Traffic information query analysis method for multiple service fields and data structures

Abstract

The invention relates to the technical field of traffic information query and analysis methods, and discloses a traffic information query and analysis method for multiple service fields and data structures, which is characterized by comprising the following steps: S1, data acquisition: collecting traffic data from service fields and data sources through an API interface, data capture and sensor acquisition modes; s2, data preprocessing: carrying out cleaning, duplicate removal and format conversion preprocessing operation on the collected data; s3, data integration: integrating the preprocessed data into a unified data view by using a data fusion technology; and S4, data indexing. According to the invention, the accuracy and efficiency of traffic information query are improved, and the traffic information query requirements of users on multiple service fields and data structures can be met; integration and analysis of heterogeneous traffic data are realized, and the data utilization rate and query efficiency are improved; abundant query result display modes are provided, and user experience and satisfaction are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of traffic information query and analysis methods, and in particular to a traffic information query and analysis method for multiple business fields and data structures. Background Art

[0002] Traffic information query refers to extracting specific information required by users from massive traffic data through various technical means. This information includes but is not limited to real-time road conditions, traffic congestion, traffic accident information, public transportation operation status, parking lot vacancies, traffic control measures, and road construction information. Users can query through mobile phone APP, web pages, traffic information display screens and other channels to better plan travel routes, avoid congested sections, and improve travel efficiency.

[0003] Traffic information query and analysis methods usually include data collection, data preprocessing, data analysis, result generation and display, etc. These methods aim to extract valuable information from complex and ever-changing traffic data and provide users with timely and accurate query services. However, with the development of traffic informatization, traffic data sources are becoming increasingly diversified, and data structures and semantics are also different, which brings new challenges to traffic information query.

[0004] In the existing technology, a major problem with traffic information query and analysis methods for multiple business fields and data structures is that it is difficult to efficiently integrate and analyze these heterogeneous traffic data. Due to the wide range of data sources, data formats, data types and data quality, traditional query and analysis methods are often not directly applicable, resulting in low query efficiency and inaccurate query results.

[0005] To solve the above problems, this application proposes a traffic information query and analysis method for multiple business fields and data structures. Summary of the invention

[0006] 1. Purpose of the invention In order to solve the technical problems existing in the background technology, the present invention proposes a traffic information query and analysis method for multiple business fields and data structures. The present invention improves the accuracy and efficiency of traffic information query, and can meet the user's traffic information query needs for multiple business fields and data structures; realizes the integration and analysis of heterogeneous traffic data, improves data utilization and query efficiency; provides a rich query result display method, and enhances user experience and satisfaction.

[0007] (II) Technical solution To solve the above problems, the present invention provides a traffic information query and analysis method for multiple business fields and data structures, comprising the following steps: S1. Data collection: Collect traffic data from business areas and data sources through API interfaces, data capture, and sensor collection; S2. Data preprocessing: The collected data is cleaned, deduplicated, and format converted to improve data quality; preprocessing algorithms include but are not limited to rule-based cleaning algorithms and statistics-based cleaning algorithms.

[0008] S3, Data Integration: Use data fusion technology to integrate the preprocessed data into a unified data view; the data fusion algorithm can use weighted averaging, Kalman filtering, and Bayesian networks to improve the accuracy and consistency of the data.

[0009] S4. Data index: Establish an efficient data indexing mechanism to improve data query speed. The indexing algorithm can use hash index, B-tree index, bitmap index; S5. Query analysis: Parse the user's query request and convert it into an executable data query statement. The query parsing algorithm can use natural language processing technology, such as word segmentation, part-of-speech tagging, and syntactic analysis; S6. Data analysis: Use data mining and machine learning techniques to conduct in-depth analysis of the integrated data and extract valuable information. Data analysis algorithms include but are not limited to association rule mining algorithms, cluster analysis algorithms, classification algorithms, and prediction algorithms; S7. Result generation: Generate user-understandable query results based on the analysis results. The result generation algorithm can use visualization techniques, such as charts and maps, to intuitively display the query results; S8. Results display: The query results are displayed to users through web pages and mobile APP channels. The display algorithm can adopt responsive design and interactive design to improve user experience.

