Low-carbon travel system based on big data analysis

Abstract

The invention discloses a low-carbon travel system based on big data analysis. The low-carbon travel system comprises a data integration module, a deep analysis module, a travel guide module and an emergency response module, the data integration module is used for integrating travel-related data from different platforms and departments; the deep analysis module is used for performing deep analysis on the integrated data and mining data associated with low-carbon travel; the travel guide module provides low-carbon travel suggestions for the user according to the deep analysis result, and guides the user to select a low-carbon travel mode; when an emergency occurs, the emergency response module rapidly integrates and analyzes related data, and provides a timely detouring scheme or an alternative travel mode suggestion for a user. The system can integrate multi-source travel data for deep analysis, provide low-carbon travel suggestions for users and provide timely detouring or alternative schemes in case of emergencies so as to guide the users to select low-carbon travel modes.

Description

Technical Field

[0001] The present invention belongs to the technical field of travel planning systems, and specifically refers to a low-carbon travel system based on big data analysis. Background Art

[0002] In today's era of vigorously advocating low-carbon travel, the efficient integration and in-depth analysis of data in the travel system is crucial. However, the existing travel system has many serious deficiencies in this regard.

[0003] At present, the sources of various types of travel-related data are extremely wide, covering traffic flow monitoring equipment, public transportation operation systems, mobile phone positioning data, etc. However, these data are scattered across different platforms and departments, lacking effective integration, which greatly hinders the promotion of low-carbon travel. For example, the real-time road traffic data held by the traffic management department and the bus and subway operation data owned by public transportation operators have not been fully integrated, making it impossible for the travel system to fully and accurately grasp the overall traffic conditions and difficult to guide users to choose low-carbon travel methods. In this case, users may choose to travel by private cars with high carbon emissions because they cannot obtain accurate public transportation information or have an incomplete understanding of traffic congestion.

[0004] In terms of data analysis, most existing travel systems only conduct simple data statistics and basic trend analysis, and fail to deeply explore the potential value of massive travel data and its relationship with low-carbon travel. For example, it is impossible to accurately predict the user's future travel needs and the most suitable low-carbon travel plan based on the user's historical travel data. At the same time, it is also difficult to optimize the allocation of transportation resources to promote low-carbon travel by conducting in-depth analysis of traffic data in different regions and time periods. For example, it is impossible to reasonably adjust bus routes, resulting in insufficient convenience of public transportation, causing users to prefer to use private cars; or it is impossible to increase public bicycle stations and optimize the setting of pedestrian paths based on data analysis results to create better conditions for low-carbon travel modes such as walking and cycling.

[0005] In addition, the existing travel system is unable to respond effectively and promptly to emergencies due to insufficient data integration and analysis capabilities, which also has a negative impact on low-carbon travel. For example, when encountering emergencies such as traffic accidents and road construction, relevant data cannot be quickly integrated and analyzed to provide users with timely detour solutions or alternative travel suggestions. This may cause users to unknowingly choose high-carbon emission travel methods to deal with emergencies, rather than choosing lower-carbon public transportation or other alternatives.

[0006] In short, the existing travel system has problems in data integration and analysis, such as data dispersion, shallow analysis, and weak ability to respond to emergencies, which seriously affects the promotion and implementation of low-carbon travel. A low-carbon travel system based on big data analysis is urgently needed to make up for these shortcomings, so as to better guide people to choose low-carbon travel modes and contribute to environmental protection and sustainable development. Summary of the invention

[0007] In order to solve the above-mentioned problems in the prior art, the present invention proposes a low-carbon travel system based on big data analysis, which can integrate multi-source travel data for in-depth analysis, provide users with low-carbon travel suggestions and provide timely detours or alternative plans in emergencies, so as to guide users to choose low-carbon travel methods.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows: a low-carbon travel system based on big data analysis, including a data integration module, a deep analysis module, a travel guidance module and an emergency response module, wherein:

[0009] The data integration module is used to integrate travel-related data from different platforms and departments, and the travel-related data includes traffic flow monitoring data, public transportation operation system data, and mobile phone positioning data;

[0010] The in-depth analysis module performs in-depth analysis on the integrated data to mine data related to low-carbon travel;

[0011] The travel guidance module provides low-carbon travel suggestions to users based on the in-depth analysis results, and guides users to choose low-carbon travel modes;

[0012] When an emergency occurs, the emergency response module quickly integrates and analyzes relevant data to provide users with timely detour solutions or alternative travel mode suggestions.

