CN116542516A

CN116542516A - Urban safety risk prediction method based on gray prediction GM (1, 1) model

Info

Publication number: CN116542516A
Application number: CN202310497010.XA
Authority: CN
Inventors: 冯云; 杨信林; 韩超; 张紫薇; 郑玲静; 张小艳; 徐正锐
Original assignee: Chengdu Institute Of Urban Safety And Emergency Management
Current assignee: Chengdu Institute Of Urban Safety And Emergency Management
Priority date: 2023-05-05
Filing date: 2023-05-05
Publication date: 2023-08-04

Abstract

The invention discloses a city safety risk prediction method based on a gray prediction GM (1, 1) model, which takes accident case data in a preset time range as a data source, utilizes the gray prediction GM (1, 1) model to generate a group of new data sequences with obvious trend through accumulation, establishes a model according to the increasing trend of the new data sequences to predict, then uses a cumulative method to reversely calculate to recover the original data sequences, further obtains the probability result data of accident occurrence.

Description

Urban safety risk prediction method based on gray prediction GM (1, 1) model

Technical Field

The invention relates to the technical field of computer information processing, in particular to a city security risk prediction method based on a gray prediction GM (1, 1) model.

Background

The urban safety risk prediction is a method for establishing automatic quantitative evaluation of safety risk early warning analysis from multiple dimensions such as accident occurrence rules, time, regional climate change and the like in an appointed area of the city. With the development of scientific technology, the continuously mature regional risk technology is widely applied to numerous public safety fields such as landslide disasters, road traffic, social security and the like, and the life safety and property safety of people are greatly protected. In 1962, the united states first applied the risk analysis method to the actual situation of the united states military industry, and obtained good analysis results, so that the risk analysis technology and method have been greatly developed and widely used. In the development process of the risk analysis method, researchers in countries around the world conduct numerous researches on the risk analysis method, and a plurality of risk analysis and evaluation methods suitable for different fields are provided. In the working environment evaluation of foreign scholars such as pia Tint, a simple risk evaluation method for reducing a six-step process to a two-step process is provided based on the occurrence probability and the result severity of the influence of risk factors on workers by using a risk evaluation technology, and the risk evaluation method is verified in an Edania wood factory.

Most of the existing prediction of safety risks is in a specific field, such as prediction of traffic safety, urban water safety and the like, and the prediction of macroscopic safety of the whole city lacks necessary research, how to scientifically analyze and judge urban safety forms, early warning prompts main safety risks of the city, and provides prevention and control suggestion measures, so that risks are effectively prevented and controlled, and accidents are reduced, so that the problem to be solved is urgent.

Disclosure of Invention

The invention provides a gray prediction GM (1, 1) model-based urban safety risk prediction method for solving the problem that the prior art lacks macroscopic analysis and judgment of urban safety, and the urban safety risk prediction method is used for warning and prompting the main urban safety risk, effectively preventing and controlling the risk and reducing the occurrence of accidents.

To achieve the above object, a first aspect of the present invention provides an urban security risk prediction method based on a gray prediction GM (1, 1) model, comprising the steps of:

s1, accident case data in a preset time range is used as a data source, and adjacent historical data of a starting report point '0 moment' and n time intervals before the starting report point '0 moment' are used as an initial operation data set;

s2, constructing a time sequence in an operation data set by using data at fixed position points in a time data plane, and determining the type of a gray prediction GM (1, 1) model using sequence;

s3, establishing a gray prediction GM (1, 1) model, outputting a predicted value at the position point, and taking the predicted value as a predicted value at the position at the next moment in the future;

s4, combining and outputting all the predicted values on the fixed position points calculated in the step S3 into a predicted value at the 1 st moment after the 0 th moment of the starting report point, and carrying out error detection on the predicted value;

s5, adding the predicted value data passing the inspection into a new operation data set, arranging according to a time sequence, updating the operation data set, repeatedly executing the steps S3-S4, sequentially outputting predicted values at subsequent moments, and constructing a predicted accident data set; and analyzing the local or whole state of the target information in the prediction product set representing the future state, so as to realize the prediction and forecast of the accident data at the future moment.

Preferably, the gray predictive GM (1, 1) model uses sequence types including: a homogeneous index sequence and a non-homogeneous index sequence.

Preferably, the gray prediction GM (1, 1) model is revised and regulated by the statistical characteristics of the initial operation data set, the model development coefficient of each modeling, the set growth factors and the set extinction factors.

Preferably, the expression of the gray prediction GM (1, 1) model is:

wherein k represents the value of time, k > 1.

In a second aspect, the present invention provides a machine-readable storage medium having stored thereon instructions for causing a machine to perform the urban security risk prediction method of the present application.

