MCP-driven city text travel data asset atlas construction and application system

Abstract

The invention discloses an MCP-driven city travel data asset atlas construction and application system, and belongs to the technical field of smart cities. According to the invention, a data acquisition module is used for inputting a data acquisition identification instruction into a large language model, so that the large language model obtains text travel data of a target city and monitoring data at the current moment based on an MCP protocol, and the data acquisition module is based on the MCP protocol; the construction module is used for generating a corresponding first text travel knowledge graph according to the urban text travel data, and adjusting the first text travel knowledge graph according to the monitoring data to obtain a second text travel knowledge graph at the current moment; the application module is used for generating a travel prompt according to the second travel knowledge graph and a travel target input by a user, and the beneficial effect of improving the utilization rate of urban travel resources is achieved.

Description

Technical Field

[0001] This invention relates to the field of smart cities, and in particular to an MCP-driven system for constructing and applying urban cultural and tourism data asset maps. Background Technology

[0002] The cultural and tourism industry is an important part of the modern service industry and one of the pillars of the national economy, directly driving the growth of industries such as transportation, accommodation, catering, and retail. However, due to differences in personal preferences, circumstances, and travel goals, different groups of people have different consumption needs when traveling to a city. At present, how to improve the tourist experience by coordinating the city's tourism resources has become an important problem to be solved. Commonly, tourism resources are digitized and provided to tourists directly through the Internet. However, due to the serious homogenization of the content pushed, some tourism resources in the city are underutilized, while other tourist attractions are overcrowded, resulting in the ineffective use of the city's tourism resources.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this invention is to provide an MCP-driven system for constructing and applying urban cultural tourism data asset maps, aiming to improve the utilization rate of urban tourism resources.

[0005] To achieve the above objectives, this invention provides an MCP-driven system for constructing and applying urban cultural tourism data asset maps. The MCP-driven system for constructing and applying urban cultural tourism data asset maps includes:

[0006] The data acquisition module is used to input data acquisition and recognition instructions into the large language model, so that the large language model can acquire the cultural and tourism data of the target city and the monitoring data at the current time based on the MCP protocol. The data acquisition module is based on the MCP protocol.

[0007] The construction module is used to generate a corresponding first cultural and tourism knowledge graph based on the urban cultural and tourism data, and adjust the first cultural and tourism knowledge graph based on the monitoring data to obtain a second cultural and tourism knowledge graph at the current moment;

[0008] The application module is used to generate travel tips based on the second cultural tourism knowledge graph and the travel goals input by the user.

[0009] Optionally, the application module includes:

[0010] The planning module generates travel suggestions based on the second cultural tourism knowledge graph and the user's input travel goals;

[0011] The prompt module generates a prompt image from the travel prompt and controls the display device to display the prompt image.

[0012] Furthermore, to achieve the above objectives, this invention also provides an MCP-driven method for constructing and applying urban cultural tourism data asset maps, the steps of which include:

[0013] The data acquisition and recognition instructions are input into the large language model, so that the large language model can acquire the cultural and tourism data of the target city and the monitoring data at the current moment based on the MCP protocol. The data acquisition module is based on the MCP protocol.

[0014] A first cultural and tourism knowledge graph is generated based on the city's cultural and tourism data.

[0015] The first cultural and tourism knowledge graph is adjusted based on the monitoring data to obtain the second cultural and tourism knowledge graph at the current moment.

[0016] Tourism suggestions are generated based on the second cultural tourism knowledge graph and the user's input tourism goals.

[0017] Optionally, the step of generating a corresponding first cultural and tourism knowledge graph based on the city's cultural and tourism data includes:

[0018] The urban cultural and tourism data is classified into preset categories to obtain multiple cultural and tourism group data, and each cultural and tourism group data corresponds to a preset category;

[0019] The frequency of occurrence of each object data in the cultural and tourism group data is counted to obtain multiple statistical results, and each statistical result corresponds to one of the cultural and tourism group data;

[0020] A first cultural tourism knowledge graph is generated based on each of the aforementioned cultural tourism group data and multiple of the aforementioned statistical results, resulting in multiple first cultural tourism knowledge graphs.

