Vehicle data integration method and device, equipment and storage medium
By dividing vehicle data into multiple dimensions and querying target data ranges, the problem of irregular vehicle data organization was solved, enabling fast and accurate data processing and anomaly detection.
Patent Information
- Application Number
- CN202410756401.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-06-12
- Publication Date
- 2025-12-12
AI Technical Summary
In existing technologies, the data reported by vehicles is irregular and scattered, which requires relevant personnel to spend a lot of time sorting and compiling vehicle data, making it impossible to detect and process abnormal data in a timely manner.
By receiving data reported by multiple vehicles, dividing it into multiple dimensions, responding to user data query commands, finding and displaying target vehicle data, and realizing data integration and statistics.
It improves the efficiency and accuracy of vehicle data processing, enabling users to quickly and accurately find and process abnormal data.
Smart Images
Figure CN121117023A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of data processing technology, and in particular to a method, apparatus, device and storage medium for integrating vehicle data. Background Technology
[0002] Currently, vehicles can monitor their own operational status and report their data to cloud servers via the network. This data is then analyzed by developers, testers, and maintenance personnel to identify and rectify problems. However, the data reported by each vehicle to the server and the reporting time are scattered and irregular. Relevant personnel need to spend a lot of time sorting and compiling the vehicle data, which makes it difficult to detect and handle abnormal vehicle data in a timely manner. Summary of the Invention
[0003] To address the aforementioned technical problems, this disclosure provides a method, apparatus, device, and storage medium for integrating vehicle data.
[0004] The first aspect of this disclosure provides a method for integrating vehicle data, including:
[0005] Receive vehicle data reported by multiple vehicles;
[0006] The vehicle data of the multiple vehicles is divided into multiple dimensions to obtain multi-dimensional vehicle data.
[0007] In response to receiving a user's data query instruction, the system retrieves at least one data range for a target dimension requested by the data query instruction.
[0008] Find target vehicle data that falls within the data range of each target dimension from vehicle data across multiple dimensions;
[0009] Display the target vehicle data.
[0010] A second aspect of this disclosure provides a vehicle data integration apparatus, comprising:
[0011] The receiving module is used to receive vehicle data reported by multiple vehicles;
[0012] The segmentation module is used to segment the vehicle data of the multiple vehicles into multiple dimensions to obtain multi-dimensional vehicle data.
[0013] The first acquisition module is used to, in response to receiving a user's data query instruction, acquire at least one data range of the target dimension requested by the data query instruction.
[0014] The search module is used to find target vehicle data that falls within the data range of each target dimension from vehicle data of multiple dimensions;
[0015] The first display module is used to display the target vehicle data.
[0016] A third aspect of this disclosure provides a computer device including a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, can implement the vehicle data integration method of the first aspect described above.
[0017] A fourth aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the vehicle data integration method of the first aspect described above.
[0018] The technical solution provided in this disclosure has the following advantages compared with the prior art:
[0019] This disclosure integrates and statistically analyzes vehicle data from multiple vehicles, divides the vehicle data into multiple dimensions to obtain multi-dimensional vehicle data, and, in response to a user's data query instruction, retrieves at least one data range of a target dimension requested by the data query instruction. It then searches for target vehicle data within each target dimension's data range from the multi-dimensional vehicle data and displays the target vehicle data. This allows users to quickly and accurately find the vehicle data they need, and promptly detect and handle abnormal vehicle data, thus improving the efficiency and accuracy of vehicle data processing. Attached Figure Description
[0020] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.
[0021] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.
[0022] Figure 1 This is a flowchart of a method for integrating vehicle data provided in an embodiment of this disclosure;
[0023] Figure 2 This is a schematic diagram of a data statistics interface of a computer device provided in an embodiment of this disclosure;
[0024] Figure 3This is a flowchart of another method for integrating vehicle data provided in this disclosure embodiment;
[0025] Figure 4 This is a flowchart of another vehicle data integration method provided in this disclosure embodiment;
[0026] Figure 5 This is a schematic diagram of the structure of a vehicle data integration device provided in an embodiment of this disclosure;
[0027] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure. Detailed Implementation
[0028] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.
