A road traffic abnormal state monitoring and analysis method, system, terminal and medium

Abstract

The present invention discloses a method, system, terminal and medium for monitoring and analyzing abnormal state of road traffic, and relates to the field of intelligent transportation technology. The key points of the technical scheme are: continuously collecting road images of the same road area at different collection times; extracting vehicle density features of each lane from the road image; arranging and combining density feature groups at different collection times to construct a density feature matrix; analyzing the density change features of each lane according to the density feature matrix to obtain a density change feature set of the road area; matching the abnormal state type of the road area from a database according to the density feature matrix and the density change feature set; outputting a corresponding warning signal according to the abnormal state type to realize abnormal state warning of road traffic; the present invention can cover the comprehensive monitoring of abnormal states in a long road with one image monitoring point, and there is no need to consider the length of the frame interval time between continuous frame images, so as to better adapt to the infrastructure of existing roads.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and more specifically, to a method, system, terminal and medium for monitoring and analyzing abnormal road traffic conditions. Background Art

[0002] Road traffic abnormalities usually refer to traffic congestion, reduced driving speed and other abnormal traffic phenomena caused by traffic accidents, construction and maintenance, vehicle failures, etc. These abnormalities will affect the road's traffic capacity, reduce traffic efficiency, and may even cause traffic safety hazards. Therefore, monitoring road traffic abnormalities is one of the important measures to ensure safe road operations.

[0003] There are obvious differences in the technologies used in the existing technologies for monitoring abnormal traffic conditions on different roads. For example, the monitoring of traffic accident conditions is mainly based on the image data of the accident scene. Since the number of image acquisition devices configured on the road is limited and the coverage of a single image acquisition device is limited, it is difficult to conduct comprehensive monitoring of traffic accidents on the entire road section. For example, for abnormal conditions such as slow-moving conditions and merging conditions, it is generally necessary to analyze the abnormal behavior of the vehicle based on continuous frame images containing the same target. In order to improve the accuracy of the analysis of abnormal behavior, the time span of the continuous frame images needs to be large enough, so it is difficult to apply to expressways.

[0004] In summary, the above technology cannot carry out comprehensive monitoring of abnormal conditions based on current road facilities. Therefore, how to research and design a road traffic abnormal state monitoring and analysis method, system, terminal and medium that can overcome the above defects is a problem that we urgently need to solve. Summary of the invention

[0005] To address the deficiencies in the prior art, the present invention aims to provide a method, system, terminal and medium for monitoring and analyzing abnormal road traffic conditions. Based on the delayed reaction characteristics caused by abnormal conditions in road traffic in the time dimension and space dimension, the present invention continuously collects road images of the same road area at different collection times, and accurately matches the abnormal condition type of the road area based on the changes in the vehicle density characteristics of each lane in the road image. One image monitoring point can cover the comprehensive monitoring of abnormal conditions in a longer road, and there is no need to consider the length of the frame interval between consecutive frame images, which can better adapt to the infrastructure of existing roads.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions:

[0007] In a first aspect, a method for monitoring and analyzing abnormal road traffic conditions is provided, comprising the following steps:

[0008] Continuously collect road images of the same road area at different collection times to obtain an image sequence;

[0009] Extract the vehicle density features of each lane from the road image to obtain the density feature group corresponding to the acquisition time;

[0010] Arrange and combine the density feature groups at different acquisition times to construct a density feature matrix;

[0011] Analyze the density change characteristics of each lane according to the density feature matrix to obtain the density change feature set of the road area;

[0012] Match the abnormal state type of the road area from the database according to the density feature matrix and the density change feature set;

[0013] Output corresponding warning signals according to the abnormal state type to realize abnormal traffic state warning.

[0014] Furthermore, the vehicle density feature is characterized by the average distance between vehicles traveling, and the value of the vehicle density feature is negatively correlated with the average distance between vehicles traveling;

[0015] Alternatively, the vehicle density feature is characterized by the number of vehicles, and the value of the vehicle density feature is positively correlated with the number of vehicles.

[0016] Furthermore, the size of the density feature matrix is ​​m×n, and is dynamically updated using a first-in-first-out method;

[0017] Wherein, m is the number of lanes in the road area, and n is the preset number of collections.

[0018] Furthermore, the analysis process of the density change characteristics is specifically as follows:

[0019] Perform linear fitting on the vehicle density features corresponding to a single lane in the density feature matrix to obtain the corresponding fitting function;

[0020] The coefficient of the first-order term in the fitting function is used as the density change feature of the corresponding lane;

[0021] Among them, the density change features of multiple lanes analyzed at the same time constitute the density change feature set of the road area.

