Distributed heterogeneous data matching and storage method of alliance chain for urban public transportation

Abstract

The present invention discloses a distributed heterogeneous data matching and storage method for alliance chain for urban public transportation, the method comprising the following steps: S1, obtaining heterogeneous data of urban public transportation within a preset period and preprocessing; S2, storing the preprocessed heterogeneous data in a corresponding data pool and packaging it on the chain to form a block; S3, based on the heterogeneous data of the formed block, determining a preset threshold according to the operation law of public transportation and the travel behavior of public transportation passengers, and judging the corresponding matching relationship according to the preset threshold; S4, reviewing all unmatched heterogeneous data, and iteratively updating the matching relationship table with a preset period as a cycle; S6, calculating the matching rate based on the matching relationship table in step S4, and adaptively setting the block packing speed of the heterogeneous data stored in the data pool according to the matching rate. The present invention matches and stores heterogeneous data based on the alliance chain, and can provide data basis for the overall planning of urban public transportation.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic data processing and storage, and in particular to a consortium chain distributed heterogeneous data matching and storage method for urban public transportation. Background Art

[0002] With the rapid advancement of urban construction, urban transportation has ushered in great development and prosperity. From a national perspective, the actual application of urban transportation is in its infancy, and the top-level design and practical exploration of related applications are still in their infancy. As an important branch of urban transportation, the urban public transportation system is a random service system that operates on a fixed schedule and fixed routes and is constantly adjusted according to changes in temporal and spatial distribution. Under such complex circumstances, the data generated in the urban public transportation system also needs to be stored for analysis. Therefore, research on it is conducive to solving urban congestion and regional economic development.

[0003] According to the current literature, the research focus of urban public transportation at home and abroad is mainly on passenger travel OD data, analyzing the passengers' boarding and alighting stations, and flexibly adjusting bus route planning, operating hours and mileage according to residents' related activities. At present, most urban buses in China implement the "one-card system" mode, which makes it impossible to record the passengers' alighting data in the data field. Therefore, the prediction of passengers' alighting points has become a hot topic in current research. However, in the actual operation of buses, the replacement of hardware equipment, real-time changes in routes, and drivers' arbitrary changes in driving routes are common, which leads to large prediction errors for subsequent passenger alighting, affecting the overall planning of urban public transportation by management departments. The data generated in the urban public transportation system is stored to achieve the availability of data without visibility, thereby ensuring privacy. How to verify and store the accuracy of the early data sources of urban public transportation is an urgent problem to be solved in promoting the application of bus data informatization. Summary of the invention

[0004] The present invention provides a consortium chain distributed heterogeneous matching data storage method for urban public transportation, which aims to solve the above-mentioned technical problems.

[0005] In order to solve the above technical problems, the present invention provides a distributed heterogeneous data matching and storage method for alliance chains for urban public transportation, comprising the following steps:

[0006] S1. Obtain heterogeneous data of urban public transportation within a preset period and perform preprocessing;

[0007] S2. The pre-processed heterogeneous data is stored in a preset data pool of the corresponding category, and the heterogeneous data stored in each preset data pool is packaged and chained based on the alliance chain member nodes to form a block;

[0008] S3, based on the heterogeneous data of the formed blocks, determining a preset threshold according to the operation rules of public transportation vehicles and the travel behavior of public transportation passengers, and judging the matching relationship between public transportation passengers and public transportation vehicles according to the preset threshold;

[0009] S4. Review all unmatched heterogeneous data, and iteratively update the matching relationship table between public transportation vehicles and public transportation passengers at a preset time period.

[0010] S6. Calculate a matching rate based on the matching relationship table obtained in step S4, and adaptively set a block packing speed for heterogeneous data stored in a preset data pool according to the matching rate.

