Data quality detection method and equipment of bus operation system, and medium

Abstract

The invention discloses a data quality detection method and device for a bus operation system and a medium, and the method comprises the steps: obtaining the parameter data of the bus operation system, and carrying out the real-time path analysis of the parameter data of the bus operation system, so as to determine the bus path parameters; carrying out bus operation feature extraction on the bus path parameters to obtain bus operation feature parameters; based on the bus operation characteristic parameters, determining a bus operation system data quality detection model through network structure configuration; and according to the bus operation system data quality detection model, obtaining a bus operation system data quality evaluation result through bus operation feature fusion. According to the method, the technical problems of insufficient intelligence and automation and poor detection precision of data quality detection of the bus operation system in the prior art are solved.

Description

Technical Field

[0001] The present application relates to the technical field of data quality analysis, and in particular to a method, device and medium for detecting data quality of a public transportation operation system. Background Art

[0002] With the continuous advancement of smart city construction, the intelligent transportation system (ITS), as its core component, is gradually realizing the comprehensive intelligent management of the public transportation system. As an important input in the intelligent transportation system, the quality of public transportation operation data is directly related to the accuracy of scheduling decisions, passenger experience satisfaction and overall operational efficiency.

[0003] In actual applications, due to the influence of various factors such as equipment failure, data transmission errors, and human operation errors, bus operation data often have problems such as inconsistent formats, timestamp errors, and path deviations. In the prior art, the data quality of the bus operation system is generally tested through system data quality detection, but the detection methods in the prior art have the problems of insufficient intelligence and automation and poor detection accuracy. Summary of the invention

[0004] The embodiments of the present application provide a method, device and medium for detecting data quality of a public transportation operation system, which solve the technical problems of insufficient intelligence and automation and poor detection accuracy of data quality detection of a public transportation operation system in the prior art.

[0005] In a first aspect, an embodiment of the present application provides a data quality detection method for a bus operation system, characterized in that the method includes: acquiring bus operation system parameter data, and performing real-time path analysis on the bus operation system parameter data to determine bus path parameters; performing bus operation feature extraction on the bus path parameters to obtain bus operation feature parameters; based on the bus operation feature parameters, determining a bus operation system data quality detection model through network structure configuration; according to the bus operation system data quality detection model, obtaining a bus operation system data quality assessment result through bus operation feature fusion.

[0006] In one implementation of the present application, real-time path analysis is performed on bus operation system parameter data to determine bus path parameters, specifically including: based on the bus operation system parameter data, bus operation text parameters are obtained by sorting dictionary item data; wherein the bus operation system parameter data includes: dictionary item data, time series data, bus path data; timestamp feature extraction is performed on the time series data to obtain bus operation time characteristic parameters; wherein the bus operation time characteristic parameters include: hours, weeks, holidays; the bus path data is converted into a bus operation coordinate sequence, and path deviation judgment is performed on the bus operation coordinate sequence to obtain real-time path deviation parameters; the bus path parameters are determined according to the bus operation text parameters, the bus operation time characteristic parameters and the real-time path deviation parameters.

[0007] In one implementation of the present application, bus path parameters are subjected to bus operation feature extraction to obtain bus operation feature parameters, specifically including: word embedding of bus operation text parameters to obtain bus operation text numerical vectors; periodic feature conversion of bus operation time feature parameters to obtain bus operation law data; setting real-time path deviation parameters as key indicators of the bus path to obtain bus path feature parameters; and obtaining bus operation feature parameters based on the bus operation text numerical vectors, bus operation law data and bus path feature parameters.

[0008] In one implementation of the present application, based on the bus operation characteristic parameters, a bus operation system data quality detection model is determined through network structure configuration, specifically including: based on the bus operation characteristic parameters, a factor decomposition machine support network is used to obtain bus text difference data; through an LSTM network, the bus operation characteristic parameters are analyzed for delay patterns to determine bus arrival pattern data; based on the bus text difference data and the bus arrival pattern data, model training data is obtained through bus vehicle form path evaluation; the model training data is input into a preset model to be trained to obtain a bus operation system data quality detection model.

[0009] In one implementation of the present application, according to the bus operation system data quality detection model, the bus operation system data quality assessment result is obtained by integrating the bus operation characteristics, which specifically includes: based on the bus operation system data quality detection model, determining the bus operation characteristic data state through bus operation characteristic assessment; quantifying the bus operation characteristic data state to obtain bus operation characteristic quantified data; wherein the bus operation characteristic quantified data includes: data missing ratio, data error ratio, error threshold; according to the bus operation characteristic quantified data, the bus operation system data quality assessment result is obtained by integrating through voting method.

