Smart city management system based on big data analysis

Abstract

The invention relates to a smart city management system based on big data analysis. The smart city management system comprises an information acquisition device, a data capture device, a history detection device and a big data analysis node. Valuable reference information can be provided for smart city data management.

Description

Technical Field

[0001] The present invention relates to the field of smart cities, and in particular to a smart city management system based on big data analysis. Background Art

[0002] Big data analysis refers to the analysis of huge amounts of data. Big data can be summarized as 5Vs: Volume, Velocity, Variety, Value, and Veracity. Big data is the hottest term in the IT industry today, and the use of the commercial value of big data, such as data warehouses, data security, data analysis, and data mining, has gradually become the focus of profit that users are vying for. With the advent of the big data era, big data analysis has also emerged.

[0003] However, big data analysis is still a new thing after all, and the specific technical solutions in some fields are not yet reliable and stable enough. For example, the application of big data needs to be continuously strengthened in the management of smart cities.

[0004] CN118540264A discloses a smart city big data traffic analysis system and method, which adopts an SDN controller, and a network perception module, a network monitoring module and a path selection module respectively connected to the SDN controller to work together, and proposes a large-volume data flow rerouting algorithm based on the characteristics of elephant flows. Based on the improvement of the existing simulated annealing algorithm, the link load is used to calculate the link cost, the link balance and the link number affect the calculation of the fitness function comparison, and the best traffic path is selected, and finally the big data traffic congestion caused by the collision of elephant flows is reduced in the SDN network of the smart city data center. Summary of the invention

[0005] In order to solve the technical problems in the related fields, compared with the prior art, the present invention provides a smart city management system based on big data analysis, the system comprising: An information collection device, used to obtain various attribute information of a set passenger high-speed rail train, wherein the various attribute information of the set passenger high-speed rail train includes the total number of carriages of the set passenger high-speed rail train, the number of seats in the economy carriage, the number of carriages in the economy carriage, the number of seats in the business carriage, the number of carriages in the business vehicle, and the total length of the carriages; A data capture device, used to obtain the two-place association data corresponding to the set passenger high-speed rail train, wherein the two-place association data corresponding to the set passenger high-speed rail train includes the distance mileage between the two places corresponding to the set passenger high-speed rail train, the number of intermediate stations, and the average number of passengers accommodated by the stations; A history detection device is used to obtain the passenger parameters corresponding to the set passenger high-speed railway train on a preset number of historical days, wherein the passenger parameters corresponding to each historical day are the number of economy seat passengers and the number of business seat passengers carried by the set passenger high-speed railway train on that day; A big data analysis node is connected to the information collection device, the data capture device and the history detection device respectively, and is used to intelligently identify the number of economy seat passengers and business seat passengers carried by the set passenger high-speed rail that did not depart on the day based on various attribute information of the set passenger high-speed rail, the two-place association data corresponding to the set passenger high-speed rail, and various passenger parameters corresponding to the set passenger high-speed rail on a preset number of historical days using a BP neural network model, wherein the number of passengers on each historical day is positively correlated with the mileage between the two places corresponding to the set passenger high-speed rail, and the nearest day among the historical days is adjacent to the day; The BP neural network model is a BP neural network that has completed multiple trainings, and the number of trainings is proportional to the sum of the number of seats in the economy car of the set passenger high-speed rail and the number of seats in the business car of the set passenger high-speed rail; Among them, obtaining the two-place association data corresponding to the set train high-speed passenger railway, the two-place association data corresponding to the set train high-speed passenger railway includes the distance mileage between the two places corresponding to the set train high-speed passenger railway, the number of intermediate stations and the average number of passengers accommodated by the station, including: the distance mileage between the two places corresponding to the set train high-speed passenger railway is the distance mileage between the starting station and the terminal station corresponding to the set train high-speed passenger railway; Among them, obtaining the two-place association data corresponding to the set train high-speed passenger railway, the two-place association data corresponding to the set train high-speed passenger railway include the distance mileage between the two places corresponding to the set train high-speed passenger railway, the number of intermediate stations and the average number of passengers accommodated by the station, including: the average number of passengers accommodated by the station corresponding to the set train high-speed passenger railway is the average value of the maximum number of passengers accommodated by each station traversed by the set train high-speed passenger railway; Among them, the passenger parameters corresponding to the set passenger high-speed railway train on a preset number of historical days are obtained, and the passenger parameters corresponding to each historical day are the number of economy seat passengers and business seat passengers carried by the set passenger high-speed railway train on that day, including: the value of the preset number is proportional to the total number of carriages of the set passenger high-speed railway train.

