Automatic driving data link generation method and device, and electronic equipment

Abstract

The present disclosure provides an automatic driving data link generation method and device and electronic equipment, relates to the technical field of big data, in particular to the technical fields of cloud computing, information flow and automatic driving, and the automatic driving data link generation method comprises the following steps: obtaining target data, the target data is data for automatic driving, and the target data comprises a scheduling job group; identifying a plurality of jobs included in the scheduling job group, wherein the plurality of jobs have a calling relationship; generating a first data link according to the calling relationship; obtaining a second data link generated in advance, and generating a first target data link according to the first data link and the second data link, wherein the first target data link is a data link for data tracking, and the second data link is a data link corresponding to data cleaning in a data warehouse.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of big data, in particular to the technical fields of cloud computing, information flow and automatic driving, and specifically relates to an automatic driving data link generation method and device and electronic equipment. BACKGROUND

[0002] With the continuous development of automatic driving technology, automatic driving data plays an increasingly important role in automatic driving. The data volume of automatic driving data is very large, and thus it is necessary to organize the automatic driving data. Currently, a specific data organization tool is usually used to organize the automatic driving data. SUMMARY

[0003] The present disclosure provides an automatic driving data link generation method, device and electronic equipment.

[0004] According to a first aspect of the present disclosure, an automatic driving data link generation method is provided, comprising:

[0005] obtaining target data, the target data being data for automatic driving, the target data comprising a scheduling job group;

[0006] identifying a plurality of jobs included in the scheduling job group, the plurality of jobs having a calling relationship therebetween;

[0007] generating a first data link according to the calling relationship;

[0008] obtaining a second data link generated in advance, and generating a first target data link according to the first data link and the second data link, the first target data link being a data link for data tracking, and the second data link being a data link corresponding to data warehouse cleaning.

[0009] According to a second aspect of the present disclosure, an automatic driving data link generation device is provided, comprising:

[0010] a first obtaining module configured to obtain target data, the target data being data for automatic driving, the target data comprising a scheduling job group;

[0011] an identifying module configured to identify a plurality of jobs included in the scheduling job group, the plurality of jobs having a calling relationship therebetween;

[0012] a generating module configured to generate a first data link according to the calling relationship;

[0013] The second acquisition module is used to acquire a pre-generated second data link and generate a first target data link based on the first data link and the second data link. The first target data link is a data link used for data tracking, and the second data link is a data link corresponding to data warehouse cleaning.

[0014] According to a third aspect of this disclosure, an electronic device is provided, comprising:

[0015] At least one processor; and

[0016] A memory that is communicatively connected to at least one processor; wherein,

[0017] The memory stores instructions that can be executed by at least one processor to enable the at least one processor to perform any of the methods in the first aspect.

[0018] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions for causing a computer to perform any of the methods in the first aspect.

[0019] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements any of the methods in the first aspect.

[0020] In this embodiment of the disclosure, a first data link can be generated based on the calling relationship between multiple jobs, and a first target data link can be generated based on the second data link corresponding to data warehouse cleaning and the first data link. The first target data link is used for data tracking, which can improve the accuracy of data tracking results. At the same time, by generating the first target data link, the relationship between data can be accurately known based on the first target data link, thereby enhancing the effect of data cleaning.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0022] Figure 1 This is a flowchart of an autonomous driving data link generation method provided in an embodiment of this disclosure;

[0023] Figure 2 This is an overall technical concept diagram provided in the embodiments of this disclosure;

[0024] Figure 3 This is a schematic diagram of the access process provided in the embodiments of this disclosure;

[0025] Figure 4 This is a schematic diagram of the research and development specifications provided in the embodiments of this disclosure;

[0026] Figure 5 This is a schematic diagram of online management provided in an embodiment of this disclosure;

[0027] Figure 6 This is a flowchart of an autonomous driving data link generation method provided in an embodiment of this disclosure;

[0028] Figure 7 This is a schematic diagram illustrating the relationships between data reports provided in the embodiments of this disclosure;

[0029] Figure 8 This is one of the schematic diagrams of online governance provided in the embodiments of this disclosure;

[0030] Figure 9 This is the second schematic diagram of online governance provided in the embodiments of this disclosure;

[0031] Figure 10 This is a schematic diagram of the structure of an autonomous driving data link generation device provided in an embodiment of this disclosure;

[0032] Figure 11 This is a schematic block diagram of an example electronic device used to implement embodiments of the present disclosure. Detailed Implementation

[0033] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0034] It should be noted that, see Figure 2 , Figure 2 For the overall concept of the embodiments of this disclosure, such as Figure 2 As shown, the embodiments disclosed herein include four parts: demand access, R&D specifications, online management, and online governance.

