Internet-based traffic law enforcement case information service method and system

Abstract

The application relates to the technical field of traffic transportation law enforcement, in particular to a traffic transportation law enforcement case information service method and system based on the Internet, which comprises the following steps: firstly, acquiring unique identification login information of a user subject; secondly, searching for the user subject case from a case library according to the unique identification login information; then, comparing and analyzing each case to generate a multi-dimensional illegal case analysis result set corresponding to the user subject; finally, pushing the illegal case situation of the user subject and the associated legal interpretation according to the multi-dimensional illegal case analysis result set. The scheme disclosed by the application can provide multi-dimensional information service for the user subject, guide the user subject to know the illegal situation, reduce illegal behaviors, and reduce the credit risk of the user subject caused by illegal behaviors, which is an urgent problem to be solved in the informationization work of traffic transportation law enforcement.

Description

Technical Field

[0001] This invention relates to the field of transportation law enforcement technology, and in particular to an internet-based method and system for providing information services for transportation law enforcement cases. Background Technology

[0002] The transportation industry is complex, with a large number of employees and businesses, and involves numerous laws and regulations, making supervision and enforcement challenging. Many practitioners lack sufficient understanding of these laws and regulations, leading to a lack of clarity regarding the status and impact of their violations, potentially resulting in frequent violations and lower professional credit ratings. Existing transportation enforcement systems focus on standardizing enforcement procedures and documents, providing practitioners with online case handling for necessary stages of enforcement, but lacking standardized and guiding measures. This fails to meet the enforcement principles of "service within enforcement" and "whoever enforces the law should also educate the public," and the requirement to improve the standardized operation capabilities of industry practitioners. Therefore, providing users with multi-dimensional information services, guiding them to understand their violations, thereby reducing violations and lowering the risk of credit defaults due to violations, is an urgent problem that transportation enforcement informatization needs to address. Summary of the Invention

[0003] The purpose of this invention is to provide an information service method and system for transportation law enforcement cases based on the Internet, so as to provide information services for practitioners and enterprises and help them understand laws and regulations.

[0004] To achieve the above objectives, the present invention provides an internet-based method for providing information services on transportation law enforcement cases, the method comprising: Obtain the unique identifier login information of the user entity; The user's main cases are retrieved from the case database based on the unique identifier login information; By comparing and analyzing each case, a multi-dimensional set of illegal case analysis results corresponding to the user entity is generated. Based on the multi-dimensional analysis results of illegal cases, the system will push information on the illegal cases of the user subject and related legal interpretations.

[0005] Optionally, retrieving user-related cases from the case database based on the unique identifier login information specifically includes: When the unique identifier login information is a citizen's ID number, it indicates that the user is a citizen; when the unique identifier login information is a unified social credit code, it indicates that the user is an enterprise. When the user is a citizen, the user's own cases are retrieved from the case database based on the unique identifier login information; When the user entity is an enterprise, the enterprise's cases are retrieved from the case database based on the unique identifier login information.

[0006] Optionally, the step of comparing and analyzing each case to generate a multi-dimensional set of illegal case analysis results corresponding to the user entity specifically includes: When the user is a citizen, a comparative analysis is performed based on the user's own cases. The results of the multi-dimensional analysis of traffic violations are: the user's own traffic violation case classification statistics, the comparison between the user's own and related industry practitioners' violation cases, the high-frequency violation situation prompts of related industry practitioners, and the personal violation warning index. When the user is an enterprise, the enterprise's cases are compared and analyzed. The enterprise's traffic violation case classification statistics, industry enterprise violation case comparison, high-frequency violation situation prompts for the enterprise and its employees, and enterprise violation warning index are used as the multi-dimensional violation case analysis result set.

[0007] Optionally, the step of pushing information on the user's illegal cases and related legal interpretations based on the multi-dimensional illegal case analysis result set specifically includes: When the user is a citizen, the system generates and pushes information on the user's traffic violations, along with corresponding legal provisions and penalty interpretations, based on the user's traffic violation statistics. It also pushes a comparison of the user's violations with those of practitioners in related industries, as well as alerts on high-frequency violations by practitioners in related industries. The system marks the user's learning status based on their personal violation warning index and case study duration. When the user is an enterprise, the system generates and pushes information on the enterprise's traffic violations, along with corresponding legal provisions and penalty clauses, based on the enterprise's traffic violation statistics. It also pushes industry enterprise violation comparisons and alerts on high-frequency violations by the enterprise and its employees. The system marks the user's learning status based on the enterprise's violation warning index and case study duration.