[0011] Preferably, in step S1, the heterogeneous data of urban public transportation is preprocessed based on data cleaning, data integration, data transformation and data reduction, and the specific steps include:

[0012] S11, deleting missing, redundant, and invalid public transportation vehicle data, and then sorting the public transportation vehicle data in chronological order;

[0013] S12. Delete irrelevant field data in public transportation passengers, and arrange the public transportation passenger data in ascending order according to chronological order.

[0014] Preferably, the step S11 is specifically as follows:

[0015] S111, matching the public transportation vehicle schedule with the station location information, and arranging the public transportation vehicle data in chronological order based on the running direction of the public transportation vehicle and the station location;

[0016] S112. Based on the arrangement information of the public transportation tool data, delete the missing data and redundant data of the public transportation tools and the invalid data that does not conform to the operation time of the public transportation tools, and then sort them in the order of the operation time of the public transportation tools.

[0017] Preferably, the specific implementation of step S2 includes:

[0018] S21, dividing the heterogeneous data of the formed block into GPS data and POS data;

[0019] S22. When the GPS data is received and verified, the GPS data is stored in a corresponding preset GPS data pool, and the heterogeneous data stored in the preset GPS data pool is packaged into a blockchain to form a block based on the alliance chain member nodes;

[0020] S23. When the POS data is received and verified, the POS data is stored in the corresponding preset POS data pool, and based on the alliance chain member nodes, the heterogeneous data stored in the preset POS data pool is packaged into the blockchain to form a block.

[0021] Preferably, the specific implementation of step S3 includes:

[0022] S31. Based on the heterogeneous data of the formed blocks, the earliest operation time point and the latest operation time point of each public transportation vehicle and the earliest travel time point and the latest travel time point of public transportation passengers are selected;

[0023] S32: determining a first preset threshold based on the earliest operating time point of each public transportation vehicle and the earliest travel time point of public transportation passengers, and determining a second preset threshold based on the latest operating time point of each public transportation vehicle and the latest travel time point of public transportation passengers;

[0024] S33. Determine the matching relationship between the public transportation vehicle and the public transportation passenger based on the first preset threshold and the second preset threshold.

[0025] Preferably, in step S33, judging the matching relationship between the public transportation vehicle and the public transportation passenger based on the first preset threshold and the second preset threshold is specifically as follows: when the following two conditions are met at the same time, it can be judged that there is a one-to-one matching correspondence between the public transportation vehicle and the public transportation passenger; otherwise, there is no matching relationship, wherein the first condition is: the absolute value of the difference between the earliest operating time point of the public transportation vehicle and the earliest travel time point of the public transportation passenger is greater than 0 and less than the first preset threshold; the second condition is: the absolute value of the difference between the latest operating time point of the public transportation vehicle and the latest travel time point of the public transportation passenger is greater than 0 and less than the second preset threshold.

[0026] Preferably, the first preset threshold is 10-20 minutes, and the second preset threshold is 30-60 minutes.

[0027] Preferably, the specific implementation method of step S4 includes: marking and counting all unmatched heterogeneous data, and checking and verifying each unmatched public transportation tool data and updating it in real time, and then iteratively updating the matching relationship table between public transportation tools and public transportation passengers at a preset time period.

[0028] Preferably, the specific implementation method of step S5 is: setting a block packing speed adjustment formula related to the matching rate, calculating the matching rate according to the matching relationship table periodically iteratively updated in a preset time period, and adaptively adjusting the block speed of the heterogeneous data stored in the preset data pool according to the set block packing speed adjustment formula and the matching rate.

[0029] Compared with the prior art, the present invention utilizes a preset data pool to store and package heterogeneous data of urban public transportation into blocks, finds the correspondence between different fields of the heterogeneous data forming the blocks and the time points, and integrates the public transportation tool data and the public transportation passenger data using threshold analysis to obtain the corresponding initial matching rate; at the same time, the unmatched public transportation tool data is statistically verified and iteratively updated in real time, and finally a complete matching relationship table is obtained, and the matching rate is calculated using the matching relationship table, and then the block output speed of the heterogeneous data in the preset data pool is adaptively set, so that the heterogeneous data in the preset data pool is available but invisible, thereby providing data basis and privacy security for the overall planning of urban public transportation, which is conducive to promoting the integration of urban public transportation data and improving the utilization rate and security of urban public transportation data. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of a consortium chain distributed heterogeneous data matching and storage method for urban public transportation in the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings.

