A large model-based traffic hub route recommendation system

Abstract

The application discloses a kind of traffic hub route recommendation systems based on large model, the present application relates to route recommendation technical field, the present application is by being arranged monitoring acquisition unit and collecting the service benchmark data of target user once, ciphertext coding unit receives service benchmark data based on traffic route recommendation platform for the response number generated this time determines all ciphertext coding of this time, in the process of determining ciphertext coding, based on two basic units 0 and 1 in binary number system it is carried out several times of recursive iteration respectively to obtain the basic ciphertext coding of the present application for ciphertext coding, without allocating fixed key for user, both avoid the burden of key verification frequently in traditional key encryption when short-term high frequency is used, solve the scene adaptation defect of key management from the root, ensure that user real-time location, travel preference and other privacy data can only be decrypted by exclusive dynamic rule, privacy security is significantly improved.

Description

Technical Field

[0001] This invention relates to the field of route recommendation technology, specifically a transportation hub route recommendation system based on a large model. Background Technology

[0002] As transportation hubs, such as airports, high-speed rail stations, and integrated transfer centers, become increasingly complex, users' demand for accurate and dynamic personalized route recommendations has surged. Large-scale models, with their ability to integrate and predict multi-source traffic data, such as real-time passenger flow and road congestion, can generate routes that balance efficiency and experience. However, the core prerequisite for achieving this function is the collection of user behavior data, including historical travel preferences and real-time travel characteristics. This data contains a large amount of private information, such as geographical location related to daily life patterns and preferences implying personal needs, which must be strictly protected to avoid the risk of leakage.

[0003] Existing user privacy data protection mostly uses key encryption, such as AES and RSA algorithms, to encrypt and decrypt data through a dedicated key. In order to ensure the security of the key, the key is dynamically changed. However, in high-frequency usage scenarios such as route recommendation in transportation hubs and daily commutes, the key is used very frequently. This not only increases the user's burden of safekeeping, but also prolongs the data processing latency.

[0004] To address the above problems, this invention proposes a solution. Summary of the Invention

[0005] The purpose of this invention is to provide a transportation hub route recommendation system based on a large model, in order to solve the problems mentioned in the background art.

[0006] This invention provides a transportation hub route recommendation system based on a large model, including a client module and a transportation route recommendation platform; the client module includes a monitoring and acquisition unit and a encrypted encoding unit, and the transportation route recommendation platform includes a user-side acquisition unit;

[0007] For any target user within the target area, whenever the target user enters an address, the monitoring and acquisition unit acquires and collects the target user's user behavior data, generates the target user's service baseline data for this time based on the address information and the user behavior data, and simultaneously generates the target user's service request instruction for this time.

[0008] The user-side information unit is used to randomly select a number from 0 to 9 as the response number for the target user after receiving the current service request instruction from the target user.

[0009] The ciphertext encoding unit is used to generate all the ciphertext codes for the target user in this instance according to a preset generation rule after receiving the response number.

[0010] The ciphertext encoding unit is also used to convert the target user's current service baseline data into binary data after receiving it, and to ciphertext encode the first data according to all the target user's current ciphertext encodings to obtain the target user's current ciphertext service data.

[0011] The user-side acquisition unit is used to restore the encrypted service data of the target user based on the response number of the target user to obtain the service baseline data of the target user after receiving the encrypted service data of the target user.

[0012] Furthermore, the address information includes the current location, the address of the destination, and the mode of transportation to be taken.

[0013] Furthermore, the traffic route recommendation platform also includes a unified recommendation unit, which, after receiving the target user's current service baseline data, inputs it together with the real-time collected traffic data of the target area into the route optimization model to output the target user's current route recommendation data.

[0014] Furthermore, the ciphertext encoding unit generates all ciphertext codes for the target user in this instance based on the following rules after receiving the response number:

[0015] S11: Assign the two basic units 0 and 1 in the binary digital system to the first variable A1 and the second variable A2 respectively. At this time, the first variable A1 = 0 and the second variable A2 = 1. The assignment method can be string A1 = "0" and string A2 = "1".