[0021] Optionally, the step of generating a first cultural tourism knowledge graph based on each of the cultural tourism group data and multiple statistical results, and obtaining multiple first cultural tourism knowledge graphs, includes:

[0022] Extract the triple data from the cultural and tourism group data to obtain a triple dataset. The triple dataset corresponds one-to-one with the cultural and tourism group data. The triple dataset includes multiple triple data.

[0023] Based on the cultural and tourism group data and the statistical results, determine the target object data corresponding to each of the cultural and tourism group data;

[0024] The target triple dataset is determined based on the triple dataset and the corresponding target object data, and the target triple dataset and the cultural tourism group data correspond one-to-one.

[0025] A first cultural tourism knowledge graph is generated based on the target triple, resulting in multiple first cultural tourism knowledge graphs.

[0026] Optionally, the step of determining the target object data corresponding to each of the cultural and tourism group data based on all the cultural and tourism group data and all the statistical results includes:

[0027] Based on the cultural and tourism group data and the statistical results, a first type of statistical result and a second type of statistical result are determined for each cultural and tourism group data. The first statistical result is the statistical result of the current corresponding cultural and tourism group data, and the second statistical result is a statistical result other than the first statistical result.

[0028] When the number of times the object data appears in the first type of statistical results is greater than or equal to the preset number of times, or when the object data in the first type of statistical results appears in the second type of statistical results, the object data is determined to be the target object data.

[0029] Optionally, the monitoring data includes: node usage data of the first cultural tourism knowledge graph, visitor flow data of each tourist area, and equipment status data of each tourist area. The step of adjusting the first cultural tourism knowledge graph according to the monitoring data to obtain the second cultural tourism knowledge graph at the current moment includes:

[0030] The first cultural tourism knowledge graph is updated based on the node usage data, the pedestrian flow data, and the device status data.

[0031] Optionally, the step of generating travel tips based on the second cultural tourism knowledge graph and the user-input travel goals includes:

[0032] Based on the stated tourism objective, a target second cultural tourism knowledge graph is selected from multiple second cultural tourism knowledge graphs;

[0033] Generate a third cultural tourism knowledge graph based on the target second cultural tourism knowledge graph;

[0034] The travel tips are generated based on the third knowledge graph and the travel objective.

[0035] Optionally, the step of generating the travel tips based on the third knowledge graph and the travel goal includes:

[0036] Based on the stated tourism objective, multiple target vertices are identified in the third knowledge graph.

[0037] Generate a target path based on multiple target vertices and the third knowledge graph;

[0038] Travel tips are determined based on the target route.

[0039] Furthermore, to achieve the above objectives, the present invention also provides an MCP-driven urban cultural tourism data asset map construction and application device, characterized in that the MCP-driven urban cultural tourism data asset map construction and application device includes: a memory, a processor, and an MCP-driven urban cultural tourism data asset map construction and application program stored on the memory and capable of running on the processor, wherein the MCP-driven urban cultural tourism data asset map construction and application program is configured to implement the steps of the MCP-driven urban cultural tourism data asset map construction and application method described above.

[0040] This invention proposes an MCP-driven method for constructing and applying urban cultural tourism data asset graphs. This method involves inputting data collection and recognition instructions into a large language model and generating a corresponding first cultural tourism knowledge graph based on the urban cultural tourism data. The first cultural tourism knowledge graph is then adjusted based on the monitoring data to obtain a second cultural tourism knowledge graph for the current time. Finally, tourism suggestions are generated based on the second cultural tourism knowledge graph and the user's input tourism goals, thereby providing personalized recommendations and improving the utilization rate of urban cultural tourism resources. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the structure of the MCP-driven urban cultural tourism data asset map construction and application device involved in the hardware operating environment of the embodiment of the present invention;

[0042] Figure 2 This is a flowchart illustrating the first embodiment of the MCP-driven method for constructing and applying urban cultural tourism data asset maps according to the present invention.

[0043] Figure 3 This is a flowchart illustrating the second embodiment of the MCP-driven method for constructing and applying urban cultural tourism data asset maps according to the present invention.

