CN113160564A - Traffic safety early warning analysis method and device and computer equipment - Google Patents
Traffic safety early warning analysis method and device and computer equipment Download PDFInfo
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
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Abstract
The invention discloses a traffic safety early warning analysis method and a traffic safety early warning analysis device, wherein the method comprises the following steps: acquiring traffic data of a target area in a preset time period and characteristic data of a target object; based on the dynamic and static angles, a plurality of first-level influence factors, a plurality of second-level influence factors and evaluation scores of the plurality of second-level influence factors are determined by combining the data, then the importance degree of each level of influence factors is determined, the traffic safety early warning degree is obtained through calculation, and a traffic safety early warning analysis result is generated. By implementing the invention, the problem that the accurate traffic early warning prompt cannot be provided for the user in the prior art is solved, the traffic accident risk of the intersection can be evaluated more systematically, comprehensively and intuitively, and the traffic early warning prompt can be accurately carried out; through judging the matrix, each index importance degree is clear and definite, and quantitative characterization can be carried out to crossing safety risk, and then accurate risk early warning is carried out to branch road pedestrian and vehicle.
Description
Technical Field
The invention relates to the field of intelligent traffic, in particular to a traffic safety early warning analysis method, a traffic safety early warning analysis device and computer equipment.
Background
With the rapid development of economic society, road mileage is continuously increased, the quantity of motor vehicles kept is continuously increased, traffic safety situations are not neglected, and particularly, traffic accidents are frequent due to factors such as insufficient sight distance and weak safety consciousness of traffic participants at urban road openings, rural branches and national provincial roads and sharp-bent road sections. In the existing traffic early warning technology, a guard rail, an indicator light and various protective measures are already arranged, but at the road section, the frequency of traffic accidents is still high, so that how to provide accurate traffic early warning prompts for users becomes a problem to be solved urgently.
Disclosure of Invention
Therefore, the technical problem to be solved by the present invention is to overcome the defect that the prior art cannot provide an accurate traffic early warning prompt for a user, so as to provide a traffic safety early warning analysis method, a traffic safety early warning analysis device and a computer device.
According to a first aspect, an embodiment of the present invention provides a traffic safety early warning analysis method, including: acquiring traffic data of a target area in a preset time period and characteristic data of a target object, wherein the characteristic data are used for representing the driving state of the target object; determining a plurality of primary influence factors, a plurality of secondary influence factors and evaluation scores of the plurality of secondary influence factors according to the preset time period data of the target area and the characteristic data of the target object, wherein the primary influence factors are used for representing road types; the secondary influence factors comprise dynamic indexes and static indexes; constructing a primary judgment matrix according to the plurality of primary influence factors; constructing a secondary judgment matrix according to the plurality of secondary influence factors; determining weights of a plurality of secondary influence factors relative to the traffic safety early warning degree according to the primary judgment matrix and the secondary judgment matrix; calculating the traffic safety early warning degree according to the weights of the secondary influence factors relative to the traffic safety early warning degree and the evaluation scores of the secondary influence factors; and generating a traffic safety early warning analysis result according to the traffic safety early warning degree.
With reference to the first aspect, in a first embodiment of the first aspect, the method further includes: constructing a primary judgment matrix by the following formula:
wherein A represents the primary judgment matrix, aijAnd the important index scale represents the primary influence factor relative to the traffic safety early warning degree.
With reference to the first aspect, in a second implementation manner of the first aspect, the two-level decision matrix is constructed by the following formula:
wherein B represents the secondary decision matrix, BijAn importance index scale representing the secondary impact factor relative to the primary impact factor.
With reference to the first aspect, in a third implementation manner of the first aspect, the determining weights of a plurality of secondary influence factors with respect to a traffic safety precaution degree according to the primary judgment matrix and the secondary judgment matrix specifically includes: calculating the maximum eigenvalue of the primary judgment matrix, and normalizing the maximum eigenvalue to obtain a first weight of the primary influence factor relative to the traffic safety early warning degree; calculating the maximum eigenvalue of the secondary judgment matrix, and normalizing the maximum eigenvalue to obtain a second weight of the secondary influence factor relative to the primary influence factor; and calculating the weights of the plurality of secondary influence factors relative to the traffic safety early warning degree according to the first weight and the second weight.