[0022] Furthermore, the matching process of the abnormal status type is specifically as follows:

[0023] Setting a first threshold and a second threshold, wherein the second threshold is greater than the first threshold;

[0024] If the density change feature of only one lane in the density change feature set is greater than the first threshold and less than the second threshold, the matched abnormal state type is a low-speed slow state;

[0025] If the density change feature of one or two lanes in the density change feature set is greater than the first threshold and less than the second threshold, and the difference between the vehicle density features of any two lanes at the current collection time does not exceed the error range, then the matched abnormal state type is the merging driving state;

[0026] If the density change feature of one lane in the density change feature set is greater than the second threshold, and the density change features of the remaining lanes are all greater than the first threshold and less than the second threshold, then the matched abnormal state type is the lane occupying state;

[0027] If the density change features of all lanes in the density change feature set are greater than the second threshold, the matched abnormal state type is a traffic accident state;

[0028] If the density change characteristics of all lanes in the density change characteristic set are less than the first threshold, and the difference between the vehicle density characteristics of one lane at different collection times exceeds the error range, the matched abnormal state type is the unsafe distance state.

[0029] Furthermore, the method further comprises:

[0030] Configuring an identifier for each of the abnormal status types;

[0031] When the abnormal state type is a low-speed slow-moving state, a merging state or an unsafe distance state, the identifier value is 1, and when the abnormal state type is a road-occupying state or a traffic accident state, the identifier value is 0;

[0032] If the identifier is 0, the warning signal is transmitted to the adjacent warning base points in the road that has been traveled; and if the identifier is 1, the warning signal is transmitted to the adjacent warning base points in the road section that has not been traveled.

[0033] Furthermore, the time interval between adjacent collection moments is specifically:

[0034] The road length in which vehicles can be identified in the road image is used as the effective monitoring distance of the road area;

[0035] The time interval between two adjacent collection moments is determined by the ratio of the effective monitoring distance to the speed limit or the actual driving speed in the road area.

[0036] In a second aspect, a road traffic abnormal state monitoring and analysis system is provided, comprising:

[0037] An image acquisition module is used to continuously acquire road images of the same road area at different acquisition times to obtain an image sequence;

[0038] A feature extraction module is used to extract the vehicle density features of each lane from the road image to obtain a density feature group corresponding to the acquisition time;

[0039] A matrix construction module is used to arrange and combine density feature groups at different acquisition times to construct a density feature matrix;

[0040] A change analysis module is used to analyze the density change characteristics of each lane according to the density feature matrix to obtain a density change feature set of the road area;

[0041] An abnormal matching module, used to match the abnormal state type of the road area from the database according to the density feature matrix and the density change feature set;

[0042] The status warning module is used to output corresponding warning signals according to the abnormal status type to realize abnormal traffic status warning.

[0043] In a third aspect, a computer terminal is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a method for monitoring and analyzing abnormal road traffic conditions as described in any one of the first aspects is implemented.

[0044] In a fourth aspect, a computer-readable medium is provided, on which a computer program is stored, and the computer program is executed by a processor to implement a method for monitoring and analyzing abnormal road traffic conditions as described in any one of the first aspects.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. A road traffic abnormal state monitoring and analysis method provided by the present invention is based on the delayed reaction characteristics caused by the abnormal state in the time dimension and the space dimension after the abnormal state in the road traffic occurs. By continuously collecting road images of the same road area at different collection times, and accurately matching the abnormal state type of the road area according to the change of vehicle density characteristics of each lane in the road image, a single image monitoring point can cover the comprehensive monitoring of abnormal states in a long road, and there is no need to consider the length of the frame interval between consecutive frame images, which can better adapt to the existing road infrastructure;

[0047] 2. The present invention performs linear fitting on the vehicle density characteristics corresponding to a single lane in the density characteristic matrix, and uses the coefficient of the first-order term in the fitting function as the density change characteristic of the corresponding lane, which can reduce the interference of the randomness of road vehicle driving on the analysis of vehicle density characteristic changes;

[0048] 3. When matching abnormal state types, the present invention not only considers the density change characteristics and vehicle density characteristics of a single lane from a local perspective, but also considers the correlation between the density change characteristics and vehicle density characteristics between two lanes from a global perspective, thereby reducing the occurrence of abnormal state type mismatching;

[0049] 4. The present invention configures an identifier for each abnormal state type, and transmits warning signals to warning base points in different directions according to the value of the identifier, thereby realizing accurate warning of abnormal road traffic conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0051] Figure 1 is a flow chart of Embodiment 1 of the present invention;

