Traffic freight employment qualification certificate handling service system and method based on artificial intelligence

Abstract

The invention relates to a traffic freight employment qualification certificate handling service system and method based on artificial intelligence. The method comprises the following steps: in response to a traffic freight employment qualification certificate service instruction triggered by a user, determining a certificate service type needing to be handled based on the service instruction; collecting identity verification information of the user, and performing identity verification on the user based on the identity verification information; if the identity verification is passed, generating and pushing a service information input interface to the user according to the type of the certificate service needing to be handled; acquiring user driving qualification information uploaded in the service information input interface, and performing service preprocessing on the user driving qualification information by adopting a driving qualification review model; pushing a certificate service confirmation interface to the user, so that the user confirms the to-be-examined information in the certificate service confirmation interface; and in response to a confirmation instruction of the target user for the certificate business confirmation interface, pushing the to-be-examined information in the certificate business confirmation interface to an examination end to complete a business handling process.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, in particular to a traffic freight practitioner qualification certificate handling service system and method based on artificial intelligence. BACKGROUND

[0002] In the process of handling qualification certificates and related certificate replacement for freight drivers, it is required that the drivers must personally handle the business on site. This approach has significant drawbacks. On the one hand, the drivers need to specially arrange time to go, and because of the lack of clear understanding of the required materials for application, the situation of incomplete information carrying often occurs, which makes the drivers have to go back and forth repeatedly, and each time at least half a day is wasted, which greatly wastes human and time resources. On the other hand, the handling process is complex, and frequent offline personal information submission and face-to-face signature confirmation operations are required, which not only greatly increases the time cost investment of users, but also seriously reduces the efficiency of the overall business handling.

[0003] Therefore, it is urgent to propose a new technical solution to solve at least one of the above technical problems in the process of handling qualification certificates for freight drivers. SUMMARY

[0004] The present application aims at the technical problems existing in the prior art, and provides a traffic freight practitioner qualification certificate handling service system and method based on artificial intelligence, to solve at least one of the technical problems in the related art, improve the execution efficiency of the traffic freight practitioner qualification certificate handling business, shorten the business handling time of users, and improve the user experience.

[0005] In a first aspect, the embodiments of the present application provide a traffic freight practitioner qualification certificate handling service method based on artificial intelligence, which comprises:

[0006] In response to a traffic freight practitioner qualification certificate business instruction triggered by a target user, determining the required certificate business type based on the business instruction; wherein the certificate business type includes: ordinary freight practitioner qualification certificate handling business, or road transport practitioner qualification certificate replacement business;

[0007] Collecting the identity verification information of the target user, and performing identity verification on the target user based on the identity verification information; the identity verification information includes at least one of: identity document information, face image information, and voiceprint information;

[0008] If the target user passes the identity verification, generating and pushing a business information input interface to the target user according to the required certificate business type; wherein the information input module contained in the business information input interface is associated with the certificate business type required by the target user; the information input module is at least used to collect individual information of the target user and business material information corresponding to the required certificate business type;

[0009] obtain user driving qualification information uploaded by the target user in the business information input interface, and perform business preprocessing on the user driving qualification information by using a driving qualification review model to obtain a certificate business confirmation interface; the user driving qualification information at least includes the following information: a motor vehicle driving license, a user ID photo, and user personal information;

[0010] push the certificate business confirmation interface to the target user to enable the target user to confirm the information to be reviewed in the certificate business confirmation interface;

[0011] In response to a confirmation instruction of the target user on the certificate business confirmation interface, push the information to be reviewed in the certificate business confirmation interface to a review end to complete a traffic freight practitioner qualification certificate business handling process.

[0012] In a second aspect, the embodiments of the present application provide a traffic freight practitioner qualification certificate handling service system based on artificial intelligence, which is connected with a transfer interface deployed in a government affair extranet server; the system at least includes the following units:

[0013] A starting unit is configured to determine a required certificate business type based on a traffic freight practitioner qualification certificate business instruction triggered by a target user in response to the business instruction; wherein the certificate business type includes a general freight practitioner qualification certificate handling business or a road transport practitioner qualification certificate replacement business;

[0014] A verification unit is configured to collect identity verification information of a target user, and perform identity verification on the target user based on the identity verification information; the identity verification information includes at least one of the following: identity certificate information, face image information, and voiceprint information;

[0015] A pushing unit is configured to generate and push a business information input interface to the target user according to the required certificate business type if the target user passes the identity verification; wherein an information input module included in the business information input interface is associated with the certificate business type required by the target user; the information input module is at least used to collect individual information of the target user and business material information corresponding to the certificate business type required by the target user;

[0016] A pre-review unit is configured to obtain user driving qualification information uploaded by the target user in the business information input interface, and perform business preprocessing on the user driving qualification information by using a driving qualification review model to obtain a certificate business confirmation interface; the user driving qualification information at least includes the following information: a motor vehicle driving license, a user ID photo, and user personal information;

[0017] The pushing unit is further configured to push the certificate service confirmation interface to the target user, so that the target user confirms the information to be examined in the certificate service confirmation interface; and in response to a confirmation instruction of the target user on the certificate service confirmation interface, the pushing unit pushes the information to be examined in the certificate service confirmation interface to the examination end, so as to complete the handling process of the traffic freight practitioner qualification certificate service.

[0018] In a third aspect, an electronic device is provided, and the electronic device comprises:

[0019] at least one processor, a memory, and an input and output unit;

[0020] The memory is configured to store a computer program, and the processor is configured to invoke the computer program stored in the memory to execute the artificial intelligence-based traffic freight practitioner qualification certificate handling service method of the first aspect.

[0021] In a fourth aspect, a computer-readable storage medium is provided, and the computer-readable storage medium comprises instructions that, when executed on a computer, cause the computer to execute the artificial intelligence-based traffic freight practitioner qualification certificate handling service method of the first aspect.

[0022] The beneficial effect of the present application is to provide an artificial intelligence-based traffic freight practitioner qualification certificate handling service system and method. In the technical solution, the execution subject of the method is connected with the transfer interface deployed in the government extranet server. First, in response to the traffic freight practitioner qualification certificate business instruction triggered by the target user, the required certificate business type is determined based on the business instruction; wherein the certificate business type includes: ordinary freight practitioner qualification certificate handling business, or road transport practitioner qualification certificate replacement business. Further, the identity verification information of the target user is collected, and the target user is authenticated based on the identity verification information; the identity verification information includes at least one of the identity information, the face image information, and the voiceprint information. Then, if the target user passes the identity verification, a business information input interface is generated and pushed to the target user according to the required certificate business type; wherein the information input module contained in the business information input interface is associated with the certificate business type required by the target user; the information input module is used at least to collect individual information of the target user and business material information corresponding to the required certificate business type. Then, the user driving qualification information uploaded by the target user in the business information input interface is obtained, and a driving qualification review model is used to pre-process the user driving qualification information to obtain a certificate business confirmation interface; the user driving qualification information at least includes the following information: motor vehicle driving license, user ID photo, and user personal information. Then, the certificate business confirmation interface is pushed to the target user to enable the target user to confirm the information to be reviewed in the certificate business confirmation interface. Finally, in response to the confirmation instruction of the target user to the certificate business confirmation interface, the information to be reviewed in the certificate business confirmation interface is pushed to the review end to complete the handling process of the traffic freight practitioner qualification certificate business.

[0023] Compared with artificial one-by-one checking and review, the technical solution of the present application greatly shortens the review time and reduces the misjudgment caused by human negligence, ensuring the accuracy and efficiency of the business handling. With the efficient processing capacity of artificial intelligence technology, each link can be quickly and accurately completed, reducing unnecessary waiting and repeated operations, realizing the whole-process optimization of the traffic freight practitioner qualification certificate handling service, and improving the overall efficiency of government services. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is a process schematic diagram of an artificial intelligence-based traffic freight practitioner qualification certificate handling service method of an embodiment of the present application;

[0025] Figures 2 to 5 is a principle schematic diagram of an artificial intelligence-based traffic freight practitioner qualification certificate handling service method of an embodiment of the present application;

[0026] Figure 6FIG. 1 is a structural schematic diagram of a traffic freight practitioner qualification certificate handling service system based on artificial intelligence according to an embodiment of the present application;

[0027] Figure 7 FIG. 2 is a structural schematic diagram of an electronic device according to an embodiment of the present application;

[0028] Figure 8 FIG. 3 is a structural schematic diagram of a medium device according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person skilled in the art without creative labor fall within the scope of protection of the present application.

[0030] In the handling of qualification certificates and related certificate replacement businesses for freight drivers, drivers must personally go to the business site to handle the business. This approach has significant drawbacks. On the one hand, drivers need to specially arrange time to go, and because of the lack of clear understanding of the required materials for application, there are often cases of incomplete data carrying, which makes drivers have to go back and forth repeatedly, each time consuming at least half a day, greatly wasting human and time resources. On the other hand, the handling process is complex, and frequent requirements for drivers to submit personal information offline and confirm in person, etc. not only greatly increases the time cost investment of users, but also seriously reduces the efficiency of the overall business handling.