[0035] Only by fully understanding and standardizing the requirements can we ensure high-quality delivery. It should be noted that... Figure 3The requirement review section can use capabilities such as exact matching, Natural Language Processing (NLP) name similarity, and NLP meaning similarity to review the meaning and calculation method of the indicators required by each role, the reports in which the indicators exist, the fields of the reports used, and the data warehouse used. Once the requirement review is approved, the specific details of the requirement can be confirmed.

[0036] It should be noted that before confirming the type of requirement, you can also search whether there is existing data that can meet the requirement. If there is data that meets the requirement, you can directly obtain the data. If there is no data that meets the requirement, you can confirm the type of requirement, review the requirement, and confirm the requirement details. After the requirement details are confirmed, you can carry out development, and create new pages, reports, etc. to meet the requirement.

[0037] For details, please refer to the R&D specifications. Figure 4 The content shown includes a principle for developing Structured Query Language (SQL) models that stipulates that data warehouse development and upper-layer data application store (ADS) development must strictly adhere to relevant SQL and big data-related language development specifications.

[0038] It should be noted that the model obtained from the research and development can be a data layering model, which can include a multi-layer structure. The multi-layer structure can include at least one of the following: Operational Data Store (ODS), Data Warehouse Detail (DWD), Data Warehouse Middle (DWM), Data Warehouse Service (DWS), Dimension Table (DWS), ADS, and summary layer. The summary layer includes a highly summarized layer and a lightly summarized layer.

[0039] The consistency principle for filter item definitions is described below: A global Dimensionality (DIM) dimension table must be constructed, and all report filter conditions must be output from this global dimension table. Simultaneously, an automatic monitoring mechanism should be established to detect changes in the values ​​corresponding to dimensions in the online production environment dimension table, promptly and automatically detect and send an approval message regarding whether the DIM dimension table has been added, enabling one-click addition and deployment.

[0040] The requirement for clear indicator definitions in reports can be understood as follows: indicator names in reports must be globally consistent, and the system will automatically check for existing exact matches or similar indicators before deployment. If an exact match exists, the system will automatically verify that the corresponding indicator definitions and calculation methods are completely consistent; otherwise, deployment will fail. Similar indicators require manual verification to ensure there are no ambiguities or other issues. Each regional section in the report configuration must be filled with detailed indicator names, definitions, and definitions for use in the subsequent automatic generation of the indicator dictionary.

[0041] The focus of this disclosure is on two parts: online management and online governance. The following are specific implementation examples of these two parts.

[0042] like Figure 1 As shown, this disclosure provides a flowchart of an autonomous driving data link generation method, as follows: Figure 1 As shown, the method for generating autonomous driving data links includes the following steps:

[0043] Step S101: Obtain target data, which is data used for autonomous driving, and the target data includes scheduling work groups.

[0044] In this embodiment, the method can be applied to a server, which can communicate with an autonomous vehicle. Therefore, the server can store target data, which is data used for autonomous driving. This can be understood as: the autonomous vehicle can perform autonomous driving based on the target data, or the target data can be data generated by the autonomous vehicle during the autonomous driving process. The specific details are not limited here.

[0045] It should be noted that the specific format of the target data is not limited here; for example, the target data can be in JSON format.

[0046] It should be noted that the number of scheduling job groups included in the target data is not limited here; the number of scheduling job groups can be one or at least two.

[0047] As an optional implementation, it also includes:

[0048] Obtain the target report, which is either a newly added report in the server or a report already stored in the server;

[0049] The target report is parsed to construct an indicator dictionary for the target report;

[0050] The target data is determined based on the indicator dictionary, and the target data is the data in the target dictionary.

[0051] When the target report is a newly added report on the server, the report can be created based on user input. The user input information can include at least one of the following: report name, online space Uniform Resource Locator (URL), report path, test space URL, and preview environment space URL.

[0052] When the target report is a report already stored on the server, the Document Object Model (DOM) file included in the target report can be parsed. When the DOM file of the target report fails to be parsed, user input can be received to supplement the DOM file to ensure data integrity.

