Data processing method and system for digital traffic service supervision

Abstract

The invention relates to a data processing method and system for digital traffic service supervision, and the method comprises the steps: obtaining supervision data of digital traffic service, and converting the supervision data into data in a specified format; the conversion process comprises a conversion process of the supervision file and a conversion process of the supervision data; storing the converted data into a database, and respectively storing different types of data in a system data table, a business data table and a digital traffic data table; the data stored in the database is inquired and counted for visual display, multiple chart types are adopted in the visual display process, and the data and the incidence relation are displayed in combination with the time sequence, classified distribution and spatial distribution of the data. Compared with the prior art, the method has the advantages that the supervision files and the supervision data are recognized and converted during data importing, a plurality of different data tables are designed for classified management, a visual visual interface is given, and data processing efficiency and user experience can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a data processing method and system for digital traffic service supervision. Background Art

[0002] Digital services have become an important support for new forms of modern service industry. However, due to the lack of supervision, digital services and various platforms have caused a series of problems such as irregular service quality and illegal content dissemination. In the field of digital transportation, establishing a basic platform for intelligent service supervision is an important measure to ensure the standardization and compliance of digital services.

[0003] The invention with publication number CN117851521A discloses a method, system, device and storage medium for hierarchical classification of traffic data. The method includes: collecting different types of data to be classified through different collection methods; preprocessing the data to be classified according to the structural type of the data to be classified; classifying the preprocessed data to be classified according to the data source and structural type, and storing them in different domain groups in the subject library in the form of data tables or fields; generating a derived data table according to the classified data, classifying the derived data table step by step to form several levels, and associating the data in corresponding categories between the levels to obtain hierarchical classification results, and storing the hierarchical classification results; obtaining a call request from a terminal, and sending the corresponding hierarchical classification results to the terminal according to the call request.

[0004] This solution improves the application accuracy and business relevance of traffic data by storing data in a hierarchical and classified manner; however, it only involves the classification storage process and still cannot provide a convenient and intuitive user interface and efficient data processing process. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a data processing method and system for digital traffic service supervision, providing user-friendly interface design and comprehensive supervision record management.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A data processing method for digital traffic service supervision comprises the following steps:

[0008] Acquire regulatory data of digital traffic services and convert the regulatory data into data in a specified format; the conversion process includes a conversion process of regulatory documents and a conversion process of regulatory data;

[0009] The converted data is stored in the database, and different types of data are stored separately in system data tables, business data tables and digital traffic data tables;

[0010] The query statistics the data stored in the database for visualization. The visualization process uses a variety of chart types and combines the time series, classification distribution and spatial distribution of the data to display the data and its associations.

[0011] Furthermore, the conversion process of the regulatory documents includes:

[0012] Generate corresponding storage paths for regulatory files uploaded by users, identify corresponding file types, parse and process them according to file types, and extract regulatory clauses;

[0013] Each extracted regulatory clause is further transformed into an atomic rule;

[0014] The obtained atomic rules are formally verified. If the verification fails, the atomic rules are automatically corrected according to the preset adjustment mechanism.

[0015] Furthermore, the parsing process according to the file type includes:

[0016] For regulatory documents in Word format, the file content is read through the file parsing interface, and regulatory clauses are identified based on the read file content structure;

[0017] For regulatory documents in Excel format, read each row of data, parse the data according to the predefined column structure, and convert it into the required standardized format, so as to extract and convert the regulatory terms in the document;

[0018] For regulatory documents in the form of flowcharts, a structured analysis of the graphic elements and connection relationships in the documents is performed through a parsing interface to identify the nodes, lines and their semantic information in the flowcharts. Through semantic analysis technology, key information in the nodes is extracted and reorganized according to logical relationships to generate regulatory clauses.

[0019] Furthermore, the atomic rule is the smallest execution unit extracted from the regulatory regulations, which includes conditions, constraints and scope of application;

[0020] The atomic rules are extracted using an atomic rule generation algorithm, and the processing of the atomic rule generation algorithm includes:

[0021] Preprocessing the input regulatory terms, including word segmentation, stop word removal and grammatical analysis, to obtain preprocessed sentences;

[0022] Perform semantic analysis and extraction on the preprocessed sentences, obtain the components of the rules through condition extraction, constraint extraction, entity extraction and behavior extraction functions, and obtain the conditions, constraints, entities and behavior information;

[0023] The extracted conditions, constraints, entities and behavior information are combined to form atomic rules, and the atomic rules are verified. If the verification passes, they are stored in the database and assigned a unique identifier; if the verification fails, they are marked as pending review.