[0031] In related technologies, in the dimension of data interaction docking, when data is interchanged with government authorities such as traffic departments through API interfaces, the existing technical architecture has shortcomings, resulting in obvious lag in data updating, and inconsistent information problems also occur frequently, seriously interfering with the accurate and efficient promotion of the business.

[0032] From the perspective of information security protection, the security performance is worrying in related technologies, and there are still many risk hidden dangers such as user personal information leakage, malicious tampering of business transaction data, etc., which are difficult to effectively protect the confidentiality, integrity and availability of user data, and have laid a major security risk for business development.

[0033] Furthermore, in related technologies, focusing on the information collection and reading link, the current technical means has poor accuracy in reading the key information of the driving license, and excessively relies on manual operation, lacking efficient and intelligent technical auxiliary tools, which not only slows down the handling rhythm at the time of handling and reduces the immediate handling efficiency, but also adds obstacles to the subsequent input and statistical work of government personnel, which is not conducive to the continuity and smoothness of the business process.

[0034] Therefore, there is an urgent need to propose a new technical solution to solve at least one technical problem existing in the above freight driver qualification certificate business.

[0035] To solve at least one technical problem in the related art, the embodiments of the present application provide a traffic freight practitioner qualification certificate handling service system and method based on artificial intelligence. In the technical solution, the execution subject of the method is connected with a transfer interface deployed in a government external network server. First, in response to a traffic freight practitioner qualification certificate business instruction triggered by a target user, the type of certificate business required to be handled is determined based on the business instruction; wherein the type of certificate business includes: ordinary freight practitioner qualification certificate handling business, or road transport practitioner qualification certificate replacement business. Further, the identity verification information of the target user is collected, and the target user is authenticated based on the identity verification information; the identity verification information includes at least one of: identity document information, face image information, and voiceprint information. Next, if the target user passes the identity verification, a business information input interface is generated and pushed to the target user according to the type of certificate business required to be handled; wherein the information input module contained in the business information input interface is associated with the type of certificate business required to be handled by the target user; the information input module is at least used to collect individual information of the target user and business material information corresponding to the type of certificate business required to be handled. Then, the user driving qualification information uploaded by the target user in the business information input interface is obtained, and a driving qualification review model is used to pre-process the user driving qualification information to obtain a certificate business confirmation interface; the user driving qualification information at least includes the following information: motor vehicle driving license, user ID photo, and user personal information. Next, the certificate business confirmation interface is pushed to the target user to enable the target user to confirm the information to be reviewed in the certificate business confirmation interface. Finally, in response to the confirmation instruction of the target user to the certificate business confirmation interface, the information to be reviewed in the certificate business confirmation interface is pushed to the review end to complete the handling process of the traffic freight practitioner qualification certificate business.

[0036] In summary, in the embodiments of the present application, compared with artificial one-by-one checking and review, the review time is greatly shortened, and the misjudgment caused by human negligence is reduced, ensuring the accuracy and efficiency of business handling. With the efficient processing capacity of artificial intelligence technology, each link can be quickly and accurately completed, reducing unnecessary waiting and repeated operations, realizing the whole-process optimization of traffic freight practitioner qualification certificate handling service, and improving the overall efficiency of government service

[0037] The service scheme for handling the traffic freight operation qualification certificate based on artificial intelligence provided by the embodiments of the present application can also be executed by an electronic device, which can be a server, a server cluster, a cloud server. The electronic device can also be a terminal device such as a mobile phone, a computer, a tablet computer, a wearable device, or a special device (such as a special terminal device with the service method system for handling the traffic freight operation qualification certificate based on artificial intelligence, etc.). The above-mentioned chip introduced in the embodiments can also be carried in these electronic devices. Alternatively, these electronic devices can also install a service program for executing the service scheme for handling the traffic freight operation qualification certificate based on artificial intelligence.

[0038] Figure 1 The flowchart of the method for handling the traffic freight operation qualification certificate based on artificial intelligence provided by the embodiments of the present application is shown in FIG. 1, which comprises the following steps: Figure 1

[0039] 101, in response to the traffic freight operation qualification certificate business instruction triggered by the target user, determining the required certificate business type based on the business instruction;

[0040] 102, collecting the identity verification information of the target user, and verifying the identity of the target user based on the identity verification information;

[0041] 103, if the target user passes the identity verification, generating and pushing a business information input interface to the target user according to the required certificate business type;

[0042] 104, obtaining the user driving qualification information uploaded by the target user in the business information input interface, and pre-processing the user driving qualification information by using a driving qualification review model to obtain a certificate business confirmation interface;

[0043] 105, pushing the certificate business confirmation interface to the target user, so that the target user confirms the information to be reviewed in the certificate business confirmation interface;

[0044] 106, in response to the confirmation instruction of the target user to the certificate business confirmation interface, pushing the information to be reviewed in the certificate business confirmation interface to the review end to complete the handling process of the traffic freight operation qualification certificate business.

[0045] ​The execution subject in the embodiment of the application is connected with the relay interface deployed in the government extranet server. The execution subject in the embodiment of the application is connected with the relay interface deployed in the government extranet server, and the relay interface plays an important role in the data transmission process. On the one hand, as a relay station of data, it guarantees the stable transmission of data from the user end to the review end, avoiding data loss or delay caused by network fluctuations or server congestion. On the other hand, combined with the security protection mechanism of the government extranet, it can effectively resist external network attacks and ensure the security and confidentiality of data in the transmission process, providing solid technical support for the smooth progress of the entire business handling process.

[0046] In the embodiment of the application, the certificate business type includes: ordinary freight operation qualification certificate handling business or road transport operation qualification certificate replacement business. The ordinary freight operation qualification certificate is a certificate that must be held by a driver engaged in ordinary freight transportation. It is applicable to personnel engaged in commercial road freight transportation using a cargo truck. Whether it is an individual transport household or a driver employed by a transport enterprise, as long as it participates in commercial ordinary freight transportation, it needs to handle this certificate. For example, truck drivers who deliver daily necessities for e-commerce enterprises and drivers who deliver raw materials for factories are within the scope of application of this qualification certificate. The road transport operation qualification certificate replacement business refers to a series of procedures that the driver needs to complete before the expiration of the certificate to obtain a new certificate within the valid period, so as to continue to engage in road transport work legally.

[0047] In the embodiment of the application, the identity verification information includes at least one of the identity certificate information, the face image information, and the voiceprint information. Specifically, the embodiment of the application collects at least one of the identity certificate information, the face image information, and the voiceprint information for identity verification. This multi-dimensional identity verification method greatly enhances security. Multiple biological characteristics and certificate information confirm each other, forming a strong security line, effectively preventing identity fraud, theft, and other situations. Whether it is local handling or remote operation, only the legal target user can enter the business handling process, protecting the legal rights and interests of users and maintaining the authority and fairness of traffic government affairs handling.

[0048] The embodiment of the application can quickly and accurately determine the required certificate business type by intelligently analyzing the business instruction triggered by the target user. Whether it is ordinary freight operation qualification certificate handling business or road transport operation qualification certificate replacement business, it can be located instantly. This avoids the time waste and error risk caused by traditional manual inquiry or manual screening, greatly improves the efficiency of the business initiation link, and lays a foundation for the smooth development of the subsequent process.

[0049] The embodiment of the application can use an advanced driving qualification review model to pre-process the driving qualification information uploaded by the user. This process relies on powerful artificial intelligence algorithms and can quickly identify and extract key data in the motor vehicle driving license, user ID photo and user personal information. The key data is compared and analyzed with the standard information in the database to efficiently determine the authenticity and validity of the ID and whether the user meets the conditions for obtaining the ID. Compared with manual review, the review time is greatly shortened, and the misjudgment caused by human negligence is reduced, ensuring the accuracy and efficiency of the business handling.

[0050] The embodiment of the application generates a business information entry interface associated with different types of ID business. The information entry module is targeted and clearly guides the user to fill in individual information and business material information. This enables the user to quickly and accurately complete information entry without wandering in irrelevant information, avoiding confusion and errors caused by complex interfaces or unclear information, and significantly improving the user's operation experience.

[0051] The embodiment of the application generates an ID business confirmation interface to let the user confirm the key information to be reviewed, ensuring the accuracy and integrity of the information. This step gives the user full right to check independently, avoiding subsequent problems caused by miscommunication or misunderstanding. At the same time, the whole process is simple and smooth, and the user can complete the submission by simply clicking the confirmation instruction, greatly improving the convenience and satisfaction of the user in handling the business.

