Ticket booking system and method and storage medium
By using pre-trained language models in the ticket booking system for keyword extraction and analysis, combined with knowledge base correction and filtering, the problem of insufficient flexibility and context understanding in existing systems in complex dialogue scenarios and multi-round dialogues is solved, and the problem of insufficient flexibility and context understanding in existing systems is realized. Efficient and accurate ticket booking services improve user experience and system adaptability.
Patent Information
- Application Number
- CN202411323757.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-23
- Publication Date
- 2025-01-10
AI Technical Summary
The existing ticket booking system is not flexible and adaptable when dealing with complex and diverse dialogue scenarios, making it difficult to meet the diverse needs of users, and traditional methods lack context understanding and information extraction accuracy in multiple rounds of dialogue.
Through the collaborative work of the client and the server, the pre-trained language model is used to extract and analyze the keywords to be processed, and the target keyword group is generated, and input it into the preset ticket query terminal to obtain ticket booking information, so as to realize the ticket confirmation booking instruction Send, correct and filter with the knowledge base to ensure the accuracy and consistency of information extraction.
Achieve a more intelligent, accurate and efficient ticket booking service, improves the user experience and convenience of the booking process, ensures accurate information extraction, seamless switching of traffic modes, reduces repeated inquiries, and improves the coherence of conversations and User satisfaction.
Smart Images
Figure CN119294564A_ABST
Abstract
Description
Technical Field
[0001] The invention mainly relates to the technical field of ticket reservation, and in particular to a ticket reservation system, method and storage medium. Background Art
[0002] In the field of ticket booking via human-computer interaction, existing technologies have made good progress. The main technical implementation methods are as follows:
[0003] (1) Rule-based dialogue system
[0004] Early human-computer dialogue systems were mostly rule-based. These systems relied on predefined dialogue rules and finite state machines to manage the flow of dialogue. They are usually applied to specific areas, such as customer service and ticketing systems. The advantages of these systems are that they are relatively simple to implement and can accurately handle predefined tasks. However, due to the fixedness and limitations of their rules, such systems perform poorly when dealing with complex and diverse dialogue scenarios.
[0005] (2) Template-based Ticket Booking System
[0006] Template-based systems are widely used in the field of ticket booking. These systems predefine some fixed dialogue templates, and the user's input will be matched to the corresponding template, extracting the corresponding elements and generating the corresponding response. This type of system is efficient in dealing with standardized problems, but has limited flexibility and scalability when faced with complex user needs and changing dialogue scenarios.
[0007] (3) Dialogue system based on traditional machine learning
[0008] With the development of machine learning technology, more and more dialogue systems have begun to adopt machine learning methods to generate responses by learning a large amount of dialogue data. Common methods include support vector machines (SVM), naive Bayes, and decision trees. Such systems perform well in processing specific tasks, but still have problems such as insufficient generalization ability and dependence on a large amount of labeled data.
[0009] (4) Dialogue system based on deep learning
[0010] The rise of deep learning technology has brought new development opportunities for human-computer dialogue systems. Through convolutional neural networks (CNNs) and recurrent neural networks (RNNs), especially long short-term memory networks (LSTMs) and gated recurrent units (GRUs), dialogue systems can better capture contextual information in dialogues and generate more natural and fluent dialogue responses. However, these models usually require a large amount of data for training and consume a lot of computing resources.
[0011] (5) Application of pre-trained language models and large models in dialogue systems
[0012] In recent years, human-computer dialogue systems based on pre-trained language models and large models have made significant progress. Pre-trained models and large models have mastered rich language knowledge and context understanding capabilities by training on massive text data. Compared with traditional methods, such systems perform better in dialogue coherence, context understanding, and response generation, but there are still problems with information extraction accuracy and knowledge hallucination.
[0013] Although existing technologies have made significant progress in the fields of human-computer dialogue and ticket booking, they still have the following limitations:
[0014] (1) Flexibility and adaptability: Rule- and template-based systems perform poorly in dealing with complex and dynamically changing conversation scenarios and are unable to meet the diverse needs of users.
[0015] (2) Context understanding: Traditional machine learning and some deep learning methods have difficulty in fully understanding and maintaining contextual information when processing multi-round conversations, resulting in incoherent conversations and insufficient accuracy of responses.
[0016] (3) Knowledge integration: Large model-based systems still face challenges in integrating and utilizing knowledge bases. How to effectively combine the information in the knowledge base with dialogue generation, reduce knowledge illusions, and improve the accuracy of information extraction is an important research topic.
