307 results about "Linear correlation" patented technology

The invention provides a prediction method and system for a rock stratum fracture disaster in tunnel engineering, and belongs to the field of tunnel engineering. Comprising the steps of multi-modal monitoring data acquisition and synchronization, data preprocessing and feature calculation, multi-modal information entropy evolution analysis, catastrophe point detection and precursor identification, risk assessment and early warning output and the like. Multiple types of sensors are arranged at key positions of a tunnel, an edge computing gateway is accessed through an industrial-grade field bus, and space-time reference unification of multi-source heterogeneous data is achieved; carrying out preprocessing and feature calculation on the collected original data; performing sliding window probability distribution modeling on each modal feature, calculating an information theory index, and analyzing non-linear relevance among the multi-modal features; judging whether a catastrophe precursor event exists or not by adopting multiple methods; and finally, based on a catastrophe point detection result, performing risk assessment on the tunnel region by using a multi-dimensional risk quantification model, and performing early warning output on high-risk and emergency states.

The invention discloses a typhoon wave forecasting method based on integrated machine learning. The method comprises the following steps: firstly, integrating historical typhoon wave data, meteorological data and marine environment data; preprocessing the data, including integration, cleaning, vacancy filling and standardization, and performing multi-source data completion by adopting a K-nearest neighbor algorithm and a spline interpolation method; secondly, screening key characteristic parameters through a Pearson's correlation coefficient, and reinforcing nonlinear correlation representation in combination with a mutual information method; then, constructing an integrated prediction model containing an LSTM (Long Short Term Memory), an XGBoost (X Goose Boost) and a Transform; and finally, dividing a training set and a verification set by adopting a dynamic time sequence division strategy, optimizing model hyper-parameters, and completing training and testing of the typhoon wave height prediction model. According to the method, the data sparsity problem is solved through multi-source data fusion and feature selection optimization, the generalization ability is improved through an integrated model architecture, and compared with a traditional single model, the training period is remarkably shortened, and the forecasting precision and timeliness are improved.

The invention discloses a vehicle-mounted monitoring system based on deep learning, particularly relates to the technical field of vehicle-mounted multi-mode cooperative sensing, and is used for solving the problems of space-time progressive deviation accumulation and high-speed scene target state estimation distortion caused by hardware synchronization limitation in existing vision and millimeter wave radar fusion. According to the method, high-precision alignment and self-adaptive correction of multi-modal data are realized through dynamic space-time baseline compensation and a closed-loop feedback optimization mechanism; a dynamic compensation coefficient is generated based on sensor space-time distribution mode prediction, and a nonlinear correlation model of a multi-modal data stream is constructed to eliminate sampling frequency difference and transmission fluctuation interference; visual motion artifacts and radar positioning jump noise are separated through a cross-modal feature decoupling technology, and high-confidence feature fragments are extracted in combination with historical trajectory screening and real-time residual analysis; and a closed-loop architecture fusing weight distribution and compensation parameter reverse correction is established, and dynamic calibration and error traceability of multi-source data collaborative sensing are realized.

The present disclosure provides a method for remote sensing blue-green wave band ratio logarithmic water depth retrieval of wavelet spline instantaneous tidal height correction, and belongs to the field of remote sensing water depth retrieval. Aiming at water depth retrieval precision reduced by blue-green light and a tidal height in a remote sensing water depth retrieval process, the model fully considers a linear correlation between an attenuation ratio of the blue-green light in water and a depth, the smoothness of the tidal height, and the characteristics of consistent convergence, first-order continuous derivation and second-order continuous derivation of the tidal height with time change, and constructs the method for remote sensing blue-green wave band ratio logarithmic water depth retrieval of wavelet spline instantaneous tidal height correction, and according to Molokai experiment verification, compared with an early model, the invention improves the remote sensing water depth retrieval precision.

