Information missing detection method and device based on time axis coverage, equipment and medium

Abstract

The invention relates to an information missing detection method and device based on time axis coverage, equipment and a medium, and belongs to the technical field of data management. The method comprises the steps of firstly obtaining a vehicle operation dump file and extracting operation data, determining operation starting and ending time according to the operation dump file, setting N time coverage intervals, calculating a time neutral position value, marking a corresponding time region meeting a preset time range as a missing candidate region, carrying out data missing analysis, determining a missing interval if a result meets a judgment condition, and carrying out data missing analysis if the result meets a judgment condition. If a missing condition is met, determining the region as a missing region, then obtaining station-crossing information in the operation starting and ending time, extracting missing time nodes of the missing region and corresponding vehicle node data, searching in the station-crossing information to obtain associated information, and analyzing and determining a detection result of the missing region. Therefore, the missing area in the vehicle operation data is automatically detected, and the missing area is analyzed to obtain a detection result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data management, and in particular to a time axis coverage-based information loss detection method, device, equipment and medium. BACKGROUND

[0002] In the field of intelligent transportation systems and vehicle operation management, the completeness and accuracy of vehicle operation data play a crucial role in ensuring safe operation of vehicles, optimizing scheduling arrangements, and fault diagnosis. A large amount of operation data is generated during vehicle operation, which is usually stored and recorded in the form of dump files. Through analysis of these files and data, the running state and performance of the vehicle can be understood in depth. However, there are many problems and challenges in the detection and analysis of vehicle operation data

[0003] Currently, when a crew member performs a leave work process, an all-in-one machine or other device is used to dump train operation monitoring record files. After the file dumping is completed, the background automatically uploads the corresponding files to the directory corresponding to the train operation monitoring record file analysis software. The corresponding post then analyzes the files based on the records. At this time, if it is found that the files are missing, the crew member needs to be called to check. The existing work process has the risk of manual checking of file loss, and the crew member may need to return to the dispatching room again to dump the train operation monitoring record files after leaving work.

[0004] Therefore, how to automatically detect missing areas in vehicle operation data to analyze the missing areas and obtain detection results has become a problem to be solved. SUMMARY

[0005] Therefore, the embodiments of the present application provide a time axis coverage-based information loss detection method, device, computer equipment and storage medium to solve the problem of how to automatically detect missing areas in vehicle operation data to analyze the missing areas and obtain detection results.

[0006] In a first aspect, the embodiments of the present application provide a time axis coverage-based information loss detection method, comprising:

[0007] Obtain a dump file of vehicle operation, and extract vehicle operation data in the dump file;

[0008] According to the vehicle operation data, determine the running start time and the running end time of the vehicle, set N time coverage intervals within the running start time and the running end time, calculate the interval time between adjacent time coverage intervals to obtain a time gap value, and N is an integer greater than zero;

[0009] mark a time region corresponding to the time gap value meeting the preset time range as a missing candidate region, perform data missing analysis on the vehicle operation data corresponding to the missing candidate region to obtain an interval data analysis result;

[0010] If the interval data analysis result meets a preset determination condition, determine a time node of the missing candidate region in the vehicle operation data, determine a missing interval of the missing candidate region according to the time node, and if the missing interval meets a preset missing condition, determine that the missing candidate region corresponding to the missing interval is a missing region;

[0011] obtain overpass information of the vehicle within the operation start time and the operation end time, extract a missing time node corresponding to the missing region in the vehicle operation data, and extract vehicle node data in the vehicle operation data according to the missing time node;

[0012] According to the vehicle node data, search in the overpass information to obtain association information, and analyze the association information to determine a detection result of the missing region.

[0013] In a second aspect, an information missing detection method based on a time axis cover is provided, and the method comprises the following steps:

[0014] A vehicle data extraction module is configured to obtain a dump file of vehicle operation, and extract vehicle operation data in the dump file;

[0015] A time calculation module is configured to determine an operation start time and an operation end time of a vehicle according to the vehicle operation data, set N time cover intervals within the operation start time and the operation end time, calculate interval times between adjacent time cover intervals to obtain time gap values, and N is an integer greater than zero;

[0016] A missing analysis module is configured to mark a time region corresponding to the time gap value meeting a preset time range as a missing candidate region, perform data missing analysis on the vehicle operation data corresponding to the missing candidate region to obtain an interval data analysis result;

[0017] A missing region determination module is configured to, if the interval data analysis result meets a preset determination condition, determine a time node of the missing candidate region in the vehicle operation data, determine a missing interval of the missing candidate region according to the time node, and if the missing interval meets a preset missing condition, determine that the missing candidate region corresponding to the missing interval is a missing region;

[0018] The node data acquisition module is configured to acquire passing information of the vehicle within the operation start time and the operation end time, extract a missing time node corresponding to the missing area in the vehicle operation data, and extract vehicle node data in the vehicle operation data according to the missing time node.