[0053] Furthermore, when adding a new target report or updating an existing report stored on the server, it is necessary to determine whether the new or updated metrics comply with the consistency principle. Only if the new or updated metrics comply with the consistency principle can they be saved to the report. This consistency principle can also be called the uniqueness principle, which means whether a target metric matching the new or updated metric is stored on the server. Only if the target metric does not exist can the new or updated metric be saved to the report.

[0054] The target report's indicator dictionary can also be referred to as the indicator dictionary db. The specific method for generating the indicator dictionary is as follows: The target report name, indicator names, and indicator SQL are extracted. The extracted information can then be used to assemble the indicator dictionary. For details, please refer to [link to relevant documentation]. Figure 6 As shown.

[0055] In this embodiment of the disclosure, target data is determined by the indicator dictionary of the target report. The indicator dictionary of the target report contains rich content, thereby increasing the richness and diversity of the target data.

[0056] As an optional implementation, determining the target data based on the indicator dictionary includes:

[0057] Obtain supplementary information, which includes at least one of the following: indicator definition information and indicator meaning information;

[0058] The indicator dictionary was revised based on the supplementary information.

[0059] The target data is determined based on the revised indicator dictionary.

[0060] Among these, the information regarding the scope of an indicator can be referred to as the indicator scope, which is the standard used in statistical data. The information regarding the meaning of an indicator can be referred to as the indicator meaning, which is the definition of the indicator or a description of its content. For details, please refer to... Figure 6 The content shown.

[0061] In this embodiment of the disclosure, the target data is determined based on the revised indicator dictionary. Since the revised indicator dictionary is richer in content, the richness and diversity of the target data can be further enhanced.

[0062] It should be noted that, see Figure 6 After obtaining the indicator dictionary, the report name can be determined based on the indicator dictionary, and the report data can be determined based on the report name.

[0063] As an optional implementation, it also includes:

[0064] Obtain the report data included in the target page, where the target page is a page on the server;

[0065] The report data is traversed and parsed to obtain the target report.

[0066] The target page can be understood as a page on the server that is in the online phase. The target page can include report data, which can include the report access URL. In addition, the report data can also include at least one of the following: regional values ​​of each section, trend chart values, table values, pie chart values, and other chart or text values. It should be noted that the target report can be obtained based on the report data.

[0067] It should be noted that the report data on the target page can be iterated over. Specifically, the following information can be iterated over:

[0068] Application Programming Interface (API) for data access: Iterate through the report hash values ​​to obtain the report API. Iterate through the hash values ​​of each area of ​​the page to obtain the path value for each area. Combine the report API and the area path values ​​to obtain the access API for each area of ​​the page.

[0069] Data storage system: parses data model addresses, stores data warehouses and data warehouse tables;

[0070] Common parameter filtering conditions: Parsing the global and local common parameter filtering regions. Combining with data APIs to achieve value calibration under certain conditions;

[0071] Region Values: Traverse and parse each region on the page, identifying the region type, such as table, numerical, trend chart, pie chart, etc. Different traversal methods are used for each region type to parse out the indicator name, indicator definition, indicator calculation code, etc.

[0072] Data dictionary: Establish a data dictionary with indicators as the primary key, which includes information such as the report name where the indicator is located, the report access URL and PATH, the data warehouse where the indicator is stored, the data warehouse table, the meaning of the indicator, the definition of the indicator, and the indicator calculation code.

[0073] It should be noted that, see Figure 6 Data lineage can be constructed based on at least one of the report name, indicator name, and parsing file, resulting in multiple data links. These data links can be saved and visualized to enhance the display effect. These data links may include the first data link and the second data link, as described below.

[0074] It should be noted that, see Figure 7 When multiple reports exist on the target page, the relationships between these reports can be as follows: Figure 7 As shown, Figure 7 The multiple reports shown can include multiple layers, such as report layer, indicator layer, ADS layer and ODS layer. In this way, data links between multiple reports can be generated based on the relationships between them, thereby enhancing the comprehensiveness and accuracy of the data in the generated data links.

[0075] In this embodiment, the report data included in the target page is traversed and parsed to obtain the target report. Since the report data included in the target page has high accuracy, the accuracy of the target report results can be enhanced. Furthermore, it can further enhance the diversity and flexibility of the method for determining the target report.

[0076] Step S102: Identify the multiple jobs included in the scheduling job group, and there is a calling relationship between the multiple jobs.

