23 results about "Estimation" patented technology

The invention belongs to the technical field of information and communication for management or supervision, and relates to a management method and system for reckoning progress and income based on cost resource usage amount, and a readable medium, and the method comprises the steps: creating a progress plan of a project; the actual progress is obtained in real time by collecting multi-source data, and the project cost is analyzed according to a progress plan; inputting the data of the actual income and the actual progress into the project calculation model, and calculating the project income by dynamically adjusting the weight of each type of resources; performing project economic activity management on the analysis result of the project cost and the calculated project income; through project economic activity management, early warning is carried out on data which does not reach a progress plan and plan income, and an early warning level is set. According to the method, multi-dimensional and multi-level management control can be performed on the project, key nodes and risk items of the project are automatically displayed through a visual analysis technology, and cost and progress monitoring of the whole life cycle from starting to ending of the project is realized.

Based on demographic information of the area being analyzed, the system estimates the distribution of people with user-specified attributes at a given location within that area. [Solution] The human flow information estimation device 2000' includes a calculation device 2100 that performs human flow information analysis based on predetermined demographic information, which includes the number of people located in each of a plurality of predetermined area meshes and information on the attributes of those people. The human flow information estimation unit 2103 identifies a set of unit sub-regions that cover the area to be analyzed without overlapping each other, and based on demographic information and predetermined lifestyle information, it distributes the number of people from the demographic information to the unit sub-regions at predetermined time intervals, based on the type of place and the size of the place corresponding to the classification of people's behavior in each area mesh, and estimates aggregated data for each unit sub-region. Based on the aggregated data, the human flow information estimation unit 2103 calculates a human absorption capacity value, which is the ratio of the number of people with the analyzed attributes for each unit sub-region to a predetermined value.

The invention relates to the technical field of economic measurement modeling, in particular to an economic prediction method and system based on weekly, monthly and quarterly big data, electronic equipment and a medium. The method comprises the following steps: acquiring multi-frequency multi-source economic time sequence data; performing frequency mixing alignment preprocessing on the multi-source economic time series data according to a preset frequency mixing data processing rule to generate a frequency mixing data set; constructing a connection model through a predefined modular architecture and a connection algorithm based on the frequency mixing data set; training the connection model through a parameter estimation algorithm; and inputting multi-source economic time sequence data obtained in real time into the trained connection model, executing high-frequency prediction, and outputting a real-time prediction result. By means of the mode, the technical problem that when an existing mixing dynamic factor model deals with multi-frequency mixing data, prediction precision is insufficient is solved, and the capacity of conducting high-frequency, accurate and real-time monitoring on the macroeconomic trend is improved.

The invention provides a workload assessment method and apparatus, and an electronic device. The method comprises the steps of obtaining a first research and development demand of a research and development project to be assessed; determining a first K-nearest neighbor classification model from the M K-nearest neighbor classification models according to the first research and development demand, wherein the value of the nearest neighbor number K in the first K-nearest neighbor classification model is a first numerical value; determining a first number of neighbor historical research and development demands in front of the first research and development demand in the first training sample according to the first K neighbor classification model; the first training sample comprises historical research and development demands of at least two historical research and development projects and corresponding historical workloads, and the first training sample is divided into at least one cluster based on a first K-nearest neighbor classification model; and estimating the workload of the first research and development demand according to the historical workload of the historical research and development demand in the first cluster, wherein the first cluster is a cluster in which the first number of neighbor research and development demands are located at the most same clusters in the at least one cluster of the first training sample. Therefore, the workload estimation accuracy is improved.

The application discloses a kind of track traffic full life cycle economy estimation system based on multidimensional data, it is related to track traffic engineering economic estimation technical field, the system includes following component parts: data acquisition module, time sequence linkage mapping module, substage estimation model module, feedback correction module and dynamic output module;The application is through covering track traffic full life cycle, establishes effective association, to substage estimation model accurately calculates each stage economic cost, provides solid data support for economic analysis;Dynamic feedback correction mechanism is built, real-time response deviation changes, improve estimation accuracy;Whole-process closed-loop optimization design, solve prior art defects, adapt to project dynamic change, provide timely reliable reference for project full cycle economic management.

The invention provides a mixed data regression model-based pm2.5 influence factor analysis method and system, and the method comprises the steps: building a mixed data regression model of which covariables are component data and numerical data and dependent variables are functional data: the mixed data regression model is a pm2.5 concentration curve of a city, is the functional data, is the proportion of first yield, second yield and third yield of the city, and is called the proportion of third yield for short; the data is component data, logarithm of per capita GDP, average temperature and numerical data, is a to-be-estimated component type coefficient changing along with time, is distributed to a component type covariable at any moment, is a to-be-estimated function type coefficient and is a function type residual error; obtaining robust M-estimation based on equidistant logarithmic ratio transformation, functional basis expansion and an iterative reweighted least square method; and according to the estimated values, analyzing the influence of the three-yield ratio, the per capita GDP and the average temperature of each city on the pm 2.5. The method can be used for analyzing the pm 2.5 influence factors.