[0013] Furthermore, the data integration module includes a data acquisition submodule, a data standardization submodule and a data fusion submodule, wherein:

[0014] The data collection submodule is used to collect travel-related data from different data sources;

[0015] The data standardization submodule performs standardization processing on the collected data so that it has a unified data format and unit;

[0016] The data fusion submodule fuses the standardized data to form a comprehensive travel data set.

[0017] Furthermore, the in-depth analysis module includes a travel history analysis unit, a traffic resource optimization unit and a low-carbon association mining unit, wherein:

[0018] The travel history analysis unit uses a machine learning algorithm to predict the user's future travel needs and preferred low-carbon travel plans based on the user's historical travel data;

[0019] The traffic resource optimization unit determines the public transportation line adjustment plan, the location of additional public bicycle stations and the optimization direction of pedestrian paths by analyzing the traffic data of different areas and different time periods;

[0020] The low-carbon association mining unit deeply mines low-carbon travel association data in the travel data and establishes an association model between the data and low-carbon travel.

[0021] Furthermore, the travel guidance module includes a travel plan input submodule, a travel suggestion generation unit, an information push unit and a user feedback processing unit, wherein:

[0022] The travel plan input submodule is used by the user to input the travel plan;

[0023] The travel suggestion generation submodule generates a personalized low-carbon travel plan for the user to choose based on the in-depth analysis results;

[0024] The information push submodule pushes travel suggestions to users via mobile phone applications, text messages, etc.

[0025] The user feedback processing submodule receives user feedback on travel suggestions, which is used to optimize the travel suggestion generating submodule.

[0026] Furthermore, the travel plan input submodule includes a precise travel plan unit and a fuzzy travel plan unit. The precise travel plan unit allows users to accurately input travel time, departure point and destination; the fuzzy travel plan unit allows users to input travel intentions in a fuzzy manner, and uses a semantic analysis model to analyze the content input by the user, automatically generate a travel destination, travel time period, and means of transportation, and feed back the plan to the user for selection.

[0027] Furthermore, the emergency response module includes a data rapid integration unit, an emergency monitoring unit, an impact analysis unit, a travel relevance assessment unit, a historical data analysis unit, an alternative solution generation unit and a feedback unit, wherein:

[0028] The data rapid integration unit quickly integrates relevant data when an emergency occurs to ensure the timeliness and comprehensiveness of the data;

[0029] The emergency monitoring unit continuously monitors various emergency situations that may affect travel and classifies the emergency situations, the types of which include but are not limited to traffic accidents, road construction, and bad weather;

[0030] The impact analysis unit is used to analyze the specific impact of various emergencies on traffic, such as the degree of traffic congestion, the extension of travel time, the difference in impact on different travel modes, etc., to provide an accurate basis for subsequent decision-making;

[0031] The travel relevance assessment unit accurately assesses the relevance of newly emerged emergencies to user travel, and by setting a reasonable relevance threshold, determines which emergencies require special attention and corresponding measures, and which can be temporarily ignored, thereby improving the pertinence and efficiency of emergency response;

[0032] The historical data analysis unit uses historical data to analyze the impact patterns and rules of similar emergencies on user travel, providing reference and lessons for handling current emergencies;

[0033] The alternative plan generation unit generates a reasonable alternative travel plan for users affected by the emergency according to the analysis results. The plan adjustment content includes adjusting the travel route, changing the travel mode, and adjusting the travel time to ensure that users can still achieve efficient and low-carbon travel in the event of an emergency;

[0034] The feedback unit will promptly feed back the generated alternative travel plans to the user, who will decide whether to modify the travel plan.