[0006] The present invention can quote a big data analysis node for using a BP neural network model to intelligently identify the number of economy seat passengers and business seat passengers on the set passenger high-speed rail train that did not depart on the day based on various attribute information of the set passenger high-speed rail train, the two-place association data corresponding to the set passenger high-speed rail train, and the various passenger parameters corresponding to the set passenger high-speed rail train on a preset number of historical days, thereby providing valuable reference information for subsequent passenger high-speed rail data management. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The embodiments of the present invention will be described below with reference to the accompanying drawings.

[0008] Figure 1 It is a schematic diagram of the internal structure of a smart city management system based on big data analysis according to the first embodiment of the present invention.

[0009] Figure 2 It is a schematic diagram of the internal structure of a smart city management system based on big data analysis according to the second embodiment of the present invention.

[0010] Figure 3 It is a schematic diagram of the internal structure of a smart city management system based on big data analysis according to the third embodiment of the present invention. DETAILED DESCRIPTION

[0011] The following is a detailed description of an embodiment of a smart city management system based on big data analysis of the present invention with reference to the accompanying drawings.

[0012] First embodiment

[0013] Figure 1 This is a schematic diagram of the internal structure of a smart city management system based on big data analysis according to the first embodiment of the present invention, wherein the system includes: An information collection device, used to obtain various attribute information of a set passenger high-speed rail train, wherein the various attribute information of the set passenger high-speed rail train includes the total number of carriages of the set passenger high-speed rail train, the number of seats in the economy carriage, the number of carriages in the economy carriage, the number of seats in the business carriage, the number of carriages in the business vehicle, and the total length of the carriages; For example, a programmable logic device may be used to implement the information collection device, which is used to obtain various attribute information of a set passenger high-speed rail train, wherein the various attribute information of the set passenger high-speed rail train includes the total number of carriages of the set passenger high-speed rail train, the number of seats in the economy carriage, the number of carriages in the economy carriage, the number of seats in the business carriage, the number of carriages in the business vehicle, and the total length of the carriages; A data capture device, used to obtain the two-place association data corresponding to the set passenger high-speed rail train, wherein the two-place association data corresponding to the set passenger high-speed rail train includes the distance mileage between the two places corresponding to the set passenger high-speed rail train, the number of intermediate stations, and the average number of passengers accommodated by the stations; A history detection device is used to obtain the passenger parameters corresponding to the set passenger high-speed railway train on a preset number of historical days, wherein the passenger parameters corresponding to each historical day are the number of economy seat passengers and the number of business seat passengers carried by the set passenger high-speed railway train on that day; A big data analysis node is connected to the information collection device, the data capture device and the history detection device respectively, and is used to intelligently identify the number of economy seat passengers and business seat passengers carried by the set passenger high-speed rail that did not depart on the day based on various attribute information of the set passenger high-speed rail, the two-place association data corresponding to the set passenger high-speed rail, and various passenger parameters corresponding to the set passenger high-speed rail on a preset number of historical days using a BP neural network model, wherein the number of passengers on each historical day is positively correlated with the mileage between the two places corresponding to the set passenger high-speed rail, and the nearest day among the historical days is adjacent to the day; The BP neural network model is a BP neural network that has completed multiple trainings, and the number of trainings is proportional to the sum of the number of seats in the economy car of the set passenger high-speed rail and the number of seats in the business car of the set passenger high-speed rail; For example, the BP neural network model is a BP neural network that has completed multiple trainings and the number of trainings is proportional to the sum of the number of seats in the economy car of the set passenger high-speed rail and the number of seats in the business car of the set passenger high-speed rail, including: a numerical mapping formula can be selected to represent a numerical mapping relationship that is proportional to the sum of the number of seats in the business car of the set passenger high-speed rail; Among them, obtaining the two-place association data corresponding to the set train high-speed passenger railway, the two-place association data corresponding to the set train high-speed passenger railway includes the distance mileage between the two places corresponding to the set train high-speed passenger railway, the number of intermediate stations and the average number of passengers accommodated by the station, including: the distance mileage between the two places corresponding to the set train high-speed passenger railway is the distance mileage between the starting station and the terminal station corresponding to the set train high-speed passenger railway; Among them, obtaining the two-place association data corresponding to the set train high-speed passenger railway, the two-place association data corresponding to the set train high-speed passenger railway include the distance mileage between the two places corresponding to the set train high-speed passenger railway, the number of intermediate stations and the average number of passengers accommodated by the station, including: the average number of passengers accommodated by the station corresponding to the set train high-speed passenger railway is the average value of the maximum number of passengers accommodated by each station traversed by the set train high-speed passenger railway; Among them, obtaining the passenger parameters corresponding to the set passenger high-speed railway train on the preset number of historical days, the passenger parameters corresponding to each historical day are the number of economy seat passengers and the number of business seat passengers carried by the set passenger high-speed railway train on that day, including: the value of the preset number is proportional to the total number of carriages of the set passenger high-speed railway train; Among them, the passenger parameters corresponding to the set passenger high-speed rail train on a preset number of historical days are obtained. The passenger parameters corresponding to each historical day are the number of economy seat passengers and business seat passengers carried by the set passenger high-speed rail train on that day, and also include: the set passenger high-speed rail train only transports one way on a single day.

