Decentralized Consortium Chain Storage and Standardization Method for TOCC Traffic Data Elements

Abstract

The present invention discloses a decentralized consortium blockchain storage and standardization method for TOCC traffic data elements. The method comprises the following steps: S1, classifying traffic data elements based on a preset traffic data element coding rule to obtain a corresponding classification number structure of the traffic data elements; S2, extracting traffic data elements in TOCC based on a preset extraction method; S3, the consortium blockchain member nodes perform block packaging and chain-up of the extracted traffic data elements, and implement consistent storage of the data according to a consensus algorithm; S4, standardize the extracted traffic data elements, and construct a standardized framework table based on the corresponding classification number structure of the traffic data elements. By standardizing traffic data elements, the present invention can guarantee the data interaction quality of the Transportation Bureau, is conducive to promoting the integration of traffic data elements, stores the integrated traffic data elements in a decentralized manner, not only improves the utilization rate of traffic data, but also stores the traffic data elements safely and effectively, and thus better serves TOCC.

Description

Technical Field

[0001] The present invention relates to the technical fields of traffic data element processing and blockchain decentralized storage, and particularly relates to a decentralized consortium chain storage and standardization method for TOCC traffic data elements. Background Art

[0002] The continuous development of information technology has led to an exponential growth in the total amount of data, and the status of data as a market factor has become increasingly prominent. According to Eric Schmidt, the chairman of the board of directors of Google, the human society created a total of 5EB (10^18 bytes) of data through written records in 2003, and it only took 10 minutes to create 5EB of data in 2013. With the rapid growth of data types and data sizes, the importance of data has undergone a fundamental change. The in-depth mining and effective utilization of massive data will promote the improvement of production efficiency in different industries. Once data is processed from static symbols into useful information, it can become knowledge, which will release huge economic value and social value.

[0003] The importance of data standardization is self-evident. However, in practical applications, the situation of emphasizing construction while neglecting implementation is not uncommon. The main reason is that the benefits of data standardization are long-term and systematic. For example, during the project construction period, the project construction party can achieve data conversion through hard coding to ensure the consistency of system data access. However, as the system construction is completed and enters the operation and maintenance period, due to reasons such as the interconnection between systems, the increase in various data types at the data level, and the update and iteration of database tables, the phenomenon of non-standard data occurs frequently. Luna (Luna-Reyesa L F, Chunb S A. Open Government and Public Participation: Issues and Challenges in Creating Public Value [J]. Information Policy, 2012, 17(1): 77-81.) et al. pointed out that the utilization rate of a large amount of "traffic" - type raw data is not high. Traffic data can only realize its use value through open utilization and application. Since the 1960s, foreign countries have started to conduct research on data elements, and the International Organization for Standardization (ISO) has successively formulated a number of international standards related to data elements. Chen Xiaojing studied the general principles for the compilation of the basic data element set of highway information in Shaanxi Province. On the basis of summarizing the existing data element classification methods, a two-dimensional classification method that adds a management dimension is adopted to classify the basic data element set of highway information in Shaanxi Province, and a two-dimensional classification mode view of data elements is given. Wen (WEN Bi-Long, ZHANG Li. Defining semantics for data element with semantic tree [C]. / / IEEE. 2008 International Symposium on Information Science and Engineering. Shanghai: IEEE, 2008: 524-527.) et al. used the method of semantic tree to intuitively and formally establish the connection between data element concepts, including attributes, object classes, and representations. Under the constraints of the semantic tree, the automatic establishment of application data elements is realized, and a data element semantic similarity algorithm based on the semantic tree is studied. Equally important is to use blockchain technology to tamper-proof and store the generated data elements in a trustworthy manner, so as to achieve the security, effectiveness, and invisible availability of data elements.

[0004] The TOCC (Transportation Operations Coordination Center) is the intelligent brain of a city's transportation, gathering a vast amount of data in the transportation field. The application of transportation data not only provides a reference for optimizing the transportation structure and improving transportation efficiency but also offers auxiliary analysis for transportation industry management and decision-making, possessing high economic value and social benefits. Currently, due to uneven regional development, there are some special situations in different regions. At the same time, the construction of transportation information systems at all levels varies in time sequence and standards. Therefore, transportation data is numerous, scattered, and significantly heterogeneous, and the vast amount of transportation data has not been utilized more effectively.