[0035] Furthermore, it also includes a user data collection module, which includes a data collection permission request unit, a moving speed collection unit, a road condition impact analysis unit, and a data storage sharing submodule, wherein:

[0036] The data collection permission request unit is used to send a clear data collection permission request to the user when the system is started or needs to collect user data, detailing the purpose, scope and usage of data collection to ensure that the user fully understands and voluntarily authorizes the system to collect data. For example, information is displayed to the user in the form of a pop-up window to inform the user that the system will collect data on his walking speed, cycling speed and the impact of different road conditions on his moving speed. These data are only used to generate more accurate travel plans for users and will not be used for other commercial purposes. At the same time, clear options are provided for users to choose whether to agree to the authorization. If the user disagrees, the system will not collect relevant data.

[0037] The mobile speed collection unit accurately collects the user's mobile speed data in a variety of ways; the mobile speed includes walking speed and cycling speed, both of which are difficult to be included in the traffic data system. By combining with the built-in acceleration sensor and positioning system of the user's smartphone, it is judged whether the user is walking or cycling according to the acceleration change feedback from the acceleration sensor, and the mobile speed is calculated according to the position change feedback from the positioning system;

[0038] The traffic condition impact analysis unit deeply analyzes the impact of different road conditions on the user's moving speed, collects traffic condition data in real time, including road congestion, road surface conditions, weather conditions, etc., establishes a traffic condition impact model, continuously obtains the traffic conditions at the user's location, records the changes in the user's moving speed when passing through different road conditions, and constructs a traffic condition impact model;

[0039] The data storage and sharing unit securely stores the collected user data and performs reasonable data sharing with the user's authorization; based on the data uploaded by other users at the same time, the speed change caused by the user passing through the road section can be predicted in turn, and the average moving speed can be calculated accordingly, which can be used as a reference when the road condition data or user data is incomplete.

[0040] Furthermore, it also includes a low-carbon travel incentive module, which includes a points reward unit, an honorary title granting unit and a preferential rights exchange unit, wherein:

[0041] The points reward unit accumulates low-carbon points for users based on factors such as the frequency and mileage of low-carbon travel modes chosen by users; for example, each time a user chooses to travel by walking, cycling or taking public transportation, he or she can obtain a certain number of points; the calculation of points can be dynamically adjusted based on the travel distance, travel time and the low-carbon degree of the travel mode; at the same time, for users who insist on low-carbon travel in a specific time period, such as travel peak hours and traffic congestion periods, additional points can be awarded;

[0042] The honorary title awarding unit awards honorary titles of different levels according to the user's low-carbon travel performance; for example, a user who mainly uses low-carbon travel for one month can be awarded the title of "Low-Carbon Pioneer"; a user who ranks high in annual low-carbon travel mileage can be awarded the title of "Low-Carbon Expert", etc. These honorary titles can not only enhance the user's sense of honor and achievement, but also be displayed in the user community and serve as a role model;

[0043] The preferential rights and interests exchange unit provides users with a channel to exchange points for preferential rights and interests; users can use accumulated low-carbon points to exchange for public transportation coupons, free riding time for shared bicycles, consumption discounts at cooperating merchants and other preferential rights and interests; at the same time, the preferential rights and interests exchange unit can also cooperate with other enterprises and institutions to continuously expand the types and scope of preferential rights and interests and increase users' enthusiasm for participating in low-carbon travel.