[0029] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.
[0030] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.
[0031] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0032] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".
[0033] The vehicle data integration method provided in this disclosure can be executed by a computer device. This device can be understood as any device with processing and computing capabilities. This device may include, but is not limited to, mobile terminals such as smartphones, laptops, tablets (PADs), and in-vehicle terminals, as well as fixed electronic devices such as digital TVs, desktop computers, and servers.
[0034] To better understand the inventive concept of the embodiments of this disclosure, the technical solutions of the embodiments of this disclosure will be described below in conjunction with exemplary embodiments.
[0035] Figure 1 This is a flowchart of a vehicle data integration method provided in an embodiment of this disclosure, such as... Figure 1 As shown, the vehicle data integration method provided in this embodiment includes the following steps:
[0036] Step 110: Receive vehicle data reported by multiple vehicles.
[0037] In this embodiment of the disclosure, a vehicle can establish communication connections with multiple vehicles. The vehicle can report various vehicle data during operation to a computer device, and the computer device can receive vehicle data reported by multiple vehicles.
[0038] Step 120: Divide the vehicle data of multiple vehicles into multiple dimensions to obtain multi-dimensional vehicle data.
[0039] In this embodiment of the disclosure, the computer device can divide the vehicle data of multiple vehicles into multiple dimensions to obtain multi-dimensional vehicle data.
[0040] Dimensions may include at least one of the following: data collection time, vehicle identification, vehicle type, vehicle configuration level, domain, vehicle purpose, vehicle status, vehicle grouping, and event type.
[0041] Data collection time can be understood as the time it takes for the vehicle to collect data.
[0042] Vehicle identification can be understood as an identifier that can uniquely identify a vehicle, such as a Vehicle Identification Number (VIN).
[0043] The vehicle model can be understood as the model number of a vehicle.
[0044] Vehicle configuration levels can be understood as different configuration levels of the same model of vehicle, such as low-end, mid-range, and high-end.
[0045] A domain can be understood as a different domain in the design architecture of a vehicle's centralized electrical / electronic system, where different manufacturers divide the system into different domains based on its functions.
[0046] Vehicle purpose can be understood as the intended use of a vehicle after it has been produced, such as a prototype vehicle, a test drive vehicle, or a vehicle for sale.
[0047] Vehicle status can be understood as the various stages a vehicle goes through from production to scrapping, such as final assembly line completion, activation status, and scrapping status.
[0048] The event name can be understood as the name of the event that occurred to the vehicle, such as the amount of computing resources occupied exceeding a preset threshold.
[0049] Event type can be understood as the type of event that occurs to a vehicle, such as normal events, abnormal events, etc.
[0050] In some embodiments, vehicle data from multiple vehicles is divided into multiple dimensions to obtain multi-dimensional vehicle data, which may include steps 1201-1202:
[0051] Step 1201: For each vehicle's data, match the vehicle data with the data types corresponding to each dimension to obtain at least one target data type that matches each vehicle's data.
[0052] In this embodiment of the disclosure, for each vehicle's data, the computer device can match the vehicle data with the data types corresponding to each dimension, that is, search for data in the vehicle data that matches the data types corresponding to each dimension, and obtain at least one target data type that matches each vehicle data.
[0053] For example, the data type corresponding to the data collection time dimension is the data collection time; the data type corresponding to the vehicle identification dimension is the vehicle identification; the data type corresponding to the vehicle model dimension is the vehicle model; the data type corresponding to the vehicle configuration level dimension is the vehicle configuration level; the data type corresponding to the domain dimension is the domain name; the data type corresponding to the vehicle purpose dimension is the purpose data; the data type corresponding to the vehicle status dimension is the vehicle status data; the data type corresponding to the vehicle grouping dimension is the group name; and the data type corresponding to the event type dimension is the event name that occurred to the vehicle.