[0032] like Figure 1 As shown, this embodiment provides a consortium chain distributed heterogeneous data matching and storage method for urban public transportation, and the method includes the following steps:

[0033] S1. Obtain heterogeneous data of urban public transportation within a preset period and perform preprocessing;

[0034] Specifically, in this embodiment, the heterogeneous data of urban public transportation are preprocessed based on data cleaning, data integration, data transformation and data reduction. The data cleaning can solve the problems of data loss, data error, redundant data, data consistency and outdated data; data integration can store multiple data source data sets in one database for analysis; data transformation is to mine the original data using data field attributes and convert it into a standardized data form; data reduction is to compress the data to maintain the integrity of the original data.

[0035] Among them, the specific steps of preprocessing urban public transportation heterogeneous data include:

[0036] S11. Delete missing, redundant, and invalid public transportation vehicle data, and then sort the public transportation vehicle data in chronological order, specifically:

[0037] S111, matching the public transportation vehicle schedule with the station location information, and arranging the public transportation vehicle data in chronological order based on the running direction of the public transportation vehicle and the station location;

[0038] S112, based on the arrangement information of the public transportation tool data, delete the missing data and redundant data of the public transportation tools and the invalid data that does not conform to the operation time of the public transportation tools, and then sort them in the order of the operation time of the public transportation tools;

[0039] S12. Delete irrelevant field data in public transportation passengers, and arrange the public transportation passenger data in ascending order according to chronological order.

[0040] S2. The pre-processed heterogeneous data is stored in a preset data pool of the corresponding category, and the heterogeneous data stored in each preset data pool is packaged and chained based on the alliance chain member nodes to form a block;

[0041] This step specifically includes:

[0042] S21, dividing the heterogeneous data of the formed block into GPS data and POS data;

[0043] S22. When the GPS data is received and verified, the GPS data is stored in a corresponding preset GPS data pool, and the heterogeneous data stored in the preset GPS data pool is packaged into a blockchain to form a first block based on the alliance chain member nodes;

[0044] S23. When the POS data is received and verified, the POS data is stored in the corresponding preset POS data pool, and based on the alliance chain member nodes, the heterogeneous data stored in the preset POS data pool is packaged into the blockchain to form a second block.

[0045] S3. Based on the heterogeneous data of the formed blocks, a preset threshold is determined according to the operation rules of public transportation vehicles and the travel behavior of public transportation passengers, and the matching relationship between public transportation passengers and public transportation vehicles is judged according to the preset threshold, specifically including:

[0046] S31. Based on the heterogeneous data of the formed blocks, the earliest operation time point and the latest operation time point of each public transportation vehicle and the earliest travel time point and the latest travel time point of public transportation passengers are selected;

[0047] S32: determining a first preset threshold based on the earliest operating time point of each public transportation vehicle and the earliest travel time point of public transportation passengers, and determining a second preset threshold based on the latest operating time point of each public transportation vehicle and the latest travel time point of public transportation passengers;

[0048] S33, determining a matching relationship between a public transportation vehicle and a public transportation passenger based on a first preset threshold and a second preset threshold;

[0049] The matching relationship between the public transportation vehicle and the public transportation passenger is determined based on the first preset threshold and the second preset threshold as follows: when the following two conditions are met at the same time, it can be determined that there is a one-to-one matching relationship between the public transportation vehicle and the public transportation passenger; otherwise, there is no matching relationship, wherein the first condition is: the absolute value of the difference between the earliest operating time point of the public transportation vehicle and the earliest travel time point of the public transportation passenger is greater than 0 and less than the first preset threshold; the second condition is: the absolute value of the difference between the latest operating time point of the public transportation vehicle and the latest travel time point of the public transportation passenger is greater than 0 and less than the second preset threshold;

[0050] The first preset threshold interval is 10-20 minutes, and the second preset threshold interval is 30-60 minutes.