The invention provides a waste glass activity rapid comprehensive evaluation method based on dissolution kinetics analysis. According to the method, the silicon ion dissolution characteristic of the waste glass in an alkaline environment is measured, the particle size effect and kinetic parameter analysis are combined, the corresponding relation with the 28-day compressive strength activity index is established, and rapid evaluation of the activity of the waste glass is achieved. The method comprises the steps of dissolution test, kinetic analysis and activity evaluation. Research finds that a power function relationship exists between the reaction rate constant and the particle size, and good linear correlation exists between the dissolution characteristic and the activity index. According to the invention, a complete evaluation system of particle size-reaction rate-activity index is established, the goal of predicting the 28-day activity index from the 24-hour dissolution characteristic is realized, and the error between the prediction result and the measured value is less than 2%. The method is short in evaluation period and accurate and reliable in result, and has important engineering application value.

The invention discloses a net cage fish school number estimation method based on fish school density, and the method comprises the steps: obtaining a sonar image corresponding to the rotation scanning of a target net cage by a sonar through a step S1 and a step S2, and extracting the fish school number corresponding to each sonar image based on image recognition; a linear correlation curve # imgabs2 # of the effective area # imgabs0 # and the sampling depth # imgabs1 # of the netting in water and a nonlinear correlation curve # imgabs5 # of the fish school density # imgabs3 # and the sampling depth # imgabs4 # are obtained through the step S3, finally, integration is conducted on the two curves according to intervals through the step S4, the fish school number of each interval is calculated in a layered mode, and the estimated value # imgabs6 # of the net cage fish school number in the target net cage is obtained through accumulation. The method fully considers the non-uniformity of the fish school density and the net cage netting area along with the depth change, can improve the measurement accuracy of the net cage fish school number, and has the advantages of high accuracy and efficient, rapid and convenient implementation.

The invention relates to a power grid accurate unit load prediction method and system based on deep learning. The method comprises the following steps: acquiring operation monitoring parameters and equipment environment parameters of electric equipment in a power grid area; dynamic voltage feature extraction is carried out on the operation monitoring parameters of the electric equipment in the power grid area, and voltage fluctuation trend feature data and multi-level load curve features are generated; performing nonlinear correlation analysis on the voltage fluctuation trend characteristic data and the multi-level load curve characteristics according to the equipment environment parameters to generate load-environment deep nonlinear correlation data; identifying power grid area electric equipment to obtain power grid area electric equipment nodes; based on the load-environment deep nonlinear associated data, carrying out accurate unit clustering division on power grid area electric equipment nodes, and constructing a power grid unit load characteristic network; a precision unit type is generated based on the plurality of grid precision units. According to the invention, accurate power grid load prediction is realized.

The invention discloses a reservoir parameter prediction method based on an improved Transform. The reservoir parameter prediction method comprises the following steps: collecting logging data and corresponding rock core lithologic data; preprocessing the logging data through a rock core homing method; selecting an input logging characteristic curve through Pearson linear correlation analysis, and carrying out standardization processing on the input logging characteristic curve; the method comprises the following steps of: improving a Transform by using a TransBlock multi-head attention mechanism and a Real-LN residual structure, and performing feature extraction on a training sample by using a TransBlock algorithm obtained after improvement to form a TransBlock feature model; and obtaining a reservoir parameter prediction result from the to-be-detected data through the TransBlock feature model. According to the method, the problems of less data and insufficient generalization ability in reservoir parameter prediction are solved by improving Transformer, the prediction performance and generalization adaptation ability of the model are improved through limited logging data and hidden correlation between the logging data, and the accuracy of reservoir parameter prediction is remarkably improved.