[0017] In summary, the existing technology has already had relatively mature solutions for realizing human-computer dialogue and ticket booking, but there are still certain limitations and room for improvement. Summary of the invention
[0018] The technical problem to be solved by the present invention is to provide a ticket booking system, method and storage medium in view of the deficiencies in the prior art.
[0019] The technical solution of the present invention to solve the above technical problem is as follows: a ticket booking system, including a client and a server,
[0020] The client is used to input a user ID and a statement to be processed, and send the user ID and the statement to be processed to the server;
[0021] The server is used to extract keywords from the sentence to be processed to obtain a plurality of initial keywords;
[0022] Perform keyword analysis on the user ID and the plurality of initial keywords to obtain a target keyword group;
[0023] The target keyword group is input into a preset ticket query terminal, and corresponding ticket reservation information is obtained from the preset ticket query terminal, and a ticket confirmation reservation instruction is generated, and the ticket reservation information and the ticket confirmation reservation instruction are sent to the client.
[0024] Another technical solution of the present invention to solve the above technical problem is as follows: a ticket booking method, comprising the following steps:
[0025] Input a user ID and a sentence to be processed, extract keywords from the sentence to be processed, and obtain a plurality of initial keywords;
[0026] Perform keyword analysis on the user ID and the plurality of initial keywords to obtain a target keyword group;
[0027] The target keyword group is input into a preset ticket query terminal, and corresponding ticket booking information is obtained from the preset ticket query terminal, and a ticket confirmation booking instruction is generated, and the ticket booking information and the ticket confirmation booking instruction are sent to the client.
[0028] The beneficial effects of the present invention are as follows: initial keywords are obtained by extracting keywords from the sentences to be processed, a target keyword group is obtained by keyword analysis of the user ID and the initial keywords, the target keyword group is input into a preset ticket query terminal, and ticket reservation information is obtained from the preset ticket query terminal, and a ticket confirmation reservation instruction is generated, and the ticket reservation information and the ticket confirmation reservation instruction are sent to the client, which can ensure that the information is extracted accurately, realize a more intelligent, accurate and efficient ticket reservation service, and provide users with a better service experience. BRIEF DESCRIPTION OF THE DRAWINGS
[0029] Figure 1 A module block diagram of a ticket booking system provided by an embodiment of the present invention;
[0030] Figure 2 A system structure diagram of a ticket booking system provided by an embodiment of the present invention;
[0031] Figure 3 A schematic diagram of a flow chart of a ticket booking method provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION
[0032] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.
[0033] Figure 1 A module block diagram of a ticket booking system provided in one embodiment of the present invention.
[0034] like Figure 1and 2 As shown, a ticket booking system includes a client and a server.
[0035] The client is used to input a user ID and a statement to be processed, and send the user ID and the statement to be processed to the server;
[0036] The server is used to extract keywords from the sentence to be processed to obtain a plurality of initial keywords;
[0037] Perform keyword analysis on the user ID and the plurality of initial keywords to obtain a target keyword group;
[0038] The target keyword group is input into a preset ticket query terminal, and corresponding ticket reservation information is obtained from the preset ticket query terminal, and a ticket confirmation reservation instruction is generated, and the ticket reservation information and the ticket confirmation reservation instruction are sent to the client.
[0039] It should be understood that the present invention successfully breaks the boundaries between traditional travel modes, allowing users to freely switch transportation modes (such as switching from train tickets to air tickets) during the booking process without having to restart the booking process, greatly improving the convenience of the booking process and user satisfaction.
[0040] In the above embodiment, initial keywords are obtained by extracting keywords from the statement to be processed, and a target keyword group is obtained by keyword analysis of the user ID and the initial keyword. The target keyword group is input into a preset ticket query terminal, and ticket reservation information is obtained from the preset ticket query terminal, and a ticket confirmation reservation instruction is generated, and the ticket reservation information and the ticket confirmation reservation instruction are sent to the client, which can ensure that the information is extracted accurately, realize a more intelligent, accurate and efficient ticket reservation service, and provide users with a better service experience.
[0041] Optionally, as an embodiment of the present invention, in the server, the process of extracting keywords from the sentence to be processed to obtain a plurality of initial keywords includes:
[0042] Keywords are extracted from the sentence to be processed using a pre-trained first language model to obtain a plurality of initial keywords.