[0051] S4. Review the heterogeneous data corresponding to all unmatched public transportation vehicles, and iteratively update the matching relationship table between public transportation vehicles and public transportation passengers at a preset time period;

[0052] This step specifically includes: marking and counting all unmatched public transportation tool data, and checking and verifying each unmatched public transportation tool data and updating it in real time, and then iteratively updating the matching relationship table between public transportation tools and public transportation passengers based on a preset period of time.

[0053] In this embodiment, first, the corresponding relationship between different fields of urban public transportation data and time points is found, and the public transportation tool data and public transportation passenger data are integrated by using threshold analysis to obtain the corresponding initial matching rate; then, the unmatched public transportation tool data are statistically verified and iteratively updated in real time, and finally a complete matching relationship table is obtained, which can provide a data basis for the overall planning of urban public transportation, which is conducive to promoting the integration of urban public transportation data and improving the utilization rate of urban public transportation data.

[0054] S6. Calculate the matching rate based on the matching relationship table obtained in step S4, and adaptively set the block packing speed of the heterogeneous data stored in the preset data pool according to the matching rate. The specific steps are: set a block packing speed adjustment formula related to the matching rate, calculate the matching rate according to the matching relationship table periodically iteratively updated in a preset time period, and adaptively adjust the block packing speed of the heterogeneous data stored in the preset data pool according to the set block packing speed adjustment formula and the matching rate.

[0055] The block packing speed adjustment formula is expressed as: x*y=k, where x is the block packing speed, y is the matching rate, and k is a preset constant. It can be seen from the block packing speed adjustment formula that when the matching rate y is lower, the heterogeneous data packing speed x in the preset data pool is faster; when the matching rate y is higher, the heterogeneous data packing speed x in the preset data pool is slower.

[0056] In order to better understand the working principle and technical effect of the present invention, the following takes Changsha urban public transportation as an example, relying on the Changsha Transportation Comprehensive Operations Coordination Center (TOCC) system, to illustrate the problem that Changsha’s current public transportation GPS data does not match the data fields of the passenger card POS machine.

[0057] The preset period is one month. The data comes from the Changsha Transportation Bureau. The data is the bus IC card swiping data and bus arrival and departure data in Changsha in November 2019 in the TOCC system, as shown in Table 1 and Table 2. Table 1 is the POS IC card data field, and Table 2 is the bus arrival and departure data, as shown in Table 1 and Table 2.

[0058] Table 1 POS IC card data fields

[0059]

[0060] Table 2 Bus arrival and departure data

[0061]

[0062]

[0063] It can be seen from Table 1 and Table 2 that there are no corresponding fields in Changsha's arrival and departure data table that correspond to the fields in the bus POS IC card table. This results in the inability to obtain passengers' travel OD and the operation status of buses on different routes is completely unknown.

[0064] In order to solve the above problem, the above data is processed using the matching method of the present invention.

[0065] The first step is to pre-process the acquired data, use SQL statements (Structured Query Language) to batch delete missing, redundant and invalid data, and sort them by order by (a keyword in SQL statements used to sort query results) to obtain standardized data, as follows:

[0066] For GPS data, we first need to match the data of a single shift with the station location information, and arrange the stations in chronological order according to the order of the bus running direction, and then delete the invalid data that does not conform to the bus operation time. As shown in Table 3, the GPS data of each GPS (Global Positioning System) device number is screened and cleaned, and then arranged in chronological order according to the station number;

[0067] Table 3 GPS data preprocessing

[0068]

[0069] For the POS (point of sale) IC card (integrated circuit card, which is a plastic card with the same size as an ordinary credit card and one or more integrated circuits embedded in it) data, first delete the data of irrelevant fields, such as: the passenger's card swipe amount and the balance on the card; then arrange each POS machine number in ascending order according to the date and time of the card swipe, as shown in Table 4.

