377 results about "Logistic regression" patented technology

The invention discloses a health care service data management method based on machine learning, and relates to the technical field of data management. The method comprises the following steps: collecting health care service data in real time, constructing a health care knowledge graph, and carrying out feature dynamic alignment on the health care knowledge graph by adopting agency attention and multi-scale contrast learning to obtain a dynamic alignment feature vector; based on the dynamic alignment feature vectors, training CatBoost, XGBoost and a random forest model through Bayesian optimization, and obtaining a health prediction model through stacking generalization fusion of a logistic regression model; inputting the dynamic alignment feature vector into a health prediction model, and outputting to obtain a prediction result; corresponding service measures are executed according to the prediction result, new data are collected again after the service measures are executed, the health care knowledge graph is updated, and therefore management of health care service data is achieved.

The invention discloses a COPD-FE risk prediction method based on disease and symptom combination, and is applied to the technical field of chronic obstructive pulmonary disease risk prediction. Comprising the following steps: acquiring chronic obstructive pulmonary frequent acute exacerbation influence factor data of a patient; the influence factors are screened through LASSO regression and an improved Boruta algorithm respectively; the LASSO independent influence factors and the Boruta independent influence factors are combined in different modes, a Logistic regression model and an XGBoost model are trained, and a plurality of COPD-FE risk prediction models are obtained; and evaluating the performance of all the COPD-FE risk prediction models, and selecting the COPD-FE risk prediction model meeting the requirement to predict the COPD-FE risk. According to the method, clinical data of patients are collected, a risk prediction model is constructed in combination with a feature selection method and machine learning, and an optimal model is screened out through comprehensive evaluation.

The invention relates to the technical field of remote sensing image recognition, in particular to a method for determining a moso bamboo forest snow disaster affected area based on a GEE platform. The method mainly solves the problems of low efficiency, high cost and difficulty in realizing large-range rapid evaluation caused by dependence on manual field investigation in the prior art. According to the technical scheme, the method comprises the following steps: firstly, obtaining a multi-temporal Sentinel-2 image before and after a snow disaster, and carrying out the preprocessing of the multi-temporal Sentinel-2 image; extracting spectral bands, vegetation indexes and texture features to form an initial feature set; then, a key feature variable combination is determined through statistical significance filtering and machine learning optimization; on this basis, establishing a logistic regression discrimination model and determining an optimal classification threshold; and finally, carrying out snow disaster state classification on the moso bamboo forest region by utilizing the trained model, and generating a disaster region spatial distribution diagram.

The invention discloses a meta-model decision-making-based intelligent prediction method for inclusion quality in an electroslag remelting process. The method comprises the following steps of: constructing a sample data set containing process and component characteristics and target variables; obtaining a first-layer basic model based on an SHAP value cumulative contribution rate screening method; a heterogeneous learner is adopted to construct a first layer structure of the stacked integrated learning model, and Bayesian is adopted to carry out adjustment and optimization; adopting logistic regression as a meta-learner to construct a second-layer structure of the stacked integrated learning model; training by adopting a five-fold cross validation strategy, and predicting the performance by using a multi-index quantitative model; and deploying the D-type inclusion prediction model in the electroslag remelting process to an actual process, collecting process parameters as input data in real time by using a multi-sensor group, performing D-type inclusion risk prediction of a corresponding heat, and outputting an inclusion risk prediction result. According to the method, the accuracy and reliability of D-type inclusion prediction can be remarkably improved.

