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Public digital life scene rule model prediction and early warning method based on deep Bayesian network

A Bayesian network and digital life technology, applied in the field of big data analysis, can solve problems such as low prediction accuracy and insufficient analysis features

Active Publication Date: 2021-06-22
HANGZHOU MAQUAN INFORMATION TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The existing prediction and early warning technology is still based on the characteristics of a single dimension or a small number of dimensions to analyze and predict people's behavior, which has the disadvantages of insufficient analysis characteristics and low prediction accuracy.

Method used

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  • Public digital life scene rule model prediction and early warning method based on deep Bayesian network
  • Public digital life scene rule model prediction and early warning method based on deep Bayesian network
  • Public digital life scene rule model prediction and early warning method based on deep Bayesian network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0058] Example 1 - public health safety early warning

[0059] 1.1 Technical route

[0060] The traditional risk prediction of infectious disease outbreak mainly includes the following four aspects: (1) select the type of infectious disease and the research area; (2) select the pathological, environmental and climatic factors related to the incidence of infectious disease; (3) select the appropriate model to Establish a risk assessment model for infectious disease outbreaks; (4) predict the probability of infectious disease outbreaks in various situations and verify the accuracy of the model established. This example has made appropriate modifications on this basis. The specific technical route is as follows: Figure 8 shown.

[0061] Among them, the exploration of the spatio-temporal law of infectious diseases mainly uses statistics, mathematics and GIS methods. In this example, the detection of spatial clustering of infectious diseases is mainly reflected in the clustering...

Embodiment 2

[0135] Example 2—Mental Health Early Warning

[0136] The form of online questionnaire can effectively screen students for depression, and the online health questionnaire-depression scale (PHQ-9) can be used to collect students' self-assessment data online, but it is time-consuming and laborious, lacking real-time and reliability, The quality and quantity of data collected was low. Research by psychologists has shown that real-time screening of depression using data from social media such as WeChat and Weibo is feasible and accurate.

[0137] Therefore, this example combines the characteristics of students and uses social media data to construct a student word cloud. On this basis, it combines card data, Internet data and mobile terminal data, access records, consumption records, video surveillance, GPS, campus wifi access logs and other data to obtain spatio-temporal information, analyze students' behavior trajectory, and construct student portraits and information behavior ...

Embodiment 3

[0165] Example 3 - Early warning of campus bullying incidents

[0166] According to methods 2.1 to 2.3, the behavioral elements and information elements of past campus bullying incidents were obtained and analyzed, combined with student information, their personality, consumption situation, behavior habits, learning status, psychological status, etc. were analyzed, and user portraits of bullying students were constructed.

[0167] Refer to the method of risk probability estimation in 1.4 to construct a deep Bayesian network, construct a feature vector according to the characteristics of student user portraits and their behavior elements and information elements, and train to obtain a campus bullying early warning model; if the risk value exceeds 0.5, an alarm will be issued, and relevant students will be given corresponding measures Pay attention, and if necessary, provide psychological counseling, home visits or punishment.

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Abstract

The invention discloses a public digital life scene rule model prediction and early warning method based on a deep Bayesian network. The method comprises the steps: carrying out the data analysis and extraction of multi-source heterogeneous data in some key life scenes in public digital life, generating an information and behavior part feature library, combining the information and behavior part feature library with a user digital portrait, and carrying out the prediction and early warning of a public digital life scene rule model. According to the method, a personalized rule mechanism is constructed, prediction and early warning are timely and accurately performed on different key life scenes, powerful support is provided for beforehand intervention, and the method can be applied to public safety and health early warning, mental health early warning, campus spoofing event early warning and the like.

Description

technical field [0001] The invention belongs to the technical field of big data analysis, and in particular relates to a public digital life scene rule model prediction and early warning method based on a deep Bayesian network. Background technique [0002] With the update and iteration of Internet technologies such as cloud computing and big data and the continuous improvement of living standards, people's demand for public services such as basic education, public health, public transportation, and elderly care continues to expand. Government departments at all levels are always thinking and paying attention to Under the background of the Internet + era, innovate the government public service model, promote the digitalization of public life, and provide life convenience. In public digital life, some key life scenarios, such as economic disputes, fires, etc., once problems occur, will have a serious impact on the interests of the people and social stability. In these key lif...

Claims

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Application Information

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IPC IPC(8): G06F16/2458G06F16/28G06F40/216G06F40/289G06F40/30G06K9/00G06N3/04G06N3/08G06N7/00G06Q10/06G06Q50/26
CPCG06F16/2465G06F16/285G06F40/216G06F40/289G06F40/30G06N3/08G06Q10/0635G06Q50/26G06V40/168G06V40/174G06N7/01G06N3/044G06N3/045Y02D10/00
Inventor 马汉杰董慧许永恩刘烈宏李柏睿
Owner HANGZHOU MAQUAN INFORMATION TECH CO LTD
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