An efficient traffic violation handling method based on cloud computing

Abstract

The present invention provides an efficient traffic violation processing method based on cloud computing, belonging to the field of intelligent transportation technology. The method includes the following steps: S1. Obtain violation data and segment the violation data into multiple segments containing violation data; S2. Extract violation features according to the data segments; S3. Calculate the average value and standard deviation of specific features in each segment; S4. Calculate the specific feature difference value of the violation according to the average value and standard deviation of the features in the segment, and determine the critical value of the specific feature difference value; S5. Allocate the data segments to different computing nodes, and each computing node processes the data segments in parallel; S6. Aggregate the results of each data segment to obtain an analysis result; S7. Generate a violation notice according to the analysis result. The present invention can effectively solve the technical problems of limited data storage and processing capabilities, slow processing speed, and low accuracy existing in the prior art.

Description

Technical Field

[0001] The present invention relates to a method for handling traffic violations, and in particular to an efficient traffic violation handling method based on cloud computing, belonging to the field of intelligent transportation technology. Background Art

[0002] With the popularization of intelligence, non-site law enforcement has become an indispensable and most important part of traffic management work. Non-site law enforcement refers to a law enforcement method in which traffic management departments use scientific and technological means such as radar, cameras, and electronic eyes to record the traffic violation behaviors of driving vehicles and form image data, and use this as evidence to punish traffic violation parties according to law afterwards. At present, traffic violation handling is a cumbersome and complex process. Traditional violation handling systems usually face problems such as limited data storage and processing capabilities, slow processing speed, and low accuracy. At the same time, there are also certain difficulties and risks in the acquisition and transmission of violation data. Therefore, an efficient traffic violation handling system based on cloud computing technology is needed to solve these problems. Summary of the Invention

[0003] A brief overview of the present invention is given below to provide a basic understanding of certain aspects of the present invention. It should be understood that this overview is not an exhaustive overview of the present invention. It is not intended to identify the key or important parts of the present invention, nor is it intended to limit the scope of the present invention. Its purpose is only to present certain concepts in a simplified form as a prelude to the more detailed description to be discussed later.

[0004] In view of this, in order to solve the technical problems of limited data storage and processing capabilities, slow processing speed, and low accuracy existing in the prior art, the present invention provides an efficient traffic violation handling method based on cloud computing.

[0005] Solution 1. An efficient traffic violation handling method based on cloud computing, comprising the following steps:

[0006] S1. Obtain violation data and segment the violation data into multiple segments, generating multiple segments containing violation data;

[0007] S2. Extract violation features according to the data segments;

[0008] S3. Calculate the average value and standard deviation of specific features in each segment;

[0009] S4. Calculate the specific feature difference value of the violation according to the average value and standard deviation of the specific features in the segment, and determine the critical value of the specific feature difference value;

[0010] S5. Allocate the data segments to different computing nodes, and each computing node processes the data segments in parallel;

[0011] S6. Aggregate the results of each data segment to obtain the analysis result;

[0012] S7. Generate a violation notice based on the analysis result, the method is: including the following steps:

[0013] S71. Generate a violation data training set from the analysis result;

[0014] S72. Train a decision tree model based on the violation data training set;

[0015] S73. Classify and judge the violation data;

[0016] S74. Generate a violation penalty decision based on the classification judgment result and the constraint conditions.

[0017] Preferably, the calculation method of the specific feature difference value is:

[0018] ;

[0019] Wherein, t represents the specific feature difference value; A represents data segment A, B represents data segment B, represents the average value of data segment A, represents the standard deviation of data segment A; represents the standard deviation of data segment B; n represents the number of specific feature values in data segment A, and m represents the number of specific feature values in data segment B.

[0020] Preferably, the method for training a decision tree model based on the violation data training set is: including the following steps:

[0021] S721. Initialize the root node and use the training data set as the input;

[0022] S722. Select the best feature, divide the data set into multiple subsets, and evaluate the quality of the division by calculating the Gini index of each feature;

[0023] S723. Recursively execute S722 for each subset until the stop condition is met;

[0024] S724. Build a decision tree and associate each node with the corresponding feature and splitting condition.

[0025] Preferably, the method for classifying and judging the violation data is: including the following steps

[0026] S731. Input the feature vector x of the next violation data;

[0027] S732. Starting from the root node, classify the data into child nodes step by step according to the characteristics and segmentation conditions of the nodes until reaching the leaf nodes, and the leaf nodes are the classification results of the new traffic violation data.