[0043] It should be understood that the pre-trained first language model may be a model such as BERT, TextRank, etc.
[0044] In the above embodiment, multiple initial keywords are obtained by extracting keywords from the sentence to be processed through the pre-trained first language model, which can ensure the accuracy of information extraction, realize a more intelligent, accurate and efficient ticket booking service, and greatly improve the convenience of the booking process and user satisfaction.
[0045] Optionally, as an embodiment of the present invention, in the server, the process of performing keyword analysis on the user ID and the multiple initial keywords to obtain a target keyword group includes:
[0046] Performing scene intent analysis on all the initial keywords to obtain initial scene intent keywords;
[0047] Obtaining corresponding historical scene intention keywords from a preset user history information library according to the user ID;
[0048] Determine whether the initial scene intent keyword is the same as the historical scene intent keyword. If so, use the corresponding historical scene intent information in the preset user history information library as the target scene intent information; if not, obtain the target scene intent information according to the initial scene intent keyword;
[0049] Perform ticket information analysis on all the initial keywords according to the target scene intention information to obtain a ticket information group;
[0050] The ticket information group is subjected to integrity analysis to obtain a target keyword group.
[0051] It should be understood that the initial scene intent keywords and the historical scene intent information may be train tickets or air tickets.
[0052] In the above embodiment, keyword analysis is performed on the user ID and multiple initial keywords to obtain a target keyword group, which can ensure the accuracy of information extraction and achieve a more intelligent, accurate and efficient ticket booking service.
[0053] Optionally, as an embodiment of the present invention, in the server, the process of performing scene intent analysis on all the initial keywords to obtain the initial scene intent keywords includes:
[0054] Determine whether any of the initial keywords is the same as the scene intent keyword in the preset scene intent database. If so, use the initial keyword as the initial scene intent keyword; if not, send the preset scene intent query statement to the client.
[0055] It should be understood that the preset scenario intention inquiry statement may be "Do you plan to take a train or a plane?".
[0056] In the above embodiment, scenario intent analysis is performed on all initial keywords to obtain initial scenario intent keywords, which can ensure the accuracy of information extraction and achieve a more intelligent, accurate and efficient ticket booking service.
[0057] Optionally, as an embodiment of the present invention, in the server, the process of performing ticket information analysis on all the initial keywords according to the target scene intention information to obtain the ticket information group includes:
[0058] Extracting departure place keywords from all the initial keywords through a pre-trained second language model to obtain a plurality of initial departure place keywords;
[0059] extracting destination keywords from all the initial keywords through a pre-trained third language model to obtain a plurality of initial destination keywords;
[0060] extracting departure dates from all the initial keywords using a pre-trained fourth language model to obtain multiple initial departure dates;
[0061] According to the target scene intention information, a target scene information database corresponding to the target scene intention information is exported from a preset target scene database;
[0062] Correcting each of the initial departure place keywords, each of the initial destination keywords, and each of the initial departure dates respectively through the target scenario information database to obtain a corrected departure place keyword corresponding to each of the initial departure place keywords, a corrected destination keyword corresponding to each of the initial destination keywords, and a corrected departure date corresponding to each of the initial departure dates;
[0063] Filter all the corrected departure place keywords according to preset filtering rules to obtain departure place filtering results;
[0064] Filter all the corrected destination keywords according to the preset filtering rules to obtain destination filtering results;
[0065] Filter all the corrected departure dates according to the preset filtering rules to obtain a departure date filtering result;
[0066] The ticket information group includes the departure place screening result, the destination screening result and the departure date screening result.
[0067] It should be understood that the present invention can deeply analyze user input, accurately identify and extract key information such as transportation, departure point, destination, travel date, etc., and convert it into a structured data format, laying a solid foundation for subsequent operations.
[0068] It should be understood that the preset screening rule may be to filter the extracted departure place keywords, destination keywords and departure date.
[0069] Specifically, the knowledge base driven factor correction and filtering of the present invention is as follows:
[0070] Element standardization: For example, from Daxing to Haikou, the departure place extracted by the big model is "Daxing", but in fact there is no "Daxing" in the airport knowledge base, so it is necessary to correct it (i.e., correction processing) based on the airport knowledge base (i.e., the target scene information database) and correct it to "Beijing Daxing Airport" before calling the airline interface to query flights.
[0071] Knowledge illusion filtering: For example, from Daxing to Haikou, in addition to extracting Daxing and Haikou, the large model may extract the date as today, resulting in incorrect extraction. In this case, filtering is required by comparing the user input with the time-related knowledge base (i.e., the preset filtering rules) to perform judgment filtering.