[0024] Furthermore, the method also includes a formal verification process: using a formal method to verify each generated atomic rule, and using natural language processing technology and an inference engine to automatically verify and correct the atomic rule;

[0025] The verification expression of the formal method is:

[0026] Valid(r)=Condition(C)∧Constraint(C)∧Entity(E)∧Action(A)

[0027] In the formula, Valid(r) indicates whether the rule r is valid, and Condition(C), Constraint(C), Entity(E) and Action(A) respectively indicate whether the condition, constraint, entity and action parts of the rule meet the logical consistency and business requirements;

[0028] The correction process includes: when the semantics of a condition or constraint is unclear, performing semantic error correction; when a rule cannot be automatically corrected, marking the corresponding atomic rule as a state to be manually reviewed.

[0029] Furthermore, the transformation process of the regulatory data includes:

[0030] Get all CSV files in the specified directory and verify the validity of each CSV file. If it is illegal, it will be recorded and skipped; if it is legal, the header field of the CSV file will be extracted and standardized;

[0031] Each extracted field value is parsed and mapped according to the database table structure. The generated data content object is associated with the CSV file and stored in the database for data query.

[0032] Furthermore, the method stores the converted data in a database through a data table, a business data table and a digital traffic data table;

[0033] The data table includes various management permissions, and the corresponding core table is the permission table, which is used to build the platform user management, permission control and log recording system;

[0034] The business data table includes various regulatory regulations, and the corresponding core table is the regulatory regulations table, which is used for the management of access specifications, atomic regulations, data interfaces, original data, regulatory documents, regulatory regulations, regulatory results and standard data;

[0035] The digital traffic data table contains digital traffic information, and the corresponding core table is the basic information table of the online car-hailing platform company, which is used to cover all aspects of the online car-hailing platform company and its related businesses.

[0036] Furthermore, during the visualization process, the original data in the database is organized into a format suitable for front-end display, and data interaction is performed through an API interface;

[0037] The data interaction process includes:

[0038] The front end uses the HTTP request framework fetch or axios to initiate an asynchronous request. After receiving the asynchronous request, the back end obtains the required data from the database through SQL query, and formats it into a standard JSON format and returns it to the front end. The front end parses the data in JSON format and binds it to the corresponding chart component.

[0039] Further, the multiple chart types include line charts, bar charts, pie charts, and geographic distribution charts;

[0040] The generation process of the line graph includes: binding the time series data to the horizontal axis, binding the data value to the vertical axis, setting a smooth curve display effect, and generating the line graph;

[0041] The generation process of the bar chart includes: using a dual-axis mode, the primary axis displays the quantity of regulatory data, and the secondary axis displays the violation rate, so that information of different dimensions can be displayed in one chart;

[0042] The pie chart generation process includes: returning classification information through a data interface, dynamically generating legends and color allocation results according to categories, and forming a pie chart;

[0043] The generation process of the geographic distribution map includes: loading map boundary data in combination with a JSON file, and marking the distribution of regulatory tasks on the map to form visual hotspots.

[0044] The present invention also provides a data processing system for the data processing method for digital traffic service supervision as described above, comprising:

[0045] A data acquisition module, used to acquire the regulatory data of digital traffic services and convert the regulatory data into data in a specified format; the conversion process includes a conversion process of regulatory documents and a conversion process of regulatory data;

[0046] A data storage module is used to store the converted data in a database, and to store different types of data separately in a system data table, a business data table and a digital traffic data table;

[0047] The visualization display module is used to query and statistically display the data stored in the database for visualization. The visualization display process uses a variety of chart types and combines the time series, classification distribution and spatial distribution of the data to display the data and its associations.

[0048] Compared with the prior art, the present invention has the following advantages:

[0049] (1) The data processing method for digital traffic service supervision provided by the present invention identifies and converts the supervision documents and supervision data respectively during data import. For different types of supervision documents, corresponding methods and rules are set to realize automatic extraction of supervision clauses, and further convert them into atomic rules through an atomic rule generation algorithm. The complex rules in the supervision documents can be converted into executable atomic rules, and the accuracy, compliance and effectiveness of the rules can be ensured through a strict verification and storage management mechanism.

