A high-speed railway station tour guide robot based on heuristic algorithm

Abstract

The invention discloses a high-speed railway station tour guide robot based on a heuristic algorithm. The tour guide robot comprises: a robot main control STM32 processing chip, a WT588D voice chip, a voice broadcast module, a servo motor moving module, a Bluetooth module, an SPP Bluetooth serial port and an infrared sensor; wherein the robot main control STM32 processing chip is respectively connected with the WT588D voice chip, the servo motor moving module, the Bluetooth module and the infrared sensor, the WT588D voice chip is connected with the voice broadcast module, the Bluetooth module is connected with the SPP Bluetooth serial port, the robot main control STM32 processing chip comprises a tourist information acquisition module, a tourism information processing module, a tour strategy output module and a travel plan output module, the tourism information processing module is used for generating an optimal tour strategy based on a heuristic algorithm according to the tourism information acquired by the tourist information acquisition module, and outputting the optimal tour strategy to the tour strategy output module; the invention can provide tourists with a reliable travel strategy plan, thereby reducing the travel difficulty of tourists.

Description

Technical Field

[0001] The invention belongs to the technical field of path planning, and in particular relates to a high-speed railway station tour guide robot based on a heuristic algorithm. Background Art

[0002] In recent years, with the improvement of people's living standards and the change of consumption concepts, the tourism industry has developed vigorously. Traditional tourism methods are often limited by time, space and other factors, and cannot meet tourists' demand for personalized and intelligent tourism.

[0003] In this regard, in order to adapt to the current fast-paced lifestyle, the existing technology has proposed an app that integrates tourism information of various regions through the Internet for tourists to choose travel plans. However, this method has the problem that there is too much tourism information, and tourists cannot quickly and effectively sort out their own clear travel plans based on this tourism information, resulting in a situation where "travel is difficult to get started", which greatly undermines the confidence of tourists in traveling, makes it impossible for tourists to quickly get a cost-effective travel plan, and increases the cost of tourists' travel.

[0004] At the same time, with the rapid development of high-speed rail business, more and more passengers choose high-speed rail travel, and most of the existing tour guide apps integrate travel guides on the Internet. However, the travel plans of high-speed rail are constantly changing, resulting in poor timeliness and reliability of travel guides on the tour guide apps, which increases the difficulty of travel for tourists. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a high-speed railway station tour guide robot based on a heuristic algorithm, which can quickly give a concise and most cost-effective optimal tour guide based on the travel information provided by tourists, saving tourists' ineffective sorting time spent in the tour guide app, solving the problem of "difficulty in starting a trip" for tourists, planning travel strategies according to actual travel budgets, saving tourists' ineffective expenses when traveling, and at the same time combining high-speed railway travel plans and optimal tour strategies to provide tourists with reliable travel plans with high timeliness and reliability, reducing the difficulty of tourists' travel and realizing convenient travel.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A high-speed railway station tour guide robot based on a heuristic algorithm, comprising:

[0008] The robot comprises a main control STM32 processing chip, a WT588D voice chip, a voice broadcast module, a servo motor movement module, a Bluetooth module, an SPP Bluetooth serial port and an infrared sensor; wherein the main control STM32 processing chip of the robot is respectively connected with the WT588D voice chip, the servo motor movement module, the Bluetooth module and the infrared sensor, the WT588D voice chip is connected with the voice broadcast module, the Bluetooth module is connected with the SPP Bluetooth serial port, the main control STM32 processing chip of the robot comprises a tourist information acquisition module, a tourism information processing module, a tour guide output module and a travel plan output module, and the tourism information processing module is used for generating an optimal tour guide based on a heuristic algorithm according to the tourism information acquired by the tourist information acquisition module and outputting the optimal tour guide to the tour guide output module.

[0009] Further, the tourist information acquisition module includes a guidance unit, an information panel unit and a tourist information acquisition unit;

[0010] The guidance unit is used to send out preset voice information according to the voice broadcast module to assist tourists in performing routine information input operations on the high-speed railway station tour guide robot;

[0011] The information panel unit is used to display a pre-set electronic digital map according to the smart panel, provide a search function, and obtain information on city attractions selected by tourists; wherein the smart panel is connected to the robot's main control STM32 processing chip;

[0012] The tourist information acquisition unit is used to obtain the city attraction information selected by the tourist, as well as the travel budget and travel duration input by the tourist on the smart panel, and integrate the tourist's travel information.

[0013] Further, the tourism information processing module performs the following operations:

[0014] Obtain travel information input by tourists;

[0015] Generate an initial digital tourist map based on the city attractions information contained in the tourist information and the starting point of the high-speed railway station where the tour guide robot is located;

[0016] According to the cost-effectiveness theory of scenic spots and the travel budget and travel duration contained in the travel information, the cost-effectiveness of the scenic spots from the i-th node to the j-th node in the initial digital tourism map is calculated; the calculation formula is:

[0017]

[0018] Among them, f ij represents the cost-effectiveness of the scenic spot from the i-th node to the j-th node, x jrepresents the initial score of traveling to the jth node, d(i, j) represents the high-speed rail travel distance from the i-th node to the j-th node, a and b are the travel weights, a+b=1, and the values ​​of a and b are determined by the travel budget and travel duration. The larger the travel budget and travel duration, the larger the a value, and the smaller the travel budget and travel duration, the larger the b value;

[0019] According to the cost-effectiveness function, the cost-effectiveness of the scenic spots from the i-th node to the j-th node in the initial tourism digital map is calculated;

[0020] Obtain the cost-effectiveness of all scenic spots to generate a path decision table;

[0021] According to the initial tourism digital map and the path decision table, the optimal tour guide is generated based on the heuristic algorithm of path planning.