[0005] In view of this, researching a decentralized consortium blockchain storage and standardization method for TOCC transportation data elements is a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0006] The present invention provides a decentralized consortium blockchain storage and standardization method for TOCC transportation data elements, aiming to solve the above technical problems.

[0007] To solve the above technical problems, a decentralized consortium blockchain storage and standardization method for TOCC transportation data elements provided by the present invention includes the following steps:

[0008] S1. Classify transportation data elements based on a preset transportation data element coding rule to obtain the corresponding classification number structure of transportation data elements;

[0009] S2. Extract transportation data elements in the TOCC based on a preset extraction method;

[0010] S3. Use the consortium blockchain member nodes to perform block packaging and chain-up of the extracted transportation data elements, and achieve the consistent storage of transportation data elements according to the consensus algorithm;

[0011] S4. Standardize the stored transportation data elements, and then construct a transportation data element standardization framework table.

[0012] As a further technical solution of the above-mentioned decentralized consortium blockchain storage and standardization method for TOCC transportation data elements, the corresponding classification number structure of the transportation data elements includes a business domain code, a first-level classification sequence number, a second-level classification sequence number, a third-level classification sequence number, and a sequence number of the classification sequence number arranged in sequence from left to right.

[0013] As a further technical solution of the above-mentioned decentralized consortium blockchain storage and standardization method for TOCC transportation data elements, the business domains of the transportation data elements include four categories: highway, waterway, urban passenger transport, and urban freight transport.

[0014] As a further technical solution of the above-mentioned decentralized consortium chain storage and standardization method for TOCC traffic data elements, the specific implementation manner of step S2 includes:

[0015] S21. Extract traffic data elements based on the bottom-up business integration method and the direct extraction method, specifically including:

[0016] S211. Analyze the traffic data elements in TOCC and summarize and organize them;

[0017] S212. Use the depth-first algorithm to analyze and sort out the business process data in TOCC, then perform information modeling on the traffic data elements, and extract the traffic data elements in TOCC based on the information modeling of the traffic data elements;

[0018] S22. Extract the data elements in the user view of TOCC based on the user view extraction method, and continuously update the data elements in the user view using the breadth-first algorithm, and then extract complete and non-redundant traffic data elements.

[0019] As a further technical solution of the above-mentioned decentralized consortium chain storage and standardization method for TOCC traffic data elements, the specific implementation manner of step S3 includes: using the consortium chain member nodes to integrate the collected, verified and transmitted traffic data elements into the block for packaging and uploading to the chain, and then based on the PBFT consensus algorithm, realizing the consistent storage of traffic data elements through five stages: request, serial number assignment, mutual interaction, serial number confirmation and response;

[0020] As a further technical solution of the above-mentioned decentralized consortium chain storage and standardization method for TOCC traffic data elements, the specific implementation manner of step S4 includes: analyzing the attributes of the stored traffic data elements and normalizing the attributes of the traffic data elements, and constructing a standardized framework table based on the corresponding classification number structure of the traffic data elements.

[0021] As a further technical solution of the above-mentioned decentralized consortium chain storage and standardization method for TOCC traffic data elements, the attributes of the traffic data elements include data element name, data element type, data format and classification number.

[0022] The present invention first classifies the traffic data elements to obtain the corresponding classification number structure, and extracts the traffic data elements based on a preset method; then standardizes the traffic data elements based on the attributes of the traffic data elements and constructs a traffic data element standardization framework table. The present invention can ensure the data interaction quality of the Transportation Management Bureau through the standardization of traffic data elements, is conducive to promoting the integration of traffic data elements, improves the utilization rate of traffic data, and thus better serves TOCC. Description of the Drawings

[0023] Figure 1 is a flowchart of a decentralized consortium chain storage and standardization method for TOCC traffic data elements in the present invention,

[0024] Figure 2 is a schematic diagram of the classification number structure of traffic data elements in the present invention,

[0025] Figure 3 is a modeling diagram of Changsha urban bus card swiping data information in an embodiment of the present invention,

[0026] Figure 4 is a schematic diagram of Changsha IC card swiping data and bus location spatio-temporal data in an embodiment of the present invention,

[0027] Figure 5 represents a flowchart of the depth-first search algorithm in the present invention,

[0028] Figure 6 represents a flowchart of the breadth-first search algorithm in the present invention. Detailed Embodiments

[0029] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0030] A data element is a data unit described by a set of attributes for its definition, identification, representation, and permitted values. It is considered the smallest indivisible data unit in a specific semantic environment. The data element specification is a method or theory for standardizing data in various industries, using this theory to uniformly define names, types, values, and classifications for industry data. A data standard refers to the standardized definition and interpretation of data within a certain range, enabling relevant personnel to form a consistent understanding and perception of data information.