[0014] Second embodiment

[0015] Figure 2 It is a schematic diagram of the internal structure of a smart city management system based on big data analysis according to the second embodiment of the present invention.

[0016] Figure 2 The smart city management system based on big data analysis can include the following components: An information collection device, used to obtain various attribute information of a set passenger high-speed rail train, wherein the various attribute information of the set passenger high-speed rail train includes the total number of carriages of the set passenger high-speed rail train, the number of seats in the economy carriage, the number of carriages in the economy carriage, the number of seats in the business carriage, the number of carriages in the business vehicle, and the total length of the carriages; A data capture device, used to obtain the two-place association data corresponding to the set passenger high-speed rail train, wherein the two-place association data corresponding to the set passenger high-speed rail train includes the distance mileage between the two places corresponding to the set passenger high-speed rail train, the number of intermediate stations, and the average number of passengers accommodated by the stations; A history detection device is used to obtain the passenger parameters corresponding to the set passenger high-speed railway train on a preset number of historical days, wherein the passenger parameters corresponding to each historical day are the number of economy seat passengers and the number of business seat passengers carried by the set passenger high-speed railway train on that day; A big data analysis node is connected to the information collection device, the data capture device and the history detection device respectively, and is used to intelligently identify the number of economy seat passengers and business seat passengers carried by the set passenger high-speed rail that did not depart on the day based on various attribute information of the set passenger high-speed rail, the two-place association data corresponding to the set passenger high-speed rail, and various passenger parameters corresponding to the set passenger high-speed rail on a preset number of historical days using a BP neural network model, wherein the number of passengers on each historical day is positively correlated with the mileage between the two places corresponding to the set passenger high-speed rail, and the nearest day among the historical days is adjacent to the day; A power detection mechanism, comprising a plurality of power detection units, for respectively detecting the current input power of the information acquisition device, the data capture device, the history detection device and the big data analysis node; The power detection mechanism includes a plurality of power detection units, and is used to respectively detect the current input power of the information acquisition device, the data capture device, the history detection device, and the big data analysis node, including: calculating the current input power of each of the information acquisition device, the data capture device, the history detection device, and the big data analysis node by respectively measuring the real-time power supply voltage and the real-time power supply current for the information acquisition device, the data capture device, the history detection device, and the big data analysis node; The calculation of the respective current input powers by measuring the real-time power supply voltage and the real-time power supply current for the information collection device, the data capture device, the history detection device and the big data analysis node respectively includes: for any device of the information collection device, the data capture device, the history detection device and the big data analysis node, its current input power is the product of its real-time power supply voltage and its real-time power supply current; Wherein, the power detection mechanism includes a plurality of power detection units for respectively detecting the current input power of the information acquisition device, the data capture device, the history detection device and the big data analysis node, and further includes: the plurality of power detection units respectively used for the information acquisition device, the data capture device, the history detection device and the big data analysis node have the same power measurement upper limit threshold and power measurement lower limit threshold; And wherein, the power detection mechanism includes multiple power detection units, which are used to respectively detect the current input power of the information collection device, the data capture device, the history detection device and the big data analysis node, and also includes: the internal structures of the multiple power detection units respectively used for the information collection device, the data capture device, the history detection device and the big data analysis node are the same.