[0070] Table 4 POS machine data preprocessing

[0071]

[0072]

[0073] In the second step, the pre-processed heterogeneous data is stored in the preset data pool of the corresponding category, and the heterogeneous data stored in each preset data pool is packaged and chained based on the alliance chain member nodes to form a block. Design the data structure and corresponding data attributes of GPS data: GPS device number, line number, arrival time, point number, departure time. Design the data structure and corresponding data attributes of POS data: consumption date, consumption time and POS number. Store the verified GPS data and POS data in the GPS data pool and POS data pool respectively, and package the heterogeneous data stored in the preset GPS data pool and the preset POS data pool into blockchain to form blocks for persistent storage.

[0074] The third step is to determine the preset threshold based on the heterogeneous data of the formed blocks and in accordance with the operation rules of public transportation vehicles and the travel behavior of public transportation passengers, and judge the matching relationship between public transportation passengers and public transportation vehicles according to the preset threshold. Specifically:

[0075] S31. Based on the heterogeneous data of the formed blocks, the earliest operation time point and the latest operation time point of each public transportation vehicle and the earliest travel time point and the latest travel time point of public transportation passengers are selected;

[0076] S32: determining a first preset threshold based on the earliest operating time point of each public transportation vehicle and the earliest travel time point of public transportation passengers, and determining a second preset threshold based on the latest operating time point of each public transportation vehicle and the latest travel time point of public transportation passengers;

[0077] S33, determining a matching relationship between a public transportation vehicle and a public transportation passenger based on a first preset threshold and a second preset threshold;

[0078] The matching relationship between the public transportation vehicle and the public transportation passenger is determined based on the first preset threshold and the second preset threshold as follows: when the following two conditions are met at the same time, it can be determined that there is a one-to-one matching relationship between the public transportation vehicle and the public transportation passenger; otherwise, there is no matching relationship, wherein the first condition is: the absolute value of the difference between the earliest operating time point of the public transportation vehicle and the earliest travel time point of the public transportation passenger is greater than 0 and less than the first preset threshold; the second condition is: the absolute value of the difference between the latest operating time point of the public transportation vehicle and the latest travel time point of the public transportation passenger is greater than 0 and less than the second preset threshold;

[0079] The matching algorithm between public transportation vehicles and public transportation passengers based on the first preset threshold and the second preset threshold is as follows:

[0080]

[0081] In the matching algorithm, the first preset threshold is set to 15 minutes, and the second preset threshold is set to 60 minutes. The preset thresholds can effectively solve the time deviation between the arrival time recorded by the GPS on the map and the actual arrival time of the bus.

[0082] Through the above matching algorithm, the corresponding relationship between the bus number and the POS machine number can be obtained, as shown in Table 5. Table 5 shows the matching result between the bus number 102100 and its associated GPS number:

[0083] Table 5 Matching results of bus number 102100 and its associated GPS number

[0084]

[0085]

[0086] From Table 5, we can see that the bus number 102100 and GPS number 22000818 have been online for 30 days, and the number of successful matches is up to 26 times, with a matching rate of 86%. It can be determined that there is a one-to-one correspondence between the bus number 102100 and the GPS number 22000818. By associating the corresponding relationship table between the bus number and the POS machine number, we can find out the POS machine number corresponding to the bus number 102100, as shown in Table 6.