[0016] S12: Perform recursive iteration on the first variable A1. Update the first variable A1 once for each recursive iteration. After three recursive iterations, obtain the current content of the first variable A1 as follows:

[0017] In the first recursive iteration, the value of the first variable A1 is updated to 10, where 10 is the numerical value obtained by converting the value of the first variable A1 before the first recursive iteration; in the second recursive iteration, the value of the first variable A1 is updated to 1110, where 1110 is the numerical value obtained by converting the value of the first variable A1 after the first recursive iteration; in the third recursive iteration, the value of the first variable A1 is updated to 3110, where 3110 is the numerical value obtained by converting the value of the first variable A1 after the second recursive iteration.

[0018] S13: Perform recursive iteration on the second variable A2. Each time the recursive iteration is performed, the second variable A2 is updated once. After four recursive iterations, obtain the second variable A2 at this time.

[0019] S14: Based on 16 different combinations of four-bit binary numbers, replace the character 0 in each combination with the value of the first variable A1 after three recursive iterations, and replace the character 1 with the value of the second variable A2 after four recursive iterations to obtain new combinations. Analyze the 16 new combinations to determine 9 ciphertext codes.

[0020] S15: Sort and label the obtained 9 ciphertext codes in order, labeling them as B1, B2, ..., B9. Based on the response number, remove the ciphertext codes whose label index matches the response number, and use all the remaining ciphertext codes as the target user's ciphertext codes for this time.

[0021] Furthermore, in S13, after four recursive iterations, the content of the second variable A2 at this point is as follows:

[0022] In the first recursive iteration, the value of the second variable A2 is updated to 11. 11 is the numerical value obtained by converting the value of the second variable A2 before the first recursive iteration.

[0023] In the second recursive iteration, the value of the second variable A2 is updated to 21. 21 is the numerical language obtained by converting the value of the second variable A2 after the first recursive iteration.

[0024] In the third recursive iteration, the value of the second variable A2 is updated to 1211. 1211 is the numerical language obtained by converting the value of the second variable A2 after the second recursive iteration.

[0025] In the fourth recursive iteration, the value of the second variable A2 is updated to 111221. 111221 is the numerical language obtained by converting the value of the second variable A2 after the third recursive iteration.

[0026] Furthermore, in S14, the nine ciphertext codes obtained are ordered, from left to right as 31, 22, 21, 13, 12, 11, 10, 03, 01, in descending order of the first digit plus the last digit.

[0027] Furthermore, the steps for ciphertext encoding the target user's initial data to obtain the target user's ciphertext service data are as follows:

[0028] S21: Take every three characters in the first data as a group of encoding arrays in order from left to right, and get several groups of encoding arrays. According to the position of each group of encoding arrays in the first data, mark all the obtained groups of encoding arrays as C1, C2, ..., Cc in order from left to right, where c≥1;

[0029] S22: Create a correspondence between all the encrypted codes of the target user in this instance and the three binary numbers 000, 001, 010, 011, 100, 101, 111 in order from left to right;

[0030] S23: Create a correspondence between all the encrypted codes of the target user in this instance and the three binary numbers 000, 001, 010, 011, 100, 101, 111, in order from left to right;

[0031] S24: Compare the encoding array C1 with the three-bit binary numbers 000, 001, 010, 011, 100, 101, and 111 respectively to obtain the ciphertext code corresponding to the three-dimensional binary number that matches the encoding array C1, and use the five-bit binary number of the ciphertext code as the ciphertext of the encoding array C1.

[0032] S25: Obtain the encoded ciphertext of the encoding arrays C2, C3, ..., Cc in sequence according to S24. Concatenate the encoded ciphertexts of the encoding arrays C1, C2, ..., Cc in order to obtain the encrypted service data for the target user.

[0033] Furthermore, the client module also includes a recommendation display unit, which is used to display the route recommendation data received from the target user for the target user to view.