[0050] In the process of regulatory data conversion, the original data files in CSV format are automatically parsed, cleaned and standardized, which enables efficient data storage and query while ensuring data integrity and consistency;

[0051] For the data obtained after conversion, several different data tables are designed to classify and manage different data separately, which is convenient for efficient and fast data query; finally, an intuitive visualization interface is given and rich chart displays are provided to present the core data of the supervision platform in an intuitive way, which is more convenient for the supervision of digital transportation service data.

[0052] (2) User-friendly interface design: The present invention provides an intuitive user interface and designs visualization components such as bar charts, line charts, and pie charts to display regulatory results, allowing users to easily select and preview regulatory rules, view historical records, and download detailed regulatory reports. This design simplifies the operation process and improves the user experience.

[0053] (3) Comprehensive regulatory record management: It records all past regulatory task information, including the regulatory rules used, data types, task types, etc., to facilitate users to conduct historical queries and audits. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic flow chart of a data processing method for digital traffic service supervision provided in an embodiment of the present invention;

[0055] Figure 2 A regulatory document conversion flow chart provided in an embodiment of the present invention;

[0056] Figure 3A supervision data conversion flow chart provided in an embodiment of the present invention;

[0057] Figure 4 An ER diagram corresponding to a supervision authority table provided in an embodiment of the present invention;

[0058] Figure 5 An ER diagram corresponding to a regulatory regulations table provided in an embodiment of the present invention;

[0059] Figure 6 An ER diagram corresponding to a basic information table of an online car-hailing platform company provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0061] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0063] Example 1

[0064] like Figure 1 As shown, this embodiment provides a data processing method for digital traffic service supervision, comprising the following steps:

[0065] S1: Obtaining regulatory data of digital traffic services, and converting the regulatory data into data in a specified format; the conversion process includes a conversion process of regulatory documents and a conversion process of regulatory data;

[0066] S2: storing the converted data in a database, and storing different types of data separately in system data tables, business data tables, and digital traffic data tables;

[0067] S3: Query and count the data stored in the database for visualization. The visualization process uses a variety of chart types and combines the time series, classification distribution and spatial distribution of the data to display the data and its associations.

[0068] Specifically, step S1 mainly includes two parts: regulatory document conversion and regulatory data conversion.

[0069] 1.1. Regulatory document conversion

[0070] The regulatory document conversion process includes:

[0071] Generate corresponding storage paths for regulatory files uploaded by users, identify corresponding file types, parse and process them according to file types, and extract regulatory clauses;

[0072] Each extracted regulatory clause is further transformed into an atomic rule;

[0073] The obtained atomic rules are formally verified. If the verification fails, the atomic rules are automatically corrected according to the preset adjustment mechanism.

[0074] Specifically, the overall process is as follows Figure 2 As shown. When a user uploads a regulatory document, a unique storage path is generated for each file. During the upload process, the file type will be automatically identified and processed accordingly. For files in Word document format, the file is parsed by the template engine, the data in the file is matched with the predefined template, and filled into the specified location; for Excel files, the file content is read, and the data in each row and column is parsed and converted into a format that meets the requirements. All file related information, such as file name, storage path, upload time, etc., will be saved in the database, which ensures the persistent storage of the file and lays the foundation for subsequent rule extraction and data processing. After the file is uploaded, it is further processed according to different file formats and contents.

[0075] Taking a regulatory document in Word format as an example, the file content is first read through the file parsing interface, and the regulatory clauses are identified based on the file structure. These clauses contain various regulatory requirements and regulations. The template engine will be used to match the file content with the external data source, and the key information in the clauses (such as rule number, clause content, etc.) will be extracted and structured through rendering technology, and finally a structured document for further processing will be generated. In this way, the rule information in the file is converted into a standardized data format, which is convenient for subsequent rule extraction and verification.

[0076] Taking the regulatory file in Excel format as an example, each line of data in the file will be read and the data will be parsed according to the predefined column structure. Excel files usually contain multiple fields, such as rule number, rule content, start and end dates, scope of application, etc., and each line represents an independent rule. These fields are matched with the predefined data model, the specific content of each rule is extracted, and it is converted into the required standardized format. The parsed rule information will be stored in the database for subsequent query, display and further intelligent processing. In this way, by parsing the Excel file, the regulatory terms in the file can be automatically extracted and converted, laying a solid foundation for the generation, verification and storage of subsequent rules.

