39results about How to "Scientific prediction" patented technology

The invention belongs the virtual simulation technical field, in particular to a macroscopic, mid-scope and microscopic multilevel urban parking demand prediction model integrated system, which comprises a system data module, an urban overall parking demand macroscopic prediction module, a comprehensive development sharing parking demand mid-scope prediction module and a parking garage entrance service level microscopic analysis module, wherein the system data module is used for importing and storing parking demand prediction data; the urban overall parking demand macroscopic prediction module is used for predicting the overall parking demand; the comprehensive development sharing parking demand mid-scope prediction module is used for predicting the largest parking demand of each block and is connected with the urban overall parking demand macroscopic prediction module; the parking garage entrance service level microscopic analysis module is used for predicting a parking garage entrance service situation; and the three prediction modules are respectively connected with the system data module. The macroscopic prediction module not only considers the parking demand of traveling vehicles, but also considers the parking demand of the vehicles which are not on trip, the mid-scope prediction module considers the shared parking demand of multi-nature buildings under the reachability situation of buses, bi-directional correction of the prediction results of the macroscopic, mid-scope and the microscopic modules can be realized, the system quantitatively predict the parking demand of different cities of different levels so as to provide important support for different parking researches.

The invention provides a gestation diabetes risk monitoring system based on dynamic physics and physical and chemical factors and belongs to the field of medical instruments in the department of obstetrics and gynecology. The gestation diabetes risk monitoring system is characterized by comprising a computer, a toggle switch array, a resistor array and a gestation diabetes pathogenesis risk LED alarming module; a pregnant women information input module of dynamic physics check and physical and chemical factors, a logic switch array module of pregnant women gestation diabetes pathogenesis risk factors and logic regression value computation module of pregnant women gestation diabetes pathogenesis risk are arranged in the computer; a voltage comparison module and an LED array are arranged in the gestation diabetes pathogenesis risk LED alarming module. According to the invention, the logic switch value is utilized to control the make-and-break state of the toggle switch; a risk factor value in the logic regression manner is utilized to control the resistance value of the resistor array, so as to indicate the relative risk degree; the resistance value of the resistor array is utilized to control a voltage comparator, so as to output electrical level and light the LED alarm. According to the invention, the risk degree of the dynamic gestation diabetes can be comprehensively evaluated from multivariate physical and chemical factors and the real-time alarm can be conducted.

The invention discloses a development zone planning land scale predicting method based on benefits of space. The development zone planning land scale predicting method based on benefits of space includes the steps: 1) predicting the planning industrial land scale based on benefits of space; 2) based on benefits of space, development potential and the like, determining distribution of the planningland scale in different industrial plates; 3)based on benefits of space of each industry, predicting the planning industrial land scale of each industry and the total planning industrial land scale; and 4) based on ratio prediction of the planning industrial land and other lands, predicting the development zone planning construction land scale, and at the same time, based on prediction of benefitsof space of the construction land, predicting the development zone planning construction land scale and performing mutual checking. The development zone planning land scale predicting method based onbenefits of space predicts and determines the development zone planning industrial land, the industry-classified land and the total planning construction land scale, can provide technical support fordevelopment zone planning making, town and country construction and land management, and can provide experience and reference for related town and country planning, and land use planning and management.

The invention provides a road traffic flow condition prediction method in a data sparse time period. When traffic flow data are insufficient, a transformation rule of a to-be-predicted road traffic flow in a time sequence is explored by using a time dynamic sequence supplementing method; a road traffic condition of a data sparse time period is restored by environment information feature extractionbased on a conditional random domain, so that a time evolution sequence of the road traffic flow in a period of time is obtained; matching with a historical time sequence of the road traffic flow isperformed to find out a time sequence fragment with a similar evolution trend and a traffic flow condition of a prediction time point is deduced. The test and on-site detection show that the prediction result of the traffic flow data sparse time period is basically accurate and reliable, so that defects of road traffic flow condition prediction in the data sparse time period in the prior art are effectively overcome, the traffic flow condition prediction weakness is eliminated, and the overall quality of the road traffic flow condition prediction method is improved.

The invention discloses a mobile network base station traffic prediction method, and belongs to the technical field of wireless communication data analysis. The method comprises the following steps: acquiring a current network cell load index, current network cell work parameter information and planning base station information; calculating a current network base station coverage area, a planningbase station coverage area, an adjacent current network base station set of the current network base station, and an adjacent current network base station set of the planning base station; obtaining akernel density model; calculating the initial prediction traffic of the base station; obtaining a base station traffic prediction model; and obtaining the predicted traffic of the planning base station. On the basis of simulation of current network actual data, an applicable kernel density model is obtained and initial traffic prediction is carried out, and then secondary prediction is carried out in combination with a machine learning algorithm, so that scientific and accurate prediction of planning base station traffic can be achieved, a reference basis is provided for network construction,and the optimal effect of the network construction cost is achieved.