[0044] Figure 4 This is a flowchart illustrating the fifth embodiment of the method for constructing and applying urban cultural tourism data asset maps driven by MCP of the present invention.

[0045] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0046] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0047] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of the MCP-driven urban cultural tourism data asset map construction and application equipment involved in the hardware operating environment of the embodiment of the present invention.

[0048] like Figure 1 As shown, the MCP-driven urban cultural tourism data asset map construction and application device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, an interactive device 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to realize communication between these components. The interactive device 1003 may include a display screen and an input unit such as a keyboard. Optionally, the interactive device 1003 may also be connected to the communication bus via standard wired or wireless interfaces. The network interface 1004 may optionally include standard wired or wireless interfaces (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0049] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the construction and application equipment of the MCP-driven urban cultural tourism data asset map. It may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0050] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and an MCP-driven application for constructing and applying urban cultural tourism data asset maps.

[0051] exist Figure 1In the MCP-driven urban cultural tourism data asset map construction and application device shown, the network interface 1004 is mainly used for data communication with other devices; the interactive device 1003 is mainly used for data interaction with users; the processor 1001 and memory 1005 in the MCP-driven urban cultural tourism data asset map construction and application device of the present invention can be set in the MCP-driven urban cultural tourism data asset map construction and application device. The MCP-driven urban cultural tourism data asset map construction and application device calls the MCP-driven urban cultural tourism data asset map construction and application stored in the memory 1005 through the processor 1001, and executes the MCP-driven urban cultural tourism data asset map construction and application method provided in the embodiment of the present invention.

[0052] This invention provides an MCP-driven method for constructing and applying urban cultural tourism data asset maps, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the method for constructing and applying an MCP-driven urban cultural tourism data asset map according to the present invention.

[0053] In this embodiment, the method for constructing and applying the urban cultural tourism data asset map driven by MCP includes:

[0054] Step S1: Input the data acquisition and recognition command into the large language model so that the large language model can acquire the cultural and tourism data of the target city and the monitoring data at the current time based on the MCP protocol. The data acquisition module is based on the MCP protocol.

[0055] The data collection and identification instructions here may include: data type, target database, data time, etc. The MCP protocol allows large language models to acquire data from the accessed database. In this embodiment, the cultural and tourism data may include: facility resource data, passenger data, passenger rating data, historical and cultural data, basic operational data, etc. Facility resource data may include: scenic spots, attractions, hotels, shopping malls, and transportation facilities, etc. Passenger data may include: tourist travel routes, passenger dwell time at various attractions, passenger behavior, etc. Historical and cultural data may include: historical task data and historical event data corresponding to each attraction, etc. Basic operational data may include facility maintenance data, service time data, activity display data, etc. In this embodiment, the monitoring data is real-time data, because the real-time nature of different data varies.

[0056] Step S2: Generate a corresponding first cultural and tourism knowledge graph based on the urban cultural and tourism data;

[0057] Specifically, the triplet data of the city's cultural and tourism data is extracted. Specifically, through template matching and syntactic analysis, the relationship between two entities is identified, thereby generating triplet data. The triplet data here can include: head entity, relation, and tail entity. A first cultural and tourism knowledge graph is generated based on the triplet data. In this embodiment, not all triplet data is used to generate a single first cultural and tourism knowledge graph. Instead, the triplet data is divided into multiple triplet datasets based on their source, data volume, etc., and a first cultural and tourism knowledge graph is generated based on each triplet dataset. Therefore, in this embodiment, a first cultural and tourism knowledge graph can be obtained.

[0058] Step S3: Adjust the first cultural tourism knowledge graph according to the monitoring data to obtain the second cultural tourism knowledge graph at the current moment;

[0059] In this embodiment, since the monitoring data is real-time data, adjusting the first cultural tourism knowledge graph using real-time data can ensure the correctness of the second cultural tourism knowledge graph obtained after adjustment, thereby effectively avoiding an increase in the error rate of subsequently generated tourism tips data due to slow data updates.