[0054] A single vehicle data point can be matched with data types corresponding to multiple dimensions simultaneously.
[0055] Step 1202: For each vehicle data, divide the vehicle data into a dimension group corresponding to at least one target data type that matches the vehicle data, to obtain vehicle data with multiple dimensions.
[0056] In this embodiment of the disclosure, each dimension corresponds to a dimension group. For vehicle data of each vehicle, the computer device can divide the vehicle data into at least one dimension group corresponding to the target data type that matches the vehicle data, thereby obtaining vehicle data with multiple dimensions.
[0057] Step 130: In response to receiving a user's data query instruction, obtain the data range of at least one target dimension requested by the data query instruction.
[0058] In this embodiment of the disclosure, the computer device can receive a user's data query instruction, which may include a data range of at least one target dimension requested. The computer device can obtain the data range of at least one target dimension requested by the data query instruction.
[0059] Step 140: Find the target vehicle data that falls within the data range of each target dimension from the vehicle data of multiple dimensions.
[0060] In this embodiment of the disclosure, the computer device can search for target vehicle data that is in the data range of each target dimension from vehicle data of multiple dimensions. That is, the target vehicle data is vehicle data that is simultaneously in the data range of each target dimension.
[0061] For example, if the target dimensions are data collection time and vehicle model, the data range for the data collection time dimension is the time period from time point A to time point B, and the data range for the vehicle model dimension is vehicle model 01 and vehicle model 02, then the target vehicle data is the vehicle data that was collected during the time period from time point A to time point B and belongs to vehicle model 01 and vehicle model 02.
[0062] Step 150: Display the target vehicle data.
[0063] In this embodiment of the disclosure, the computer device can display target vehicle data on the display interface.
[0064] For example, target vehicle data can be displayed based on a preset data display method. The preset data display method can be set as needed, such as a rectangle chart, pie chart, etc., which is not limited here.
[0065] For example, in response to receiving a user's data display instruction, the system can obtain the data display method from the instruction and display the target vehicle data based on that method.
[0066] In some embodiments, the above-described display of target vehicle data may include steps 1501-1503:
[0067] Step 1501: In response to receiving a first statistical instruction from the user for target vehicle data, obtain the target statistical method and at least one target data object requested for statistical analysis from the first statistical instruction.
[0068] In this embodiment of the disclosure, the computer device can respond to receiving a first statistical instruction from a user for target vehicle data, and obtain the target statistical method and at least one target data object for which statistics are requested from the first statistical instruction.
[0069] A data object can be understood as the data object to which the data contained in the target vehicle data belongs. For example, the target vehicle data is the vehicle data corresponding to model 01 and model 02, which was collected between time point A and time point B. Here, each time point within the time period from time point A to time point B is a data object, and model 01 and model 02 are data objects.
[0070] The target data object can be understood as any data object among the data objects contained in the target vehicle data.
[0071] The target statistics can be presented in various formats, such as rectangle charts, pie charts, trend charts, and distribution charts.
[0072] Step 1502: Extract the target data corresponding to each target data object from the target vehicle data.
[0073] In this embodiment of the disclosure, a computer device can extract target data corresponding to each target data object from target vehicle data.
[0074] For example, if the target vehicle data is the data collected between time point A and time point B and belongs to vehicle models 01 and 02, and the target data objects are vehicle models 01 and 02, then the computer equipment can extract the target data corresponding to vehicle model 01 and the target data corresponding to vehicle model 02 from the target vehicle data.
[0075] Step 1503: Display the target data corresponding to each target data object according to the target statistical method.
[0076] In this embodiment of the disclosure, the computer device can display the target data corresponding to each target data object in a target statistical manner.
[0077] In other embodiments, the above-described display of target vehicle data may include steps 1511-1514:
[0078] Step 1511: In response to receiving the user's second statistical instruction on the target vehicle data, obtain the target statistical items in the second statistical instruction.
[0079] In this embodiment of the disclosure, the computer device can, in response to receiving a second statistical instruction from a user for target vehicle data, obtain the target statistical items in the second statistical instruction.