[0072] In the above embodiment, ticket information analysis is performed on all initial keywords according to the target scenario intention information to obtain a ticket information group, which can ensure the accuracy of information extraction, realize a more intelligent, accurate and efficient ticket booking service, and provide users with a better service experience.
[0073] Optionally, as an embodiment of the present invention, in the server, the process of performing integrity analysis on the ticket information group to obtain the target keyword group includes:
[0074] S351: Determine whether the departure place screening result is no screening result, if so, send the preset departure place query statement to the client; if not, execute S352;
[0075] S352: Determine whether the destination screening result is no screening result, if yes, send the preset destination query statement to the client; if no, execute S353;
[0076] S353: Determine whether the departure date screening result is no screening result, if so, send the preset departure date query statement to the client; if not, execute S354;
[0077] S354: The departure place screening result, the destination screening result and the departure date screening result are taken as a target keyword group.
[0078] Specifically, the present invention has an intelligent active inquiry function. Based on the core elements required for ticket booking, namely, departure place, destination, and departure date, the system dynamically analyzes the acquired information, automatically identifies and asks the user for details about the missing elements. For example, when the system knows the departure place and destination, it will automatically ask: "When do you plan to leave?" This mechanism effectively guides the user to gradually provide complete information, ensuring a smooth booking process.
[0079] In the above embodiment, the integrity analysis of the ticket information group is performed to obtain the target keyword group, which effectively guides the user to gradually provide complete information, ensures the smooth progress of the booking process, realizes a more intelligent, accurate and efficient ticket booking service, and provides users with a better service experience.
[0080] Optionally, as an embodiment of the present invention, the server further includes:
[0081] The corresponding historical scene intent keywords in the preset user history information library are updated according to the target scene intent information to obtain updated historical scene intent keywords.
[0082] In the above embodiment, the corresponding historical scene intention keywords in the preset user historical information library are updated according to the target scene intention information, which can ensure the accuracy of information extraction, realize a more intelligent, accurate and efficient ticket booking service, and provide users with a better service experience.
[0083] Optionally, as another embodiment of the present invention, the present invention aims to combine the powerful language understanding ability of the pre-trained language model with the precise knowledge of the knowledge base, and realize a more intelligent, accurate and efficient ticket booking service by flexibly processing multiple rounds of dialogue. This innovative direction is not only expected to overcome the limitations of the existing technology, but also to provide users with a better service experience.
[0084] Optionally, as another embodiment of the present invention, the present invention aims to innovatively integrate a large-scale language model with a structured knowledge base to create an advanced human-machine multi-round dialogue ticket booking system. The system not only gives full play to the excellent ability of the large model in information extraction, but also cleverly uses the knowledge base to correct and optimize the output of the large model to ensure the accuracy of information extraction, thereby realizing efficient and accurate ticket booking services.
[0085] Optionally, as another embodiment of the present invention, the present invention adopts a parameter transfer mechanism to intelligently record the core elements extracted in the conversation process, avoid repeated inquiries for the same information, ensure the continuity of the conversation and optimize the user experience. This design makes human-computer interaction more natural and smooth, and significantly improves booking efficiency. The present invention is committed to providing a highly intelligent and user-friendly ticket booking solution to redefine the future travel booking experience.
[0086] Optionally, as another embodiment of the present invention, the beneficial effects of the present invention are as follows:
[0087] 1. Innovation of integrated travel booking experience
[0088] Seamless scene switching: Through intent fusion technology, the present invention successfully breaks the boundaries between traditional travel modes, allowing users to freely switch transportation modes during the booking process (such as switching from train tickets to air tickets) without having to restart the booking process, greatly improving the convenience of the booking process and user satisfaction.
[0089] 2. Proactively ask questions to improve information integrity
[0090] Intelligent guidance mechanism: The built-in active inquiry function of the present invention can dynamically adjust questions according to the currently acquired information, helping users to quickly supplement the missing key information (such as departure date) to ensure the efficient completion of the booking process.
[0091] 3. Deep understanding and accurate extraction of core elements
[0092] Accurate information extraction: With the help of advanced big model technology, the present invention identifies and extracts key elements in user input, such as transportation type, location and date, and converts them into structured data. At the same time, through deep integration with the professional knowledge base, the present invention can automatically correct the non-standard expression of the big model extraction results (such as correcting "Daxing" to "Beijing Daxing Airport"), and filter out possible erroneous information (such as unreasonable time input), ensuring the accuracy and reliability of the booking information.