[0060] Step S4: Generate travel tips based on the second cultural tourism knowledge graph and the travel goals entered by the user.

[0061] In this embodiment, the vertex corresponding to the travel goal is determined based on the user's input travel goal, and the path to achieve the travel goal is determined through graph reasoning, thereby generating travel tips based on the path to achieve the travel goal.

[0062] In this embodiment, by inputting data collection and recognition instructions into a large language model, a corresponding first cultural and tourism knowledge graph is generated based on the urban cultural and tourism data; the first cultural and tourism knowledge graph is adjusted based on the monitoring data to obtain a second cultural and tourism knowledge graph at the current moment; and tourism tips are generated based on the second cultural and tourism knowledge graph and the user's input tourism goals, thereby obtaining personalized recommendations and improving the utilization rate of urban cultural and tourism resources.

[0063] Furthermore, based on the first embodiment, a second embodiment of the method for constructing and applying urban cultural tourism data asset maps driven by MCP of the present invention is proposed. In this embodiment, referring to... Figure 3 The step of generating the corresponding first cultural tourism knowledge graph based on the city's cultural tourism data includes:

[0064] Step S21: Classify the urban cultural and tourism data according to preset categories to obtain multiple cultural and tourism group data, each of the cultural and tourism group data corresponding to a preset category;

[0065] In this embodiment, the preset categories include: facility resource category, passenger data category, passenger rating category, historical and cultural category, basic operation category, etc. Since different types of data are actually acquired from different databases, the data type stored in the database can be identified. For example, the preset category classification of the city's cultural and tourism data corresponding to that database can be determined based on the database's identifier.

[0066] Step S22: Count the number of occurrences of each object data in the cultural and tourism group data to obtain multiple statistical results, each statistical result corresponding to one of the cultural and tourism group data;

[0067] The frequency of occurrence of each object data in the cultural and tourism group data is determined by vocabulary statistics. Here, each statistical result includes the frequency of occurrence of each object data in a corresponding cultural and tourism group data. Since each cultural and tourism group data corresponds to one statistical result, and there are multiple cultural and tourism group data, multiple statistical results are obtained.

[0068] Step S23: Generate a first cultural tourism knowledge graph based on each of the cultural tourism group data and multiple statistical results, and obtain multiple first cultural tourism knowledge graphs.

[0069] In this embodiment, the statistical results are used to identify key and less important object data. For example, popular tourist attractions often have their corresponding object data appearing multiple times in the cultural and tourism group data. This allows for the generation of a first cultural and tourism knowledge graph based on the key object data. Due to varying computing power and storage capabilities, the scale of the first cultural and tourism knowledge graph can differ depending on the statistical results. For instance, when computing power and storage are sufficient, the first cultural and tourism knowledge graph is generated based on all the cultural and tourism group data; when computing power or storage is insufficient, only a portion of the data in the cultural and tourism group data is selected to generate the first cultural and tourism knowledge graph. The specific selection method is based on the statistical results.

[0070] In this embodiment, by classifying the urban cultural and tourism data according to preset categories, multiple cultural and tourism group data are obtained. The occurrence frequency of each object data in the cultural and tourism group data is counted to obtain multiple statistical results. A first cultural and tourism knowledge graph is generated based on each of the cultural and tourism group data and the multiple statistical results, resulting in multiple first cultural and tourism knowledge graphs, thereby obtaining a first cultural and tourism knowledge graph that can be dynamically changed.

[0071] Furthermore, the step of generating a first cultural tourism knowledge graph based on each of the cultural tourism group data and multiple statistical results, and obtaining multiple first cultural tourism knowledge graphs, includes:

[0072] Extract the triple data from the cultural and tourism group data to obtain a triple dataset. The triple dataset corresponds one-to-one with the cultural and tourism group data. The triple dataset includes multiple triple data.

[0073] Based on the cultural and tourism group data and the statistical results, determine the target object data corresponding to each of the cultural and tourism group data;

[0074] The target triple dataset is determined based on the triple dataset and the corresponding target object data, and the target triple dataset and the cultural tourism group data correspond one-to-one.