[0077] Multiple jobs can be distributed in multiple layers, and the calling relationship between multiple jobs can include at least one of the following relationships: layer-by-layer calling and skip-layer calling.

[0078] Among the multiple assignments, each assignment can be of the same type or different types.

[0079] Optionally, job types can include the following: triggered (Spark) SQL, data dump, Layered Service Provider (LSP), remote execution, dependency checking, data export, etc. This increases the diversity of job types and enriches the variety of jobs.

[0080] Step S103: Generate the first data link according to the calling relationship.

[0081] The first data link can be a tree structure, and the first data link can be generated based on the calling relationship.

[0082] Step S104: Obtain the pre-generated second data link, and generate a first target data link based on the first data link and the second data link. The first target data link is a data link used for data tracking, and the second data link is a data link corresponding to data warehouse cleaning.

[0083] It should be noted that target parameters can also be detected through the first target data link. The specific content of the target parameters is not limited here. For example, target parameters may include at least one of the following: data security and data quality.

[0084] Furthermore, the first target data link can also be applied during indicator iteration and data development, thereby further improving the efficiency of indicator iteration and data development.

[0085] The aforementioned data links, such as the first data link, the second data link, and the first target data link, can be referred to as the data lineage. The data lineage specifically reflects information such as the source of the data, the transmission of the data, and the application of the data.

[0086] The first data link and the second data link can both be tree structures, and the first target data link can also be a tree structure.

[0087] The second data link can be generated using data processing tools and SQL flow services. This second data link corresponds to the data warehouse cleaning process. It can be understood as: performing data warehouse cleaning on the server and generating the second data link based on the cleaned data. This reduces redundant and erroneous data on the server, improves data accuracy, and thus improves the accuracy of the second data link.

[0088] In this embodiment of the disclosure, through steps S101 to S104, a first data link can be generated based on the calling relationship between multiple jobs, and a first target data link can be generated based on the second data link corresponding to data warehouse cleaning and the first data link. The first target data link is used for data tracking, which can improve the accuracy of data tracking results. At the same time, by generating the first target data link, the relationship between data can be accurately known based on the first target data link, thereby enhancing the effect of data cleaning.

[0089] As an optional implementation, it also includes:

[0090] Obtain application layer report metrics;

[0091] The first target data link is associated with the application layer report metrics to obtain the second target data link, which is a data link used for data tracking.

[0092] In this embodiment of the disclosure, by associating the first target data link with application layer report indicators, a second target data link is obtained, thereby further enhancing the richness of the content of the second target data link, and thus further improving the accuracy of data tracking results when performing data tracking.

[0093] It should be noted that the specific content of application layer report metrics is not limited here.

[0094] As an optional implementation, the dimensional information of the second target data link includes at least one of the following: indicator dimension, data warehouse table dimension, and indicator dictionary dimension.

[0095] In this embodiment of the disclosure, the dimensional information of the second target data link includes at least one of the following: indicator dimension, data warehouse table dimension, and indicator dictionary dimension. In this way, the more content the dimensional information of the second target data link includes, the richer the content of the second target data link, thereby further improving the accuracy of data tracking results when performing data tracking.

[0096] It should be noted that the dimensional information of the second target data link includes at least one of the following: indicator dimension, data warehouse table dimension, and indicator dictionary dimension, which can increase the diversity of the dimensional information of the second target data link.

[0097] It should be noted that the stages corresponding to the above implementation methods can be understood as the server's online phase.

[0098] As an optional implementation, it also includes:

[0099] Display the first target data link;

[0100] If an alarm message exists at a target node in the first target data link, the alarm message corresponding to the target node is recalled.

[0101] This implementation method can be understood as the online governance phase, and the content of the online governance phase can be found in [link to relevant documentation]. Figure 8 and Figure 9 As shown. See also Figure 8During the online governance phase, monitoring configuration and monitoring loop can be performed first. Monitoring configuration can include the following settings: setting indicator names and network access conditions. Monitoring loop can include the following settings: alarm problem classification and resolution time. After the monitoring configuration and monitoring loop are completed, online governance can be carried out. When alarm information occurs, alarm details can be recalled, and consistency measurement processing can be performed on the alarm details. If the consistency measurement processing is successful, troubleshooting can be performed based on the alarm information.

[0102] The aforementioned consistency measurement process may include screening and statistical analysis of at least one of the following: the number of monitoring items, the number of problems, and the number of false alarm messages.

