Prompt automatic optimization method applied to traffic rule model

Abstract

The invention discloses an automatic prompt optimization method applied to a traffic rule model, and the method carries out the automatic optimization of the input prompt of the traffic rule model through a particle swarm optimization algorithm, achieves the comprehensive improvement of prediction accuracy, optimization efficiency, labor cost and system robustness, and provides a technical support for the development of an intelligent traffic system. The innovative method not only effectively solves the problems in the prior art, but also brings significant performance improvement for traffic data analysis and prediction, can dynamically adjust the prompt according to the change of real-time traffic data, ensures that the model is always in the optimal operation state, realizes real-time response and optimization of the traffic data, and improves the real-time performance of the traffic data through an automatic prompt optimization method. According to the method, the optimal or close-to-optimal prompt combination can be accurately found, the efficiency is remarkably improved through the automatic optimization process, the time and energy of manual intervention are reduced, and traffic data analysis and prediction are more economical and efficient.

Description

Technical Field

[0001] The present invention relates to the technical field of computer data processing, and particularly relates to a method for automatically optimizing prompts applied to a traffic rule model. Background Art

[0002] In modern intelligent transportation systems, traffic rule models are widely used to analyze and predict traffic conditions to achieve more efficient traffic management and planning. These models process a large amount of traffic data (such as traffic flow, traffic rules, violation records, etc.) to provide decision-making support for traffic management departments. However, the design of the input prompts for the models is crucial for the model performance, and reasonable prompts can significantly improve the prediction accuracy and efficiency of the models.

[0003] Currently, the design of prompts usually relies on manual experience and has the following main problems:

[0004] Manually designing prompts requires a large amount of time and effort, and the process is cumbersome and complex. The accuracy of manually designed prompts is easily affected by the subjective factors of the designers, resulting in the inability to guarantee the accuracy and consistency of the model output results. In the prior art, the process of optimizing prompts is difficult to automate and cannot meet the requirements of analyzing large-scale and complex traffic data. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method for automatically optimizing prompts applied to a traffic rule model to solve the technical problems in the above background art.

[0006] To solve the above technical problems, the technical solution of the present invention is as follows:

[0007] A method for automatically optimizing prompts applied to a traffic rule model includes the following steps:

[0008] (1) Data preprocessing, using the Pandas library in Python for data preprocessing, cleaning, denoising, and normalizing the original traffic rule data to ensure data quality;

[0009] (2) Generating initial prompts, generating an initial prompt set based on the preprocessed traffic rule data;

[0010] (3) Setting optimization objectives, the accuracy rate and running time of the model output;

[0011] (4) Selecting the particle swarm optimization algorithm, selecting the particle swarm optimization algorithm to optimize the initial prompts;

[0012] (5) Iterative optimization: Use the particle swarm optimization algorithm to iteratively optimize the initial prompt, evaluate the quality of the model output after each iteration, and make adjustments according to the optimization objective.

[0013] Furthermore, in step 2, the initial prompt is generated through templates or simple rules.

[0014] Furthermore, in step 3, the function defined for the optimization objective is:

[0015] f(x) = Accuracy(x) + β · Time(x)

[0016] where α and β are weight parameters, Accuracy(x) is the accuracy of the model output, and Time(x) is the running time.

[0017] Furthermore, the particle swarm optimization algorithm includes the following steps:

[0018] a. Initialize the particle swarm. Each particle represents a prompt. Initialize the position x i and velocity v i ;

[0019]

[0020] where rand is a random number in the range [0, 1];

[0021] b. Fitness evaluation

[0022] Calculate the fitness value of each particle Evaluate the performance of each particle according to the optimization objective function;

[0023] c. Update the individual extreme value and the global extreme value. Update the individual extreme value (pbest) of each particle and the global extreme value (gbest) of the group;

[0024]

[0025] d. Update the velocity and position

[0026] Update the velocity and position of each particle according to the interaction rules between particles:

[0027]

[0028] where w is the inertia weight, c1 and c2 are learning factors, and r1 and r2 are random numbers in the range [0, 1];

[0029] e. Iteration

[0030] Repeat the steps of fitness evaluation, individual extreme value and global extreme value update, velocity and position update until the preset number of iterations or fitness threshold is reached;

[0031] f. Optimization result output

[0032] Finally, output the optimal prompt for practical application in the traffic rule model.