[0022] Furthermore, according to the initial digital tourist map and the path decision table, the optimal tour guide is generated based on the heuristic algorithm of path planning, including:

[0023] Step 1: Get the starting point and destination in the initial tourism digital map, and calculate the travel time T required from the starting point to the destination and back to the starting point * ;

[0024] Step 2: If T * <T, check the path decision table to calculate the first most cost-effective node to be visited, and calculate the remaining time t1 after visiting the first most cost-effective node;

[0025] t1=T-t1 *

[0026] Among them, T is the travel time, t1 * The estimated time to reach the second most cost-effective node;

[0027] Calculate the travel time t required from the first most cost-effective node to the destination 1，p , and calculate the high-speed rail return time t from the destination to the starting point p ;

[0028] If t 1，p +t p <t1, then determine to travel to the first most cost-effective node;

[0029] If 1，p +t p ≥t1, then travel from the starting point to the destination and return to the starting point;

[0030] Step 3: After successfully traveling to the first most cost-effective node, continue to look up the table to obtain the kth most cost-effective node to be traveled, and calculate the travel time t required from the k-1th most cost-effective node to the kth most cost-effective node k-1，k , and calculate the travel time t required from the kth most cost-effective node to the destination k，p , and calculate the remaining time t after traveling to the kth most cost-effective node k ;

[0031] t k =Tt k *

[0032] Among them, t k * is the estimated time spent before visiting the k+1th most cost-effective node, where k is an integer greater than 1;

[0033] If k-1，k +t k，p +t p <t k , then travel to the kth most cost-effective node;

[0034] If k-1，k +t k，p +t p ≥t k , then travel from the k-1th most cost-effective node to the destination and return to the starting point;

[0035] Step 4: Obtain the starting point and destination in the initial tourism digital map, as well as all the cost-effective nodes that have been visited, and generate the optimal travel strategy.

[0036] Furthermore, step 3 also includes:

[0037] When t k-1，k +t k，p +t p ≥t k When the kth most cost-effective node is reached, the path decision table is searched to obtain the kth second cost-effective node to be visited, and the travel time required from the k-1th most cost-effective node to the kth second cost-effective node is calculated. And calculate the travel time required from the kth second cost-effective node to the destination And calculate the remaining time after traveling to the kth second cost-effective node

[0038] like Then travel to the kth second cost-effective node;

[0039] like Then travel from the k-1th most cost-effective node to the destination and return to the starting point.

[0040] Furthermore, the tour guide output module performs the following operations:

[0041] Get all the nodes in the optimal tour guide;

[0042] Search the built-in database to obtain the tourism introduction information corresponding to each node to generate an introduction label card, associate the corresponding nodes, and generate a tourist map;

[0043] Obtain the designated device of the visitor through the Bluetooth module;

[0044] Send tourist guide maps to tourists’ designated devices;

[0045] The voice broadcast module recognizes the introduction label card of each node in the guide map and provides voice broadcast to tourists.

[0046] Furthermore, the robot main control STM32 processing chip also includes a tour guide reset module, wherein the tour guide reset module performs the following operations:

[0047] Get the original tourist map and new tourist information input by the user;

[0048] The original tourist map is updated according to the new tourist information, and the new tourist map is sent to the tourist guide output module.

[0049] Furthermore, the travel plan output module performs the following operations:

[0050] When it is determined that the tourists have no objection to the guide map sent by the tour guide output module;

[0051] Obtain the high-speed rail travel schedule for each node included in the guide map through a network connection;

[0052] Filter the high-speed train numbers in the high-speed rail travel schedule that meet the travel time planning of each node included in the tourist map;

[0053] Integrate all high-speed rail train numbers to generate a high-speed rail travel preparation plan, and send it to the tourist's designated device through the tour guide output module; among them, the high-speed rail travel preparation plan contains repeated dates, and repeated high-speed rail train numbers of the departure station and destination;

[0054] Obtain high-speed rail travel plans from tourists and generate integrated ticket purchase links in conjunction with relevant ticket purchase apps; the high-speed rail travel plans do not contain repeated dates, or repeated high-speed rail train numbers at the departure and destination stations;

[0055] The integrated ticket purchase link is sent to the tourist's designated device through the tour guide output module.

[0056] The beneficial effects of the present invention are:

[0057] The present invention provides a high-speed railway station tour guide robot based on a heuristic algorithm, which can quickly give a concise and most cost-effective optimal tour guide based on the travel information given by tourists, save the tourists' ineffective sorting time spent in the tour guide app, solve the problem of "difficulty in starting a trip" for tourists, plan the travel guide according to the actual travel budget, save the tourists' ineffective expenses when traveling, and provide tourists with a reliable travel plan with high timeliness and reliability by combining the high-speed railway passage plan and the optimal tour guide.

[0058] Other advantages, objectives and features of the present invention will be described in the following description and will be apparent to those skilled in the art to some extent, or they may be taught from the practice of the present invention. The purposes and other advantages of the present invention may be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0059] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0061] Figure 1 This is a system structure diagram of a high-speed railway station tour guide robot based on a heuristic algorithm in an embodiment of the present invention;

[0062] Figure 2 This is a module schematic diagram of a robot main control STM32 processing chip in a high-speed railway station tour guide robot based on a heuristic algorithm in an embodiment of the present invention;

[0063] Figure 3 This is a flow chart of the execution process of a tourism information processing module in a high-speed railway station tour guide robot based on a heuristic algorithm in an embodiment of the present invention;

[0064] Figure 4 A schematic diagram of non-cost-effective nodes for multi-node path planning in a high-speed railway station tour guide robot based on a heuristic algorithm in an embodiment of the present invention;

[0065] Figure 5 A schematic diagram of a cost-effective node for multi-node path planning in a high-speed railway station tour guide robot based on a heuristic algorithm in an embodiment of the present invention;

[0066] Figure 6 The diagram is a schematic diagram of two calculation nodes for multi-node path planning in a high-speed railway station tour guide robot based on a heuristic algorithm in an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0068] See also Figure 1 , Figure 2 The present invention proposes a high-speed railway station tour guide robot based on a heuristic algorithm, comprising:

[0069] The robot main control STM32 processing chip, WT588D voice chip, voice broadcast module, servo motor movement module, Bluetooth module, SPP Bluetooth serial port and infrared sensor; wherein, the robot main control STM32 processing chip is respectively connected with the WT588D voice chip, servo motor movement module, Bluetooth module and infrared sensor, the WT588D voice chip is connected with the voice broadcast module, the Bluetooth module is connected with the SPP Bluetooth serial port, the robot main control STM32 processing chip includes a tourist information acquisition module, a tourism information processing module, a tour strategy output module and a travel plan output module, and the tourism information processing module is used to generate an optimal tour strategy based on the heuristic algorithm according to the tourism information acquired by the tourist information acquisition module and output it to the tour strategy output module;

[0070] The working principle of the above technical solution is as follows: with the popularization of high-speed rail, the travel mode of most tourists has changed from self-driving tours and round-trip flights to high-speed rail travel. The present invention provides a high-speed rail station tour guide robot based on a heuristic algorithm to provide more reliable and cost-effective travel strategies for tourists who have travel plans but incomplete travel plans and have arrived at the high-speed rail station. Specifically, the main structure of the robot includes a robot main control STM32 processing chip, a WT588D voice chip, a voice broadcast module, a servo motor movement module, a Bluetooth module, an SPP Bluetooth serial port and an infrared sensor; wherein the robot main control STM32 processing chip serves as a core processor, and is respectively connected to the WT588D voice chip, the servo motor movement module, the Bluetooth module and the infrared sensor for distributing various instructions and processing data, the Bluetooth module is connected to the SPP Bluetooth serial port, and is used to receive the tourists' remote app instructions according to Bluetooth and connect to the tourists' mobile devices to facilitate the transmission of subsequent guide maps, and the WT588D voice chip is connected to the voice broadcast module for voice for tourists. The robot main control STM32 processing chip includes a tourist information acquisition module, a tourism information processing module, a tour strategy output module and a travel plan output module. The robot main control STM32 processing chip is used to combine the heuristic algorithm theory and the acquired tourism information to provide tourists with more efficient tourism strategies and feedback to the designated equipment of tourists. Among them, the application of the heuristic algorithm theory is mainly concentrated in the tourism information processing module. This module is used to generate the optimal tour strategy based on the heuristic algorithm according to the tourism information acquired by the tourist information acquisition module and output it to the tour strategy output module; the servo motor moving module is used to provide power when the tourism robot needs to move. It is worth mentioning that all the power sources of the robot are undescribed power modules. The infrared sensor is used to sense obstacles when moving. At the same time, the infrared sensor is also used to detect whether there is an obstacle in front of the robot for a long time. If there is, the guidance mode is triggered, which is beneficial to improve the subjective initiative of the robot;

[0071] The beneficial effects of the above technical solution are: through the above technical solution, a concise and most cost-effective optimal tour guide is quickly given based on the travel information provided by tourists, which saves tourists' ineffective sorting time spent in the tour guide app, solves the problem of "difficulty in starting a trip" for tourists, and plans travel guides according to actual travel budgets, saving tourists' ineffective expenses when traveling. At the same time, combined with the high-speed rail travel plan and the optimal tour guide, a reliable travel plan is provided for tourists, which has high timeliness and reliability, reduces the difficulty of tourists' travel and realizes convenient travel.

[0072] In one embodiment, the tourist information acquisition module includes a guidance unit, an information panel unit, and a tourist information acquisition unit;

[0073] The guidance unit is used to send out preset voice information according to the voice broadcast module to assist tourists in performing routine information input operations on the high-speed railway station tour guide robot;

[0074] The information panel unit is used to display a pre-set electronic digital map according to the smart panel, provide a search function, and obtain information on city attractions selected by tourists; wherein the smart panel is connected to the robot's main control STM32 processing chip;

[0075] The tourist information acquisition unit is used to acquire the information of the city attractions selected by the tourist, as well as the travel budget and travel duration entered by the tourist on the smart panel, and integrate the tourist's travel information;

[0076] The working principle of the above technical solution is as follows: the present invention includes a tourist information acquisition module for acquiring tourist information input by tourists. To realize the above function, the tourist information acquisition module includes a guidance unit, an information panel unit and a tourist information acquisition unit, wherein the guidance unit is connected to the voice broadcast module, and sends out the voice information preset in the storage data corresponding to the robot main control STM32 processing chip according to the voice broadcast module to assist tourists in performing routine information input operations on the high-speed railway station tour guide robot; it is worth noting that in addition to being triggered by a tourist click, the guidance unit will also start automatically when the guidance mode is triggered after the infrared sensor recognizes it, and the guidance unit will also guide tourists to connect their own mobile devices to the Bluetooth module through Bluetooth, so that The information panel unit is connected to the smart panel, and displays the electronic digital map pre-set in the storage data corresponding to the robot main control STM32 processing chip according to the smart panel. The electronic digital map is preferably set according to the national land map. At the same time, the information panel unit provides a search function and obtains the city scenic spot information selected by the tourist on the smart panel; finally, the tourist information acquisition unit obtains the city scenic spot information selected by the tourist, as well as the travel budget and travel duration input by the tourist on the smart panel according to the guidance, and integrates the tourist's travel information; it is worth noting that when inputting travel information, the tourist can choose the city he does not want to travel, and the robot temporarily sets the cost-effectiveness of the scenic spots in the city he does not want to travel to to 0;

[0077] The beneficial effect of the above technical solution is: through the above technical solution, the input information of tourists is obtained in a guided manner, which is beneficial to ensure the rationality of the information input by tourists, and at the same time integrates the input information in advance to provide reliable data support for the subsequent generation of tour guides.