The invention provides a large language model generation code detection method and system, which is applied to the technical field of artificial intelligence, and comprises the following steps: obtaining a to-be-detected code; a to-be-detected code is input to a trained shared encoder, a code feature vector is obtained, the shared encoder is obtained through multi-target joint training, and the multi-target joint training is used for optimizing classification loss, domain confrontation loss, comparison loss and difficult sample loss at the same time; l2 normalization is carried out on the code feature vector, and the code feature vector is mapped to a hyperspherical space to obtain spherical embedding; the sphere is embedded and input into a sphere category classifier based on sphere logistic regression for classification processing, a detection result of the to-be-detected code output by the sphere category classifier is obtained, the detection result comprises AI generation and human writing, and a decision boundary of the sphere category classifier is an intersection line of a hyperplane and a hypersphere for classification. According to the invention, the AI generation code and the human compiled code can be accurately distinguished.

The invention discloses a disastrous big wave occurrence probability and wave height combined prediction method, device and equipment, a medium and a product, and relates to the technical field of meteorological ocean forecasting. The method comprises the following steps: acquiring an effective wave height measured value and environmental factor data of a to-be-predicted region in a historical time period; key environment factors are screened through spatial correlation analysis and linear fitting verification, a first training data set and a second training data set are constructed, and a multiple linear regression model and a logic regression model are trained respectively to obtain a wave height prediction model and a big wave occurrence probability prediction model; and inputting the key environmental factors of the current time period of the to-be-predicted region into the model to obtain the predicted occurrence probability of the disastrous waves and the predicted value of the significant wave height in the predicted time period. According to the method, the key environmental factors are screened and the two models are used for respective prediction, the influence of the various environmental factors on the big waves is considered, the occurrence probability and the wave height of the disastrous big waves can be predicted more accurately, and the ocean safety is effectively guaranteed.

The invention discloses a context poisoning attack defense method oriented to an RAG application, and relates to the technical field of RAG. the method comprises the following steps: inputting a target query statement, and retrieving the target query statement to obtain multiple pieces of context information; taking representative sentences in the retrieved context information, and identifying and filtering potential malicious template clusters; the big language model gives all candidate answers according to existing context information, the logarithmic probability of all contexts to different candidate answers is calculated, and after the influence of parameter knowledge of the big language model is removed from the logarithmic probability, the support degree of all contexts to different candidate answers is obtained; the whole logarithmic probability vector is used as a support degree distribution condition of the context to the candidate answers; identifying a single piece of harmful information from the support degree distribution condition of the context to the candidate answers through a logistic regression model so as to filter wrong answers; according to the attack defense method provided by the invention, centralized injection of multiple malicious texts and sparse injection of a small number of malicious texts can be defended.

The invention provides a light color cast correction method and device based on a target area, and is applied to the technical field of data processing. The method comprises the following steps: acquiring a light original image and a shooting environment parameter through an image acquisition module; a light source point is positioned by using a YOLO algorithm, a white connected domain is extracted by combining an HSV threshold value, a luminous body contour is obtained through closed contour search, and whether correction is needed or not is judged according to the proportion of 80%; a U-Net model with specific parameters is adopted to segment outer-layer halo, and a color reference annular area is obtained through XOR operation; on the basis of pixel data of the region, through formula calculation, abnormal value filtering and class logistic regression function fitting, deducing and compensating an HSV (Hue, Saturation and Value) parameter; and performing non-target area invariance on the color cast area of the inner layer of the light source, and finally outputting a corrected image and verifying the effect, thereby realizing accurate color cast correction.

The invention discloses a decentralized AI service credible management system and a decentralized AI service credible management method. According to the method, the client and the server of each network domain are constructed through the credible AI model based on federal learning, so that the credibility of the AI model obtained by training can be ensured; the block chain node in each network domain can evaluate the credibility of the AI model uploaded to the block chain by the server of each network domain through the AI model credibility evaluation based on logistic regression and clustering, thereby supporting the subsequent personalized AI service. Through on-chain registration, under-chain storage and authorization access control of the AI model realized based on the client, the server and the block chain network, block chain-based AI service management is realized, and decentralized management of the whole life cycle of the AI service can be realized.