The invention provides a marine meteorological data quality control method based on a cross-parameter correlation network, which belongs to the technical field of marine meteorology, and comprises the following steps: constructing a cross-parameter correlation network model comprising an air temperature and air pressure correlation rule, a humidity and air temperature linear correlation rule and a wind speed and air pressure gradient extraction correlation rule; a sliding window algorithm is adopted to calculate a correlation coefficient in real time and identify correlation abnormity, a multivariate abnormity detection mechanism of four dimensions of parameter threshold overrun, spatio-temporal change rate abnormity, probability density distribution offset and correlation verification failure is established, a fuzzy comprehensive evaluation method is adopted to calculate a comprehensive abnormity index, and data abnormity is judged. And the authenticity of the abnormal data is confirmed in combination with a multi-sensor cross validation mechanism, and finally the abnormal data is restored by adopting a virtual sensor data reconstruction algorithm based on correlation network reverse calculation, so that the technical problem of poor abnormal data detection effect in the prior art is solved.

The invention discloses an electric power resource scheduling method and device suitable for extreme weather, terminal equipment and a storage medium, and belongs to the technical field of electric power scheduling, and the method adopts nonparametric kernel density estimation to construct a wind and light output joint probability distribution function representing output characteristics of wind power output and photovoltaic output in extreme weather. When flexibility demand analysis is carried out, the advantages of nonlinear correlation between variables and tail risks can be analyzed by means of a joint distribution probability function, wind power prediction errors and photovoltaic prediction errors are accurately analyzed, and then adjustment demand risks of net load flexibility demands are quantified in combination with convolution operation. The problems that the joint probability distribution characteristics of wind and light output in extreme weather cannot be accurately described and the regulation demand risk cannot be ignored in the current flexibility demand evaluation method depending on the normal distribution independent assumption are solved. And the power supply reliability and the operation stability of the power system in extreme weather can be ensured by the formulated power dispatching plan.

The invention relates to a method for detecting pyridine in air, a test paper sheet is placed in an air environment to be detected, the color change of the test paper sheet is observed, the pyridine concentration is qualitatively judged or quantitatively judged according to the color change of the test paper sheet, and the test paper sheet is made of a filter paper sheet and a zinc-based organic-inorganic hybrid halide material. According to the method, qualitative or semi-quantitative judgment of the pyridine concentration in the air can be performed by adopting a test paper sheet and a color card or a linear correlation model standard curve graph, so that whether the pyridine content in the air exceeds the standard or not or the exceeding amplitude can be monitored, and the method has the advantages of convenience in operation and more convenience in detection.

The invention discloses a multi-source heterogeneous data processing method and system based on a test chip, and the method comprises the steps: employing a distributed message queue to access a multi-protocol data stream in real time, collecting the multi-source heterogeneous data related to a chip test, and carrying out the normalization preprocessing; performing cross-domain spatio-temporal feature alignment and mapping on the preprocessed multi-source heterogeneous data, constructing a hybrid incidence matrix based on linear correlation degree and nonlinear correlation degree, extracting three-dimensional joint features, and generating a dynamic metadata tag; generating nodes and edges by using the incidence matrix and the extracted three-dimensional joint features, constructing a parameter association graph structure, performing graph embedding training and generating an index structure, positioning Top-K candidates based on the index structure by using query features and labels, and sorting and returning a result according to a comprehensive distance; the problem that cross-domain association analysis is difficult to carry out due to the fact that the format difference of multi-source heterogeneous data generated by existing chip testing is large is solved, and the precision and efficiency of multi-source heterogeneous data processing are improved.

The invention discloses an image genetics correlation analysis method based on comparative learning and a conditional generative network, and the method comprises the steps: firstly, carrying out the nonlinear mapping of multi-modal image data to a potential space, and extracting the features of the image data; then, implementing comparative learning among the multi-modal image features to enhance feature embedding; in order to adaptively learn the importance of feature embedding, an attention mechanism is introduced to effectively fuse multi-modal image data; in addition, single nucleotide polymorphism (SNP) data is preprocessed into numeric data through a sample population mapping matrix, and then the SNP data is further encoded using a variational autoencoder to extract potential genetic features; in order to construct a non-linear correlation between nerve image data and genetic data, SNP data and demographic data are utilized, a dual-identification condition generative adversarial network is proposed to generate a multi-modal nerve image and a modal attention vector, and early diagnosis of brain diseases can be realized.