[0019] The detection module is configured to search in the passing information according to the vehicle node data, obtain associated information, analyze the associated information, and determine a detection result of the missing area.

[0020] In a third aspect, an embodiment of the present application provides a computer device, which comprises a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor implements the above-mentioned information missing detection method based on time axis coverage when executing the computer program.

[0021] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the above-mentioned information missing detection method based on time axis coverage.

[0022] The beneficial effects of the present application compared with the prior art are: obtaining a dump file of vehicle operation, extracting vehicle operation data in the dump file, determining the operation start time and the operation end time of the vehicle according to the vehicle operation data, setting N time coverage intervals within the operation start time and the operation end time, calculating the interval time between adjacent time coverage intervals to obtain a time gap value, N is an integer greater than zero, marking the time region corresponding to the time gap value meeting the preset time range as a missing candidate region, performing data missing analysis on the missing candidate region in the vehicle operation data to obtain an interval data analysis result, if the interval data analysis result meets the preset determination condition, determining the time node of the missing candidate region in the vehicle operation data, determining the missing interval of the missing candidate region according to the time node, if the missing interval meets the preset missing condition, determining that the missing candidate region corresponding to the missing interval is a missing region, obtaining the passing information of the vehicle within the operation start time and the operation end time, extracting the missing time node of the missing region in the vehicle operation data according to the missing time node, extracting the vehicle node data in the vehicle operation data according to the vehicle node data, searching in the passing information according to the vehicle node data to obtain associated information, and analyzing the associated information to determine the detection result of the missing region. By first obtaining the vehicle operation dump file and extracting the operation data, the operation start and end time is determined, N time coverage intervals are set to calculate the time gap value, the corresponding time region meeting the preset time range is marked as a missing candidate region and data missing analysis is performed, if the result meets the determination condition, the missing interval is determined, if it meets the missing condition, it is determined as a missing region, then the passing information within the operation start and end time is obtained, the missing time node of the missing region and the corresponding vehicle node data are extracted, the associated information is obtained by searching in the passing information, and the detection result of the missing region is determined by analysis. Thus, the missing region in the vehicle operation data is automatically detected, and the missing region is analyzed to obtain the detection result. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0024] Figure 1 is an application environment schematic diagram of a time axis coverage based information missing detection method provided by the first embodiment of the present application;

[0025] Figure 2 is a flowchart of a time axis coverage based information missing detection method provided by the second embodiment of the present application;

[0026] Figure 3 This is a flowchart illustrating an information missing detection method based on time axis coverage provided in Embodiment 3 of the present invention;

[0027] Figure 4 This is a flowchart illustrating an information missing detection method based on time axis coverage provided in Embodiment 4 of the present invention;

[0028] Figure 5 This is a flowchart illustrating an information missing detection method based on time axis coverage provided in Embodiment 5 of the present invention;

[0029] Figure 6 This is a schematic diagram of the structure of an information missing detection device based on time axis coverage provided in Embodiment Six of the present invention;

[0030] Figure 7 This is a schematic diagram of the structure of a computer device provided in Embodiment 7 of the present invention. Detailed Implementation

[0031] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0032] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0033] It should also be understood that the term “and / or” as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0034] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [described condition or event] is detected" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once [described condition or event] is detected," or "in response to detection of [described condition or event]."

[0035] Furthermore, in the description of this invention and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0036] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0037] It should be understood that the sequence number of each step in the following embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0038] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0039] The first embodiment of this invention provides a method for detecting missing information based on time axis coverage, which can be applied to applications such as... Figure 1 In this application environment, the client and server communicate with each other. Users can provide conditions, requirements, and operation instructions for information missing detection based on time axis coverage by operating the client. The server is used to generate control instructions for the information missing detection method based on time axis coverage according to the relevant content sent by the client.

[0040] The client side includes, but is not limited to, PDAs, desktop computers, laptops, ultra-mobile personal computers (UMPCs), netbooks, cloud terminal devices, and personal digital assistants (PDAs). The server side can be a standalone server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0041] like Figure 2The diagram shown is a flowchart illustrating an information missing detection method based on time axis coverage provided in Embodiment 2 of the present invention.

[0042] The information missing detection method based on time axis coverage may include the following steps:

[0043] Step S201: Obtain the vehicle operation dump file and extract the vehicle operation data from the dump file;

[0044] Optionally, after step S201, the following steps may also be included:

[0045] Based on the dump file, determine the vehicle operation time point corresponding to the vehicle operation data;

[0046] Based on the vehicle operation time points, the vehicle operation data is sorted in ascending order to obtain sorted data. Based on the sorted data, the start time and end time of vehicle operation are determined.