[0008] Optionally, the personal violation warning index The formula is: ; in, For the first The value of each indicator, For the first Individual utility function for each indicator For the first Each indicator has a weight value.

[0009] Optionally, the enterprise violation early warning index The formula is: ; in, For the first The value of each indicator, For the first A single indicator of firm utility function, For the first Each indicator has a weight value.

[0010] This invention also discloses an Internet-based information service system for traffic law enforcement cases, the system comprising: The acquisition module is used to obtain the unique identifier login information of the user entity; The case retrieval module is used to retrieve user-related cases from the case database based on the unique identifier login information. The multi-dimensional illegal case analysis result set generation module is used to compare and analyze each case and generate a multi-dimensional illegal case analysis result set corresponding to the user subject. The information push module is used to push information on the user's illegal cases and related legal interpretations based on the multi-dimensional illegal case analysis result set.

[0011] Optionally, the multi-dimensional illegal case analysis result set generation module specifically includes: The first result set generation unit is used to conduct comparative analysis based on the retrieved cases of the user when the user is a citizen. The result set includes the user's traffic violation case classification statistics, the comparison between the user's violation cases and those of practitioners in related industries, the high-frequency violation situation prompts of practitioners in related industries, and the personal violation warning index as a multi-dimensional violation case analysis result set. The second result set generation unit is used to conduct comparative analysis based on the retrieved cases of the enterprise when the user is an enterprise. It uses the enterprise's traffic violation case classification statistics, industry enterprise violation case comparison, high-frequency violation situation prompts for the enterprise and its employees, and enterprise violation warning index as a multi-dimensional violation case analysis result set.

[0012] The present invention also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0013] The present invention also discloses an electronic device, including a processor, which implements the above-described method when executing a computer program.

[0014] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: This invention provides a method and system for providing information services on transportation law enforcement cases based on the internet. First, it obtains the unique login information of the user entity. Second, it retrieves the user entity's cases from the case database based on the unique login information. Then, it performs comparative analysis on each case to generate a multi-dimensional set of violation case analysis results for the user entity. Finally, it pushes information on the user entity's violation cases and related legal interpretations based on the multi-dimensional violation case analysis results. The solution disclosed in this invention can provide users with multi-dimensional information services, guide users to understand their violations, reduce illegal behavior, and lower the risk of credit default due to violations—a problem urgently needing to be solved in the informatization of transportation law enforcement. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of an Internet-based method for providing information services for transportation law enforcement cases, as described in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] The purpose of this invention is to provide an information service method and system for transportation law enforcement cases based on the Internet, so as to provide information services for practitioners and enterprises and help them understand laws and regulations.

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] like Figure 1 As shown, this invention discloses an internet-based method for providing information services on transportation law enforcement cases, the method comprising: Step S1: Obtain the unique identifier login information of the user entity.

[0021] Step S2: Retrieve the user's main case from the case database based on the unique identifier login information.

[0022] Step S3: Compare and analyze each case to generate a multi-dimensional set of illegal case analysis results for the user entity.

[0023] Step S4: Push the user's illegal case information and related legal interpretations based on the multi-dimensional illegal case analysis result set.

[0024] The following is a detailed discussion of each step: Step S1: Obtain the unique identifier login information of the user entity. This information can be entered by pressing a key, taking a photo, or using voice input. The specific input method can be selected according to actual needs.

[0025] Step S2: Retrieve user-specific cases from the case database based on the unique login information. Specifically, this includes: if the unique login information is a citizen's identification number, it indicates that the user is a citizen; if the unique login information is a unified social credit code, it indicates that the user is an enterprise; if the user is a citizen, retrieve their own cases from the case database based on the unique login information; if the user is an enterprise, retrieve the enterprise's cases from the case database based on the unique login information. The aforementioned citizen's identification number can be an ID card number, professional qualification certificate number, etc.; retrieving the user's own cases or the enterprise's cases can be retrieving cases within a set time period, or all cases prior to that period; the set time period can be the most recent login time or a period prior to that, and the length of this time period can be set according to actual needs, such as one year or one month, and is not limited here.