[0044] The beneficial effects of a low-carbon travel system based on big data analysis of the present invention are as follows: through the data integration module, travel-related data from different platforms and departments are collected, standardized and integrated, solving the problem of data dispersion in the existing travel system; the deep analysis module uses advanced technologies such as machine learning algorithms to mine data related to low-carbon travel and predict users' future travel needs and preferred low-carbon travel plans; the emergency response module can quickly integrate relevant data and analyze them when an emergency occurs, and provide users with timely detour plans or alternative travel mode suggestions. This greatly improves the system's ability to respond to emergencies and improves users' low-carbon travel experience; the low-carbon travel incentive module encourages users to give priority to low-carbon travel modes through reward settings; the travel plan input submodule provides users with two input methods, precise and fuzzy, so that users who know the travel time, departure point and destination clearly, or users who only have a general travel intention, can easily plan their travel; the user data collection module can generate more accurate time calculations for travel plans based on the user's walking speed, riding speed and road conditions, and can combine historical data and real-time road conditions to provide more accurate time calculations, so that users can better arrange travel and reduce waiting and uncertainty. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a system architecture diagram of a low-carbon travel system based on big data analysis of the present invention;

[0046] Figure 2 This is a system architecture diagram of a data integration module of a low-carbon travel system based on big data analysis in the present invention;

[0047] Figure 3 This is a system architecture diagram of a deep analysis module of a low-carbon travel system based on big data analysis in the present invention;

[0048] Figure 4 This is a system architecture diagram of a travel guidance module of a low-carbon travel system based on big data analysis in the present invention;

[0049] Figure 5 A system architecture diagram of the travel plan input submodule of the travel guidance module;

[0050] Figure 6 This is a system architecture diagram of an emergency response module of a low-carbon travel system based on big data analysis in the present invention;

[0051] Figure 7 This is a system architecture diagram of a user data collection module of a low-carbon travel system based on big data analysis in the present invention;

[0052] Figure 8This is a system architecture diagram of a low-carbon travel incentive module of a low-carbon travel system based on big data analysis in the present invention.

[0053] Among them, 1-data integration module, 11-data collection submodule, 12-data standardization submodule, 13-data fusion submodule, 2-deep analysis module, 21-travel history analysis unit, 22-traffic resource optimization unit, 23-low-carbon association mining unit, 3-travel guidance module, 31-travel plan input submodule, 311-precise travel plan unit, 312-fuzzy travel plan unit, 32-travel suggestion generation unit, 33-information push unit, 34-user feedback processing unit, 4-emergency response module, 41 -Data rapid integration unit, 42-Emergency monitoring unit, 43-Impact analysis unit, 44-Travel relevance assessment unit, 45-Historical data analysis unit, 46-Alternative solution generation unit, 47-Feedback unit, 5-User data collection module, 51-Data collection permission request unit, 52-Moving speed collection unit, 53-Road condition impact analysis unit, 54-Data storage and sharing unit, 6-Low-carbon travel incentive module, 61-Point reward unit, 62-Honorary title awarding unit, 63-Preferential rights exchange unit. DETAILED DESCRIPTION

[0054] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] like Figure 1-8 As shown, the present invention is a low-carbon travel system based on big data analysis, including a data integration module 1, a deep analysis module 2, a travel guidance module 3 and an emergency response module 4, wherein:

[0056] The data integration module 1 is used to integrate travel-related data from different platforms and departments, and the travel-related data includes traffic flow monitoring data, public transportation operation system data, and mobile phone positioning data;

[0057] The in-depth analysis module 2 performs in-depth analysis on the integrated data to mine data related to low-carbon travel;

[0058] The travel guidance module 3 provides low-carbon travel suggestions to users based on the in-depth analysis results, and guides users to choose low-carbon travel modes;

[0059] When an emergency occurs, the emergency response module 4 quickly integrates and analyzes relevant data to provide users with timely detour solutions or alternative travel mode suggestions.

[0060] Furthermore, the data integration module 1 includes a data acquisition submodule 11, a data standardization submodule 12 and a data fusion submodule 13, wherein:

[0061] The data collection submodule 11 is used to collect travel-related data from different data sources;

[0062] The data standardization submodule 12 performs standardization processing on the collected data so that it has a unified data format and unit;

[0063] The data fusion submodule 13 fuses the standardized data to form a comprehensive travel data set.