With reference to the third embodiment of the first aspect, in the fourth embodiment of the first aspect, the first weight is calculated by the following formula:
wherein the content of the first and second substances,
represents said first weight, λmaxRepresenting the maximum characteristic root of a primary judgment matrix;
calculating the second weight by the following formula:
wherein the content of the first and second substances,
represents said second weight, λmaxRepresenting the largest feature root of the secondary decision matrix.
With reference to the fourth embodiment of the first aspect, in the fifth embodiment of the first aspect, the weights of the plurality of secondary influence factors with respect to the traffic safety precaution degree are calculated by the following formula:
wherein the content of the first and second substances,
representing the weight of the secondary influence factor relative to the traffic safety early warning degree;
represents the ith secondary influence factor PiRelative to the jth first order influence factor BjA second weight of (a);
represents the jth primary influence factor BjA first weight relative to a traffic safety precaution.
With reference to the fifth embodiment of the first aspect, in the sixth embodiment of the first aspect, the traffic safety precaution degree is calculated by the following formula:
wherein Z represents the traffic safety early warning degree; a represents the number of called preset databases; fijExpressing the evaluation scores of the jth preset database on the secondary influence factors;
and representing the weight of the secondary influence factor relative to the traffic safety early warning degree.
According to a second aspect, an embodiment of the present invention provides a traffic safety early warning analysis apparatus, including: the data acquisition module is used for acquiring traffic data of a target area in a preset time period and characteristic data of a target object, wherein the characteristic data is used for representing the driving state of the target object; the first determining module is used for determining a plurality of primary influence factors, a plurality of secondary influence factors and evaluation scores of the plurality of secondary influence factors according to the preset time period data of the target area and the characteristic data of the target object, wherein the primary influence factors are used for representing road types; the secondary influence factors comprise dynamic indexes and static indexes; the primary judgment matrix construction module is used for constructing a primary judgment matrix according to the plurality of primary influence factors; the second-level judgment matrix construction module is used for constructing a second-level judgment matrix according to the plurality of second-level influence factors; the second determining module is used for determining the weights of the secondary influence factors relative to the traffic safety early warning degree according to the primary judgment matrix and the secondary judgment matrix; the calculation module is used for calculating the traffic safety early warning degree according to the weights of the secondary influence factors relative to the traffic safety early warning degree and the evaluation scores of the secondary influence factors; and the traffic early warning analysis result generation module is used for generating a traffic safety early warning analysis result according to the traffic safety early warning degree.
According to a third aspect, an embodiment of the present invention provides a computer device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the processor, and the instructions are executed by the at least one processor to cause the at least one processor to perform the steps of the traffic safety warning analyzing method according to the first aspect or any embodiment of the first aspect.
According to a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the traffic safety warning analysis method according to the first aspect or any of the embodiments of the first aspect.
The technical scheme of the invention has the following advantages:
the invention provides a traffic safety early warning analysis method and a traffic safety early warning analysis device, wherein the method comprises the following steps: acquiring traffic data of a target area in a preset time period and characteristic data of a target object; determining a plurality of first-level influence factors, a plurality of second-level influence factors and evaluation scores of the plurality of second-level influence factors based on a dynamic static angle according to the data of the preset time period of the target area and the characteristic data of the target object, determining the importance degree of each level of influence factor through a chromatographic analysis method, and then calculating to obtain the traffic safety early warning degree; and generating a traffic safety early warning analysis result according to the traffic safety early warning degree. By implementing the invention, the problem that the accurate traffic early warning prompt cannot be provided for the user in the prior art is solved, the driving behavior can be analyzed through the driving track of the current target object and the historical data of each intersection, the traffic accident risk of the intersection can be evaluated more systematically, comprehensively and intuitively, and the traffic early warning prompt is accurately carried out; through judging the matrix, each index importance degree is clear and definite, and quantitative characterization can be carried out to crossing safety risk, and then accurate risk early warning is carried out to branch road pedestrian and vehicle.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a specific example of a traffic safety warning analysis method according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of a specific example of a traffic safety warning analysis apparatus according to an embodiment of the present invention;
FIG. 3 is a diagram of an embodiment of a computer device.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
In order to guarantee road traffic safety of rural cities, particularly for urban road openings, national and provincial road and rural branch intersections, sharp curve road sections and the like, traffic early warning prompts need to be provided for passerby and vehicles.