The invention discloses a content abstract generation method based on chapter structure analysis, and belongs to the technical field of natural language processing. The method comprises the steps that firstly, an original text is preprocessed, then text structure deep analysis is carried out, the text type of the text is recognized, an explicit / implicit text relation is extracted, and special symbols are introduced through a Prompt normal form for implicit text relation extraction to strengthen logic semantics; secondly, scoring sentences by adopting a double-path scoring mechanism in combination with a deep neural network model of chapter structure features and an optimized text sorting algorithm, and fusing scores through a logistic regression model; then, screening target sentences based on a chapter relation weighted secondary modulus function and a greedy algorithm, and finally, carrying out post-processing to generate an abstract. According to the abstract generation method, the chapter structure logic is deeply utilized, so that the problems of logic unsmoothness, information redundancy or key relation missing in the existing abstract generation are solved, and the semantic coherence and information integrity of the abstract are improved.

The invention relates to the technical field of circuit board analysis, in particular to an integrated circuit defect analysis method and system, and the method comprises the steps: extracting and screening E-test data of a target product from a big data platform for integrated circuit manufacturing; performing defect classification modeling processing on the screened E-test test data; calculating a defect correlation index according to the classification accuracy and the correlation metric value of each candidate E-test item; determining a defect judgment threshold value and a numerical value distribution direction related to the defect based on a logic regression classifier result of the key test item, and generating a binary defect distribution diagram corresponding to each jointed board; calculating the image similarity between the bad jointed board graph and each good jointed board graph; judging whether the image similarity is higher than a preset threshold value or not, and adding the jointed boards of the good jointed board graph into a virtual bad jointed board list; and based on the virtual bad jointed board list, analyzing and comparing the manufacturing process flow data of the virtual bad jointed board and the original defective jointed board, and determining the potential root cause causing the defect. According to the method, the problems that in the E-test scene of the circuit board, the number of defect samples is extremely small, categories are extremely unbalanced, an experience threshold value is difficult to set, and root causes are difficult to trace can be solved.

The invention relates to the technical field of insulation degradation detection, in particular to an insulation characteristic degradation detection method for an underwater wet-plugging electric connector, which is characterized in that a multi-angle appearance image of the connector is acquired through a pressure-resistant underwater camera device, and the image quality and the recognition accuracy are improved by adopting a defogging and color correction technology. And further extracting defect characteristics such as cracks, corrosion and aging on the surface of the connector through an image by virtue of a graph neural network model, realizing automatic identification and positioning of defect types, and then executing a local electrical test on the identified defect region to obtain electrical parameters such as insulation resistance, capacitance value and medium absorption ratio. Image recognition information and electrical test data are constructed into a feature matrix, a fusion evaluation model composed of a logistic regression model and a weighted scoring mechanism is input, an insulation degradation degree value is output, and a detection result is generated, so that the accuracy of insulation detection of the underwater wet-plugging electric connector is improved, and the detection time is effectively shortened.

The invention discloses an in-vitro fertilization-based method and system for predicting the cumulative live productivity of multiple complete cycles, and the method comprises the steps: obtaining clinical data, embryo information and pregnancy follow-up visit data of a patient, and screening and preprocessing a sample in combination with a collection and discharge standard; feature variables are extracted before treatment, after treatment and at the early stage of pregnancy, an optimal set is determined, and key factors influencing live birth at all stages are accurately captured; after the nonlinear variables are processed by the RCS, a pre-treatment / post-treatment COX regression model and a Logistic regression model are adaptively constructed, and the prediction precision is improved by fitting data characteristics; and meanwhile, by evaluating the iterative optimization model, the variable weight is visually displayed by using a Nomogram graph after the model is qualified. The method can accurately predict the multi-cycle cumulative live productivity, assists a doctor in formulating a personalized scheme, helps a patient to reasonably plan treatment, reduces the time and economic burden of blind treatment, and has clinical practical value and benefits of the patient.