The invention relates to the field of education big data mining, graph neural network and learner behavior modeling, and provides a knowledge tracking method based on test question heterogeneous graph representation and learner embedding, the method adopts a heterogeneous graph neural network technology in the deep learning field to represent multi-dimensional features of test questions, and meanwhile, complex learner characteristics are modeled in combination with a project reaction theory, and ability characteristics of learners are captured by adopting a clustering mode and the like; finally, the test question and learner mixed features are fused to a traditional knowledge tracking model, and knowledge tracking and learner performance prediction for different learner groups are achieved. According to the invention, the learning condition of the learner can be scientifically and comprehensively predicted, and the purpose of assisting the teacher in precise teaching is achieved.

The invention discloses a copy selection method based on copy response time prediction. Compared with an existing copy response time prediction algorithm, the method provided by the invention emphatically considers that the influence degree of sampling values of copy response time of different moments on prediction of copy response time is different. Closer is a sample to a prediction point, larger is a weight value, and otherwise, the weight value is smaller. Such a model solves the problem of different influence degree of influence factors on copy response time in different periods, thereby improving prediction precision.

The present invention provides a prediction method of a gas emission rate. The method comprises the steps of: 1. arranging a gas sensor, performing real-time monitoring of gas concentrations, and transmitting data to a database to calculate and store a gas emission rate; 2. extracting safety gas emission rates in the database, constructing a gas emission rate prediction model, performing gas emission rate prediction of input parameters according to the gas emission rate prediction model, and outputting a predicted value; 3. comparing the predicted value with a real-time monitoring value, forming error analysis, and updating the gas emission rate prediction model; and 4. repeatedly performing the steps 1-3, and performing real-time monitoring of the gas emission rate. Through adoption of high non-linear mapping of a neural network model and an Matlab language computing environment, data in the data is extracted in real time to correct the gas emission rate data model, and therefore, predicted errors are small, the prediction is more scientific and accurate, and scientization and quantification guidance is provided for avoiding outburst of gas.

The invention discloses a comprehensive humidity detection system, especially to a comprehensive humidity detection system for grain detection. A remote monitoring analysis management platform is adopted to monitor the water content of grain in a grain bin, and a sampling procedure is not needed, so the workload of grain monitoring is minimized and the labor cost is reduced. Large apparatuses with complex structures are not needed, so the detection cost is reduced. The system is capable of automatically identifying the position of a detection terminal in the grain bin, a manual system input step and other steps are minimized, the intelligent level of the system is improved, and grain monitoring is more convenient and accurate.

The invention belongs to the field of hydraulic engineering, and particularly relates to a dredging channel back-silting amount predicting method based on a timing sequence analysis-Markov chain method. The method includes the following steps that an original sequence of spatial dispersion is selected; a predicted value sequence is generated; the relative residual sequence of the original sequence and the predicted value sequence is calculated; the relative residual sequence is divided into intervals; Markov chain prediction is performed among the intervals; the water depth prediction interval is averaged to obtain a predicted water depth value, and an original water depth value is substracted from the water depth value to obtain a back-silting value. A timing sequence analysis method and the Markov chain are combined to obtain the timing sequence analysis-Markov chain prediction method, the change trends and random fluctuation of the sequences can be reflected at the same time, and the predicting method conforms to the actual change conditions of the dredging channel back-silting sequence better. By using the predicting method, the relative error rate is only 1.04%, the timing sequence analysis-Markov chain method can improve the precision by 97% compared with a single timing sequence method, and the prediction of the timing sequence analysis-Markov chain method conforms to actual measurement data better compared with the grey theory.

The invention provides a situation awareness virtualization method and a situation awareness virtualization device. The method comprises the steps of packaging interfaces for acquiring different information sources; facilitating customer calls, obtaining data streams in a unified format through preprocessing; extracting high-frequency project group elements from the data stream; generating high frequency association rules, carrying out situation evaluation for evaluation quantification; carrying out fusion with different evaluation systems; and obfuscating the data elements, obtaining situation values of a single device and a local network; combining the architecture composition of the whole network; and obtaining the situation value of the whole device. According to the method, situationvalues of different levels are imported into a neural network model for prediction. Finally, a prediction result is visually displayed. The whole network and each single device are fully evaluated. The association is established between each device and each layer, so that future devices can be scientifically predicted. Valuable reference suggestions are provided for users.