[0026] Step S3: Compare and analyze each case to generate a multi-dimensional analysis result set of illegal cases corresponding to the user entity, specifically including: Step S31: When the user is a citizen, a comparative analysis is performed based on the retrieved cases against the user. The results of the multi-dimensional violation case analysis are: the user's own traffic violation case classification statistics, the comparison between the user's own and related industry practitioners' violation cases, the high-frequency violation alerts for related industry practitioners, and the individual violation warning index. The user's occupation type is obtained from the occupational qualification database. If the user does not have an occupational qualification, their occupation is extracted from the case information. The analysis period is the current year up to the user's login time.

[0027] When the user is a citizen, the aforementioned statistical values ​​for traffic violation cases committed by the user themselves include the number of cases committed by the user. Publicly disclose the number of cases. The number of cases with fines and total amount of fines .

[0028] The comparison of illegal cases involving the user and related industry practitioners includes a comparison of illegal cases involving the user and practitioners in the same industry, and a comparison of illegal cases involving the user and practitioners in their own company. High-frequency illegal activities alerts for related industry practitioners include alerts for high-frequency illegal activities involving practitioners in the same industry and practitioners in their own company.

[0029] The calculation method for the contrast between the number of illegal cases committed by the user and those of other practitioners in the same industry is as follows: Based on the total number of cases committed by all practitioners in the industry, the contrast between the user's number of illegal cases and that of other practitioners in the same industry is represented by a cumulative percentage value. Specifically, illegal cases involving citizens in the same industry as the user are extracted, accumulated by citizen identification number, sorted, and then the cumulative percentage is calculated to obtain the number of cases committed by the user. In contrast to the number of cases involving all practitioners It also notifies users that the number of cases is among the highest in the industry. level.

[0030] The comparison of illegal cases between a user and their company's employees is calculated as follows: Based on the total number of cases handled by all employees of the user's company, a cumulative percentage value is used to represent the contrast between the user's own illegal case count and that of their company's employees. Specifically, the system retrieves all employees of the user's company from the industry regulatory system, extracts all illegal cases handled by these employees, accumulates them by citizen identification number, sorts them, and calculates the cumulative percentage to obtain the user's case count. In contrast to the number of cases involving all practitioners It also notifies users that the number of cases is among the highest in the company. level.

[0031] The alerts for high-frequency violations by industry practitioners are displayed by accumulating violations by practitioners with the same discretionary standards and then sorting them in descending order, showing either a default number or a number selected by the user. Specifically, violations by practitioners with the same discretionary standards are accumulated and sorted from highest to lowest according to the discretionary standard code. Users can choose different numbers to view violations to obtain a summary of industry-wide violations.

[0032] The alerts for high-frequency violations by employees of the same company are displayed by accumulating violations by employees of the same company with the same discretionary standards and sorting them in descending order, with the number displayed by default or by the user.

[0033] Specifically, violations by employees of the same company with the same discretionary standards are cumulatively counted according to the discretionary standard code and sorted from high to low. Users can choose different numbers to view the violations in order to obtain the concentrated content of violations by employees of their company.

[0034] Personal violation warning index The calculation is based on the number of traffic operations, inspections, and cases involving citizens, using the following formula: ;in, For the first The value of each indicator, For the first Individual utility function for each indicator For the first Each indicator has a weight value.

[0035] The percentage of illegal operations is calculated using the following formula:

[0036] To check the percentage of non-conforming items, the calculation formula is as follows:

[0037] The proportion of serious violations is calculated using the following formula:

[0038] Indicator weight value It is calculated by comparing matrices.

[0039] In this invention, importance .but The value could be 0, meaning that there are no key cases among the user's illegal activities. Key cases are identified by data items in the case information and are determined by law enforcement agencies based on factors such as discretionary standards, the time of the incident, and the location of the incident.

[0040] according to The range of values ​​is given, and the individual utility function is also provided. In this invention, , To adjust the coefficients, in this invention patent Maximum percentage of illegal operations .

[0041] The maximum percentage of non-compliance in this invention patent , In this invention patent, the constant coefficient constant coefficient The highest percentage of serious violations .