[0093] 4. Contextual memory optimizes multi-round conversations
[0094] Continuous dialogue management: Using contextual memory technology, the present invention can record the dialogue history, avoid repeatedly asking the same information, make multiple rounds of dialogue more coherent and natural, greatly improve the user experience, and make the booking process more smooth and user-friendly.
[0095] In summary, through a series of innovative designs, the present invention not only greatly simplifies the complexity of travel booking and improves the user experience, but also significantly enhances the intelligence and adaptability of the system, providing users with unprecedented personalized, efficient and seamless travel booking experience, heralding a new trend in future travel services.
[0096] Optionally, as another embodiment of the present invention, the intelligent ticket booking solution of the present invention is widely applicable to various travel service platforms, including but not limited to:
[0097] 1. Online travel platforms: such as Ctrip and Qunar, can integrate this technology to optimize their air ticket and train ticket booking services and provide a more personalized and efficient user interaction experience.
[0098] 2. Official websites of airlines and railway companies: directly embed the intelligent booking system to improve the booking efficiency and user experience of official channels and reduce the burden on customer service.
[0099] 3. Comprehensive travel applications: such as Didi Chuxing and Uber, which provide users with one-stop travel solutions by integrating booking services of multiple modes of transportation.
[0100] 4. Intelligent voice assistants and chatbots: such as Siri, Google Assistant, etc., can use this technology to enhance their conversation understanding and processing capabilities, enabling more natural and efficient travel booking conversations.
[0101] 5. Corporate travel management system: Provide fast and accurate travel booking services for corporate employees, simplify the approval process, and improve travel management efficiency.
[0102] Optionally, as another embodiment of the present invention, the application prospects of the present invention are as follows:
[0103] 1. User experience upgrade: This invention significantly improves the user experience during the booking process through technologies such as active inquiry, core element extraction, knowledge base-driven element correction and filtering, and context memory. It is expected to become an industry standard and lead the upgrade direction of future travel booking experience.
[0104] 2. Efficiency and cost optimization: The automated and intelligent booking process reduces the need for manual intervention, reduces operating costs, and improves booking efficiency. For large travel service providers, this means huge economic benefits.
[0105] 3. Improved market competitiveness: Service providers who are the first to adopt such advanced technologies will have an advantage in market competition and attract more users, especially among young consumers who pursue convenient and personalized services, thus forming brand loyalty.
[0106] 4. Data-driven value-added services: The large amount of user behavior data collected by the system can be used to analyze user preferences and develop customized products and services, such as recommending the best travel plans and providing personalized discounts, to further improve user satisfaction.
[0107] 5. Cross-industry cooperation opportunities: As the technology matures and becomes more popular, the application scenarios of this invention will continue to expand, and it may be combined with industries such as hotel reservations, car rental services, and travel insurance to form a more complete travel ecosystem.
[0108] In summary, the present invention can not only significantly improve the quality and efficiency of existing travel booking services, but also is expected to inspire new business models and service innovations, promote the digital transformation of the entire travel industry, and bring users an unprecedented travel booking experience.
[0109] Figure 3 A schematic diagram of a flow chart of a ticket booking method provided in accordance with an embodiment of the present invention.
[0110] Optionally, as another embodiment of the present invention, Figure 3 As shown, a ticket booking method comprises the following steps:
[0111] Input a user ID and a sentence to be processed, extract keywords from the sentence to be processed, and obtain a plurality of initial keywords;
[0112] Perform keyword analysis on the user ID and the plurality of initial keywords to obtain a target keyword group;
[0113] The target keyword group is input into a preset ticket query terminal, and corresponding ticket booking information is obtained from the preset ticket query terminal, and a ticket confirmation booking instruction is generated, and the ticket booking information and the ticket confirmation booking instruction are sent to the client.
[0114] Optionally, another embodiment of the present invention provides a ticket booking system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the ticket booking method described above is implemented. The system may be a computer or other system.
[0115] Optionally, another embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the ticket booking method as described above is implemented.
[0116] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.
[0117] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0118] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.
[0119] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.
[0120] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
[0121] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. For such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.
[0122] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.