[0064] Furthermore, the in-depth analysis module 2 includes a travel history analysis unit 21, a traffic resource optimization unit 22 and a low-carbon association mining unit 23, wherein:

[0065] The travel history analysis unit 21 uses a machine learning algorithm to predict the user's future travel needs and preferred low-carbon travel plans based on the user's historical travel data;

[0066] The traffic resource optimization unit 22 determines the public transportation line adjustment plan, the location of additional public bicycle stations and the optimization direction of pedestrian paths by analyzing the traffic data of different areas and different time periods;

[0067] The low-carbon association mining unit 23 deeply mines low-carbon travel association data in the travel data and establishes an association model between the data and low-carbon travel.

[0068] Furthermore, the travel guidance module 3 includes a travel plan input submodule 31, a travel suggestion generation unit 32, an information push unit 33 and a user feedback processing unit 34, wherein:

[0069] The travel plan input submodule 31 is used by the user to input the travel plan;

[0070] The travel suggestion generation submodule 32 generates a personalized low-carbon travel plan for the user to choose based on the in-depth analysis results;

[0071] The information push submodule 33 pushes travel suggestions to the user, which can be done through mobile phone applications, text messages, etc.;

[0072] The user feedback processing submodule 34 receives user feedback on travel suggestions, and is used to optimize the travel suggestion generating submodule 32 .

[0073] Furthermore, the travel plan input submodule 31 includes a precise travel plan unit 311 and a fuzzy travel plan unit 312. The precise travel plan unit 311 allows the user to accurately input the travel time, departure point and destination; the fuzzy travel plan unit 312 allows the user to input the travel intention in a fuzzy manner, and uses a semantic analysis model to analyze the content input by the user, automatically generate the travel destination, travel time period, and means of transportation, and feed back the plan to the user for the user to choose.

[0074] Furthermore, the emergency response module 4 includes a data rapid integration unit 41, an emergency monitoring unit 42, an impact analysis unit 43, a travel relevance evaluation unit 44, a historical data analysis unit 45, an alternative solution generation unit 46 and a feedback unit 47, wherein:

[0075] The data rapid integration unit 41 rapidly integrates relevant data when an emergency occurs, ensuring the timeliness and comprehensiveness of the data;

[0076] The emergency monitoring unit 42 continuously monitors various emergency situations that may affect travel and classifies the emergency situations, the types of which include but are not limited to traffic accidents, road construction, and bad weather;

[0077] The impact analysis unit 43 is used to analyze the specific impact of various emergencies on traffic, such as the degree of traffic congestion, the extension of travel time, the difference in impact on different travel modes, etc., to provide an accurate basis for subsequent decision-making;

[0078] The travel relevance evaluation unit 44 accurately evaluates the relevance of the newly emerged emergency situation and the user's travel, and by setting a reasonable relevance threshold, determines which emergency situations need to be focused on and corresponding measures taken, and which can be temporarily ignored, thereby improving the pertinence and efficiency of emergency response;

[0079] The historical data analysis unit 45 uses historical data to analyze the impact patterns and rules of similar emergencies on user travel, providing reference and lessons for handling current emergencies;

[0080] The alternative plan generating unit 46 generates a reasonable alternative travel plan for users affected by the emergency according to the analysis results. The plan adjustment content includes adjusting the travel route, changing the travel mode, and adjusting the travel time to ensure that the user can still achieve efficient and low-carbon travel in the event of an emergency;

[0081] The feedback unit 47 promptly feeds back the generated alternative travel plan to the user, and the user decides whether to modify the travel plan.