[0034] Compared with existing technologies, it has the following advantages:

[0035] This invention collects service baseline data from a target user by setting up a monitoring and acquisition unit. Each time the ciphertext encoding unit receives service baseline data, it determines all ciphertext codes based on the response numbers generated by the traffic route recommendation platform. In the process of determining the ciphertext codes, it performs several recursive iterations based on the two basic units 0 and 1 in the binary number system to obtain the basic ciphertext codes used for ciphertext encoding in this invention. The service baseline data is then converted based on the ciphertext codes of the baseline. In this way, there is no need to assign a fixed key to the user. This avoids the burden of frequent key verification and replacement during short-term high-frequency use in traditional key encryption, and also avoids the risk of key theft during intermittent or long-term idle periods. It fundamentally solves the scenario adaptation defects of key management, ensuring that the user's real-time location, travel preferences, and other private data can only be decrypted through exclusive dynamic rules during the collection, transmission, and storage process. Privacy and security are significantly improved, and the final form of the service baseline data is a binary data stream, which is deceptive and greatly ensures the security of data transmission. Attached Figure Description

[0036] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Please see Figure 1 This application provides a transportation hub route recommendation system based on a large model, including a client module and a transportation route recommendation platform;

[0039] The client module is used for target users in the target area to apply for traffic route recommendation services. The client module includes a monitoring and collection unit, a encrypted encoding unit, and a recommendation display unit.

[0040] For any target user within the target area, whenever the target user enters address information, the monitoring and acquisition unit acquires and collects the target user's user behavior data. Based on the address information and the user behavior data, the unit generates the target user's service baseline data for this instance and simultaneously generates the target user's service request instruction for this instance. The service baseline data includes the address information and the target user's user behavior data. The address information includes the current location, the address of the destination, and the intended mode of transportation. The user behavior data includes the user's frequently used destinations, avoidance routes, mobile phone number, login account, and other user privacy information.

[0041] It should be noted that the service baseline data is stored and transmitted as a string; the service baseline data acquired by the monitoring and acquisition unit is stored and transmitted in string format.

[0042] The monitoring and acquisition unit transmits the service baseline data to the encrypted encoding unit and the service request instruction to the traffic route recommendation platform;

[0043] The transportation route recommendation platform is used to provide route recommendation services to users in the target area. The transportation route recommendation platform includes a user-side information unit and a unified recommendation unit.

[0044] After receiving the service request instruction, the traffic route recommendation platform transmits it to the user-side information unit. After receiving the service request instruction, the user-side information unit randomly selects a number from 0 to 9 as the target user's response number and transmits the response number to the client module.

[0045] The selection of random numbers can be achieved using the random function;

[0046] After receiving the transmitted response number, the client module transmits it to the ciphertext encoding unit for temporary storage.

[0047] After receiving the transmitted service baseline data, the ciphertext encoding unit temporarily stores it, and after receiving the response number, generates all the ciphertext codes for the target user's current task according to a preset generation rule, which is as follows:

[0048] S11: Assign the two basic units 0 and 1 in the binary digital system to the first variable A1 and the second variable A2 respectively. At this time, the first variable A1=0 and the second variable A2=1.

[0049] S12: Perform recursive iteration on the first variable A1. Update the first variable A1 once for each recursive iteration. After three recursive iterations, obtain the current value of the first variable A1 and establish a correspondence between the current value of the first variable A1 and 0. The specific content is as follows:

[0050] In the first recursive iteration, the value of the first variable A1 is updated to 10, that is, A1=10. The value of 10 is obtained by converting the value of the first variable A1 before the first recursive iteration. The explanation is as follows:

[0051] Before the first recursive iteration, the value of the first variable A1 is 0, which consists of one 0. Therefore, 10 can be used as its numerical language after the first recursive iteration.

[0052] In the second recursive iteration, the value of the first variable A1 is updated to 1110, that is, A1 = 1110. 1110 is the numerical expression obtained by converting the value of the first variable A1 after the first recursive iteration, as explained below:

[0053] The value of the first variable A1 after the first recursive iteration is 10, which is composed of one 1 and one 0. Therefore, 1110 can be used as the numerical language after the second recursive iteration.

[0054] In the third recursive iteration, the value of the first variable A1 is updated to 3110, that is, A1=3110. 3110 is the numerical language obtained by converting the value of the first variable A1 after the second recursive iteration.

[0055] S13: Perform recursive iteration on the second variable A2. Update the second variable A2 once for each recursive iteration. After four recursive iterations, obtain the current value of the second variable A2 and establish a correspondence between the current value of the second variable A2 and 1. The specific content is as follows:

[0056] In the first recursive iteration, the value of the second variable A2 is updated to 11, that is, A2=11. The value of 11 is derived from the value of the second variable A2 before the first recursive iteration, expressed in numerical terms as follows:

[0057] Before the first recursive iteration, the value of the second variable A2 is 1, which consists of one 1. Therefore, 11 can be used as its numerical language after the first recursive iteration.

