79 results about "Target population" patented technology

The present disclosure provides a population variation detection method and apparatus, an electronic device, and a storage medium. The method comprises: according to population information of a plurality of samples, determining a division mode of genome variation data; according to the division mode, dividing the genome variation data to obtain a plurality of variation data fragments corresponding to the genome variation data; combining variation data fragments in the genome variation data having the same ranking to obtain combined variation data; respectively performing population variation detection on the combined variation data to obtain population genome variation data of the combined variation data; and combining the population genome variation data to obtain target population genome variation data of the plurality of samples. Therefore, in the population variation detection process, population variation detection is respectively performed on a plurality pieces of local combined data, and detection results are combined, so that the efficiency of acquiring complete population genome variation data can be improved.

The invention provides a data recommendation method and device based on artificial intelligence, computer equipment and a storage medium. The method comprises the following steps: acquiring key business data of an initial crowd; analyzing the key business data based on an index analysis strategy to generate analysis reasons and optimization suggestions; performing data integration on the key business data, the analysis reasons, the optimization suggestions and the user behavior logs to obtain target data; based on the target data, performing crowd optimization processing on the initial crowd by using a preset large model, a diffusion model and a hybrid model to determine an optimized target crowd; performing effect prediction on the target crowd, and judging whether effect prediction data accords with expectation or not; if yes, binding the target crowd with a marketing strategy, and verifying the target crowd based on an AB experiment; and if the experiment result of the AB experiment is a forward result, performing product recommendation processing on the target crowd based on a total delivery strategy. According to the method, the marketing recommendation efficiency and accuracy are effectively improved.

The invention provides a target population-oriented re-crime analysis and early warning system and method, and the system comprises a data collection module which is used for obtaining the related management data of a target person from a pre-integrated related data system; the data analysis module is used for processing and analyzing the related management data according to a pre-stored data processing and analysis tool to obtain a data analysis report, and adding the data analysis report into a sample database; the early warning module is used for inputting the data analysis report into a pre-constructed behavior mode and psychological tendency prediction model to obtain an early warning signal, and performing early warning according to the early warning signal; and the control module is used for inputting the early warning signal into a pre-constructed intelligent decision support model to obtain the risk level of the target personnel and corresponding management measures. According to the invention, the re-crime analysis and early warning which is high in efficiency and universal and faces the target crowd is realized without manual participation.

The present application is applicable to the field of data analysis technology, and in particular, relates to a policy relevance analysis method and system. The policy relevance analysis method includes: comprehensively grasping the basic situation of the target population and the historical background of the policy by obtaining the target population information and the policy information of multiple policies; determining the policy description mark of each policy based on the policy information of multiple policies to reflect the impact of the policy on different populations in the past, so as to accurately evaluate the potential impact of the policy on the target population in subsequent analysis; generating multi-dimensional matching analysis data based on the policy description mark and the target population information, and comprehensively evaluating the relevance between the policy and the target population from multiple dimensions; finally, determining the optimal matching policy for the target population information based on the multi-dimensional matching analysis data of the policy description mark and the target population information, so that the enterprise can effectively grasp the policy direction, optimize resource allocation, and enhance the competitiveness of the enterprise.

The invention relates to a partial discharge fault diagnosis method and device, equipment, a medium and a product. The method comprises the following steps: performing feature extraction on sample partial discharge data of sample gas insulated switchgear under different fault types to obtain sample partial discharge features; constructing an initialized population; population individuals in the initialized population correspond to parameter combinations of to-be-trained parameters; updating individual positions of population individuals in the initialized population according to the sample partial discharge features to optimize the initialized population and obtain a target population; according to a target parameter combination in the target population, initializing model parameters of the extreme learning machine model; and performing model training on the initialized extreme learning machine model according to the sample partial discharge characteristics to obtain a fault diagnosis model which is used for diagnosing the fault type of the partial discharge signal. By adopting the method, the fault type of partial discharge can be accurately diagnosed.

The invention develops a sarcopenia prediction system for an adult group. The sarcopenia prediction method comprises the following steps: determining a crowd range and a standard included in the prediction system; determining a sarcopenia diagnosis standard and collecting outcomes in the target population; selecting a tree-based machine learning model according to the data features; screening the features by using a Boruta algorithm to construct a first set of screening model with high recall rate; screening features by using a pre-SHAP analysis method to construct a second set of auxiliary models for balancing the recall rate and the accuracy rate; optimal models of the system are determined by comparing model performance parameters, and finally prediction is achieved by combining the models. Features used by the system only depend on basic demographic features and physical activity questionnaires without clinical indexes, meanwhile, high performance is achieved in a model parallel connection mode, and the system is suitable for early screening detection of sarcopenia.