[0052] In the embodiment of the application, the entire handling process starts from the business instruction trigger, and then goes through identity verification, information entry, qualification review, information confirmation, and finally completes the business handling. Each link is closely connected to form an organic whole. With the efficient processing capacity of artificial intelligence technology, each link can be quickly and accurately completed, reducing unnecessary waiting and repeated operations, realizing the whole-process optimization of the traffic and freight practitioner qualification certificate handling service, improving the overall efficiency of government services, and providing a strong guarantee for the standardized management and development of the transportation industry.

[0053] As an optional embodiment, in 101, in response to the traffic and freight practitioner qualification certificate business instruction triggered by the target user, the required ID business type is determined based on the business instruction, including:

[0054] 201, receiving the business instruction through the input layer of the business classification model;

[0055] 202, determining the region where the target user is located through the calling layer of the business classification model, and calling the branch business classification module corresponding to the region;

[0056] 203、The branch business classification module of the business classification model performs semantic recognition and business classification on the business instruction to obtain the type of certificate business indicated by the business instruction.

[0057] Among them, the branch business classification module corresponding to different regions is deployed in the business classification model.

[0058] Specifically, the calling layer of the business classification model determines the region where the target user is located, which lays the foundation for subsequent accurate business processing. Different regions may have slight differences in the handling of transportation qualification certificates, including the handling process, required materials, and special policy requirements. Accurate acquisition of user region information enables the system to conduct targeted processing according to the characteristics of different regions, avoiding the problem of inadaptation caused by the "one-size-fits-all" mode.

[0059] The branch business classification module corresponding to the region where the user is located is called, which means that the system can accurately interpret and classify the business instruction according to the actual situation of different regions. For example, some regions may have special qualification certificate handling regulations for certain goods transportation, or have unique time node requirements for certificate replacement. The branch business classification module can quickly identify these regional characteristics to ensure the accuracy and practicality of business processing.

[0060] The branch business classification module performs semantic recognition on the business instruction, using advanced natural language processing technology to understand complex expressions, colloquial expressions, and ambiguous meanings in the instruction. Even if the driver's instruction is not sufficiently standardized, the model can accurately extract key information and determine its intent. For example, if the driver says "I want to get a certificate that can carry building materials", the model can accurately determine that this is a business instruction about the handling of ordinary freight qualification certificates through semantic analysis.

[0061] Based on accurate semantic understanding, the model can quickly classify the business instruction and determine its type of certificate business, i.e., ordinary freight qualification certificate handling or road transport qualification certificate replacement. This efficient classification capability greatly improves the speed of business processing, reduces the time cost of manual judgment and classification, and makes the entire handling process more smooth and efficient.

[0062] The business classification model is deployed with branch business classification modules corresponding to different regions, making the system highly flexible. When the business regulations of a region change, only the corresponding branch business classification module needs to be adjusted and optimized, without the need for large-scale modification of the entire system. This allows the system to quickly adapt to policy adjustments and business changes in different regions, providing continuous and stable services to users. The model architecture is easy to extend. As the business develops and new regions are added, only the corresponding branch business classification module needs to be added to the model and trained and configured accordingly to include the new region in the system's service range. This scalability ensures that the system can continue to grow with the industry and meet the needs of more users.

[0063] It can be understood that the branch business classification modules corresponding to different regions deployed in the business classification model are a functional module architecture for business classification processing based on regional differences. Different regions often have different policies, regulations and business requirements in traffic and freight management. For example, some regions may have special regulations for handling dangerous goods transport qualification, some coastal regions may have unique requirements for freight qualification related to sea transportation, and some inland regions may focus more on the regulations of land transportation related business. Based on these differences, the business classification model sets up corresponding branch business classification modules for each region to ensure accurate processing of local business instructions.

[0064] Further optionally, a large amount of historical business data and local policy and regulation information is used to train the branch business classification module for each region. These data include different types of business instructions, handling procedures, required materials, etc. Through machine learning and other technologies, the module can learn the characteristics and rules of the region's business instructions, enabling accurate semantic recognition and business classification.

[0065] Specifically, the input layer is primarily responsible for receiving business instructions for freight transport qualification certificates triggered by target users. These instructions can be text messages entered by users through various channels, such as input into the application system's input box or text converted from speech. The call layer determines the target user's region. This can be achieved through various methods, such as using the address information provided during user registration, IP address location, mobile phone base station location, and other technical means to determine the user's region. Based on this regional information, the corresponding branch business classification module is then invoked. The branch business classification module is the core component, with independent branch modules for each region. It includes a semantic recognition model and business classification rules specific to the business in that region. The semantic recognition model performs natural language processing on the input business instructions, understands the meaning of the instructions, and extracts key information. The business classification rules classify the recognized information based on local policies and regulations and actual business conditions, determining the specific business type for the certificate, such as general freight transport qualification certificate application or road transport qualification certificate renewal. This can even be further broken down into more specific business subtypes.

[0066] Based on the above architecture, the input layer of the business classification model receives a business instruction for a transportation and freight qualification certificate from a target user. The call layer uses relevant technical means to determine the user's region and, based on this regional information, locates the corresponding branch business classification module. The branch business classification module performs semantic recognition on the business instruction, converting the natural language instruction into a computer-interpretable feature vector. Then, based on predefined business classification rules and a trained model, it classifies the instruction and determines the specific business type for the certificate. The classification result is output, providing accurate business type information for subsequent business processing, allowing the system to proceed according to the corresponding process and requirements.

[0067] This enables precise business classification tailored to the specific circumstances of different regions, avoiding classification errors caused by standardized standards and improving the accuracy and efficiency of business processing. When regional business policies or processes change, only the branch business classification modules for that region need to be updated and adjusted, eliminating the need for large-scale modifications to the entire system. This makes the system highly adaptable and flexible. If new regions join or new business types emerge, simply add new branch business classification modules and perform appropriate training and configuration, allowing for easy system expansion to meet business development needs.

[0068] Further optionally, the branch business classification modules corresponding to different regions are loaded with respective corresponding dialect correction modules, and each dialect correction module is used to implement dialect error correction and colloquial optimization of the service instruction. Further optionally, each dialect correction module is trained based on dialect voice materials and / or dialect image materials of each region. The dialect correction module aims to solve the understanding error that may occur in the business classification model when the user uses a dialect or colloquial expression to express the service instruction. The core principle is to use the dialect data of a specific region to preprocess the input service instruction, so that it is more consistent with the standard language expression, and the model can be accurately classified. By collecting dialect voice materials and dialect image materials (such as identification, files containing dialect characters, etc.) of each region, these materials become the basic data for training the dialect correction module. For voice materials, voice recognition is performed to convert them into text; image materials are extracted by OCR technology to extract text information.

[0069] Specifically, the collected dialect data is cleaned to remove noise and error data. The text converted from voice and the text extracted by OCR are sorted out, and the difference part from the standard language is labeled to construct a training data set. Natural language processing technology is used to extract features in dialect text, such as specific vocabulary, grammar structure, etc. For example, unique vocabulary substitution and word order changes in dialects in some regions, etc. These features are the key basis for correction. For example, a sequence-to-sequence (Seq2Seq) model based on deep learning is used, combined with an attention mechanism. The model inputs the dialect text sequence, converts it into a feature vector through an encoder, and generates a corrected standard language text through a decoder. The attention mechanism helps the model focus on the key parts of the dialect text during generation, improving the correction accuracy.

[0070] In actual operation, the service instruction enters the dialect correction module, and the model corrects the dialect expression in the instruction according to the dialect conversion rules learned by training, optimizes the colloquial expression, and transmits the processed text to the subsequent business classification module, improving the accuracy of business classification.

[0071] Further optionally, when the target user triggers the traffic and freight industry qualification certificate service instruction, a neural network model in deep learning is called to process the instruction. This neural network model has been trained with a large amount of service instruction data and has strong semantic understanding ability. It can deeply analyze the user's input instruction, not only easily handle regular service instruction expressions, but also accurately understand instructions with regional characteristics and colloquial expressions. In the processing process, the model can quickly and accurately find out the ambiguous and ambiguous parts in the instruction and correct them.

[0072] For example, for some instructions with local dialects such as "I want to do a running goods certificate" or "I want to change a transport certificate", the model can accurately identify it as a general freight qualification certificate business or a road transport qualification certificate change business according to its training results, and provide accurate business type information for the subsequent certificate handling process. Just like starting a precise navigation system for the entire process, guiding the subsequent business operation smoothly.

[0073] Through this optimization step, the system can accurately grasp the user's business needs from the beginning, avoid various errors and delays caused by inaccurate instruction understanding, and improve the efficiency and quality of the entire traffic freight qualification certificate handling service

[0074] As an optional embodiment, in 203, the branch business classification module of the business classification model performs semantic recognition and business classification on the business instruction to obtain the certificate business type indicated by the business instruction, including:

[0075] 301. Extract the corresponding business instruction features from the business instruction through the feature extraction layer of the branch business classification module;

[0076] 302. The dialect error correction and spoken language optimization module of the branch business classification module corrects the business instruction features to obtain the first optimized instruction features;

[0077] 303. The ambiguity correction layer of the branch business classification module corrects the first optimized instruction features to obtain the second optimized instruction features;

[0078] 304. The semantic recognition layer of the branch business classification module performs semantic recognition on the second optimized instruction features to obtain the semantic recognition result;

[0079] 305. The branch business classification module of the business classification layer matches the corresponding business classification result for the semantic recognition result.

