Traffic equipment data processing method and device

Abstract

According to the traffic equipment data processing method and device provided by the embodiment of the invention, a multi-dimensional data preprocessing mechanism is innovatively constructed, and standardized representation of characteristics such as equipment types and geographic positions is realized through abnormal value identification and one-hot coding. And designing a classification model based on a multi-label random forest, and establishing a feature division and decision tree construction strategy in combination with an information gain criterion and Gini coefficient optimization. And a voting integration mechanism is introduced, and accurate recommendation of illegal configuration is realized through fusion of multi-tree classification results. According to the method, the defects of the traditional technology in the aspects of data processing, classification modeling, result generation and the like are effectively overcome, and the accuracy and reliability of traffic equipment data processing are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and specifically to a method and device for processing traffic equipment data. Background Art

[0002] Existing methods for processing traffic equipment data have significant shortcomings. Traditional systems lack systematicity in processing feature data, making it difficult to effectively clean and encode multidimensional data such as equipment type, geographic location, installation location, and captured lanes, thus affecting the accuracy of recommendations.

[0003] Furthermore, existing technologies face bottlenecks in building classification models. Most systems fail to fully leverage the advantages of random forests and lack a feature partitioning mechanism based on information gain and the Gini coefficient, resulting in suboptimal multi-label classification results.

[0004] Existing systems have technical shortcomings in generating recommendation results. They lack the ability to effectively integrate decision tree classification results, making it difficult to accurately recommend illegal configurations through voting mechanisms, which hinders practical application effectiveness. Addressing these issues is crucial for improving traffic equipment data processing capabilities. Summary of the Invention

[0005] In response to the problems in the existing technology, the present application provides a method and device for processing traffic equipment data, which can effectively solve the shortcomings of traditional technology in data processing, classification modeling and result generation, and significantly improve the accuracy and reliability of traffic equipment data processing.

[0006] In order to solve at least one of the above problems, the present application provides the following technical solutions: In a first aspect, the present application provides a method for processing traffic equipment data, comprising: Acquire equipment type data from a traffic equipment monitoring platform, acquire geographic location data from a geographic information system, acquire installation location data and shooting lane data from a device deployment system, clean the equipment type data, the geographic location data, the installation location data, and the shooting lane data, remove missing records, identify and remove outliers using a box plot method, construct an equipment type feature set, perform one-hot encoding conversion on the equipment type feature set, construct a geographic location feature set, perform one-hot encoding conversion on the geographic location feature set, construct an installation location feature set and a shooting lane feature set, perform one-hot encoding conversion on the installation location feature set and the shooting lane feature set, and divide the encoded feature set into a training sample set and a test sample set according to time series information; Constructing a multi-label random forest classifier based on the training sample set, performing random sampling with replacement on the training sample set to generate multiple sub-datasets, randomly selecting feature subsets from the sub-datasets, constructing a decision tree based on the feature subsets, performing feature partitioning on tree nodes using an information gain criterion, calculating the Gini coefficient of each node, selecting the feature with the smallest Gini coefficient as the splitting feature, iteratively constructing the decision tree until a preset depth is reached, and combining the constructed decision trees to generate an illegal configuration recommendation model; The test sample set is input into the illegal configuration recommendation model, multi-label classification is performed on each test sample based on the illegal configuration recommendation model, the classification results of each decision tree are counted, the final illegal configuration recommendation result is determined by voting, and the illegal configuration recommendation result is output to the traffic equipment configuration system.

[0007] Furthermore, the method further includes: obtaining equipment type data from a traffic equipment monitoring platform, obtaining geographic location data from a geographic information system, and obtaining installation location data and captured lane data from a device deployment system based on a data interface, aligning the equipment type data, the geographic location data, the installation location data, and the captured lane data according to timestamps, constructing a data association table, establishing a data quality assessment indicator system, and calculating a completeness score for each record in the data association table; Records with integrity scores below the threshold are removed, and the quartile interval is constructed. The upper quartile and lower quartile of each feature in the data association table are calculated. The outlier judgment boundary is determined based on the upper quartile and the lower quartile. Data exceeding the outlier judgment boundary is marked as outliers and removed to generate a cleaned data set.

[0008] Furthermore, the method further includes: classifying and arranging the cleaned data set according to device type, geographic location, installation location, and shooting lane, respectively constructing a device type feature set, a geographic location feature set, an installation location feature set, and a shooting lane feature set, performing one-hot encoding conversion on the device type feature set to generate a device type encoding matrix, performing one-hot encoding conversion on the geographic location feature set to generate a geographic location encoding matrix, and performing one-hot encoding conversion on the installation location feature set and the shooting lane feature set to generate an installation location encoding matrix and a shooting lane encoding matrix, respectively; The device type coding matrix, the geographic location coding matrix, the installation location coding matrix and the shooting lane coding matrix are spliced ​​in the sample dimension to generate a feature fusion matrix, the feature fusion matrix is ​​normalized, and the feature fusion matrix is ​​divided into a training sample set and a test sample set according to a preset ratio based on time series information.

[0009] Furthermore, the method further includes: constructing a multi-label random forest classifier based on the training sample set, setting parameters for the number of trees, maximum depth, and minimum number of split samples of the classifier, performing random sampling with replacement on the training sample set to generate multiple sub-datasets, calculating an importance score for each feature, sorting the features according to the importance score, randomly selecting a feature subset from the sub-datasets according to a preset feature number threshold, and constructing an initial node of a decision tree based on the feature subset; The sub-dataset is input into the initial node, the node is feature-partitioned using the information gain criterion, the Gini coefficient of each candidate feature is calculated, the optimal split feature is selected by comparing the Gini coefficients, the data set is divided into a left child node data set and a right child node data set according to the optimal split feature, and the Gini coefficients of the left child node data set and the right child node data set are calculated.

[0010] Furthermore, the method further includes: calculating the Gini coefficients of candidate features for the left child node data set and the right child node data set, selecting the feature with the smallest Gini coefficient as a split feature, performing binary classification on the node data based on the split feature, repeating the node splitting process until a preset depth is reached or the number of node samples is less than a minimum number of split samples, calculating the sample distribution probability of each category for each leaf node, and using the sample distribution probability as the prediction output of the leaf node; The prediction outputs of multiple decision trees are combined to construct a random forest voting matrix. Based on the voting matrix, the predicted probability distribution of each sample in each violation category is calculated. A violation category probability threshold is set, and categories above the violation category probability threshold are marked as positive samples to generate a violation configuration recommendation model.

[0011] Furthermore, the method further includes: inputting the test sample set into the illegal configuration recommendation model, performing feature matching on each test sample in each decision tree, dividing the sample into corresponding leaf nodes according to the sample feature value, extracting the predicted probability distribution stored in the leaf node, calculating the predicted probability value for each illegal category, constructing a prediction result matrix based on the predicted probability value, and combining the prediction result matrix on the decision tree dimension; The classification results of each decision tree for each test sample in each violation category are counted, a decision tree voting statistics table is constructed, the number of decision trees predicted as positive samples for each violation category is calculated, the number of decision trees is divided by the total number of decision trees to obtain the category prediction probability, the category prediction probability is normalized, and a standardized prediction probability matrix is ​​generated.

[0012] Furthermore, the method further includes: setting voting weight coefficients based on the standardized prediction probability matrix, performing weighted voting on the prediction results of each decision tree, calculating weighted voting scores for each illegal category, comparing the weighted voting scores with a preset illegal category threshold, marking categories above the illegal category threshold as recommended configuration items, sorting the recommended configuration items in descending order according to the weighted voting scores, and generating illegal configuration recommendation results; The illegal configuration recommendation results are encapsulated into a standard data format, a data transmission interface is constructed, a communication connection with the traffic equipment configuration system is established, the illegal configuration recommendation results are pushed to the traffic equipment configuration system in real time through the data transmission interface, a push log is recorded, and a recommendation result history is saved.

[0013] In a second aspect, the present application provides a traffic equipment data processing device, comprising: a data set determination module, configured to obtain equipment type data from a traffic equipment monitoring platform, obtain geographic location data from a geographic information system, obtain installation location data and shooting lane data from a device deployment system, clean the equipment type data, the geographic location data, the installation location data, and the shooting lane data, remove missing records, identify and remove outliers using a box plot method, construct an equipment type feature set, perform one-hot encoding conversion on the equipment type feature set, construct a geographic location feature set, perform one-hot encoding conversion on the geographic location feature set, construct an installation location feature set and a shooting lane feature set, perform one-hot encoding conversion on the installation location feature set and the shooting lane feature set, and divide the encoded feature set into a training sample set and a test sample set according to time series information; a model training module for constructing a multi-label random forest classifier based on the training sample set, performing random sampling with replacement on the training sample set to generate multiple sub-datasets, randomly selecting feature subsets from the sub-datasets, constructing a decision tree based on the feature subsets, performing feature partitioning on the tree nodes using an information gain criterion, calculating the Gini coefficient of each node, selecting the feature with the smallest Gini coefficient as the splitting feature, iteratively constructing the decision tree until a preset depth is reached, and combining the constructed decision trees to generate an illegal configuration recommendation model; The recommended configuration module is used to input the test sample set into the illegal configuration recommendation model, perform multi-label classification on each test sample based on the illegal configuration recommendation model, count the classification results of each decision tree, determine the final illegal configuration recommendation result by voting, and output the illegal configuration recommendation result to the traffic equipment configuration system.