[0080] Statistical items can include the average value, peak value, etc. of data for a specific target vehicle.
[0081] Step 1512: Extract vehicle data that meets the requirements of the target statistical items from the target vehicle data as statistical data.
[0082] In this embodiment of the disclosure, the computer device can extract vehicle data that meets the requirements of the target statistical items from the target vehicle data as statistical data.
[0083] For example, if the target statistical item is the number of online vehicles, then the number of online vehicles is extracted from the target vehicle data as the statistical data; if the target statistical item is peak data, then the peak data is extracted from the target vehicle data as the statistical data.
[0084] Step 1513: Based on the data collection time and / or the numerical value of the statistical data, sort the statistical data and generate a statistical chart of the statistical data.
[0085] In this embodiment of the disclosure, the computer device can obtain the data collection time and / or the numerical value of the statistical data, and then sort the statistical data based on the data collection time and / or the numerical value of the statistical data to generate a statistical chart of the statistical data.
[0086] In some embodiments, the computer device can obtain the data collection time of the statistical data; based on the chronological order of the data collection time, the statistical data is sorted to generate a trend chart of the statistical data, in which case the statistical chart is a trend chart.
[0087] In other embodiments, the computer device can sort the statistical data based on the numerical value to obtain a data sequence of the statistical data; according to a preset data distribution rule, the statistical data in the data sequence is divided into at least one data interval to obtain a distribution map of the statistical data, where the statistical map is a distribution map.
[0088] The types of distribution maps can include tree diagrams, rectangle diagrams, pie charts, etc.
[0089] Step 1514: Display statistical charts of the statistical data.
[0090] In this embodiment of the disclosure, the computer device can display statistical charts of statistical data on the display interface.
[0091] Therefore, vehicle data from different dimensions can be statistically analyzed and displayed to users, and relevant data trends and distributions can be statistically analyzed. Users can quickly and accurately find the vehicle data they need, enabling them to promptly identify potential problems and handle abnormal vehicle data, thus improving the efficiency and accuracy of vehicle data processing.
[0092] For example, such as Figure 2 As shown, Figure 2This is a schematic diagram of a data statistics interface for a computer device. Figure 2 The statistical data in the data collection period is from time point A to time point B, the vehicle identification range is 0000-9999, and the vehicle models are X01 and X02.
[0093] The vehicle configuration level is MAX, the domains are XCU, FSD-A, and FSD-B, the vehicle purpose is for sales, the vehicle status is activated, the vehicle group is test item 100 vehicles, the event type is target vehicle data of test failure; the statistical data is presented as a pie chart.
[0094] This embodiment of the disclosure receives vehicle data reported by multiple vehicles; divides the vehicle data of multiple vehicles into multiple dimensions to obtain multi-dimensional vehicle data; responds to receiving a user's data query instruction, obtains at least one data range of the target dimension requested by the data query instruction; searches for target vehicle data belonging to each target dimension data range from the multi-dimensional vehicle data; and displays the target vehicle data. This allows for the integration and statistical analysis of vehicle data from multiple vehicles, dividing the vehicle data into multiple dimensions and searching and displaying the target vehicle data corresponding to the target dimension data range according to the user's data query instruction. This enables users to quickly and accurately find the required vehicle data and promptly detect and handle abnormal vehicle data, improving the efficiency and accuracy of vehicle data processing.
[0095] In some embodiments of this disclosure, users can update the dimensions of vehicle data according to actual needs. The computer device can respond to the user's update operation on the data dimensions by adding or deleting dimensions of the vehicle data to obtain multiple updated dimensions.
[0096] This improves the applicability of dimensional division.
[0097] Figure 3 This is a flowchart of a vehicle data integration method provided in an embodiment of this disclosure, such as... Figure 3 As shown, the vehicle data integration method provided in this embodiment includes the following steps:
[0098] Step 310: Receive vehicle data reported by multiple vehicles.