[0033] The advantages of the present invention are as follows:

[0034] The method of the present invention automatically optimizes the input prompt of the traffic rule model through the particle swarm optimization algorithm, achieving an overall improvement in prediction accuracy, optimization efficiency, labor cost and system robustness, providing technical support for the development of intelligent transportation systems. This innovative method not only effectively solves the problems in the prior art, but also brings significant performance improvements to traffic data analysis and prediction. It can dynamically adjust the prompt according to the changes in real-time traffic data to ensure that the model is always in the best operating state, realizing real-time response and optimization of traffic data. Through the automated prompt optimization method, it can accurately find the optimal or near-optimal prompt combination, thereby improving the prediction accuracy of the traffic rule model. Description of the Drawings

[0035] Figure 1 It is a schematic flowchart of the present invention. Detailed Embodiments

[0036] The following further describes the detailed embodiments of the present invention with reference to the drawings. It should be noted here that the description of these embodiments is for helping to understand the present invention, but does not constitute a limitation to the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0037] A method for automatically optimizing prompts applied to a traffic rule model includes the following steps:

[0038] 1. Data preprocessing

[0039] Clean, denoise and normalize the original traffic rule data to ensure data quality. Use the Pandas library in Python for data preprocessing.

[0040] 2. Initial Prompt generation

[0041] Based on the preprocessed traffic rule data, generate an initial prompt set. The initial prompt can be generated through templates or simple rules, for example: "Predict the violation situation in the next hour based on the traffic rule data of a certain section of the road".

[0042] 3. Optimization objective setting

[0043] Set the optimization objective, such as the accuracy of the model output, running time, etc. The objective function can be defined as:

[0044] f(x) = Accuracy(x) + β·Time(x)

[0045] Where α and β are weight parameters, Accuracy(x) is the accuracy of the model output, and Time(x) is the running time.

[0046] 4. Particle swarm optimization algorithm selection

[0047] Select the particle swarm optimization algorithm (PSO) to optimize the initial prompt.

[0048] 5. Iterative optimization

[0049] Use the particle swarm optimization algorithm to iteratively optimize the initial prompt, evaluate the quality of the model output after each iteration, and adjust according to the optimization objective.

[0050] The particle swarm optimization algorithm is as follows:

[0051] The particle swarm optimization algorithm simulates the foraging process of a bird flock and finds the optimal solution through information sharing among individuals. The algorithm steps are as follows:

[0052] 1. Initialize the particle swarm. Each particle represents a prompt, and initialize the position xi and velocity vi of the particle;

[0053]

[0054] Where rand is a random number in the range of [0, 1];

[0055] 2. Fitness evaluation

[0056] Calculate the fitness value of each particle Evaluate the performance of each particle according to the optimization objective function;

[0057] 3. Update individual extremum and global extremum

[0058] Update the individual extremum (pbest) of each particle and the global extremum (gbest) of the group:

[0059]

[0060] 4. Update velocity and position

[0061] Update the velocity and position of each particle according to the interaction rules among particles:

[0062]

[0063] Among them, w is the inertia weight, c1 and c2 are learning factors, and r1 and r2 are random numbers within the range of [0, 1].

[0064] 5. Iteration

[0065] Repeat the steps of fitness evaluation, individual extreme value and global extreme value update, velocity and position update until the preset number of iterations or fitness threshold is reached.

[0066] 6. Output of Optimization Results

[0067] Finally, the optimal prompt is output for practical application in the traffic rule model.

[0068] The present invention has the following advantages:

[0069] Improve prediction accuracy

[0070] Through the automated prompt optimization method, the optimal or near-optimal prompt combination can be accurately found, thereby improving the prediction accuracy of the traffic rule model. This eliminates the errors and subjective biases that may occur in the process of manually designing prompts, ensuring that the model output is more reliable and accurate.

[0071] Enhance optimization efficiency

[0072] The particle swarm optimization algorithm has the ability of global search and fast convergence characteristics, and can find the optimization solution in a short time. Compared with the traditional method of manually designing prompts, the automated optimization process significantly improves the efficiency and reduces the time and effort of manual intervention.