[0052] Figure 2 It is a system block diagram in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0053] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0054] Embodiment 1: A method for monitoring and analyzing abnormal road traffic conditions, such as Figure 1 As shown, the following steps are included:

[0055] S1: continuously collect road images of the same road area at different collection times to obtain an image sequence;

[0056] S2: extracting the vehicle density features of each lane from the road image to obtain the density feature group corresponding to the acquisition time;

[0057] S3: Arrange and combine the density feature groups at different acquisition times to construct a density feature matrix;

[0058] S4: Analyze the density change characteristics of each lane according to the density feature matrix to obtain a density change feature set of the road area;

[0059] S5: Match the abnormal state type of the road area from the database according to the density feature matrix and the density change feature set;

[0060] S6: Outputting a corresponding warning signal according to the abnormal state type to implement abnormal traffic state warning.

[0061] In step S1, the road area is a road section of a certain length, which includes all lanes in the corresponding road section. To this end, the road image can be collected according to the camera device configured at the road test monitoring point. Since the camera device is fixed, different road images are collected for the same road area.

[0062] In addition, since the present invention mainly analyzes the changes in vehicle density characteristics of the lane, it is not necessary for the two road images to have the same vehicle characteristics. For this reason, the time interval between the acquisition moments can be as large as possible, but in order to adapt to the expressway, the time interval between adjacent acquisition moments should maintain full road section coverage, specifically: the road length in the road image where vehicles can be identified is used as the effective monitoring distance of the road area; the time interval between two adjacent acquisition moments is determined by the ratio of the effective monitoring distance to the speed limit in the road area, or the time interval between two adjacent acquisition moments is determined by the ratio of the effective monitoring distance to the actual driving speed in the road area.

[0063] In step S2, as an optional implementation, the vehicle density feature can be characterized by the average distance between vehicles, and the value of the vehicle density feature is negatively correlated with the average distance between vehicles. The average distance between vehicles is generally determined by the ratio of the number of vehicles in the lane to the length of the lane.

[0064] As an optional implementation manner, the vehicle density feature is characterized by the number of vehicles, and the value of the vehicle density feature is positively correlated with the number of vehicles.

[0065] It should be noted that for the recognition of the number of vehicles in the road image, it is not necessary to accurately identify the information of each vehicle. It is only necessary to recognize that the vehicle is an independent target, which can expand the scope of the road area to a certain extent.

[0066] In addition, considering that the recognition of the number of vehicles is easily affected by environmental conditions, such as rain, fog and other weather conditions, the size of the road area can also be dynamically adjusted according to environmental conditions.

[0067] In step S3, the size of the density feature matrix is ​​m×n, and it is dynamically updated using a first-in-first-out method; wherein m is the number of lanes in the road area, and n is the preset number of acquisitions.

[0068] For example, the vehicle density feature is determined by the number of vehicles. The density feature groups of lane A at 120 km / h, lane A at 100 km / h, and lane A at 80 km / h at 6 consecutive acquisition times are (6,7,7), (6,7,7), (7,8,7), (7,8,8), (8,7,8), and (8,8,8). The density feature matrix constructed is as follows:

[0069] .

[0070] When the density feature group at the next moment is (8,8,8), the updated density feature matrix is ​​as follows:

[0071] .

[0072] In step S4, the analysis process of the density change characteristics is specifically as follows: linear fitting is performed on the vehicle density characteristics corresponding to a single lane in the density characteristic matrix to obtain the corresponding fitting function; the coefficient of the first-order term in the fitting function is used as the density change characteristic of the corresponding lane; wherein, the density change characteristics of multiple lanes analyzed at the same time constitute a density change feature set of the road area.

[0073] From the above density feature matrix, we can see that the vehicle density features of lane A at 120 km / h at 6 consecutive collection times are {6, 6, 7, 7, 8, 8}. The fitting function obtained by the linear fitting method with minimum error is: y=0.5t+5.25, t is the collection time, y is the vehicle density feature. The density change feature of lane A when t is 6 is 0.5.

[0074] The present invention performs linear fitting on the vehicle density characteristics corresponding to a single lane in the density characteristic matrix, and uses the coefficient of the first-order term in the fitting function as the density change characteristic of the corresponding lane, which can weaken the interference of the randomness of road vehicle driving on the analysis of vehicle density characteristic changes.