[0014] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the traffic equipment data processing method when executing the program.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the traffic equipment data processing method when executed by a processor.

[0016] In a fifth aspect, the present application provides a computer program product, comprising a computer program / instruction, which implements the steps of the traffic equipment data processing method when executed by a processor.

[0017] As can be seen from the above technical solution, the present application provides a method and device for processing traffic equipment data. By innovatively constructing a multi-dimensional data preprocessing mechanism, it realizes the standardized representation of features such as equipment type and geographical location through outlier identification and one-hot encoding. A classification model based on multi-label random forest is designed, and a feature partitioning and decision tree construction strategy is established by combining the information gain criterion and Gini coefficient optimization. A voting integration mechanism is introduced to achieve accurate recommendation of illegal configurations through the fusion of multi-tree classification results. This method effectively solves the shortcomings of traditional technologies in data processing, classification modeling, and result generation, and significantly improves the accuracy and reliability of traffic equipment data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 Schematic diagram of the flow of the traffic equipment data processing method in the embodiment of the present application; Figure 2 This is a structural diagram of a traffic equipment data processing device in an embodiment of the present application; Figure 3 Schematic diagram of the structure of the electronic device in the embodiment of the present application.

[0020] Reference numerals: Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver program storage unit 9144, antenna 9111, speaker 9131, microphone 9132. DETAILED DESCRIPTION

[0021] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0022] The acquisition, storage, use, and processing of data in the technical solution of this application comply with relevant laws and regulations.

[0023] Taking into account the problems existing in the prior art, the present application provides a method and device for processing traffic equipment data. By innovatively constructing a multi-dimensional data preprocessing mechanism, the standardized representation of features such as equipment type and geographical location is achieved through outlier identification and one-hot encoding. A classification model based on multi-label random forest is designed, and a feature partitioning and decision tree construction strategy is established by combining the information gain criterion and Gini coefficient optimization. A voting integration mechanism is introduced to achieve accurate recommendations for illegal configurations by fusing multi-tree classification results. This method effectively solves the shortcomings of traditional technologies in data processing, classification modeling, and result generation, and significantly improves the accuracy and reliability of traffic equipment data processing.

[0024] In order to effectively solve the deficiencies of traditional technologies in data processing, classification modeling and result generation, and significantly improve the accuracy and reliability of traffic equipment data processing, this application provides an embodiment of a traffic equipment data processing method, see Figure 1 , the traffic equipment data processing method specifically includes the following contents: Step S101: Obtain device type data from a traffic equipment monitoring platform, obtain geographic location data from a geographic information system, obtain installation location data and shooting lane data from a device deployment system, clean the device type data, the geographic location data, the installation location data, and the shooting lane data, remove missing records, identify and remove outliers using a box plot method, construct a device type feature set, perform one-hot encoding conversion on the device type feature set, construct a geographic location feature set, perform one-hot encoding conversion on the geographic location feature set, construct an installation location feature set and a shooting lane feature set, perform one-hot encoding conversion on the installation location feature set and the shooting lane feature set, and divide the encoded feature set into a training sample set and a test sample set according to time series information; Optionally, this embodiment addresses the issues of unstable data quality and insufficient feature expression in traffic equipment data processing by innovatively designing a feature processing solution based on multi-source data fusion. This embodiment first constructs a data acquisition framework and achieves comprehensive capture of equipment status through multi-level data cleaning. The system designs a data quality assessment formula: Quality_Score = α Completeness +β Consistency - γ × Anomaly_Factor, where Completeness represents data completeness, Consistency represents data consistency, Anomaly_Factor represents the anomaly factor, and α, β, and γ are dynamic adjustment coefficients. In traffic equipment configuration scenarios, this multi-dimensional quality assessment method can effectively ensure data availability.

[0025] This embodiment deeply optimizes the data collection strategy. In view of the multi-source nature of traffic data, a collection mechanism based on time alignment is designed. Accurate timestamps are used to achieve synchronous collection of data from different sources. Special attention is paid to the timeliness of the data. When a data stream interruption or delay is detected, the system performs supplementary collection through a backup channel. For example, when processing the configuration data of urban electronic police equipment, multi-source collection can simultaneously obtain key information such as device type, geographic location, installation location, and shooting lane, which is crucial for building a complete configuration feature.

[0026] This embodiment innovatively implements an outlier processing mechanism. In response to the abnormal characteristics of different types of data, the system constructs a boxplot-based cleaning framework. By analyzing the distribution characteristics of the data, accurate identification of outliers is achieved. Special attention is paid to the rationality of the data, and the reliability of the cleaning results is ensured by designing an adaptive threshold strategy. This statistical-based cleaning method can effectively maintain the distribution characteristics of the data. This embodiment adopts the outlier identification formula: Outlier_Boundary = Q ± k×IQR, where Q represents the quartile, IQR represents the interquartile range, and k is the adjustment coefficient.

[0027] This embodiment deeply optimizes the feature encoding strategy. The system constructs a feature representation framework based on one-hot encoding, achieving standardized representation of various types of information through feature classification. Special attention is paid to encoding uniqueness, and a mapping dictionary mechanism is designed to improve the accuracy of encoding conversion. This comprehensive encoding method provides a standardized data foundation for subsequent analysis.

[0028] This embodiment achieves structured data organization through feature partitioning. The system builds a time-series-based partitioning framework, combining it with multi-dimensional feature encoding for comprehensive modeling. Particular attention is paid to the balance of the partitioning, and a stratified sampling mechanism is established to achieve a reasonable partitioning of the dataset. This systematic organization scheme provides standardized data support for model training.

[0029] The innovative design of this embodiment not only addresses data quality issues encountered in traditional methods but also establishes a sustainable preprocessing framework. Through multi-level data cleaning and feature construction, the system is able to extract effective configuration features from complex raw data. This multi-source fusion-based processing mechanism ensures that the system maintains effective feature expression capabilities even in complex traffic scenarios. This intelligent preprocessing solution significantly improves the accuracy of traffic equipment configuration recommendations.

[0030] This embodiment achieves intelligent analysis and upgrades for equipment configuration by establishing a complete data processing chain. The system dynamically adjusts processing strategies based on real-time data quality, avoiding the limitations of traditional fixed-rule solutions. Multi-dimensional data cleaning and feature construction significantly improve the quality and reliability of feature representation, providing reliable data support for subsequent configuration recommendations. This intelligent processing mechanism demonstrates strong adaptability and optimization effects in traffic equipment configuration.

[0031] This embodiment not only improves data processing accuracy but also establishes a continuously evolving preprocessing system through continuous strategy optimization and performance analysis. This optimization mechanism, based on real-time feedback, ensures that the system can continuously improve as traffic scenarios change, providing increasingly accurate feature representations for subsequent recommendations. In practical applications, this self-optimization mechanism has significantly improved the system's long-term service quality and recommendation effectiveness, providing reliable technical support for traffic equipment management.

[0032] Step S102: constructing a multi-label random forest classifier based on the training sample set, performing random sampling with replacement on the training sample set to generate multiple sub-datasets, randomly selecting feature subsets from the sub-datasets, constructing a decision tree based on the feature subsets, performing feature partitioning on the tree nodes using the information gain criterion, calculating the Gini coefficient of each node, selecting the feature with the smallest Gini coefficient as the splitting feature, iteratively constructing the decision tree until a preset depth is reached, and combining the constructed decision trees to generate an illegal configuration recommendation model; Optionally, this embodiment addresses the issues of complex feature associations and unstable classification results in traffic equipment data processing by innovatively designing a classification optimization solution based on multi-label random forests. This embodiment first constructs a classifier framework to achieve accurate identification of violation types through multi-level feature selection. The system designs a feature importance evaluation formula: Feature_Score = α Information_Gain +β Gini_Index, where Information_Gain represents the information gain value, Gini_Index represents the Gini coefficient, and α and β are dynamic adjustment coefficients. In traffic equipment configuration scenarios, this multi-dimensional feature evaluation method can effectively improve classification accuracy.

[0033] This embodiment deeply optimizes the sampling strategy. In view of the diversity of traffic equipment configuration data, a sampling mechanism based on the self-service method is designed. Through random sampling with replacement, full utilization of training data is achieved. Special attention is paid to the representativeness of the samples. When an imbalance in the category distribution is detected, the system will balance the samples through stratified sampling. For example, when dealing with illegal configurations on different road sections, random sampling can effectively balance the number of samples of various types of violations, including running red lights, speeding, illegal parking, and other types of violations. This is crucial to improving the generalization ability of the model.

[0034] This embodiment innovatively implements a feature selection mechanism. In response to the feature complexity of traffic scenarios, the system constructs a feature selection framework based on random subspaces. By sorting the feature importance, accurate screening of key features is achieved. Special attention is paid to the relevance of features, and the diversity of feature combinations is ensured by designing feature subset strategies. This feature processing method based on random selection can effectively improve the robustness of the model. This embodiment adopts the Gini coefficient calculation formula: Gini = 1-∑(pi^2), where pi represents the proportion of samples in the i-th category.

[0035] This embodiment deeply optimizes the tree node splitting strategy. The system constructs a feature partitioning framework based on information gain and accurately selects splitting features through Gini coefficient calculation. It pays special attention to node purity and improves the effectiveness of splitting by designing a dynamic threshold mechanism. This comprehensive splitting method provides reliable technical support for decision tree construction.