This invention provides a method and system for calculating the construction progress and cost variances of power transmission and transformation projects. The method includes performing multivariate correlation analysis on construction period data to calculate the planned duration of each process; identifying critical path processes that affect the total project duration, determining the time percentage of each critical path process within the critical path, and calculating the total project duration based on the time percentage of each process within the critical path and the planned duration of each process; determining alternative project execution plans based on the total project duration; calculating the personnel and machinery costs of each process based on the alternative project execution plans; accumulating the costs of each process, and comparing the cost differences between different alternative project execution plans. This invention develops a quantitative analysis tool for the correlation between construction period and cost through an innovative method, constructing a multi-dimensional linkage model of "schedule-cost-resources" to accurately quantify the intrinsic relationship between construction efficiency losses and cost fluctuations, avoiding errors from empirical estimations.

The invention discloses a transpiration and evapotranspiration ratio data set fusion method, system and device based on triple matching, a medium and a product, and relates to the field of ecological hydrology. The method comprises the following steps: firstly, acquiring three sets of transpiration T and evapotranspiration ET data sets from different sources, and preprocessing to obtain three preprocessed T and ET data sets; for the three preprocessed T and ET data sets, respectively calculating error variances of T and ET in each data source by using a triple matching method; according to the error variance of the T and the ET in each data source, distributing the weight of the T and the ET in each data source; performing fusion calculation according to the weight of the T and the weight of the ET in each data source to obtain the fused T and the fused ET; and calculating the ratio of T to ET after fusion to obtain a T / ET data set after fusion. According to the method, the problem of systematic deviation of multi-source T / ET data can be effectively solved, the estimation precision is remarkably improved, and the adaptability to regional heterogeneity is enhanced.

The application discloses a kind of data missing scenario under city low carbon level evaluation method and device, method includes the following steps: step S1, the multi-source index data of the city to be evaluated is obtained, the completeness of the multi-source index data is analyzed, and the data completeness is calculated;Step S2, based on the data completeness corresponding evaluation mode is enabled and corresponding index set is called;Step S3, the index set called is judged to be missing, according to the index type of missing data from the proxy rule library corresponding estimation rule is selected, and the estimated value of missing index is generated based on estimation rule;Step S4, the real observation data in the index set called is fused with the estimated data generated based on estimation rule, and the complete evaluation data set is constructed;Step S5, the evaluation model of city low carbon development level is constructed, and the city low carbon level evaluation is carried out based on the evaluation data set and the evaluation model.The application can reasonably evaluate the city low carbon level when data is incomplete.

The invention discloses a geostatistical modeling method uncertainty quantification method based on a mutual information entropy framework, and relates to the technical field of geostatistical modeling quality control, and the method comprises the steps: collecting the space sample data of an estimation domain, determining target methods, constructing a parameter matrix for each target method, and carrying out the calculation of the parameter matrix; carrying out multiple modeling on each target method according to the parameter matrix on the estimation domain to generate a plurality of modeling results; dispersing each realization modeling result to a unified grid to obtain a realization modeling result set of each grid, counting distribution of a plurality of realization modeling results for each grid, constructing probability distribution of each grid, calculating point entropy of each grid based on the probability distribution, and forming a spatial uncertainty entropy field. According to the method, quantification of spatial uncertainty is achieved by achieving probability distribution and entropy measurement, IMUI indexes are constructed through joint entropy, conditional entropy and mutual information, and evaluation of output convergence and structural consistency of the method is achieved.

This invention provides a fine-scale population spatialization method that integrates geospatial and internet data. The method includes: obtaining residential building footprint data within the study area; constructing geographic grid data for the study area and determining the proportion of residential building footprint area in each geographic grid unit; obtaining the number of floors in internet-connected buildings within the study area, establishing a residential building floor estimation model, and determining the initial input values ​​for the residential building floor estimation model; calculating the estimated population for each administrative region, performing error verification with the corresponding administrative region's population statistics, iterating based on the error verification results, and optimizing and adjusting the parameters of the residential building floor estimation model. This invention constructs a population spatialization model by integrating land use (residential land), surface-covered building areas, and the number of floors in internet-connected buildings, classifying the population living in urban and rural areas to estimate the floor height of residential buildings, achieving fine-scale population spatialization with high accuracy.