[0010] In one implementation of the present application, a bus operation system data quality assessment result is obtained based on the quantified data of bus operation characteristics through a voting method, specifically including: determining the bus characteristic voting weight based on the quantified data of bus operation characteristics; obtaining initial assessment data through bus characteristic assessment based on the bus voting weight; converting the initial assessment data into an assessment result, and performing weighted summation on the data after the assessment result conversion to obtain a bus operation system data quality assessment score; determining the bus operation system data quality assessment result through an assessment threshold based on the bus operation system data quality assessment score.

[0011] In one implementation of the present application, after obtaining a bus operation system data quality assessment result by integrating bus operation characteristics according to a bus operation system data quality detection model, the method further includes: determining bus system data adjustment items through real-time monitoring of the bus system according to the bus operation system data quality assessment result; performing data correction on the bus system data adjustment items to obtain bus operation system iterative data; and determining bus operation system optimization data based on the bus operation system iterative data by optimizing the bus operation system data.

[0012] In one implementation of the present application, after determining the optimized data of the bus operation system through optimization of the bus operation system data based on the iterative data of the bus operation system, the method further includes: obtaining system data to be improved through a passenger satisfaction survey based on the optimized data of the bus operation system; and updating the operation strategy of the bus operation system according to the system data to be improved.

[0013] In a second aspect, an embodiment of the present application further provides a data quality detection device for a bus operation system, characterized in that the device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: obtain bus operation system parameter data, and perform real-time path analysis on the bus operation system parameter data to determine bus path parameters; perform bus operation feature extraction on the bus path parameters to obtain bus operation feature parameters; determine a bus operation system data quality detection model based on the bus operation feature parameters through network structure configuration; and obtain a bus operation system data quality assessment result through bus operation feature fusion according to the bus operation system data quality detection model.

[0014] In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium for data quality detection of a bus operation system, storing computer executable instructions, characterized in that the computer executable instructions are configured to: obtain bus operation system parameter data, and perform real-time path analysis on the bus operation system parameter data to determine bus path parameters; perform bus operation feature extraction on the bus path parameters to obtain bus operation feature parameters; determine a bus operation system data quality detection model based on the bus operation feature parameters through network structure configuration; and obtain a bus operation system data quality assessment result through bus operation feature fusion according to the bus operation system data quality detection model.

[0015] The embodiments of the present application provide a method, device and medium for detecting data quality of a public transportation operation system. By fusion of multi-network features and automatic optimization and iteration of models, the technical problems of insufficient intelligence and automation and poor detection accuracy of data quality detection of a public transportation operation system in the prior art are solved, and the intelligence and automation of data quality detection of a public transportation operation system are realized, thereby improving the accuracy of data quality detection of a public transportation operation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 A flow chart of a data quality detection method for a public transportation operation system provided in an embodiment of the present application;

[0018] Figure 2 A schematic diagram of the internal structure of a data quality detection device for a public transportation operation system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0020] The embodiments of the present application provide a method, device and medium for detecting data quality of a public transportation operation system. By fusion of multi-network features and automatic optimization and iteration of models, the technical problems of insufficient intelligence and automation and poor detection accuracy of data quality detection of a public transportation operation system in the prior art are solved, and the intelligence and automation of data quality detection of a public transportation operation system are realized, thereby improving the accuracy of data quality detection of a public transportation operation system.

[0021] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0022] Figure 1 The following is a flow chart of a data quality detection method for a public transportation operation system provided in an embodiment of the present application. Figure 1 As shown, a data quality detection method for a public transportation operation system provided in an embodiment of the present application specifically includes the following steps:

[0023] Step 101: Obtain bus operation system parameter data, and perform real-time path analysis on the bus operation system parameter data to determine bus path parameters.

[0024] Specifically, it includes: based on the bus operation system parameter data, obtaining the bus operation text parameters through dictionary item data sorting; wherein, the bus operation system parameter data includes: dictionary item data, time series data, bus path data; extracting the timestamp feature of the time series data to obtain the bus operation time characteristic parameters; wherein, the bus operation time characteristic parameters include: hours, weeks, holidays; converting the bus path data into a bus operation coordinate sequence, and performing path deviation judgment on the bus operation coordinate sequence to obtain real-time path deviation parameters; determining the bus path parameters according to the bus operation text parameters, the bus operation time characteristic parameters and the real-time path deviation parameters.

[0025] The present application obtains the bus operation system parameter data and performs real-time path analysis on the bus operation system parameter data to determine the bus path parameters, thereby realizing the deviation analysis of the bus path and the predetermined path.