The embodiment of the invention provides a traffic safety early warning analysis method, as shown in fig. 1, the traffic safety early warning analysis method comprises the following steps:
step S11: acquiring traffic data of a target area in a preset time period and characteristic data of a target object, wherein the characteristic data is used for representing the driving state of the target object; in this embodiment, the target area may be each intersection, or may be a traffic road section with frequent accidents, such as an urban road opening section, a national and provincial road and rural branch intersection, each sharp-bending road section, or actually may be various road sections that need to provide an early warning prompt for passing vehicles and pedestrians; the traffic data in the preset time period can be data of each road section in three-month or six-month time periods, which are used for representing the passing conditions of vehicles on the road section, such as the number of accidents, the accident reasons and the like; the target object may be each vehicle traveling on the road segment and a pedestrian passing through the road segment; the running state of the target object may be the running condition of the vehicle as well as the pedestrian itself. Specifically, when a traffic safety early warning analysis prompt needs to be provided for a vehicle driving on a sharp-curved road section, firstly, traffic data information in a preset event section of the sharp-curved road section and driving state information of the driving vehicle need to be acquired.
Step S12: determining a plurality of primary influence factors, a plurality of secondary influence factors and evaluation scores of the plurality of secondary influence factors according to the data of the target area in the preset time segment and the characteristic data of the target object, wherein the primary influence factors are used for representing the road type; the secondary influence factors comprise dynamic indexes and static indexes; in this embodiment, the primary influence factor may be various influence indexes used for characterizing a criterion layer, and may include intersections, environments, main roads, branches, and the like; the second-level influence factor can be various influence indexes used for representing a scheme layer and corresponds to the first-level influence factor; for example, the secondary influence factors corresponding to the primary influence factor intersection may include a gradient, an intersection road grade difference, a historical accident condition, and a sight distance; the secondary influence factors corresponding to the primary influence factor main road can comprise line shapes, speeds, vehicle types, distances and driving behaviors; the secondary influence factors corresponding to the primary influence factor branches can include types of pedestrians and vehicles; the second-level influence factor corresponding to the first-level influence factor environment can comprise visibility and road surface wet and slippery degree. Specifically, an evaluation index system based on traffic safety early warning analysis, that is, based on traffic safety early warning degree, may be established according to the target area preset time period data and the characteristic data of the target object, where the traffic safety early warning degree is used to represent the target area, that is, the quantitative representation of the risk size of the traffic accident occurring on the target road segment, and the specific evaluation index system is shown in table 1 below:
TABLE 1
As shown in table 1, the target layer may be the traffic safety precaution degree to be calculated, the criterion layer is each primary index having an influence on the target layer, the solution layer is each primary index having an influence on the alignment layer, and each secondary index having an influence on the target layer. According to the difference of specific reference information of each secondary index, the secondary indexes are divided into dynamic indexes and static indexes, namely, the traffic safety early warning analysis method provided by the embodiment can select index parameters which have influence on the traffic accident risk at the road junction from the dynamic and static aspects.
Specifically, the target area may be matched with a preset expert database according to preset time period data of the target area, feature data of the target object, and the determined multiple secondary influence factors, and an evaluation score of each secondary influence factor is determined according to a matching result.
Step S13: constructing a primary judgment matrix according to a plurality of primary influence factors; in this embodiment, the primary influence factor, that is, the criterion layer influence index in table 1 above, may be a factor that determines influence on the road section traffic accident risk based on the principles of systematicness, scientificity, and pertinence, such as an intersection, a main road, a branch road, and an environment; the primary judgment matrix is a matrix for comparing importance degrees between primary influence factors, specifically, the influence strength of the primary influence factors on the traffic accident risk of the road section can be compared pairwise, and the relative importance of pairwise comparison elements can adopt a 1-9 scale method, as shown in the following table 2:
TABLE 2
| Scale | Means of |
| 1 | PiAnd PjHas the same influence on the early warning degree |
| 3 | PiCompare PjSlightly strong influence on the early warning degree |
| 5 | PiCompare PjHas strong influence on the early warning degree |
| 7 | PiCompare PjHas stronger influence on the early warning degree |
| 9 | PiCompare PjHas very strong influence on the early warning degree |
| 2，4，6，8 | Intermediate values of two adjacent scales |
| 1/2,1/3，…，1/9 | The above-mentioned opposite explanation for the influence result of the early warning degree |
When P is shown in Table 2 aboveiRelative to PjWhen the scale of (A) is 1, P is illustratediAnd PjFor preThe alarm degree influence is the same; when P is presentiRelative to PjWhen the scale of (A) is 3, P is illustratediAnd PjHas a slightly strong influence on the early warning degree.