[0099] Step 320: Divide the vehicle data of multiple vehicles into multiple dimensions to obtain multi-dimensional vehicle data.
[0100] Step 330: In response to receiving the user's data query instruction, obtain the data range of the first target dimension and the data range of the second target dimension requested by the data query instruction. The first target dimension is the data collection time dimension, and the data range under the data collection time dimension is the target collection time range. The second target dimension is the vehicle identification dimension, and the data range of the second target dimension is the target vehicle identification set.
[0101] In this embodiment of the disclosure, the computer device can respond to receiving a user's data query instruction by obtaining the data range of the first target dimension and the data range of the second target dimension requested by the data query instruction.
[0102] The first target dimension can be understood as the data collection time dimension, and the data range under the data collection time dimension can be understood as the target collection time range. For example, the target collection time range can be between 0:00 on June 10, 2024 and 20:00 on June 11, 2024.
[0103] The second target dimension can be understood as the vehicle identification dimension. The data range of the second target dimension can be understood as the target vehicle identification set, which is a collection of multiple vehicle identifications.
[0104] Step 340: Obtain at least one data range of a third target dimension other than the data collection time dimension and the vehicle identification dimension from the data query instruction.
[0105] The third objective dimension can be at least one of the following: vehicle type, vehicle configuration level, domain, vehicle purpose, vehicle status, and vehicle grouping.
[0106] Step 350: From the vehicle data of multiple dimensions, find the first vehicle data whose data collection time is within the target collection time range.
[0107] In this embodiment of the disclosure, the computer device can search for the first vehicle data whose data collection time falls within the target collection time range from vehicle data of multiple dimensions.
[0108] Step 360: From the vehicle identifiers corresponding to the first vehicle data, find the target vehicle identifier that belongs to the target vehicle identifier set, and determine the vehicle data corresponding to the target vehicle identifier as the second vehicle data.
[0109] In this embodiment of the present disclosure, after obtaining the first vehicle data, the computer device can search for the target vehicle identifier belonging to the target vehicle identifier set from the vehicle identifiers corresponding to the first vehicle data, and determine the vehicle data corresponding to the target vehicle identifier as the second vehicle data.
[0110] Step 370: Obtain the target vehicle data corresponding to the data range of the third target dimension from the second vehicle data.
[0111] In this embodiment of the disclosure, after obtaining the second vehicle data, the computer device can obtain the target vehicle data corresponding to the data range of the third target dimension from the second vehicle data.
[0112] Step 380: Display the target vehicle data.
[0113] Therefore, vehicle data from multiple vehicles can be integrated and statistically analyzed, and the vehicle data from multiple vehicles can be divided into multiple dimensions. According to the user's data query instructions, the target vehicle data corresponding to the data range of the target dimension can be found and displayed. This allows users to quickly and accurately find the vehicle data they need, and promptly detect and handle abnormal vehicle data, thereby improving the efficiency and accuracy of vehicle data processing.
[0114] In other embodiments of this disclosure, after the vehicle data of multiple vehicles is divided into multiple dimensions to obtain multi-dimensional vehicle data, the computer device can execute... Figure 4 A flowchart of a method for integrating vehicle data is provided, such as... Figure 4 As shown, the vehicle data integration method provided in this embodiment includes the following steps:
[0115] Step 410: For each dimension of vehicle data, monitor the first degree of change of the vehicle data in that dimension within a preset time period or monitor the second degree of change of the vehicle data in that dimension relative to the target time.
[0116] In some embodiments, for each dimension of vehicle data, the computer device can monitor the first degree of change of that dimension of vehicle data within a preset time period. The preset time period can be set as needed, such as the most recent day, week, month, etc., and is not limited here.
[0117] In other embodiments, for each dimension of vehicle data, the computer device can monitor a second degree of change in that dimension of vehicle data relative to a target time. The preset duration can be set as needed, such as the previous day, the same day of the previous week, the same day of the previous month, etc., and is not limited here.