The invention provides a chronic disease risk early warning system and method based on artificial intelligence. The method comprises the following steps: acquiring historical monitoring information of a diabetic patient; determining development trends of different disease course stages through historical monitoring information, and performing trend evolution based on all the development trends to obtain evolution characteristics of each disease course stage; determining the risk contribution degree of each health index to the chronic disease risk according to the linear correlation among different health indexes, and determining a risk monitoring model of the target patient through all the risk contribution degrees; performing confidence adjustment on the chronic disease risk in the risk monitoring model according to each evolution feature, and further obtaining a confidence risk value of the current disease course stage of the target patient; and carrying out risk prompting on the target patient based on the confidence risk value. By adopting the scheme of the invention, real-time modeling can be carried out on the dynamic change of the course of disease of the patient under the background of massive health data.

The invention discloses a water level time sequence data cleaning method based on regression statistics, which comprises the following steps: S1, sorting flood data of a historical session of a monitoring station to form a complete flood sample set; s2, establishing a function model according to the nonlinear change characteristics of the flood water level; s3, performing regression function fitting on the historical flood sample data, and calculating linear correlation between each fitting function and actual data; s4, based on a fitting result of the optimal regression function, establishing a Gaussian distribution model of a difference value; s5, determining a confidence interval of Gaussian distribution through the verification sample, and judging whether the data is abnormal data or not according to the confidence interval; s6, the detected abnormal data are corrected, and the cleaned data are obtained; and S7, processing the first N time sequence points in the flood time sequence data by adopting a moving average algorithm, and eliminating the interference of accidental fluctuation on regression fitting. The method has the advantages of being high in cleaning precision, high in adaptability and good in flood nonlinear change characteristic processing effect.

The invention discloses a circulating water culture pond fish school biomass evaluation method, which comprises the following steps of: firstly, acquiring a sonar image corresponding to each circle of rotary scanning of a circulating water culture pond by sonar through steps S1 and S2, and extracting a spatial three-dimensional coordinate of each identified cultured fish in each sonar image based on image identification; filtering noise points generated due to repeated identification in the identified cultured fish coordinate points through the step S3, and counting the number of fish schools corresponding to each sonar image according to the actual cultured fish coordinate points obtained through point cloud merging, and then obtaining a linear correlation curve Si (hi) of the effective area Si of the pool wall and the sampling depth hi through the step S4, and finally, performing integration on the two curves according to intervals through the step S5, performing layered calculation on the number of the fish schools in each interval, and performing accumulation to obtain an estimated value P of the fish school biomass in the circulating water culture pond, so that the measurement accuracy of the fish school biomass in the circulating water culture pond can be improved.

The invention discloses a model processing method and device, equipment, a storage medium and a program product, and relates to the technical field of artificial intelligence, in the method, singular value decomposition is carried out on a network weight matrix of a hybrid expert model, and in an obtained right singular vector matrix, columns with singular values smaller than a threshold value are screened to serve as a null-space basis matrix. In this way, the null-space basis matrix can represent the activation mode direction which contributes little to the activation of the expert network. Furthermore, a sub-network weight matrix is constructed based on each column of the null-space basis matrix and the network weight matrix. Therefore, according to the maximum singular value and the minimum singular value of the sub-network weight matrix, the closeness degree of linear correlation between each column of the network weight matrix and the null-space base matrix can be determined, then the invalid feature sensitivity of each expert network can be determined, and according to the invalid feature sensitivity, the invalid feature sensitivity of each expert network can be determined. And cutting network parameters of the hybrid expert model. In this way, the model parameter quantity can be reduced.