[0047] The dump file list contains key information such as vehicle model and license plate number. When step S201 is executed to obtain the dump files of vehicle operation, these files should contain the information required by the list, as they may be used in subsequent analysis of vehicle operation data. For example, starttime and endtime can help us initially define the time period of vehicle operation, while begintime and endtime can help us understand the starting and ending points of the vehicle's journey.

[0048] The LMD interface can be selected to provide information such as station passing events, vehicle type / number, passing time, and station name. This information is closely related to vehicle operation. The passing time can help determine the time point corresponding to the vehicle operation data, and the station name information is helpful for subsequent analysis of the vehicle's travel route and station passing situation.

[0049] The time data in the dump file is processed, filtering out null and incorrectly formatted data. This is because after step S201, it is necessary to determine the corresponding time points of vehicle operation data based on the dump file. If the time data is incorrect or missing, it will seriously affect subsequent time-related analyses, such as determining the start and end times of vehicle operation. Filtering invalid time data ensures the accuracy of subsequent time calculations.

[0050] The station information is processed, including removing extra spaces, replacing full-width spaces with half-width spaces, and mapping aliases. Station information is a crucial reference when analyzing vehicle operation data. Inconsistent station names can lead to data matching errors, affecting the judgment of vehicle routes and stop status. Station standardization ensures the consistency and accuracy of station information. Records are then sorted in ascending order by starttime. This step facilitates the sorting of vehicle operation data in ascending order based on vehicle operation time after step S201, as the preprocessed data is already ordered by start time, making subsequent processing more efficient and accurate.

[0051] After the access and preprocessing are completed, step S201 can successfully obtain a high-quality dump file and extract vehicle operation data from it. This data may include operation-related information such as vehicle speed, location, and driving status.

[0052] Based on the time information contained in the dump file (such as starttime, endtime, and the pass-through time provided by the LMD interface), determine the specific time point corresponding to the vehicle operation data.

[0053] Based on the determined vehicle operation time points, the vehicle operation data is sorted in ascending order to obtain an ordered data arrangement. By analyzing the arranged data, the start and end times of vehicle operation can be accurately determined. These two time points are the basis for subsequent operations such as setting time coverage intervals and detecting missing data areas.

[0054] Step S202: Based on the vehicle operation data, determine the vehicle's operation start time and operation end time, set N time coverage intervals within the operation start time and operation end time, calculate the interval time between adjacent time coverage intervals to obtain the time gap value, where N is a positive integer.

[0055] Optionally, setting N time coverage intervals within the operation start time and the operation end time may include the following steps:

[0056] During the start and end times of the operation, the vehicle operation data is analyzed to determine the valid operation records of the vehicle.

[0057] N time coverage intervals are set within the time interval corresponding to the valid operation records.

[0058] In step S201, the start and end times of vehicle operation have been determined based on the dump file. These two time points define the complete time range of this vehicle operation, providing boundaries for subsequently setting the time coverage interval.

[0059] Within a defined start and end time range, N time coverage intervals are defined, where N is a positive integer. These intervals can be equally spaced or non-equally spaced depending on specific needs and data characteristics. For example, if you want to focus on certain specific time periods, you can divide those time periods into more intervals.

[0060] Analyze vehicle operation data to filter out data records that truly reflect the vehicle's effective operating status. For example, exclude data from when the vehicle is stationary or undergoing maintenance, which are considered ineffective operating states. The time points and time periods corresponding to these effective operation records are the parts of normal vehicle operation that we are truly interested in. Based on the time intervals corresponding to the effective operation records, set N time coverage intervals. The advantage of this approach is that it can more accurately cover the vehicle's effective operating periods, avoid including ineffective operating time in the analysis, and improve the accuracy of subsequent analysis.

[0061] The time interval between adjacent time coverage intervals is calculated to obtain the time gap value. This time gap value is an important basis for determining whether data may be missing. If a certain time gap value is too large, exceeding a certain threshold (such as the default 30-minute gapMinutes), then the time period corresponding to this interval may be a candidate region for missing data.

[0062] By dividing the time interval and calculating the intervals, vehicle operation data can be systematically checked to identify potential data gaps, providing a foundation for subsequent data gap analysis and processing.

[0063] Step S203: Mark the time region corresponding to the time gap value that meets the preset time range as a missing candidate region, and perform data missing analysis in the vehicle operation data at the corresponding missing candidate region to obtain the interval data analysis result.

[0064] Step S204: If the interval data analysis result meets the preset judgment conditions, then determine the time node of the missing candidate region within the vehicle operation data, and determine the missing interval of the missing candidate region based on the time node. If the missing interval meets the preset missing conditions, then determine the missing candidate region corresponding to the missing interval as the missing region.

[0065] In step S202, the time gap values ​​between adjacent time coverage intervals have been calculated. The preset time range is a pre-defined time limit, which serves as a preliminary standard for judging whether data may be missing. When a certain time gap value falls within this preset time range, it means that there may be missing data in that time period, and the time area corresponding to this time gap value is marked as a missing candidate area.