Specifically, the first-level judgment matrix can be constructed by the following formula:
wherein A represents a primary decision matrix, aijAnd (3) representing the important index scale of the primary influence factors relative to the traffic safety early warning degree, specifically, the important index degree between the primary influence factors relative to the traffic safety early warning degree can be a value obtained by analyzing according to a preset expert database.
Step S14: constructing a secondary judgment matrix according to the plurality of secondary influence factors; in this embodiment, the secondary influence factor, that is, the influence index of the scheme layer in table 1 above, may be a specific index factor that determines influence on the road section traffic accident risk, such as an intersection, a main road, a branch road, and an environment, based on the principles of systematicness, scientificity, and pertinence; the multiple secondary influence factors under the primary influence factor intersection can be gradient, grade difference of the intersected roads, historical accident condition and sight distance; wherein, the gradient represents the target area, that is, the degree of steepness of the ground surface unit of the target road section, and may be the ratio of the vertical height of the slope to the distance in the horizontal direction; the intersection road level difference represents a level difference between two roads to which the intersection belongs, and for example, the correspondence between the level and the name of each traffic road is shown in table 3 below:
TABLE 3
| Order of the bit | Form (B) of | Name (R) |
| 1 | Highway with a light-emitting diode | High speed |
| 2 | Expressway | Fast speed |
| 3 | Main road | Road for highway |
| 4 | Secondary trunk road | Road |
| 5 | Main collecting and distributing road | Road surface |
| 6 | Secondary distributed road | Road surface |
| 7 | Regional road | Street |
| 8 | Access | Lane, work and nameless |
Specifically, if the two roads to which the intersection of the target area belongs are the high speed a road and the big speed B road, respectively, the level difference of the intersection road of the target area can be calculated to be 4 as shown in table 3.
Specifically, the secondary judgment matrix is a matrix for comparing importance degrees between secondary influence factors, for example, according to each secondary influence factor under the intersection: the method comprises the steps of constructing a first secondary judgment matrix according to the gradient, the grade difference of the crossed road, the historical accident condition and the sight distance, and obtaining the weight of the gradient, the grade difference of the crossed road, the historical accident condition and the sight distance relative to an intersection; similarly, according to each secondary influence factor subordinate to the main road, a second secondary judgment matrix is constructed, and the weight of the linear shape, the speed, the vehicle type, the distance and the driving behavior relative to the main road can be obtained; constructing a third secondary judgment matrix according to each secondary influence factor subordinate to the branch, so as to obtain the weight of the types of pedestrians and vehicles relative to the branch; and constructing a fourth secondary judgment matrix according to each secondary influence factor under the environment, so as to obtain the weight of visibility and road surface slippery relative to the environment.
Specifically, each secondary judgment matrix can be constructed by the following formula:
wherein B represents a secondary decision matrix, BijAnd the important index scale of the secondary influence factor relative to the primary influence factor is represented, and the important index degree of the secondary influence factor relative to the primary influence factor can be a value obtained by analyzing according to a preset expert database.
Step S15: determining weights of a plurality of secondary influence factors relative to the traffic safety early warning degree according to the primary judgment matrix and the secondary judgment matrix; in this embodiment, according to the constructed primary judgment matrix and the plurality of secondary judgment matrices, normalization processing is performed by calculating the maximum feature root and the feature vector of the primary judgment matrix and each secondary judgment matrix, so as to obtain the weight of each secondary influence factor relative to the traffic safety early warning degree.
Step S16: calculating the traffic safety early warning degree according to the weights of the secondary influence factors relative to the traffic safety early warning degree and the evaluation scores of the secondary influence factors; in this embodiment, the traffic safety warning degree may be a value calculated by the method provided in the embodiment of the present invention when the target object travels in the target area, and is used for performing a traffic safety warning prompt for the driver or the passerby.
Step S17: and generating a traffic safety early warning analysis result according to the traffic safety early warning degree. In this embodiment, when the calculated traffic safety early warning degree is higher than the first preset threshold, it indicates that the target area is a road section with multiple accidents, and the driver needs to carefully drive, that is, the traffic safety early warning analysis result generated according to the traffic safety early warning degree indicates that the area is the road section with multiple accidents, and the traffic safety early warning degree is higher, and the driver needs to carefully drive.