The invention provides a Joint-Calling acceleration method and device for high-performance sequencing data, electronic equipment and a storage medium, and the method comprises the following steps: determining a division mode of genome variation data according to group information of a plurality of samples, and dividing each piece of genome variation data according to the division mode, obtaining a plurality of variation data fragments corresponding to each piece of genome variation data; merging variation data fragments with the same sequence in the genome variation data to obtain merged variation data; and respectively performing group variation detection on each piece of combined variation data to obtain group genome variation data of each piece of combined variation data, and combining each piece of group genome variation data to obtain target group genome variation data of a plurality of samples. Therefore, in the population variation detection process, the efficiency of obtaining complete population genome variation data can be improved by performing population anomaly detection on the multiple pieces of local merged data and merging the detection results.

The invention provides a causal inference method and device based on genetic variation, electronic equipment and a medium. The causal inference method comprises the following steps: acquiring whole genome SNP data and proteome data of a detection sample of a target population from a detection platform; performing whole genome association analysis on the whole genome SNP data to obtain outcome SNP data associated with the target phenotype; performing protein quantitative trait site analysis based on the whole genome SNP data and the proteome data to obtain exposure SNP data associated with the target exposure factor; and performing data preprocessing on the outcome SNP data and the exposed SNP data, and performing Mendel randomization analysis on the preprocessed outcome SNP data and exposed SNP data to obtain a causal relationship between the target exposure factor and the target phenotype. According to the method, the accuracy and reliability of Mendel stochastic analysis results are improved.

The invention discloses a population prediction method and device, electronic equipment and a storage medium. Comprising the following steps: acquiring satellite image data, night light image data and base station communication data of a target area; determining plane area data, floor number and building type of the building in the target area based on the satellite image data; for any building, determining the population density of the building based on the plane area data, the floor number and the building type of the building, and determining an initial population prediction result based on the plane area data, the floor number and the population density of the building; and determining a population calibration coefficient of the target area based on at least one of the night light image data, the base station communication data and the initial population prediction result of each building, so as to calibrate the initial population prediction result corresponding to each building, and obtain a corresponding target population data prediction result. Dynamic calibration of the population prediction data is realized, and the accuracy of the population prediction result is improved.

The present invention discloses a method and device for locating a fault section of a distribution network based on a dynamic weight update strategy, and relates to the field of distribution networks or other related fields. The method comprises: responding to a detection instruction, using a chaotic mapping strategy to generate a population, receiving a segment actual vector, calculating an expected vector, using an improved fitness function to obtain segment fitness, forward sorting the fitness set, selecting the top three individuals in the sorting as wolves of the gray wolf algorithm, using a dynamic weight update strategy to update the position of the wolf, using a mutation and crossover strategy for the individuals of the population to obtain a mutated crossover individual, when the fitness of the mutated crossover individual is less than that of the individuals of the population, using the mutated crossover individual to replace the individuals of the population to obtain a new population, and obtaining a target population by iterating the new population, and locating the fault section in the segment vector represented by the optimal individual in the target population. The present invention solves the technical problems in the related art that the algorithm converges slowly and is prone to fall into the dilemma of local optimal solutions.

The present invention discloses a method for constructing a disease recognition model of medical data based on multi-task self-supervised learning, which belongs to the field of laboratory medicine and disease recognition technology, and includes: S1, extracting medical data of a target population to form an original sample data set; S2, preprocessing the original sample data set; S3, constructing a missing value filling model and a sample data homology test model using the preprocessed missing value filling data set and the sample data homology analysis data set; S4, using a multi-task self-supervised learning model, taking missing value filling and sample data homology prediction as auxiliary tasks, taking known disease screening as a downstream task, and realizing disease risk assessment of the original sample data through transfer learning. The present invention adopts the above-mentioned method for constructing a disease recognition model of medical data based on multi-task self-supervised learning, which can extract more comprehensive and accurate features to achieve the purpose of improving the generalization performance of downstream tasks.

The invention provides a method and system for predicting the individual exposure level of air pollutants, and the method comprises the steps: S1, collecting the original sample data information of a subject population to form an original data set, and dividing the original data set into a training subset and a test subset; s2, constructing an air pollutant crowd exposure level initial model; s3, training the initial model by using the training subset, and optimizing the initial model based on the combination of a regularization method of Lasso regression, an SHAP value weighting method and a recursive feature elimination algorithm; step S4-S5, obtaining model prediction data and evaluating prediction accuracy; and step S6, collecting input characteristic variables of a target population different from the subject population, and substituting the input characteristic variables of the target population into the prediction model subjected to accuracy evaluation to realize prediction of the air pollutant individual exposure level of the target population. The prediction model is better in fitting performance and high in accuracy.