[0028] Preferably, the constraint conditions include:

[0029] Constraint 1: Constraint on traffic violation handling requirements;

[0030] Constraint 2: Constraint on legal regulations.

[0031] Preferably, the objective function of the traffic violation punishment decision is:

[0032] ;

[0033] where punishment_amount is the punishment amount and handling_method is the handling method.

[0034] Preferably, the constraint conditions for traffic violation handling requirements are:

[0035] a. Handling timeliness;

[0036] b. Handling method selection;

[0037] C. Appeal procedure;

[0038] d. Handling record preservation;

[0039] e. Punishment decision notification;

[0040] The constraint conditions of legal regulations are:

[0041] a. Minimum legal punishment;

[0042] b. Legal punishment amount;

[0043] c. Legal handling method;

[0044] d. Legal right of appeal;

[0045] e. Legal timeliness.

[0046] Solution 2. An electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method for efficient traffic violation handling based on cloud computing described in Solution 1.

[0047] Solution 3. A computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the method for efficient traffic violation handling based on cloud computing described in Solution 1.

[0048] The beneficial effects of the present invention are as follows: The present invention effectively improves the efficiency and accuracy of traffic violation handling, reduces the handling cost, effectively solves the technical problems of limited data storage and processing capabilities, slow processing speed, and low accuracy existing in the prior art, and at the same time improves the level of traffic management and service quality; The present invention also has broad application prospects and has important practical significance in the field of traffic management. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0050] Figure 1 is a flowchart of an efficient traffic violation handling method based on cloud computing. DETAILED DESCRIPTION OF THE INVENTION

[0051] In order to make the technical solutions and advantages in the embodiments of the present invention clearer and more understandable, the following further details the exemplary embodiments of the present invention with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than an exhaustive list of all embodiments. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0052] Embodiment 1: Refer to Figure 1 To illustrate this embodiment, an efficient traffic violation handling method based on cloud computing includes the following steps:

[0053] S1. Obtain violation data and segment the violation data into multiple segments, generating multiple segments containing violation data;

[0054] The method for obtaining violation data can be to obtain it in real time through monitoring cameras or through radar collection;

[0055] The violation data includes vehicle information, violation time, and violation location;

[0056] The vehicle information includes license plate number and vehicle type;

[0057] S2. Extract violation features according to the data segments;

[0058] Count the number of violations in the data segments;

[0059] Count the number of different types of violations in the data segments and calculate the frequency of different types of violations;

[0060] Count the locations where violations occur in the data segments and the distribution of violations at different locations;

[0061] S3. Calculate the mean and standard deviation of specific features in each segment;

[0062] Calculate the data of specific features in each segment. The specific feature in data segment A is , and the specific feature in data segment B is ;

[0063] Mean of data segment A:

[0064] ;

[0065] Standard deviation of data segment A:

[0066] ;

[0067] Mean of data segment B:

[0068] ;

[0069] Standard deviation of data segment B:

[0070] ;

[0071] S4. Calculate the specific feature difference value of violations according to the mean and standard deviation of features in the segment;

[0072] Specific feature difference value:

[0073] ;

[0074] Among them, t represents the specific feature difference value; A represents data segment A, B represents data segment B, represents the mean of data segment A, represents the standard deviation of data segment A; represents the standard deviation of data segment B; n represents the number of specific feature values in data segment A, and m represents the number of specific feature values in data segment B;

[0075] S5. Allocate data segments to different computing nodes, and each computing node processes data segments in parallel;

[0076] Each computing node of the present invention can process one or more data segments in parallel. By adopting distributed processing of data segments, the computing efficiency can be improved;

[0077] S6. Aggregate the results of each data segment to obtain the analysis result;

[0078] The analysis result can be visually displayed in a bar chart. Use the matplotlib library to draw a bar chart so as to be able to visualize the feature differences between different data segments;

[0079] S7. Generate a traffic violation notice based on the analysis results, which can analyze the patterns and trends of traffic violation data and help improve traffic management strategies. The method includes the following steps:

[0080] S71. Generate a training set of traffic violation data from the analysis results , where is a feature vector of traffic violation data, is the corresponding classification label, is the number of training samples; , where is the number of features; , where 0 indicates no traffic violation and 1 indicates a traffic violation;