[0010] Preferably, S1 specifically includes: API interface: obtain real-time traffic information, such as road conditions, accident reports, and public transportation operation status, by calling the API interface provided by the traffic management department or third-party service provider; Data crawling: Using crawler technology to crawl traffic-related information from web pages and social media platforms, such as road construction notices and traffic control measures; Sensor collection: Real-time data on traffic flow, vehicle speed, and congestion levels are collected through sensors installed at key locations on roads, bridges, and tunnels.

[0011] Preferably, S2 specifically includes: Data preprocessing: Data preprocessing is a key step to ensure data quality. Its goal is to clean, remove duplicates, and convert formats to improve data accuracy and usability. Rule-based cleaning algorithm: Cleans data according to preset rules, such as the range of data values ​​and data format, and removes data that does not conform to the rules; Statistical cleaning algorithm: Use statistical methods such as mean, median, and standard deviation to clean data and identify and process outliers or missing values; Deduplication: Remove duplicate data records by comparing key fields in the data records, such as timestamps and locations; Format conversion: Convert data into a unified format for subsequent analysis and processing.

[0012] Preferably, S3 specifically includes: Data Integration: Data integration is to integrate the pre-processed data into a unified data view for subsequent analysis and query; Weighted average method: For data from different data sources but describing the same entity, the weighted average method can be used to integrate the data to reduce errors and improve accuracy; Kalman filter method: For time series data, such as vehicle speed and traffic flow, Kalman filter method can be used to predict and integrate data to improve the real-time and accuracy of data; Bayesian network: For data with complex dependencies, such as the relationship between traffic accidents and weather and road conditions, Bayesian networks can be used to integrate and analyze the data.

[0013] Preferably, S4 specifically includes: Data indexing: Data indexing is a key step to improve data query speed. Its goal is to establish an efficient data indexing mechanism to quickly locate the required data; Hash index: For data that needs to be found quickly, such as license plate numbers and driver information, hash indexes can be used to increase query speed; B-tree index: For data that requires sorting and range query, such as timestamps and locations, B-tree index can be used to improve query efficiency; Bitmap index: For data with multiple attribute values, such as vehicle type and color, bitmap index can be used to reduce storage space and improve query speed.

[0014] Preferably, S5 specifically includes: Word segmentation: Split the user's query request into independent words for subsequent part-of-speech tagging and syntactic analysis; Part-of-speech tagging: tagging each word with its part of speech, such as noun, verb, adjective, for subsequent analysis and processing; Syntactic analysis: Analyze the sentence structure in the query request and identify the subject, predicate, and object sentence components in order to construct an accurate query statement.

[0015] Preferably, S6 specifically includes: Association rule mining algorithms: such as the Apriori algorithm or the FP-Growth algorithm, are used to discover association rules in data, such as "during peak hours, a certain road section is often congested."

[0016] Cluster analysis algorithms: such as the K-means algorithm or the DBSCAN algorithm, are used to divide data into different categories or clusters to better understand the distribution and characteristics of the data.

[0017] Classification algorithms: such as support vector machines (SVM), decision trees, or random forests, are used to classify data into different categories, such as "traffic accident" or "traffic jam".

[0018] Prediction algorithms: such as time series analysis and neural networks, are used to predict future traffic conditions, such as “the degree of congestion on a certain road section in the next hour.”

[0019] The above technical solution of the present invention has the following beneficial technical effects: The accuracy and efficiency of traffic information query have been improved, and can meet users' traffic information query needs in multiple business fields and data structures.

[0020] It realizes the integration and analysis of heterogeneous traffic data, and improves data utilization and query efficiency.

[0021] It provides a variety of query result display methods, enhancing user experience and satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The present invention provides a schematic diagram of the structure of a traffic information query and analysis method for multiple business fields and data structures. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0024] like Figure 1As shown, the present invention proposes a traffic information query and analysis method for multiple business fields and data structures, comprising the following steps: S1. Data collection: Collect traffic data from business areas and data sources through API interfaces, data capture, and sensor collection; Specifically include: API interface: obtain real-time traffic information, such as road conditions, accident reports, and public transportation operation status, by calling the API interface provided by the traffic management department or third-party service provider; Data crawling: Using crawler technology to crawl traffic-related information from web pages and social media platforms, such as road construction notices and traffic control measures; Sensor collection: Real-time data on traffic flow, vehicle speed, and congestion levels are collected through sensors installed at key locations on roads, bridges, and tunnels.