[0031] Standardization is the fundamental work of informatization construction, and the standard system is the core of standardization work. Through the construction of the standard system, each business link is organically combined to play the guiding role of standardization and ensure the realization of technical coordination and overall effectiveness. The construction of the standardization system should follow the following principles:

[0032] (1) Simplification principle. It is a standardization form that reduces the number of object types within a certain range to meet general needs within a given time.

[0033] (2) Unification principle. It is a standardization form that merges two or more forms of expression of the same kind of things into one or limits them within a certain range. The purpose of unification is to eliminate the chaos caused by unnecessary diversification and establish a common order for the normal use of system users.

[0034] (3) Coordination principle. Everything is in extensive connection and has relevance. As a functional unit in the system, it is both restricted and affects the overall function. It must be coordinated with other functional units to find consistency at the connection points to optimize the overall function.

[0035] (4) Optimization principle. Under certain restrictive conditions, select, design, or adjust the components and their relationships of the standard system to achieve the most ideal effect.

[0036] As Figure 1 shown in the figure, this embodiment provides a standardization method for TOCC traffic data elements, and the method includes the following steps:

[0037] S1. Classify traffic data elements based on a preset traffic data element coding rule to obtain the corresponding classification number structure of the traffic data elements;

[0038] In this step, the preset traffic data element coding rule refers to the coding rule in the standard document "Basic Traffic Data Elements - Part 1: General Rules" (JT / T 672).

[0039] The corresponding classification number structure of the classified traffic data elements includes, from left to right, the business domain code, the first-level classification sequence number, the second-level classification sequence number, the third-level classification sequence number, and the sequence number of the classification sequence number; the business domains of the traffic data elements include four categories: highway, waterway, urban passenger transport, and urban freight transport.

[0040] S2. Extract traffic data elements in TOCC based on a preset extraction method;

[0041] In this step, the extraction method of traffic data elements is as follows:

[0042] S21. Extract traffic data elements based on the bottom-up business integration method and the direct extraction method, specifically including:

[0043] S211. Analyze, summarize, and organize the traffic data elements in TOCC;

[0044] S212. Analyze and sort out the business process data in TOCC using the depth-first algorithm, then perform information modeling on the traffic data elements, and extract the traffic data elements in TOCC based on the information modeling of the traffic data elements.

[0045] In this step, since the connections between various business processes in the business process data are very close and do not exist in isolation, belonging to a network structure, different from a chain structure and a tree structure, it is impossible to find a first node or a root node, nor can one start from a certain point to achieve the purpose of obtaining traffic data elements. Therefore, the depth-first algorithm is used to extract traffic data elements of the business process type, as Figure 5 shown. The specific steps for the depth-first algorithm to extract traffic data elements of the business process type are as follows:

[0046] Step 1: Construct a set D of business process data elements. Based on the data element coding rule R, divide the set D into different subsets Di, where i ∈ [1, N]. Define a function Ai on the set D. The function Ai represents that there is a business process data element d ∈ D, which is expressed by the formula:

[0047]

[0048] Define a function W on the set D. For the business process data element d ∈ D, W(d) represents the weight of the business process data element d under the rule R. W(d) is expressed by the formula:

[0049]

[0050] In the formula, w i represents the weight of each subset Di;

[0051] Define a function S on the set D. For the business process data element d ∈ D, S(d) represents the multiplicity of the business process data element d under all subsets, which is expressed by the formula:

[0052]

[0053] Construct a relationship set P for recording all relationships of the business process data element d under the rule R;

[0054] Step 2: Now assume there is a business B1, and the corresponding data element set is D1, which contains a total of N1 data elements. D1 = {d1,1, d1,2, d1,3, ……, d1,N1}, denoted as d1, d ∈ D1, dj ∈ D1, j = 1, N1;

[0055] Step 3: Initialize the set D, let i = 1, d = 1, select the set D1 corresponding to the business B1, and let M = N1. Create M new elements d1, d2, d3, ……, dM in the set D, denoted as dm, m = 1, M. Then assign the elements in the set D1 to the newly created elements dm in sequence, dm = d1,m. Then for any dm ∈ D, S(dm) = 1, P(dm) = {D1}. The set of cut subsets of the set D is denoted as D′, and the number of cut subset sets is K. At this time, K = 1, then the set D′ = {D1};

[0056] Step 4: Determine whether d <= M is true. If it is, proceed to Step 5; if not, execute i = i + 1 and proceed to Step 7.