[0081] S72. Train a decision tree model based on the training set of traffic violation data

[0082] S721. Initialize the root node and use the training data set as the input;

[0083] S722. Select the best feature, divide the data set into multiple subsets, and evaluate the quality of the division by calculating the Gini index of each feature;

[0084] The method for selecting the best feature is to calculate the information entropy of each feature. Information entropy is a metric for measuring the uncertainty of data, defined as H(D) = - ∑ p(x) log2 p(x), where x is the sample in the data set and p(x) is the probability of the sample occurring. Calculate the information gain Gain(D, A) of each feature, Gain(D, A) = H(D) - H(D|A), where A is the feature to be evaluated, H(D) is the information entropy of the data set, and H(D|A) is the conditional entropy, representing the uncertainty of the data set considering feature A. The formula for conditional entropy is H(D|A) = ∑p(a) H(D|A=a), where a is the value of feature A, p(a) is the probability of feature A taking value a, and H(D|A=a) is the conditional information entropy of the sample when feature A takes value a. Finally, select the feature with the maximum information gain as the best feature;

[0085] For each possible value a of feature A, divide the data set into subsets . Calculate the Gini index of each subset . Then calculate the weighted Gini index of feature A.

[0086] The formula for the weighted Gini index of feature A is: , where, , where, is the upper limit of the value, Represents the proportion of the cases where the feature A takes the value a.

[0087] Evaluating the quality of the partition depends on the value of the Gini index. When the value of the Gini index is less than the threshold, the evaluation result is good; otherwise, the evaluation result is bad.

[0088] When the evaluation result is bad, , when the evaluation result is good, ;

[0089] S723. Recursively execute S722 for each subset until the stopping condition is met;

[0090] Stopping condition: The number of samples in the subset is less than the threshold, and the range of the threshold depends on the severity of the violation that needs to be punished. For example, when lighter violations need to be punished, the threshold value is low, while when only serious violations need to be punished, the threshold is raised, specifically depending on the local penalty intensity for violations.

[0091] S724. Construct a decision tree and associate each node with the corresponding feature and splitting condition;

[0092] S73. Classify and judge the violation data;

[0093] S731. Input the feature vector x of the next violation data;

[0094] S732. Starting from the root node, gradually classify the data to the child nodes according to the features and splitting conditions of the nodes until reaching the leaf node, and the leaf node is the classification result of the new violation data.

[0095] S74. Generate a violation penalty decision based on the classification judgment result and the constraint conditions;

[0096] The constraint conditions include:

[0097] Constraint 1: Violation handling requirement constraint;

[0098] Constraint 2: Legal regulation constraint;

[0099] The constraint conditions of the violation handling requirements:

[0100] a. Handling timeliness: It is required to complete the handling of the violation within a certain time to ensure timely disposal;

[0101] b. Handling method selection: Specifies the available handling method options, such as fines, demerit points, educational courses, etc., which may vary according to the circumstances of the violation.

[0102] C. Appeal procedure: Specifies the appeal process, allowing violators to raise objections or appeals at different stages;

[0103] d. Handling record preservation: Require to record and store detailed information of the handling process for verification and auditing;

[0104] e. Penalty decision notice: Require the violator to be promptly notified of the penalty decision to ensure transparency and legality;

[0105] Constraints stipulated by laws:

[0106] a. Minimum legal penalty: The minimum legal penalty for certain violations to ensure a certain degree of punishment for illegal acts;

[0107] b. Legal penalty amount: The range of penalty amounts for different types of violations for decision-making within this range;

[0108] c. Legal handling method: Specify that specific types of violations must adopt specific handling methods, such as revoking the driving license;

[0109] d. Legal right of appeal: The right of appeal of the violator, including the procedures and conditions for raising objections or appeals;

[0110] e. Legal timeliness: The timeliness requirements for handling violations to ensure disposal within a reasonable time;

[0111] The objective function of the penalty decision for violations is:

[0112] ;

[0113] Among them, punishment_amount is the penalty amount and handling_method is the handling method.

[0114] The present invention can not only generate violation notices using the above method, but also automatically generate suggestions for penalty decisions for violations.

[0115] The violation notice generated by the present invention includes information such as the violation time, violation location, and violation type.

[0116] Embodiment 2: The computer device of the present invention may be a device including a processor and a memory, such as a single-chip microcomputer including a central processing unit. Moreover, when the processor executes the computer program stored in the memory, it implements the steps of the above-mentioned efficient traffic violation handling method based on cloud computing.