[0026] In the examples of the present application, detailed explanation is given by the following Example 1.

[0027] Example 1: For text information such as bus stops and routes, perform text cleaning, remove redundant characters, and unify the format. Parse timestamp information such as vehicle arrival time and departure time, extract time features such as hours, days of the week, holidays, etc., and perform normalization. Use GIS technology to convert the vehicle's driving path into a coordinate sequence and calculate the deviation between the actual path and the planned path.

[0028] Step 102: extract bus operation characteristics from bus route parameters to obtain bus operation characteristic parameters.

[0029] Specifically, it includes: embedding words into bus operation text parameters to obtain bus operation text numerical vectors; converting bus operation time characteristic parameters into periodic characteristics to obtain bus operation law data; setting real-time path deviation parameters as key indicators of bus paths to obtain bus path characteristic parameters; obtaining bus operation characteristic parameters based on bus operation text numerical vectors, bus operation law data and bus path characteristic parameters.

[0030] The present application extracts bus operation characteristics from bus route parameters to obtain bus operation characteristic parameters, thereby achieving feature extraction corresponding to the bus operation characteristic parameters and improving the accuracy of the relevant parameter rules of bus operation.

[0031] In the examples of the present application, detailed explanation is given by the following Example 2.

[0032] Example 2: Use Word2Vec word embedding to convert text data such as bus stops and routes into numerical vectors. Convert time data into periodic features to reflect the time pattern of bus operations. Calculate key indicators such as path deviation and driving speed as path features.

[0033] Step 103: Based on the bus operation characteristic parameters, a bus operation system data quality detection model is determined through network structure configuration.

[0034] Specifically, it includes: based on the bus operation characteristic parameters, the bus text difference data is obtained through the factor decomposition machine support network; the delay pattern analysis of the bus operation characteristic parameters is carried out through the LSTM network to determine the bus arrival pattern data; according to the bus text difference data and the bus arrival pattern data, the model training data is obtained through the bus vehicle form path evaluation; the model training data is input into the preset model to be trained to obtain the bus operation system data quality detection model.

[0035] This application is based on the bus operation characteristic parameters, determines the bus operation system data quality detection model through network structure configuration, and realizes the construction of the bus operation system data quality detection model.

[0036] In the examples of the present application, this is explained in detail through the following Example 3.

[0037] Example 3: Use FNN to process dictionary item data and identify incorrectly formatted or inconsistent station and route information. Use LSTM to process time series data and analyze the regularity and delays of vehicle arrival times. Combined with GIS technology, evaluate the accuracy and efficiency of vehicle travel routes.

[0038] Step 104: According to the bus operation system data quality detection model, the bus operation feature fusion is used to obtain the bus operation system data quality assessment result.

[0039] Specifically, it includes: based on the bus operation system data quality detection model, through the bus operation characteristics evaluation, determine the bus operation characteristic data status; quantify the characteristics of the bus operation characteristic data status to obtain the bus operation characteristic quantitative data; wherein, the bus operation characteristic quantitative data includes: data missing ratio, data error ratio, error threshold; according to the bus operation characteristic quantitative data, through the voting method fusion, obtain the bus operation system data quality evaluation result.

[0040] Based on the quantified data of bus operation characteristics, the bus operation system data quality assessment results are obtained through voting fusion, which specifically includes: determining the bus characteristic voting weight based on the quantified data of bus operation characteristics; obtaining initial assessment data through bus characteristic assessment according to the bus voting weight; converting the initial assessment data into assessment results, and performing weighted summation on the data after the assessment result conversion to obtain the bus operation system data quality assessment score; determining the bus operation system data quality assessment result through assessment threshold judgment according to the bus operation system data quality assessment score.

[0041] After obtaining the bus operation system data quality assessment result by integrating the bus operation characteristics according to the bus operation system data quality detection model, the method also includes: determining the bus system data adjustment items through real-time monitoring of the bus system according to the bus operation system data quality assessment result; performing data correction on the bus system data adjustment items to obtain bus operation system iterative data; and determining the bus operation system optimization data based on the bus operation system iterative data by optimizing the bus operation system data.

[0042] After determining the optimized data of the bus operation system through optimization of the bus operation system data based on iterative data of the bus operation system, the method further includes: obtaining system data to be improved through a passenger satisfaction survey based on the optimized data of the bus operation system; and updating the operation strategy of the bus operation system according to the system data to be improved.

[0043] This application obtains the bus operation system data quality assessment result based on the bus operation system data quality detection model by integrating the bus operation characteristics, realizes the bus operation system data quality assessment, and improves the accuracy of bus operation system data quality detection.