[0057] Step 5: Explore other services related to dd. Determine whether there exists a service Bx with its corresponding set Dx. Determine whether Dx ⊆ dd is true. If it is, proceed to Step 6; if not, execute d = d + 1 and proceed to Step 4.

[0058] Step 6: Determine whether Dx ⊆ D' is true and whether x <= K is true. If both are true, proceed to Step 7; if not, then execute K = K + 1, x = K, D' = D' ∪ {Dx}, and proceed to Step 7.

[0059] Step 7: Update the attributes S and P of the data element dd, S(dd) = S(dd) + 1, P(dd) = P(dd) ∪ {Dx}, and return to Step 8.

[0060] Step 8: Determine whether i <= K is true. If it is, proceed to Step 9; if not, proceed to Step 10.

[0061] Step 9: Take the data element set Di, which contains Ni data elements. Compare each data element in this set with the data elements in D. If there are no data elements in D that do not exist in Di, then i = i + 1 and return to Step 8; if there are y data elements in D that do not exist in Di, then create y new elements dM+1, dM+2, dM+3,..., dM+y in D. For dm ∈ D, where m ∈ {M + 1, M + y}, dm.DS = 1, dm.DD' = {Di}. Let M = M + y and proceed to Step 4.

[0062] Step 10: Output the data element set D.

[0063] S22. Extract data elements in the user view of TOCC using the user view extraction method, and continuously update the data elements in the user view using the breadth - first algorithm, thereby extracting comprehensive and non - overlapping traffic data elements.

[0064] In this step, since there are many duplicate data elements in the data elements of the user view extracted by the user view extraction method, in order to obtain comprehensive and non - overlapping data elements, the breadth - first algorithm is used to continuously update the duplicate data elements, thereby extracting comprehensive and non - overlapping traffic data elements. As Figure 6 shown, the comprehensive and non - overlapping traffic data elements extracted by the breadth - first algorithm specifically include:

[0065] Step 1': First, construct K data element sets D1, D2, D3, ……, DK according to the data element coding rule R, denoted as Di, where i ∈ [1, K]. Each data element set Di contains Ni elements, where Di = {di,1, di,2, di,3, ……, di,Ni}, denoted as di,j, where di,j ∈ Di and j ∈ [1, Ni];

[0066] Step 2': Initialize the set D, let i = 1, select the set Di, and let M = Ni. Create M elements d1, d2, d3, ……, dM in the set D, denoted as dm, where m ∈ [1, M]. Then, assign the elements in the set Di to the newly created elements dm in sequence, dm = di,m, where m ∈ [1, Ni]. Then, for any dmi ∈ D, S(dm) = 1, P(dm) = {D1}; the set of cut subsets of the set D is denoted as D′, and the number of cut subsets is K. At this time, K = 1, so the set D′ = {D1};

[0067] Step 3': Judge whether i <= K is true. If it is true, enter Step 4'; if not, enter Step 9';

[0068] Step 4': Select the set Di and let d = 1;

[0069] Step 5': Judge whether d <= Ni is true. If it is true, enter Step 6'; if not, execute i = i + 1 and return to Step 3';

[0070] Step 6': Take the element di,d in the set Di and compare it with each element in the set D one by one to judge whether there exists m, where m ∈ [1, M], such that dm has the same data element name as di,d. If it is true, enter Step 7'; if not, enter Step 8';

[0071] Step 7': Update the attributes S and P of the data element dm, S(dm) = S(dm) + 1, P(dm) = P(dm) ∪ {Di}, d = d + 1, and return to Step 5';

[0072] Step 8': Create a new data element, let M = M + 1, create an element dM in the set D, dM = di,d, S(dM) = 1, P(dM) = {Di}, d = d + 1, and return to Step 5';

[0073] Step 9': Output the data element set D;

[0074] S3. Use the member nodes of the consortium chain to integrate the collected, verified, and transmitted traffic data elements into a block for packaging and uploading to the chain. Then, based on the PBFT consensus algorithm, achieve the consistent storage of the traffic data elements through five stages: request, serial number allocation, mutual interaction, serial number confirmation, and response;