The traffic safety early warning analysis method provided by the embodiment of the invention comprises the following steps: acquiring traffic data of a target area in a preset time period and characteristic data of a target object; determining a plurality of first-level influence factors, a plurality of second-level influence factors and evaluation scores of the plurality of second-level influence factors based on a dynamic static angle according to the data of the preset time period of the target area and the characteristic data of the target object, determining the importance degree of each level of influence factor through a chromatographic analysis method, and then calculating to obtain the traffic safety early warning degree; and generating a traffic safety early warning analysis result according to the traffic safety early warning degree. By implementing the invention, the problem that the accurate traffic early warning prompt cannot be provided for the user in the prior art is solved, the driving behavior can be analyzed through the driving track of the current target object and the historical data of each intersection, the traffic accident risk of the intersection can be evaluated more systematically, comprehensively and intuitively, and the traffic early warning prompt is accurately carried out; through judging the matrix, each index importance degree is clear and definite, and quantitative characterization can be carried out to crossing safety risk, and then accurate risk early warning is carried out to branch road pedestrian and vehicle.
As an optional embodiment of the present invention, in the step S15, determining weights of the multiple secondary influence factors with respect to the traffic safety precaution degree according to the primary judgment matrix and the secondary judgment matrix specifically includes:
firstly, calculating a maximum characteristic value of a primary judgment matrix, and normalizing the maximum characteristic value to obtain a first weight of a primary influence factor relative to a traffic safety early warning degree; that is to say, according to the primary judgment matrix, the first weight of the intersection relative to the traffic safety precaution degree, the first weight of the main road relative to the traffic safety precaution degree, the first weight of the branch road relative to the traffic safety precaution degree, and the first weight of the environment relative to the traffic safety precaution degree can be obtained, and specifically, the first weight can be calculated by the following formula:
wherein the content of the first and second substances,
denotes a first weight, λmaxRepresenting the largest feature root of the primary decision matrix.
Secondly, calculating the maximum eigenvalue of the secondary judgment matrix, normalizing the maximum eigenvalue, and respectively obtaining the second weight of each secondary influence factor relative to the corresponding primary influence factor; respectively calculating the maximum eigenvalue and eigenvector of each secondary judgment matrix and carrying out normalization processing to obtain the weight of the slope, the grade difference of the crossed roads, the historical accident condition and the sight distance relative to the intersection; the weight of the alignment, speed, vehicle type, distance, driving behavior relative to the main road; weight of pedestrian, vehicle type relative to leg; visibility, road surface wet skid weight relative to the environment. Specifically, each second weight is calculated by the following formula:
wherein the content of the first and second substances,
denotes a second weight, λmaxRepresenting the largest feature root of the secondary decision matrix.
And thirdly, calculating the weights of the plurality of secondary influence factors relative to the traffic safety early warning degree according to the first weight and the second weight. Specifically, the weights of the plurality of secondary influence factors relative to the traffic safety precaution degree can be calculated through the following formula:
wherein the content of the first and second substances,
representing the weight of the secondary influence factor relative to the traffic safety early warning degree;
represents the ith secondary influence factor PiRelative to the jth first order influence factor BjA second weight of (a);
represents the jth primary influence factor BjA first weight relative to a traffic safety precaution.
As an alternative embodiment of the present invention, the step S16: in the calculation of the traffic safety early warning degree according to the weights of the secondary influence factors relative to the traffic safety early warning degree and the evaluation scores of the secondary influence factors, the traffic safety early warning degree can be calculated through the following formula:
wherein Z represents the traffic safety early warning degree; a represents the number of the called preset expert databases; fijExpressing the evaluation scores of the jth preset database on the secondary influence factors;
and representing the weight of the secondary influence factor relative to the traffic safety early warning degree.
According to the traffic safety early warning analysis method provided by the embodiment of the invention, the importance degree of each influence factor is determined by combining the preset time period data of the target area, the characteristic data of the target object and the determined multiple secondary influence factors through an analytic hierarchy process and a constructed judgment matrix, the risk of a road section traffic accident can be comprehensively evaluated, the quantitative characterization of the road intersection ventilation risk can be carried out through the traffic safety early warning degree, and further, accurate prompt can be carried out on drivers and passers-by.
An embodiment of the present invention provides a traffic safety early warning analysis apparatus, as shown in fig. 2, including:
the data acquisition module 21 is configured to acquire traffic data of a target area in a preset time period and feature data of a target object, where the feature data is used to represent a driving state of the target object; the detailed implementation can be referred to the related description of step S11 in the above method embodiment.