A system and method for determining and managing reputation of an entity or industry includes determining a sample size of the population that provides at least a pre-defined percent confidence interval, and which has the predefined level of familiarity (unique group); determining a measure of likelihood that the targeted population will perform a positive action on behalf of an entity (behavioral connection score), wherein determining the measure of the likelihood that the targeted population will perform a positive action on behalf of an entity comprises: receiving survey ratings from behavioral connection survey questions where each survey rating is provided by a party within the unique group; weighting the received survey ratings to accommodate for at least one of the group consisting of cultural bias and missed demographic quotas; and aggregating the ratings within each individual question to provide a single aggregated behavioral connection score for each behavioral connection survey question.

The invention relates to the technical field of inverters, and discloses a VSG-based inverter parameter identification method and device, equipment and a storage medium, and the method comprises the steps: generating an initial population through an initial search space and a preset number Q of individuals, the initial search space reflecting the value range of model parameters of a VSG-based inverter model, and the value range of the model parameters of the VSG-based inverter model; the initial population comprises Q initial individuals, and each individual corresponds to a group of candidate model parameters; performing iterative updating on the model parameters through evolution operations such as variation, crossover and out-of-group competition, and finally determining target fitness according to a predetermined real response value of the inverter, an estimated response value of the inverter under the candidate model parameters in the target population and a preset criterion function. According to the method, the optimal individual corresponding to the optimal identification result of the model parameters is evaluated, the parameter identification accuracy is improved, and the problems of modeling and simulation of a high-proportion new energy power system can be solved while the power grid voltage support capability is improved.

The invention relates to the technical field of inverters, and discloses an inverter parameter identification method based on multi-point crossover and a related device, and the method comprises the steps: determining a first population corresponding to a model parameter of an inverter model under a current iteration number G; performing evolution processing on the first population by using a preset multi-point crossover rule to obtain a target population; determining target fitness according to a predetermined real response value of the inverter, an estimated response value of the inverter under the model parameter individuals in the target population and a preset criterion function; if the target fitness does not meet the preset convergence condition, G is made to be equal to G + 1, the step of determining the first population corresponding to the model parameters of the inverter model under the current iteration number is executed again, and G is the current iteration number; and if the target fitness meets a preset convergence condition, taking a model parameter corresponding to the optimal model parameter individual in the target population as an optimal identification result. Parameter identification is carried out through a multi-point crossing rule, so that a parameter identification result is more reliable and accurate.

The system according to the embodiment aims to efficiently collect and analyze reviews from personas tailored to a target demographic. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a collection unit, an analysis unit, and a provision unit. The reception unit inputs attributes of a target demographic. The generation unit generates a persona based on the attributes input by the reception unit. The collection unit collects reviews based on the persona generated by the generation unit. The analysis unit analyzes the reviews collected by the collection unit. The provision unit provides the analysis results obtained by the analysis unit.

The present invention relates to the field of medical technology, and in particular to a method and system for reviewing and homologous matching of blood routine test results, the method comprising: obtaining blood routine test data of a target population to form a training data set and a test data set; constructing an anomaly detection model, formulating evaluation rules for applicable scenarios, re-examination decisions and homologous predictions; using the training data set to train the anomaly detection model; during testing, in the sample quality review scenario, the input of the anomaly detection model is the difference in indicators of each item between the current subject's blood routine sample and the previous blood routine sample of the subject and the data of related interference factors, and the output is whether re-examination is required; in the homologous matching scenario, the input of the anomaly detection model is the difference in indicators of each item between the current subject's blood routine sample and the blood routine sample of a suspected homologous patient and the data of related interference factors, and the output is whether it matches. The present invention can quickly and accurately determine whether a patient sample is qualified, and perform homologous matching for mismatched samples.

The present invention discloses a seafloor inversion method, apparatus, device, and medium based on genetic clonal selection. The method comprises: initializing a first population and using the first population as a target population; performing inversion and evaluation based on inversion parameters of the target population to obtain the root mean square value corresponding to each individual; setting random parameters based on the minimum root mean square value and the number of population iterations; updating the population by improving a genetic algorithm based on the random parameters; when an individual in the updated population has a root mean square value that meets an optimal iteration threshold, using the corresponding individual as the target individual; otherwise, cyclically updating the population until the target individual is obtained or the number of population iterations equals a preset maximum number of population iterations; and finally determining the seafloor inversion result of the area to be inverted based on the seafloor topography data corresponding to the target individual. The present invention can accurately achieve seafloor inversion and can be widely applied in the field of data processing technology.