The invention discloses a system and a method for producing a glue injection industrial brush by using an injection molding machine, and relates to the technical field of industrial brush processing, a multi-parameter coupling index is generated according to acquired working parameters, and the working parameters, the multi-parameter coupling index and a batch identifier in a corresponding time period are bound; the method comprises the following steps: performing multi-angle imaging on brushes, adding mark information sections, performing preprocessing, extracting candidate areas which may have defects, after detecting a plurality of defect areas, constructing a morphological synergy index to quantify the overall defect degree, and obtaining defect data of each brush; after a comprehensive index structure is constructed, an injection molding process feature vector is constructed, kernel mapping and an AWCCA method are utilized to calculate nonlinear correlation and local sensitivity between each working parameter and a form cooperation index, a working parameter interval is formulated, and a parameter modification suggestion is generated; in the production process, parameters are continuously corrected in a small-stride mode, the defect degree of the brush and the stability of the injection molding process are balanced, and more flexible regulation and control are achieved.

The invention discloses an attention mechanism and deep learning-based electric tug energy consumption prediction method, which comprises the following steps of: acquiring ship power battery parameters, propulsion system parameters, navigation state parameters and navigation environment parameters in real time, and performing data preprocessing; the tug working conditions are classified and recognized, working condition recognition results are coded, and a data set for energy consumption prediction is constructed; and a pure electric tug energy consumption prediction model based on the LSTM-Transform hybrid architecture is constructed, the constructed prediction model is trained and tested, and finally energy consumption prediction per minute and energy consumption prediction per sea are realized. According to the method, the attention mechanism of Transform is combined with a deep learning method to fully mine the time sequence characteristics and non-linear correlation of the electric tug energy consumption data, the prediction precision is improved by adaptively adjusting model parameters, and reliable data support can be provided for energy optimization scheduling and cruising ability evaluation of the electric tug; and the economy and safety of ship operation are effectively improved.

The invention discloses a coal seam roof pre-grouting optimization method based on data driving. The coal seam roof pre-grouting optimization method comprises the following steps: S1, fine exploration and original information extraction of a geologic body in a research area; s2, construction information extraction; s3, performing grouting project classification by adopting a DBSCAN clustering algorithm; s4, establishing a quantitative relation model based on a classification result; the method is suitable for the technical field of coal water prevention and control, multi-source geological exploration data and dynamic grouting engineering parameters are fused, a geological-engineering feature coupling model is established, a nonlinear association rule of a geological structure and grouting response is quantitatively analyzed based on a machine learning algorithm, and the method has the advantages of being high in practicability and high in reliability. And a multi-objective collaborative optimization system is developed, and intelligent matching of grouting parameters, accurate prediction of the construction effect and dynamic optimization of an engineering scheme are achieved. Finally, the technical purposes of improving the historical data utilization rate, enhancing the grouting and water plugging success rate and reducing the engineering cost are achieved, and scientific decision support is provided for coal mine water disaster prevention and control.

The invention provides a gas leakage detection method and circuit, and the method comprises the steps: obtaining a plurality of calibration electric signals of calibration concentrations of target gas through employing a gas concentration sensor, and forming a concentration electric signal calibration table; determining the linear correlation between the gas concentration of the target gas and the detection electric signal based on a concentration electric signal calibration table; obtaining a leakage threshold value corresponding to the gas alarm concentration by combining the concentration electric signal calibration table and the linear correlation; a gas concentration sensor is adopted to obtain the real-time detection concentration of target gas in real time, and when the real-time detection concentration reaches a leakage threshold value, an alarm signal is sent out. The method has no destructive influence on the detection environment and no material loss, and is simple in structure, stable in performance, convenient to use, stable in signal and long in service life by detecting the concentration and leakage state of the target gas, and the corresponding detection circuit is small in structure and convenient to mount and dismount.