[0077] Taking the regulation document in the form of a flowchart as an example, we first perform a structured analysis of the graphic elements and connection relationships in the document through the parsing interface to identify the nodes, lines and their semantic information in the flowchart. Each node usually represents a specific regulatory condition, behavior or decision point, while the line represents the logical relationship and execution order between the nodes. By parsing the flowchart structure and combining semantic analysis technology, the key information in the node (such as conditions, operations, constraints, etc.) is extracted and reorganized according to the logical relationship to generate a clear description of the regulatory rules. During the parsing process, a rule standardization mechanism will also be introduced to ensure that the converted rules comply with the predefined data model and logical specifications. Ultimately, these rule descriptions will be stored in the database to provide support for subsequent query, verification and execution, thereby achieving efficient conversion of the regulation document in the form of a flowchart to standardized regulatory rules.

[0078] Each extracted regulatory rule will be further converted into an atomic rule. An atomic rule is the smallest execution unit extracted from a regulatory regulation, which contains clear conditions, constraints, and scope of application. Through multi-level data parsing and semantic processing, the information in the original regulation is mapped into the atomic rule model to ensure that the rule is logically correct and meets business needs. The generation of atomic rules is not just a simple rule decomposition, but also involves semantic understanding and data mining of the rule content. The atomic rule generation algorithm is shown in Algorithm 1. The atomic rule generation algorithm deeply analyzes the content of the regulatory regulations, extracts the conditions, constraints, entities, and behaviors in each rule, and converts these elements into standardized atomic rules to ensure the correctness and compliance of the rule logic. In the implementation process, the input regulatory text is first preprocessed, including word segmentation, stop word removal, grammatical analysis, etc., in order to extract meaningful sentences. Then, semantic parsing is performed on each sentence to extract the key elements. Specifically, the components of the rule are obtained through functions such as condition extraction, constraint extraction, entity extraction, and behavior extraction. For example, the function extractCondition(s) can be used to extract the conditional part of a sentence, extractConstraint(s) can be used to extract the constraint part, and extractEntity(s) and extractAction(s) can be used to extract the entity and action parts respectively.

[0079] After extracting conditions, constraints, entities, and behaviors, the algorithm checks whether each rule meets the basic logical requirements. If the conditions and behaviors are successfully extracted and conform to the predetermined rule format, a new atomic rule will be created through createRule(condition, constraint, entity, action). On this basis, the generated atomic rules are verified to ensure their compliance. The verification process may involve checking the legality and integrity of the rules. If there is a problem with the rule, such as non-compliance with regulatory requirements or logical loopholes, the rule will be marked as "pending review" for manual intervention and correction. If the rule passes the verification, it will be stored in the database and assigned a unique identifier (ID) to ensure the traceability of the rule.

[0080] In terms of rule storage and management, the algorithm also implements version control. Each time a rule is updated, metadata such as creation time, update time, and verification status are recorded to ensure that the entered rules are always up-to-date and valid. Rule version management can be implemented through the versionControl(r) function, which ensures that historical information is not lost during rule updates and maintenance, and each version can be traced and audited.

[0081] During the algorithm execution, the workflow is as follows:

[0082] 1) Text preprocessing: First, the original regulatory regulations are preprocessed to clean the text through steps such as tokenization and stop-word removal.

[0083] 2). Semantic analysis and extraction: Each sentence is structured through syntactic analysis to extract conditions, constraints, entities and behaviors.

[0084] 3) Rule generation and verification: Combine the extracted elements into atomic rules through the createRule() function, and verify the rules. Use validateRule(r) to ensure the compliance of the rules.

[0085] 4) Rule storage and version management: store compliance rules through storeRuleInDatabase(r), assign a unique identifier to each rule, and perform version management.

[0086] In practical applications, the above steps can automatically process a large number of regulatory regulations, improve the efficiency and accuracy of rule generation through intelligent algorithms, and handle complex rule logic and the correlation between rules.

[0087] Algorithm 1 Atomic rule generation algorithm

[0088]

[0089] In order to ensure the correctness and applicability of atomic rules, a formal verification process is also introduced. Formal methods are used to verify each generated atomic rule, and natural language processing technology and reasoning engines are used to automatically verify and correct the rules. The key to formal verification is to perform strict logic checks and consistency verifications on each component element of the rule to ensure that these elements conform to the predetermined logical structure and can be executed unambiguously in practical applications. In the verification process, the rule conditions and constraints are first analyzed to confirm whether they meet the specific logical form. For example, for the condition part C in a rule, verify whether it is compatible with the corresponding entity and behavior part A and meets the predefined compliance standards. Specifically, formal verification can be expressed as:

[0090] Valid(r)=Condition(C)∧Constraint(C)∧Entity(E)∧Action(A)

[0091] Among them, Valid(r) indicates whether the rule r is valid, and Condition(C), Constraint(C), Entity(E) and Action(A) respectively indicate whether the condition, constraint, entity and behavior parts of the rule meet the logical consistency and business requirements. In addition, the automatic reasoning ability of the reasoning engine is used to perform in-depth reasoning on the rules to check possible logical loopholes or conflicts between the rules. For example, by introducing a formal model, it is possible to check whether there are contradictions between multiple rules, or whether certain rules can be covered or conflicted by other rules under certain conditions. If the verification passes, the rule will be marked as valid and stored in the rule database to provide support for subsequent regulatory tasks. The formalization of this process can be expressed as:

[0092] Store(r)if Valid(r)=True

[0093] However, if the verification fails, the rules need to be automatically corrected according to the preset adjustment mechanism. For example, when the semantics of the conditions or constraints are unclear, semantic correction needs to be performed automatically, or the rules that cannot be automatically corrected need to be marked as pending manual review, waiting for manual intervention by experts. This part of the process can be expressed as:

[0094] Adjust(r)if Valid(r)=False

[0095] At this point, the rule will enter the review queue for further manual confirmation and correction.

[0096] After this series of rule generation and verification processes, it can be ensured that each atomic rule is logically correct and compliant, and at the same time has a high degree of applicability. When atomic rules are stored in the database, a unique identifier is assigned to each rule, and the related metadata (such as creation time, update time, verification status, etc.) is saved together. These metadata enable the rules to be efficiently managed and traced. At the same time, it also supports version control and history management of atomic rules. Whenever an atomic rule is updated, a new version is automatically generated, and the historical version is retained. Through this version control mechanism, the change process of each rule can be accurately managed to ensure the stability and traceability of the rules.

[0097] Through the above processing flow, the complex rules in the regulatory documents can be converted into executable atomic rules, and through strict verification and storage management mechanisms, the accuracy, compliance and effectiveness of the rules can be ensured.

[0098] 1.2. Regulatory data conversion

[0099] The overall process is as follows Figure 3As shown in the figure, the original data files in CSV format are automatically parsed, cleaned and standardized to achieve efficient data storage and query while ensuring data integrity and consistency. It is suitable for large-scale regulatory data management and can significantly improve the automation level of data processing, reduce manual intervention and operational errors, and thus greatly improve work efficiency and accuracy.

[0100] The regulatory data processing algorithm based on CSV files is shown in Algorithm 2. In the implementation process, the algorithm obtains all CSV files in the specified directory D through functions such as getCSVFiles(D) and uses isValidCSV(f) to verify the legitimacy of each file. If the file is invalid, it is recorded and skipped through LogError(f). Next, the algorithm uses extractHeader(f) to extract the header field of the CSV file and standardizes it through standardizeHeader(H). For each record r, extractFieldValue(r,h) is used to obtain the field value, formatField(v,S[h]) is used for type conversion, and finally LogError(r,h) is used to record the invalid field. The processed data is stored in a temporary record object R[h], and after all records are processed, the Batch insert method is used to write the data in batches to the database. This algorithm can efficiently and accurately process regulatory data in CSV format and seamlessly convert it into information in a structured database to support subsequent query and analysis.

[0101] Algorithm 2 Regulatory data processing algorithm based on CSV files

[0102]

[0103] Secondly, by parsing the header information of the CSV file, the name and location of each column of data are obtained, and the column names are cleaned and normalized, and special characters and spaces are automatically removed to ensure the consistency of column names. Next, the field values ​​of each CSV record are parsed and mapped according to the database table structure. Each field will be converted according to the predetermined format, such as converting numeric type fields to BigDecimal and time fields to Timestamp to ensure consistency in data type and precision. An exception handling mechanism is also implemented for each field. For example, empty fields will be filled with default values, and numeric fields will be validated to ensure that their values ​​are within a reasonable range.

[0104] The formula is as follows:

[0105] v cleaned =formatField(v,S[h])

[0106] Where v represents the original field value, S[h] is the database table structure definition, and formatField(v,S[h]) converts the field value. During the field conversion process, if the format or type is found to be inconsistent with expectations, the exception handling mechanism will be triggered, the error will be recorded and the current record will be skipped to ensure that the data processing flow is not disturbed. After all data is cleaned and converted, a data content object is generated and associated with a CSV file, and stored in batches in the database. During the insertion process, data operations use transaction control to ensure data consistency. Finally, in order to improve query efficiency, a paging query function based on standard data content is provided, which supports efficient data query through dynamically generated SQL statements.