[0075] In step S5, the matching process of the abnormal state type is specifically as follows: a first threshold value and a second threshold value are set, and the second threshold value is greater than the first threshold value; if the density change feature of only one lane in the density change feature set is greater than the first threshold value and less than the second threshold value, the matched abnormal state type is a low-speed slow-moving state; if the density change feature of one or two lanes in the density change feature set is greater than the first threshold value and less than the second threshold value, and the difference between the vehicle density features of any two lanes at the current collection time does not exceed the error range, the matched abnormal state type is a merging driving state; if the density change feature of one lane in the density change feature set is greater than the second threshold value, and the density change features of the remaining lanes are all greater than the first threshold value and less than the second threshold value, the matched abnormal state type is a lane occupying state; if the density change features of all lanes in the density change feature set are greater than the second threshold value, the matched abnormal state type is a traffic accident state; if the density change features of all lanes in the density change feature set are less than the first threshold value, and the difference between the vehicle density features of one lane at different collection times exceeds the error range, the matched abnormal state type is a non-safe distance state.

[0076] It should be noted that the first threshold and the second threshold can be obtained by statistical analysis based on historical data. The first threshold is mainly designed to distinguish between random fluctuations and abnormal conditions, while the second threshold is mainly designed to distinguish between slow changes in density and rapid changes. The above matrix represents the merging state.

[0077] When matching abnormal state types, the present invention not only considers the density change characteristics and vehicle density characteristics of a single lane from a local perspective, but also considers the correlation between the density change characteristics and the vehicle density characteristics between the two lanes from a global perspective, thereby reducing the occurrence of incorrect matching of abnormal state types.

[0078] In step S6, the warning signal is output to eliminate abnormal conditions and weaken the impact of abnormal conditions on road traffic safety. For example, low-speed slow driving and merging driving are likely to reduce vehicle speed, which may cause road congestion in severe cases; non-safe distance conditions are likely to cause traffic accidents; and occupying the road and traffic accident conditions are likely to cause road congestion, traffic accidents, etc.

[0079] To this end, the present invention configures an identifier for each abnormal state type; when the abnormal state type is a slow-speed state, a merging state, or an unsafe distance state, the identifier value is 1, and when the abnormal state type is a road-occupying state or a traffic accident state, the identifier value is 0. It should be noted that the value of the identifier is only an example, and it can also be other values ​​that can distinguish the two situations, which is not limited here.

[0080] Among them, if the identifier is 0, the warning signal is transmitted to the adjacent warning base point in the road that has been passed, mainly to warn the vehicles behind; and if the identifier is 1, the warning signal is transmitted to the adjacent warning base point in the road section that has not been passed, mainly to warn the vehicles in front.

[0081] In this embodiment, each warning base point can be configured with a camera device, a communication device and a warning device. Two adjacent warning base points are connected by a communication device. The warning device is activated after responding to the warning signal, such as playing voice content corresponding to the abnormal state type in a voice playback manner.

[0082] The present invention configures an identifier for each abnormal state type, and transmits a warning signal to warning base points in different directions according to the value of the identifier, thereby realizing accurate warning of abnormal road traffic conditions.

[0083] Embodiment 2: A road traffic abnormal state monitoring and analysis system, the system is used to implement a road traffic abnormal state monitoring and analysis method as described in Embodiment 1, such as Figure 2As shown, it includes an image acquisition module, a feature extraction module, a matrix construction module, a change analysis module, an abnormal matching module and a status warning module.

[0084] Among them, the image acquisition module is used to continuously collect road images of the same road area at different acquisition times to obtain an image sequence; the feature extraction module is used to extract the vehicle density features of each lane from the road image to obtain the density feature group at the corresponding acquisition time; the matrix construction module is used to arrange and combine the density feature groups at different acquisition times to construct a density feature matrix; the change analysis module is used to analyze the density change features of each lane according to the density feature matrix to obtain the density change feature set of the road area; the abnormal matching module is used to match the abnormal state type of the road area from the database according to the density feature matrix and the density change feature set; the state warning module is used to output the corresponding warning signal according to the abnormal state type to realize the abnormal state warning of road traffic.

[0085] The present invention also records a computer terminal, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a method for monitoring and analyzing abnormal road traffic conditions as described in Example 1 is implemented.

[0086] The present invention also records a computer-readable medium on which a computer program is stored. The computer program is executed by a processor to implement a method for monitoring and analyzing abnormal road traffic conditions as described in Example 1.

[0087] Working principle: The present invention is based on the delayed reaction characteristics caused by abnormal conditions in road traffic in the time dimension and space dimension. It continuously collects road images of the same road area at different collection times, and accurately matches the abnormal condition type of the road area according to the changes in the vehicle density characteristics of each lane in the road image. One image monitoring point can cover the comprehensive monitoring of abnormal conditions in longer roads, and there is no need to consider the length of the frame interval between consecutive frame images, which can better adapt to the infrastructure of existing roads.