[0025] S2. Data preprocessing: The collected data is cleaned, deduplicated, and format converted to improve data quality; preprocessing algorithms include but are not limited to rule-based cleaning algorithms and statistics-based cleaning algorithms.

[0026] Specifically include: Data preprocessing: Data preprocessing is a key step to ensure data quality. Its goal is to clean, remove duplicates, and convert formats to improve data accuracy and usability. Rule-based cleaning algorithm: Cleans data according to preset rules, such as the range of data values ​​and data format, and removes data that does not conform to the rules; Statistical cleaning algorithm: Use statistical methods such as mean, median, and standard deviation to clean data and identify and process outliers or missing values; Deduplication: Remove duplicate data records by comparing key fields in the data records, such as timestamps and locations; Format conversion: Convert data into a unified format for subsequent analysis and processing.

[0027] S3, Data Integration: Use data fusion technology to integrate the preprocessed data into a unified data view; the data fusion algorithm can use weighted averaging, Kalman filtering, and Bayesian networks to improve the accuracy and consistency of the data.

[0028] Specifically include: Data Integration: Data integration is to integrate the pre-processed data into a unified data view for subsequent analysis and query; Weighted average method: For data from different data sources but describing the same entity, the weighted average method can be used to integrate the data to reduce errors and improve accuracy; Kalman filter method: For time series data, such as vehicle speed and traffic flow, Kalman filter method can be used to predict and integrate data to improve the real-time and accuracy of data; Bayesian network: For data with complex dependencies, such as the relationship between traffic accidents and weather and road conditions, Bayesian networks can be used to integrate and analyze the data.

[0029] S4. Data index: Establish an efficient data indexing mechanism to improve data query speed. The indexing algorithm can use hash index, B-tree index, bitmap index; Specifically include: Data indexing: Data indexing is a key step to improve data query speed. Its goal is to establish an efficient data indexing mechanism to quickly locate the required data; Hash index: For data that needs to be found quickly, such as license plate numbers and driver information, hash indexes can be used to increase query speed; B-tree index: For data that requires sorting and range query, such as timestamps and locations, B-tree index can be used to improve query efficiency; Bitmap index: For data with multiple attribute values, such as vehicle type and color, bitmap index can be used to reduce storage space and improve query speed.

[0030] S5. Query analysis: Parse the user's query request and convert it into an executable data query statement. The query parsing algorithm can use natural language processing technology, such as word segmentation, part-of-speech tagging, and syntactic analysis; Specifically include: Word segmentation: Split the user's query request into independent words for subsequent part-of-speech tagging and syntactic analysis; Part-of-speech tagging: tagging each word with its part of speech, such as noun, verb, adjective, for subsequent analysis and processing; Syntactic analysis: Analyze the sentence structure in the query request and identify the subject, predicate, and object sentence components in order to construct an accurate query statement.

[0031] S6. Data analysis: Use data mining and machine learning techniques to conduct in-depth analysis of the integrated data and extract valuable information. Data analysis algorithms include but are not limited to association rule mining algorithms, cluster analysis algorithms, classification algorithms, and prediction algorithms; Specifically include: Association rule mining algorithms: such as the Apriori algorithm or the FP-Growth algorithm, are used to discover association rules in data, such as "during peak hours, a certain road section is often congested."

[0032] Association rule mining algorithm (Apriori algorithm): formula: ; Among them, L k+1 is a frequent item set of length k+1, C k+1 is a candidate item set of length k+1, support(c) is the support of item set c, and min_sup is the minimum support threshold.

[0033] Cluster analysis algorithms: such as the K-means algorithm or the DBSCAN algorithm, are used to divide data into different categories or clusters to better understand the distribution and characteristics of the data.

[0034] Classification algorithms: such as support vector machines (SVM), decision trees, or random forests, are used to classify data into different categories, such as "traffic accident" or "traffic jam".

[0035] Classification algorithm (Support Vector Machine SVM): formula: ; Among them, α i is the Lagrange multiplier, N is the number of training samples, y i is the sample label, K(x i ,x j ) is the kernel function, which is used to calculate the similarity between samples.

[0036] Prediction algorithms: such as time series analysis and neural networks, are used to predict future traffic conditions, such as “the degree of congestion on a certain road section in the next hour.”