[0073] Reduce labor costs

[0074] The present invention reduces the dependence on professional knowledge and experience and reduces the cost of manually designing prompts. This not only saves human resources but also improves the overall work efficiency, making traffic data analysis and prediction more economical and efficient.

[0075] Provide stable and consistent optimization results

[0076] The particle swarm optimization algorithm provides a stable and consistent optimization method by simulating swarm intelligence. It avoids the problem of inconsistent results caused by individual differences in the manual design process, ensuring the stability and consistency of the model in different scenarios.

[0077] Adapt to complex data structures

[0078] With the continuous increase in traffic data volume and the complexity of data structures, automated optimization methods can better adapt to complex and changing data environments. The particle swarm optimization algorithm performs excellently in dealing with large-scale and complex data, improving the adaptability and performance of the model.

[0079] Support the development of intelligent transportation systems

[0080] By optimizing the input prompts of the traffic rule model, the application effect and efficiency of the model are improved, providing strong technical support for the development of intelligent transportation systems. The optimized model can more accurately predict traffic flow, analyze illegal behaviors, and make traffic management decisions, enhancing the overall intelligence level of the traffic system.

[0081] Enhance system robustness

[0082] The automated prompt optimization method makes the system more robust in the face of different types of traffic rule data through continuous adjustment and optimization. Whether it is data quality fluctuations or data volume changes, it can ensure the stable operation of the system and high-quality output.

[0083] Achieve dynamic adjustment

[0084] The method of the present invention can dynamically adjust the prompt according to the changes in real-time traffic data to ensure that the model is always in the best operating state. This feature is particularly important in practical applications and can respond to changes in traffic conditions in real-time, improving the flexibility and real-time nature of traffic management and prediction.

[0085] The present invention automatically optimizes the input prompts of the traffic rule model through the particle swarm optimization algorithm, achieving a comprehensive improvement in prediction accuracy, optimization efficiency, labor cost, and system robustness, providing technical support for the development of intelligent transportation systems. This innovative method not only effectively solves the problems in the prior art but also brings significant performance improvements to traffic data analysis and prediction.

[0086] The embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, without departing from the principles and spirit of the present invention, various changes, modifications, substitutions, and variations to these embodiments still fall within the protection scope of the present invention.

Claims

1. A prompt automatic optimization method applied to a traffic rule model, characterized in that It includes the following steps: (1) Data preprocessing: Use the Pandas library in Python to perform data preprocessing on the original traffic rule data, including cleaning, denoising, and normalization to ensure data quality; (2) Initial Prompt generation: Generate an initial prompt set based on the preprocessed traffic rule data; (3) Optimization objective setting: The accuracy and running time of the model output; (4) Particle swarm optimization algorithm selection: Select the particle swarm optimization algorithm to optimize the initial prompt; (5) Iterative optimization: Use the particle swarm optimization algorithm to iteratively optimize the initial prompt, evaluate the quality of the model output after each iteration, and adjust according to the optimization objective.

2. The prompt automatic optimization method applied to the traffic rule model according to claim 1, wherein: In step 2, the initial prompt is generated through templates or simple rules.

3. The prompt automatic optimization method applied to the traffic rule model according to claim 2, characterized in that: In step 3, the function for setting the optimization objective is defined as: f(x) = Accuracy(x) + β·Time(x) where α and β are weight parameters, Accuracy(x) is the accuracy of the model output, and Time(x) is the running time.

4. The prompt automatic optimization method applied to the traffic rule model according to claim 2, characterized in that, The particle swarm optimization algorithm includes the following steps: a. Initialize the particle swarm, where each particle represents a prompt, and initialize the position x i and velocity v i ; where rand is a random number in the range [0, 1]; b. Fitness evaluation Calculate the fitness value of each particle Evaluate the performance of each particle according to the optimization objective function; c. Update of individual extreme value and global extreme value: Update the individual extreme value (pbest) of each particle and the global extreme value (gbest) of the population; d. Update of velocity and position Update the velocity and position of each particle according to the interaction rules between particles: where w is the inertia weight, c1 and c2 are learning factors, and r1 and r2 are random numbers in the range [0, 1]; e. Iteration Repeat the steps of fitness evaluation, update of individual extreme value and global extreme value, and update of velocity and position until the preset number of iterations or fitness threshold is reached; f. Output of optimization result Finally, output the optimal prompt for practical application in the traffic rule model.

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