[0088] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0089] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0090] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0092] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for monitoring and analyzing abnormal road traffic conditions, characterized in that: The following steps are involved: Continuously collect road images of the same road area at different collection times to obtain an image sequence; Extract the vehicle density features of each lane from the road image to obtain the density feature group corresponding to the acquisition time; Arrange and combine the density feature groups at different acquisition times to construct a density feature matrix; Analyze the density change characteristics of each lane according to the density feature matrix to obtain the density change feature set of the road area; Match the abnormal state type of the road area from the database according to the density feature matrix and the density change feature set; Output corresponding warning signals according to the abnormal state type to realize abnormal traffic state warning; The size of the density feature matrix is ​​m×n, and is dynamically updated using a first-in-first-out method; Where m is the number of lanes in the road area, and n is the preset number of collections; The analysis process of the density change characteristics is specifically as follows: Perform linear fitting on the vehicle density features corresponding to a single lane in the density feature matrix to obtain the corresponding fitting function; The coefficient of the first-order term in the fitting function is used as the density change feature of the corresponding lane; Among them, the density change features of multiple lanes analyzed at the same time constitute the density change feature set of the road area; The specific matching process of the abnormal status type is as follows: Setting a first threshold and a second threshold, wherein the second threshold is greater than the first threshold; If the density change feature of only one lane in the density change feature set is greater than the first threshold and less than the second threshold, the matched abnormal state type is a low-speed slow state; If the density change feature of one or two lanes in the density change feature set is greater than the first threshold and less than the second threshold, and the difference between the vehicle density features of any two lanes at the current collection time does not exceed the error range, then the matched abnormal state type is the merging driving state; If the density change feature of one lane in the density change feature set is greater than the second threshold, and the density change features of the remaining lanes are all greater than the first threshold and less than the second threshold, then the matched abnormal state type is the lane occupying state; If the density change features of all lanes in the density change feature set are greater than the second threshold, the matched abnormal state type is a traffic accident state; If the density change characteristics of all lanes in the density change characteristic set are less than the first threshold, and the difference between the vehicle density characteristics of one lane at different collection times exceeds the error range, the matched abnormal state type is the unsafe distance state.

2. A method for monitoring and analyzing abnormal road traffic conditions according to claim 1, characterized in that: The vehicle density feature is characterized by the average distance between vehicles traveling, and the value of the vehicle density feature is negatively correlated with the average distance between vehicles traveling; Alternatively, the vehicle density feature is characterized by the number of vehicles, and the value of the vehicle density feature is positively correlated with the number of vehicles.

3. The method for monitoring and analyzing abnormal road traffic conditions according to claim 1, characterized in that: The method further includes: Configuring an identifier for each of the abnormal status types; When the abnormal state type is a low-speed slow-moving state, a merging state or an unsafe distance state, the identifier value is 1, and when the abnormal state type is a road-occupying state or a traffic accident state, the identifier value is 0; If the identifier is 0, the warning signal is transmitted to the adjacent warning base points in the road that has been traveled; and if the identifier is 1, the warning signal is transmitted to the adjacent warning base points in the road section that has not been traveled.

4. The method for monitoring and analyzing abnormal road traffic conditions according to claim 1, characterized in that: The time interval between adjacent collection moments is specifically: The road length in which vehicles can be identified in the road image is used as the effective monitoring distance of the road area; The time interval between two adjacent collection moments is determined by the ratio of the effective monitoring distance to the speed limit or the actual driving speed in the road area.

5. A road traffic abnormal state monitoring and analysis system, characterized in that: The system is used to implement a road traffic abnormal state monitoring and analysis method as described in any one of claims 1 to 4, comprising: An image acquisition module is used to continuously acquire road images of the same road area at different acquisition times to obtain an image sequence; A feature extraction module is used to extract the vehicle density features of each lane from the road image to obtain a density feature group corresponding to the acquisition time; A matrix construction module is used to arrange and combine density feature groups at different acquisition times to construct a density feature matrix; A change analysis module is used to analyze the density change characteristics of each lane according to the density feature matrix to obtain a density change feature set of the road area; An abnormal matching module, used to match the abnormal state type of the road area from the database according to the density feature matrix and the density change feature set; The status warning module is used to output corresponding warning signals according to the abnormal status type to realize abnormal traffic status warning.

6. A computer terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for monitoring and analyzing abnormal road traffic conditions as described in any one of claims 1 to 4 is implemented.

7. A computer readable medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement a method for monitoring and analyzing abnormal road traffic conditions as described in any one of claims 1 to 4.

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