[0042] The above violation warning index can be calculated to obtain an index between 1 and 100. The smaller the value, the less serious the user's overall violation situation; the larger the value, the more serious the user's overall violation situation.

[0043] Step S32: When the user is an enterprise, a comparative analysis is performed based on the retrieved cases of the enterprise. The results of the multi-dimensional violation case analysis are: the enterprise's traffic violation case classification statistics, the comparison of violation cases among industry enterprises, the enterprise's and its employees' high-frequency violation alerts, and the enterprise's violation warning index. The enterprise's industry type is obtained from the industry approval database, and the analysis period is the user's login time up to the end of the current year.

[0044] The statistical values ​​for traffic violations by this company include both the company's own categorized statistics and the statistics for violations committed by its employees. The industry-wide violation comparison includes the comparison of this company's violations with those of other companies in the industry, and the comparison of the average number of violations per employee across all employees in this company and other companies in the industry. The high-frequency violation alerts for this company and its employees include alerts for high-frequency violations by this company, high-frequency violations by other companies in the industry, high-frequency violations by this company's employees, and high-frequency violations by other employees in the industry.

[0045] The statistics on illegal cases categorized by this enterprise include the number of illegal cases committed by the enterprise. Publicly disclose the number of cases. The number of cases with fines and total amount of fines The statistical values ​​for illegal cases involving employees of this company include the number of illegal cases involving employees of this company. Publicly disclose the number of cases. The number of employees of this company who have been investigated and dealt with. Number of cases per person And also the company's employees who frequently commit illegal acts. The calculation method for the company's employees who frequently commit illegal acts is as follows: calculate the number of cases separately according to the identification number of the company's employees and sort them in descending order. Enterprise users can choose different numbers to view the employees who frequently commit illegal acts.

[0046] The formula for calculating the contrast between this company's illegal cases and those of other companies in the industry is as follows: based on the total number of cases of all companies in the industry according to their unified social credit codes, the cumulative percentage value is used to mark this company's case. Contrast with the number of illegal cases in the industry .

[0047] The formula for calculating the average number of illegal cases per employee of this company compared to other companies in the industry is as follows: Based on the number of illegal cases of each company in the industry and the number of employees of each company obtained from the industry regulatory system, the average number of cases per employee of each company in the industry is calculated and ranked. The cumulative percentage is then used as the current average number of cases per employee for this company. Compared with the average number of cases per employee in this industry .

[0048] This page displays high-frequency violations by the company. Cases against the company are accumulated and sorted in descending order based on the same discretionary standards. Company users can select different numbers to view high-frequency violations to identify concentrated violations by the company.

[0049] This section provides a list of frequently occurring violations by companies in this industry. Cases involving all companies in this industry are aggregated and sorted in descending order based on the same discretionary standards. Company users can select different numbers of cases to view to identify their company's most frequent violations.

[0050] This alert displays frequently occurring violations by employees of this company. Cases involving employees of this company are accumulated and sorted in descending order based on the same discretionary standards, with the alert displayed using either a default number or a user-selected number.

[0051] High-frequency violations by practitioners in this industry are highlighted. Cases involving any practitioner in the industry are accumulated and sorted in descending order based on the same discretionary standards, and the number is displayed by default or user selection.

[0052] Enterprise violation early warning index Calculated based on the number of transportation operations, the number of inspections, and the number of cases involving the enterprise. Enterprise Violation Warning Index. The formula is: ,in, For the first The value of each indicator, For the first A single indicator of firm utility function, For the first Each indicator has a weight value.

[0053] The percentage of illegal operations is calculated using the following formula:

[0054] To check the percentage of non-conforming items, the calculation formula is as follows:

[0055] The proportion of serious violations is calculated using the following formula:

[0056] The unrepaired credit ratio is calculated using the following formula:

[0057] Establish The comparison matrix And a consistency check is performed. In this invention, importance is... . The value could be 0, meaning there are no key cases among the user's violations. Key cases are identified by data items in the case information, determined by law enforcement agencies based on discretionary standards, the time of the incident, the location of the incident, and other factors. Cases where credit has not been repaired are marked as repaired if the law enforcement agency receives and approves the application materials for credit repair from the party through the credit website; otherwise, they are considered unrepaired cases.