The invention discloses a screening method of a protein marker combination, which is used for risk prediction of abdominal aortic aneurysm or ruptured abdominal aortic aneurysm, and optimizes the stability of a protein expression profile by constructing an initial protein expression profile; screening differential expression proteins by adopting statistical analysis, and preliminarily locking candidate proteins remarkably associated with a disease endpoint based on survival analysis or a risk regression model; performing cross validation by combining a sparse constraint algorithm and a nonlinear feature selection algorithm, and extracting a robust core marker; the independent contribution degree of each marker is evaluated through the standardized weight, and finally the optimal protein combination for disease prediction or diagnosis is determined. According to the method, six core proteins obtained through multi-algorithm cross screening can be detected in serum, multivariable logistic regression and external verification set performance both show good robustness, and an aorta protein fingerprint feature set which can be popularized and explained is formed.

The invention relates to the technical field of intelligent traffic control, and discloses a highway emergency lane dynamic start decision method and system, and the method comprises the steps: obtaining video monitoring data, employing a deep learning-based target detection framework to extract a traffic flow parameter set, and constructing a time sequence database; a traffic jam index prediction model and a dynamic traffic jam index threshold system which are constructed by combining a long-short-term memory network model and a Greenberg model are utilized to predict a traffic jam index in real time and carry out emergency lane multi-level early warning; a pre-constructed random forest multi-criterion decision model is adopted, and a nonlinear decision boundary is dynamically generated to perform emergency lane starting control; and carrying out quantitative evaluation on the emergency lane starting effect by adopting a pre-constructed logistic regression model so as to evaluate road section congestion trend optimization starting control and verify the reasonability of S3 starting control. According to the invention, a decision-making system fusing multi-source data, a dynamic prediction algorithm and intelligent resource scheduling is constructed, and scientific, dynamic and refined management and control of emergency lane starting are realized.

The invention discloses a civil aviation risk cause network association analysis method based on a semantic enhancement model, and the method comprises the steps: building an EnhancedBertLdaModel model fusing BERT semantic representation and quality adaptive adjustment, and achieving the high-precision topic mining of a civil aviation risk event text; designing an improved Apriori algorithm which introduces a factor number adaptive threshold mechanism, and dynamically capturing a strong correlation cause rule; and constructing an optimized Logistic regression model and cause association network, quantifying risk contribution through multi-source feature fusion, and identifying a core risk path. According to the method, deep semantic analysis and dynamic rule mining are fused, civil aviation risk causes can be comprehensively identified, multi-source data features are fused, and contribution of the multi-source data features to the risk is quantified; by improving the algorithm, the analysis complexity is reduced, and the accuracy and stability of the result are improved, so that the purposes of precisely mining and quantifying civil aviation risk causes are achieved, and a scientific decision basis is provided for civil aviation safety management.

The invention relates to the technical field of gastrointestinal tumor chemotherapy risk assessment, and particularly discloses a gastrointestinal tumor chemotherapy risk scoring model and a construction method thereof, and the method comprises the steps: obtaining the individualized feature data and basic physiological data of a historical patient, and constructing an individualized difference library with a unique ID, and a core influence library; the method comprises the following steps: standardizing and coding double-library data, synchronizing clinical data containing treatment effect codes, forming a data dictionary, acquiring and coding current gastrointestinal tumor patient data, and matching the data dictionary to judge individual difference abnormity; when the first data set is abnormal, constructing a first data set, screening core independent variables through Logistic regression, and constructing a scoring model by using an LSTM (Long Short Term Memory) model; when no abnormity exists, redundant codes are removed to obtain a second data set, and modeling is conducted through the same method. Through double-library linkage and scene-divided modeling, the three-level toxicity risk prediction precision is improved, data support is provided for clinical chemotherapy dose adjustment and toxicity prevention, and the risk of excessive treatment or insufficient treatment is reduced.