[0066] After identifying the candidate missing regions, a detailed analysis should be performed on the corresponding locations of these regions in the vehicle operation data. Data missing analysis may include checking whether records of key data points exist within the region and whether the data values ​​are reasonable.

[0067] For example, regarding the missing candidate region of 10:00-10:30, examining vehicle speed data reveals that if no speed data is recorded during this time period, or the recorded speed values ​​are clearly illogical (e.g., negative speeds), or the vehicle location data is not updated during this time period, it can be preliminarily determined that data may be missing in this region. Through this analysis, the final result will be an interval data analysis of the missing candidate region, which may indicate different scenarios such as missing data, potential missing data, or normal data.

[0068] The preset judgment criteria are a series of rules formulated based on actual business needs and data characteristics. When the interval data analysis results obtained in step S203 meet these rules, it indicates that the data missing situation in the missing candidate area has reached the standard for further in-depth processing. For example, the judgment criteria may stipulate that the missing proportion of key data (such as speed, location, status, etc.) in the missing candidate area exceeds 30%. If, after analysis, it is found that the missing proportion of key data such as speed and location in the missing candidate area of ​​10:00-10:30 reaches 40%, the preset judgment criteria are met, and the next step of processing is initiated.

[0069] Once the interval data analysis results meet the preset judgment conditions, it is necessary to determine the specific time range of the missing candidate region. The time node is the start and end time of the region; for example, in the above example, 10:00 is the start time node and 10:30 is the end time node. The missing interval is the difference between the end time node and the start time node; in this example, the missing interval is 30 minutes.

[0070] The preset missing condition is also set based on the actual situation and is used to further filter out the real missing data regions. When the calculated missing interval meets this condition, the candidate missing region can be determined as the real missing data region.

[0071] For example, the preset missing condition stipulates that the missing interval must be greater than 15 minutes. Since the missing interval in the above example is 30 minutes, this condition is met, so the missing candidate region of 10:00 - 10:30 can be determined as the missing region.

[0072] During steps S203 and S204, the result of the "Site Continuity and Rest Exemption Determination" may affect the final determination of the missing area. If site continuity is not met, it will directly affect the evaluation of the candidate missing area; while if the exemption rules are met, even if there is a time gap value that meets the preset range, it may not be upgraded to a missing area. For example, although a time gap value of a certain time area meets the requirements and is marked as a candidate missing area, according to the site continuity and exemption rules, this may be a normal rest interval for vehicles, and therefore it will not be determined as a missing area.

[0073] Step S205: Obtain the station-passing information of the vehicle during the start and end times of the operation, extract the missing time nodes corresponding to the missing regions in the vehicle operation data, and extract vehicle node data from the vehicle operation data based on the missing time nodes.

[0074] Step S206: Based on the vehicle node data, retrieve the associated information from the station information to obtain related information, analyze the associated information, and determine the detection result of the missing area.

[0075] First, it's necessary to obtain the vehicle's station-passing information during the start and end times of its journey. The start and end times typically define the entire timeframe of the vehicle's operation. Station-passing information may include the time the vehicle passed through each station, the station name, and the vehicle's status. This information may come from various data sources, such as station recording systems or vehicle positioning devices. For example, if a vehicle departs from point A to point B, with a start time of 8:00 and an end time of 12:00, then detailed information about the stations the vehicle passed through during the time period from 8:00 to 12:00 is required.

[0076] Extract the missing time points from the missing regions identified in the previous steps. The missing time points are the start and end times of the missing region. These time points are key time points for determining data loss. For example, if a previously identified missing region is 9:30 - 9:45, then 9:30 and 9:45 are the missing time points.

[0077] Based on the extracted missing time points, vehicle node data for the corresponding time points is extracted from the vehicle operation data. Vehicle node data may contain information such as vehicle speed, location, and operating status. By extracting this data, the vehicle's operation within the missing region can be further analyzed. For the missing region of 9:30 - 9:45, relevant data such as vehicle speed and location for the time points of 9:30 and 9:45 are extracted from the vehicle operation data.

[0078] Step S206 involves retrieving and analyzing the data extracted in step S205 within the transit information to determine the detection results of the missing region, and to judge whether the missing region truly has a data problem and the specific details of the problem.

[0079] The search within the transit information aims to find transit information related to the missing area. This information may be correlated with vehicle node data. Correlated information might include information about stations passed by vehicles near the missing time points, transit times, etc. For example, based on vehicle node data for the missing time points of 9:30 and 9:45, the search within the transit information would look for stations passed by vehicles around these times and their corresponding transit times.

[0080] A thorough analysis of the retrieved related information is conducted. This analysis may include assessing the consistency between the related information and vehicle node data, and identifying any anomalies. Through this analysis, the detection results for missing regions can be determined. Detection results may vary, including confirmed data loss, incorrect data recording, or normal data but with other interfering factors.