[0017] Third embodiment

[0018] Figure 3 It is a schematic diagram of the internal structure of a smart city management system based on big data analysis according to the third embodiment of the present invention.

[0019] Figure 3 The smart city management system based on big data analysis can include the following components: An information collection device, used to obtain various attribute information of a set passenger high-speed rail train, wherein the various attribute information of the set passenger high-speed rail train includes the total number of carriages of the set passenger high-speed rail train, the number of seats in the economy carriage, the number of carriages in the economy carriage, the number of seats in the business carriage, the number of carriages in the business vehicle, and the total length of the carriages; A data capture device, used to obtain the two-place association data corresponding to the set passenger high-speed rail train, wherein the two-place association data corresponding to the set passenger high-speed rail train includes the distance mileage between the two places corresponding to the set passenger high-speed rail train, the number of intermediate stations, and the average number of passengers accommodated by the stations; A history detection device is used to obtain the passenger parameters corresponding to the set passenger high-speed railway train on a preset number of historical days, wherein the passenger parameters corresponding to each historical day are the number of economy seat passengers and the number of business seat passengers carried by the set passenger high-speed railway train on that day; A big data analysis node is connected to the information collection device, the data capture device and the history detection device respectively, and is used to intelligently identify the number of economy seat passengers and business seat passengers carried by the set passenger high-speed rail that did not depart on the day based on various attribute information of the set passenger high-speed rail, the two-place association data corresponding to the set passenger high-speed rail, and various passenger parameters corresponding to the set passenger high-speed rail on a preset number of historical days using a BP neural network model, wherein the number of passengers on each historical day is positively correlated with the mileage between the two places corresponding to the set passenger high-speed rail, and the nearest day among the historical days is adjacent to the day; An on-site notification mechanism, connected to the multiple power detection units respectively used by the information collection device, the data capture device, the history detection device and the big data analysis node, for performing corresponding power notification operations based on power measurement results of the multiple power detection units respectively used by the information collection device, the data capture device, the history detection device and the big data analysis node; Wherein, the on-site notification mechanism is respectively connected to a plurality of power detection units respectively used for the information collection device, the data capture device, the history detection device and the big data analysis node, and is used to perform corresponding power notification operations based on power measurement results of the plurality of power detection units respectively used for the information collection device, the data capture device, the history detection device and the big data analysis node, including: the on-site notification mechanism includes a static storage unit for storing a power notification threshold; And wherein, the on-site notification mechanism is respectively connected to multiple power detection units respectively used for the information collection device, the data capture device, the history detection device and the big data analysis node, and is used to perform corresponding power notification operations based on the power measurement results of the multiple power detection units respectively used for the information collection device, the data capture device, the history detection device and the big data analysis node. It also includes: the on-site notification mechanism performs corresponding power notification operations for the information collection device, the data capture device, the history detection device and the big data analysis node based on the voice playback mode.

[0020] In addition, in the smart city management system based on big data analysis, a BP neural network model is used to intelligently identify the number of economy seat passengers and business seat passengers carried by the set passenger high-speed rail that has not departed on the day based on various attribute information of the set passenger high-speed rail, the two-place association data corresponding to the set passenger high-speed rail, and the various passenger parameters corresponding to the set passenger high-speed rail on a preset number of historical days. The number of each historical day is positively correlated with the mileage between the two places corresponding to the set passenger high-speed rail, and the most recent day among the historical days is adjacent to the day, including: inputting various attribute information of the set passenger high-speed rail, the two-place association data corresponding to the set passenger high-speed rail, and the various passenger parameters corresponding to the set passenger high-speed rail on a preset number of historical days into the BP neural network model, and executing the BP neural network model to obtain the number of economy seat passengers and business seat passengers carried by the set passenger high-speed rail that has not departed on the day output by the BP neural network model.