In a third aspect, the present invention provides a processor for executing a program, wherein the program is executed to perform: the urban safety risk prediction method is characterized by comprising the following steps of.

Through the technical scheme, the urban safety risk can be accurately early-warned, the accident occurrence and development process is combed, the accident cause is deeply analyzed, the urban safety form is scientifically analyzed and judged, the urban main safety risk is warned and prompted, the prevention and control recommended measures are provided, the prevention and control risks are effectively reduced, and the accident occurrence is reduced.

Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.

Drawings

The accompanying drawings are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain, without limitation, the embodiments of the invention. In the drawings:

FIG. 1 is a schematic flow diagram of the method of the present invention;

fig. 2 is a data diagram of an embodiment of the present invention.

Detailed Description

The following describes the detailed implementation of the embodiments of the present invention with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present application. One skilled in the relevant art will recognize, however, that the aspects of the application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the application.

The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.

As shown in fig. 1, a first aspect of the present invention provides an urban security risk prediction method based on a gray prediction GM (1, 1) model, comprising the steps of:

the city initial operation data set is related data of the related field, and comprises a risk value, a hidden danger value and an accident value.

(1) Let the sequence x= (X (1), X (2), X (n)), if

x(k)＝ce ^ak (c,a≠0),k＝1,2,...,n

X is a homogeneous exponential sequence; if it is

x(k)＝ce ^ak +b(c,a,b≠0),k＝1,2,...,n

X is a non-homogeneous exponential sequence.

(2) Set X ⁽⁰⁾ As the original sequence, then:

X ⁽⁰⁾ ＝(x ⁽⁰⁾ (1),x ⁽⁰⁾ (2),...,x ⁽⁰⁾ (n))

d is a sequence operator, and the sequence operator,

X ⁽⁰⁾ D＝(x ⁽⁰⁾ (1)d,x ⁽⁰⁾ (2)d,...,x ⁽⁰⁾ (n)d)

wherein,,d is X ⁽⁰ ) The one-time accumulation of (2) generates an operator, which is denoted as 1-AGO.

(3) Set X ⁽⁰⁾ Is a non-negative sequence:

X ⁽⁰⁾ ＝(x ⁽⁰⁾ (1),x ⁽⁰⁾ (2),...,x ⁽⁰⁾ (n))

wherein x is% ⁰ )(k)≥0,k＝1,2,...,n；

Set X ⁽¹⁾ Is X ⁽⁰⁾ 1-AGO sequence of (a):

X ⁽¹⁾ ＝(x ⁽¹⁾ (1),x ⁽¹⁾ (2),...,x ⁽¹⁾ (n))

wherein the method comprises the steps of

Let Z be ⁽¹⁾ Is X ⁽¹⁾ Is a sequence of immediately adjacent mean generation:

Z ⁽¹⁾ ＝(z ⁽¹⁾ (2),z ⁽¹⁾ (3),...,x ⁽¹⁾ (n))

wherein,,

The specific algorithm is as follows:

(1) Is provided with X ⁽¹⁾ 、X ⁽⁰⁾ Then

x ⁽⁰⁾ (k)+ax ⁽¹⁾ (k)＝b,k＝1,2,...,n

For the original form of the gray predictive GM (1, 1) model, "G" represents "Grey", M represents "model", the first "1" of the function represents the 1 st order equation, and the second "1" represents 1 variable.

(2) Is provided with X ⁽¹⁾ 、X ⁽⁰⁾ 、Z ⁽¹⁾ Then

x ⁽⁰⁾ (k)+az ⁽¹⁾ (k)＝b,k＝1,2,...,n

The mean form of the GM (1, 1) model is predicted in gray, and is also a differential equation.

(3) With non-negative sequence X ⁽⁰⁾ ＝(x ⁽⁰⁾ (1),x ⁽⁰⁾ (2),...,x ⁽⁰⁾ (n)) which once accumulates the sequence X ⁽¹⁾ ＝(x ⁽¹⁾ (1),x ⁽¹⁾ (2),...,x ⁽¹⁾ (n))，Z ⁽¹⁾ Is X ⁽¹⁾ Is adjacent to the mean generating sequence, Z ⁽¹⁾ ＝(z ⁽¹⁾ (2),z ⁽¹⁾ (3),...,x ⁽¹⁾ (n)) if it isIs a parameter array, and

model x ⁽⁰⁾ (k)+az ⁽¹⁾ (k) The least squares estimation parameter column of =b satisfies:

(4) Set X ⁽⁰⁾ X is a non-negative sequence ⁽¹⁾ Is X ⁽⁰⁾ 1-AGO sequence, Z of (A) ⁽¹⁾ Is X ⁽¹⁾ Is a sequence of generation of the immediate mean of [ a, b ]] ^T ＝(B ^T B) ^-1 B ^T Y is then

Predicting GM (1, 1) model x for gray ⁽⁰⁾ (k)+az ⁽¹⁾ (k) Whitening equation of =b, also called shadow equation.