[0036] This example achieves deep growth of decision trees through iterative optimization. The system constructs a termination framework based on a preset depth and controls growth in conjunction with node purity. Special attention is paid to tree complexity, and a pruning mechanism is established to effectively control overfitting. This systematic growth scheme provides stable model support for classification tasks.

[0037] The innovative design of this embodiment not only solves the classification optimization issues inherent in traditional methods but also establishes a continuously optimized recommendation framework. Through multi-level feature selection and tree construction, the system is able to learn effective violation patterns from complex traffic data. This random forest-based classification mechanism ensures that the system maintains effective recommendation capabilities in diverse traffic scenarios. This intelligent optimization solution significantly improves the accuracy and reliability of recommendations for traffic equipment configuration.

[0038] This embodiment achieves intelligent recommendation upgrades for traffic equipment by establishing a complete classification and processing chain. The system dynamically adjusts optimization strategies based on real-time classification results, avoiding the limitations of traditional fixed model solutions. Through multi-dimensional model optimization and integration, the quality and reliability of recommendations are significantly improved, providing reliable decision support for traffic equipment configuration. This intelligent classification mechanism demonstrates strong adaptability and optimization effects in traffic violation analysis.

[0039] This embodiment not only improves the accuracy of recommendations but also establishes a continuously evolving classification system through continuous strategy optimization and performance analysis. This optimization mechanism, based on real-time feedback, ensures that the system can continuously improve as traffic patterns change, providing increasingly accurate recommendations for device configuration. In practical applications, this self-optimization mechanism has significantly improved the system's long-term service quality and recommendation effectiveness, providing reliable technical support for traffic management.

[0040] Step S103: Input the test sample set into the illegal configuration recommendation model, perform multi-label classification on each test sample based on the illegal configuration recommendation model, count the classification results of each decision tree, determine the final illegal configuration recommendation result by voting, and output the illegal configuration recommendation result to the traffic equipment configuration system.

[0041] Optionally, this embodiment addresses the issues of unstable model predictions and insufficient reliability of results in traffic equipment data processing by innovatively designing a prediction optimization solution based on integrated voting. This embodiment first constructs a prediction evaluation framework to achieve accurate prediction of violation types through multi-level result statistics. The system designs a voting weight evaluation formula: Vote_Weight = α Tree_Accuracy +βClass_Confidence, where Tree_Accuracy represents the decision tree accuracy, Class_Confidence represents the class confidence, and α and β are dynamic adjustment coefficients. In traffic equipment configuration scenarios, this multi-dimensional voting evaluation method can effectively improve the reliability of recommendations.

[0042] This embodiment deeply optimizes the prediction strategy. In view of the complexity of traffic equipment configuration scenarios, a multi-label classification mechanism is designed. Through the independent prediction of each decision tree, a comprehensive evaluation of the test samples is achieved. Special attention is paid to the stability of the prediction. When fluctuations in the prediction results are detected, the system will optimize the results through an integrated learning method. For example, when dealing with illegal configurations at urban traffic checkpoints, multi-label classification can simultaneously identify the monitoring needs of multiple violations, including speeding, running red lights, illegal lane changes, and other dimensions. This is crucial to improving the pertinence of equipment configuration.

[0043] This embodiment innovatively implements a result statistics mechanism. In view of the predictive characteristics of multiple decision trees, the system constructs a voting-based statistical framework. Through weight calculation, accurate summary of classification results is achieved. Special attention is paid to the consistency of the results, and the representativeness of the voting results is ensured by designing a weighting strategy. This integration-based statistical method can effectively improve the accuracy of recommendations. This embodiment adopts the weighted voting calculation formula: Final_Score = ∑(wi×vi) / ∑wi, where wi represents the weight of the i-th decision tree and vi represents its voting result.

[0044] This embodiment deeply optimizes the recommendation output strategy. The system builds a recommendation generation framework based on voting results and uses threshold screening to precisely control the recommendation results. Special attention is paid to the practicality of recommendations, and a dynamic threshold mechanism is designed to improve recommendation accuracy. This comprehensive output method provides reliable decision support for device configuration.

[0045] This embodiment achieves reliable output of results through system integration. The system builds a data transmission framework based on standard interfaces and integrates real-time push notifications for result distribution. Special attention is paid to data integrity, and a logging mechanism is established to effectively track the recommendation process. This systematic output solution provides high-quality service support for configuration management.

[0046] The innovative design of this embodiment not only solves the prediction optimization problems of traditional methods but also establishes a sustainable optimization recommendation framework. Through multi-level result statistics and voting optimization, the system is able to extract reliable configuration solutions from complex prediction results. This integrated recommendation mechanism ensures that the system maintains effective recommendation capabilities in diverse traffic scenarios. In traffic equipment configuration, this intelligent optimization solution significantly improves the accuracy and reliability of recommendations.

[0047] This embodiment achieves intelligent configuration upgrades for traffic equipment by establishing a complete recommendation processing chain. The system dynamically adjusts optimization strategies based on real-time prediction results, avoiding the limitations of traditional fixed-rule solutions. Through multi-dimensional result optimization and integration, the quality and reliability of recommendations are significantly improved, providing reliable decision support for traffic equipment management. This intelligent recommendation mechanism demonstrates strong adaptability and optimization effects in traffic violation analysis.

[0048] This embodiment not only improves the accuracy of recommendations but also establishes a continuously evolving recommendation system through continuous strategy optimization and performance analysis. This optimization mechanism, based on real-time feedback, ensures that the system can continuously improve as traffic patterns change, providing increasingly accurate recommendations for device configuration. In practical applications, this self-optimization mechanism has significantly improved the system's long-term service quality and recommendation effectiveness, providing reliable technical support for traffic management.

[0049] As can be seen from the above description, the traffic equipment data processing method provided in the embodiment of the present application can realize the standardized representation of features such as equipment type and geographical location by innovatively constructing a multi-dimensional data preprocessing mechanism, through outlier identification and one-hot encoding. A classification model based on multi-label random forest is designed, and a feature partitioning and decision tree construction strategy is established by combining the information gain criterion and Gini coefficient optimization. A voting integration mechanism is introduced to achieve accurate recommendation of illegal configurations by fusing the classification results of multiple trees. This method effectively solves the shortcomings of traditional technologies in data processing, classification modeling and result generation, and significantly improves the accuracy and reliability of traffic equipment data processing.

[0050] In one embodiment of the traffic equipment data processing method of the present application, the following contents may also be specifically included: Step S201: Obtaining device type data from a traffic equipment monitoring platform, obtaining geographic location data from a geographic information system, and obtaining installation location data and captured lane data from a device deployment system based on a data interface, aligning the device type data, the geographic location data, the installation location data, and the captured lane data according to timestamps, constructing a data association table, establishing a data quality assessment indicator system, and calculating a completeness score for each record in the data association table; Step S202: Eliminate records with integrity scores below a threshold, construct quartile intervals, calculate the upper quartile and lower quartile of each feature in the data association table, determine the outlier determination boundary based on the upper quartile and the lower quartile, mark the data beyond the outlier determination boundary as outliers and eliminate them, and generate a cleaned data set.

[0051] Optionally, this embodiment innovatively designs a quality optimization solution based on multi-source data fusion to address issues such as unstable data quality and inaccurate feature associations for illegal traffic equipment configurations. This embodiment first constructs a data acquisition framework to achieve comprehensive acquisition of equipment information through multi-level data synchronization. The system designs a data integrity assessment formula: Completeness_Score = α Field_Coverage +β Time_Consistency - γ × Missing_Rate, where Field_Coverage represents field coverage, Time_Consistency represents time consistency, Missing_Rate represents the missing rate, and α, β, and γ are dynamic adjustment coefficients. In traffic equipment management scenarios, this multi-dimensional quality assessment method can effectively ensure data availability.

[0052] This embodiment deeply optimizes the data collection strategy. In view of the multi-source characteristics of traffic data, a collection mechanism based on a unified interface is designed. Through a standardized data interface, unified acquisition of data from different systems is achieved. Special attention is paid to the real-time nature of the data. When an abnormal response from the data source is detected, the system will perform supplementary collection through the backup channel. For example, when processing the configuration data of urban electronic police equipment, multi-source collection can simultaneously obtain key information such as device type, installation location, geographic coordinates, and monitored lanes. The integrity of this information is crucial for subsequent illegal configuration recommendations.

[0053] This embodiment innovatively implements a data alignment mechanism. In response to the time consistency requirements of multi-source data, the system constructs a timestamp-based alignment framework. Through precise time series matching, unified organization of data from different sources is achieved. Special attention is paid to the accuracy of alignment, and the time series correlation of data is ensured by designing a time window strategy. This timestamp-based alignment method can effectively ensure data consistency. This embodiment adopts the outlier determination formula: Boundary = Q ± k×IQR, where Q represents the quartile, IQR represents the interquartile range, and k is the adjustment coefficient.

[0054] This embodiment deeply optimizes the quality assessment strategy. The system constructs an assessment framework based on multi-dimensional indicators, achieving precise measurement of data quality through completeness score calculation. Special attention is paid to the comprehensiveness of the assessment, and a hierarchical assessment mechanism is designed to improve the accuracy of quality assessment. This comprehensive assessment method provides a reliable basis for judgment during data cleaning.