[0058] according to The range of values ​​is given for each firm utility function. In this invention, , To adjust the coefficients, in this invention patent Maximum percentage of illegal operations .

[0059] The maximum percentage of non-compliance in this invention patent , In this invention patent, the constant coefficient constant coefficient The highest percentage of serious violations . In this invention patent, the maximum value of the unrepaired credit ratio. .

[0060] The above violation warning index can be calculated to obtain an index between 1 and 100. The smaller the value, the lighter the overall violation situation of the current enterprise user; the larger the value, the more serious the overall violation situation of the current enterprise user.

[0061] Step S4: Based on the multi-dimensional analysis results of illegal cases, push the user's illegal case information and related legal interpretations, specifically including: Step S41: When the user is a citizen, the system generates and pushes information on the user's traffic violation cases, along with corresponding legal provisions and penalty interpretations, based on the user's traffic violation case classification statistics. It also pushes a comparison of the user's violation cases with those of employees in related industries, as well as alerts on high-frequency violations by employees in related industries. The system marks the user's learning status based on their personal violation warning index and case study duration. Furthermore, it can push typical cases of high-frequency violations by industry employees and their respective company employees, along with their corresponding legal provisions and penalty interpretations, according to the same discretionary standards. These can be displayed as videos, text, or images. The aforementioned user traffic violation case classification statistics and the user's violation case comparison with those of employees in related industries can be displayed as numbers or charts; the aforementioned alerts on high-frequency violations by employees in related industries can be displayed as text or images; and the aforementioned personal violation warning index can be displayed as numbers or images.

[0062] Based on the user's personal violation warning index The time spent viewing case studies (i.e., case study learning time) is used to mark the user's self-study status. The specific formula is as follows: in, This invention provides a personal violation warning index threshold for each user. , This refers to the duration a user spends on the case study page. As a threshold for dwell time, the present invention .

[0063] This indicates that the user's self-study status is abnormal. This abnormal status can be pushed to their company, regulatory authorities, and industry associations to guide relevant institutions to strengthen the management of practitioners who have committed serious violations and have not engaged in self-study.

[0064] Step S42: When the user is an enterprise, generate and push information on the enterprise's traffic violation cases based on the enterprise's traffic violation case classification statistics, along with corresponding legal and regulatory provisions and penalty interpretations. Also push industry-wide enterprise violation case comparisons and high-frequency violation alerts for the enterprise and its employees. Mark the user's learning status based on the enterprise's violation warning index and case study duration. Furthermore, recommend typical high-frequency violation cases for the enterprise, its industry, its employees, and its employees, along with their corresponding legal and regulatory provisions and penalty interpretations, based on the same discretionary standards within the enterprise's industry. These can be displayed as videos, text, or images. The aforementioned enterprise traffic violation case classification statistics and industry-wide enterprise violation case comparisons can be displayed as numbers or charts; the aforementioned high-frequency violation alerts for the enterprise and its employees can be displayed as text or images; and the aforementioned enterprise violation warning index can be displayed as numbers or images.

[0065] Based on the statistical values ​​of the company's traffic violations, the system generates and pushes information on the company's violations, along with corresponding legal provisions and penalty interpretations. Specifically, based on the company's case statistics in the statistical values ​​of its traffic violations, the system generates information on the company's violations, along with corresponding legal provisions and penalty interpretations.

[0066] Based on the enterprise violation warning index and the page time spent viewing cases (i.e., case study time), the self-learning status of enterprise users is marked. The specific formula is as follows: in, This invention provides a threshold for the enterprise violation early warning index. , The duration of time a company stays on the case study page. As a threshold for dwell time, the present invention . This indicates that the company's self-learning status is abnormal. This abnormal status can be reported to regulatory authorities and industry associations to guide relevant agencies to strengthen supervision and management of company managers who have committed serious violations and have not engaged in self-learning.

[0067] The high-frequency violations mentioned above in this invention can actually be set with an upper limit value according to actual needs. When the violation exceeds this upper limit value, it can be called a high-frequency violation.