[0058] In the second recursive iteration, the value of the second variable A2 is updated to 21, that is, A2=21. 21 is the numerical language obtained by converting the value of the second variable A2 after the first recursive iteration.

[0059] In the third recursive iteration, the value of the second variable A2 is updated to 1211, that is, A2=1211. 1211 is the numerical language obtained by converting the value of the second variable A2 after the second recursive iteration.

[0060] In the fourth recursive iteration, the value of the second variable A2 is updated to 111221, that is, A2=111221. 111221 is the numerical language obtained by converting the value of the second variable A2 after the third recursive iteration.

[0061] S14: Based on 16 different combinations of four-bit binary numbers, replace the character 0 in each combination with the value of the first variable A1 after three recursive iterations, and replace the character 1 with the value of the second variable A2 after four recursive iterations to obtain new combinations. Analyze the 16 new combinations to determine 9 ciphertext codes, namely 31, 11, 10, 12, 21, 22, 01, 13, 03, 22;

[0062] The 16 different combinations of the four binary digits are 0000, 0001, 0010, 0011, 0100, 0101, 0110, 0111, 1000, 1001, 1010, 1011, 1100, 1101, 1110, 1111;

[0063] Taking a combination of four binary numbers, 0101, as an example, the character 0 in the combination is replaced bit by bit with the value of the first variable A1 after three recursive iterations, and the character 1 is replaced with the value of the second variable A2 after four recursive iterations to obtain a new combination, 31101112213110111221. At this time, the characters located at the 1st and 2nd positions, the 2nd and 3rd positions, ..., the 19th and 20th positions are concatenated in the order from left to right to obtain 31, 11, 10, 01, 11, 11, 12, 22, 21, 13, 31, 11, 10, 01, 11, 12, 22, 21. After removing duplicates, the ciphertext encoding is obtained as 31, 11, 10, 01, 12, 22, 21, 13.

[0064] It should be noted that the nine ciphertext codes obtained are ordered, from left to right as 31, 22, 21, 13, 12, 11, 10, 03, 01. They are ordered by descending order of the first digit and descending order of the last digit. From left to right, the leftmost digit is prioritized in descending order. If the leftmost digits are the same, the leftmost digits are ordered in descending order.

[0065] S15: Sort and mark the obtained 9 ciphertext codes in order, labeling them as B1, B2, ..., B9. According to the response number, remove the ciphertext codes whose label index matches the response number, and use all the remaining ciphertext codes as the target user's ciphertext codes for this time.

[0066] Then, the service baseline data is converted into binary to obtain the first data. The binary conversion is achieved by calling a publicly available online character or text to binary tool. In this application, the publicly available online character or text to binary tool is the ToolTT online toolbox, and the tool link is https: / / tooltt.com / txt2bin / .

[0067] The first data is encrypted using all the encrypted codes of the target user to obtain the encrypted service data of the target user. The steps are as follows:

[0068] S21: Take every three characters in the first data as a group of encoding arrays in order from left to right to obtain several groups of encoding arrays. According to the position of each group of encoding arrays in the first data, mark all the obtained groups of encoding arrays as C1, C2, ..., Cc from left to right, where c≥1. It should be noted that if the number of remaining characters is greater than 0 and less than 3, then all the remaining characters are temporarily stored as a supplementary array.

[0069] S22: Create a correspondence between all the encrypted codes of the target user in this instance and the three binary numbers 000, 001, 010, 011, 100, 101, 111 in order from left to right;

[0070] For example, if the response number is 7, then according to the order of the ciphertext encoding, B1, B2, ..., B9 are 31, 22, 21, 13, 12, 11, 10, 03, 01 respectively. Based on the response number 7, the ciphertext encoding 10 is removed from the ciphertext encodings 31, 22, 21, 13, 12, 11, 10, 03, 01, resulting in the target user's current ciphertext encodings 31, 22, 21, 13, 12, 11, 03, 01.

[0071] S23: From left to right, create a correspondence between all the encrypted codes of the target user and the three binary numbers 000, 001, 010, 011, 100, 101, 111. Then, 31 corresponds to 000, 22 to 001, 21 to 010, 13 to 011, 12 to 100, 11 to 101, 03 to 110, and 01 to 111.

