Police condition prediction parity data method and device

Abstract

The invention relates to a police condition prediction parity data method and device. The police condition prediction parity data method includes the steps that previous police condition data are introduced; the police condition data per day are classified and combined according to a two-dimension address field and time slot, and the previous police condition data in each address field during the same time slot at different dates are obtained; the previous police condition data in the same address field during the same time slot at different dates are sequenced in a serial mode according to the date sequence to obtain the police condition parity data at different dates in the same address field and during the same time slot; the prediction police condition data possibly happening in the specified address field and during the specified time slot at the date to be predicted are calculated according to the police condition parity data through a predetermining estimation method. The police condition parity data have the relative crowd stability and the two-dimension features of the address field and time slot, estimation of future police condition data is realized according to the police condition parity data, police condition prediction of a specified address field and time slot is facilitated, the estimation result is more accurate, the actual police work efficiency is improved, and the actual work requirements are better met.

Description

technical field [0001] The invention relates to the technical field of public safety, in particular to a method and device for predicting co-location data of police situations. Background technique [0002] In the public security 110 command and dispatch system, the prediction of the police situation is the basis for relevant departments to conduct scientific analysis, scientific decision-making, and scientific deployment. The traditional forecast calculation method is to perform statistical forecast calculation according to the urban police situation data according to the time sequence analysis method, that is, the sequential data analysis method. The police situation data is actually closely related to the crowd and the quality of the crowd, and the population of a region is not a closed system parameter, but the population is an activity variable with inflow and outflow. These essential factors affect the actual results of the police situation, so the habitual use of tim...

AI Technical Summary

Problems solved by technology

[0004] (1) The prediction calculation method referring to the time dimension cannot predict specific regions:

[0005] The urban police situation is predicted and calculated according to the traditional time-series data, and the change of the police situation according to the time domain is emphasized in a single dimension. It is impossible to consider the impact of specific address domain factors on the change of the population group, and it is difficult to realize the police situation valuation prediction of the concerned address domain. , can not meet the actual work needs

[0006] (2) According to the time series data analysis method of year, month and day, it is impossible to predict the police situation for a specific time period:

[0007] The traditional method of analyzing the urban police situation according to time series data of year, month and day does not take into account the influence of the aggregation-discrete change factors of population groups in specific time periods, such as the influence of population changes such as holidays and peak hours. Therefore, based on traditional time series data analysis, regardless of The valuation error of which calculation method is generally unable to meet the needs of practical applications and cannot play a role in actual work

[0008] (3) Unable to adapt to the needs of new technology development:

[0009] The police situation is a state variable closely related to the population group. The traditional time-series police situation data prediction method, due to the certainty of the calculation method, is difficult to realize the information processing of the sudden increase or decrease of the population group.

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