[0021] Compared with the prior art, the present invention has at least the following two important innovations: Firstly: construct a BP neural network model for intelligent identification of passenger information of a set passenger high-speed train that has not departed on the same day, wherein the BP neural network model is a BP neural network that has completed multiple trainings and the number of trainings is proportional to the sum of the number of seats in the economy car of the set passenger high-speed train and the number of seats in the business car of the set passenger high-speed train, thereby achieving structural customization of the BP neural network model and improving the reliability and stability of subsequent intelligent identification results; Secondly: a BP neural network model is used to intelligently identify the number of economy seat passengers and business seat passengers on the set passenger high-speed rail train that did not depart on the day based on various attribute information of the set passenger high-speed rail train, the related data between the two places corresponding to the set passenger high-speed rail train, and the passenger parameters corresponding to the set passenger high-speed rail train on a preset number of historical days, wherein the number of each historical day is positively correlated with the mileage between the two places corresponding to the set passenger high-speed rail train, and the nearest day among the historical days is adjacent to the day, thereby providing important reference information for subsequent passenger data management.

[0022] Therefore, the smart city management system based on big data analysis of the present invention is used to address the technical problem in the prior art that the lack of a mature and effective passenger flow prediction mechanism for each set train transport has resulted in a lack of reliable reference information for subsequent passenger high-speed rail data management. By referencing the big data analysis node and adopting the BP neural network model, the number of economy seat passengers and business seat passengers on the set passenger high-speed rail trains that have not departed on the day are intelligently identified, thereby providing valuable reference information for the subsequent passenger high-speed rail data management.

[0023] While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all modifications, equivalent structures and functions.

Claims

1. A smart city management system based on big data analysis, characterized in that: The system comprises: An information collection device, used to obtain various attribute information of a set passenger high-speed rail train, wherein the various attribute information of the set passenger high-speed rail train includes the total number of carriages of the set passenger high-speed rail train, the number of seats in the economy carriage, the number of carriages in the economy carriage, the number of seats in the business carriage, the number of carriages in the business vehicle, and the total length of the carriages; A data capture device, used to obtain the two-place association data corresponding to the set passenger high-speed rail train, wherein the two-place association data corresponding to the set passenger high-speed rail train includes the distance mileage between the two places corresponding to the set passenger high-speed rail train, the number of intermediate stations, and the average number of passengers accommodated by the stations; A history detection device is used to obtain the passenger parameters corresponding to the set passenger high-speed railway train on a preset number of historical days, wherein the passenger parameters corresponding to each historical day are the number of economy seat passengers and the number of business seat passengers carried by the set passenger high-speed railway train on that day; A big data analysis node is connected to the information collection device, the data capture device and the history detection device respectively, and is used to intelligently identify the number of economy seat passengers and business seat passengers carried by the set passenger high-speed rail that did not depart on the day based on various attribute information of the set passenger high-speed rail, the two-place association data corresponding to the set passenger high-speed rail, and various passenger parameters corresponding to the set passenger high-speed rail on a preset number of historical days using a BP neural network model, wherein the number of passengers on each historical day is positively correlated with the mileage between the two places corresponding to the set passenger high-speed rail, and the nearest day among the historical days is adjacent to the day; The BP neural network model is a BP neural network that has completed multiple trainings, and the number of trainings is proportional to the sum of the number of seats in the economy car of the set passenger high-speed rail and the number of seats in the business car of the set passenger high-speed rail; Among them, obtaining the two-place association data corresponding to the set train high-speed passenger railway, the two-place association data corresponding to the set train high-speed passenger railway includes the distance mileage between the two places corresponding to the set train high-speed passenger railway, the number of intermediate stations and the average number of passengers accommodated by the station, including: the distance mileage between the two places corresponding to the set train high-speed passenger railway is the distance mileage between the starting station and the terminal station corresponding to the set train high-speed passenger railway; Among them, obtaining the two-place association data corresponding to the set train high-speed passenger railway, the two-place association data corresponding to the set train high-speed passenger railway include the distance mileage between the two places corresponding to the set train high-speed passenger railway, the number of intermediate stations and the average number of passengers accommodated by the station, including: the average number of passengers accommodated by the station corresponding to the set train high-speed passenger railway is the average value of the maximum number of passengers accommodated by each station traversed by the set train high-speed passenger railway; Among them, the passenger parameters corresponding to the set passenger high-speed railway train on a preset number of historical days are obtained, and the passenger parameters corresponding to each historical day are the number of economy seat passengers and business seat passengers carried by the set passenger high-speed railway train on that day, including: the value of the preset number is proportional to the total number of carriages of the set passenger high-speed railway train.