[0044] In the examples of the present application, a detailed explanation is given by the following Example 4.

[0045] Example 4: First, it is necessary to clarify which features or indicators will be used to evaluate the quality of bus data. These features may include but are not limited to: Data integrity: Check whether the data records are complete, such as whether all bus trips have corresponding departure and arrival time records.

[0046] Check whether the data is internally consistent, such as whether the departure time and arrival time are reasonable (the departure time is earlier than the arrival time), data accuracy: evaluate the accuracy of the data by comparing it with other reliable data sources (such as GPS trajectory data, passenger card swiping records). Check whether the data is updated in a timely manner to avoid using outdated information. Identify and mark data points that are obviously deviated from the normal range (such as extremely fast driving speed, unreasonable number of passengers, etc.).

[0047] After defining the evaluation features and quantitative standards, the voting method can be used to fuse the quality evaluation results of these features. According to the importance of each feature to the overall data quality, set the corresponding voting weight for each feature. The weight can be determined based on expert experience, historical data analysis, or model prediction. Feature evaluation result conversion: Convert the evaluation result of each feature into a voting value. For example, if the quality evaluation result of a feature meets the requirements (such as data integrity), vote in favor (recorded as a positive score); if it does not meet the requirements, vote against (recorded as a negative score or zero score). A more detailed scoring mechanism can also be used, such as giving different negative scores according to the degree of deviation. According to the set weight, the voting value of each feature is weighted and summed. The result of the weighted summation will be used as the final score for the comprehensive evaluation of data quality. Set a judgment threshold for the comprehensive score. If the final score is higher than the threshold, the data quality is considered to be qualified; otherwise, it is considered that there is a problem with the data quality. Establish a data quality feedback mechanism to feed back the fusion results to the data provider or maintainer so that they can improve the data quality in a timely manner.

[0048] According to the actual application effects and changes in data characteristics, parameters such as evaluation features, quantification standards, voting weights and judgment thresholds are dynamically adjusted to optimize the performance of the fusion strategy.

[0049] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this application embodiment also provides a data quality detection device for a public transportation operation system, and its structure is as follows: Figure 2 shown.

[0050] Figure 2 This is a schematic diagram of the internal structure of a data quality detection device for a public transportation operation system provided in an embodiment of the present application. Figure 2 As shown, the device includes:

[0051] at least one processor 201;

[0052] and, a memory 202 communicatively connected to the at least one processor;

[0053] The memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 201 to enable at least one processor 201 to:

[0054] Obtain the bus operation system parameter data, and perform real-time path analysis on the bus operation system parameter data to determine the bus path parameters; extract the bus operation characteristics of the bus path parameters to obtain the bus operation characteristic parameters; based on the bus operation characteristic parameters, determine the bus operation system data quality detection model through network structure configuration; according to the bus operation system data quality detection model, obtain the bus operation system data quality assessment result through bus operation characteristic fusion.

[0055] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for data quality detection of a public transportation operation system stores computer executable instructions, wherein the computer executable instructions are set as follows:

[0056] Obtain the bus operation system parameter data, and perform real-time path analysis on the bus operation system parameter data to determine the bus path parameters; extract the bus operation characteristics of the bus path parameters to obtain the bus operation characteristic parameters; based on the bus operation characteristic parameters, determine the bus operation system data quality detection model through network structure configuration; according to the bus operation system data quality detection model, obtain the bus operation system data quality assessment result through bus operation characteristic fusion.

[0057] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the IoT device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0058] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0059] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0060] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0061] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0062] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0063] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0064] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0065] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0066] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0067] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A data quality detection method for a public transportation operation system, characterized in that: The method comprises: Acquiring bus operation system parameter data, and performing real-time path analysis on the bus operation system parameter data to determine bus path parameters; Extracting bus operation characteristics from the bus path parameters to obtain bus operation characteristic parameters; Based on the bus operation characteristic parameters, a bus operation system data quality detection model is determined through network structure configuration; According to the bus operation system data quality detection model, the bus operation system data quality assessment result is obtained by integrating bus operation characteristics.

2. The data quality detection method of a public transportation operation system according to claim 1 is characterized in that: Performing real-time path analysis on the bus operation system parameter data to determine bus path parameters, specifically including: Based on the bus operation system parameter data, the bus operation text parameter is obtained by sorting the dictionary item data; wherein the bus operation system parameter data includes: dictionary item data, time series data, and bus route data; Extracting timestamp features from the time series data to obtain bus operation time feature parameters; wherein the bus operation time feature parameters include: hours, days of the week, and holidays; Converting the bus path data into a bus running coordinate sequence, and performing path deviation determination on the bus running coordinate sequence to obtain a real-time path deviation parameter; The bus path parameter is determined according to the bus operation text parameter, the bus operation time characteristic parameter and the real-time path deviation parameter.