[0103] See Figure 9 During the online governance phase, monitoring projects and configurations can be set up. Monitoring projects can include data processing, data warehousing, and data application, while monitoring configurations can include quality monitoring, task monitoring, service monitoring, and indicator monitoring. After the monitoring projects and configurations are completed, monitoring can be displayed to enhance the monitoring effect. When alarm information occurs, the node corresponding to the alarm information can be identified in a timely and rapid manner through the monitoring screen, thereby improving the efficiency of node identification.

[0104] Optionally, displaying the first target data link includes:

[0105] If the first target data link passes the review and verification, the first target data link is displayed.

[0106] In this way, the first target data link is only displayed when the audit and verification are passed. If the audit and verification of the first target data link fails, the first target data link can be returned to the previous node to prompt that node to make modifications. This can improve the security of the first target data link and reduce the occurrence of leakage of sensitive information in the first target data link.

[0107] It should be noted that the first target data link can be manually reviewed. Once the manual review is passed, the verification service can be started. The verification service can be automated. For example, the server can generate an automatic monitoring service through a graphical configuration and API, and perform result verification periodically according to the monitoring frequency.

[0108] In addition, by verifying the data link of the first target, the consistency of user-visible data is verified, and at the same time, an accuracy scheme is combined to ensure the authority of the data.

[0109] Simultaneously, it achieves both "monitoring" (monitoring anomalies) and "control" (managing and controlling) to promptly stop losses. This includes addressing issues such as data quality, indicator consistency, task scheduling, and service problems exposed through multiple channels at the minute level, achieving intelligent service recovery through self-healing measures. It also enables visualized dashboards for alarm indicators, facilitating more accurate problem recall and closed-loop management for system and business development.

[0110] In this embodiment of the disclosure, a first target data link can be displayed, and if there is alarm information at the target node in the first target data link, the alarm information corresponding to the target node can be recalled. In this way, by parsing the alarm information, problems can be discovered and solved in a timely and accurate manner, thereby improving the efficiency of problem solving.

[0111] It should be noted that, in this embodiment, visualization of at least one of the following information can also be provided: alarm analysis display, number of monitoring, number of problems, number of false alarms, and alarm details.

[0112] See Figure 10 , Figure 10 This is a schematic diagram of the structure of an autonomous driving data link generation device provided in an embodiment of the present disclosure, as shown below. Figure 10 As shown, the autonomous driving data link generation device 1000 includes:

[0113] The first acquisition module 1001 is used to acquire target data, which is data for autonomous driving, and the target data includes scheduling work groups.

[0114] The identification module 1002 is used to identify multiple jobs included in the scheduling job group, and there is a calling relationship between the multiple jobs;

[0115] Generation module 1003 is used to generate a first data link based on the calling relationship;

[0116] The second acquisition module 1004 is used to acquire a pre-generated second data link and generate a first target data link based on the first data link and the second data link. The first target data link is a data link used for data tracking, and the second data link is a data link corresponding to data warehouse cleaning.

[0117] Optionally, the autonomous driving data link generation device 1000 further includes:

[0118] The third acquisition module is used to acquire application layer report metrics;

[0119] The association module is used to associate the first target data link with the application layer report indicators to obtain a second target data link, which is a data link used for data tracking.

[0120] Optionally, the dimensional information of the second target data link includes at least one of the following: indicator dimension, data warehouse table dimension, and indicator dictionary dimension.

[0121] Optionally, the autonomous driving data link generation device 1000 further includes:

[0122] The fourth acquisition module is used to acquire the target report, which is a newly added report in the server or a report already stored in the server;

[0123] The first parsing module is used to parse the target report in order to construct the indicator dictionary of the target report;

[0124] The determination module is used to determine the target data based on the indicator dictionary, wherein the target data is the data in the target dictionary.

[0125] Optionally, the determining module includes:

[0126] The acquisition submodule is used to acquire supplementary information, which includes at least one of the following: indicator definition information and indicator meaning information;

[0127] The correction submodule is used to correct the indicator dictionary based on the supplementary information;

[0128] The determination submodule is used to determine the target data based on the revised indicator dictionary.

[0129] Optionally, the autonomous driving data link generation device 1000 further includes:

[0130] The fifth acquisition module is used to acquire report data included in the target page, wherein the target page is a page of the server;

[0131] The second parsing module is used to traverse and parse the report data to obtain the target report.