[0081] If the station information shows that a vehicle should have passed through a station between 9:30 and 9:45, but there is no record of it in the vehicle operation data, and the associated information shows that the station's passage is normal, then it can be determined that the missing data is indeed missing. If there is a contradiction between the station information and the vehicle node data, such as the passage time not matching the time calculated for the vehicle speed, there may be a data recording error.

[0082] In this embodiment, a vehicle operation dump file is obtained, and vehicle operation data is extracted from the dump file. Based on the vehicle operation data, the start and end times of vehicle operation are determined. N time coverage intervals are set within the start and end times of operation. The interval between adjacent time coverage intervals is calculated to obtain time gap values, where N is a positive integer. The time regions corresponding to the time gap values ​​that meet the preset time range are marked as missing candidate regions. Data missing analysis is performed at the corresponding missing candidate regions in the vehicle operation data to obtain interval data analysis results. If the interval data analysis results meet the preset judgment conditions, the time nodes of the missing candidate regions are determined within the vehicle operation data. Based on the time nodes, the missing interval of the missing candidate regions is determined. If the missing interval meets the preset missing conditions, the missing candidate region corresponding to the missing interval is determined as a missing region. Station-passing information of the vehicle within the start and end times of operation is obtained, and the missing time nodes corresponding to the missing regions in the vehicle operation data are extracted. Based on the missing time nodes, vehicle node data is extracted from the vehicle operation data. Based on the vehicle node data, a search is performed in the station-passing information to obtain related information. The related information is analyzed to determine the detection results of the missing regions. First, vehicle operation dump files are acquired and operational data is extracted to determine the start and end times of operation. N time coverage intervals are set to calculate time gap values. Time regions matching the preset time range are marked as candidate missing regions, and data missing analysis is performed. If the results meet the judgment criteria, the missing interval is determined; if the missing criteria are met, the region is identified as missing. Next, station-passing information within the start and end times of operation is acquired, and the missing time nodes and corresponding vehicle node data of the missing regions are extracted. Related information is retrieved from the station-passing information, and the detection results of the missing regions are analyzed to determine the missing regions. This automatically detects missing regions in vehicle operation data and analyzes them to obtain detection results.

[0083] like Figure 3 The diagram shown is a flowchart of an information gap detection method based on time axis coverage provided in Embodiment 3 of the present invention. Step S202, which calculates the time interval between adjacent time coverage intervals to obtain the time gap value, may include the following steps:

[0084] Step S301: Extract the start time point and end time point of the coverage of the time coverage interval.

[0085] Step S302: Determine the time gap value based on the coverage start time point and the coverage end time point between adjacent time coverage intervals.

[0086] Optionally, data analysis is performed on the vehicle operation data corresponding to the coverage end time point. If the data analysis result is empty, the data corresponding to the coverage start time point of the adjacent time coverage interval is filled to obtain filled data.

[0087] In this context, a time coverage interval represents a period during which vehicle operation data is completely covered. To calculate the interval between adjacent time coverage intervals, it is first necessary to define the start and end boundaries of each interval, i.e., the start time point and the end time point of coverage.

[0088] After obtaining the start and end times of adjacent time coverage intervals, the time gap value between these two intervals can be obtained by calculating the difference between them. The time gap value can help determine if there are any missing data. This is calculated by subtracting the end time of the previous interval from the start time of the later interval.

[0089] Data analysis is performed at the corresponding coverage end time point in the vehicle operation data. If the data analysis result is null, the data corresponding to the coverage start time point of the adjacent time coverage interval is filled to obtain the filled data.

[0090] In some cases, there may be gaps in vehicle operation data at the end of the coverage period. To ensure data continuity and integrity, data from the start times of adjacent coverage intervals can be used to fill these gaps. This helps to mitigate the impact of missing data on subsequent analysis.

[0091] In this embodiment, by extracting key time points within a time coverage interval and calculating the differences between them, the intervals between adjacent data coverage intervals can be quantified. Furthermore, an optional data filling operation is provided to address potential data gaps, ensuring data integrity as much as possible.

[0092] like Figure 4 The diagram shown is a flowchart of an information missing detection method based on time axis coverage provided in Embodiment 4 of the present invention. Step S204, which states that if the interval data analysis result meets a preset judgment condition, then the time node of the missing candidate region is determined within the vehicle operation data; based on the time node, the missing interval of the missing candidate region is determined; and if the missing interval meets a preset missing condition, then the missing candidate region corresponding to the missing interval is determined to be a missing region, may include the following steps:

[0093] Step S401: If the interval data analysis result of the missing candidate region is that the data is not empty, then the time node of the missing candidate region is determined within the vehicle operation data.