[0107] In summary, the regulatory data conversion method provided by the present invention combines data cleaning, field format conversion and batch processing, improves the intelligence and efficiency of data processing, and ensures the high efficiency and accuracy of large-scale data management.

[0108] Step S2 is specifically as follows:

[0109] The converted data is stored in a data table. Specifically, the present invention designs three types of data tables, including a data table, a business data table, and a digital traffic data table. The data table mainly includes various management permissions, and its core table is the permission table; the business data table mainly includes various regulatory regulations, and its core table is the regulatory regulations table; the digital traffic data table mainly contains various information on digital traffic, and its core table is the basic information table of the online car-hailing platform company. The design of the core data table is listed here:

[0110] (1) Permission table: stores information about different permissions of the platform, as shown in Table 1.

[0111] Table 1 Permission table attribute design

[0112]

[0113] The permission table corresponds to the ER diagram as follows Figure 4 shown.

[0114] (2) Regulatory regulations table: stores relevant information of regulatory regulations, as shown in Table 2.

[0115]

[0116] The regulatory regulations table corresponds to the ER diagram as follows Figure 5 shown.

[0117] (3) Basic information table of online ride-hailing platform companies: stores the basic information of online ride-hailing platform companies, as shown in Table 3.

[0118] Table 3 Attribute design of basic information table of online car-hailing platform companies

[0119]

[0120] The ER diagram corresponding to the basic information table of the online car-hailing platform company is as follows Figure 6 shown.

[0121] Step S3 specifically includes:

[0122] Query statistical stored data for visual display. Specifically, this solution aims to present the core data of the regulatory platform in an intuitive way. It mainly uses a variety of chart types such as line charts, bar charts, pie charts and geographic distribution charts, combined with the time series, classification distribution and spatial distribution of data, to provide comprehensive data insights. At the same time, the background organizes the raw data in the database into a format suitable for front-end display through efficient data processing logic, and interacts through API interfaces.

[0123] (1) AJAX data interaction

[0124] Data interaction uses AJAX technology, which sends requests from the front end to the back end, dynamically obtains the required data from the database, and returns it to the front end in JSON format. The core database tables include the supervision record table and the regional information table. The supervision record table stores the core information of each supervision task, including fields such as task ID, task date, regional code, and whether there is a violation, providing basic data for all statistical and visualization functions. The regional information table stores the mapping between regional codes and specific provincial and municipal names to support geographic distribution statistics.

[0125] The front end uses the HTTP request framework fetch or axios to initiate an asynchronous request. After receiving the request, the back end obtains the required data from the database through SQL query and formats it into a standard JSON format and returns it to the front end. Through the JSON format, the front end can directly parse date, inspection_count and violation_rate and bind them to the corresponding chart components. For example, a query based on time statistics may return the following data:

[0126]

[0127] The backend API provides multi-dimensional interfaces such as time range, regional distribution and classification statistics. For example, through the " / api / inspections?start_date=2024-12-01&end_date=2024-12-02" interface, you can obtain task data queried by time range, through " / api / regions / statistics" to obtain the total number of tasks and violation rate data by region, and through " / api / categories / violations" to obtain the proportion of different violation types.

[0128] Through SQL query, the total number of tasks and the number of illegal tasks in each region can be counted, and then the violation rate can be calculated. The query uses the CASE statement conditional counting and combines the ROUND function to calculate the exact violation rate:

[0129]

[0130] The above query returns the number of violations, total number of tasks, and violation rate for each region, providing data support for the bar chart and pie chart on the visualization page.

[0131] (2) Display and organization of charts

[0132] The core of the front-end visualization chart is a dynamic chart based on the ECharts library. Line charts are used to display time series data, such as the number of completed daily regulatory tasks and the trend of violation rates. Bar charts are suitable for displaying classified data, such as the comparison of the number of regulatory tasks and violation rates in various regions. Pie charts are more suitable for representing proportional data, such as the proportion of different types of violations. Geographic distribution maps show the distribution hotspots of regulatory data in different regions by combining maps.