[0078] See also Figure 3 In one embodiment, the travel information processing module performs the following operations:

[0079] S101, obtaining travel information input by tourists;

[0080] S102, generating an initial digital tourist map according to the city scenic spot information contained in the tourist information and the starting point of the high-speed railway station where the tour guide robot is located;

[0081] S103, according to the cost-effectiveness theory of scenic spots and the travel budget and travel duration contained in the travel information, the cost-effectiveness of the scenic spots from the i-th node to the j-th node in the initial digital tourism map is calculated; the calculation formula is:

[0082]

[0083] Among them, f i，j represents the cost-effectiveness of the scenic spot from the i-th node to the j-th node, x j represents the initial score of traveling to the jth node, d(i, j) represents the high-speed rail travel distance from the i-th node to the j-th node, a and b are the travel weights, a+b=1, and the values ​​of a and b are determined by the travel budget and travel duration. The larger the travel budget and travel duration, the larger the a value, and the smaller the travel budget and travel duration, the larger the b value;

[0084] S104, obtaining the cost-effectiveness of all scenic spots to generate a path decision table;

[0085] S105, generating an optimal tour strategy based on the initial digital tourism map and the path decision table and the heuristic algorithm of path planning;

[0086] The working principle of the above technical solution is as follows: the present invention includes a tourism information processing module, which is used to generate an optimal tour guide based on the tourism information and a heuristic algorithm. To achieve the above functions, the tourism information processing module preferably performs the following operations: first, the tourism information integrated in the tourist information acquisition module is obtained, and then the city scenic spot information contained in the tourism information and the starting point of the high-speed railway station where the tour guide robot is located generate an initial tourism digital map. The starting point of the high-speed railway station is determined by the administrator in advance according to the high-speed railway station deployed by the robot; and according to the scenic spot cost-effectiveness theory and the travel budget and travel duration contained in the tourism information, the cost-effectiveness of the scenic spot from the i-th node to the j-th node in the initial tourism digital map is calculated. It is worth noting that the i-th node and the j-th node are preferably two adjacent cost-effective scenic spots. If the a value is much larger than the b value, there is no restriction on whether the i-th node and the j-th node are adjacent, and the i-th node and the j-th node are not the starting point and the destination;

[0087] In the calculation formula of cost-effectiveness, x j It represents the initial score of visiting the jth node, that is, the original score of the node. The source of the original score can be obtained by extracting the public score, tourism score and other scores of the node through big data and calculating the weighted sum. The more elements in the weighted sum, the higher the accuracy of the initial score.

[0088] d(i, j) represents the high-speed rail travel distance from the i-th node to the j-th node. The distance is preferably determined based on the data published in the railway app;

[0089] a and b are tourism weights, a+b=1, and the values ​​of a and b are determined by the travel budget and travel duration. The larger the travel budget and travel duration, the larger the a value, and the smaller the travel budget and travel duration, the larger the b value, that is, the larger the travel budget and travel duration, the higher the score is when selecting the most cost-effective attractions, providing tourists with a better travel experience, while the smaller the travel budget and travel duration, the shorter the distance is, providing tourists with a more cost-effective travel experience within the limited travel budget and travel duration;

[0090] Then, the cost-effectiveness of all scenic spots is obtained to generate a path decision table, which includes the cost-effectiveness of traveling between all city nodes. It is worth noting that the path decision table is predetermined, and subsequent changes will only be updated with the version update, and will not change with the change of tourists' travel routes, which reduces the generation time of the path decision table, is beneficial to improve the output time of the travel guide, and reduce the waiting time of tourists;

[0091] Finally, according to the initial digital tourist map and the path decision table, the optimal tour guide is generated based on the heuristic algorithm of path planning;

[0092] The beneficial effects of the above technical solution are as follows: through the above technical solution, the optimal tour guide is generated, which can save tourists' ineffective time and money when traveling compared to the existing technology. At the same time, the concept of the most cost-effective attractions is introduced in the selection of the optimal route, which is beneficial to improving the rationality of the tour guide, and formulating more reasonable travel plans for different tourists, which is beneficial to improving the tourists' travel experience.

[0093] In one embodiment, according to the initial digital tourist map and the path decision table, an optimal tour guide is generated based on a heuristic algorithm for path planning, including:

[0094] Step 1: Get the starting point and destination in the initial tourism digital map, and calculate the travel time T required from the starting point to the destination and back to the starting point * ;

[0095] Step 2: If T * <T, check the path decision table to calculate the first most cost-effective node to be visited, and calculate the remaining time t1 after visiting the first most cost-effective node;

[0096] t1=T-t1 *

[0097] Among them, T is the travel time, t1 *The estimated time to reach the second most cost-effective node;

[0098] Calculate the travel time t required from the first most cost-effective node to the destination 1，p , and calculate the high-speed rail return time t from the destination to the starting point p ;

[0099] If 1，p +t p <t1, then determine to travel to the first most cost-effective node;

[0100] If 1，p +t p ≥t1, then travel from the starting point to the destination and return to the starting point;

[0101] Step 3: After successfully traveling to the first most cost-effective node, continue to look up the table to obtain the kth most cost-effective node to be traveled, and calculate the travel time t required from the k-1th most cost-effective node to the kth most cost-effective node k-1，k , and calculate the travel time t required from the kth most cost-effective node to the destination k，p , and calculate the remaining time t after traveling to the kth most cost-effective node k ;

[0102] t k =Tt k *

[0103] Among them, t k * is the estimated time spent before visiting the k+1th most cost-effective node, where k is an integer greater than 1;

[0104] If k-1，k +t k，p +t p <t k , then travel to the kth most cost-effective node;

[0105] If k-1，k +t k，p +t p ≥t k , then travel from the k-1th most cost-effective node to the destination and return to the starting point;

[0106] Step 4: Obtain the starting point and destination in the initial tourism digital map, as well as all the cost-effective nodes that have been visited, and generate the optimal tour guide;