[0094] Step S402: Based on the time node, determine the missing interval of the missing candidate region. If the missing interval is greater than a preset interval threshold, then determine the missing candidate region corresponding to the missing interval as a missing region.

[0095] In step S203, a data missing analysis was performed on the missing candidate regions, yielding interval data analysis results. This result is required to be non-empty, meaning data exists within the missing candidate regions, but this does not guarantee the data is complete and normal; further evaluation is needed. Time nodes are crucial boundary information for missing candidate regions, clarifying their start and end positions on the timeline. Determining time nodes helps in accurately calculating the missing interval and conducting more detailed data analysis.

[0096] The missing interval is calculated based on the time nodes determined in step S401, which is the end time node minus the start time node. It represents the time span of the missing candidate region.

[0097] Preset interval threshold: The preset interval threshold is a time limit set in advance based on actual business needs and data characteristics. This threshold is an important criterion for determining whether a missing candidate region is a truly missing region.

[0098] The calculated missing interval is compared with a preset interval threshold. If the missing interval is greater than the preset interval threshold, it indicates that the time span of the missing candidate region is large, and there is a high probability that data is missing. Therefore, it is identified as a missing region.

[0099] In this embodiment, a process for determining missing vehicle operation data is established. The identified missing areas may subsequently be used for data repair, anomaly analysis, and other operations, and will cooperate with steps such as "station continuity and rest exemption determination" and "LMD secondary verification" to ensure accurate detection and handling of missing vehicle operation data.

[0100] like Figure 5 The diagram shown is a flowchart of an information missing detection method based on time axis coverage provided in Embodiment 5 of the present invention. The step of retrieving related information from the station information based on the vehicle node data, analyzing the related information, and determining the detection result of the missing region may include the following steps:

[0101] Step S501: Extract vehicle model information from the vehicle node data, and search the station information according to the vehicle model information to obtain the model association information of the corresponding vehicle model.

[0102] Step S502: Extract vehicle stop information from the vehicle node data, and search the station information according to the vehicle stop information to obtain the stop association information of the corresponding vehicle stop.

[0103] Step S503: Analyze the vehicle model association information and the stop point association information to determine the detection result of the missing area.

[0104] This process involves extracting vehicle model information from vehicle node data. Vehicle model information is a crucial characteristic of a vehicle, as different models may differ in operating rules and station stops. Based on the extracted vehicle model information, a search is performed on the station transit information. Station transit information typically contains transit records for different vehicle models; by searching, the corresponding transit information for a specific model can be found—this information constitutes the vehicle model association information.

[0105] Similarly, vehicle stop information is extracted from vehicle node data. Stop information indicates the stations where vehicles stop during operation and is key information for determining vehicle trajectory and data integrity. Based on the extracted vehicle stop information, a search is performed in the station information to find the corresponding station information, i.e., stop association information.

[0106] The vehicle model association information obtained in step S501 and the stop point association information obtained in step S502 are combined for analysis. The analysis may include whether the vehicle model association information and the stop point association information match, and whether any anomalies exist. Based on the analysis results, the detection results for the missing areas are determined. The detection results may vary, such as data being genuinely missing, data recording errors, or normal data but with other interfering factors.

[0107] In this embodiment, targeted searches are performed on station information to obtain relevant information, which is then analyzed to determine the detection results for missing areas. This helps to more accurately determine the actual situation of vehicle operation data in missing areas, providing a basis for subsequent data processing and decision-making.

[0108] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0109] Corresponding to the methods in the above embodiments, such as Figure 6The diagram shown is a schematic of an information missing detection device based on time axis coverage provided in Embodiment Six of the present invention. This information missing detection device based on time axis coverage corresponds one-to-one with the information missing detection method based on time axis coverage in the above embodiments. The authentication device for information missing detection based on time axis coverage is applied to a computer device, which connects to a target database through a preset application programming interface (API). When the target database is driven to run and execute corresponding tasks, corresponding task logs are generated, which can be collected through the API. For ease of explanation, only the parts related to the embodiments of the present invention are shown.

[0110] The information missing detection device based on time axis coverage includes:

[0111] The vehicle data extraction module 61 is used to obtain a dump file of vehicle operation and extract vehicle operation data from the dump file.

[0112] The time calculation module 62 is used to determine the start time and end time of the vehicle's operation based on the vehicle operation data, set N time coverage intervals within the start time and end time, calculate the interval between adjacent time coverage intervals to obtain a time gap value, where N is a positive integer.

[0113] Missing data analysis module 63 is used to mark the time region corresponding to when the time gap value meets the preset time range as missing candidate region, and to perform data missing data analysis in the vehicle operation data at the corresponding missing candidate region to obtain interval data analysis results;

[0114] The missing region determination module 64 is used to determine the time node of the missing candidate region in the vehicle operation data if the interval data analysis result meets the preset judgment conditions, determine the missing interval of the missing candidate region according to the time node, and determine the missing candidate region corresponding to the missing interval as the missing region if the missing interval meets the preset missing conditions.