[0087] Table 6 POS machine number comparison table

[0088]

[0089] By preprocessing and matching the above-mentioned city bus data, a total of 4,385 buses were matched, and a total of 2,804 buses were not matched, and the matching rate was significantly lower;

[0090] Step 4: Check and verify all unmatched bus data one by one. The main reasons for failure to accurately match include:

[0091] (1) There is redundancy loss in bus data, that is, some GPS data do not match the corresponding POS data, as shown in Table 7. Table 7 shows some unmatched data. The reason is that there are too few samples of bus GPS data and POS data, and the number of days online is less than 10 days;

[0092] Table 7 Unmatched data

[0093]

[0094] (2) The bus GPS data and POS equipment were replaced within a preset period (i.e., one month) but the data was not updated in real time, resulting in a single bus GPS data matching multiple POS numbers, as shown in Table 8. Table 8 shows the situation where the GPS data of one bus matched multiple POS numbers. The reason is: the hardware equipment is damaged, etc., and the equipment number in the system is not updated in time according to the actual situation, resulting in the passenger's card swiping data being recorded in different POS equipment, causing errors;

[0095] Table 8: Matching of GPS data of a single bus with multiple POS numbers

[0096]

[0097]

[0098] (3) Some bus drivers fail to operate according to the prescribed routes, there are temporary adjustments to bus routes within a single month, or the personal behavior of bus drivers;

[0099] Based on the reasons for the above investigation and verification, the unmatched bus data was updated in real time, and the matching operations of the first and second steps were repeated in the next preset period (December 2019). The bus matching rate of the next preset period (December 2019) was compared with the bus matching rate of the previous preset period (November 2019). The bus matching rate increased from the initial 61% to 75%;

[0100] Step 6: Continue to iterate according to this method until March 2020, when the matching rate of Changsha’s urban buses reached 86%;

[0101] The seventh step is to calculate the matching rate according to the matching relationship table obtained by iterative updating, and adjust the block speed of the heterogeneous data stored in the preset data pool according to the matching rate. Specifically, a block packing speed adjustment formula is set, and the formula is expressed as: x*y=k, where x is the block packing speed, y is the matching rate, and k is a preset constant. The block speed of the heterogeneous data stored in the preset data pool is adaptively adjusted based on the calculated matching rate y.

[0102] In summary, the alliance chain distributed heterogeneous data matching and storage method for urban public transportation provided by the present invention can effectively integrate the matching rate between public transportation tool data and public transportation passenger data, and at the same time use the matching relationship table to calculate the matching rate, and then adaptively set the block speed of heterogeneous data in the preset data pool, so that the heterogeneous data in the preset data pool can be available but not visible, thereby providing data basis and privacy security for the overall planning of urban public transportation, which is conducive to promoting the integration of urban public transportation data and improving the utilization rate and security of urban public transportation data.

[0103] The above is a detailed introduction to the alliance chain distributed heterogeneous data matching and storage method for urban public transportation provided by the present invention. This article uses specific examples to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the core idea of ​​the present invention. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, the present invention can also be improved and modified, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A distributed heterogeneous data matching and storage method for alliance chains for urban public transportation, characterized in that: The method comprises the following steps: S1. Obtain heterogeneous data of urban public transportation within a preset period and perform preprocessing; S2. The pre-processed heterogeneous data is stored in a preset data pool of the corresponding category, and the heterogeneous data stored in each preset data pool is packaged and chained based on the alliance chain member nodes to form a block; S3. Based on the heterogeneous data of the formed blocks, a preset threshold is determined according to the operation rules of public transportation vehicles and the travel behavior of public transportation passengers, and the matching relationship between the GPS data on the public transportation vehicle and the POS card swiping data is determined according to the preset threshold, specifically including: S31, based on the heterogeneous data of the formed blocks, selecting the earliest arrival time point and the latest arrival time point of the GPS device of each public transportation vehicle, as well as the earliest card swiping time point and the latest card swiping time point of the POS machine; S32, determining a first preset threshold based on the earliest arrival time of the GPS device of each public transportation means and the earliest card swiping time of the POS machine, and determining a second preset threshold based on the latest arrival time of the GPS device of each public transportation means and the latest card swiping time of the POS machine; S33, based on the first preset threshold and the second preset threshold, determine the matching relationship between the GPS data on the public transportation and the POS card swiping data, specifically: when the following two conditions are met at the same time, it can be determined that there is a one-to-one matching relationship between the GPS data on the public transportation and the POS card swiping data; otherwise, there is no matching relationship, wherein the first condition is: the absolute value of the difference between the earliest arrival time point of the GPS device of the public transportation and the earliest card swiping time point of the POS machine is greater than 0 and less than the first preset threshold; the second condition is: the absolute value of the difference between the latest arrival time point of the GPS device of the public transportation and the latest card swiping time point of the POS machine is greater than 0 and less than the second preset threshold; S4. Review the heterogeneous data corresponding to all unmatched public transportation vehicles, and iteratively update the matching relationship table between the GPS data on the public transportation vehicles and the POS card swiping data at a preset period; S5. Calculate the matching rate based on the matching relationship table obtained in step S4, and adaptively set the block packing speed of the heterogeneous data stored in the preset data pool according to the matching rate, wherein the matching rate is related to the number of matched public transportation vehicles and the number of unmatched public transportation vehicles, and the matched public transportation vehicles refer to the public transportation vehicles whose GPS data and POS card swiping data have a one-to-one matching correspondence.