Based on demographic information of the area being analyzed, the system estimates the distribution of people with user-specified attributes at a given location within that area. [Solution] The human flow information estimation device 2000' includes a calculation device 2100 that performs human flow information analysis based on predetermined demographic information, which includes the number of people located in each of a plurality of predetermined area meshes and information on the attributes of those people. The human flow information estimation unit 2103 identifies a set of unit sub-regions that cover the area to be analyzed without overlapping each other, and based on demographic information and predetermined lifestyle information, it distributes the number of people from the demographic information to the unit sub-regions at predetermined time intervals, based on the type of place and the size of the place corresponding to the classification of people's behavior in each area mesh, and estimates aggregated data for each unit sub-region. Based on the aggregated data, the human flow information estimation unit 2103 calculates a human absorption capacity value, which is the ratio of the number of people with the analysis target attributes for each unit sub-region to a predetermined value.

The application discloses a kind of land space planning management system and method based on big data, it is related to spatial planning management technical field, in the flood season waterlogging and project approval collaborative scene, using risk and utility double threshold value automatic parameter estimation desensitization, sensitive data can be shared and available are realized, through event time anchoring synchronization, time uncertainty and version pedigree, make cross-source data alignment traceable, can be calculated, control line and index threshold are constructed as constraint atlas, conflict identification and repair candidate are automatically output, and optimal planning scheme is selected with non-dominated solution set, generates approval and land adjustment signal issue, improves decision interpretability and landing efficiency.

This invention relates to the field of computer technology for temperature estimation, aiming to address the pain points of existing technologies, such as reliance on single-phase data, insufficient capture of temporal and global information, and lack of spatial uncertainty assessment. The technical solution includes: preprocessing multi-source data, extracting and standardizing key features related to climate, geography, and time in multiple dimensions, constructing a multi-scale time-series dataset to determine the optimal time series; designing and testing various models, selecting the HyTC-TaNet model, which combines Transformer and CNN through a specific layer sequence to capture global temporal dependence and local spatial features respectively; using SHAP technology to analyze the impact of features on the estimation results; and constructing a difference index through AOA evaluation to define the model's applicable area and extrapolation area. This invention significantly improves the accuracy of daily average temperature estimation, overcomes the "black box" problem of deep learning, compensates for the inability to quantify spatial error distribution, adapts to complex and heterogeneous surface environments, and provides a technical framework reference for similar research.

The application discloses a subjective city performance visualization question and answer method based on a large language model, which is characterized by using a large language model (LLM) to perform query rewriting, knowledge base retrieval and prompt integration on the natural language query of a user, effectively identifying the user's intention, combining semantic information and spatial distribution of points of interest (POIs), generating an accurate semantic density map through a semantic self-adaptive kernel density estimation algorithm, positioning a significant area based on a density-based spatial clustering algorithm, analyzing geographical relationships of different scales, and providing a detailed interactive visual answer. Compared with the prior art, the application solves the complex visualization automatic question and answer problem of city performance, enhances the adaptability of subjective city performance analysis to different scenarios through the large language model, and improves the accuracy and generalization ability of the analysis. The application can be widely applied to various practical application scenarios such as city site selection and travel planning, and has high practical value and good development prospect.

A headcount estimation system (1001) according to the present disclosure comprises an image capture unit (11), an image processing unit (21), a congestion level evaluation unit (22), and a correction unit (23). The image capture unit (11) captures an image of a prescribed area. On the basis of the image captured by the image capture unit (11), the image processing unit (21) calculates the number of people in the prescribed area and the number of people in each of a plurality of sections obtained by dividing the prescribed area. The congestion level evaluation unit (22) evaluates the level of congestion in the prescribed area. The correction unit (23) identifies the section to be referred to on the basis of the level of congestion, and corrects the number of people in the prescribed area on the basis of the identified section.

The invention discloses a power transmission and transformation project cost prediction method and system based on data mining, and relates to the technical field of project cost prediction, and the method comprises the steps: collecting multi-dimensional data covering project attributes, environmental factors and historical cost, and building a project database; performing missing value filling and preprocessing on the data, and calculating a correlation coefficient matrix; determining the number of principal components based on principal component analysis, calculating a principal component load matrix, and extracting a candidate key field set; and carrying out partial correlation analysis, cross validation and regression significance test on the candidate fields and the project cost data, identifying key factors which have significant influence on the cost, constructing a project cost prediction model based on the key factors, and carrying out scientific prediction on the cost of a new project. According to the method, the problems of high dependence and low data utilization rate of traditional experience estimation are solved, systematic identification and cost prediction of key factors of the cost are realized, and support is provided for investment decision and cost control of power grid construction.