[0115] The node data acquisition module 65 is used to acquire the station passing information of the vehicle during the start time and end time of the operation, extract the missing time nodes corresponding to the missing area in the vehicle operation data, and extract vehicle node data in the vehicle operation data according to the missing time nodes.

[0116] The detection module 66 is used to retrieve related information from the station information based on the vehicle node data, analyze the related information, and determine the detection result of the missing area.

[0117] Optionally, the information missing detection device based on time axis coverage includes:

[0118] The time point determination module is used to determine the vehicle operation time point corresponding to the vehicle operation data based on the dump file.

[0119] The sorting module is used to sort the vehicle operation data in ascending order according to the vehicle operation time points to obtain sorted data, and to determine the start time and end time of vehicle operation based on the sorted data.

[0120] Optionally, the time calculation module 62 includes:

[0121] The valid record determination unit is used to analyze the vehicle operation data during the operation start time and the operation end time to determine the valid operation record of the vehicle.

[0122] The interval setting unit is used to set N time coverage intervals according to the time interval corresponding to the valid operation record.

[0123] Optionally, the time calculation module 62 includes:

[0124] The coverage time point determination unit is used to extract the coverage start time point and coverage end time point of the time coverage interval;

[0125] The coverage gap determination unit is used to determine the time gap value based on the coverage start time point and the coverage end time point between adjacent time coverage intervals.

[0126] Optionally, the information missing detection device based on time axis coverage includes:

[0127] The data filling module is used to mark the time area corresponding to the time gap value that meets the preset time range as the missing candidate area, and then perform data analysis on the vehicle operation data at the corresponding coverage end time point. If the data analysis result is a null value, the data corresponding to the coverage start time point of the adjacent time coverage interval is filled to obtain the filled data.

[0128] Optionally, the missing region determination module 64 includes:

[0129] A node determination unit is used to determine the time node of the missing candidate region within the vehicle operation data if the interval data analysis result of the missing candidate region is that the data is not empty.

[0130] The missing region determination unit is used to determine the missing interval of the missing candidate region based on the time node. If the missing interval is greater than a preset interval threshold, the missing candidate region corresponding to the missing interval is determined to be a missing region.

[0131] Optionally, the detection module 66 includes:

[0132] The vehicle model association information acquisition unit is used to extract vehicle model information from the vehicle node data, and retrieve the corresponding vehicle model association information from the station information based on the vehicle model information.

[0133] The stopover association information acquisition unit is used to extract vehicle stopover information from the vehicle node data, and search the transit information according to the vehicle stopover information to obtain the stopover association information of the corresponding vehicle stopover.

[0134] The retrieval unit is used to analyze the vehicle model association information and the stop point association information to determine the detection result of the missing area.

[0135] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0136] Figure 7 This is a schematic diagram of the structure of a computer device provided in Embodiment Seven of the present invention. Figure 7 As shown, the computer device of this embodiment includes: at least one processor ( Figure 7 Only one is shown in the diagram), a memory, and a computer program stored in the memory and capable of running on at least one processor. When the processor executes the computer program, it implements the steps in any of the above embodiments of the information missing detection method based on time axis coverage.

[0137] This computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 7 The examples of computer devices are merely examples and do not constitute a limitation on computer devices. Computer devices may include more or fewer components than shown in the illustration, or combinations of certain components, or different components, such as network interfaces, displays, and input devices.

[0138] The processor referred to can be a CPU, but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0139] Memory includes readable storage media, internal memory, etc., wherein internal memory can be the RAM of a computer device, providing an environment for the operation of the operating system and computer-readable instructions stored in the readable storage media. The readable storage media can be the hard drive of a computer device, or in other embodiments, it can be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, memory can include both internal storage units and external storage devices of the computer device. Memory is used to store the operating system, applications, bootloader, data, and other programs, such as program code for computer programs. Memory can also be used to temporarily store data that has been output or will be output.

[0140] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the functions described above can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here. If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the processes in the methods of the above embodiments by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium can include at least: any entity or device capable of carrying computer program code, a recording medium, a computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0141] The present invention can implement all or part of the processes in the methods of the above embodiments, or it can be accomplished by a computer program product. When the computer program product is run on a computer device, the computer device executes the steps in the above method embodiments.