[0075] In this step, a request message is sent to the master node during the request phase. When the master node receives the request, it assigns a sequence number to the request, and then broadcasts the sequence number message. The slave nodes receive the message and broadcast the message to other nodes for mutual interaction. After receiving 2f + 1 messages, the slave nodes verify the request and the order, broadcast the confirmation message, and then enter the response phase. Thus, the consistent storage of traffic data elements is achieved;

[0076] S4. Standardize the stored traffic data elements, and then construct a standardized framework table for traffic data elements;

[0077] This step is specifically as follows: Analyze the attributes of the stored traffic data elements and standardize the attributes of the traffic data elements. Based on the corresponding classification number structure of the traffic data elements, construct a standardized framework table. Among them, the attributes of the traffic data elements include data element name, data element type, data format, and classification number, as well as the English name of the data element, Chinese full spelling, version, registration agency, definition, value range, measurement unit, remarks, etc. In this embodiment, the data element name, data element type, data format, and classification number are used as the research objects.

[0078] In this step, the preset extraction methods include the bottom-up business integration method, the direct extraction method, and the user view extraction method. Among them, the direct extraction method refers to directly extracting data elements from materials such as tables, databases, entity-relationship diagrams, and development design documents of existing systems. This method is based on existing information systems with complete functions but heterogeneous data. The way of counting data is direct, which is convenient for discovering heterogeneous data items. However, this method completely depends on the original system, making it difficult to improve the quality of the extracted data and being unfavorable for forming global data requirements;

[0079] The bottom-up business integration method refers to analyzing each small business module and then gradually integrating upward to extract data elements. This method can accurately extract the data requirements in the business chain. However, since many business processes repeat during the business process, the workload of this analysis method is huge, and there are many repetitive tasks. It is a passive method that needs to start from process standardization;

[0080] The user view extraction method refers to extracting data elements from various input / output forms, vouchers, and other materials. This method has strong operability, direct collection of data views, and simple analysis methods, which are convenient for end-users to participate. However, it is very difficult to completely collect user views, and the analysis process is highly subjective and has a large workload.

[0081] In this embodiment, first, traffic data elements are classified based on a preset traffic data element coding rule to obtain the classification number structure of the corresponding traffic data elements; then, traffic data elements are extracted from the TOCC based on a preset extraction method; finally, the traffic data elements are standardized according to the attributes of the traffic data elements, and a standardized framework table of the traffic data elements is constructed. This method can ensure the quality of data interaction of the Transportation Bureau, is conducive to promoting the integration of traffic data elements, effectively improves the utilization rate of traffic data, and can better serve the TOCC.

[0082] It should be noted that in this embodiment, in the breadth-first algorithm for extracting comprehensive and non-redundant traffic data elements and the depth-first algorithm for extracting traffic data elements of business processes, except for the different business types, the meanings represented by each letter and function are the same. Therefore, in this embodiment, the corresponding meanings represented by the letters here are not specifically distinguished.

[0083] To better understand the working principle and technical effect of the present invention, the actual situation of traffic data in Changsha City is taken as an example for illustration below.

[0084] The current situation of the transportation industry in Changsha City is as follows:

[0085] 1.1 Highway status

[0086] Changsha is one of the areas with the most intensive highway networks in Hunan Province. At present, a trunk highway network centered on Changsha and reaching all cities and counties in the province has been formed. With the improvement of road traffic capacity, the transportation market is showing a rapid development trend. By 2021, the average monthly volume of highway passenger transportation can reach 1 million people, and the average monthly passenger turnover can reach 70 million people. The average monthly volume of highway freight transportation is close to 100 million tons, and the average monthly turnover of highway freight can reach 6.8 billion tons.

[0087] 1.2 Waterway status

[0088] Changsha is located in the lower reaches of the main stream of the Xiangjiang River. Changsha Port is an important water and land transportation hub in central China. It has been navigable with major cities along the Yangtze River and is one of the 28 major inland ports in the country. The current docks are mainly distributed in 10 port areas such as Xialing Port Area, Muyun Port Area, Tongguan Port Area, and Xinkang Port Area. Except for Xialing Port Area, which mainly focuses on container and general cargo transportation, the other port areas mainly serve the development of local towns and surrounding areas and mainly transport mineral construction materials. In 2021, the monthly waterway freight volume in Changsha was close to 2 million tons, and the monthly passenger volume was about 10,000 people. The total monthly throughput of goods in Changsha Port was more than 2.8 million tons on average, and the monthly throughput of foreign trade goods was about 100,000 tons.