[0080] Specifically, the feature extraction layer extracts key features from the business instruction, which can be vocabulary, phrases, sentence structures, etc. For example, core vocabulary such as "cargo qualification certificate", "handling", "renewal" and structure information such as subject-predicate-object in the sentence are extracted, and the business instruction is converted into a feature vector form that the model can understand, providing a basis for subsequent processing. The dialect correction module uses a model trained based on regional dialect speech and image materials to identify and correct dialect expressions and colloquial parts in the business instruction features. For example, replace dialect vocabulary with standard language, adjust the order of colloquial language, and make the instruction more standardized. For example, "I want to handle a cargo certificate" is corrected to "I want to handle a cargo qualification certificate", improving the universality of the instruction. The ambiguity correction layer analyzes the possible ambiguities in the first optimized instruction features based on a language knowledge base and deep learning algorithms. For example, "I want to renew the certificate", through the context and industry knowledge, it is determined whether it is a cargo qualification certificate renewal or other certificates, eliminating potential understanding bias and ensuring the uniqueness and accuracy of the instruction. The semantic recognition layer uses natural language processing techniques such as word vector models and neural network algorithms to perform deep semantic analysis on the second optimized instruction features. Match the instruction features with pre-defined semantic patterns to understand the true intent behind the instruction and output an accurate understanding of the instruction meaning. The business classification layer matches the semantic recognition result with pre-defined business classification labels. For example, after recognizing the "handle the cargo qualification certificate" semantic, it is matched to the general cargo industry qualification certificate handling business category, completing the mapping of the business instruction to the specific business type.

[0081] In this way, various interference factors in the business instruction are gradually eliminated through multiple steps to ensure the accuracy of business classification. Whether it is dialect, ambiguity or complex semantics, it can be correctly processed to reduce business handling errors caused by misunderstanding. It can adapt to users with different regional and expression habits. The dialect correction module enables the system to handle various regional dialects, allowing drivers from different regions to interact smoothly with the system and expanding the service coverage. Users do not need to worry about the non-standard expression of the instruction, and the system can automatically process and understand it to improve the convenience of business handling. From the user triggering the instruction to the clear business type, the whole process is efficient and accurate, improving the user's satisfaction with the handling service. It shows strong language understanding and processing capabilities, which is an important embodiment of intelligent service systems. Through complex semantic analysis and multi-module cooperation, it realizes the intelligent conversion from natural language instruction to business classification, providing strong support for the intelligent upgrading of traffic and cargo industry qualification certificate handling services.

[0082] As an optional embodiment, in 102, the identity verification information of the target user is collected, and the identity of the target user is verified based on the identity verification information, including:

[0083] 401、based on the business instruction, determine the business handling mode corresponding to the target user; wherein the business handling mode includes: online handling and / or offline handling;

[0084] 402、based on the target user's corresponding business handling method, collecting at least one of the target user's identity document information, face image information, and voiceprint information;

[0085] 403、based on the user name information in the business instruction, selecting matched first user identity information from the user identity database;

[0086] 404、input the collected identity document information, face image information, and voiceprint information into the multi-modal identity recognition model, and perform comprehensive identity recognition processing on the target user to obtain second user identity information of the target user;

[0087] 405、compare whether the first user identity information and the second user identity information are consistent;

[0088] 406、if the first user identity information and the second user identity information are consistent, it is determined that the target user passes the identity verification; or if the first user identity information and the second user identity information are inconsistent, it is determined that the target user does not pass the identity verification.

[0089] Specifically, according to the business instruction triggered by the user, the handling method selected by the user is determined, which helps to collect identity verification information targetedly. Under different handling methods, the focus and method of information collection may be different. For example, online handling may rely more on remotely collected face image information and voiceprint information, while offline handling may focus more on on-site collection of identity document information.

[0090] The first user identity information matched with the user name in the business instruction is extracted from the user identity database, and the collected identity document information, face image information, and voiceprint information are comprehensively recognized by the multi-modal identity recognition model to generate second user identity information. The multi-modal identity recognition model combines the advantages of multiple biological characteristics, uses deep learning algorithm to fuse and analyze different modal data, and can improve the accuracy and reliability of identity recognition. Finally, the two information are compared, and if they are consistent, the verification is passed, and if they are inconsistent, the verification is not passed, so as to determine the authenticity of the user's identity.

[0091] Exemplarily, it is assumed that driver Xiao Li triggers a business instruction of handling a freight qualification certificate through a mobile phone APP, and the system determines that the handling mode is online. The system first collects the face image information (through the mobile phone camera for self-shooting) and voiceprint information (through voice recording) of Xiao Li, and obtains the first user identity information matched with the user name "Xiao Li" from the database. After the face and voiceprint information of Xiao Li is input into the multi-modal identity recognition model, the second user identity information is generated. If the two are consistent, Xiao Li can continue to handle the business online; if they are not consistent, such as the voiceprint is too different from the database record, the system determines that the verification fails, and Xiao Li needs to recheck the information or contact customer service.

[0092] In the offline handling scene, Wang goes to the government service window to handle the qualification certificate replacement business, and the window staff scans the identity card of Wang to obtain the identity card information, and collects the face image information through the on-site camera. The system extracts the first user identity information matched with "Wang" from the database, and inputs the collected information into the multi-modal identity recognition model to obtain the second user identity information. If the two are consistent, Wang can continue to handle the replacement business; if they are not consistent, such as the identity card photo is obviously different from the on-site face image, the staff will further verify the situation.

[0093] Therefore, the multi-modal identity recognition model combines multiple biological characteristics for verification, greatly increasing the difficulty of identity fraud, effectively preventing others from misusing the identity to handle the business, and protecting the legitimate rights and interests of users and the security of business handling. According to different business handling modes, the identity verification information is collected flexibly, which adapts to the diversified handling scenes online and offline, facilitates users to handle the business, and improves the user experience. Through the double comparison of database information and multi-modal recognition information, it can effectively avoid the misjudgment caused by single information collection error or inaccuracy, and improve the accuracy of identity verification.

[0094] As an optional embodiment, at least one of the fingerprint image information and the iris image information of the target user can also be collected based on the business handling mode corresponding to the target user. Based on this, in the above step 404, after the collected identity card information, face image information and voiceprint information are input into the multi-modal identity recognition model for comprehensive identity recognition processing of the target user to obtain the second user identity information of the target user, the fingerprint image information, iris image information, face image information and voiceprint information of the target user are biologically fused, and a correlation relationship model between the multi-modal biological characteristics is constructed; the correlation relationship model is cross-validated to obtain the verification information of the second user identity information; and the confidence degree of the second user identity information is determined based on the verification information, wherein the confidence degree is used to represent the accuracy degree of the second user identity information.

[0095] Specifically, in the identity verification process, the collected fingerprint image information, iris image information, face image information, and voiceprint information are input into a multi-modal identity recognition model, and then these biometric features are further fused. Through deep learning algorithms, a correlation model between multi-modal biometric features is constructed, which learns the internal relationship and feature distribution between different biometric features. For example, the distribution pattern of certain biometric features in the population, the correlation between different features, etc. Then the correlation model is cross-validated, i.e. using existing feature correlation knowledge and verification algorithms, the input biometric feature data is cross-compared and verified. According to the verification result, the verification information of the second user identity information is obtained, and then the confidence of the second user identity information is calculated based on this information, which reflects the probability of the accuracy of the identity information.

[0096] In this way, combining multiple biometric features and constructing a correlation model greatly increases the difficulty of identity forgery, making identity verification more secure and reliable, effectively resisting identity fraud risks. Using cross-validation and correlation, the accuracy of user identity information can be determined more accurately, reducing the possibility of misjudgment due to a single biometric feature, and improving the overall accuracy of identity verification. The calculation of confidence provides quantitative evaluation of the accuracy of user identity information, which helps the system to make different processing decisions based on different confidence levels, such as high confidence for direct verification, and low confidence for further review or manual review, realizing intelligent identity verification.

[0097] In the process of multi-modal biometric feature fusion, it is crucial to ensure data privacy and security, which can be achieved through technical means, management systems, and other measures. After data collection, symmetric encryption algorithms or asymmetric encryption algorithms are used to encrypt biometric feature data, ensuring that even if the data is stolen during storage and transmission, attackers will have difficulty obtaining the real data. For example, AES (Advanced Encryption Standard) algorithm is used to encrypt fingerprint images, iris images, etc. Before the data enters the fusion system, the biometric feature data is anonymized and desensitized, removing or replacing sensitive information that can directly or indirectly identify the user's identity, such as replacing the user's name, ID number, etc. with randomly generated codes. In the process of multi-modal biometric feature fusion calculation, secure multi-party computation technology is used, allowing parties involved in the calculation to perform joint calculations without revealing the original data, ensuring data privacy. Using the decentralized and tamper-proof features of blockchain, the operation log and ownership information of biometric feature data are recorded, ensuring data integrity and traceability, and preventing illegal tampering and misuse of data.