[0118] Step 420: When the first degree of change is greater than the first change threshold or the second degree of change is greater than the second change threshold, determine that the vehicle data in this dimension is abnormal vehicle data.
[0119] In this embodiment of the present disclosure, the computer device can determine whether the first degree of change of vehicle data in each dimension within a preset time period is greater than a first change threshold. When the first degree of change is greater than the first change threshold, the vehicle data in that dimension can be determined to be abnormal vehicle data.
[0120] Computer equipment can determine whether the second degree of change of vehicle data in each dimension relative to the target time is greater than the second change threshold. When the second degree of change is greater than the second change threshold, the vehicle data in that dimension can be determined to be abnormal vehicle data.
[0121] The first and second change thresholds can be set as needed, and are not limited here.
[0122] In some embodiments, a user may adjust the change threshold of at least one dimension as needed. In response to the user's adjustment of the change threshold of at least one dimension, the computer device may adjust the first change threshold or the second change threshold corresponding to each of the at least one dimension to obtain the adjusted first change threshold or the adjusted second change threshold corresponding to each dimension.
[0123] Step 430: Display abnormal vehicle data and corresponding warning information. The warning information includes at least one of the following: the degree of change of abnormal vehicle data, the vehicle identifier corresponding to the abnormal vehicle data, and the time period in which the abnormal vehicle data occurred.
[0124] In this embodiment of the disclosure, the computer device can display abnormal vehicle data and corresponding warning information. The warning information may include at least one of the following: the degree of change of the abnormal vehicle data, the vehicle identifier corresponding to the abnormal vehicle data, and the time period in which the abnormal vehicle data occurred.
[0125] Therefore, abnormal vehicle data can be displayed to users and warnings can be issued. Vehicles that experience sudden changes under selected conditions can be alerted. The problematic vehicle and the time of the change can be quickly located. Users can quickly assess the severity and scope of the abnormal vehicle data and process it in a timely manner, thus improving the efficiency and accuracy of vehicle data processing.
[0126] Figure 5 This is a schematic diagram of a vehicle data integration device provided in an embodiment of this disclosure. This device can be understood as the aforementioned computer equipment or a functional module within the aforementioned computer equipment. Figure 5 As shown, the vehicle data integration device 500 may include:
[0127] The receiving module 510 is used to receive vehicle data reported by multiple vehicles;
[0128] The segmentation module 520 is used to segment vehicle data of multiple vehicles into multiple dimensions to obtain multi-dimensional vehicle data.
[0129] The first acquisition module 530 is used to, in response to receiving a user’s data query instruction, acquire at least one data range of the target dimension requested by the data query instruction.
[0130] The search module 540 is used to search for target vehicle data that falls within the data range of each target dimension from vehicle data of multiple dimensions.
[0131] The first display module 550 is used to display the target vehicle data.
[0132] Optionally, the above-mentioned partitioning modules include:
[0133] The matching submodule is used to match the vehicle data of each vehicle with the data types corresponding to each dimension to obtain at least one target data type that matches each vehicle data.
[0134] The segmentation submodule is used to segment each piece of vehicle data into a dimension group corresponding to at least one target data type that matches the vehicle data, thereby obtaining vehicle data with multiple dimensions.
[0135] Optionally, the first acquisition module mentioned above includes:
[0136] The first acquisition submodule is used to respond to the user's data query instruction by acquiring the data range of the first target dimension and the data range of the second target dimension requested by the data query instruction. The first target dimension is the data collection time dimension, and the data range under the data collection time dimension is the target collection time range. The second target dimension is the vehicle identification dimension, and the data range of the second target dimension is the target vehicle identification set.
[0137] The second acquisition submodule is used to acquire at least one data range of a third target dimension other than the data collection time dimension and the vehicle identification dimension from the data query instruction.
[0138] The above search module includes:
[0139] The first search submodule is used to search for the first vehicle data whose data collection time is within the target collection time range from vehicle data from multiple dimensions;
[0140] The second search submodule is used to search for the target vehicle identifier belonging to the target vehicle identifier set from the vehicle identifiers corresponding to the first vehicle data, and to determine the vehicle data corresponding to the target vehicle identifier as the second vehicle data.