The invention discloses a comprehensive detection and evaluation method and system for the lap joint construction quality of new and old roadbeds, and the method comprises the steps: carrying out the fitting processing of j differential settlement values of a certain monitored section before a certain moment T and the time t, drawing a differential settlement-time relation curve, and obtaining the change rate of the differential settlement values at the moment T; obtaining differential settlement values and differential settlement value change rates of n monitoring sections at the T moment along the line, respectively drawing a distribution linear association diagram of the differential settlement values along with the course change of the length L of the detection route, and calculating to obtain an average value and a variance of the differential settlement values; calculating to obtain an average value and a variance of the differential settlement value change rate according to a distribution linear correlation graph of the differential settlement value change rate along with the course change of the detection route length L; constructing a construction quality comprehensive detection evaluation index, setting a new and old roadbed lap joint construction quality comprehensive detection evaluation index standard value, and performing evaluation according to the new and old roadbed lap joint construction quality comprehensive detection evaluation grade division table.

The invention discloses an intelligent analysis system and method for rehabilitation treatment of lumbar intervertebral disc herniation, and relates to the field of intelligent analysis. Abstract clinical information amounts such as lesion part features, pain perception description and movement function requirements are converted into high-dimensional vectors by adopting a semantic coding technology based on deep learning; the system can more accurately understand and simulate the condition of a patient in a multi-dimensional space, further, a dynamic mapping mechanism between a rehabilitation starting point and a target is established to capture non-linear correlation between the current state of the patient and an ideal rehabilitation target, and intelligent derivation of a rehabilitation path is achieved based on the non-linear correlation. Through the dynamic analysis of the multi-dimensional information of the patient, the system can better understand the unique demand of each patient, the matching precision of the rehabilitation treatment scheme is improved, and the treatment is more personalized and effective.

The invention provides a shale oil reservoir dessert intelligent identification method and system, relates to the field of shale oil reservoirs, and aims to obtain original logging data and extract curve deep space-time coding features by fusing a hybrid model of a convolutional neural network and a recurrent neural network. Then, multi-source logging features are dynamically fused through a cross-curve feature fusion algorithm, and spatial compression is carried out through feature principal component analysis; thirdly, implicit features strongly related to the geological signs of the sweet spots are screened through semantic level selection based on feature principal components, sweet spot probability prediction is achieved based on a lightweight classification model, an intelligent recognition closed loop without manual intervention is formed, and the dependence of a traditional method on expert experience is broken through; in this way, non-linear correlation in the logging curve is automatically mined through deep learning, and the interpretability and generalization ability of complex reservoir dessert recognition are improved.

The invention provides a ratio fluorescent probe for detecting aureomycin in meat food and a detection method. After an aureomycin solution is added into the prepared Au NCs ratiometric fluorescent probe solution, a fluorescence emission band of a reaction system at 620 nm is almost unchanged, and a fluorescence emission band at 425 nm is remarkably enhanced. Meanwhile, the color of a reaction system under an ultraviolet lamp is gradually changed from red to purple along with the increase of the concentration of the aureomycin, and quantitative detection of the aureomycin can be realized by utilizing the characteristics. The detection method disclosed by the invention comprises two detection methods, one is a ratio fluorescent probe detection method, and quantitative detection is realized by utilizing linear correlation between the F425 / F620 fluorescence intensity ratio and the chlortetracycline concentration. The other method is a visual quantitative detection method, a fluorescent paper chip image is collected by using a smart phone and converted into a corresponding RGB value, and quantitative detection is realized by using a linear relationship between an R / B value and the chlortetracycline concentration. The detection method disclosed by the invention has the advantages of high selectivity, high sensitivity, simplicity in operation, obvious phenomenon and good stability and repeatability.

The invention belongs to the technical field of hydraulics, and particularly relates to an earth and rockfill dam overtopping dam break peak flow prediction method, which comprises the following steps: collecting a water level-area-reservoir capacity curve, a dam crest elevation and a dam site section reservoir bottom elevation data of a prediction target reservoir; according to the dam crest elevation and the water level-reservoir capacity curve, the reservoir capacity is obtained through linear interpolation, and the maximum dam break height is obtained by subtracting the dam site section reservoir bottom elevation from the dam crest elevation; according to the water level-area curve, converting the water level into false height by taking the reservoir bottom elevation as a reference surface, and fitting the false height to obtain a power coefficient; acquiring a flow coefficient by adopting a linear fitting mode according to the database data of the broken dam case; and predicting the reservoir dam break peak flow according to the breach peak flow calculation formula. The dam break peak flow prediction index provided by the invention is clear in physical significance, has a remarkable linear correlation characteristic with the dam break peak flow, is easy to obtain, and can be used as a trend analysis index to test the rationality of a calculation result.