2. The alliance chain distributed heterogeneous data matching and storage method for urban public transportation according to claim 1 is characterized in that: In step S1, the heterogeneous data of urban public transportation is preprocessed based on data cleaning, data integration, data transformation and data reduction, and the specific steps include: S11, deleting missing, redundant, and invalid GPS data, and then sorting the GPS data in chronological order; S12, deleting irrelevant field data in the POS card swiping data, and arranging the POS card swiping data in ascending order according to the chronological order.

3. The alliance chain distributed heterogeneous data matching and storage method for urban public transportation as claimed in claim 2 is characterized in that: The step S11 is specifically as follows: S111, matching the public transportation vehicle schedule with the station location information, and arranging the GPS data in chronological order based on the running direction of the public transportation vehicle and the station location; S112. Based on the arrangement information of the GPS data, delete the missing data and redundant data in the GPS data as well as the invalid data that does not conform to the operation time of the public transportation tools, and then sort the data in the order of the operation time of the public transportation tools.

4. The alliance chain distributed heterogeneous data matching and storage method for urban public transportation as claimed in claim 3 is characterized in that: The specific implementation of step S2 includes: S21, dividing the heterogeneous data of the formed block into GPS data and POS data; S22. When the GPS data is received and verified, the GPS data is stored in a corresponding preset GPS data pool, and the heterogeneous data stored in the preset GPS data pool is packaged into a blockchain to form a block based on the alliance chain member nodes; S23. When the POS data is received and verified, the POS data is stored in the corresponding preset POS data pool, and based on the alliance chain member nodes, the heterogeneous data stored in the preset POS data pool is packaged into the blockchain to form a block.

5. The alliance chain distributed heterogeneous data matching and storage method for urban public transportation as claimed in claim 4 is characterized in that: The first preset threshold is 10-20 minutes, and the second preset threshold is 30-60 minutes.

6. The alliance chain distributed heterogeneous data matching and storage method for urban public transportation according to claim 5 is characterized in that: The specific implementation method of step S4 includes: marking and counting all unmatched GPS data, and checking and verifying each unmatched GPS data and updating it in real time, and then iteratively updating the matching relationship table between the GPS data on public transportation and the POS card swiping data at a preset time period.

7. The alliance chain distributed heterogeneous data matching and storage method for urban public transportation according to claim 6 is characterized in that: The specific implementation method of step S5 is: setting a block packing speed adjustment formula related to the matching rate, calculating the matching rate according to the matching relationship table periodically iteratively updated in a preset time period, and adaptively adjusting the block speed of the heterogeneous data stored in the preset data pool according to the set block packing speed adjustment formula and the matching rate.

Images