[0141] The third acquisition submodule is used to acquire the target vehicle data corresponding to the data range of the third target dimension from the second vehicle data.
[0142] Optionally, the above display modules include:
[0143] The fourth acquisition submodule is used to respond to receiving a first statistical instruction from the user for target vehicle data, and to obtain the target statistical method and at least one target data object requested for statistical analysis from the first statistical instruction;
[0144] The first extraction submodule is used to extract the target data corresponding to each target data object from the target vehicle data;
[0145] The first display submodule is used to display the target data corresponding to each target data object according to the target statistical method.
[0146] Optionally, the above display modules include:
[0147] The fifth acquisition submodule is used to acquire the target statistical items in the second statistical instruction in response to receiving a second statistical instruction from the user for the target vehicle data;
[0148] The second extraction submodule is used to extract vehicle data that meets the requirements of the target statistical items from the target vehicle data corresponding to the target dimension as statistical data.
[0149] A generation submodule is used to sort the statistical data based on the data collection time and / or the numerical value of the statistical data, and generate a statistical chart of the statistical data.
[0150] The second display submodule is used to display statistical charts of the statistical data.
[0151] Optionally, the device for integrating the aforementioned vehicle data includes:
[0152] The monitoring module is used to monitor the first degree of change of vehicle data in each dimension within a preset time period or to monitor the second degree of change of vehicle data in that dimension relative to a target time.
[0153] The determination module is used to determine that vehicle data in this dimension is abnormal vehicle data when the first degree of change is greater than the first change threshold or the second degree of change is greater than the second change threshold.
[0154] The early warning module is used to display abnormal vehicle data and the corresponding early warning information. The early warning information includes at least one of the following: the degree of change of the abnormal vehicle data, the vehicle identifier corresponding to the abnormal vehicle data, and the time period in which the abnormal vehicle data occurred.
[0155] The vehicle data integration apparatus provided in this disclosure can implement the methods of any of the above embodiments, and its execution and beneficial effects are similar, so they will not be described again here.
[0156] This disclosure also provides a computer device, which includes a processor and a memory, wherein the memory stores a computer program. When the computer program is executed by the processor, it can implement the methods of any of the above embodiments. The execution method and beneficial effects are similar, and will not be described again here.
[0157] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure, such as... Figure 6 As shown, the computer device 600 may include a processor 610 and a memory 620. The memory 620 stores a computer program 621. When the computer program 621 is executed by the processor 610, it can implement the method provided in any of the above embodiments. The execution mode and beneficial effects are similar and will not be described again here.
[0158] Of course, for the sake of simplicity, Figure 6 Only some of the components of the computer device 600 relevant to the present invention are shown in this illustration; components such as buses, input / output interfaces, input devices, and output devices are omitted. In addition, the computer device 600 may include any other suitable components depending on the specific application.
[0159] This disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement the methods of any of the above embodiments. The execution method and beneficial effects are similar, and will not be described again here.
[0160] The aforementioned computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0161] The computer program described above can be written in any combination of one or more programming languages to perform the operations of the embodiments of this disclosure. The programming languages include object-oriented programming languages such as Java and C++, as well as conventional procedural programming languages such as C or similar languages. The program code can be executed entirely on the user's computer device, partially on the user's device, as a standalone software package, partially on the user's computer device and partially on a remote computer device, or entirely on a remote computer device or server.
[0162] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.
[0163] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.
[0164] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for integrating vehicle data, characterized in that, include: Receive vehicle data reported by multiple vehicles; The vehicle data of the multiple vehicles is divided into multiple dimensions to obtain multi-dimensional vehicle data. In response to receiving a user's data query instruction, the system retrieves at least one data range of a target dimension requested by the data query instruction. Find the target vehicle data that falls within the data range of each of the target dimensions from vehicle data of multiple dimensions; Display the target vehicle data.