[0107] The working principle of the above technical solution is as follows: in order to achieve the optimal planning of tourists in tourism, this solution combines the initial tourism digital map given by tourists and the pre-designed path decision table, and jointly plans the optimal tour strategy based on the heuristic algorithm of path planning; in order to reduce the difficulty of understanding of this solution for technical personnel in this field, in the following description, the initial tourism digital map preferably selects tourists only from the starting point and the destination. First, the starting point and the destination in the initial tourism digital map are obtained, and the travel time T required from the starting point to the destination and back to the starting point is calculated. * , where the required travel time T * The calculation parameters include the high-speed rail round-trip time required from the starting point to the destination and the average travel time of tourists at the destination estimated based on big data. Then, the travel time T input by the tourists is obtained. If T * <T, indicating that there is extra free time in this tour. At this time, the robot checks the path decision table to calculate the first most cost-effective node to be visited. The calculation method is preferably to take the starting point as the initial node, determine the cost-effectiveness of each node in the path decision table where there is the same path or only one fork node between the starting point and the destination node, select the node with the highest cost-effectiveness as the most cost-effective node, and then calculate the remaining time t1 after visiting the first most cost-effective node. In the calculation process, t1 * It represents the estimated time to travel to the second most cost-effective node. This estimated time includes the average playing time at the node, and the travel time t required from the first most cost-effective node to the destination is calculated. 1，p , and calculate the high-speed rail return time t from the destination to the starting point p , t 1，p Including high-speed rail travel time and average travel time at the destination, after obtaining the above calculation data, determine whether to visit the first most cost-effective attraction. If the judgment result is yes, perform the following steps to continue to look up the table to obtain the kth most cost-effective node to be visited, and calculate the travel time t required from the k-1th most cost-effective node to the kth most cost-effective node k-1，k , and calculate the travel time t required from the kth most cost-effective node to the destination k，p , and calculate the remaining time t after traveling to the kth most cost-effective node k , where t k-1，k Including high-speed rail travel time and the average travel time at the kth most cost-effective node, t k，p Including the high-speed rail travel time and the average time spent at the destination, the remaining time t is calculated. k In the process, t k *Indicates the estimated time spent before visiting the k+1th most cost-effective node, which also includes the average playing time and high-speed rail travel time at the node; after obtaining the above calculation data, determine whether to visit the kth most cost-effective attraction, and finally determine several cost-effective nodes according to the above steps and the travel time T, and finally obtain the starting point and destination in the initial tourism digital map, as well as all the cost-effective nodes that have been visited, and generate the optimal tour guide;

[0108] Furthermore, when a visitor enters multiple nodes, the execution of this solution includes the following operations:

[0109] Obtain the starting point, transfer points and destination in the initial tourism digital map, and calculate the travel time T required to travel from the starting point to each transfer point to the destination and back to the starting point 1* The travel time needs to be planned for the starting point, transfer point and destination in the initial tourism digital map. The path planning is preferably planned according to the shortest total distance goal, and then T is determined according to the planned path. 1* ;

[0110] If T 1* <T, the starting point, transfer point and destination are segmented. For example, if the starting point is A, the transfer points are BCDEF, and the destination is p, then it is divided into AB, BC, CD, DE, EF, and FP segments; Figure 4 As shown;

[0111] Look up the path decision table to calculate the most cost-effective node for each trip, sort the attractions at each most cost-effective node according to their cost-effectiveness, and calculate the remaining time after visiting the first most cost-effective node in the sorting.

[0112]

[0113] in, The estimated time to the second most cost-effective node in the travel sequence, that is, the estimated time to the first most cost-effective node in the travel sequence and the related transfer points;

[0114] Calculate the time required to reach the destination by traveling from the last most cost-effective node as the starting point to each remaining transfer point And calculate the high-speed rail return time t from the destination to the starting point p ;like Figure 5 As shown, it is the time required to travel from point V to the destination after traveling through each of the remaining transfer points.

[0115] like Then determine the most cost-effective node that is first in the tour order;

[0116] Otherwise, travel according to the starting point, transit point and destination in the initial tourism digital map;

[0117] After successfully visiting the first most cost-effective node in the sorting, calculate the remaining time after visiting the second most cost-effective node in the sorting

[0118]

[0119] in, The estimated time to the third most cost-effective node in the travel sequence, that is, the estimated time to the first most cost-effective node in the travel sequence, the second most cost-effective node in the travel sequence, and the relevant transfer points;

[0120] Calculate the time required to reach the destination by traveling from the last most cost-effective node as the starting point to each remaining transfer point And calculate the high-speed rail return time t from the destination to the starting point p ;like Figure 6 As shown, it is the time required to travel from point V to the destination after traveling through each of the remaining transfer points.

[0121] like Then determine the second most cost-effective node in the travel order;

[0122] Otherwise, travel according to the starting point, transit point and destination in the initial tourism digital map, as well as the most cost-effective node that is first in the ranking;

[0123] In the case of cumulative order in the sorting, the above technical solution is repeated until Stop when n is the sorting order, n is a positive integer;

[0124] Obtain the most cost-effective nodes, starting points, transfer points and destinations visited during the tour, and generate the best tour guide;

[0125] Furthermore, when the visitor does not enter any nodes he wants to visit, the execution of this solution includes the following operations:

[0126] Obtain the starting point in the initial tourism digital map, check the path decision table to obtain the first most cost-effective node to be traveled and travel to the first most cost-effective node;

[0127] The selection of the cost-effective point is to select the node with the highest cost-effectiveness of the scenic spot from among the nodes adjacent to the starting point as the first most cost-effective node;

[0128] In the process of traveling to the kth most cost-effective node, calculate the remaining time t after traveling to the kth most cost-effective node k , k ≥ 1;