[0082] Furthermore, it also includes a user data collection module 5, which includes a data collection permission request unit 51, a moving speed collection unit 52, a road condition impact analysis unit 53, and a data storage sharing submodule 54, wherein:

[0083] The data collection permission request unit 51 is used to send a clear data collection permission request to the user when the system is started or needs to collect user data, detailing the purpose, scope and usage of data collection to ensure that the user fully understands and voluntarily authorizes the system to collect data. For example, information is displayed to the user in the form of a pop-up window to inform the user that the system will collect data on his walking speed, cycling speed and the impact of different road conditions on his moving speed. These data are only used to generate more accurate travel plans for users and will not be used for other commercial purposes. At the same time, clear options are provided for users to choose whether to agree to the authorization. If the user disagrees, the system will not collect relevant data.

[0084] The moving speed acquisition unit 52 accurately collects the moving speed data of the user in a variety of ways; the moving speed includes walking speed and cycling speed, both of which are difficult to be included in the traffic data system. By combining with the built-in acceleration sensor and positioning system of the user's smartphone, it is judged whether the user is walking or cycling according to the acceleration change feedback from the acceleration sensor, and the moving speed is calculated according to the position change feedback from the positioning system;

[0085] The traffic condition impact analysis unit 53 deeply analyzes the impact of different traffic conditions on the user's moving speed, collects traffic condition data in real time, including road congestion, road surface conditions, weather conditions, etc., establishes a traffic condition impact model, continuously obtains the traffic conditions at the user's location, records the changes in the user's moving speed when passing through different road conditions, and constructs a traffic condition impact model;

[0086] The data storage and sharing unit 54 securely stores the collected user data and performs reasonable data sharing with the user's authorization; based on the data uploaded by other users at the same time, the speed change caused by the user passing through the road section can be predicted in turn, and the average moving speed can be calculated accordingly, which can be used as a reference when the road condition data or user data is incomplete.

[0087] Furthermore, it also includes a low-carbon travel incentive module 6, which includes a points reward unit 61, an honorary title granting unit 62 and a preferential rights exchange unit 63, wherein:

[0088] The points reward unit 61 accumulates low-carbon points for the user based on factors such as the frequency and mileage of the user's choice of low-carbon travel mode; for example, each time the user chooses to walk, ride a bicycle or take public transportation to travel, a certain number of points can be obtained; the calculation of points can be dynamically adjusted according to the travel distance, travel time and the low-carbon degree of the travel mode; at the same time, for users who insist on low-carbon travel in a specific time period, such as travel peak hours and traffic congestion periods, additional points can be awarded;

[0089] The honorary title granting unit 62 grants honorary titles of different levels according to the user's low-carbon travel performance; for example, a user who mainly uses low-carbon travel for one month can be awarded the title of "Low-Carbon Pioneer"; a user who ranks high in annual low-carbon travel mileage can be awarded the title of "Low-Carbon Expert", etc. These honorary titles can not only enhance the user's sense of honor and achievement, but also be displayed in the user community and serve as a role model;

[0090] The preferential rights exchange unit 63 provides users with a channel to exchange points for preferential rights; users can use accumulated low-carbon points to exchange for public transportation coupons, free riding time for shared bicycles, consumption discounts at cooperating merchants and other preferential rights; at the same time, the preferential rights exchange unit 63 can also cooperate with other enterprises and institutions to continuously expand the types and scope of preferential rights and interests, and increase users' enthusiasm for participating in low-carbon travel.

[0091] The beneficial effects of a low-carbon travel system based on big data analysis of the present invention are as follows: through the data integration module, travel-related data from different platforms and departments are collected, standardized and integrated, solving the problem of data dispersion in the existing travel system; the deep analysis module uses advanced technologies such as machine learning algorithms to mine data related to low-carbon travel and predict users' future travel needs and preferred low-carbon travel plans; the emergency response module can quickly integrate relevant data and analyze them when an emergency occurs, and provide users with timely detour plans or alternative travel mode suggestions. This greatly improves the system's ability to respond to emergencies and improves users' low-carbon travel experience; the low-carbon travel incentive module encourages users to give priority to low-carbon travel modes through reward settings; the travel plan input submodule provides users with two input methods, precise and fuzzy, so that users who know the travel time, departure point and destination clearly, or users who only have a general travel intention, can easily plan their travel; the user data collection module can generate more accurate time calculations for travel plans based on the user's walking speed, riding speed and road conditions, and can combine historical data and real-time road conditions to provide more accurate time calculations, so that users can better arrange travel and reduce waiting and uncertainty.