[0055] This example optimizes data quality through exception handling. The system builds a quartile-based anomaly detection framework, incorporating statistical features for boundary demarcation. Special attention is paid to the rationality of anomalies, and an adaptive threshold mechanism is established to accurately identify outliers. This systematic cleaning solution provides high-quality data support for subsequent analysis.

[0056] The innovative design of this embodiment not only addresses data quality issues encountered in traditional methods but also establishes a sustainable preprocessing framework. Through multi-level data processing and quality control, the system is able to extract effective configuration features from complex raw data. This multi-source fusion-based processing mechanism ensures that the system maintains effective data quality even in complex traffic scenarios. This intelligent preprocessing solution significantly improves the accuracy of traffic equipment configuration recommendations.

[0057] This embodiment achieves intelligent analysis and upgrades for equipment configuration by establishing a complete data processing chain. The system dynamically adjusts processing strategies based on real-time data quality, avoiding the limitations of traditional fixed-rule solutions. Multi-dimensional data cleaning and quality control significantly improves data reliability and validity, providing reliable data support for subsequent configuration recommendations. This intelligent processing mechanism demonstrates strong adaptability and optimization effects in traffic equipment configuration.

[0058] This embodiment not only improves data processing accuracy but also establishes a continuously evolving preprocessing system through continuous strategy optimization and performance analysis. This optimization mechanism, based on real-time feedback, ensures that the system can continuously improve as traffic scenarios change, providing increasingly accurate data support for subsequent analysis. In practical applications, this self-optimization mechanism has significantly improved the system's long-term service quality and processing effectiveness, providing reliable technical support for traffic equipment management.

[0059] In one embodiment of the traffic equipment data processing method of the present application, the following contents may also be specifically included: Step S301: Classify and organize the cleaned data set according to device type, geographic location, installation location, and shooting lane, and construct a device type feature set, a geographic location feature set, an installation location feature set, and a shooting lane feature set, respectively. Perform one-hot encoding conversion on the device type feature set to generate a device type encoding matrix, perform one-hot encoding conversion on the geographic location feature set to generate a geographic location encoding matrix, and perform one-hot encoding conversion on the installation location feature set and the shooting lane feature set to generate an installation location encoding matrix and a shooting lane encoding matrix, respectively. Step S302: splicing the device type coding matrix, the geographic location coding matrix, the installation location coding matrix and the shooting lane coding matrix in the sample dimension to generate a feature fusion matrix, normalizing the feature fusion matrix, and dividing the feature fusion matrix into a training sample set and a test sample set according to a preset ratio based on time series information.

[0060] Optionally, this embodiment addresses the issues of insufficient feature expression and incomplete data fusion in traffic equipment data processing by innovatively designing a data processing solution based on multi-dimensional feature coding. This embodiment first constructs a feature processing framework to achieve a comprehensive expression of the equipment status through multi-level feature coding. The system designs a feature importance evaluation formula: Feature_Value = α Encoding_Dimension +β Information_Gain - γ × Correlation_Factor, where Encoding_Dimension represents the encoding dimension, Information_Gain represents information gain, Correlation_Factor represents the correlation factor, and α, β, and γ are dynamic adjustment coefficients. In traffic equipment configuration scenarios, this multi-dimensional feature evaluation method can effectively improve data representation.

[0061] This embodiment deeply optimizes the feature classification strategy. In view of the diversity of traffic equipment data, an attribute-based classification mechanism is designed. Through precise feature classification, effective organization of data with different attributes is achieved. Special attention is paid to the rationality of classification. When feature association is detected, the system will perform classification optimization through association analysis methods. For example, when processing the configuration data of urban electronic police, feature classification can clearly distinguish the characteristic attributes of equipment type (such as bayonet cameras, illegal parking capture, etc.), geographical location (such as intersections, road sections, etc.), installation location (such as poles, cross arms, etc.) and shooting lanes (such as left turn, straight, etc.), which is crucial for building a complete configuration model.

[0062] This embodiment innovatively implements a coding conversion mechanism. In response to the expression requirements of different types of features, the system constructs a conversion framework based on one-hot encoding. Through feature mapping, the numerical representation of category data is achieved. Special attention is paid to the integrity of the encoding, and the accuracy of the conversion results is ensured by designing a mapping dictionary strategy. This feature processing method based on one-hot encoding can effectively maintain the original information of the feature. This embodiment adopts the feature fusion calculation formula: Fusion_Matrix=[E1; E2; E3; E4], where E1 to E4 represent the coding matrices of the device type, geographical location, installation location, and shooting lane, respectively.

[0063] This embodiment deeply optimizes the matrix fusion strategy. The system constructs a feature fusion framework based on sample dimensions, achieving unified representation of multi-source features through matrix concatenation. Special attention is paid to dimensional alignment, and synchronization mechanisms are designed to improve fusion accuracy. This comprehensive fusion approach provides a standardized data foundation for model training.

[0064] This example achieves standardized feature expression through normalization. The system constructs a normalization framework based on statistical features, combining multidimensional features for distribution adjustment. Particular attention is paid to data scale consistency, and an adaptive normalization mechanism is established to effectively control feature distribution. This systematic processing solution provides high-quality feature support for subsequent analysis.

[0065] The innovative design of this embodiment not only solves the feature processing issues of traditional methods but also establishes a continuously optimized feature learning framework. Through multi-level feature encoding and fusion, the system is able to extract effective configuration features from complex equipment data. This multi-dimensional encoding-based processing mechanism ensures that the system maintains effective feature expression capabilities even in complex traffic scenarios. This intelligent processing solution significantly improves the accuracy of recommendations for traffic equipment configuration.

[0066] This embodiment achieves intelligent analysis and upgrades for device configuration by establishing a complete feature processing chain. The system dynamically adjusts processing strategies based on real-time feature distribution, avoiding the limitations of traditional fixed encoding schemes. Multi-dimensional feature fusion and normalization significantly improve the quality and reliability of feature representation, providing reliable data support for subsequent configuration recommendations. This intelligent processing mechanism demonstrates strong adaptability and optimization effects in traffic equipment configuration.

[0067] This embodiment not only improves the accuracy of feature processing but also establishes an evolving feature learning system through continuous strategy optimization and performance analysis. This optimization mechanism, based on real-time feedback, ensures that the system can continuously improve as traffic scenarios change, providing increasingly accurate feature representations for subsequent recommendations. In practical applications, this self-optimization mechanism has significantly improved the system's long-term service quality and recommendation effectiveness, providing reliable technical support for traffic equipment management.

[0068] In one embodiment of the traffic equipment data processing method of the present application, the following contents may also be specifically included: Step S401: constructing a multi-label random forest classifier based on the training sample set, setting the classifier's parameters for the number of trees, maximum depth, and minimum number of split samples, performing random sampling with replacement on the training sample set to generate multiple sub-datasets, calculating the importance score of each feature, sorting the features according to the importance score, randomly selecting feature subsets from the sub-datasets according to a preset feature number threshold, and constructing initial nodes of a decision tree based on the feature subsets; Step S402: Input the sub-dataset into the initial node, perform feature partitioning on the node using the information gain criterion, calculate the Gini coefficient of each candidate feature, select the optimal split feature by comparing the Gini coefficients, divide the data set into a left child node data set and a right child node data set according to the optimal split feature, and calculate the Gini coefficients of the left child node data set and the right child node data set.

[0069] Optionally, this embodiment addresses the issues of insufficient model expression and inaccurate feature selection in traffic equipment data processing by innovatively designing a classification optimization solution based on multi-label random forests. This embodiment first constructs a classifier framework and achieves accurate modeling of violation types through multi-level parameter configuration. The system designs a feature importance evaluation formula: Feature_Importance = α Information_Gain +β Gini_Decrease, where Information_Gain represents the information gain value, Gini_Decrease represents the reduction in Gini impurity, and α and β are dynamic adjustment coefficients. In traffic equipment configuration scenarios, this multi-dimensional feature evaluation method can effectively improve classification accuracy.

[0070] This embodiment deeply optimizes the classifier initialization strategy. A parameter grid-based initialization mechanism is designed to address the complexity of traffic equipment configuration data. Through precise parameter settings, a reasonable definition of the model structure is achieved. Particular attention is paid to model complexity. When poor training results are detected, the system dynamically adjusts parameters for optimization. For example, when processing illegal configurations at urban traffic checkpoints, properly setting the number and depth of trees can effectively balance the model's expressiveness and generalization capabilities, which is crucial for improving recommendation accuracy.

[0071] This embodiment innovatively implements a sampling mechanism. In view of the distribution characteristics of the training data, the system constructs a sampling framework based on the bootstrap method. Through random sampling with replacement, full utilization of the training data is achieved. Special attention is paid to the representativeness of the samples, and the category balance of the sub-datasets is ensured by designing a stratified sampling strategy. This data processing method based on random sampling can effectively improve the robustness of the model. This embodiment adopts the Gini coefficient calculation formula: Gini = 1 - ∑(pi^2), where pi represents the proportion of samples in the i-th category.

[0072] This embodiment deeply optimizes the feature selection strategy. The system builds a feature evaluation framework based on importance scores, enabling precise screening of key features through a ranking mechanism. It pays special attention to feature contributions and improves the effectiveness of feature selection by designing a threshold screening mechanism. This comprehensive selection approach provides reliable feature support for decision tree construction.