[0075] A first cultural tourism knowledge graph is generated based on the target triple, resulting in multiple first cultural tourism knowledge graphs.

[0076] The triple data extracted from the cultural and tourism grouping data can be obtained using relation recognition methods. Optionally, triple data can be extracted from the cultural and tourism grouping data according to the data format. Alternatively, triple data can also be generated by extracting the cultural and tourism grouping data using a large language model. In this embodiment, the target object data is the data used to construct the first cultural and tourism knowledge graph. Generally, the target object data can be determined by sorting the frequency of occurrence in statistical data. The specific steps for determining the target triple dataset based on the triple dataset and the corresponding target object data are as follows: determine whether each triple data in the triple dataset includes the target object data, and combine all triple data that include the target object data into the target triple dataset.

[0077] In this embodiment, the target object data corresponding to each cultural and tourism group data is determined by the cultural and tourism group data and the statistical results; the target triple dataset is determined according to the triple dataset and the corresponding target object data; a first cultural and tourism knowledge graph is generated according to the target triple, resulting in multiple first cultural and tourism knowledge graphs. This effectively limits the size of the first cultural and tourism knowledge graph while ensuring that the first cultural and tourism knowledge graph can effectively include important or hot cultural and tourism data.

[0078] Furthermore, based on the first or second embodiment, a third embodiment of the method for constructing and applying urban cultural tourism data asset maps driven by MCP of the present invention is proposed. In this embodiment, the step of determining the target object data corresponding to each cultural tourism group data according to all the cultural tourism group data and all the statistical results includes:

[0079] Based on the cultural and tourism group data and the statistical results, a first type of statistical result and a second type of statistical result are determined for each cultural and tourism group data. The first statistical result is the statistical result of the current corresponding cultural and tourism group data, and the second statistical result is a statistical result other than the first statistical result.

[0080] When the number of times the object data appears in the first type of statistical results is greater than or equal to the preset number of times, or when the object data in the first type of statistical results appears in the second type of statistical results, the object data is determined to be the target object data.

[0081] In other embodiments, the target object data can be determined solely based on the first statistical result. In this embodiment, when the object data in the first type of statistical result is in the second type of statistical result, it indicates that the object data will actually appear in at least two first cultural tourism knowledge graphs. Therefore, it needs to be determined as the target object data.

[0082] In this embodiment, based on the cultural and tourism group data and the statistical results, a first type of statistical result and a second type of statistical result corresponding to each cultural and tourism group data are determined. When the number of occurrences of object data in the first type of statistical result is greater than or equal to a preset number of occurrences, or when object data in the first type of statistical result is in the second type of statistical result, the object data is determined to be target object data. This allows for the selection of important data, thereby increasing the proportion of key cultural and tourism data assets in the subsequent first cultural and tourism knowledge graph.

[0083] Furthermore, based on any of the above embodiments, a fourth embodiment of the MCP-driven urban cultural tourism data asset graph construction and application method of the present invention is proposed. The monitoring data includes: node usage data of the first cultural tourism knowledge graph, traffic flow data of each tourist area, and equipment status data of each tourist area. The step of adjusting the first cultural tourism knowledge graph according to the monitoring data to obtain the second cultural tourism knowledge graph at the current moment includes:

[0084] The first cultural tourism knowledge graph is updated based on the node usage data, the pedestrian flow data, and the device status data.

[0085] Optionally, the first cultural tourism knowledge graph can be adjusted based on visitor flow data. For example, if the visitor flow data of a passage connecting two attractions is too high, the connection between the two entity locations in the first cultural tourism knowledge graph can be deleted. Optionally, when some entertainment facilities in some attractions are temporarily closed, the corresponding vertices in the first cultural tourism knowledge graph can be deleted, and the relationships related to the corresponding vertices can also be deleted at the same time.