This invention proposes a posture detection and automatic zoom method, comprising: S11, using a target detection algorithm to identify and locate human information in the image; the human information includes the number of human bodies in the image and the position data of several human bodies; the position data includes the center coordinates and dimensions of the detection box; S12, filtering the target people to select those for posture detection; S13, classifying the target people using a clustering algorithm to obtain several target group classes; S14, segmenting the image according to the target group classes to obtain segmented images with the same number of target group classes; S15, enlarging the segmented images to a preset resolution; S16, performing posture detection on the enlarged segmented images; the above method can be applied to various sports competition scenarios, achieving adaptive image segmentation and posture detection, and finding more human postures with fewer detections.

The invention provides a parameter identification method and device of a permanent magnet synchronous motor, equipment and a storage medium, and the method comprises the steps: obtaining a to-be-processed parameter of the permanent magnet synchronous motor, the to-be-processed parameter being used for representing an initial population individual in a genetic algorithm; identifying a permanent magnet synchronous motor working condition corresponding to the initial population individuals, and determining a pre-calibration weight coefficient corresponding to the preset target function through the working condition; determining fitness values of the initial population individuals through a preset objective function; target population individuals are screened out through the fitness values, and iterative operation is carried out on the target population individuals; for each iterative operation, determining an update vector corresponding to the target population individuals; adjusting the to-be-processed parameters corresponding to the target population individuals through the update vectors to obtain adjustment parameters corresponding to the target population individuals; and identifying the target parameter of the permanent magnet synchronous motor through the adjustment parameter corresponding to the target population individual under the condition that the preset iteration termination condition is satisfied, thereby improving the accuracy of the identification result.

The present invention discloses a method and system for urban carbon emission prediction and control based on deep learning, which relates to the technical field of carbon emission prediction. It includes obtaining carbon content data released by residents in public areas based on smoking behavior, obtaining action data of residents' smoking behavior in public areas based on the carbon content data, where the action data is composed of the moving positions of limb nodes, and obtaining the target population in public areas based on the carbon content data and the action data. By comparing and analyzing the action data of the target population with that of other target individuals, the present invention can extract differential data from the behavior patterns and use these differential data to adjust the values of the correction data, thereby improving the accuracy and real-time performance of carbon emission prediction. In addition, by automatically adjusting the correction data and comparing it with the real-time action data, the future smoking behavior and carbon emission settings of residents can be predicted, improving the accuracy of carbon emission prediction.

The invention provides a product recommendation method which can be applied to the technical field of financial science and technology. The product recommendation method comprises the steps of obtaining interaction data of a user, the interaction data comprising a plurality of products having an interaction relationship with the user, and each of the plurality of products having the interaction relationship with the user has at least one product attribute; generating a population according to the interaction data, the population including a plurality of individuals, the individuals including N products having an interaction relationship with the user; according to the number of the categories of the products in the individuals, the excitation coefficients of the individuals are determined, and the excitation coefficients of the individuals and the product attributes of the products included in the individuals are used for constructing an objective function of an evolutionary algorithm; performing evolution operation on the population by using an evolutionary algorithm and a target function to obtain a target population; and determining a set of target individuals in the target population through the function value of the target function, and taking products included in the target individuals as recommendation results. The invention further provides a product recommendation device and system, a medium and a program product.

We provide the system. [Solution] The primary means of receiving data input from users, A second means for obtaining related information from information storage based on the data input, A third means that uses the acquired information to generate the optimal question using artificial intelligence, A fourth means for formatting the generated questions and creating an electronic questionnaire, A fifth method for distributing the questionnaire to the target persons, A sixth method involves collecting and analyzing responses from the target individuals after distribution. A system that includes this.

This invention provides a genome-wide selection breeding method based on pooled simplified genome sequencing, relating to the field of breeding. The method includes obtaining the minor allele ratios and genotype data of SNP loci, mixing them according to the target group size, statistically analyzing the probability density distribution of allele frequencies corresponding to each possible outcome of genotype mixing, and obtaining the parameters α and β of the β distribution. After mixing the genotype and phenotypic data of the target species according to the target group size, the minor allele ratios simulating real pooled sequencing are obtained by sampling using the fitted β distribution, which serves as the genotype. A regression model is then selected to estimate the marker effect value, and the breeding value of each individual in the target population is estimated. Using this method avoids the problem of single-sample data uniformity and improves the accuracy of breeding value prediction with a reference population establishment cost of one-fifth or even one-tenth, thus better enabling breeding of species such as Litopenaeus vannamei and dairy cows.