[0073] This example achieves precise tree structure construction through node optimization. The system builds a node partitioning framework based on information gain and incorporates the Gini coefficient for feature evaluation. It pays special attention to node purity and establishes a binary classification mechanism to effectively partition the dataset. This systematic construction approach provides stable model support for classification tasks.

[0074] The innovative design of this embodiment not only solves the classification optimization issues inherent in traditional methods but also establishes a sustainable optimization modeling framework. Through multi-level feature selection and node optimization, the system is able to learn effective violation patterns from complex traffic data. This random forest-based classification mechanism ensures that the system maintains effective recommendation capabilities in diverse traffic scenarios. This intelligent optimization solution significantly improves the accuracy and reliability of recommendations for traffic equipment configuration.

[0075] This embodiment achieves intelligent recommendation upgrades for traffic equipment by establishing a complete classification and processing chain. The system dynamically adjusts optimization strategies based on real-time classification results, avoiding the limitations of traditional fixed model solutions. Through multi-dimensional model optimization and construction, the quality and reliability of recommendations are significantly improved, providing reliable decision support for traffic equipment configuration. This intelligent classification mechanism demonstrates strong adaptability and optimization effects in traffic violation analysis.

[0076] This embodiment not only improves the accuracy of recommendations but also establishes a continuously evolving classification system through continuous strategy optimization and performance analysis. This optimization mechanism, based on real-time feedback, ensures that the system can continuously improve as traffic patterns change, providing increasingly accurate recommendations for device configuration. In practical applications, this self-optimization mechanism has significantly improved the system's long-term service quality and recommendation effectiveness, providing reliable technical support for traffic management.

[0077] In one embodiment of the traffic equipment data processing method of the present application, the following contents may also be specifically included: Step S501: Calculate the Gini coefficients of candidate features for the left child node dataset and the right child node dataset, select the feature with the smallest Gini coefficient as the split feature, and divide the node data into two categories based on the split feature. Repeat the node splitting process until the preset depth is reached or the number of node samples is less than the minimum number of split samples. Calculate the sample distribution probability of each category for each leaf node, and use the sample distribution probability as the prediction output of the leaf node. Step S502: Combine the prediction outputs of multiple decision trees to construct a random forest voting matrix, calculate the predicted probability distribution of each sample in each violation category based on the voting matrix, set a violation category probability threshold, mark the category above the violation category probability threshold as a positive sample, and generate a violation configuration recommendation model.

[0078] Optionally, this embodiment addresses the issues of unstable decision tree growth and unreliable prediction results in traffic equipment data processing by innovatively designing a tree optimization solution based on adaptive splitting. This embodiment first constructs a node splitting framework and achieves accurate modeling of violation types through multi-level feature selection. The system designs a node purity evaluation formula: Node_Score = α Gini_Index -β Sample_Size + γ × Tree_Depth, where Gini_Index represents the Gini coefficient, Sample_Size represents the sample size, Tree_Depth represents the tree depth, and α, β, and γ are dynamic adjustment coefficients. In traffic equipment configuration scenarios, this multi-dimensional node evaluation method can effectively improve the quality of tree growth.

[0079] This embodiment deeply optimizes the splitting strategy. In view of the complexity of traffic equipment configuration data, a splitting mechanism based on the Gini coefficient is designed. Through recursive feature evaluation, the optimal division of nodes is achieved. Special attention is paid to the effectiveness of the split. When node impurity is detected, the system will control the splitting through a dynamic threshold method. For example, when processing illegal configurations on urban roads, node splitting can effectively distinguish the characteristic patterns of different types of violations, including speeding violations, red light running violations, and other scenarios. This is crucial to improving the accuracy of configuration recommendations.

[0080] This embodiment innovatively implements a termination judgment mechanism. In response to the growth control requirements of the decision tree, the system constructs a termination framework based on multiple conditions. Through depth limitation and sample quantity control, precise management of tree growth is achieved. Special attention is paid to the rationality of growth, and the structure optimization of the tree is ensured by designing an adaptive termination strategy. This multi-dimensional termination judgment method can effectively avoid the overfitting problem. This embodiment adopts the integrated prediction calculation formula: Ensemble_Prob =∑(wi×pi) / ∑wi, where wi represents the weight of the i-th tree and pi represents its predicted probability.

[0081] This embodiment deeply optimizes the prediction combination strategy. The system builds a result fusion framework based on the voting matrix, achieving precise integration of prediction results through probability distribution calculations. Special attention is paid to prediction reliability, and a dynamic threshold mechanism is designed to improve prediction accuracy. This comprehensive combination approach provides reliable decision support for recommendation tasks.

[0082] This embodiment achieves recommendation capabilities through model generation. The system builds a model framework based on ensemble learning, combining multi-tree predictions for comprehensive decision-making. Special attention is paid to the model's generalization capabilities, and a validation mechanism is established to effectively evaluate model performance. This systematic approach provides high-quality model support for configuration recommendations.

[0083] This innovative design not only solves the tree optimization problem inherent in traditional methods but also establishes a continuously optimized recommendation framework. Through multi-level node splitting and result combination, the system is able to learn effective violation patterns from complex traffic data. This adaptive optimization-based learning mechanism ensures that the system maintains effective recommendation capabilities in diverse traffic scenarios. In traffic equipment configuration, this intelligent optimization solution significantly improves the accuracy and reliability of recommendations.

[0084] This embodiment achieves intelligent recommendation upgrades for traffic equipment by establishing a complete model-building chain. The system dynamically adjusts optimization strategies based on real-time prediction results, avoiding the limitations of traditional fixed model solutions. Through multi-dimensional tree optimization and result fusion, the quality and reliability of recommendations are significantly improved, providing reliable decision support for traffic equipment configuration. This intelligent recommendation mechanism demonstrates strong adaptability and optimization effects in traffic violation analysis.

[0085] This embodiment not only improves the accuracy of recommendations but also establishes a continuously evolving recommendation system through continuous strategy optimization and performance analysis. This optimization mechanism, based on real-time feedback, ensures that the system can continuously improve as traffic patterns change, providing increasingly accurate recommendations for device configuration. In practical applications, this self-optimization mechanism has significantly improved the system's long-term service quality and recommendation effectiveness, providing reliable technical support for traffic management.

[0086] In one embodiment of the traffic equipment data processing method of the present application, the following contents may also be specifically included: Step S601: Input the test sample set into the illegal configuration recommendation model, perform feature matching on each test sample in each decision tree, divide the sample into corresponding leaf nodes according to the sample feature value, extract the predicted probability distribution stored in the leaf node, calculate the predicted probability value for each illegal category, construct a prediction result matrix based on the predicted probability values, and combine the prediction result matrix on the decision tree dimension; Step S602: Count the classification results of each decision tree for each test sample in each violation category, construct a decision tree voting statistics table, calculate the number of decision trees predicted as positive samples for each violation category, divide the number of decision trees by the total number of decision trees to obtain the category prediction probability, standardize the category prediction probability, and generate a standardized prediction probability matrix.

[0087] Optionally, this embodiment addresses the issues of unstable prediction results and inaccurate classification confidence in traffic equipment data processing by innovatively designing a prediction optimization solution based on multi-tree ensembles. This embodiment first constructs a prediction evaluation framework to achieve accurate prediction of violation types through multi-level result statistics. The system designs a prediction reliability evaluation formula: Reliability_Score = α Tree_Consensus +βProbability_Confidence - γ × Variance_Factor, where Tree_Consensus represents inter-tree consistency, Probability_Confidence represents probability confidence, Variance_Factor represents variance factor, and α, β, and γ are dynamic adjustment coefficients. In traffic equipment configuration scenarios, this multi-dimensional prediction and evaluation method can effectively improve the reliability of recommendations.

[0088] This embodiment deeply optimizes the feature matching strategy. In view of the complexity of traffic equipment configuration scenarios, a path-based matching mechanism is designed. Through recursive node traversal, the precise positioning of the test sample is achieved. Special attention is paid to the accuracy of the matching. When an abnormal feature value is detected, the system will optimize the matching through a fault-tolerant mechanism. For example, when processing illegal configurations of urban road monitoring points, feature matching can accurately identify illegal feature combinations in different scenarios, including running red lights at intersections, speeding on roads, and other situations. This is crucial to improving the accuracy of configuration recommendations.

[0089] This embodiment innovatively implements a probability extraction mechanism. In view of the prediction characteristics of leaf nodes, the system constructs a probability calculation framework based on distribution. Through category statistics, an accurate estimation of the prediction probability is achieved. Special attention is paid to the rationality of the probability, and the stability of the prediction results is ensured by designing a smoothing strategy. This distribution-based probability processing method can effectively improve the reliability of the prediction. This embodiment adopts the standardized calculation formula: Norm_Prob = (P - μ) / (σ + ε), where P represents the original probability, μ represents the mean, σ represents the standard deviation, and ε is the smoothing factor.

[0090] This embodiment deeply optimizes the result combination strategy. The system builds a prediction fusion framework based on matrix operations, achieving a unified representation of multi-tree results through dimensional combination. Special attention is paid to the effectiveness of the combination, and a weighting mechanism is designed to improve the accuracy of the fusion. This comprehensive combination method provides a reliable data foundation for result statistics.