[0086] Furthermore, based on any of the above embodiments, a fifth embodiment of the MCP-driven urban cultural tourism data asset map construction and application method of the present invention is proposed, referring to... Figure 4 The step of generating travel tips based on the second cultural tourism knowledge graph and the user's input travel goals includes:

[0087] Step S41: Select a target second cultural tourism knowledge graph from multiple second cultural tourism knowledge graphs according to the tourism goal;

[0088] In this embodiment, the tourism objective is not limited to a tourist destination; it can be learning about historical sites or exploring local cuisine. The relevant tourism objective is selected from multiple second cultural tourism knowledge graphs to form the desired target second cultural tourism knowledge graph. Preferably, there is more than one target second cultural tourism knowledge graph, and each target second cultural tourism knowledge graph must include a geographical cultural tourism knowledge graph. The geographical cultural tourism knowledge graph specifically includes the location relationships of various attractions for green line planning in tourism. The corresponding second cultural tourism knowledge graph is determined based on the tourism objective. For example, if the tourism objective is to visit the former residence of a famous person in the city, the second cultural tourism knowledge graph containing data on the famous person in the city would also be used as the target second cultural tourism knowledge graph.

[0089] Step S42: Generate a third cultural tourism knowledge graph based on the target second cultural tourism knowledge graph;

[0090] Combine more than one second cultural tourism knowledge graph into a third cultural tourism knowledge graph.

[0091] Step S43: Generate the travel tips based on the third knowledge graph and the travel goal.

[0092] Furthermore, the step of generating the travel tips based on the third knowledge graph and the travel goal includes:

[0093] Based on the stated tourism objective, multiple target vertices are identified in the third knowledge graph.

[0094] Generate a target path based on multiple target vertices and the third knowledge graph;

[0095] Travel tips are determined based on the target route.

[0096] In this embodiment, a path passing through multiple target vertices is searched, and a path ranking algorithm is used to determine the target path among the multiple paths. Optionally, the travel tips are generated using a large language model and the target path.

[0097] Furthermore, this invention also proposes an MCP-driven urban cultural tourism data asset map construction and application system, characterized in that the MCP-driven urban cultural tourism data asset map construction and application system includes:

[0098] The data acquisition module is used to input data acquisition and recognition instructions into the large language model, so that the large language model can acquire the cultural and tourism data of the target city and the monitoring data at the current time based on the MCP protocol. The data acquisition module is based on the MCP protocol.

[0099] The construction module is used to generate a corresponding first cultural and tourism knowledge graph based on the urban cultural and tourism data, and adjust the first cultural and tourism knowledge graph based on the monitoring data to obtain a second cultural and tourism knowledge graph at the current moment;

[0100] The application module is used to generate travel tips based on the second cultural tourism knowledge graph and the travel goals input by the user.

[0101] Furthermore, the application module includes:

[0102] The planning module generates travel suggestions based on the second cultural tourism knowledge graph and the user's input travel goals;

[0103] The prompt module generates a prompt image from the travel prompt and controls the display device to display the prompt image.

[0104] The MCP-driven urban cultural tourism data asset map construction and application system can implement the steps of any of the above-mentioned MCP-driven urban cultural tourism data asset map construction and application methods.

[0105] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0106] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0107] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0108] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A MCP-driven urban travel data asset map construction and application system, characterized in that, The MCP-driven urban cultural tourism data asset graph construction and application system comprises: A data acquisition module configured to input a data acquisition identification instruction into a large language model, so that the large language model acquires cultural tourism data of a target city and monitoring data at a current time based on an MCP protocol, and the data acquisition module is based on the MCP protocol; A construction module configured to generate a first cultural tourism knowledge graph according to the urban cultural tourism data, and adjust the first cultural tourism knowledge graph according to the monitoring data to obtain a second cultural tourism knowledge graph at the current time; An application module configured to generate a tourism prompt according to the second cultural tourism knowledge graph and a user-input tourism target. 2.The MCP-driven urban travel data asset map construction and application system of claim 1, wherein, The application module comprises: A planning module configured to generate a tourism prompt according to the second cultural tourism knowledge graph and a user-input tourism target; A prompt module configured to generate a prompt image of the tourism prompt, and control a display device to display the prompt image.