The embodiment of the application provides a dynamic multi-objective evolutionary method based on migration learning and special point strategy, and relates to the field of evolutionary computation.The method comprises the following steps: analyzing a dynamic multi-objective optimization scene, and obtaining an initial population of the optimization scene;optimizing the initial population by using a multi-objective estimation distribution method, and obtaining a front optimization set of the initial population;obtaining a special point according to the front optimization set;when the decision variable changes, mapping the special point to a high-dimensional space based on a migration learning method of joint distribution adaptation to obtain the special point at the next moment;optimizing the initial population at the next moment by using the multi-objective estimation distribution method, and obtaining a front optimization set of the initial population at the next moment.Through the special point, the accurate prediction of the special point under a new environment is realized, other individuals are randomly generated, the population diversity is increased, and therefore the convergence speed and convergence precision of the algorithm under the new environment are improved.

An estimation apparatus includes a memory; and a processor configured to execute: receiving spatiotemporal population data and a probability of movement between areas as input; constructing a collective graphical model (CGM) in a path graph for estimating a number of people who have moved between areas from the spatiotemporal population data and the probability of movement between areas; generating an instance of a minimum cost flow problem for performing MAP estimation on the constructed CGM; solving the instance of the minimum cost flow problem to estimate the number of people who have moved between areas at individual time steps; and outputting the estimated number of people who have moved between the areas at the individual time steps.

This invention discloses a method for assessing the affected population based on dynamically corrected disaster-reported population data. The method includes the following steps: Step 1, collecting basic data of the study area; Step 2, initializing the population density field; Step 3, constructing a spatial adjacency graph; Step 4, determining the dynamic flood inundation range; Step 5, comparing the reported data to correct population errors. This method, even with limited reported data, dynamically optimizes local population density estimation by combining the spatiotemporal patterns of flood evolution, while ensuring total population conservation, non-negativity, and spatial smoothness. It achieves dynamic updates of population density at the grid scale within administrative regions, thereby obtaining a more accurate and realistic spatial distribution of the population and the number of affected people. This provides a basis for decision support such as population relocation and precise rescue, demonstrating strong practicality.

The invention provides a dynamic population monitoring method and system based on multi-source heterogeneous data and a medium, and the method comprises the steps: firstly, receiving multi-source monitoring data from a mobile communication base station, Internet of Things sensing equipment, a public transport system and image collection equipment; performing deduplication, space-time unification and anonymization preprocessing; extracting data according to space-time grid units and inputting the data into a pre-training estimation model to generate an estimation evidence matrix; then, identifying short-time data exception and sustainable events by adopting a consistency threshold and continuity threshold dual comparison mechanism, dynamically adjusting a model weight or generating an evidence replacement value, fusing an adjacent grid evidence matrix to calculate a confidence coefficient when no exception exists, and correcting the evidence matrix through standby data when the confidence coefficient is lower than a threshold; and finally, outputting a dynamic population monitoring result through a weighted fusion algorithm. Therefore, the accuracy, reliability and real-time performance of population monitoring are improved, and data security and system robustness are ensured through multi-stage verification and a privacy protection mechanism.

The invention discloses a disaster-affected population assessment method based on disaster-reported population data dynamic correction. The method comprises the following steps: step 1, collecting basic data of a research area; 2, initializing a population density field; step 3, constructing a spatial adjacency relation graph; 4, determining a dynamic flood inundation range; and 5, comparing the reported data to carry out population error correction. According to the method, local population density estimation can be dynamically optimized in combination with a spatio-temporal mode of flood routing under the condition that only a small amount of reported data exists, total population conservation, non-negativity and spatial smoothness are guaranteed, grid scale population density dynamic updating in an administrative region is achieved, and the population density estimation efficiency is improved. Therefore, accurate and practical population spatial distribution and the number of affected population are obtained, a basis is provided for decision support such as population transfer and accurate rescue, and the practicability is high.

The invention relates to a flexible arrangement-based self-adaptive demand measurement and calculation model generation method and device, and belongs to the technical field of material data processing. The device comprises a measurement and calculation standard normalization module, a calculation formula construction module, a calculation model arrangement engine, a public parameter management module and an empirical coefficient learning recommendation module. Through normalization and atomization of a measurement standard and a calculation formula, the device can quickly combine a measurement model for an unknown scene like building blocks, and the problems that a traditional system is rigid and needs secondary development are fundamentally solved; the technical threshold and the development period are greatly reduced, extremely complex guarantee logic in reality can be represented, the long-term maintenance and expansion cost of the system is greatly reduced, and the intelligent degree is obviously improved.