[0089] 1.3 Urban traffic status

[0090] (1) Buses

[0091] As of now, there are a total of 7,575 operating buses in Changsha, including 5,256 pure electric vehicles and 2,319 hybrid vehicles. Currently, there are 291 urban bus lines in total, with a total line length of 5,584.09 kilometers.

[0092] (2) Taxis

[0093] Currently, there are 8,370 taxis in Changsha, all of which are clean energy taxis, and the daily passenger volume can reach up to more than 500,000 person-times.

[0094] (3) Online car-hailing

[0095] Changsha took the lead nationwide in building a regulatory information interaction platform for online car-hailing, comprehensively supervising operators, vehicles, business operation data, drivers, etc. It is connected to the national platform and has accessed data from online car-hailing companies such as Shenzhou, Didi, and Yidao, promoting the healthy and orderly development of this business format.

[0096] (4) Shared mobility vehicles

[0097] Shared mobility vehicles include shared bicycles and shared electric vehicles. The shared mobility vehicle industry has developed rapidly in Changsha and has become the third largest urban travel mode after buses and subways. They have real-time positioning and precise search functions, and are equipped with intelligent locks with in-vehicle satellite positioning and intelligent communication control modules. Shared mobility vehicles can be seen everywhere on the streets and alleys of Changsha.

[0098] 1.4 Urban rail transit

[0099] As of now, there are 6 urban rail transit lines in Changsha, including 5 subway lines and 1 maglev line, with a total mileage of 161 km and a total of 102 rail stations. On April 29, 2021, it was the seventh anniversary of the opening of Changsha Metro for passenger operation. The operating line length has exceeded 161.02 kilometers, the number of operating stations has increased to 114, with a cumulative safe operation of 2,557 days, running more than 48 million train-kilometers, a punctuality rate of 99.9% and a train operation graph fulfillment rate of 99.9%. Since its operation, it has set a single-day passenger flow record of 2.8512 million person-times. On May 6, 2021, it was the fifth anniversary of the trial operation of Changsha Maglev Express. It has had a cumulative safe operation of 1,826 days, with 262,615 train trips, a total operating mileage of 4.8708 million train-kilometers, and a total passenger flow of 16.2576 million person-times, once setting a historical highest daily passenger flow of 18,012 person-times.

[0100] The first step is to classify traffic data elements based on the coding rules of traffic data elements to obtain the corresponding classification number structure of traffic data elements, specifically as Figure 2As shown in the figure, AB represents the business area to which the traffic data element belongs (in this article, it is abbreviated by the first letter of the Chinese pinyin of the business area. For example, highway is represented as GL, waterway is represented as SL, urban passenger transport is represented as CK, and urban freight transport is represented as CH); CD is a number, which represents the serial number of the first-level classification to which the traffic data element belongs; EF is a number, which represents the serial number of the second-level classification to which the traffic data element belongs; GH is a number, which represents the serial number of the third-level classification to which the traffic data element belongs; OPQ is a number, which represents the serial number of the classification sequence number, starting from 001 and encoded in sequence; among them, the first-level classification sequence number, the second-level classification sequence number, and the third-level classification sequence number are all arranged in sequence from left to right. Each level of classification sequence number starts from 01. When there is no classification at a certain level, the number of that level is set to 00, that is, it means that the classification name of that level is empty;

[0101] In the second step, based on the current situation of the transportation industry in Changsha, traffic data elements are collected, which includes a three-level architecture: the first-level platform of the Municipal Transportation Bureau, the second-level platform of the industry (directly affiliated units, such as the Bus Affairs Center), and the third-level platform of enterprises (such as Hunan Bus, Longxiang Bus, Baojun Bus, etc.). For the bus data in Changsha, the Bus Affairs Center belongs to the second-level platform of the industry, which connects enterprises and the Municipal Transportation Bureau.

[0102] Currently, the existing relevant traffic data business systems in Changsha include the municipal transportation system, the dangerous goods intelligent supervision system, the highway system, and the maritime system, etc. Based on a preset extraction method, data elements of this traffic data business system are extracted, which includes:

[0103] S211. Analyze the traffic data elements in the existing business systems and collect, summarize, and sort them;

[0104] S212. Use the depth-first algorithm to analyze and sort out the business process data in TOCC, then conduct information modeling on traffic data elements, and extract traffic data elements in TOCC based on the information modeling of traffic data elements;