3. A method for constructing and applying a city cultural tourism data asset map driven by an MCP, characterized in that, The steps of the MCP-driven urban cultural tourism data asset graph construction and application method comprise: Inputting a data acquisition identification instruction into a large language model, so that the large language model acquires cultural tourism data of a target city and monitoring data at a current time based on an MCP protocol, and the data acquisition module is based on the MCP protocol; Generating a first cultural tourism knowledge graph according to the urban cultural tourism data; Adjusting the first cultural tourism knowledge graph according to the monitoring data to obtain a second cultural tourism knowledge graph at the current time; Generating a tourism prompt according to the second cultural tourism knowledge graph and a user-input tourism target.

4. The MCP-driven urban travel data asset map construction and application method of claim 3, wherein, The step of generating a first cultural tourism knowledge graph according to the urban cultural tourism data comprises: Classifying the urban cultural tourism data according to preset categories to obtain a plurality of cultural tourism grouping data, each of which corresponds to a preset category; Counting the number of occurrences of each object data in the cultural tourism grouping data to obtain a plurality of statistical results, each of which corresponds to a cultural tourism grouping data; Generating a first cultural tourism knowledge graph according to each of the cultural tourism grouping data and a plurality of statistical results to obtain a plurality of first cultural tourism knowledge graphs.

5. The MCP-driven urban travel data asset map construction and application method of claim 4, wherein, The step of generating a first cultural tourism knowledge graph according to each of the cultural tourism grouping data and a plurality of statistical results to obtain a plurality of first cultural tourism knowledge graphs comprises: Extracting triple data of the cultural tourism grouping data to obtain a triple data set, which corresponds one-to-one to the cultural tourism grouping data, and the triple data set comprises a plurality of triple data; Determining target object data corresponding to each of the cultural tourism grouping data according to the cultural tourism grouping data and the statistical results; Determining a target triple data set according to the triple data set and the corresponding target object data, which corresponds one-to-one to the cultural tourism grouping data; Generating a first cultural tourism knowledge graph according to the target triple to obtain a plurality of first cultural tourism knowledge graphs.

6. The MCP-driven urban travel data asset map construction and application method of claim 5, wherein, The step of determining target object data corresponding to each of the cultural tourism grouping data according to all of the cultural tourism grouping data and all of the statistical results comprises: According to the travel group data and the statistical results, determine the first type of statistical results and the second type of statistical results corresponding to each travel group data, the first statistical result is the statistical result of the current corresponding travel group data, and the second statistical result is the statistical result other than the first statistical result; When the number of occurrences of the object data in the first type of statistical results is greater than or equal to a preset number of occurrences, or when the object data in the first type of statistical results is in the second type of statistical results, the object data is determined as target object data.

7. The MCP-driven urban travel data asset map construction and application method of claim 3, wherein, The monitoring data includes node usage data of a first travel knowledge graph, people flow data of each tourism area, and device state data of each tourism area, and the step of adjusting the first travel knowledge graph according to the monitoring data to obtain a second travel knowledge graph at the current time includes: According to the node usage data, the people flow data and the device state data, the first travel knowledge graph is updated.

8. The MCP-driven urban travel data asset map construction and application method of claim 3, wherein, The step of generating a travel prompt according to the second travel knowledge graph and a user input travel target includes: According to the travel target, a target second travel knowledge graph is selected from a plurality of second travel knowledge graphs; According to the target second travel knowledge graph, a third travel knowledge graph is generated; According to the third knowledge graph and the travel target, the travel prompt is generated.

9. The MCP-driven urban travel data asset map construction and application method of claim 3, wherein, The step of generating the travel prompt according to the third knowledge graph and the travel target includes: According to the travel target, a plurality of target vertices corresponding to the third knowledge graph are determined; According to the plurality of target vertices and the third knowledge graph, a target path is generated; According to the target path, a travel prompt is determined.

10. A MCP-driven urban travel data asset map construction and application device, characterized in that, The MCP-driven urban travel data asset graph construction and application device includes a memory, a processor, and an MCP-driven urban travel data asset graph construction and application program stored on the memory and executable on the processor, the MCP-driven urban travel data asset graph construction and application program is configured to implement the steps of the MCP-driven urban travel data asset graph construction and application method according to any one of claims 3 to 9.

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