[0098] It can be understood that a strict access control mechanism is established, the personnel and systems that can access the biometric data are strictly managed in terms of permissions, the principle of minimum authorization is implemented, and only necessary access permissions are granted. The biometric data is backed up regularly, and the backup data is stored in a secure location. At the same time, a perfect data recovery mechanism is established to deal with possible data loss or damage. A security audit system is established to monitor and audit the whole process of biometric data collection, storage, fusion, use, etc. in real time, and abnormal behaviors are discovered and handled in time. The personnel involved in the processing of biometric data are trained in data privacy and security to improve their safety awareness and operation specifications to prevent data leakage due to human negligence.

[0099] In step 103, if the target user passes the identity verification, a business information entry interface is generated and pushed to the target user according to the type of the certificate business to be handled. In the embodiment of the present application, the information entry module contained in the business information entry interface is associated with the type of the certificate business to be handled by the target user. Further optionally, the information entry module is used at least to collect individual information of the target user and business material information corresponding to the type of the certificate business to be handled. For example, the business information entry interface can be implemented as the interface shown in Figure 2 Further, if the individual information of the target user and the business material information corresponding to the type of the certificate business to be handled uploaded in the business information entry interface contains errors, the target user can be prompted to retransmit the corresponding information by using the prompt information as shown in Figure 3

[0100] Specifically, according to the type of the certificate business to be handled by the target user determined in the foregoing, such as the ordinary cargo transport practitioner qualification certificate handling business or the road transport practitioner qualification certificate replacement business, a corresponding template preset in advance is called. Different business types have different information requirements. For example, the ordinary cargo transport practitioner qualification certificate handling may require information such as the initial certificate issuance time and the driving license vehicle type, while the replacement business pays more attention to the original certificate validity period and the completion of continuing education. The system generates an adaptive business information entry interface according to these differences. Further, the information entry module is closely associated with the business type to ensure that each module can accurately collect individual information and business material information required for handling the business. The individual information covers basic content such as name, gender, and contact information, and the business material information is specific to specific files or data for a specific business, such as continuing education proof materials for practitioner qualification certificate replacement. Through this association, the pertinence and completeness of information collection are ensured.

[0101] ​In this way, a clear, concise and targeted entry interface is provided for the user, avoiding the user's confusion with complex and irrelevant information. The user does not need to filter among numerous options and can quickly find the content to be filled in, improving operational efficiency, making the process smoother, and enhancing the user's satisfaction with the entire certificate service. The information entry module is associated with the business type, which can effectively prevent the user from missing important information or incorrectly filling in unnecessary content. The system guides the user to accurately input as required, reducing the delay or failure of business processing due to missing or incorrect information, improving the success rate of business processing, and also reducing the workload of subsequent audit links. Precise collection of required information enables subsequent audit, certificate issuance and other processes to proceed smoothly. Because the entered information is complete and meets the requirements, the links are more closely connected, reducing repeated communication and repeated processes due to information problems, and overall improving the efficiency and quality of the traffic and freight operation qualification certificate service.

[0102] In 104, the user driving qualification information uploaded by the target user in the business information entry interface is obtained, and a driving qualification review model is used to pre-process the user driving qualification information, obtaining a certificate business confirmation interface. In the embodiments of the present application, the user driving qualification information at least includes the following information: motor vehicle driving license, user certificate photo, and user personal information.

[0103] For example, the certificate business confirmation interface can be implemented as Figure 4 the certificate business confirmation interface of the ordinary freight operation qualification certificate shown in FIG. 8, or Figure 5 the certificate business confirmation interface of the road transport operation qualification certificate replacement shown in FIG. 9.

[0104] Specifically, first, from the motor vehicle driving license image uploaded by the user on the business information entry interface, the key information such as driving license number, qualified vehicle type, and validity period is extracted using optical character recognition (OCR) technology and deep learning algorithms. For the user's ID photo, it is confirmed whether the photo meets the certificate production standards, including size, background, and clarity. At the same time, the user's personal information is sorted out to ensure the completeness and accuracy of the information, such as the consistency of the name, ID number, and driving license information. The driving qualification review model is based on machine learning algorithms, which learn from a large amount of historical driving qualification data and relevant regulations and policies to build review rules and judgment standards. After inputting the user's driving qualification information into the model, the model will conduct a comprehensive review of the user's driving qualification according to these rules and standards. For example, it checks whether the driving license is in the state of suspension or temporary suspension, whether the qualified vehicle type matches the application for the freight transport qualification certificate, and whether the user meets the age, health, and other relevant employment requirements. According to the review results, the system generates a certificate business confirmation interface. The key information extracted during the review process and the preliminary review conclusion are displayed on the interface for the user to confirm. If there are problems or doubts, the interface will also clearly prompt the user to supplement or correct the content.

[0105] Therefore, compared with manual review one by one, the driving qualification review model can quickly process a large amount of data and complete the review of the user's driving qualification information in an instant, greatly shortening the business handling time and improving the overall work efficiency. The model reviews based on established rules and standards, avoiding human factors that lead to negligence and errors, ensuring the accuracy and consistency of the review results. This helps to ensure that traffic and freight transport industry practitioners have legal and compliant driving qualifications and maintain road transport safety. By generating a certificate business confirmation interface, users can clearly understand their business handling situation and confirm the review results. If there are problems, they can also be aware of them in time and handle them, increasing the transparency and controllability of business handling and improving user satisfaction with the handling service. Accurate and efficient driving qualification review helps traffic management departments regulate industry order and ensure that only qualified personnel engage in freight transport work, promoting the healthy development of the entire traffic and freight transport industry.

[0106] On the generated certificate business confirmation interface, the following functions can be added to further optimize user experience and business processes:

[0107] Real-time information correction and intelligent prompts: When the user finds that the information is incorrect and modifies it, the system checks whether the modified content meets the format requirements and business rules in real time. For example, when modifying the driving license validity period, if the input format is incorrect, the system will immediately pop up a prompt box to inform the correct format. For some easily confused information items, such as the filling specifications of the qualified vehicle type, intelligent prompts are provided to guide the user to input accurately.

[0108] Material Preview and Download: Provide users with a preview function for uploaded business materials (such as motor vehicle license images, ID scans, etc.), making it easy for users to confirm the clarity and completeness of the materials again. At the same time, set up a download button to facilitate users to keep a copy when needed. This provides great convenience for users who may face other related business needs in the future or need to resubmit materials for some reason.

[0109] Related Regulations and Policies Interpretation Link: Set up a "Regulations and Policies" button on the interface. After clicking, a pop-up window or a new page will appear, showing relevant traffic regulations, industry qualification requirements, etc. For example, explain the specific provisions of the freight qualification certificate on driver age, health status, and the correspondence between the vehicle type and freight business. Help users better understand the basis and standards for business handling, and enhance transparency and trust.

[0110] Progress Tracking Visualization: Use progress bars or flowcharts to visually display the entire process from business application submission to final certificate handling completion, as well as the current stage. For example, display status information such as "information entry and qualification review completed, waiting for review department review" to let users know the progress of business handling and reduce anxiety and uncertainty.

[0111] Online Customer Service One-Click Consultation: Add an online customer service function entry. If users encounter any questions during the confirmation process, they can click to communicate with customer service personnel in real time. Customer service personnel can answer user questions through text, images, etc., such as explaining the meaning of some review conclusions, guiding users to supplement materials, etc., providing instant human support and improving user experience.

[0112] Historical Business Record Inquiry: For users who have experience with freight qualification certificate-related business handling, provide a historical business record inquiry function. Users can view information such as past business types, handling times, and handling results, making it easy to compare and review, and also helping users understand their business handling trajectory and related information.

[0113] Business Handling Risk Warning: Based on user input information and review results, warn about possible business handling risks. For example, if the user's driver's license is about to expire, which may affect the qualification certificate handling, the system will pop up a risk prompt box, reminding the user to handle the driver's license renewal in a timely manner to avoid being hindered by external factors.

[0114] Personalized Handling Suggestions: Based on user personal information, driving qualification information, and past business records, provide personalized handling suggestions for users. For example, if the user often transports across regions, prompt them to pay attention to the differences in freight policies in different regions; if the user's driver's license vehicle type has room for upgrade, suggest they consider upgrading at the appropriate time to expand their business scope.