[0091] This embodiment achieves reliable integration of prediction results through voting statistics. The system constructs a statistical framework based on majority voting and combines inter-tree consistency for result evaluation. Special attention is paid to statistical comprehensiveness, and a standardization mechanism is established to achieve effective calibration of prediction probabilities. This systematic statistical approach provides high-quality decision support for configuration recommendations.

[0092] The innovative design of this embodiment not only solves the prediction optimization problems of traditional methods but also establishes a sustainable optimization recommendation framework. Through multi-level result statistics and probabilistic optimization, the system is able to extract reliable configuration solutions from complex prediction results. This integrated prediction mechanism ensures that the system maintains effective recommendation capabilities in diverse traffic scenarios. In traffic equipment configuration, this intelligent optimization solution significantly improves the accuracy and reliability of recommendations.

[0093] This embodiment achieves intelligent recommendation upgrades for traffic equipment by establishing a complete prediction and processing chain. The system dynamically adjusts optimization strategies based on real-time prediction results, avoiding the limitations of traditional fixed-rule solutions. Through multi-dimensional result optimization and statistics, the quality and reliability of recommendations are significantly improved, providing reliable decision support for traffic equipment configuration. This intelligent prediction mechanism demonstrates strong adaptability and optimization effects in traffic violation analysis.

[0094] This embodiment not only improves the accuracy of recommendations but also establishes a continuously evolving prediction system through continuous strategy optimization and performance analysis. This optimization mechanism, based on real-time feedback, ensures that the system can continuously improve as traffic patterns change, providing increasingly accurate recommendations for device configuration. In practical applications, this self-optimization mechanism has significantly improved the system's long-term service quality and recommendation effectiveness, providing reliable technical support for traffic management.

[0095] In one embodiment of the traffic equipment data processing method of the present application, the following contents may also be specifically included: Step S701: Setting voting weight coefficients based on the standardized prediction probability matrix, performing weighted voting on the prediction results of each decision tree, calculating weighted voting scores for each violation category, comparing the weighted voting scores with a preset violation category threshold, marking categories above the violation category threshold as recommended configuration items, sorting the recommended configuration items in descending order according to the weighted voting scores, and generating a violation configuration recommendation result; Step S702: Encapsulate the illegal configuration recommendation result into a standard data format, build a data transmission interface, establish a communication connection with the traffic equipment configuration system, push the illegal configuration recommendation result to the traffic equipment configuration system in real time through the data transmission interface, record the push log and save the recommendation result history.

[0096] Optionally, this embodiment addresses issues such as inaccurate result integration and imperfect push mechanisms in traffic equipment data processing, and innovatively designs a recommendation optimization solution based on weighted voting. This embodiment first constructs a voting evaluation framework, and achieves accurate generation of recommendation results through multi-level weight calculations. The system designs a voting weight evaluation formula: Vote_Weight = α Tree_Reliability +β Prediction_Stability - γ × Bias_Factor, where Tree_Reliability represents tree reliability, Prediction_Stability represents prediction stability, Bias_Factor represents the bias factor, and α, β, and γ are dynamic adjustment coefficients. In traffic equipment configuration scenarios, this multi-dimensional voting evaluation method can effectively improve the accuracy of recommendations.

[0097] This embodiment deeply optimizes the voting weight strategy. In view of the complexity of traffic equipment configuration scenarios, a reliability-based weight distribution mechanism is designed. Through tree performance evaluation, accurate weighting of prediction results is achieved. Special attention is paid to the rationality of the weights. When a prediction deviation is detected, the system will optimize the weights through adaptive adjustment methods. For example, when dealing with illegal configurations at urban traffic checkpoints, weighted voting can effectively balance the prediction contributions of different decision trees, including the ability to identify various illegal behaviors such as speeding, running red lights, and illegal lane changes. This is crucial to improving the accuracy of configuration recommendations.

[0098] This embodiment innovatively implements a threshold judgment mechanism. In response to the recommendation requirements of illegal categories, the system constructs a score-based screening framework. By setting dynamic thresholds, precise control of recommendation results is achieved. Special attention is paid to the reliability of recommendations, and the practicality of recommendation results is ensured by designing a grading strategy. This threshold-based screening method can effectively improve the accuracy of recommendations. This embodiment adopts the weighted score calculation formula: Final_Score = ∑(wi×vi) / ∑wi, where wi represents the weight of the i-th decision tree and vi represents its voting result.

[0099] This embodiment deeply optimizes the data encapsulation strategy. The system builds a result encapsulation framework based on a standard format, achieving standardized representation of recommendation results through interface design. Special attention is paid to data integrity, and verification mechanisms are designed to improve transmission reliability. This comprehensive encapsulation approach provides reliable data support for system integration.

[0100] This embodiment achieves timely distribution of results through real-time push notifications. The system builds a push notification framework based on communication connections and integrates logging for process tracking. Special attention is paid to push notification stability, with a backup mechanism established to ensure reliable delivery of recommendation results. This systematic push notification solution provides high-quality service support for configuration management.

[0101] The innovative design of this embodiment not only solves the recommendation integration problem in traditional methods but also establishes a continuously optimized push framework. Through multi-level result integration and push optimization, the system is able to extract reliable configuration solutions from complex prediction results. This weighted recommendation mechanism ensures that the system maintains effective recommendation capabilities in diverse traffic scenarios. In traffic equipment configuration, this intelligent optimization solution significantly improves the accuracy and reliability of recommendations.

[0102] This embodiment achieves intelligent configuration upgrades for traffic equipment by establishing a complete recommendation processing chain. The system dynamically adjusts optimization strategies based on real-time recommendation results, avoiding the limitations of traditional fixed-rule solutions. Through multi-dimensional result optimization and push notifications, the quality and reliability of recommendations are significantly improved, providing reliable decision-making support for traffic equipment management. This intelligent recommendation mechanism demonstrates strong adaptability and optimization effects in traffic violation analysis.

[0103] This embodiment not only improves the accuracy of recommendations but also establishes a continuously evolving recommendation system through continuous strategy optimization and performance analysis. This optimization mechanism, based on real-time feedback, ensures that the system can continuously improve as traffic patterns change, providing increasingly accurate recommendations for device configuration. In practical applications, this self-optimization mechanism has significantly improved the system's long-term service quality and recommendation effectiveness, providing reliable technical support for traffic management.

[0104] In order to effectively solve the deficiencies of traditional technologies in data processing, classification modeling, and result generation, and significantly improve the accuracy and reliability of traffic equipment data processing, the present application provides an embodiment of a traffic equipment data processing device for implementing all or part of the content of the traffic equipment data processing method, see Figure 2 , the traffic equipment data processing device specifically includes the following contents: a data set determination module 10 for acquiring equipment type data from a traffic equipment monitoring platform, acquiring geographic location data from a geographic information system, acquiring installation location data and shooting lane data from a device deployment system, cleaning the equipment type data, the geographic location data, the installation location data, and the shooting lane data, removing missing records, identifying and removing outliers using a box plot method, constructing an equipment type feature set, performing one-hot encoding conversion on the equipment type feature set, constructing a geographic location feature set, performing one-hot encoding conversion on the geographic location feature set, constructing an installation location feature set and a shooting lane feature set, performing one-hot encoding conversion on the installation location feature set and the shooting lane feature set, and dividing the encoded feature set into a training sample set and a test sample set according to time series information; A model training module 20 is configured to construct a multi-label random forest classifier based on the training sample set, perform random sampling with replacement on the training sample set to generate multiple subsets, randomly select feature subsets from the subsets, construct a decision tree based on the feature subsets, perform feature partitioning on the tree nodes using an information gain criterion, calculate the Gini coefficient of each node, select the feature with the smallest Gini coefficient as the splitting feature, iteratively construct the decision tree until a preset depth is reached, and combine the constructed decision trees to generate an illegal configuration recommendation model; The recommended configuration module 30 is used to input the test sample set into the illegal configuration recommendation model, perform multi-label classification on each test sample based on the illegal configuration recommendation model, count the classification results of each decision tree, determine the final illegal configuration recommendation result by voting, and output the illegal configuration recommendation result to the traffic equipment configuration system.

[0105] From the above description, it can be seen that the traffic equipment data processing device provided in the embodiment of the present application can realize the standardized representation of features such as equipment type and geographical location by innovatively constructing a multi-dimensional data preprocessing mechanism, through outlier identification and one-hot encoding. A classification model based on multi-label random forest is designed, and a feature partitioning and decision tree construction strategy is established by combining the information gain criterion and Gini coefficient optimization. A voting integration mechanism is introduced to achieve accurate recommendation of illegal configurations by fusing the classification results of multiple trees. This method effectively solves the shortcomings of traditional technologies in data processing, classification modeling and result generation, and significantly improves the accuracy and reliability of traffic equipment data processing.

[0106] From a hardware perspective, in order to effectively address the deficiencies of conventional technologies in data processing, classification modeling, and result generation, and significantly improve the accuracy and reliability of traffic equipment data processing, this application provides an embodiment of an electronic device for implementing all or part of the traffic equipment data processing method. The electronic device specifically includes the following: A processor, memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to transmit information between the traffic equipment data processing device and related devices such as core business systems, user terminals, and related databases; the logic controller can be a desktop computer, a tablet computer, a mobile terminal, etc., but this embodiment is not limited thereto. In this embodiment, the logic controller can be implemented with reference to the embodiments of the traffic equipment data processing method and the embodiments of the traffic equipment data processing device in the embodiments, and their contents are incorporated herein, and any repetitions are not repeated.