Claims
1. A ticket booking system, characterized in that: include: Client and Server, The client is used to input a user ID and a statement to be processed, and send the user ID and the statement to be processed to the server; The server is used to extract keywords from the sentence to be processed to obtain a plurality of initial keywords; Perform keyword analysis on the user ID and the plurality of initial keywords to obtain a target keyword group; The target keyword group is input into a preset ticket query terminal, and corresponding ticket reservation information is obtained from the preset ticket query terminal, and a ticket confirmation reservation instruction is generated, and the ticket reservation information and the ticket confirmation reservation instruction are sent to the client.
2. The ticket reservation system according to claim 1, characterized in that: In the server, the process of extracting keywords from the sentence to be processed to obtain a plurality of initial keywords includes: Keywords are extracted from the sentence to be processed using a pre-trained first language model to obtain a plurality of initial keywords.
3. The ticket reservation system according to claim 1, characterized in that: In the server, the process of performing keyword analysis on the user ID and the plurality of initial keywords to obtain a target keyword group includes: Performing scene intent analysis on all the initial keywords to obtain initial scene intent keywords; Obtaining corresponding historical scene intention keywords from a preset user history information library according to the user ID; Determine whether the initial scene intent keyword is the same as the historical scene intent keyword. If so, use the corresponding historical scene intent information in the preset user history information library as the target scene intent information; if not, obtain the target scene intent information according to the initial scene intent keyword; Perform ticket information analysis on all the initial keywords according to the target scene intention information to obtain a ticket information group; The ticket information group is subjected to integrity analysis to obtain a target keyword group.
4. The ticket reservation system according to claim 3, characterized in that: In the server, the process of performing scene intent analysis on all the initial keywords to obtain the initial scene intent keywords includes: Determine whether any of the initial keywords is the same as a scene intent keyword in a preset scene intent database. If so, use the initial keyword as the initial scene intent keyword; if not, send the preset scene intent query statement to the client.
5. The ticket reservation system according to claim 3, characterized in that: In the server, the process of performing ticket information analysis on all the initial keywords according to the target scene intention information to obtain the ticket information group includes: Extracting departure place keywords from all the initial keywords through a pre-trained second language model to obtain a plurality of initial departure place keywords; extracting destination keywords from all the initial keywords through a pre-trained third language model to obtain a plurality of initial destination keywords; extracting departure dates from all the initial keywords using a pre-trained fourth language model to obtain multiple initial departure dates; According to the target scene intention information, a target scene information database corresponding to the target scene intention information is exported from a preset target scene database; Correcting each of the initial departure place keywords, each of the initial destination keywords, and each of the initial departure dates respectively through the target scenario information database to obtain a corrected departure place keyword corresponding to each of the initial departure place keywords, a corrected destination keyword corresponding to each of the initial destination keywords, and a corrected departure date corresponding to each of the initial departure dates; Filter all the corrected departure place keywords according to preset filtering rules to obtain departure place filtering results; Filter all the corrected destination keywords according to the preset filtering rules to obtain destination filtering results; Filter all the corrected departure dates according to the preset filtering rules to obtain a departure date filtering result; The ticket information group includes the departure place screening result, the destination screening result and the departure date screening result.
6. The ticket reservation system according to claim 5, characterized in that: In the server, the process of performing integrity analysis on the ticket information group to obtain the target keyword group includes: S351: Determine whether the departure place screening result is no screening result, if so, send the preset departure place query statement to the client; if not, execute S352; S352: Determine whether the destination screening result is no screening result, if yes, send the preset destination query statement to the client; if no, execute S353; S353: Determine whether the departure date screening result is no screening result, if so, send the preset departure date query statement to the client; if not, execute S354; S354: The departure place screening result, the destination screening result and the departure date screening result are taken as a target keyword group.
7. The ticket reservation system according to claim 3, characterized in that: The server also includes: The corresponding historical scene intent keywords in the preset user history information library are updated according to the target scene intent information to obtain updated historical scene intent keywords.
8. A ticket booking method, characterized in that: The steps include: Input a user ID and a sentence to be processed, extract keywords from the sentence to be processed, and obtain a plurality of initial keywords; Perform keyword analysis on the user ID and the plurality of initial keywords to obtain a target keyword group; The target keyword group is input into a preset ticket query terminal, and corresponding ticket booking information is obtained from the preset ticket query terminal, and a ticket confirmation booking instruction is generated, and the ticket booking information and the ticket confirmation booking instruction are sent to the client.
9. A ticket reservation system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the ticket booking method according to claim 8 is implemented.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the ticket booking method as claimed in claim 8 is implemented.