[0105] From Figure 3 it can be seen that the common data information existing in each business link can be interacted, but in the actual situation of Changsha's traffic data, the same business information exists in different business links but cannot be interacted, resulting in the inability to reasonably utilize the data. For example, the Changsha IC card swiping data and the bus location spatio-temporal data belong to two different table structures. Among them Figure 4 a and Figure 4 b respectively represent the bus POS machine swiping data and the bus location spatio-temporal data. From these two figures, the buses and POS machines cannot be corresponding one by one, so the role of the data cannot be maximally exerted. Therefore, it is necessary to conduct information modeling and integration on traffic data elements. Based on the three-layer architecture model for collecting Changsha urban bus swiping data, as Figure 3 shownFigure 3 An information modeling diagram of Changsha city bus card swiping data is shown;

[0106] S213, extracting traffic data elements in TOCC based on information modeling of traffic data elements;

[0107] In this step, the traffic data elements finally extracted include: bus card POS machine number, card swiping station information, IC card number, card swiping amount, IC card balance, card swiping time, card swiping POS machine number, bus vehicle number, POS machine model specification, vehicle company name, license plate number, operation route, operation time, bus fuel type, passenger load factor, number of seats, company name, company mailing address, company contact number, company legal person, number of company vehicles, etc.;

[0108] S22, extracting data elements in the user view in TOCC based on the user view extraction method, and continuously updating the data elements in the user view using the breadth-first algorithm, thereby extracting complete and non-duplicate traffic data elements;

[0109] In this step, firstly, the authoritative, latest released and latest version user views of Changsha traffic data are collected, and then decomposed and standardized, that is, complex tables are disassembled layer by layer to form multiple simple tables, and finally traffic data elements are extracted based on the user view extraction method;

[0110] In this step, the user view is an application form for road passenger transport route operation, and the application form for road passenger transport route operation can be disassembled into a sub-table of applicant basic information, a sub-table of existing operating buses, a sub-table of application for licensed passenger routes, and a sub-table of buses to be put into operation. The finally extracted traffic data elements include but are not limited to the applicant's name, legal representative's name, person in charge's name, mailing address, zip code, contact number, e-mail address, business license number, total number of operating buses, number of high-end operating buses, number of mid-level operating buses, starting point of passenger routes, end point of passenger routes, end point of passenger routes, intermediate passenger stops, operating mileage, minimum daily departure frequency, application operation period, passenger bus type, vehicle type, vehicle grade, vehicle technical grade, number of vehicles to be purchased, and current number of vehicles.

[0111] The third step is to use the alliance chain member nodes to collect, verify and transmit the extracted traffic data elements into blocks for block packaging and chain-up, and realize consistent storage of traffic data elements based on the consensus algorithm.

[0112] The fourth step is to standardize the stored traffic data elements and then construct a standardized framework table for traffic data elements.

[0113] Specifically, analyze the attributes of the stored traffic data elements, standardize the attributes of the traffic data elements, and construct a standardized framework table based on the corresponding classification number structure of the traffic data elements.

[0114] In this step, the attributes of the traffic data elements include name, type, format, measurement unit, and number.

[0115] Before standardizing the traffic data elements, the following situations may exist:

[0116] (1) The names of traffic data elements are not standardized

[0117] Generally, traffic data elements are the smallest data units in a specific context and cannot be further divided. However, there is a traffic data element "passenger station site location", whose actual connotation includes specific items such as "site longitude" and "site latitude", and "bus operation time" is also divided into specific items such as "winter departure time", "winter last bus time", "summer departure time", and "summer last bus time". In such cases, it is inappropriate to use "passenger station site location" and "bus operation time" as traffic data element names. If the name is not suitable as a traffic data element name, it can be considered to use the specific content it contains as the name of the traffic data element. Another situation is that the name of the traffic data element is ambiguous or has multiple meanings, causing conflicts and violating the principle of unambiguous expression of data elements in a certain context.

[0118] (2) Traffic data elements are heterogeneous but synonymous

[0119] When sorting, number the traffic data elements. For the naming of this number, there are "number", "code", "serial number", "ID", etc. At this time, it is necessary to uniformly name this type of number.

[0120] (3) The incorrect type is selected for traffic data elements

[0121] The meanings of some traffic data elements are accurate to specific "hours, minutes, and seconds", but the traffic data element type is selected as "date type", which is only accurate to "year, month, and day". Therefore, "time and date type" needs to be selected. For the data type of the traffic data element "ID number", although it is a string of numbers, the data type should not be selected as "numeric type", but should be selected as "character type". The data type needs to be determined based on the specific meaning expressed in the definition of the traffic data element.