[0117] The so-called processor may be a Central Processing Unit (CPU), or 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, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0118] The memory may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices.

[0119] Embodiment 3: Embodiment of a computer-readable storage medium.

[0120] The computer-readable storage medium of the present invention may be any form of storage medium readable by the processor of a computer device, including but not limited to non-volatile memory, volatile memory, ferroelectric memory, etc. A computer program is stored on the computer-readable storage medium. When the processor of the computer device reads and executes the computer program stored in the memory, the steps of the above-mentioned efficient traffic violation processing method based on cloud computing can be implemented.

[0121] The computer program includes computer program code, which may be in the form of source code, object code, executable files, or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, mobile hard disks, magnetic disks, optical disks, computer memories, read-only memories (ROMs), random access memories (RAMs), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0122] Although the present invention has been described based on a limited number of embodiments, those skilled in the art in this technical field will understand that other embodiments can be conceived within the scope of the present invention thus described. In addition, it should be noted that the language used in this specification is mainly selected for readability and teaching purposes, rather than for the purpose of interpreting or limiting the subject matter of the present invention. Therefore, many modifications and variations will be obvious to those of ordinary skill in the art in this technical field without departing from the scope and spirit of the appended claims. For the scope of the present invention, the disclosure made of the present invention is illustrative rather than restrictive, and the scope of the present invention is defined by the appended claims.

Claims

1. An efficient traffic violation handling method based on cloud computing, characterized in that, It includes the following steps: S1. Obtain the traffic violation data and segment the traffic violation data to generate multiple segments containing the traffic violation data; S2. Extract traffic violation features according to the data segments; S3. Calculate the average value and standard deviation of specific features in each segment; S4. Calculate the specific feature difference value of the traffic violation according to the average value and standard deviation of the specific features in the segment; S5. Allocate the data segments to different computing nodes, and each computing node processes the data segments in parallel; S6. Aggregate the results of each data segment to obtain the analysis result; S7. Generate a traffic violation notice according to the analysis result, and the method includes the following steps: S71. Generate a traffic violation data training set from the analysis result; S72. Train a decision tree model based on the traffic violation data training set; S73. Classify and judge the traffic violation data, and the method includes the following steps: S731. Input the feature vector x of the next traffic violation data; S732. Starting from the root node, gradually classify the data to child nodes according to the features and splitting conditions of the nodes until reaching the leaf node, and the leaf node is the classification result of the new traffic violation data; S74. Generate a traffic violation penalty decision based on the classification and judgment result and the constraint conditions.

2. The efficient traffic violation handling method based on cloud computing according to claim 1, characterized in that, The calculation method of the specific feature difference value is: Among them, t represents the specific feature difference value; A represents data segment A, B represents data segment B, μ A represents the average value of data segment A, represents the standard deviation of data segment A; represents the standard deviation of data segment B; n represents the number of specific feature values in data segment A, and m represents the number of specific feature values in data segment B.

3. An efficient traffic violation handling method based on cloud computing according to claim 2, characterized in that, The method S7 for training a decision tree model based on the traffic violation data training set includes the following steps: S721. Initialize the root node and use the training data set as the input; S722. Select the best feature, divide the data set into multiple subsets, and evaluate the quality of the division by calculating the Gini index of each feature; S723. Recursively execute S722 for each subset until the stopping condition is met; S724. Construct a decision tree and associate each node with the corresponding feature and splitting condition.

4. An efficient traffic violation handling method based on cloud computing according to claim 3, characterized in that, The constraint conditions include: Constraint 1: Constraint on traffic violation handling requirements; Constraint 2: Constraint on legal regulations.

5. The efficient traffic violation handling method based on cloud computing according to claim 1, wherein, The constraint conditions for traffic violation handling requirements are: a. Handling timeliness; b. Handling method selection; c. Appeal procedure; d. Preservation of handling records; e. Notice of penalty decision; The constraint conditions for legal regulations are: a. Minimum legal penalty; b. Legal penalty amount; c. Legal handling method; d. Legal right of appeal; e. Legal timeliness.

6. An electronic device, characterized in that, It includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of an efficient traffic violation handling method based on cloud computing as described in claims 1-5.

7. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, it implements an efficient traffic violation handling method based on cloud computing as described in claims 1-5.

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