2. The smart city management system based on big data analysis as claimed in claim 1, characterized in that: Obtain the passenger parameters corresponding to the set passenger high-speed rail train on a preset number of historical days, the passenger parameters corresponding to each historical day are the number of economy seat passengers and business seat passengers carried by the set passenger high-speed rail train on that day, and also include: the set passenger high-speed rail train only transports one way on a single day.

3. The smart city management system based on big data analysis as claimed in claim 2, characterized in that: The system further comprises: A power detection mechanism, comprising a plurality of power detection units, for respectively detecting the current input power of the information acquisition device, the data capture device, the history detection device and the big data analysis node; Among them, the power detection mechanism includes multiple power detection units, which are used to respectively detect the current input power of the information acquisition device, the data capture device, the history detection device and the big data analysis node, including: calculating the current input power of each by measuring the real-time power supply voltage and the real-time power supply current for the information acquisition device, the data capture device, the history detection device and the big data analysis node respectively.

4. The smart city management system based on big data analysis as claimed in claim 3, characterized in that: The calculation of the respective current input powers is completed by measuring the real-time power supply voltage and the real-time power supply current for the information collection device, the data capture device, the history detection device and the big data analysis node respectively, including: for any device of the information collection device, the data capture device, the history detection device and the big data analysis node, its current input power is the product of its real-time power supply voltage and its real-time power supply current.

5. The smart city management system based on big data analysis as claimed in claim 3, characterized in that: The power detection mechanism includes multiple power detection units, which are used to respectively detect the current input power of the information collection device, the data capture device, the history detection device and the big data analysis node. It also includes: the multiple power detection units respectively used for the information collection device, the data capture device, the history detection device and the big data analysis node have the same power measurement upper limit threshold and power measurement lower limit threshold.

6. The smart city management system based on big data analysis as claimed in claim 5, characterized in that: The power detection mechanism includes multiple power detection units, which are used to respectively detect the current input power of the information collection device, the data capture device, the history detection device and the big data analysis node. It also includes: the internal structures of the multiple power detection units respectively used for the information collection device, the data capture device, the history detection device and the big data analysis node are the same.

7. The smart city management system based on big data analysis as described in any one of claims 3 to 6, characterized in that: The system further comprises: The on-site notification mechanism is respectively connected to the multiple power detection units respectively used for the information collection device, the data capture device, the history detection device and the big data analysis node, and is used to perform corresponding power notification operations based on the power measurement results of the multiple power detection units respectively used for the information collection device, the data capture device, the history detection device and the big data analysis node.

8. The smart city management system based on big data analysis as claimed in claim 7, characterized in that: An on-site notification mechanism is respectively connected to a plurality of power detection units respectively used for the information collection device, the data capture device, the history detection device and the big data analysis node, and is used to perform corresponding power notification operations based on power measurement results of the plurality of power detection units respectively used for the information collection device, the data capture device, the history detection device and the big data analysis node, including: the on-site notification mechanism includes a static storage unit for storing a power notification threshold.

9. The smart city management system based on big data analysis as claimed in claim 8, characterized in that: The on-site notification mechanism is respectively connected to multiple power detection units respectively used for the information collection device, the data capture device, the history detection device and the big data analysis node, and is used to perform corresponding power notification operations based on the power measurement results of the multiple power detection units respectively used for the information collection device, the data capture device, the history detection device and the big data analysis node. It also includes: the on-site notification mechanism performs corresponding power notification operations for the information collection device, the data capture device, the history detection device and the big data analysis node based on the voice playback mode.

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