reflectivity at the peak of a growing season by using Landsat8 satellite data, and calculating a mixed vegetation index; synchronously measuring vegetation indexes of pure vegetation and bare soil on site to obtain a vegetation coverage rate, and obtaining water transparency, suspended solid concentration and chlorophyll a concentration through on-site measurement on the basis of green light, red light, blue light and near-infrared reflectivity in a heavy rainfall period and a dry season; the method comprises the following steps: constructing a linear regression model of reflectivity and water quality parameters through a least square method, calculating a water quality index, constructing an extreme weather influence factor based on ten-year extreme weather data, and finally fusing a vegetation coverage rate, water quality, the extreme weather influence factor, annual precipitation, annual average temperature, optimal regional vegetation temperature and historical rainfall extremum. And constructing an ecological quality index, and dividing ecological environment quality grades.

The invention discloses a low-voltage transformer area hidden danger identification method and system based on degradation risk analysis, and relates to the technical field of data processing, and the method comprises the steps: constructing an initial data set according to measurement data; constructing an abnormal data set according to the instantaneous drop event criterion in combination with the initial data set; according to a joint melting identification algorithm, determining meter front-end abnormity and a corresponding front-end abnormity degree; according to a tail end abnormity identification algorithm, meter tail end abnormity and a corresponding tail end abnormity degree are determined; combining and determining a front-end risk weight and a tail-end risk weight through a linear regression model; determining a front-end hidden danger risk value according to the meter front-end abnormity, the front-end abnormity degree and the front-end risk weight or / and determining a tail-end hidden danger risk value according to the meter tail-end abnormity, the tail-end risk weight and the tail-end abnormity degree; and determining a hidden danger emergency strategy corresponding to the transformer area according to the front-end hidden danger risk value or / and the tail-end hidden danger risk value. According to the scheme, the low-voltage transformer area hidden danger identification accuracy and efficiency are remarkably improved.

The invention provides a plate anchor ultimate bearing capacity calculation method and device. The method comprises the steps that soil body strain softening, anchor plate geometry and ocean clay initial mechanical parameters are obtained; the method comprises the following steps: constructing a Drucker-Prager elastic-plastic numerical model based on a Cosseerat continuum theory; calculating bearing capacity under different burial depth ratios, inclination angles beta and softening moduli; determining a burial depth ratio demarcation threshold value; constructing a burial depth and dip angle coupled reference bearing capacity formula, which is divided into burial depth ratio lt; 4 and > = 4, respectively using quadratic polynomial and linear regression analysis; constructing a strain softening correction coefficient formula, and fitting in two working conditions; and generating a comprehensive ultimate bearing capacity empirical formula in combination with the reference formula and the correction coefficient formula. According to the method, the rotational degree of freedom and the bending moment balance item are introduced, the grid dependence is reduced in combination with the characteristic length parameter, the comprehensive empirical formula is constructed through large-scale parameterized numerical calculation, and the prediction error is effectively reduced.

The invention discloses a live pig body size and weight measuring method based on RGB-D. The method comprises the following steps: S1, obtaining a left vision rotation translation matrix and a right vision rotation translation matrix; s2, acquiring a three-view RGB image and a depth image of a single pig; s3, acquiring a body scale measurement key point; s4, acquiring a left viewpoint cloud, a right viewpoint cloud and an overlook point cloud, and performing point cloud registration to obtain a complete pig point cloud C1; projecting the body size measurement key points to the complete pig point cloud C1; s5, obtaining a final complete pig point cloud C2; s6, calculating the body length, the body width, the body height and the abdominal girth of the pig by using the final complete pig point cloud C2, and forming body size data; and S7, substituting the body size data of the pig into the multiple linear regression prediction model to predict the weight. According to the method for measuring the body size and the weight of the live pig, the accuracy and the stability of extracting the position of the body size measuring point are improved, and the accuracy of the body size and the weight measuring result of the pig is improved.

The invention discloses a forest carbon sink dynamic monitoring system based on multi-source data fusion, and relates to the technical field of carbon sink monitoring, and the system comprises a multi-source data collection and fusion module which collects forest-related multi-source data, and generates a standardized fusion data set; the carbon sink reserve estimation module is used for constructing a carbon sink reserve estimation model according to the fused data set, and calculating forest carbon sink reserves based on a model output result; the carbon sink reserve prediction module is used for predicting the forest carbon sink reserve at the next moment by using a time sequence analysis algorithm; the state change monitoring module is used for analyzing a carbon sink dynamic change trend based on forest carbon sink reserve prediction data and historical reserve data; and the early warning and decision support module is used for triggering an early warning signal of a corresponding level based on the abnormal change information. Through fusion of federated learning, ARIMA time sequence analysis, a sliding window and a linear regression technology, efficient and continuous tracking of a carbon sink reserve change trend is realized, and change abnormity can be identified in time.

The invention relates to the technical field of cost assessment, in particular to a civil construction project cost assessment optimization system, which comprises a feature analysis module, a cost estimation module, a real-time monitoring module, a model training and tuning module, a risk analysis module, a decision support module, a financial simulation module and a reinforcement learning cost adjustment module. According to the invention, a convolutional neural network algorithm and a principal component analysis method in the feature analysis module excavate building information model data and screen key features, and the cost estimation module combines a multi-layer perceptron neural network and a linear regression model to enhance the cost mode analysis capability and calculate the project cost. The digital twinborn technology and the dynamic programming algorithm realize tracking of project progress and real-time monitoring of cost change in the real-time monitoring module, and the genetic algorithm and the machine learning algorithm realize continuous optimization and self-learning of model parameters in the model training and tuning module. And a Monte Carlo simulation method and a sensitivity analysis method are used for identifying and evaluating project risks in the risk analysis module.

The invention discloses an OFDM radar vital sign signal separation method based on FGO-VMD and multi-feature clustering, and belongs to the technical field of radar communication integration. According to the method, an OFDM signal radar is used for carrying out human body detection, multi-subcarrier phase difference linear regression is used for estimating displacement, thoracic cavity micro-motion signals are extracted, an FGO-VMD joint optimization model is provided, the modal number and penalty factor parameters of a VMD decomposition algorithm are adaptively determined through minimum envelope entropy, and a multi-feature clustering and weighted reconstruction strategy is designed. And in combination with frequency band screening and correlation coefficient weighting, the separation robustness of the respiratory signal and the heartbeat signal is enhanced, and high-precision separation of the vital sign signal is realized. According to the method, non-contact vital sign detection based on OFDM radar signals is achieved, the problems that in a traditional algorithm, parameters of modal numbers and penalty factors need to be manually set, and signal amplitude attenuation and modal aliasing are caused under complex noise are solved, the vital sign monitoring precision is remarkably improved, and high-precision separation of vital sign signals is achieved.