3. The data quality detection method of a public transportation operation system according to claim 2 is characterized in that: The bus path parameters are subjected to bus operation feature extraction to obtain bus operation feature parameters, specifically including: Performing word embedding on the bus operation text parameter to obtain a bus operation text numerical vector; Performing periodic characteristic conversion on the bus running time characteristic parameters to obtain bus running regularity data; The real-time path deviation parameter is set as a key indicator of the bus path to obtain a bus path characteristic parameter; The bus operation characteristic parameters are obtained based on the bus operation text numerical vector, the bus operation rule data and the bus path characteristic parameters.

4. The data quality detection method of a public transportation operation system according to claim 1 is characterized in that: Based on the bus operation characteristic parameters, a bus operation system data quality detection model is determined through network structure configuration, specifically including: Based on the bus operation characteristic parameters, bus text difference data is obtained through a factorization machine support network; Through the LSTM network, the delay law analysis of the bus operation characteristic parameters is carried out to determine the bus arrival law data; According to the bus text difference data and the bus arrival regularity data, model training data is obtained by evaluating the bus vehicle form path; The model training data is input into a preset model to be trained to obtain the public transportation operation system data quality detection model.

5. The data quality detection method for a public transportation operation system according to claim 1 is characterized in that: According to the bus operation system data quality detection model, the bus operation system data quality assessment results are obtained by integrating bus operation characteristics, which specifically include: Based on the bus operation system data quality detection model, determining the state of the bus operation characteristic data through bus operation characteristic evaluation; Quantifying the characteristics of the bus operation characteristic data state to obtain bus operation characteristic quantified data; wherein the bus operation characteristic quantified data includes: data missing ratio, data error ratio, and error threshold; The bus operation system data quality assessment result is obtained by integrating the bus operation characteristic quantified data through voting method.

6. The data quality detection method for a public transportation operation system according to claim 5 is characterized in that: According to the quantified data of the bus operation characteristics, the data quality evaluation result of the bus operation system is obtained by fusion through voting method, which specifically includes: Determining the bus characteristic voting weight based on the bus operation characteristic quantified data; According to the bus voting weight, initial evaluation data is obtained by evaluating bus characteristics; The initial evaluation data is converted into evaluation results, and the data after the evaluation results conversion is weighted and summed to obtain the data quality evaluation score of the operation system; The public transportation operation system data quality evaluation result is determined according to the public transportation operation system data quality evaluation score through evaluation threshold judgment.

7. The data quality detection method for a public transportation operation system according to claim 1 is characterized in that: After obtaining a bus operation system data quality assessment result by integrating bus operation features according to the bus operation system data quality detection model, the method further includes: According to the data quality assessment results of the public transportation operation system, the public transportation system data adjustment items are determined through real-time monitoring of the public transportation system; Performing data correction on the public transportation system data adjustment item to obtain public transportation operation system iteration data; Based on the iterative data of the public transportation operation system, the optimized data of the public transportation operation system is determined by optimizing the data of the public transportation operation system.

8. The data quality detection method for a public transportation operation system according to claim 7, characterized in that: After determining the optimized data of the bus operation system through the bus operation system data optimization based on the bus operation system iteration data, the method further includes: Based on the optimization data of the public transportation operation system, obtain the data for system improvement through passenger satisfaction survey; The operation strategy of the public transportation operation system is updated according to the system improvement data.

9. A data quality detection device for a public transportation operation system, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Acquiring bus operation system parameter data, and performing real-time path analysis on the bus operation system parameter data to determine bus path parameters; Extracting bus operation characteristics from the bus path parameters to obtain bus operation characteristic parameters; Based on the bus operation characteristic parameters, a bus operation system data quality detection model is determined through network structure configuration; According to the bus operation system data quality detection model, the bus operation system data quality assessment result is obtained by integrating bus operation characteristics.

10. A non-volatile computer storage medium for data quality detection of a public transportation operation system, storing computer executable instructions, characterized in that: The computer executable instructions are configured to: Acquiring bus operation system parameter data, and performing real-time path analysis on the bus operation system parameter data to determine bus path parameters; Extracting bus operation characteristics from the bus path parameters to obtain bus operation characteristic parameters; Based on the bus operation characteristic parameters, a bus operation system data quality detection model is determined through network structure configuration; According to the bus operation system data quality detection model, the bus operation system data quality assessment result is obtained by integrating bus operation characteristics.

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