[0115] Evaluation and feedback portal: The interface includes an evaluation and feedback function. After completing a transaction, users can evaluate and provide feedback on the entire transaction process, system experience, information accuracy, and other aspects. This helps system developers and business management departments promptly understand user needs and issues, and continuously optimize business services.

[0116] As an optional embodiment, in step 104, the driving qualification review model is used to perform business pre-processing on the user's driving qualification information to obtain a certificate business confirmation interface, including:

[0117] The feature extraction layer of the driving qualification review model extracts certificate content features from the user's driving qualification information; the type of the certificate content features is determined by the corresponding review business regulations; the identification layer of the driving qualification review model identifies the certificate content features to obtain corresponding certificate content information; the pre-review layer of the driving qualification review model pre-reviews the certificate content information to determine whether the certificate content information meets pre-configured review conditions; the construction layer of the driving qualification review model, if the certificate content information meets the pre-configured review conditions, loads the certificate content information into the interface information display position corresponding to the first certificate business confirmation interface as pending review information; the construction layer, if the certificate content information does not meet the pre-configured review conditions, loads the certificate content information into the interface information display position corresponding to the second certificate business confirmation interface as pending review information, and marks the pending review information that does not meet the review conditions in the interface information display position to prompt the target user to re-upload the unqualified pending review information.

[0118] Specifically, the feature extraction layer extracts key document content features from the user's driver's license, ID photo, personal information, and other driving qualification information provided by the user, based on the specific regulations of the review process. For example, for a driver's license, information such as the license number, vehicle type, validity period, and issuing authority will be extracted. For a user's ID photo, relevant features such as photo size, background color, and facial features will be extracted to determine whether the photo meets the standards. These features form the basis for subsequent identification and review.

[0119] The recognition layer uses advanced recognition technologies, such as optical character recognition (OCR) and image recognition algorithms, to accurately identify the extracted document content features. It accurately converts the text on the driver's license into editable text and quantitatively analyzes the various features of the ID photo to obtain the corresponding document content information. For example, it can identify the exact expiration date of the driver's license and determine whether the ID photo meets the required size.

[0120] The pre-examination layer compares the identified certificate content information with pre-configured examination qualified conditions in detail. These examination qualified conditions cover various requirements such as traffic regulations, industry standards, etc., such as the driver's license must be within the valid period, the qualified vehicle type must meet the requirements of the freight qualification certificate application, the certificate photo must meet the specified format, etc. Through this comparison, it is determined whether the certificate content information meets the requirements.

[0121] If the certificate content information completely meets the examination qualified conditions, the construction layer loads these information into the corresponding interface information display position of the first certificate business confirmation interface. These information are presented to the user as to-be-examined information. The user can clearly see the information extracted by the system and confirm that there is no error before proceeding to the next step.

[0122] If the certificate content information does not meet the examination qualified conditions, the construction layer loads it into the interface information display position of the second certificate business confirmation interface, and clearly marks the to-be-examined information that does not meet the conditions. For example, if the driver's license has expired, a red warning will be marked at this information, and the user will be prompted to upload valid driver's license information again. In this way, the user can intuitively understand which information has problems, so as to timely correct and re-upload.

[0123] 105, pushing the certificate business confirmation interface to the target user, so that the target user confirms the to-be-examined information in the certificate business confirmation interface.

[0124] 106, in response to the confirmation instruction of the target user to the certificate business confirmation interface, pushing the to-be-examined information in the certificate business confirmation interface to the examination end to complete the freight qualification certificate business handling process.

[0125] Exemplarily, assuming that driver Xiao Zhang applies for a general freight qualification certificate, and the driving qualification information provided by him meets all conditions after being processed by the examination model. The system generates a first certificate business confirmation interface, which displays the name, ID number, driving license vehicle type, and valid period of Xiao Zhang. After Xiao Zhang logs in to the system, he sees that the information is accurate and clicks the confirmation button. The system receives the confirmation instruction and pushes the to-be-examined information to the examination end, and the examination end starts the subsequent audit process.

[0126] In another example, driver Xiao Li applies for a certificate replacement business, and the expired information of his driver's license is identified by the examination model. The system generates a second certificate business confirmation interface, and the driver's license valid period column is marked in red and prompts "Driver's license has expired, please upload valid driver's license information again". After seeing the prompt, Xiao Li updates the driver's license information and re-submits. After the system passes the examination again, it pushes a new first certificate business confirmation interface to Xiao Li, and Xiao Li confirms that there is no error and clicks the confirmation. The system pushes the information to the examination end.

[0127] Thus, the system generates a corresponding certificate business confirmation interface according to the previous review results, and pushes the interface to the target user through network communication technology. This process is based on HTTP / HTTPS protocol to ensure the safety and stability of data transmission in the network. The user can receive the interface by logging in to the system through an account on the client side (such as a computer or a mobile phone APP). When the user confirms the information to be reviewed in the interface, clicks the confirmation button, and the client generates a confirmation instruction and sends it back to the system server. After the server receives the instruction, it packages and arranges the information to be reviewed in the certificate business confirmation interface, and pushes it to the review end according to the pre-set interface specification and data transmission protocol. The review end is generally the business audit system of the transportation management department, which starts the formal audit process after receiving the data.

[0128] Thus, by allowing the user to confirm the information in the confirmation interface, the user can clearly understand the information submitted by himself and the results processed by the system, enhancing the user's sense of participation and control in the business handling process. The user confirmation link effectively avoids information errors caused by system error information extraction or user negligence. Before the final submission for review, the user is given the opportunity to check again to ensure that the review process can be based on accurate information, improving the efficiency and pass rate of the review. The entire process realizes the automatic transfer from user information submission to review end information reception, reduces manual intervention and intermediate links, improves the overall efficiency of the transportation and freight qualification certificate business handling, and makes the business handling more standardized and smooth. Clear feedback and operation guidance are provided to the user, especially when the information does not meet the conditions, the user is clearly informed of the content that needs to be corrected, which helps to improve the user's satisfaction with the business handling service.

[0129] In the embodiments of the present application, further optionally, an intelligent voice interaction assistant is embedded with technical means such as air drop. The driver only needs to dictate instructions, and the assistant can assist in completing operations such as finding files, switching input interfaces, and confirming submission, freeing the hands, especially suitable for the scene of hurried business handling during the driver's driving interval, and comprehensively improving the operation convenience. For example, the historical business data of the same target user is obtained from different devices through air drop; the target field recognized by the above-mentioned OCR is fused to correct the deviation in the recognition result.

[0130] The present application uses its own encryption technology to protect the safety of all user information data. Further optionally, comprehensive encryption processing is started for the handling process of the transportation and freight qualification certificate business.

[0131] Further optionally, an intrusion detection system (IDS) based on neural network is implanted to monitor network traffic and user behavior patterns in real time. Once an anomaly such as suspected hacker intrusion or malicious software penetration is found, an automatic blocking and early warning mechanism is immediately started.

[0132] Exemplarily, after implanting the neural network-based intrusion detection system (IDS) and the self-owned encryption technology, taking the case of Xiaozhang applying for a general freight qualification certificate as an example, in the process of generating the first certificate business confirmation interface by the system and pushing it to Xiaozhang, the IDS monitors the network traffic in real time. If a hacker tries to obtain Xiaozhang's name, ID number, driving license vehicle type, validity period and other transmission information through network sniffing, the IDS will analyze the network traffic pattern based on the neural network, and as soon as it identifies abnormal traffic characteristics, such as a large number of requests for the transmission data initiated by non-Xiaozhang clients in a short period of time, it will immediately start the automatic blocking mechanism to prevent the hacker from obtaining the data. At the same time, the system administrator is sent a warning message to inform the possible intrusion behavior.

[0133] All information of Xiaozhang is encrypted by the self-owned encryption technology of the application during transmission and storage. From the moment he inputs information on the client, the data is encrypted into ciphertext for transmission. Even if the hacker intercepts the data, without the corresponding decryption key, he cannot obtain the real information. When stored on the server side, it is also saved in encrypted form to ensure information security.

[0134] Taking Xiaoli's application for a new certificate as an example, when Xiaoli first submits the expired driving license information identified by the review model, the system generates a second certificate business confirmation interface. In this process, the IDS continuously monitors the user behavior pattern. If it is found that malicious software tries to penetrate the system through Xiaoli's client, modify the prompt information or steal Xiaoli's subsequent updated driving license information, the IDS can learn and judge the abnormal behavior pattern in time, and start the automatic blocking mechanism to prevent further damage of the malicious software. At the same time, the system administrator is warned to deal with it in time.

[0135] In the whole process of Xiaoli re-uploading the updated driving license information and subsequently receiving the new first certificate business confirmation interface and confirming it, all his information is transmitted and stored under the protection of the self-owned encryption technology. This ensures that whether in the network link of data transmission or in the server storage link of the system, Xiaoli's information will not be illegally stolen or tampered with, ensuring the safety and reliability of the business handling.