A system for real-time prediction of heart disease and to support clinical decisions; the system includes: a patient data acquisition module configured to receive multimodal inputs, including physiological signals selected from electrocardiographic waveforms, blood pressure readings, heart rate readings, and oxygen saturation readings, as well as demographic and lifestyle information, including age, gender, cholesterol levels, smoking habits, and family medical history; a preprocessing engine configured to perform data cleansing, imputation of missing values, categorical coding, and feature scaling, so that the input data is normalized and converted into a format suitable for machine learning; a processing core comprising a multi-core CPU / GPU system-on-chip operationally coupled with a secure storage unit, wherein the processing core is configured to execute a variety of pre-trained machine learning models, including Naive Bayes, Random Forest, Logistic Regression and Decision Tree classifiers; a model evaluation unit configured to calculate validation metrics such as precision, recall, F1 score and area under the curve for each of the models and dynamically select the model with optimal performance to generate real-time predictions for the risk of heart disease; a display interface configured to present predictive results in the form of risk probability values, confidence indices, and actionable recommendations that are mapped to clinical treatment guidelines; and a communication interface configured to transmit emergency alerts based on high-risk predictions to remote caregivers, hospitals, and emergency response systems via wireless communication protocols such as WLAN, Bluetooth, and 4G / 5G cellular connections.

The invention discloses a mangrove forest recognition method and system based on ensemble learning, and relates to the technical field of remote sensing data processing.A mangrove forest integrated recognition model driven by feature indexes is constructed, and the model takes various heterogeneous random forest models, gradient elevator models, support vector machine models and K neighbor models as base classifiers; the mangrove forest identification method effectively utilizes respective learning capabilities for different data models, reduces single model deviation, and then couples with the meta-classifier comprising the logistic regression model, so that the classification performance of the model can be further improved, the mangrove forest identification efficiency and accuracy are remarkably improved, and the mangrove forest identification efficiency and accuracy are improved. And meanwhile, the adaptability and the recognition capability of the model under a complex background condition are improved.

The invention provides a recognition and early warning method and device for irritable hyperglycemia, electronic equipment and a storage medium, and is applied to the technical field of medical data processing. According to the method, data type information (including FreeStyle LibreH dynamic blood glucose and glycosylated hemoglobin data), influence factor data (improved and preset irritable hyperglycemia index and the like) and abnormal state information (fasting interference and detection difference) are taken; acute-stage and chronic-stage average blood glucose basic data are generated through preprocessing. The index weight is enhanced and improved by means of a dynamic fusion engine, the probability is predicted by combining space-time correlation analysis and binary logistic regression, and the abnormal characteristics are obtained by comparing the difference of the AUC by adopting Delog test. The multi-modal features are processed to obtain an identification result, clinical indexes screened by LASSO are fused, index differences are compared to generate risk influence factors, finally, a dynamic early warning result is generated based on a space-time correlation early warning engine and a multi-target strategy, and accurate identification and early warning are achieved.

The invention discloses a state sensing and fault prediction system and method for a low-voltage switch cabinet, and relates to the technical field of power equipment monitoring, and the system comprises a multi-source feature fusion sensing module which is used for collecting multi-source data of the low-voltage switch cabinet to construct an original state data set, fusing credibility weighting and dynamic and static features, and constructing an enhanced state feature vector; the health assessment and fault prediction module is used for generating a health index and a state label through logistic regression mapping based on the enhanced state feature vector, performing health index prediction by adopting attention LSTM to calculate the remaining available life, and estimating the fault probability through structure perception k-NN; and the adaptive control optimization module is used for modulating the control advance based on the fault probability and generating a differential control instruction. According to the method, the accuracy of state sensing of the low-voltage switch cabinet and the credibility of fault prediction are remarkably improved, and the early warning capability and the operation and maintenance decision reliability are improved.