[0072] S24: Compare the encoding array C1 with the three-bit binary numbers 000, 001, 010, 011, 100, 101, and 111 respectively to obtain the ciphertext code corresponding to the three-bit binary number that matches the encoding array C1. Use the five-bit binary number of the ciphertext code as the ciphertext of the encoding array C1. The five-bit binary number of the ciphertext number is obtained by first converting the ciphertext code into an integer type and then calculating the five-bit binary number of the integer type.

[0073] S25: Obtain the encoded ciphertext of the encoding arrays C2, C3, ..., Cc in sequence according to S24. Concatenate the encoded ciphertexts of the encoding arrays C1, C2, ..., Cc in order to obtain the encrypted service data of the target user. It should be noted that if a supplementary array is temporarily stored, the supplementary array will be concatenated to the rightmost end of the encrypted service data.

[0074] The encrypted encoding unit transmits the encrypted service data to the traffic route recommendation platform. After receiving the encrypted service data of the target user, the traffic route recommendation platform transmits it to the user-side information unit. After receiving the transmitted encrypted service data, the user-side information unit determines all the encrypted codes of the target user according to steps S11 to S15, and reverses steps S21 to S25 to restore the service base data of the target user.

[0075] The user-side information unit transmits the target user's current service baseline data to the overall recommendation unit;

[0076] The overall recommendation unit collects traffic data of the target area in real time. The traffic data contains dynamic status information and attribute parameters of several traffic nodes and road segments.

[0077] The information on traffic nodes includes: real-time passenger flow density, such as the number of people waiting at bus stops, the queue length at hub gates, equipment operating status, such as the current phase and duration of traffic lights, the opening and closing status of turnstiles, elevator operating modes, and emergency event indicators, such as temporary control measures and facility failure alarms within the node area.

[0078] The information for road segments includes: real-time traffic parameters, such as average vehicle speed, congestion index, and traffic volume for motor vehicle segments; pedestrian movement speed, congestion, and changes in traffic conditions for pedestrian segments, such as the scope of road construction areas, location of temporary obstacles, road surface friction coefficient under rain and snow weather; and related time-sensitive data, such as the predicted travel time for the current period and the comparison of historical traffic conditions for the same period.

[0079] These data are collected in real time through sensor networks, such as cameras, millimeter-wave radar, IoT devices, such as vehicle terminals, gate sensors, and external system interfaces, such as traffic management platforms and meteorological services. After standardization processing, they form a structured dataset, providing real-time basis for the overall recommendation unit to dynamically optimize the path candidate set.

[0080] The overall recommendation unit has a pre-trained route optimization model stored in it. The overall recommendation unit inputs the received service baseline data of the target user and traffic data of the target area into the route optimization model, outputs the route recommendation data of the target user, and transmits the route recommendation data to the client module.

[0081] After receiving the route recommendation data, the client module transmits it to the recommendation display unit, which then displays the route recommendation data to the target user for viewing.

[0082] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0083] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A transportation hub route recommendation system based on a large model, characterized in that, It includes a client module and a traffic route recommendation platform; the client module includes a monitoring and data collection unit and a encrypted encoding unit, and the traffic route recommendation platform includes a user-side data collection unit; For any target user within the target area, whenever the target user enters an address, the monitoring and acquisition unit acquires and collects the target user's user behavior data, generates the target user's service baseline data for this time based on the address information and the user behavior data, and simultaneously generates the target user's service request instruction for this time. The user-side information unit is used to randomly select a number from 0 to 9 as the response number for the target user after receiving the current service request instruction from the target user. The ciphertext encoding unit is used to generate all the ciphertext codes for the target user in this instance by performing a number of recursive iterations on the two basic units 0 and 1 in the binary number system after receiving the response number. The ciphertext encoding unit is also used to convert the received service baseline data of the target user into binary data to obtain first data, and to ciphertext encode the first data according to all the ciphertext encodings of the target user to obtain the ciphertext service data of the target user. The steps are as follows: S21: Take every three characters in the first data as a group of encoding arrays in order from left to right, and get several groups of encoding arrays. According to the position of each group of encoding arrays in the first data, mark all the obtained groups of encoding arrays as C1, C2, ..., Cc in order from left to right, where c≥1; S22: Create a correspondence between all the encrypted codes of the target user in this instance and the three binary numbers 000, 001, 010, 011, 100, 101, 111 in order from left to right; S23: Perform consistency comparison between the encoded array C1 and the three-bit binary numbers 000, 001, 010, 011, 100, 101, and 111 respectively, obtain the ciphertext code corresponding to the three-dimensional binary number that matches the encoded array C1, and use the ciphertext code as the encoded ciphertext of the encoded array C1. S24: Obtain the encoded ciphertext of the encoding arrays C2, C3, ..., Cc in sequence according to S23, and concatenate the encoded ciphertexts in the order of the encoding arrays C1, C2, ..., Cc to obtain the encrypted service data of the target user for this time; The user-side acquisition unit is used to restore the encrypted service data of the target user based on the response number of the target user to obtain the service baseline data of the target user after receiving the encrypted service data of the target user.