[0133] In the organizational logic of the front-end, the line chart mainly binds the time series data to the horizontal axis and the data value to the vertical axis, and sets a smooth curve display effect to make the trend more intuitive. It is used to display time series data, such as the number of daily supervision tasks, completion status, and violation rate. The data interface returns the time (date) and the corresponding value (inspection_count) data. The front-end binds the time data through xAxis and the value data through yAxis. For example, the changing trend of the number of daily tasks and violation rate:

[0134]

[0135] The bar chart is usually combined with a dual-axis mode, with the primary axis showing the number of regulatory data and the secondary axis showing the violation rate, so that information of different dimensions can be fully presented in one chart. It is used to show the distribution of regional data, such as the number of regulatory data and the violation rate in each province or city. The data interface packages the region name, total data, and violation rate into JSON and returns it, and the front end binds the bar and line series of the chart.

[0136] The pie chart returns classification information through the data interface, and dynamically generates legends and color assignments by category, such as the proportion of different types of violations. The data interface returns type and count according to the violation type. The front-end is configured as a ring pie chart, and the legend and color are dynamically generated.

[0137] The geographic distribution map combines the JSON file to load the map boundary data, and marks the distribution of supervision tasks on the map to form visual hotspots.

[0138]

[0139] (3) Query and statistics of time and region

[0140] In the time dimension statistics, the SQL DATE_FORMAT function can be used to achieve daily or monthly statistics. These query results are returned in JSON format and returned to the front end through the back-end interface, supporting dynamic updates of line charts. For example, when performing monthly statistics, the number of tasks for each month is calculated by extracting the year-month grouping:

[0141]

[0142] When counting by day, directly extract the date field and group it:

[0143]

[0144] The geographic distribution map needs to associate the region code with the actual province and city name. This function depends on the design of the region information table. The region information table stores the region code, province name, and city name. In this way, the number of supervision tasks in each province can be counted to provide basic data for the geographic distribution map. When counting task data, the region code is associated with the province and city information through SQL query:

[0145]

[0146] The preferred embodiments of the present invention are described in detail above.

[0147] This embodiment also provides a data processing system for the data processing method for digital traffic service supervision as described above, comprising:

[0148] A data acquisition module, used to acquire the regulatory data of digital traffic services and convert the regulatory data into data in a specified format; the conversion process includes a conversion process of regulatory documents and a conversion process of regulatory data;

[0149] A data storage module is used to store the converted data in a database, and to store different types of data separately in a system data table, a business data table and a digital traffic data table;

[0150] The visualization display module is used to query and statistically display the data stored in the database for visualization. The visualization display process uses a variety of chart types and combines the time series, classification distribution and spatial distribution of the data to display the data and its associations.

[0151] It should be noted that the specific content and beneficial effects of the system of the present application can be found in the above-mentioned method embodiment, which will not be repeated here.

[0152] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A data processing method for digital traffic service supervision, characterized in that: The following steps are involved: Acquire regulatory data of digital traffic services and convert the regulatory data into data in a specified format; the conversion process includes a conversion process of regulatory documents and a conversion process of regulatory data; The converted data is stored in the database, and different types of data are stored separately in system data tables, business data tables and digital traffic data tables; The query statistics the data stored in the database for visualization. The visualization process uses a variety of chart types and combines the time series, classification distribution and spatial distribution of the data to display the data and its associations.

2. A data processing method for digital traffic service supervision according to claim 1, characterized in that: The regulatory document conversion process includes: Generate corresponding storage paths for regulatory files uploaded by users, identify corresponding file types, parse and process them according to file types, and extract regulatory clauses; Each extracted regulatory clause is further transformed into an atomic rule; The obtained atomic rules are formally verified. If the verification fails, the atomic rules are automatically corrected according to the preset adjustment mechanism.

3. A data processing method for digital traffic service supervision according to claim 2, characterized in that: The parsing process according to the file type includes: For regulatory documents in Word format, the file content is read through the file parsing interface, and regulatory clauses are identified based on the read file content structure; For regulatory documents in Excel format, read each row of data, parse the data according to the predefined column structure, and convert it into the required standardized format, so as to extract and convert the regulatory terms in the document; For regulatory documents in the form of flowcharts, a structured analysis of the graphic elements and connection relationships in the documents is performed through a parsing interface to identify the nodes, lines and their semantic information in the flowcharts. Through semantic analysis technology, key information in the nodes is extracted and reorganized according to logical relationships to generate regulatory clauses.