The invention relates to the technical field of computers, and particularly discloses a security product self-evaluation report intelligent auditing method based on artificial intelligence assistance, and the method comprises the steps: analyzing a self-evaluation report to generate structured detection item data; carrying out multi-modal feature extraction and fusion on the text and image proof materials; semantic conflicts, configuration compliance, evidence credibility, image-text consistency and historical risk matching degree are analyzed in parallel through an artificial neural network, a support vector machine, a random forest, logistic regression and a weighted neighbor algorithm; according to a preset weight, dynamically fusing results of all dimensions to determine a compliance probability; similar historical cases are retrieved in combination with a security product compliance analysis knowledge graph, and auditing instructions and improvement suggestions with violation positioning bases are generated; and finally, outputting a structured auditing report and supporting continuous optimization of a manual reexamination feedback driving model. According to the method, high-precision, full-dimension and automatic security product self-evaluation report auditing can be realized, and the auditing efficiency, objectivity and large-scale processing capability are remarkably improved.

The application provides a hallucination inhibition large model question and answer method and system, the method comprises the following steps: inputting a user question into a pre-trained industry large model and a hallucination control large model respectively, the industry large model and the hallucination control large model split the user question into word pieces; using the industry large model and the hallucination control large model, processing the word pieces split from the user question one by one to obtain the generation probability of the current word piece, and determining the large model question and answer result according to the generation probability of all word pieces; processing the word pieces split from the user question one by one to obtain the generation probability of the current word piece comprises the following steps: using the industry large model to generate the first logistic regression output of the current word piece, using the hallucination control large model to generate the second logistic regression output of the current word piece; performing softmax processing to obtain the first probability and the second probability; and determining the generation probability of the current word piece by comparing the first probability and the second probability through contrast learning.

The invention discloses a construction method of a malignant pleural effusion prediction model, a marker combination and application, equipment and a medium. The construction method comprises the following steps: S1, obtaining characteristics of a biomarker combination; the biomarker combination is composed of three biomarkers of NGAL, CEA and CA50; s2, based on the characteristics of the biomarker combination, adopting Logistic regression analysis to respectively construct regression models corresponding to the three biomarkers, and respectively outputting parameters alpha NGAL, beta NGAL, alpha CEA, beta CEA, alpha CA50 and beta CA50; and S3, based on the output parameters in the step S2, adopting Bayesian analysis to construct a malignant pleural effusion prediction model. The method can be used for predicting the MPE probability in the pleural effusion patient, the identification and prediction accuracy is high, and compared with pleural biopsy and thoracoscopic sampling biopsy, the method has the advantages of being rapid, minimally invasive and the like.

The invention discloses a method, device and equipment for directly and qualitatively evaluating a media structure type based on logistic regression and a storage medium, and the method comprises the steps: obtaining logging data of a plurality of wells in a research range, carrying out the standardization processing of the logging data, and obtaining the standardized logging data, the plurality of wells in the research range comprise a reference well and a key well; the reference well is selected, and the coal body structure of the reference well is divided; evaluating the effectiveness of the standardized logging data of the reference well in the aspect of distinguishing coal body structure types by utilizing likelihood ratio test, and screening the standardized logging data according to an evaluation result; the screened standardized logging data are extracted, and a coal body structure evaluation model is constructed through logistic regression analysis; and substituting the standardized logging data of the prediction well into the coal body structure evaluation model, and evaluating the coal body structure type of the prediction well. According to the method, the defect that a single logging index cannot truly reflect the coal body structure can be overcome, and the calculation method is simple, direct, effective, high in accuracy and high in objectivity.