[0122] (4) The inapplicable representation format is selected for traffic data elements

[0123] The representation of many traffic data elements is in integer digits. For example, there is no such thing as half a person in "number of people". If decimals are selected, it will not only not appear more accurate, but will instead consume more storage space. Additionally, for traffic data elements with text descriptions, since the text expressions are uncertain and the number of characters can be long or short, if the "fixed length" format is used for representation, it will limit the number of characters. In this case, "variable length" should be selected;

[0124] In this embodiment, based on the analysis of the attributes of the stored traffic data elements and the corresponding classification number structure of the traffic data elements obtained in step S1, a traffic data element standardization framework table is constructed, as shown in Table 1. Only the object names under the first-level classification are listed in Table 1. For example, if there is a basic data element of IC card number first, then the business area of this basic data source is urban passenger transport, the code is CK, the first-level classification is road transportation, its classification sequence number is 04, the second-level classification is bus, its classification sequence number is 01, the third-level classification is IC card data, its classification sequence number is 01, and the basic data source is IC card number, its classification sequence number is 001. Then the code of the basic data element IC card number is CK040101001.

[0125] Table 1 Traffic data element standardization framework table in this embodiment

[0126]

[0127]

[0128] In summary, standardizing the traffic data elements of urban buses in Changsha can ensure the quality of data interaction, facilitate the integration of traffic data elements, improve the utilization rate of traffic data, and thus better serve TOCC.

[0129] The above has introduced in detail a decentralized consortium chain storage and standardization method for traffic data elements oriented to TOCC provided by the present invention. Specific examples are used in this article to elaborate on the principle and implementation manner 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 noted that for those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and modifications can still be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims

1. Decentralized Consortium Chain Storage and Standardization Method for TOCC Traffic Data Elements, Characterized in that, The method comprises the following steps: S1. Classify traffic data elements based on a preset traffic data element coding rule to obtain the corresponding classification number structure of traffic data elements; S2. Extract traffic data elements in TOCC based on a preset extraction method, and its specific implementation manner includes: S21. Extract traffic data elements from TOCC based on the bottom-up business integration method and the direct extraction method, specifically including: S211. Analyze, summarize and sort out traffic data elements in TOCC; S212. Analyze and sort out business process data in TOCC by using the depth-first algorithm, then perform information modeling on traffic data elements, and extract traffic data elements in TOCC based on the information modeling of traffic data elements; S22. Extract data elements in the user view in TOCC based on the user view extraction method, and continuously update the data elements in the user view by using the breadth-first algorithm, and then extract complete and non-redundant traffic data elements; S3. Use the consortium chain member nodes to block and package the extracted traffic data elements and upload them to the chain, and achieve the consistent storage of traffic data elements according to the consensus algorithm; S4. Standardize the stored traffic data elements, and construct a standardization framework table based on the corresponding classification number structure of traffic data elements.

2. The decentralized consortium chain storage and standardization method for TOCC traffic data elements according to claim 1, Characterized in that, The corresponding classification number structure of the traffic data elements includes a business domain code, a first-level classification sequence number, a second-level classification sequence number, a third-level classification sequence number, and a sequence number of the classification sequence number arranged in sequence from left to right.

3. The decentralized consortium chain storage and standardization method for TOCC traffic data elements according to claim 2, Characterized in that, The business domains of the traffic data elements include four categories: highway, waterway, urban passenger transport, and urban freight transport.

4. The decentralized consortium chain storage and standardization method for TOCC traffic data elements according to claim 3, Characterized in that, The specific implementation manner of step S3 includes: using the consortium chain member nodes to integrate the collected, verified and transmitted extracted traffic data elements into a block for packaging and uploading to the chain, and then based on the PBFT consensus algorithm, realizing the consistent storage of the traffic data elements through five stages: request, sequence number allocation, mutual interaction, sequence number confirmation, and response.

5. The decentralized consortium chain storage and standardization method for TOCC traffic data elements according to claim 4, Characterized in that, The specific implementation manner of step S4 includes: analyzing the attributes of the stored traffic data elements and standardizing the attributes of the traffic data elements, and constructing a standardization framework table based on the corresponding classification number structure of the traffic data elements.

6. The decentralized consortium chain storage and standardization method for TOCC traffic data elements according to claim 5, Characterized in that, The attributes of the traffic data elements include data element name, data element type, data format, and classification number.

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