The application includes systems and methods for phenotyping. A request is received to identify a target population having a phenotype. Predefined characteristics associated with the phenotype are obtained. Using a retriever model, a set of subjects is identified as potential members of the target population by searching databases. Medical information is obtained by searching a second set of one or more databases and providing corresponding information to an artificial intelligence (Al) component. Natural language instructions are provided to the Al component to provide context to the Al component to determine whether a respective subject in the set of subjects has at least one of the one or more predefined characteristics. A subset of subjects is identified by the Al system to be, or to have a high likelihood of being, a member of the target population.

The invention relates to a physiological data fused comprehensive assessment method and system for the exercise ability of old people. The method comprises the following steps: acquiring physiological health information of a target population in a specific region, and dividing exercise health risk levels through a preset machine learning classification algorithm to obtain a preliminary sample set; integrating the data with regional climate indexes and regional high-incidence disease related indexes to form a comprehensive input data set, constructing an evaluation framework based on SPPB to perform hierarchical evaluation to obtain a preliminary risk hierarchical result, and determining a final risk group through a clustering algorithm; inputting the final risk group into a preset classification model adaptive to the region and health characteristics to generate a risk level tag, and generating a final intervention scheme according to the risk level tag; and allocating the scheme to a target population, dynamically evaluating and calculating a deviation value based on a preset health target and a risk threshold, and if the deviation exceeds the threshold, generating a personalized participation report in combination with the real-time health state. According to the method, the accuracy and the region adaptability can be improved, the reduction of the exercise ability is assisted and delayed, and the risk of bad health events is reduced.

The invention discloses a cerebrovascular disease risk assessment method, and relates to the technical field of risk prediction, and the method comprises the steps: determining a target population, and collecting first multi-dimensional data including demographic information, biochemical index information, lifestyle information and target risk type exclusive data; preprocessing the second multi-dimensional data to obtain second multi-dimensional data, and screening key feature variables by adopting a category-type feature improvement algorithm; constructing a machine learning model matched with the target risk, and outputting a risk assessment result or analyzing a variable risk association relationship after training; combining crowd characteristics or variable dependency relationship optimization model output, and verifying efficiency through decision curve analysis, an ROC curve and a confusion matrix; and finally integrating results to generate an evaluation report containing risk levels and key influence factors. According to the invention, through multi-dimensional data acquisition, scenarized model construction and two-dimensional verification, the assessment is more suitable for clinical requirements, and effective guidance can be provided for cerebrovascular disease risk prevention and control.

The invention relates to the technical field of detection experiments, in particular to an automatic scheduling method and system for assembly line experiments and related equipment, and the method comprises the steps: randomly generating a full-permutation array as an initial population, the full-permutation array representing different detection sequences of each detection experiment; taking the initial population as a parent, performing crossover variation by introducing linkage gene probability in biology, determining a target population, and obtaining total detection duration of each individual in the target population; and taking the individual corresponding to the minimum value in the total detection duration as a target individual, and detecting each detection experiment according to the detection sequence corresponding to the target individual. According to the method, the tasks are scheduled and arranged by adopting the genetic algorithm, and the detection efficiency is optimized.

The present invention discloses a terrain processing method, apparatus, device, and medium based on a genetic algorithm. The method includes: initializing a first population; using the first population as a target population; performing an inversion operation and fitness evaluation of a target area based on the target population to obtain a root mean square value of each individual in the target population; when a root mean square value meets a preset threshold condition, outputting the corresponding individual as an adapted individual; otherwise, performing a genetic operation on the target population to obtain a second population; using the second population as the target population; returning to the steps of performing an inversion operation and fitness evaluation of the target area based on the target population until an adapted individual is output or the maximum number of iterations of the population is reached; and using the terrain data obtained from the inversion operation using the inversion parameters corresponding to the adapted individuals as the terrain processing result of the target area. The present invention can efficiently and accurately perform terrain processing and can be widely applied in the field of data processing technology.

A multi-stratified sampling system using a machine learning algorithm, according to one embodiment of the present invention, comprises: a sample size calculation unit for calculating a sample size on the basis of the size of a target population; a multi-stratified sampling unit for performing multi-stratified sampling on the target population on the basis of the calculated sample size; and a multi-stratified sample group calculation unit for calculating a multi-stratified sample group from the target population on the basis of the performed multi-stratified sampling.