[0107] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0108] In practical applications, portions of the traffic equipment data processing method can be executed on the electronic device side as described above, or all operations can be performed on the client device. The specific selection can be based on the processing capabilities of the client device and the limitations of the user's usage scenario. This application does not impose any restrictions on this. If all operations are performed on the client device, the client device may also include a processor.

[0109] The aforementioned client device may include a communication module (i.e., a communication unit) capable of establishing a communication connection with a remote server to facilitate data transmission with the server. The server may include a server at the task scheduling center or, in other implementation scenarios, a server on an intermediate platform, such as a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may comprise a single computer device, a server cluster consisting of multiple servers, or a distributed server configuration.

[0110] Figure 3 Schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Figure 3 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that the Figure 3 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0111] In one embodiment, the traffic equipment data processing method function may be integrated into the central processing unit 9100. The central processing unit 9100 may be configured to perform the following control: Step S101: Obtain device type data from a traffic equipment monitoring platform, obtain geographic location data from a geographic information system, obtain installation location data and shooting lane data from a device deployment system, clean the device type data, the geographic location data, the installation location data, and the shooting lane data, remove missing records, identify and remove outliers using a box plot method, construct a device type feature set, perform one-hot encoding conversion on the device type feature set, construct a geographic location feature set, perform one-hot encoding conversion on the geographic location feature set, construct an installation location feature set and a shooting lane feature set, perform one-hot encoding conversion on the installation location feature set and the shooting lane feature set, and divide the encoded feature set into a training sample set and a test sample set according to time series information; Step S102: constructing a multi-label random forest classifier based on the training sample set, performing random sampling with replacement on the training sample set to generate multiple sub-datasets, randomly selecting feature subsets from the sub-datasets, constructing a decision tree based on the feature subsets, performing feature partitioning on the tree nodes using the information gain criterion, calculating the Gini coefficient of each node, selecting the feature with the smallest Gini coefficient as the splitting feature, iteratively constructing the decision tree until a preset depth is reached, and combining the constructed decision trees to generate an illegal configuration recommendation model; Step S103: Input the test sample set into the illegal configuration recommendation model, perform multi-label classification on each test sample based on the illegal configuration recommendation model, count the classification results of each decision tree, determine the final illegal configuration recommendation result by voting, and output the illegal configuration recommendation result to the traffic equipment configuration system.

[0112] As can be seen from the above description, the electronic device provided in the embodiment of the present application, through the innovative construction of a multi-dimensional data preprocessing mechanism, realizes the standardized representation of features such as device type and geographical location through outlier identification and one-hot encoding. A classification model based on multi-label random forest is designed, and a feature partitioning and decision tree construction strategy is established by combining the information gain criterion and Gini coefficient optimization. A voting integration mechanism is introduced to achieve accurate recommendation of illegal configurations through the fusion of multi-tree classification results. This method effectively solves the shortcomings of traditional technologies in data processing, classification modeling and result generation, and significantly improves the accuracy and reliability of traffic equipment data processing.

[0113] In another embodiment, the traffic equipment data processing device can be configured separately from the central processor 9100. For example, the traffic equipment data processing device can be configured as a chip connected to the central processor 9100, and the traffic equipment data processing method function is implemented under the control of the central processor.

[0114] like Figure 3 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Figure 3 In addition, the electronic device 9600 may also include all components shown in Figure 3 For components not shown, reference may be made to the prior art.

[0115] like Figure 3 As shown, the central processing unit 9100 is sometimes also referred to as a controller or operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0116] Memory 9140 can be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It can store the aforementioned failure-related information and also store programs that execute the relevant information. The CPU 9100 can execute the programs stored in memory 9140 to implement information storage or processing.

[0117] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 may be, for example, a keypad or touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display objects such as images and text. The display may be, for example, an LCD display, but is not limited thereto.

[0118] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), or SIM card. Alternatively, it may be a memory that retains information even when power is off, can be selectively erased, and is capable of storing additional data. Examples of such memory are sometimes referred to as EPROMs. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs, or processes used by the central processing unit 9100 to execute operations of the electronic device 9600.

[0119] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, images, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various driver programs for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0120] The communication module 9110 is a transmitter / receiver that transmits and receives signals via the antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0121] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless local area network modules. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130, providing audio output via the speaker 9131 and receiving audio input from the microphone 9132, thereby implementing common telecommunication functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor 9130 is coupled to the central processing unit 9100, enabling local recording via the microphone 9132 and playback of stored audio via the speaker 9131.

[0122] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all steps of the traffic device data processing method in the above-mentioned embodiments, where the execution subject is a server or a client. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the computer program implements all steps of the traffic device data processing method in the above-mentioned embodiments, where the execution subject is a server or a client. For example, when the processor executes the computer program, the following steps are implemented: Step S101: Obtain device type data from a traffic equipment monitoring platform, obtain geographic location data from a geographic information system, obtain installation location data and shooting lane data from a device deployment system, clean the device type data, the geographic location data, the installation location data, and the shooting lane data, remove missing records, identify and remove outliers using a box plot method, construct a device type feature set, perform one-hot encoding conversion on the device type feature set, construct a geographic location feature set, perform one-hot encoding conversion on the geographic location feature set, construct an installation location feature set and a shooting lane feature set, perform one-hot encoding conversion on the installation location feature set and the shooting lane feature set, and divide the encoded feature set into a training sample set and a test sample set according to time series information; Step S102: constructing a multi-label random forest classifier based on the training sample set, performing random sampling with replacement on the training sample set to generate multiple sub-datasets, randomly selecting feature subsets from the sub-datasets, constructing a decision tree based on the feature subsets, performing feature partitioning on the tree nodes using the information gain criterion, calculating the Gini coefficient of each node, selecting the feature with the smallest Gini coefficient as the splitting feature, iteratively constructing the decision tree until a preset depth is reached, and combining the constructed decision trees to generate an illegal configuration recommendation model; Step S103: Input the test sample set into the illegal configuration recommendation model, perform multi-label classification on each test sample based on the illegal configuration recommendation model, count the classification results of each decision tree, determine the final illegal configuration recommendation result by voting, and output the illegal configuration recommendation result to the traffic equipment configuration system.

[0123] As can be seen from the above description, the computer-readable storage medium provided in the embodiment of the present application realizes the standardized representation of features such as equipment type and geographical location by innovatively constructing a multi-dimensional data preprocessing mechanism, through outlier identification and one-hot encoding. A classification model based on multi-label random forest is designed, and a feature partitioning and decision tree construction strategy is established by combining the information gain criterion and Gini coefficient optimization. A voting integration mechanism is introduced to achieve accurate recommendation of illegal configurations by fusing the classification results of multiple trees. This method effectively solves the shortcomings of traditional technologies in data processing, classification modeling and result generation, and significantly improves the accuracy and reliability of traffic equipment data processing.

[0124] The embodiments of the present application also provide a computer program product capable of implementing all steps of the traffic equipment data processing method in the above-mentioned embodiments, where the execution subject is a server or a client. When the computer program / instructions are executed by a processor, the computer program / instructions implement the steps of the traffic equipment data processing method. For example, the computer program / instructions implement the following steps: Step S101: Obtain device type data from a traffic equipment monitoring platform, obtain geographic location data from a geographic information system, obtain installation location data and shooting lane data from a device deployment system, clean the device type data, the geographic location data, the installation location data, and the shooting lane data, remove missing records, identify and remove outliers using a box plot method, construct a device type feature set, perform one-hot encoding conversion on the device type feature set, construct a geographic location feature set, perform one-hot encoding conversion on the geographic location feature set, construct an installation location feature set and a shooting lane feature set, perform one-hot encoding conversion on the installation location feature set and the shooting lane feature set, and divide the encoded feature set into a training sample set and a test sample set according to time series information; Step S102: constructing a multi-label random forest classifier based on the training sample set, performing random sampling with replacement on the training sample set to generate multiple sub-datasets, randomly selecting feature subsets from the sub-datasets, constructing a decision tree based on the feature subsets, performing feature partitioning on the tree nodes using the information gain criterion, calculating the Gini coefficient of each node, selecting the feature with the smallest Gini coefficient as the splitting feature, iteratively constructing the decision tree until a preset depth is reached, and combining the constructed decision trees to generate an illegal configuration recommendation model; Step S103: Input the test sample set into the illegal configuration recommendation model, perform multi-label classification on each test sample based on the illegal configuration recommendation model, count the classification results of each decision tree, determine the final illegal configuration recommendation result by voting, and output the illegal configuration recommendation result to the traffic equipment configuration system.

[0125] As can be seen from the above description, the computer program product provided in the embodiment of the present application realizes the standardized representation of features such as equipment type and geographical location by innovatively constructing a multi-dimensional data preprocessing mechanism, through outlier identification and one-hot encoding. A classification model based on multi-label random forest is designed, and a feature partitioning and decision tree construction strategy is established by combining the information gain criterion and Gini coefficient optimization. A voting integration mechanism is introduced to achieve accurate recommendation of illegal configurations by fusing the classification results of multiple trees. This method effectively solves the shortcomings of traditional technologies in data processing, classification modeling, and result generation, and significantly improves the accuracy and reliability of traffic equipment data processing.