[0092] The present invention and its embodiments are described above, which is not restrictive. What is shown in the accompanying drawings is only one of the embodiments of the present invention, and the actual content is not limited to this. In short, if ordinary technicians in this field are inspired by it and do not deviate from the purpose of the invention, they can creatively design structural methods and embodiments similar to the technical scheme, which should all fall within the scope of protection of the present invention.

Claims

1. A low-carbon travel system based on big data analysis, characterized by: It includes a data integration module (1), a deep analysis module (2), a travel guidance module (3) and an emergency response module (4), wherein: The data integration module (1) is used to integrate travel-related data from different platforms and departments, wherein the travel-related data includes traffic flow monitoring data, public transportation operation system data, and mobile phone positioning data; The in-depth analysis module (2) performs in-depth analysis on the integrated data to mine data related to low-carbon travel; The travel guidance module (3) provides low-carbon travel suggestions to users based on the in-depth analysis results, and guides users to choose low-carbon travel modes; When an emergency occurs, the emergency response module (4) quickly integrates relevant data and performs analysis to provide users with timely detour solutions or alternative travel mode suggestions.

2. A low-carbon travel system based on big data analysis according to claim 1, characterized in that: The data integration module (1) comprises a data acquisition submodule (11), a data standardization submodule (12) and a data fusion submodule (13), wherein: The data collection submodule (11) is used to collect travel-related data from different data sources; The data standardization submodule (12) performs standardization processing on the collected data so that the data has a unified data format and unit; The data fusion submodule (13) fuses the standardized data to form a comprehensive travel data set.

3. A low-carbon travel system based on big data analysis according to claim 2, characterized in that: The in-depth analysis module (2) comprises a travel history analysis unit (21), a traffic resource optimization unit (22) and a low-carbon association mining unit (23), wherein: The travel history analysis unit (21) uses a machine learning algorithm to predict the user's future travel needs and preferred low-carbon travel plans based on the user's historical travel data; The traffic resource optimization unit (22) determines the public transportation line adjustment plan, the location of additional public bicycle stations and the optimization direction of pedestrian paths by analyzing the traffic data of different areas and different time periods; The low-carbon association mining unit (23) deeply mines low-carbon travel association data in the travel data and establishes an association model between the data and low-carbon travel.

4. A low-carbon travel system based on big data analysis according to claim 1, characterized in that: The travel guidance module (3) comprises a travel plan input submodule (31), a travel suggestion generation unit (32), an information push unit (33) and a user feedback processing unit (34), wherein: The travel plan input submodule (31) is used by the user to input a travel plan; The travel suggestion generation submodule (32) generates a personalized low-carbon travel plan for the user to choose according to the in-depth analysis result; The information push submodule (33) pushes the travel suggestions to the user, which may be through mobile phone applications, text messages, etc.; The user feedback processing submodule (34) receives user feedback on travel suggestions, which is used to optimize the travel suggestion generating submodule (32).

5. A low-carbon travel system based on big data analysis according to claim 4, characterized in that: The travel plan input submodule (31) comprises a precise travel plan unit (311) and a fuzzy travel plan unit (312). The precise travel plan unit (311) allows the user to accurately input the travel time, departure point and destination; the fuzzy travel plan unit (312) allows the user to input the travel intention in a fuzzy manner, uses a semantic analysis model to analyze the content input by the user, automatically generates the travel destination, travel time period, and transportation, and feeds back the plan to the user for the user to choose.