[0129] t k =Tt k *

[0130] Among them, T is the travel time, t k * is the estimated time spent on visiting the k+1th most cost-effective node;

[0131] Continue to look up the table to get the k+1th most cost-effective node to be traveled, and calculate the travel time t required from the kth most cost-effective node to the k+1th most cost-effective node 1k , and calculate the high-speed rail return time t from the k+1th most cost-effective node back to the starting point 2k ;

[0132] If 1k +t 2k <t k , then visit the k+1th most cost-effective node;

[0133] If 1k +t 2k ≥t k , then return to the starting point from the kth most cost-effective node;

[0134] Until the remaining time does not meet the travel conditions;

[0135] Get all visited nodes and starting points during the tour and generate the best tour strategy;

[0136] The beneficial effects of the above technical solution are: through the above technical solution, combined with the path planning of the most cost-effective theory and heuristic algorithm, the node selection of the tour guide is completed, and the travel guide is planned according to the actual travel budget and travel duration, which saves tourists' ineffective expenses during travel and improves tourists' travel experience.

[0137] In one embodiment, step 3 further includes:

[0138] When t k-1，t +t k，p +t p ≥t k When the kth most cost-effective node is reached, the path decision table is searched to obtain the kth second cost-effective node to be visited, and the travel time required from the k-1th most cost-effective node to the kth second cost-effective node is calculated. And calculate the travel time required from the kth second cost-effective node to the destination And calculate the remaining time after traveling to the kth second cost-effective node

[0139] like Then travel to the kth second cost-effective node;

[0140] like Then travel from the k-1th most cost-effective node to the destination and return to the starting point;

[0141] The working principle of the above technical solution is: in order to further meet the actual needs of tourists when traveling and improve the reasonable utilization rate of tourists' travel time, this solution changes the most cost-effective mechanism when the remaining time is no longer enough to visit the most cost-effective attractions, from a mechanism that comprehensively considers the travel budget and travel time to a mechanism that considers the travel distance, thereby reducing the travel time consumed by the next travel node. Specifically, when t k-1，k +t k，p +t p ≥t k When the kth most cost-effective node is reached, the path decision table is searched to obtain the kth second cost-effective node to be visited, and the travel time required from the k-1th most cost-effective node to the kth second cost-effective node is calculated. And calculate the travel time required from the kth second cost-effective node to the destination And calculate the remaining time after traveling to the kth second cost-effective node like Then travel to the kth second cost-effective node; if Then travel from the k-1th most cost-effective node to the destination and return to the starting point;

[0142] Furthermore, when a visitor enters multiple nodes, the execution of this solution includes the following operations:

[0143] when When , skip the most cost-effective node of the nth order in the sort, and calculate whether the remaining time after traveling to the most cost-effective node of the n+1th order is greater than If the result is negative, the most cost-effective node of the n+1th node in the sorting process is skipped and recalculated and judged until all nodes are judged as negative.

[0144] When the above situations are all negative, the mechanism of comprehensively considering travel budget and travel duration is changed to considering travel distance, and the following technical solutions are proposed:

[0145] Continue to check the path decision table to obtain the second cost-effective node corresponding to the most cost-effective node from the nth to the mth in the travel order, where m is the total number of segments of the trip. Taking the AB, BC, CD, DE, EF, and Fp segments in the above technical solution as an example, m is 6;

[0146] Assume that the nth second cost-effective node has been visited;

[0147] Calculate the time required to reach the destination by traveling from the last most cost-effective node as the starting point to each remaining transfer point And calculate the high-speed rail return time t from the destination to the starting point p ;

[0148] like Then determine the second cost-effective node of the nth in the tour sorting;

[0149] Otherwise, skip the second cost-effective node of the nth order in the sort, and calculate whether the remaining time after traveling to the second cost-effective node of the n+1th order is greater than If the result is negative, the second calculation node of the n+1th node in the sorting is skipped and recalculated and judged until all nodes are judged as negative;

[0150] Furthermore, when the visitor does not enter any nodes he wants to visit, the execution of this solution includes the following operations:

[0151] When t 1k +t 2k ≥t k When , continue to look up the table to get the k+1th second cost-effective node to be traveled, and calculate the travel time t required from the kth most cost-effective node to the k+1th second cost-effective node 3k , and calculate the high-speed rail return time t from the k+1th second calculation node back to the starting point 4k ;

[0152] If 3k +t 4k <t k , then visit the k+1th second cost-effective node;

[0153] If 3k +t 4k ≥t k , then return to the starting point from the kth most cost-effective node;

[0154] The beneficial effect of the above technical solution is: through the above technical solution, the improved path planning heuristic algorithm is more in line with the actual needs of tourists when traveling, which is beneficial to improving the reliability of the tour guide.

[0155] In one embodiment, the tour guide output module performs the following operations:

[0156] Get all the nodes in the optimal tour guide;

[0157] Search the built-in database to obtain the tourism introduction information corresponding to each node to generate an introduction label card, associate the corresponding nodes, and generate a tourist map;

[0158] Obtain the designated device of the visitor through the Bluetooth module;

[0159] Send tourist guide maps to tourists’ designated devices;

[0160] The voice broadcast module recognizes the introduction label card of each node in the guide map and broadcasts the information to tourists;

[0161] The working principle of the above technical solution is as follows: the present invention includes a tour strategy output module, which is used to generate a tourist map according to the optimal tour strategy, send it to the designated device of the tourist, and perform voice broadcast to the tourist according to the tourist introduction information of each node in the tourist map. To achieve the above function, the tour strategy output module preferably obtains all nodes in the optimal tour strategy, searches the built-in database to obtain the tourist introduction information corresponding to each node to generate an introduction label card, associates the corresponding nodes, and generates a tourist map. The built-in database is preferably the storage data corresponding to the robot main control STM32 processing chip. The associated tourist map can enable users to click on the corresponding node to view the tourist attraction information of the node. The tourist attraction information includes but is not limited to ticket prices, attraction star ratings, surrounding accommodation and other tourism related information; then, through the Bluetooth module, obtain the designated device of the tourist, preferably a mobile phone device connected by Bluetooth, and send the tourist map to the designated device of the tourist. Furthermore, the tourist downloads the travel app or travel public account associated with the robot, and realizes the generation and transmission of the tourist map online; finally, the robot recognizes the introduction label card of each node in the tourist map through the voice broadcast module and performs voice broadcast to the tourist;

[0162] The beneficial effect of the above technical solution is: through the above technical solution, a tourist map is generated for feedback, which is beneficial for tourists to view their travel plans in real time and improve the practical value of the tourist map.