The invention provides a nonlinear correlation pair screening method for photovoltaic power generation output power influence factors. The nonlinear correlation pair screening method comprises the following steps: acquiring photovoltaic power generation output power data and corresponding meteorological factor data in a preset time period; constructing a plurality of time window pairs according to the photovoltaic power generation output power data and the meteorological factor data; calculating a nonlinear correlation coefficient of the time window pair, and obtaining a candidate set of a nonlinear correlation pair according to a calculation result; and performing reduction processing on the candidate set of the nonlinear correlation pair to obtain an optimal nonlinear correlation pair. According to the method, influences of different regularity, different periodicity and volatility of the influence factors of the output power, namely meteorological factors on the time scale are fully considered, and a more reliable analysis result can be obtained.

The invention discloses a ship maintenance inspection robot and a use method, and relates to the technical field of ship maintenance inspection robots. Through mutual cooperation of the telescopic springs and the anti-collision plates, it is conveniently guaranteed that in the collision process of the robot, impact on a robot body is reduced, the use safety of the robot is improved, and then the function of protecting the robot and ship equipment is achieved; a linear relation model of obstacle length, width, walkway width and passing time is constructed to quantify the influence of the obstacle on the passing efficiency, and the limitation that only the route length is used as a single optimization index is avoided; the actual passing time of each route is calculated based on historical inspection data, the consumed time of the optimal route and the consumed time of the to-be-selected route are dynamically compared, real-time adjustment of the route priority is achieved, and the inspection efficiency in a complex environment is improved; and the distribution rule of abnormal positions is judged by using a linear correlation coefficient, and if abnormal points are linearly arranged, the corrosion possibility is eliminated, so that the misjudgment rate is reduced.

The invention relates to the technical field of application of a neural network to interval prediction of regional power system load, and provides a method for reasonably and accurately predicting and estimating power load which is a key element of a novel power system, so that safe and stable operation of the power system is ensured, and efficient supply and demand balance is realized. According to the interval prediction method based on an adaptive cooperative myxomycete algorithm and parallel prediction, firstly, an improved grey correlation degree analysis IGRA is adopted to screen climate, holiday and festival factors, and factors having high non-linear correlation with a load sequence are identified; then, a neural network model of time convolutional neural network-deep learning is built; carrying out parameter optimization on the TCN-Transformer model by adopting an adaptive cooperative mucus algorithm (ACSMA); and utilizing the trained TCN-Transform to carry out interval prediction on the load of the regional power system. The method is mainly applied to power system load prediction management occasions.

The invention relates to a hematocrit measurement method and device, and the method comprises the steps: applying an AC signal with a preset frequency to a to-be-measured blood sample, obtaining an impedance real part and an impedance imaginary part of the to-be-measured blood sample, substituting the ratio of the impedance real part to the impedance imaginary part into a preset linear equation, and obtaining the hematocrit of the to-be-measured blood, the method comprises the following steps: respectively applying alternating current signals with a preset frequency to a plurality of blood samples with different concentrations and different hematocrites to obtain impedances and phase angles of the plurality of blood samples under the preset frequency, and obtaining a ratio of an impedance real part to an impedance imaginary part of the plurality of blood samples under the preset frequency according to the impedances and phase angles of the plurality of blood samples under the preset frequency; according to the method, linear fitting is carried out on the ratio of the impedance real part to the impedance imaginary part of the plurality of blood samples under the preset frequency and the corresponding hematocrit, so that the linear correlation coefficient of the fitted linear equation can be effectively improved, and the measurement accuracy of the hematocrit can be effectively improved.