The first determining module 22 is configured to determine a plurality of primary influence factors, a plurality of secondary influence factors and evaluation scores of the plurality of secondary influence factors according to target area preset time segment data and feature data of a target object, where the primary influence factors are used for representing road types; the secondary influence factors comprise dynamic indexes and static indexes; the detailed implementation can be referred to the related description of step S12 in the above method embodiment.
A primary judgment matrix construction module 23, configured to construct a primary judgment matrix according to the multiple primary influence factors; the detailed implementation can be referred to the related description of step S13 in the above method embodiment.
A secondary judgment matrix construction module 24, configured to construct a secondary judgment matrix according to the plurality of secondary influence factors; the detailed implementation can be referred to the related description of step S14 in the above method embodiment.
The second determining module 25 is configured to determine weights of the multiple secondary influence factors with respect to the traffic safety early warning degree according to the primary judgment matrix and the secondary judgment matrix; the detailed implementation can be referred to the related description of step S15 in the above method embodiment.
The calculation module 26 is configured to calculate the traffic safety early warning degree according to the weights of the plurality of secondary influence factors relative to the traffic safety early warning degree and the evaluation scores of the plurality of secondary influence factors; the detailed implementation can be referred to the related description of step S16 in the above method embodiment.
And a traffic early warning analysis result generation module 27, configured to generate a traffic safety early warning analysis result according to the traffic safety early warning degree. The detailed implementation can be referred to the related description of step S17 in the above method embodiment.
The embodiment of the invention provides a traffic safety early warning analysis device, which comprises: acquiring traffic data of a target area in a preset time period and characteristic data of a target object through a data acquisition module 21; determining a plurality of first-level influence factors, a plurality of second-level influence factors and evaluation scores of the plurality of second-level influence factors by a first determination module 22 based on a dynamic static angle according to data of a preset time period of a target area and feature data of a target object, determining the importance degree of each level of influence factor by a chromatographic analysis method by a first-level judgment matrix construction module 23 and a second-level judgment matrix construction module 24, and calculating to obtain a traffic safety early warning degree by a second determination module 25, a calculation module 26 and a traffic early warning analysis result generation module 27; and generating a traffic safety early warning analysis result according to the traffic safety early warning degree. By implementing the invention, the problem that the accurate traffic early warning prompt cannot be provided for the user in the prior art is solved, the driving behavior can be analyzed through the driving track of the current target object and the historical data of each intersection, the traffic accident risk of the intersection can be evaluated more systematically, comprehensively and intuitively, and the traffic early warning prompt is accurately carried out; through judging the matrix, each index importance degree is clear and definite, and quantitative characterization can be carried out to crossing safety risk, and then accurate risk early warning is carried out to branch road pedestrian and vehicle.
An embodiment of the present invention further provides a computer device, as shown in fig. 3, the computer device may include a processor 31 and a memory 32, where the processor 31 and the memory 32 may be connected by a bus 30 or in another manner, and fig. 3 takes the example of connection by a bus as an example.
The processor 31 may be a Central Processing Unit (CPU). The Processor 31 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof.
The memory 32 is a non-transitory computer-readable storage medium, and can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the traffic safety warning analysis method in the embodiment of the present invention (for example, the data acquisition module 21, the first determination module 22, the primary judgment matrix construction module 23, the secondary judgment matrix construction module 24, the second determination module 25, the calculation module 26, and the traffic warning analysis result generation module 27 shown in fig. 2). The processor 31 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory 32, that is, the traffic safety warning analysis method in the above method embodiment is implemented.
The memory 32 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 31, and the like. Further, the memory 32 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 32 may optionally include memory located remotely from the processor 31, and these remote memories may be connected to the processor 31 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 32 and, when executed by the processor 31, perform the traffic safety warning analysis method in the embodiment shown in fig. 1.
The details of the computer device can be understood with reference to the corresponding related descriptions and effects in the embodiment shown in fig. 1, and are not described herein again.