[0163] In one embodiment, the robot main control STM32 processing chip also includes a tour guide reset module, wherein the tour guide reset module performs the following operations:

[0164] Get the original tourist map and new tourist information input by the user;

[0165] The original tourist map is updated according to the new tourist information, and the new tourist map is sent to the tourist guide output module;

[0166] The working principle of the above technical solution is: when tourists' travel plans change due to other reasons during their travels, in order to improve tourists' subsequent travel experience, the present invention proposes the following technical solution to improve tourists' travel experience, firstly obtaining the original guide map input by the user and the new travel information, then updating the original guide map according to the new travel information, and sending the new guide map to the tour strategy output module. The specific update operation can be known from the above technical solution. It is worth noting that when the route planning is re-performed for the places that have been visited, the cost-effectiveness of the corresponding attractions will be reduced to 0;

[0167] The beneficial effects of the above technical solution are: through the above technical solution, it is beneficial to improve the reliability of the tourist map when facing unexpected situations. At the same time, through the secondary update of the tourist map, since a large amount of information robots can obtain through the tourist map, the amount of information that tourists need to enter is reduced, further improving the tourists' user experience.

[0168] In one embodiment, the travel plan output module performs the following operations:

[0169] When it is determined that the tourists have no objection to the guide map sent by the tour guide output module;

[0170] Obtain the high-speed rail travel schedule for each node included in the guide map through a network connection;

[0171] Filter the high-speed train numbers in the high-speed rail travel schedule that meet the travel time planning of each node included in the tourist map;

[0172] Integrate all high-speed rail train numbers to generate a high-speed rail travel preparation plan, and send it to the tourist's designated device through the tour guide output module; among them, the high-speed rail travel preparation plan contains repeated dates, and repeated high-speed rail train numbers of the departure station and destination;

[0173] Obtain high-speed rail travel plans from tourists and generate integrated ticket purchase links in conjunction with relevant ticket purchase apps; the high-speed rail travel plans do not contain repeated dates, or repeated high-speed rail train numbers at the departure and destination stations;

[0174] Send the integrated ticket purchase link to the tourist's designated device through the tour guide output module;

[0175] The working principle of the above technical solution is as follows: In the existing technology, the tourism apps on the market only provide tourists with a way to purchase tickets, such as apps like T Travel, Z Travel, and X Trip, or provide tourists with travel guides for a certain place, and cannot meet the one-stop travel needs of users, that is, one way to meet all the travel needs of users; for this reason, this solution proposes a travel plan output module. When it is determined that the tourist has no objection to the tourist map sent by the tour guide output module, through the network connection method, obtain the high-speed rail driving schedule of each node included in the tourist map in the relevant database; and screen the high-speed rail driving schedule to obtain the high-speed rail EMU numbers that meet the travel time plan of each node included in the tourist map; then integrate all the high-speed rail EMU numbers to generate a high-speed rail travel preliminary plan, and send it to the tourist's designated device through the tour guide output module; among them, there are duplicate dates and high-speed rail EMU numbers with duplicate departure stations and destinations in the high-speed rail travel preliminary plan. Since different tourists have different travel times, for example, some tourists don't like to get up early, this step facilitates the realization of the personalized needs of tourists; obtain the high-speed rail travel plan feedback by tourists, and jointly generate an integrated ticket purchase link with the relevant ticket purchase app; among them, there are no duplicate dates and high-speed rail EMU numbers with duplicate departure stations and destinations in the high-speed rail travel plan. This step facilitates tourists to remove the time periods they don't like. The way of integrating the ticket purchase link reduces the time waste of tourists when purchasing tickets, and at the same time helps tourists book tickets in advance to avoid the phenomenon of no tickets in the subsequent itinerary; finally, send the integrated ticket purchase link to the tourist's designated device through the tour guide output module.

[0176] The beneficial effects of the above technical solution are as follows: Through the above technical solution, it reduces the time for users to query the ticket purchase plan according to the tourist map, helps tourists quickly plan their travel plans, and there is no need to spend too much time on ticket purchase, which is beneficial to improving the tourist quality of tourists.

[0177] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solution of the present invention and not to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A high-speed railway station tour guide robot based on a heuristic algorithm, characterized in that: include: The robot main control STM32 processing chip, WT588D voice chip, voice broadcast module, servo motor movement module, Bluetooth module, SPP Bluetooth serial port and infrared sensor; wherein, the robot main control STM32 processing chip is respectively connected with the WT588D voice chip, servo motor movement module, Bluetooth module and infrared sensor, the WT588D voice chip is connected with the voice broadcast module, the Bluetooth module is connected with the SPP Bluetooth serial port, the robot main control STM32 processing chip includes a tourist information acquisition module, a tourism information processing module, a tour strategy output module and a travel plan output module, the tourism information processing module is used to generate the optimal tour strategy based on the heuristic algorithm according to the tourism information acquired by the tourist information acquisition module and output it to the tour strategy output module; wherein, the tourism information processing module performs the following operations: Obtain travel information input by tourists; Generate an initial digital tourist map based on the city attractions information contained in the tourist information and the starting point of the high-speed railway station where the tour guide robot is located; According to the cost-effectiveness theory of scenic spots and the travel budget and travel duration contained in the travel information, the cost-effectiveness of the scenic spots from the i-th node to the j-th node in the initial digital tourism map is calculated; the calculation formula is: Among them, f i,j represents the cost-effectiveness of the scenic spot from the i-th node to the j-th node, x j represents the initial score of traveling to the jth node, d(i,j) represents the high-speed rail travel distance from the ith node to the jth node, a and b are the travel weights, a+b=1, and the values ​​of a and b are determined by the travel budget and travel duration. The larger the travel budget and travel duration, the larger the a value, and the smaller the travel budget and travel duration, the larger the b value; According to the cost-effectiveness function, the cost-effectiveness of the scenic spots from the i-th node to the j-th node in the initial tourism digital map is calculated; Obtain the cost-effectiveness of all scenic spots to generate a path decision table; According to the initial tourism digital map and the path decision table, the optimal tour guide is generated based on the heuristic algorithm of path planning.