2. The method according to claim 1, characterized in that, The process of dividing the vehicle data of the multiple vehicles into multiple dimensions to obtain multi-dimensional vehicle data includes: For each vehicle's data, the vehicle data is matched with the data types corresponding to each dimension to obtain at least one target data type that matches each vehicle data. For each piece of vehicle data, the vehicle data is divided into a dimension group corresponding to at least one target data type that matches the vehicle data, resulting in vehicle data with multiple dimensions.
3. The method according to claim 1, characterized in that, The step of responding to receiving a user's data query instruction and obtaining a data range for at least one target dimension requested by the data query instruction includes: In response to receiving a user's data query instruction, the system obtains the data range of the first target dimension and the data range of the second target dimension requested by the data query instruction. The first target dimension is the data collection time dimension, and the data range under the data collection time dimension is the target collection time range. The second target dimension is the vehicle identification dimension, and the data range of the second target dimension is the target vehicle identification set. Obtain a data range of at least one third target dimension other than the data collection time dimension and the vehicle identification dimension from the data query instruction; The step of searching for target vehicle data that falls within the data range of each target dimension from vehicle data across multiple dimensions includes: From vehicle data across multiple dimensions, locate the first vehicle data whose data collection time falls within the target collection time range; From the vehicle identifiers corresponding to the first vehicle data, find the target vehicle identifiers belonging to the target vehicle identifier set, and determine the vehicle data corresponding to the target vehicle identifiers as the second vehicle data; From the second vehicle data, obtain the target vehicle data corresponding to the data range of the third target dimension.
4. The method according to claim 1, characterized in that, The display of the target vehicle data includes: In response to receiving a first statistical instruction from a user regarding the target vehicle data, the system obtains the target statistical method and at least one target data object requesting statistical analysis from the first statistical instruction. Extract the target data corresponding to each target data object from the target vehicle data; The target data corresponding to each target data object is displayed according to the target statistical method described above.
5. The method according to claim 1, characterized in that, The display of the target vehicle data includes: In response to receiving a second statistical instruction from the user regarding the target vehicle data, the target statistical items in the second statistical instruction are obtained; Extract vehicle data that meets the requirements of the target statistical items from the target vehicle data corresponding to the target dimension as statistical data; Based on the data collection time and / or the numerical value of the statistical data, the statistical data is sorted to generate a statistical chart of the statistical data; A statistical chart is used to display the statistical data.
6. The method according to claim 1, characterized in that, After dividing the vehicle data of the multiple vehicles into multiple dimensions to obtain multi-dimensional vehicle data, the method further includes: For each dimension of vehicle data, monitor the first degree of change of the vehicle data in that dimension within a preset time period or monitor the second degree of change of the vehicle data in each dimension relative to a target time. When the first degree of change is greater than the first change threshold or the second degree of change is greater than the second change threshold, the vehicle data in the dimension is determined to be abnormal vehicle data. Display the abnormal vehicle data and the corresponding warning information. The warning information includes at least one of the following: the degree of change of the abnormal vehicle data, the vehicle identifier corresponding to the abnormal vehicle data, and the time period in which the abnormal vehicle data occurred.
7. The method according to claim 1, characterized in that, The method further includes: In response to user update operations on data dimensions, the dimensions of vehicle data are added or deleted to obtain multiple updated dimensions.
8. A vehicle data integration device, characterized in that, include: The receiving module is used to receive vehicle data reported by multiple vehicles; The segmentation module is used to segment the vehicle data of the multiple vehicles into multiple dimensions to obtain multi-dimensional vehicle data. The first acquisition module is configured to, in response to receiving a user's data query instruction, acquire a data range of at least one target dimension requested by the data query instruction. The search module is used to search for target vehicle data that falls within the data range of each target dimension from vehicle data of multiple dimensions; The first display module is used to display the target vehicle data.
9. A computer device, characterized in that, include: A memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the method for integrating vehicle data as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method for integrating vehicle data as described in any one of claims 1-7.