Optionally, an embodiment of the present invention further provides a non-transitory computer readable medium, where the non-transitory computer readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the traffic safety warning analysis method described in any of the above embodiments, where the storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid-State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are illustrative for clarity and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (10)
1. A traffic safety early warning analysis method is characterized by comprising the following steps:
acquiring traffic data of a target area in a preset time period and characteristic data of a target object, wherein the characteristic data are used for representing the driving state of the target object;
determining a plurality of primary influence factors, a plurality of secondary influence factors and evaluation scores of the plurality of secondary influence factors according to the preset time period data of the target area and the characteristic data of the target object, wherein the primary influence factors are used for representing road types; the secondary influence factors comprise dynamic indexes and static indexes;
constructing a primary judgment matrix according to the plurality of primary influence factors;
constructing a secondary judgment matrix according to the plurality of secondary influence factors;
determining weights of a plurality of secondary influence factors relative to the traffic safety early warning degree according to the primary judgment matrix and the secondary judgment matrix;
calculating the traffic safety early warning degree according to the weights of the secondary influence factors relative to the traffic safety early warning degree and the evaluation scores of the secondary influence factors;
and generating a traffic safety early warning analysis result according to the traffic safety early warning degree.
2. The method of claim 1, wherein the first-order decision matrix is constructed by the following formula:
wherein A represents the primary judgment matrix, aijAnd the important index scale represents the primary influence factor relative to the traffic safety early warning degree.
3. The method of claim 1, wherein the secondary decision matrix is constructed by the following equation:
wherein B represents the secondary decision matrix, BijAn importance index scale representing the secondary impact factor relative to the primary impact factor.
4. The method according to claim 1, wherein the determining weights of the plurality of secondary influence factors with respect to the traffic safety precaution degree according to the primary judgment matrix and the secondary judgment matrix specifically comprises:
calculating the maximum eigenvalue of the primary judgment matrix, and normalizing the maximum eigenvalue to obtain a first weight of the primary influence factor relative to the traffic safety early warning degree;
calculating the maximum eigenvalue of the secondary judgment matrix, and normalizing the maximum eigenvalue to obtain a second weight of the secondary influence factor relative to the primary influence factor;
and calculating the weights of the plurality of secondary influence factors relative to the traffic safety early warning degree according to the first weight and the second weight.
5. The method of claim 4, wherein the first weight is calculated by the following equation:
wherein the content of the first and second substances,
represents said first weight, λmaxRepresenting the maximum characteristic root of a primary judgment matrix;
calculating the second weight by the following formula:
wherein the content of the first and second substances,
represents said second weight, λmaxRepresenting the largest feature root of the secondary decision matrix.
6. The method of claim 5, wherein the weights of the plurality of secondary impact factors are calculated with respect to the traffic safety precaution by the following formula:
wherein the content of the first and second substances,
representing the weight of the secondary influence factor relative to the traffic safety early warning degree;
represents the ith secondary influence factor PiRelative to the jth first order influence factor BjA second weight of (a);
represents the jth primary influence factor BjA first weight relative to a traffic safety precaution.
7. The method of claim 6, wherein the traffic safety precaution degree is calculated by the following formula:
wherein Z represents the traffic safety early warning degree; a represents the number of called preset databases; fijExpressing the evaluation scores of the jth preset database on the secondary influence factors;
and representing the weight of the secondary influence factor relative to the traffic safety early warning degree.
8. A traffic safety early warning analysis device, characterized by, includes:
the data acquisition module is used for acquiring traffic data of a target area in a preset time period and characteristic data of a target object, wherein the characteristic data is used for representing the driving state of the target object;
the first determining module is used for determining a plurality of primary influence factors, a plurality of secondary influence factors and evaluation scores of the plurality of secondary influence factors according to the preset time period data of the target area and the characteristic data of the target object, wherein the primary influence factors are used for representing road types; the secondary influence factors comprise dynamic indexes and static indexes;
the primary judgment matrix construction module is used for constructing a primary judgment matrix according to the plurality of primary influence factors;
the second-level judgment matrix construction module is used for constructing a second-level judgment matrix according to the plurality of second-level influence factors;
the second determining module is used for determining the weights of the secondary influence factors relative to the traffic safety early warning degree according to the primary judgment matrix and the secondary judgment matrix;
the calculation module is used for calculating the traffic safety early warning degree according to the weights of the secondary influence factors relative to the traffic safety early warning degree and the evaluation scores of the secondary influence factors;
and the traffic early warning analysis result generation module is used for generating a traffic safety early warning analysis result according to the traffic safety early warning degree.
9. A computer device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the one processor to cause the at least one processor to perform the steps of the traffic safety warning analysis method according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the traffic safety warning analysis method according to any one of claims 1 to 7.
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