[0142] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0143] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0144] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / computer devices and methods can be implemented in other ways. For example, the apparatus / computer device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0145] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0146] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A time axis overlay based information absence detection method, characterized by, The method comprises the following steps: acquiring a dump file of vehicle operation, and extracting vehicle operation data in the dump file; determining a running start time and a running end time of the vehicle according to the vehicle operation data, setting N time coverage intervals within the running start time and the running end time, calculating interval time between adjacent time coverage intervals to obtain a time gap value, and N is an integer greater than zero; when the time gap value meets a preset time range, marking a time region corresponding to the time gap value as a missing candidate region, and performing data missing analysis on the vehicle operation data corresponding to the missing candidate region to obtain an interval data analysis result; if the interval data analysis result meets a preset determination condition, determining a time node of the missing candidate region in the vehicle operation data, determining a missing interval of the missing candidate region according to the time node, and if the missing interval meets a preset missing condition, determining that a missing candidate region corresponding to the missing interval is a missing region; acquiring over-station information of the vehicle within the running start time and the running end time, extracting missing time nodes corresponding to the missing region in the vehicle operation data, and extracting vehicle node data in the vehicle operation data according to the missing time nodes; according to the vehicle node data, searching in the over-station information to obtain associated information, and analyzing the associated information to determine a detection result of the missing region.

2. The time axis overlay based information absence detection method of claim 1, wherein, After the extraction of the vehicle operation data in the dump file, the method further comprises the following steps: determining a vehicle operation time point corresponding to the vehicle operation data according to the dump file; arranging the vehicle operation data in ascending order according to the vehicle operation time point to obtain arranged data, and determining the running start time and the running end time of the vehicle according to the arranged data. 3.The time axis overlay based information absence detection method of claim 1, wherein, The setting of N time coverage intervals within the running start time and the running end time comprises the following steps: analyzing the vehicle operation data within the running start time and the running end time to determine an effective running record of the vehicle; setting N time coverage intervals within a time interval corresponding to the effective running record.

4. The time axis overlay based information absence detection method of claim 1, wherein, The calculation of the interval time between adjacent time coverage intervals to obtain the time gap value comprises the following steps: extracting a coverage start time point and a coverage end time point of the time coverage interval; determining the time gap value according to the coverage start time point and the coverage end time point between adjacent time coverage intervals.

5. The time axis overlay based information absence detection method of claim 4, wherein, After the marking of the time region corresponding to the time gap value that meets the preset time range as the missing candidate region, the method further comprises the following steps: performing data analysis on the vehicle operation data corresponding to the coverage end time point, and if the data analysis result is a null value, filling data corresponding to the coverage start time point of adjacent time coverage intervals to obtain filled data.

6. The time axis overlay based information absence detection method of claim 1, wherein, If the interval data analysis result of the missing candidate region meets a preset judgment condition, a time node of the missing candidate region is determined in the vehicle operation data, a missing interval of the missing candidate region is determined according to the time node, and if the missing interval meets a preset missing condition, the missing candidate region corresponding to the missing interval is determined as a missing region, comprising: If the interval data analysis result of the missing candidate region is that the data is not empty, a time node of the missing candidate region is determined in the vehicle operation data; If the missing interval is greater than a preset interval threshold, the missing candidate region corresponding to the missing interval is determined as a missing region.

7. The time axis overlay based information absence detection method of claim 1, wherein, The searching in the passing information according to the vehicle node data is performed to obtain associated information, the associated information is analyzed, and a detection result of the missing region is determined, comprising: Vehicle model information in the vehicle node data is extracted, the vehicle model information is used for searching in the passing information to obtain vehicle model associated information corresponding to the vehicle model information; Vehicle stop point information in the vehicle node data is extracted, the vehicle stop point information is used for searching in the passing information to obtain stop point associated information corresponding to the vehicle stop point information; The vehicle model associated information and the stop point associated information are analyzed to determine the detection result of the missing region.

8. A device for detecting missing information based on time axis coverage, characterized in that, Comprising: A vehicle data extraction module is configured to acquire a dump file of vehicle operation and extract vehicle operation data in the dump file; A time calculation module is configured to determine a running start time and a running end time of a vehicle according to the vehicle operation data, set N time coverage intervals in the running start time and the running end time, calculate interval times between adjacent time coverage intervals to obtain time gap values, and N is an integer greater than zero; A missing analysis module is configured to mark a time region corresponding to the time gap values meeting a preset time range as a missing candidate region, and perform data missing analysis on the missing candidate region in the vehicle operation data to obtain an interval data analysis result; A missing region determination module is configured to determine a time node of the missing candidate region in the vehicle operation data if the interval data analysis result meets a preset judgment condition, determine a missing interval of the missing candidate region according to the time node, and determine the missing candidate region corresponding to the missing interval as a missing region if the missing interval meets a preset missing condition; A node data acquisition module is configured to acquire passing information of a vehicle in the running start time and the running end time, extract missing time nodes of the missing region in the vehicle operation data, and extract vehicle node data in the vehicle operation data according to the missing time nodes; A detection module is configured to search in the passing information according to the vehicle node data to obtain associated information, analyze the associated information, and determine a detection result of the missing region.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The computer program is executed by the processor to implement the time axis coverage based information missing detection method in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the time axis coverage based information missing detection method in any one of claims 1 to 7.

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