Background Art: As car ownership continues to increase, the road traffic safety situation has become particularly severe. Identifying road traffic accident-prone sections is of great importance for traffic police departments to reasonably allocate police force and rescue equipment deployment according to different times and weather conditions, thereby improving traffic control capabilities. significance. At present, there are mainly the following types of identification methods for traffic accident-prone sections at home and abroad: The first type is to use Traffic Conflict Analysis Technology (TCT) to judge possible traffic conflict points by predicting vehicle trajectory, and to judge locations where traffic conflicts are more serious. As accident-prone sections. This method relies less on historical traffic accident data, but due to the large workload of the traffic conflict analysis method, it is only suitable for identifying traffic accident black spots on small-scale urban roads. The second category is based on historical traffic accident data. First, the road under study is artificially segmented, and then directly uses the accident number method and the accident rate method to identify accident-prone road sections, or models such as Poisson regression, negative binomial regression, and empirical Bayes are constructed to regression analyze accident data and judge accident-prone road sections based on predictions of accident development trends. Since this type of method requires manual segmentation of the road before identification, the segmentation results will directly affect the impact of traffic accident-prone sections. On the other hand, time and weather conditions have a significant impact on traffic safety, but the above prior art identification methods for road sections prone to traffic accidents do not consider time and weather conditions, which greatly reduces the accuracy of the identification results.  Project description: In order to avoid the shortcomings of the above-mentioned existing technologies, we provide a method for identifying traffic accident-prone road sections based on accident risk to improve the accuracy of the recognition results and provide a basis for traffic police departments to reasonably deploy police forces and rescue equipment according to different time and weather factors. Provide basis. In order to solve the technical problem, the following technical scheme is adopted: the method for identifying traffic accident-prone road sections based on accident risk is characterized by following steps: 1. For historical traffic accidents that have occurred in the identified road section A, determining the occurrence time, occurrence weather and occurrence location of each historical traffic accident; 2. Among the historical traffic accidents, selecting all historical traffic accidents that match the set time and weather to form a traffic accident data set S; 3: According to the set time and weather, determining the qualified time length T in days; 4: calculating and obtaining the influence value f(xi) of each historical traffic accident Si in the traffic accident data set S on the accident risk degree of the identified point Aj in the identified road section A according to the Gaussian distribution function represented by Equation (1)| Aj): In Equation (1), m0 is the distance from the identified point Aj to the starting point A0 of the identified road section;xi is the distance from the place where the historical traffic accident Si occurred to the starting point A0 of the identified road section;k is a coefficient related to the number of casualties in the historical traffic accident Si, and σ is a coefficient related to the maximum speed limit of the identified road section A; the accident risk f(Aj) of the identified point Aj is calculated from Equation (2):| S| refers to the number of historical traffic accidents in the traffic accident data set S; 5: Set the accident risk degree safety threshold to L, and judge the accident-prone road section: If there is: f(Aj)≥L, judge the identified point Aj as an accident-prone point; 6. According to steps 4 and 5, obtain the accident risk degree f(Aj) of each identified point Aj in the identified road section A, judge whether it is an accident-prone point, and identify the interval composed of all accident-prone points as an accident-prone road section. The method for identifying traffic accident-prone road sections based on accident risk is also characterized in that in step 1, the occurrence time of historical traffic accidents is set to different description forms such as season, week, day and night, and time; the occurrence weather of historical traffic accidents is set to different description forms such as temperature, weather, wind force and wind direction, and the accident occurrence location is defined as the distance between the accident occurrence place and the starting point of the road.