[0068] This invention, based on the unique identifier of a user's login subject, generates a default case description of the violation and its handling when the subject is a citizen. Users can view all case information by filtering criteria and retrieve the corresponding legal provisions and penalty interpretations from the discretionary benchmark database based on the case's corresponding discretionary benchmark code. Based on high-frequency violations by industry practitioners and company employees, this invention retrieves case and discretionary benchmark databases using discretionary benchmark codes, recommending related cases and corresponding legal provisions and penalty interpretations to users. It also considers the user's personal violation warning index and the duration of case and case viewing. When the user's subject is a company, a default case description of the violation and its handling is generated. Users can view all case information for the company by filtering criteria and retrieve the corresponding legal provisions and penalty interpretations from the discretionary benchmark database based on the case's corresponding discretionary benchmark code. Based on high-frequency violations by the company and industry enterprises, and high-frequency violations by the company's employees and industry practitioners, the invention retrieves case and discretionary benchmark databases using discretionary benchmark codes, recommending related cases and corresponding legal provisions and penalty interpretations to company users.

[0069] This invention analyzes a subject's illegal cases to obtain statistical values ​​from different dimensions, comparing the subject's illegal behavior with that of other subjects in the industry, thereby clearly understanding the quantitative comparison status of the subject's illegal behavior. It also identifies high-frequency illegal activities within the industry and clarifies the severity of the subject's existing illegal cases through a quantified illegal warning index. Based on the results of the illegal case analysis, it accurately pushes related cases and legal interpretations.

[0070] The beneficial effects of this invention on citizens and enterprises working in the transportation industry are as follows: by providing efficient and convenient case information services through an internet platform, compared to an internet case handling platform that provides users with individual online case handling, it is beneficial for entities to understand the current quantitative comparison status and situation of violations and irregularities, to know the meaning of the transportation industry laws and regulations they have violated, and thus to effectively fulfill their main responsibilities and management obligations, and reduce illegal behavior.

[0071] This invention also discloses an Internet-based information service system for traffic law enforcement cases, the system comprising: The acquisition module is used to obtain the unique identifier login information of the user entity.

[0072] The case retrieval module is used to retrieve user-related cases from the case database based on the unique identifier login information.

[0073] The multi-dimensional illegal case analysis result set generation module is used to compare and analyze each case and generate a multi-dimensional illegal case analysis result set corresponding to the user subject.

[0074] The information push module is used to push information on the user's illegal cases and related legal interpretations based on the multi-dimensional illegal case analysis result set.

[0075] As an optional embodiment, the multi-dimensional illegal case analysis result set generation module of the present invention specifically includes: The first result set generation unit is used to conduct comparative analysis based on the retrieved cases of the user when the user is a citizen. The result set includes the user's own traffic violation case classification statistics, the comparison between the user's own and related industry practitioners' violation cases, the high-frequency violation situation prompts of related industry practitioners, and the personal violation warning index as a multi-dimensional violation case analysis result set.

[0076] The second result set generation unit is used to conduct comparative analysis based on the retrieved cases of the enterprise when the user is an enterprise. It uses the enterprise's traffic violation case classification statistics, industry enterprise violation case comparison, high-frequency violation situation prompts for the enterprise and its employees, and enterprise violation warning index as a multi-dimensional violation case analysis result set.

[0077] The present invention also discloses a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the above-described method for providing information services for Internet-based traffic law enforcement cases.

[0078] The present invention also discloses an electronic device, including a processor, which executes a computer program to implement the above-described method for providing information services for Internet-based traffic law enforcement cases.

[0079] The same steps as those in the Internet-based transportation law enforcement case information service method will not be repeated here; please refer to the Internet-based transportation law enforcement case information service method for details.

[0080] This invention provides an internet-based information service method and system for transportation law enforcement cases, offering information services to practitioners and enterprises. This helps practitioners assess the impact of cases, understand laws and regulations, and achieve the goal of "explaining the law through cases and improving services."

[0081] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0082] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for providing information services for transportation law enforcement cases based on the Internet, characterized in that, The method includes: Obtain the unique identifier login information of the user entity; The user's main cases are retrieved from the case database based on the unique identifier login information; By comparing and analyzing each case, a multi-dimensional set of illegal case analysis results corresponding to the user entity is generated. Based on the multi-dimensional analysis results of illegal cases, the system will push information on the illegal cases of the user subject and related legal interpretations.