[0136] Further optionally, a new homomorphic encryption technology is adopted, so that data can still be operated in an encrypted state. For example, when a government official needs to count the age distribution of drivers applying for a freight qualification certificate or the number of vehicles they can drive, the encrypted information of drivers such as Xiao Zhang and Xiao Li does not need to be decrypted. The system can directly perform operations such as addition and multiplication on encrypted driver personal information and driving license information according to the homomorphic encryption rule, so as to obtain the statistical result.

[0137] From the effect, homomorphic encryption greatly reduces the risk of data exposure. In the whole data transmission and operation process, the data always exists in the form of ciphertext. Even if the data is intercepted during transmission or faces potential attacks during operation, the attacker cannot obtain the original data. This not only ensures the privacy of drivers such as Xiao Zhang and Xiao Li, but also ensures the confidentiality and integrity of the data in the business process, further enhances the security and reliability of the system, and builds a strong line of defense for the information security of the traffic freight qualification certificate business.

[0138] In the technical solution of the present application, compared with manual one-by-one checking, the examination time is greatly shortened, and the misjudgment caused by human negligence is reduced, ensuring the accuracy and efficiency of the business process. With the help of the efficient processing capacity of artificial intelligence technology, each link can be quickly and accurately completed, reducing unnecessary waiting and repeated operations, realizing the whole-process optimization of the traffic freight qualification certificate service, and improving the overall efficiency of government services. The present application optimizes the data connection with the traffic bureau, simplifies the declaration process, and improves the security performance of the system, thereby solving the above problems and improving the efficiency and security of ordinary freight qualification certificate handling and road transport qualification certificate replacement handling. The present application combines software and hardware to improve user experience and reduce the difficulty of handling matters.

[0139] In another embodiment of the present application, an artificial intelligence-based traffic freight qualification certificate handling service system is also provided. Referring to Figure 6 The system includes the following units:

[0140] The starting unit is configured to determine the required certificate business type based on the business instruction triggered by the target user in response to the traffic freight qualification certificate business instruction. The certificate business type includes ordinary freight qualification certificate handling business or road transport qualification certificate replacement business.

[0141] a verification unit configured to collect identity verification information of the target user, and perform identity verification on the target user based on the identity verification information; the identity verification information includes at least one of identity card information, face image information, and voiceprint information;

[0142] a pushing unit configured to, if the target user passes the identity verification, generate and push a business information entry interface to the target user according to a required certificate business type; wherein the information entry module contained in the business information entry interface is associated with the certificate business type required by the target user; the information entry module is used at least for collecting individual information of the target user and business material information corresponding to the certificate business type required by the target user;

[0143] a pre-examination unit configured to obtain user driving qualification information uploaded by the target user in the business information entry interface, and perform business preprocessing on the user driving qualification information by using a driving qualification examination model to obtain a certificate business confirmation interface; the user driving qualification information includes at least the following information: a motor vehicle driving license, a user certificate photo, and user personal information;

[0144] the pushing unit is further configured to push the certificate business confirmation interface to the target user, so that the target user confirms the information to be examined in the certificate business confirmation interface; and in response to a confirmation instruction of the target user on the certificate business confirmation interface, the pushing unit pushes the information to be examined in the certificate business confirmation interface to an examination end, so as to complete a traffic and freight operation qualification certificate business process.

[0145] Further optionally, a starting unit is configured to, in response to a traffic and freight operation qualification certificate business instruction triggered by the target user, determine a required certificate business type based on the business instruction; the starting unit is configured to receive the business instruction through an input layer of a business classification model; determine a region where the target user is located through a calling layer of the business classification model, and call a branch business classification module corresponding to the region; wherein the business classification model is deployed with branch business classification modules corresponding to different regions; the starting unit is configured to perform semantic recognition and business classification on the business instruction through the branch business classification module of the business classification model, so as to obtain the certificate business type indicated by the business instruction; wherein each branch business classification module corresponding to a different region is loaded with a corresponding dialect correction module, each dialect correction module is used to correct dialect errors and optimize spoken language of the business instruction; each dialect correction module is obtained by training based on dialect voice materials and / or dialect image materials of each region.

[0146] Further optionally, the starting unit is configured to perform semantic recognition and business classification on the service instruction through a branch business classification module of the service classification model to obtain a certificate service type indicated by the service instruction, and is configured to: extract corresponding service instruction features from the service instruction through a feature extraction layer of the branch business classification module; perform dialect error correction and spoken language optimization processing on the service instruction features through a dialect correction module of the branch business classification module to obtain first optimized instruction features; perform ambiguity correction on the first optimized instruction features through an ambiguity correction layer of the branch business classification module to obtain second optimized instruction features; perform semantic recognition on the second optimized instruction features through a semantic recognition layer of the branch business classification module to obtain a semantic recognition result; and match a corresponding business classification result to the semantic recognition result through a business classification layer of the branch business classification module.

[0147] Further optionally, the verification unit is configured to collect identity verification information of the target user and perform identity verification on the target user based on the identity verification information, and is configured to: determine a service handling mode corresponding to the target user based on the service instruction; wherein the service handling mode includes online handling and / or offline handling; collect at least one of identity document information, face image information, and voiceprint information of the target user based on the service handling mode corresponding to the target user; select matched first user identity information from a user identity database based on user name information in the service instruction; input the collected identity document information, face image information, and voiceprint information into a multi-modal identity recognition model to perform comprehensive identity recognition processing on the target user to obtain second user identity information of the target user; compare whether the first user identity information and the second user identity information are consistent; if the first user identity information and the second user identity information are consistent, it is determined that the target user passes the identity verification; or if the first user identity information and the second user identity information are inconsistent, it is determined that the target user fails the identity verification.

[0148] Further optionally, the verification unit is configured to: based on a service handling manner corresponding to the target user, collect at least one of the fingerprint image information and the iris image information of the target user; after the verification unit inputs the collected identity document information, the face image information, and the voiceprint information into the multi-modal identity recognition model to perform comprehensive identity recognition processing on the target user to obtain second user identity information of the target user, the verification unit is further configured to: perform biological feature fusion on the fingerprint image information, the iris image information, the face image information, and the voiceprint information of the target user, and construct a correlation relationship model between the multi-modal biological features; cross-verify the correlation relationship model to obtain verification information of the second user identity information; and determine a confidence degree of the second user identity information based on the verification information, wherein the confidence degree is used to represent a degree of accuracy of the second user identity information.

[0149] Further optionally, the pre-examination unit is configured to: through a feature extraction layer of the driving qualification examination model, extract document content features from the user driving qualification information; the type of the document content features is determined by a corresponding examination business; through an identification layer of the driving qualification examination model, identify the document content features to obtain corresponding document content information; through a pre-examination layer of the driving qualification examination model, pre-examine the document content information to determine whether the document content information meets a pre-configured examination qualified condition; through a construction layer of the driving qualification examination model, if the document content information meets the pre-configured examination qualified condition, load the document content information into an interface information display position corresponding to the first document business confirmation interface as to-be-examined information; and through the construction layer, if the document content information does not meet the pre-configured examination qualified condition, load the document content information into an interface information display position corresponding to the second document business confirmation interface as to-be-examined information, and mark the to-be-examined information that does not meet the examination qualified condition in the interface information display position to prompt the target user to re-upload the unqualified to-be-examined information.

[0150] Further optionally, before the pre-examination unit pre-examines the document content information through the pre-examination layer of the driving qualification examination model to determine whether the document content information meets a pre-configured examination qualified condition, the pre-examination unit is further configured to: through an optimization layer of the driving qualification examination model, perform integrity detection on the document content information to determine whether all to-be-examined information types required by the user driving qualification examination are contained in the document content information; if the document content information does not pass the integrity detection, query historical business information corresponding to the target user, and update the document content information based on the historical business information, so that the updated document content information passes the integrity detection.

[0151] The system can implement various steps in the above method embodiments, which are not expanded here.

[0152] In the embodiments of the present application, the traffic freight practitioner qualification certificate handling service system based on artificial intelligence greatly shortens the examination time compared with artificial one-by-one checking and examination, and can reduce the misjudgment caused by human negligence, thereby ensuring the accuracy and efficiency of business handling. With the efficient processing capacity of artificial intelligence technology, each link can be quickly and accurately completed, unnecessary waiting and repeated operation are reduced, the whole process optimization of traffic freight practitioner qualification certificate handling service is realized, and the overall efficiency of government service is improved.

[0153] Please refer to Figure 7 , Figure 7 An embodiment of the electronic device provided in the embodiments of the present application is shown. As shown in Figure 7 , the present application provides an electronic device 500, which includes a memory 510, a processor 520, and a computer program 511 stored in the memory 510 and executable on the processor 520, and the processor 520 implements the steps of the foregoing embodiments when executing the computer program 511. Please refer to Figure 8 , Figure 8 An embodiment of a computer-readable storage medium provided in the embodiments of the present application is shown. As shown in Figure 8 , the present embodiment provides a computer-readable storage medium 600, which stores a computer program 611, and the computer program 611 is executed by a processor to implement the steps of the foregoing embodiments.