The invention provides an innovative model based on the correlation between the PLEKHA4 gene expression level and the low-grade glioma radiotherapy sensitivity and a prediction method. According to the model, a multi-factor Logistic regression analysis framework is constructed, and the PLEKHA4 gene expression level and key clinical pathological parameters are organically combined, so that a radiotherapy sensitivity prediction model is established. The method comprises a series of steps of sample collection, gene expression detection, clinical information collection, model calculation, result interpretation and the like, and can realize accurate prediction of radiotherapy response of low-grade glioma patients. Compared with the prior art, the method has the remarkable advantages of simplicity and convenience in operation, high prediction accuracy, high clinical transformability and the like. The method has great potential in the aspect of guiding individualized radiotherapy scheme formulation, can significantly improve the treatment effect, and has important value for medical application. Besides, the model can be optimized through further clinical verification, so that clinical practice can be better served, and the life quality and prognosis effect of patients are improved.

The invention discloses an electricity larceny identification method, system and device based on multi-source electricity utilization characteristic fusion and a medium, and belongs to the technical field of electricity larceny diagnos.The method comprises the steps that a sample set containing electricity larceny samples and normal electricity utilization samples is obtained, the characteristic judgment capacity is calculated through adjacent sample difference analysis, and electricity utilization behavior characteristics are screened according to the judgment capacity; constructing an electricity consumption trend decrease index based on the electricity consumption change trend, calculating a user theoretical line loss contribution factor according to the correlation between the user electricity consumption ratio and the voltage sequence, and constructing an electricity larceny suspicion index based on the theoretical line loss contribution factor; analyzing the relevance change characteristics of the user electric quantity and the court line loss rate through a superposition verification method, and calculating the electricity stealing suspicion degree according to the relevance change characteristics; and constructing a logistic regression model, and judging an electricity stealing user in combination with the electricity stealing suspicion degree. According to the method, the feature quality is improved through adjacent sample difference analysis, the electricity larceny suspicion degree is accurately quantified through Monte Carlo multi-round iteration verification, and the electricity larceny judgment precision is optimized and improved through the quasi-Newton method.

Provided is a method for correcting bias introduced by weighted training in machine learning, comprising: labeling the number of examples of each class in weighted data used by a machine learning classifier; adding a weight to training data, and calculating a weight wij of each data example j in class i of the training data according to a user-given data weighting method; calculating a mean weight wi for examples of each class; conducting classification and logistic regression against features of the examples in the weighted data and labels corresponding to the features; after training, when calculating probabilities Pw(i) of class after machine training using the machine learning classifier, correcting the probabilities P(i) by applying a deweighting formula to obtain corrected probabilities P(i); making a classification decision based on the corrected probabilities P(i). The method improves the accuracy of classifiers in assigning probabilities to new data in machine learning applications.

An inventory prediction system is described that outputs predicted inventory items not included in a user's known inventory using a cross-category directed graph representing item categories as nodes. The inventory prediction system implements a prediction model trained using machine learning that outputs predicted inventory items using graphics and at least one item from a user's known inventory. The inventory prediction system is also configured to generate entry recommendations that predict inventory items. To this end, an inventory prediction system implements a logistic regression model trained using machine learning to calculate a probability that entry recommendations should be generated using attributes of predicted inventory items and attributes of current popular items. An entry recommendation is generated to include a description of a predicted inventory item and an estimated value for the predicted inventory item, and an option for a sale entry for the predicted inventory item is generated.

The application discloses a kind of data acquisition method and system for equipment service management platform, it is related to data acquisition technical field, it is proposed that a kind of intelligent screening reduction sequencing is used to solve the problem of low data acquisition efficiency, waste of computing resources, by collecting data call frequency, data fluctuation value, data effective period length and data area coverage range value, establish data analysis model, carry out logistic regression calculation, obtain screening evaluation coefficient, and compare with preset screening threshold value, according to the comparison result, obtain the equipment data paragraph of priority screening, then according to the data similarity of each priority screening equipment data paragraph, determine data screening paragraph, according to data screening paragraph, according to data screening paragraph call frequency user end real-time average upload speed and data screening paragraph in different area data distribution frequency degree A set of fuzzy rules are formulated to carry out fuzzy reasoning, determine data screening paragraph call sequencing result.