(5) Setting the formula B, Y,as indicated by the definition above->Then there are:

1) Whitening equationThe solution of (2) is also a time response function, which is

2) Gray predictive GM (1, 1) model x ⁽⁰⁾ (k)+az ⁽¹⁾ (k) The time response sequence of =b is

3) Reduction value

The parameter-a in the gray prediction GM (1, 1) model is the development coefficient and b is the gray contribution.

The development coefficient-a in the gray prediction GM (1, 1) model has the following relation with model prediction:

(1) when-a is less than or equal to 0.3, the gray prediction GM (1, 1) model can be used as the medium-long term prediction;

(2) when the value of-a is less than or equal to 0.3 and less than or equal to 0.5, the gray prediction GM (1, 1) model can be used as short-term prediction, and medium-term prediction is cautious;

(3) when the value of-a is less than or equal to 0.5 and less than or equal to 0.8, the gray prediction GM (1, 1) model can be used as short-term prediction with cautions;

(4) when 0.8 < -a is less than or equal to 1, the residual gray prediction GM (1, 1) model is needed;

(5) when-a > 1, the gray predictive GM (1, 1) model is not suitable.

preferably, the expression of the gray prediction GM (1, 1) model is:

wherein k represents the value of time, k > 1.

The specific algorithm for error checking of the predicted value is as follows:

let the original sequence be: x is X ⁽⁰⁾ ＝(x ⁽⁰⁾ (1),x ⁽⁰⁾ (2),...,x ⁽⁰⁾ (n))，

The simulation sequence of the prediction model is as follows:

the residual sequence is:

the relative error sequence is:

1) For k.ltoreq.n, thenFor the k point model, the relative error is simulated>Is the average relative error;

2) ThenFor average relative accuracy, 1-delta _k For the simulation accuracy of k points, k=1, 2, …, n;

3) Given alpha, whenAnd delta is _n <And when alpha is established, the model is a residual qualified model.

Substituting the initial operation data set (risk value, hidden danger value, accident value, etc.) of the city into the calculation process to obtain a model:

According to the invention, a gray prediction GM (1, 1) model is utilized to generate a group of new data sequences with obvious trend through accumulation, a model is built according to the growth trend of the new data sequences to predict, then reverse calculation is carried out by using a cumulative method to recover the original data sequences, and further accident occurrence probability result data is obtained.

In one embodiment, accident case data within 3 years of the preset time range is used as a data source, the number of occurrence of accidents is predicted to be 36 in 12 months in 2019, and the average relative error is 24.8%. The number of accidents is predicted to be more in 12 months in 2019. Compared with the analysis of the number of the accidents in the 12 th 2017 and the 12 th 2018, the number of the accidents in the 12 th 2017 is reduced by 14.3% and the number of the accidents in the 12 th 2018 are increased by 37.5%, so that the phenomenon that the accidents are driven to work in the 12 th month is prominent, and the accident prevention working pressure is high.

In a third aspect, the present invention provides a processor for running a program, wherein,

the processor includes a kernel, and the kernel fetches the corresponding program unit from the memory. The kernel can set one or more than one of the kernel parameters, generate a group of new data sequences with obvious trend through an accumulation mode for the past urban safety accident history case data sequences, build a model according to the increasing trend of the new data sequences to predict, and then use an accumulation and subtraction method to perform reverse calculation to recover the original data sequences so as to obtain the accident occurrence possibility result data.

The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.

The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.

Claims

1. The urban security risk prediction method based on the gray prediction GM (1, 1) model is characterized by comprising the following steps of:

2. The prediction method according to claim 1, wherein the gray prediction GM (1, 1) model uses sequence types comprising: a homogeneous index sequence and a non-homogeneous index sequence.

3. The prediction method according to claim 1, wherein the gray prediction GM (1, 1) model is revised and regulated by the model output result through the statistical characteristics of the initial operation dataset, the model development coefficient of each modeling, and the set growth factors and death factors.

4. The prediction method according to claim 1, wherein the expression of the gray prediction GM (1, 1) model is:

wherein k represents the value of time, k > 1.

5. A machine-readable storage medium having stored thereon instructions for causing a machine to perform any of the city security risk prediction methods of the present application described above.

6. A processor configured to execute a program, wherein the program is configured to, when executed, perform: the urban security risk prediction method according to any one of claims 1 to 4.