2. The high-speed railway station tour guide robot based on heuristic algorithm according to claim 1, characterized in that: The tourist information acquisition module includes a guidance unit, an information panel unit and a tourist information acquisition unit; The guidance unit is used to send out preset voice information according to the voice broadcast module to assist tourists in performing routine information input operations on the high-speed railway station tour guide robot; The information panel unit is used to display a pre-set electronic digital map according to the smart panel, provide a search function, and obtain information on city attractions selected by tourists; wherein the smart panel is connected to the robot's main control STM32 processing chip; The tourist information acquisition unit is used to obtain the city attraction information selected by the tourist, as well as the travel budget and travel duration input by the tourist on the smart panel, and integrate the tourist's travel information.

3. The high-speed railway station tour guide robot based on heuristic algorithm according to claim 1, characterized in that: According to the initial digital tourist map and the path decision table, the optimal tour guide is generated based on the heuristic algorithm of path planning, including: Step 1: Get the starting point and destination in the initial tourism digital map, and calculate the travel time T required from the starting point to the destination and back to the starting point * ; Step 2: If T * < T, look up the path decision table to calculate the first most cost-effective node to be visited, and calculate the remaining time t1 after visiting the first most cost-effective node; t1=T-t1 * Among them, T is the travel time, t1 * The estimated time to reach the second most cost-effective node; Calculate the travel time t required from the first most cost-effective node to the destination 1,p , and calculate the high-speed rail return time t from the destination to the starting point p ; If t 1,p +t p < t1, then determine to visit the first most cost-effective node; If 1,p +t p ≥t1, then travel from the starting point to the destination and return to the starting point; Step 3: After successfully traveling to the first most cost-effective node, continue to look up the table to obtain the kth most cost-effective node to be traveled, and calculate the travel time t required from the k-1th most cost-effective node to the kth most cost-effective node k-1,k , and calculate the travel time t required from the kth most cost-effective node to the destination k,p , and calculate the remaining time t after traveling to the kth most cost-effective node k ; t k =T-t k * Among them, t k * is the estimated time spent before visiting the k+1th most cost-effective node, where k is an integer greater than 1; If k-1,k +t k,p +t p <t k , then travel to the kth most cost-effective node; If k-1,k +t k,p +t p ≥t k , then travel from the k-1th most cost-effective node to the destination and return to the starting point; Step 4: Obtain the starting point and destination in the initial tourism digital map, as well as all the cost-effective nodes that have been visited, and generate the optimal travel strategy.

4. The high-speed railway station tour guide robot based on heuristic algorithm according to claim 3, characterized in that: Step 3 also includes: When t k-1,k +t k,p +t p ≥t k When the kth most cost-effective node is reached, the path decision table is searched to obtain the kth second cost-effective node to be visited, and the travel time required from the k-1th most cost-effective node to the kth second cost-effective node is calculated. And calculate the travel time required from the kth second cost-effective node to the destination And calculate the remaining time after traveling to the kth second cost-effective node like Then travel to the kth second cost-effective node; like Then travel from the k-1th most cost-effective node to the destination and return to the starting point.

5. The high-speed railway station tour guide robot based on heuristic algorithm according to claim 1, characterized in that: The tour guide output module performs the following operations: Get all the nodes in the optimal tour guide; Search the built-in database to obtain the tourism introduction information corresponding to each node to generate an introduction label card, associate the corresponding nodes, and generate a tourist map; Obtain the designated device of the visitor through the Bluetooth module; Send tourist guide maps to tourists’ designated devices; The voice broadcast module recognizes the introduction label card of each node in the guide map and provides voice broadcast to tourists.

6. The high-speed railway station tour guide robot based on heuristic algorithm according to claim 5, characterized in that: The robot's main control STM32 processing chip also includes a tour guide reset module, wherein the tour guide reset module performs the following operations: Get the original tourist map and new tourist information input by the user; The original tourist map is updated according to the new tourist information, and the new tourist map is sent to the tourist guide output module.

7. The high-speed railway station tour guide robot based on heuristic algorithm according to claim 1, characterized in that: The travel plan output module performs the following operations: When it is determined that the tourists have no objection to the guide map sent by the tour guide output module; Obtain the high-speed rail travel schedule for each node included in the guide map through a network connection; Filter the high-speed train numbers in the high-speed rail travel schedule that meet the travel time planning of each node included in the tourist map; Integrate all high-speed rail train numbers to generate a high-speed rail travel preparation plan, and send it to the tourist's designated device through the tour guide output module; among them, the high-speed rail travel preparation plan contains repeated dates, and repeated high-speed rail train numbers of the departure station and destination; Obtain high-speed rail travel plans from tourists and generate integrated ticket purchase links in conjunction with relevant ticket purchase apps; the high-speed rail travel plans do not contain repeated dates, or repeated high-speed rail train numbers at the departure and destination stations; The integrated ticket purchase link is sent to the tourist's designated device through the tour guide output module.

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