2. The transportation hub route recommendation system based on a large model according to claim 1, characterized in that, The address information includes the current location, the address of the destination, and the mode of transportation to be used.

3. The transportation hub route recommendation system based on a large model according to claim 1, characterized in that, The traffic route recommendation platform also includes a unified recommendation unit, which, after receiving the target user's current service baseline data, inputs it together with the real-time collected traffic data of the target area into the route optimization model to output the target user's current route recommendation data.

4. The transportation hub route recommendation system based on a large model according to claim 1, characterized in that, After receiving the response number, the ciphertext encoding unit, based on the two basic units 0 and 1 in the binary number system, performs several recursive iterations to generate the ciphertext encoded content for the target user in this instance, as follows: S11: Assign the two basic units 0 and 1 in the binary digital system to the first variable A1 and the second variable A2 respectively. At this time, the first variable A1=0 and the second variable A2=1. S12: Recursively iterate over the first variable A1. Update the first variable A1 once for each recursive iteration. After three recursive iterations, obtain the current value of the first variable A1, as follows: In the first recursive iteration, the value of the first variable A1 is updated to 10, where 10 is the numerical value obtained by converting the value of the first variable A1 before the first recursive iteration; in the second recursive iteration, the value of the first variable A1 is updated to 1110, where 1110 is the numerical value obtained by converting the value of the first variable A1 after the first recursive iteration; in the third recursive iteration, the value of the first variable A1 is updated to 3110, where 3110 is the numerical value obtained by converting the value of the first variable A1 after the second recursive iteration. S13: Perform recursive iteration on the second variable A2. Each time the recursive iteration is performed, the second variable A2 is updated once. After four recursive iterations, obtain the second variable A2 at this time. S14: Based on 16 different combinations of four-bit binary numbers, replace the character 0 in each combination with the value of the first variable A1 after three recursive iterations, and replace the character 1 with the value of the second variable A2 after four recursive iterations to obtain new combinations. Analyze the 16 new combinations to determine 9 ciphertext codes. S15: Sort and label the obtained 9 ciphertext codes in order, labeling them as B1, B2, ..., B9. Based on the response number, remove the ciphertext codes whose label index matches the response number, and use all the remaining ciphertext codes as the target user's ciphertext codes for this time.

5. The transportation hub route recommendation system based on a large model according to claim 4, characterized in that, S13, after four recursive iterations, the content of the second variable A2 at this point is as follows: In the first recursive iteration, the value of the second variable A2 is updated to 11. 11 is the numerical value obtained by transforming the value of the second variable A2 before the first recursive iteration. In the second recursive iteration, the value of the second variable A2 is updated to 21. 21 is the numerical language obtained by converting the value of the second variable A2 after the first recursive iteration. In the third recursive iteration, the value of the second variable A2 is updated to 1211. 1211 is the numerical language obtained by converting the value of the second variable A2 after the second recursive iteration. In the fourth recursive iteration, the value of the second variable A2 is updated to 111221. 111221 is the numerical language obtained by converting the value of the second variable A2 after the third recursive iteration.

6. The transportation hub route recommendation system based on a large model according to claim 5, characterized in that, S14 yields nine ciphertext codes in order, from left to right: 31, 22, 21, 13, 12, 11, 10, 03, 01, following the descending order of the first and last digits.

7. A transportation hub route recommendation system based on a large model according to claim 3, characterized in that, The client module also includes a recommendation display unit, which is used to display the route recommendation data received from the target user for the target user to view.

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