[0154] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code. Although the preferred embodiments of the present application have been described, those skilled in the art can make further changes and modifications to these embodiments once they understand the basic inventive concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application. Obviously, those skilled in the art can make various modifications and changes to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and changes of the present application fall within the scope of the claims of the present application and their equivalents, the present application also intends to include these modifications and changes.

Claims

1. A method for handling transportation freight qualification certificates based on artificial intelligence, characterized in that: The execution subject of the method is connected to the transfer interface deployed in the government extranet server; the method includes: In response to a traffic freight practitioner qualification certificate business instruction triggered by a target user, the type of certificate business required is determined based on the business instruction; wherein the certificate business type includes: general freight practitioner qualification certificate processing business, or road transport practitioner qualification certificate renewal business; Collecting identity verification information of a target user and authenticating the target user based on the identity verification information; the identity verification information includes at least one of identity document information, facial image information, and voiceprint information; If the target user passes identity verification, a business information entry interface is generated and pushed to the target user based on the type of certificate business required. The information entry module included in the business information entry interface is associated with the type of certificate business required by the target user. The information entry module is used to collect at least the target user's individual information and business material information corresponding to the type of certificate business required. Obtain the target user's driving qualification information uploaded in the business information entry interface, and perform business pre-processing on the user's driving qualification information using a driving qualification review model to obtain a certificate business confirmation interface; the user's driving qualification information includes at least the following information: a motor vehicle driver's license, a user ID photo, and user personal information; Pushing the certificate service confirmation interface to the target user, so that the target user can confirm the information to be reviewed in the certificate service confirmation interface; In response to the target user's confirmation instruction on the certificate business confirmation interface, the information to be reviewed in the certificate business confirmation interface is pushed to the review end to complete the processing flow of the transportation and freight practitioner qualification certificate business.

2. The method for handling transportation freight qualification certificates based on artificial intelligence according to claim 1 is characterized in that: The step of responding to the target user's triggering of a transportation and freight practitioner qualification certificate business instruction and determining the required certificate business type based on the business instruction includes: Receiving the business instruction through the input layer of the business classification model; The target user's region is determined through the calling layer of the business classification model, and the branch business classification module corresponding to the region is called; wherein the business classification model is deployed with branch business classification modules corresponding to different regions; The business instructions are semantically recognized and classified through the branch business classification modules of the business classification model to obtain the certificate business type indicated by the business instructions; wherein, the branch business classification modules corresponding to different regions are loaded with their own corresponding dialect correction modules, and each dialect correction module is used to realize dialect error correction and spoken language optimization of the business instructions; each dialect correction module is trained based on the dialect voice materials and / or dialect image materials of each region.

3. The artificial intelligence-based transportation freight practitioner qualification certificate processing service method according to claim 2 is characterized in that: The branch business classification module of the business classification model performs semantic recognition and business classification on the business instruction to obtain the certificate business type indicated by the business instruction, including: Extracting corresponding service instruction features from the service instructions through a feature extraction layer of a branch service classification module; Performing dialect error correction and spoken language optimization processing on the service instruction feature by the dialect correction module of the branch service classification module to obtain a first optimized instruction feature; Performing ambiguity correction on the first optimization instruction feature by an ambiguity correction layer of a branch service classification module to obtain a second optimization instruction feature; Performing semantic recognition on the second optimization instruction feature through the semantic recognition layer of the branch business classification module to obtain a semantic recognition result; The semantic recognition result is matched with a corresponding business classification result through the business classification layer of the branch business classification module.

4. The method for handling transportation freight qualification certificates based on artificial intelligence according to claim 1 is characterized in that: The collecting of identity authentication information of the target user and authenticating the target user based on the identity authentication information includes: Determine the business processing method corresponding to the target user based on the business instruction; wherein the business processing method includes: online processing and / or offline processing; Based on the business handling method corresponding to the target user, at least one of the target user's identity document information, facial image information, and voiceprint information is collected; Based on the user name information in the service instruction, selecting matching first user identity information from a user identity database; Input the collected identity document information, facial image information, and voiceprint information into the multimodal identity recognition model to perform comprehensive identity recognition processing on the target user to obtain the target user's second user identity information; comparing the first user identity information with the second user identity information to determine whether they are consistent; If the first user identity information is consistent with the second user identity information, it is determined that the target user has passed the identity authentication; or, If the first user identity information is inconsistent with the second user identity information, it is determined that the target user has failed identity authentication.

5. The artificial intelligence-based transportation freight qualification certificate processing service method according to claim 4 is characterized in that: The method further comprises: Based on the service handling method corresponding to the target user, collecting at least one of the fingerprint image information and iris image information of the target user; After inputting the collected identity document information, facial image information, and voiceprint information into the multimodal identity recognition model and performing comprehensive identity recognition processing on the target user to obtain the second user identity information of the target user, the method further includes: Fusing the target user's fingerprint image information, iris image information, face image information, and voiceprint information into biometric features, and constructing a correlation model between the multimodal biometric features; Cross-validating the association relationship model to obtain verification information of the second user identity information; The confidence level of the second user identity information is determined based on the verification information, wherein the confidence level is used to indicate a degree of accuracy of the second user identity information.

6. The method for handling transportation freight qualification certificates based on artificial intelligence according to claim 1 is characterized in that: The driving qualification review model is used to perform business pre-processing on the user's driving qualification information to obtain a certificate business confirmation interface, including: Extracting certificate content features from the user's driving qualification information through the feature extraction layer of the driving qualification review model; the type of the certificate content features is determined by the corresponding review business regulations; Identify the content features of the certificate through the recognition layer of the driving qualification review model to obtain corresponding certificate content information; Pre-examination of the certificate content information is performed through the pre-examination layer of the driving qualification review model to determine whether the certificate content information meets the pre-configured review qualification conditions; Through the construction layer of the driving qualification review model, if the certificate content information meets the pre-configured review qualification conditions, the certificate content information is loaded into the interface information display position corresponding to the first certificate service confirmation interface as information to be reviewed; Through the construction layer, if the certificate content information does not meet the pre-configured review qualification conditions, the certificate content information will be loaded into the interface information display position corresponding to the second certificate business confirmation interface as the information to be reviewed, and the information to be reviewed that does not meet the review qualification conditions will be marked in the interface information display position to prompt the target user to re-upload the unqualified information to be reviewed.

7. The artificial intelligence-based transportation freight practitioner qualification certificate processing service method according to claim 6 is characterized in that: Before the pre-examination layer of the driving qualification review model pre-examines the certificate content information to determine whether the certificate content information meets the pre-configured review qualification conditions, the method further includes: Through the optimization layer of the driving qualification review model, the integrity of the certificate content information is checked to determine whether the certificate content information contains all the types of information to be reviewed required for the user's driving qualification review; If the certificate content information fails the integrity check, historical service information corresponding to the target user is queried, and the certificate content information is updated based on the historical service information, so that the updated certificate content information passes the integrity check.

8. An artificial intelligence-based transportation and freight qualification certificate processing service system, characterized by: The system is connected to a transfer interface deployed in a government extranet server; the system includes the following units, wherein: The initiating unit is configured to respond to a traffic freight practitioner qualification certificate service instruction triggered by a target user and determine a required certificate service type based on the service instruction; wherein the certificate service type includes: general freight practitioner qualification certificate service or road transport practitioner qualification certificate renewal service; a verification unit configured to collect identity verification information of a target user and authenticate the target user based on the identity verification information; the identity verification information includes at least one of identity document information, facial image information, and voiceprint information; The push unit is configured to generate and push a business information entry interface to the target user based on the type of certificate business required if the target user passes identity verification; wherein the information entry module included in the business information entry interface is associated with the type of certificate business required by the target user; and the information entry module is configured to collect at least the individual information of the target user and the business material information corresponding to the type of certificate business required; The pre-examination unit is configured to obtain the user driving qualification information uploaded by the target user in the service information entry interface, and perform service pre-processing on the user driving qualification information using the driving qualification review model to obtain a certificate service confirmation interface; the user driving qualification information includes at least the following information: a motor vehicle driver's license, a user ID photo, and user personal information; The push unit is also configured to push the certificate business confirmation interface to the target user so that the target user can confirm the information to be reviewed in the certificate business confirmation interface; in response to the target user's confirmation instruction on the certificate business confirmation interface, the information to be reviewed in the certificate business confirmation interface is pushed to the review end to complete the processing procedure of the transportation and freight practitioner qualification certificate business.

9. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby implementing the artificial intelligence-based transportation and freight practitioner qualification certificate processing service method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that The storage medium stores a computer software program, which, when executed by a processor, implements the artificial intelligence-based transportation and freight qualification certificate processing service method as described in any one of claims 1 to 7.

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