4. A data processing method for digital traffic service supervision according to claim 2, characterized in that: The atomic rules are the smallest execution units extracted from regulatory regulations, including conditions, constraints and scope of application; The atomic rules are extracted using an atomic rule generation algorithm, and the processing of the atomic rule generation algorithm includes: Preprocessing the input regulatory terms, including word segmentation, stop word removal and grammatical analysis, to obtain preprocessed sentences; Perform semantic analysis and extraction on the preprocessed sentences, obtain the components of the rules through condition extraction, constraint extraction, entity extraction and behavior extraction functions, and obtain the conditions, constraints, entities and behavior information; The extracted conditions, constraints, entities and behavior information are combined to form atomic rules, and the atomic rules are verified. If the verification passes, they are stored in the database and assigned a unique identifier; if the verification fails, they are marked as pending review.

5. A data processing method for digital traffic service supervision according to claim 1, characterized in that: The method also includes a formal verification process: using a formal method to verify each generated atomic rule, and using natural language processing technology and an inference engine to automatically verify and correct the atomic rule; The verification expression of the formal method is: Valid(r)=Condition(C)∧Constraint(C)∧Entity(E)∧Action(A) In the formula, Valid(r) indicates whether the rule r is valid, and Condition(C), Constraint(C), Entity(E) and Action(A) respectively indicate whether the condition, constraint, entity and action parts of the rule meet the logical consistency and business requirements; The correction process includes: when the semantics of a condition or constraint is unclear, performing semantic error correction; when a rule cannot be automatically corrected, marking the corresponding atomic rule as a state to be manually reviewed.

6. A data processing method for digital traffic service supervision according to claim 1, characterized in that: The regulatory data conversion process includes: Get all CSV files in the specified directory and verify the validity of each CSV file. If it is illegal, it will be recorded and skipped; if it is legal, the header field of the CSV file will be extracted and standardized; Each extracted field value is parsed and mapped according to the database table structure. The generated data content object is associated with the CSV file and stored in the database for data query.

7. A data processing method for digital traffic service supervision according to claim 1, characterized in that: The method stores the converted data into a database through a data table, a business data table and a digital traffic data table; The data table includes various management permissions, and the corresponding core table is the permission table, which is used to build the platform user management, permission control and log recording system; The business data table includes various regulatory regulations, and the corresponding core table is the regulatory regulations table, which is used for the management of access specifications, atomic regulations, data interfaces, original data, regulatory documents, regulatory regulations, regulatory results and standard data; The digital traffic data table contains digital traffic information, and the corresponding core table is the basic information table of the online car-hailing platform company, which is used to cover all aspects of the online car-hailing platform company and its related businesses.

8. A data processing method for digital traffic service supervision according to claim 1, characterized in that: During the visualization process, the original data in the database is organized into a format suitable for front-end display, and data interaction is performed through an API interface; The data interaction process includes: The front end uses HTTP request framework fetch or axios to initiate asynchronous requests; After receiving the asynchronous request, the backend obtains the required data from the database through SQL query, formats it into standard JSON format and returns it to the frontend; the frontend parses the data in JSON format and binds it to the corresponding chart component.

9. A data processing method for digital traffic service supervision according to claim 1, characterized in that: The multiple chart types include line charts, bar charts, pie charts, and geographic distribution charts; The generation process of the line graph includes: binding the time series data to the horizontal axis, binding the data value to the vertical axis, setting a smooth curve display effect, and generating the line graph; The generation process of the bar chart includes: using a dual-axis mode, the primary axis displays the quantity of regulatory data, and the secondary axis displays the violation rate, so that information of different dimensions can be displayed in one chart; The pie chart generation process includes: returning classification information through a data interface, dynamically generating legends and color allocation results according to categories, and forming a pie chart; The generation process of the geographic distribution map includes: loading map boundary data in combination with a JSON file, and marking the distribution of regulatory tasks on the map to form visual hotspots.

10. A data processing system based on a data processing method for digital traffic service supervision as claimed in any one of claims 1 to 9, characterized in that: include: A data acquisition module is used to acquire the regulatory data of digital transportation services and convert the regulatory data into data in a specified format; The conversion process includes a conversion process for regulatory documents and a conversion process for regulatory data; A data storage module is used to store the converted data in a database, and to store different types of data separately in a system data table, a business data table and a digital traffic data table; The visualization display module is used to query and statistically display the data stored in the database for visualization. The visualization display process uses a variety of chart types and combines the time series, classification distribution and spatial distribution of the data to display the data and its associations.

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