2. The method for providing information services for transportation law enforcement cases based on the Internet according to claim 1, characterized in that, The step of retrieving user-related cases from the case database based on the unique identifier login information specifically includes: When the unique identifier login information is a citizen's ID number, it indicates that the user is a citizen; when the unique identifier login information is a unified social credit code, it indicates that the user is an enterprise. When the user is a citizen, the user's own cases are retrieved from the case database based on the unique identifier login information; When the user entity is an enterprise, the enterprise's cases are retrieved from the case database based on the unique identifier login information.

3. The method for providing information services for transportation law enforcement cases based on the Internet according to claim 2, characterized in that, The process involves comparing and analyzing each case to generate a multi-dimensional set of illegal case analysis results for each user entity, specifically including: When the user is a citizen, a comparative analysis is performed based on the user's own cases. The results of the multi-dimensional analysis of traffic violations are: the user's own traffic violation case classification statistics, the comparison between the user's own and related industry practitioners' violation cases, the high-frequency violation situation prompts of related industry practitioners, and the personal violation warning index. When the user is an enterprise, the enterprise's cases are compared and analyzed. The enterprise's traffic violation case classification statistics, industry enterprise violation case comparison, high-frequency violation situation prompts for the enterprise and its employees, and enterprise violation warning index are used as the multi-dimensional violation case analysis result set.

4. The method for providing information services for transportation law enforcement cases based on the Internet according to claim 3, characterized in that, The process of pushing information on a user's illegal activities and related legal interpretations based on the multi-dimensional analysis results set specifically includes: When the user is a citizen, the system generates and pushes information on the user's traffic violations, along with corresponding legal provisions and penalty interpretations, based on the user's traffic violation statistics. It also pushes a comparison of the user's violations with those of practitioners in related industries, as well as alerts on high-frequency violations by practitioners in related industries. The system marks the user's learning status based on their personal violation warning index and case study duration. When the user is an enterprise, the system generates and pushes information on the enterprise's traffic violations, along with corresponding legal provisions and penalty clauses, based on the enterprise's traffic violation statistics. It also pushes industry enterprise violation comparisons and alerts on high-frequency violations by the enterprise and its employees. The system marks the user's learning status based on the enterprise's violation warning index and case study duration.

5. The method for providing information services for transportation law enforcement cases based on the Internet according to claim 4, characterized in that, The personal violation warning index The formula is: ； in, For the first The value of each indicator, For the first Individual utility function for each indicator For the first Each indicator has a weight value.

6. The method for providing information services for transportation law enforcement cases based on the Internet according to claim 4, characterized in that, The enterprise violation early warning index The formula is: ； in, For the first The value of each indicator, For the first A single indicator of firm utility function, For the first Each indicator has a weight value.

7. An internet-based information service system for traffic law enforcement cases, characterized in that, The system includes: The acquisition module is used to obtain the unique identifier login information of the user entity; The case retrieval module is used to retrieve user-related cases from the case database based on the unique identifier login information. The multi-dimensional illegal case analysis result set generation module is used to compare and analyze each case and generate a multi-dimensional illegal case analysis result set corresponding to the user subject. The information push module is used to push information on the user's illegal cases and related legal interpretations based on the multi-dimensional illegal case analysis result set.

8. The Internet-based transportation law enforcement case information service system according to claim 7, characterized in that, The multi-dimensional illegal case analysis result set generation module specifically includes: The first result set generation unit is used to conduct comparative analysis based on the retrieved cases of the user when the user is a citizen. The result set includes the user's traffic violation case classification statistics, the comparison between the user's violation cases and those of practitioners in related industries, the high-frequency violation situation prompts of practitioners in related industries, and the personal violation warning index as a multi-dimensional violation case analysis result set. The second result set generation unit is used to conduct comparative analysis based on the retrieved cases of the enterprise when the user is an enterprise. It uses the enterprise's traffic violation case classification statistics, industry enterprise violation case comparison, high-frequency violation situation prompts for the enterprise and its employees, and enterprise violation warning index as a multi-dimensional violation case analysis result set.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

10. An electronic device, characterized in that, Includes a processor that executes a computer program to implement the method of any one of claims 1 to 6.

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