[0126] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatuses, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0127] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 produce 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 flowcharts and / or block diagrams. 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.

[0128] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0129] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0130] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A method for processing traffic equipment data, characterized in that: The method comprises: Acquire equipment type data from a traffic equipment monitoring platform, acquire geographic location data from a geographic information system, acquire installation location data and shooting lane data from a device deployment system, clean the equipment type data, the geographic location data, the installation location data, and the shooting lane data, remove missing records, identify and remove outliers using a box plot method, construct an equipment type feature set, perform one-hot encoding conversion on the equipment type feature set, construct a geographic location feature set, perform one-hot encoding conversion on the geographic location feature set, construct an installation location feature set and a shooting lane feature set, perform one-hot encoding conversion on the installation location feature set and the shooting lane feature set, and divide the encoded feature set into a training sample set and a test sample set according to time series information; Constructing a multi-label random forest classifier based on the training sample set, performing random sampling with replacement on the training sample set to generate multiple sub-datasets, randomly selecting feature subsets from the sub-datasets, constructing a decision tree based on the feature subsets, performing feature partitioning on tree nodes using an information gain criterion, calculating the Gini coefficient of each node, selecting the feature with the smallest Gini coefficient as the splitting feature, iteratively constructing the decision tree until a preset depth is reached, and combining the constructed decision trees to generate an illegal configuration recommendation model; The test sample set is input into the illegal configuration recommendation model, multi-label classification is performed on each test sample based on the illegal configuration recommendation model, the classification results of each decision tree are counted, the final illegal configuration recommendation result is determined by voting, and the illegal configuration recommendation result is output to the traffic equipment configuration system.

2. The method for processing traffic equipment data according to claim 1, wherein: The device type data is obtained from the traffic equipment monitoring platform, the geographic location data is obtained from the geographic information system, and the installation location data and the captured lane data are obtained from the equipment deployment system. The device type data, the geographic location data, the installation location data and the captured lane data are cleaned, missing records are removed, and outliers are identified and removed using a box plot method, including: Obtaining equipment type data from a traffic equipment monitoring platform, obtaining geographic location data from a geographic information system, and obtaining installation location data and captured lane data from a device deployment system based on a data interface, aligning the equipment type data, the geographic location data, the installation location data, and the captured lane data according to timestamps, constructing a data association table, establishing a data quality assessment indicator system, and calculating a completeness score for each record in the data association table; Records with integrity scores below the threshold are removed, and the quartile interval is constructed. The upper quartile and lower quartile of each feature in the data association table are calculated. The outlier judgment boundary is determined based on the upper quartile and the lower quartile. Data exceeding the outlier judgment boundary is marked as outliers and removed to generate a cleaned data set.

3. The method for processing traffic equipment data according to claim 1, wherein: The steps of constructing a device type feature set, performing one-hot encoding conversion on the device type feature set, constructing a geographic location feature set, performing one-hot encoding conversion on the geographic location feature set, constructing an installation location feature set and a shooting lane feature set, performing one-hot encoding conversion on the installation location feature set and the shooting lane feature set, and dividing the encoded feature set into a training sample set and a test sample set according to time series information include: The cleaned data set is classified and sorted according to device type, geographic location, installation location, and shooting lane, and a device type feature set, a geographic location feature set, an installation location feature set, and a shooting lane feature set are constructed respectively. The device type feature set is converted into a device type encoding matrix, the geographic location feature set is converted into a geographic location encoding matrix, and the installation location feature set and the shooting lane feature set are converted into an installation location encoding matrix and a shooting lane encoding matrix respectively. The device type coding matrix, the geographic location coding matrix, the installation location coding matrix and the shooting lane coding matrix are spliced ​​in the sample dimension to generate a feature fusion matrix, the feature fusion matrix is ​​normalized, and the feature fusion matrix is ​​divided into a training sample set and a test sample set according to a preset ratio based on time series information.

4. The method for processing traffic equipment data according to claim 1, wherein: The method comprises the following steps: constructing a multi-label random forest classifier based on the training sample set, performing random sampling with replacement on the training sample set to generate multiple sub-datasets, randomly selecting feature subsets from the sub-datasets, constructing a decision tree based on the feature subsets, performing feature partitioning on the tree nodes using the information gain criterion, and calculating the Gini coefficient of each node. Constructing a multi-label random forest classifier based on the training sample set, setting parameters for the number of trees, maximum depth, and minimum number of split samples of the classifier, performing random sampling with replacement on the training sample set to generate multiple sub-datasets, calculating the importance score of each feature, sorting the features according to the importance score, randomly selecting feature subsets from the sub-datasets according to a preset feature number threshold, and constructing initial nodes of a decision tree based on the feature subsets; The sub-dataset is input into the initial node, the node is feature-partitioned using the information gain criterion, the Gini coefficient of each candidate feature is calculated, the optimal split feature is selected by comparing the Gini coefficients, the data set is divided into a left child node data set and a right child node data set according to the optimal split feature, and the Gini coefficients of the left child node data set and the right child node data set are calculated.

5. The method for processing traffic equipment data according to claim 1, wherein: The feature with the smallest Gini coefficient is selected as the split feature, the decision tree is iteratively constructed until a preset depth is reached, and the constructed decision trees are combined to generate an illegal configuration recommendation model, including: Calculate the Gini coefficients of candidate features for the left child node data set and the right child node data set respectively, select the feature with the smallest Gini coefficient as the split feature, divide the node data into two categories based on the split feature, repeat the node splitting process until the preset depth is reached or the number of node samples is less than the minimum number of split samples, calculate the sample distribution probability of each category for each leaf node, and use the sample distribution probability as the prediction output of the leaf node; The prediction outputs of multiple decision trees are combined to construct a random forest voting matrix. Based on the voting matrix, the predicted probability distribution of each sample in each violation category is calculated. A violation category probability threshold is set, and categories above the violation category probability threshold are marked as positive samples to generate a violation configuration recommendation model.

6. The method for processing traffic equipment data according to claim 1, wherein: Inputting the test sample set into the illegal configuration recommendation model, performing multi-label classification on each test sample based on the illegal configuration recommendation model, and counting the classification results of each decision tree include: Inputting the test sample set into the illegal configuration recommendation model, performing feature matching on each test sample in each decision tree, dividing the sample into corresponding leaf nodes according to the sample feature value, extracting the predicted probability distribution stored in the leaf node, calculating the predicted probability value for each illegal category, constructing a prediction result matrix based on the predicted probability values, and combining the prediction result matrix on the decision tree dimension; The classification results of each decision tree for each test sample in each violation category are counted, a decision tree voting statistics table is constructed, the number of decision trees predicted as positive samples for each violation category is calculated, the number of decision trees is divided by the total number of decision trees to obtain the category prediction probability, the category prediction probability is normalized, and a standardized prediction probability matrix is ​​generated.

7. The method for processing traffic equipment data according to claim 1, wherein: The method of determining the final illegal configuration recommendation result by voting and outputting the illegal configuration recommendation result to the traffic equipment configuration system includes: Setting voting weight coefficients based on the standardized prediction probability matrix, performing weighted voting on the prediction results of each decision tree, calculating weighted voting scores for each violation category, comparing the weighted voting scores with a preset violation category threshold, marking categories above the violation category threshold as recommended configuration items, sorting the recommended configuration items in descending order according to the weighted voting scores, and generating a violation configuration recommendation result; The illegal configuration recommendation results are encapsulated into a standard data format, a data transmission interface is constructed, a communication connection with the traffic equipment configuration system is established, the illegal configuration recommendation results are pushed to the traffic equipment configuration system in real time through the data transmission interface, a push log is recorded, and a recommendation result history is saved.

8. A traffic equipment data processing device, characterized in that: The device comprises: a data set determination module, configured to obtain equipment type data from a traffic equipment monitoring platform, obtain geographic location data from a geographic information system, obtain installation location data and shooting lane data from a device deployment system, clean the equipment type data, the geographic location data, the installation location data, and the shooting lane data, remove missing records, identify and remove outliers using a box plot method, construct an equipment type feature set, perform one-hot encoding conversion on the equipment type feature set, construct a geographic location feature set, perform one-hot encoding conversion on the geographic location feature set, construct an installation location feature set and a shooting lane feature set, perform one-hot encoding conversion on the installation location feature set and the shooting lane feature set, and divide the encoded feature set into a training sample set and a test sample set according to time series information; a model training module for constructing a multi-label random forest classifier based on the training sample set, performing random sampling with replacement on the training sample set to generate multiple sub-datasets, randomly selecting feature subsets from the sub-datasets, constructing a decision tree based on the feature subsets, performing feature partitioning on the tree nodes using an information gain criterion, calculating the Gini coefficient of each node, selecting the feature with the smallest Gini coefficient as the splitting feature, iteratively constructing the decision tree until a preset depth is reached, and combining the constructed decision trees to generate an illegal configuration recommendation model; The recommended configuration module is used to input the test sample set into the illegal configuration recommendation model, perform multi-label classification on each test sample based on the illegal configuration recommendation model, count the classification results of each decision tree, determine the final illegal configuration recommendation result by voting, and output the illegal configuration recommendation result to the traffic equipment configuration system.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the traffic equipment data processing method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the traffic equipment data processing method according to any one of claims 1 to 7 are implemented.

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