6. A low-carbon travel system based on big data analysis according to claim 1, characterized in that: The emergency response module (4) includes a data rapid integration unit (41), an emergency monitoring unit (42), an impact analysis unit (43), a travel relevance assessment unit (44), a historical data analysis unit (45), an alternative solution generation unit (46) and a feedback unit (47), wherein: The data rapid integration unit (41) rapidly integrates relevant data when an emergency occurs, thereby ensuring the timeliness and comprehensiveness of the data; The emergency monitoring unit (42) continuously monitors various emergency situations that may affect travel and classifies the emergency situations, wherein the types of the emergency situations include but are not limited to traffic accidents, road construction, and bad weather; The impact analysis unit (43) is used to analyze the specific impact of various emergencies on traffic, such as the degree of traffic congestion, the extension of travel time, the difference in impact on different travel modes, etc., to provide an accurate basis for subsequent decision-making; The travel relevance assessment unit (44) accurately assesses the relevance between the newly emerged emergency and the user's travel, and by setting a reasonable relevance threshold, determines which emergency situations need to be focused on and corresponding measures taken, and which can be temporarily ignored, thereby improving the pertinence and efficiency of emergency response; The historical data analysis unit (45) uses historical data to analyze the impact patterns and rules of similar emergencies on user travel, providing reference and lessons for handling current emergencies; The alternative plan generating unit (46) generates a reasonable alternative travel plan for users affected by the emergency situation according to the analysis result, and the plan adjustment content includes adjusting the travel route, changing the travel mode, and adjusting the travel time to ensure that the user can still achieve efficient and low-carbon travel in the event of an emergency; The feedback unit (47) promptly feeds back the generated alternative travel plan to the user, and the user decides whether to modify the travel plan.

7. The low-carbon travel system based on big data analysis according to claim 1 is characterized by: It also includes a user data collection module (5), the user data collection module (5) includes a data collection authority request unit (51), a moving speed collection unit (52), a road condition impact analysis unit (53), and a data storage sharing submodule (54), wherein: The data collection permission request unit (51) is used to send a clear data collection permission request to the user when the system is started or needs to collect user data, detailing the purpose, scope and usage of data collection, to ensure that the user fully understands and voluntarily authorizes the system to collect data. The movement speed acquisition unit (52) accurately acquires the movement speed data of the user in a variety of ways; the movement speed includes walking speed and riding speed, and is combined with the built-in acceleration sensor and positioning system of the user's smart phone to judge whether the user is walking or riding according to the acceleration change fed back by the acceleration sensor, and calculates the movement speed according to the position change fed back by the positioning system; The road condition impact analysis unit (53) deeply analyzes the impact of different road conditions on the user's moving speed, collects road condition data in real time, including road congestion, road surface conditions, weather conditions, etc., establishes a road condition impact model, continuously obtains the road condition at the user's location, records the change in the user's moving speed when passing through different road conditions, and constructs a road condition impact model; The data storage and sharing unit (54) safely stores the collected user data and performs reasonable data sharing with the user's authorization; based on the data uploaded by other users at the same time, the speed change caused by the user passing through the road section can be predicted in turn, and the average moving speed can be calculated based on this, which can be used as a reference when the road condition data or user data is incomplete.

8. The low-carbon travel system based on big data analysis according to claim 1 is characterized by: It also includes a low-carbon travel incentive module (6), which includes a point reward unit (61), an honorary title awarding unit (62) and a preferential rights exchange unit (63), wherein: The point reward unit (61) accumulates low-carbon points for the user according to factors such as the frequency and mileage of the user's choice of low-carbon travel mode; The honorary title granting unit (62) grants honorary titles of different levels according to the user's low-carbon travel performance; The preferential rights exchange unit (63) provides a channel for users to exchange points for preferential rights; users can use the accumulated low-carbon points to exchange for public transportation coupons, free riding time for shared bicycles, consumption discounts at cooperative merchants and other preferential rights.

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