[0037] S7. Result generation: Generate user-understandable query results based on the analysis results. The result generation algorithm can use visualization techniques, such as charts and maps, to intuitively display the query results; S8. Results display: The query results are displayed to users through web pages and mobile APP channels. The display algorithm can adopt responsive design and interactive design to improve user experience.

[0038] In the present invention, a series of steps including data collection, preprocessing, integration, indexing, query parsing, analysis, result generation and display are used to realize comprehensive query and analysis of traffic information in multiple business fields and data structures. Among them, the data integration module uses data fusion to integrate data from different sources and formats into a unified data view; the data analysis module uses data mining and machine learning to conduct in-depth analysis of the integrated data and extract valuable information; the result generation and display module uses visualization technology and interactive design to display the analysis results to users in an intuitive and easy-to-understand manner. This enables the present invention to efficiently handle complex traffic information query needs and provide users with timely and accurate query services.

[0039] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalent forms of such scope and boundaries.

Claims

1. A traffic information query and analysis method for multiple business fields and data structures, characterized in that: The following steps are involved: S1. Data collection: Collect traffic data from business areas and data sources through API interfaces, data capture, and sensor collection; S2. Data preprocessing: Perform pre-processing operations such as cleaning, deduplication, and format conversion on the collected data; S3, Data Integration: Use data fusion technology to integrate pre-processed data into a unified data view; S4. Data index: Establish an efficient data indexing mechanism; S5. Query analysis: Parse the user's query request and convert it into executable data query statement; S6. Data analysis: Use data mining and machine learning to conduct in-depth analysis of the integrated data and extract valuable information; S7. Result generation: Generate user-understandable query results based on the analysis results; S8. Results display: The query results are displayed to users through web pages and mobile APP channels.

2. A traffic information query and analysis method for multiple business fields and data structures according to claim 1, characterized in that: S1 specifically includes: API interface: obtain real-time traffic information by calling the API interface provided by the traffic management department or third-party service provider; Data crawling: Using crawler technology to crawl traffic-related information from web pages and social media platforms; Sensor collection: Real-time data on traffic flow, vehicle speed, and congestion levels are collected through sensors installed at key locations on roads, bridges, and tunnels.

3. A traffic information query and analysis method for multiple business fields and data structures according to claim 2, characterized in that: S2 specifically includes: Data preprocessing: Data preprocessing is a key step to ensure data quality, and its goal is to clean, remove duplicates, and convert formats; Rule-based cleaning algorithm: Cleans data according to preset rules and removes data that does not conform to the rules; Statistical cleaning algorithm: Use statistical methods to clean data, identify and process outliers or missing values; Deduplication: Remove duplicate data records by comparing key fields in the data records; Format conversion: Convert data into a unified format.

4. A traffic information query and analysis method for multiple business fields and data structures according to claim 3, characterized in that: S3 specifically includes: Data Integration: Data integration is to integrate the pre-processed data into a unified data view; Weighted average method: For data from different data sources but describing the same entity, the weighted average method is used to integrate these data; Kalman filter method: For time series data, Kalman filter method is used to predict and integrate data; Bayesian Network: For data with complex dependencies, Bayesian Network is used to integrate and analyze the data.

5. A traffic information query and analysis method for multiple business fields and data structures according to claim 4, characterized in that: S4 specifically includes: Data indexing: Data indexing is a key step to improve data query speed. Its goal is to establish an efficient data indexing mechanism; Hash index: For data that needs to be found quickly, hash index is used to improve query speed; B-tree index: For data that needs to be sorted and range-queried, B-tree index is used to improve query efficiency; Bitmap index: For data with multiple attribute values, a bitmap index is used to reduce storage space and improve query speed.

6. A traffic information query and analysis method for multiple business fields and data structures according to claim 5, characterized in that: S5 specifically includes: Word segmentation: split the user's query request into independent words; Part-of-speech tagging: tag each word with its part of speech; Syntactic analysis: Analyze the sentence structure in the query request and identify the subject, predicate, and object sentence components in order to construct an accurate query statement.

7. A traffic information query and analysis method for multiple business fields and data structures according to claim 6, characterized in that: S6 specifically includes: Association rule mining algorithm: used to discover association rules in data; Cluster analysis algorithms: used to divide data into different categories or clusters to better understand the distribution and characteristics of the data; Classification algorithms: support vector machines, decision trees, or random forests, to classify data into different categories; Prediction algorithms: used to predict future traffic conditions.

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