[0132] Optionally, the autonomous driving data link generation device 1000 further includes:

[0133] A display module is used to display the first target data link;

[0134] The recall module is used to recall the alarm information corresponding to the target node when there is alarm information in the target node in the first target data link.

[0135] The autonomous driving data link generation device 1000 provided in this disclosure can implement all the processes implemented in the autonomous driving data link generation method embodiment and can achieve the same beneficial effects. To avoid repetition, it will not be described again here.

[0136] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0137] Figure 11 A schematic block diagram of an example electronic device 1100 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0138] like Figure 11 As shown, device 1100 includes a computing unit 1101, which can perform various appropriate actions and processes according to a computer program stored in read-only memory (ROM) 1102 or a computer program loaded into random access memory (RAM) 1103 from storage unit 1108. The RAM 1103 may also store various programs and data required for the operation of device 1100. The computing unit 1101, ROM 1102, and RAM 1103 are interconnected via bus 1104. Input / output (I / O) interface 1105 is also connected to bus 1104.

[0139] Multiple components in device 1100 are connected to I / O interface 1105, including: input unit 1106, such as keyboard, mouse, etc.; output unit 1107, such as various types of monitors, speakers, etc.; storage unit 1108, such as disk, optical disk, etc.; and communication unit 1109, such as network card, modem, wireless transceiver, etc. Communication unit 1109 allows device 1100 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0140] The computing unit 1101 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1101 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1101 performs the various methods and processes described above, such as the autonomous driving data link generation method. For example, in some embodiments, the autonomous driving data link generation method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 1108. In some embodiments, part or all of the computer program can be loaded and / or installed on device 1100 via ROM 1102 and / or communication unit 1109. When the computer program is loaded into RAM 1103 and executed by the computing unit 1101, one or more steps of the autonomous driving data link generation method described above can be performed. Alternatively, in other embodiments, computing unit 1101 may be configured to perform an autonomous driving data link generation method by any other suitable means (e.g., by means of firmware).

[0141] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0142] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0143] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0144] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0145] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0146] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0147] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0148] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for generating an autonomous driving data link, comprising: Acquire target data, which is data used for autonomous driving, and the target data includes scheduling job groups; The scheduling job group is identified as comprising multiple jobs, and there are calling relationships between the multiple jobs; wherein, the calling relationships between the multiple jobs include at least one of the following relationships: hierarchical calling and skip-hierarchical calling; A first data link is generated based on the aforementioned call relationship; Obtain a pre-generated second data link, and generate a first target data link based on the first data link and the second data link. The first target data link is a data link used for data tracking, and the second data link is a data link corresponding to data warehouse cleaning. Obtain application layer report metrics; The first target data link is associated with the application layer report metrics to obtain the second target data link, which is a data link used for data tracking. The dimensional information of the second target data link includes at least one of the following: indicator dimension, data warehouse table dimension, and indicator dictionary dimension; Obtain the target report, which is either a newly added report in the server or a report already stored in the server; The target report is parsed to construct an indicator dictionary for the target report; The target data is determined according to the indicator dictionary, and the target data is the data in the indicator dictionary; The method further includes: Display the first target data link; If an alarm message exists at a target node in the first target data link, the alarm message corresponding to the target node is recalled.

2. The method according to claim 1, wherein, Determining the target data based on the indicator dictionary includes: Obtain supplementary information, which includes at least one of the following: indicator definition information and indicator meaning information; The indicator dictionary was revised based on the supplementary information. The target data is determined based on the revised indicator dictionary.

3. The method according to claim 1, further comprising: Obtain the report data included in the target page, where the target page is a page on the server; The report data is traversed and parsed to obtain the target report.

4. An autonomous driving data link generation device, comprising: The first acquisition module is used to acquire target data, which is data for autonomous driving, and the target data includes scheduling work groups. The identification module is used to identify multiple jobs included in the scheduled job group, wherein there are calling relationships between the multiple jobs; wherein the calling relationships between the multiple jobs include at least one of the following relationships: layer-by-layer calling and skip-layer calling; The generation module is used to generate a first data link based on the calling relationship; The second acquisition module is used to acquire a pre-generated second data link and generate a first target data link based on the first data link and the second data link. The first target data link is a data link used for data tracking, and the second data link is a data link corresponding to data warehouse cleaning. The third acquisition module is used to acquire application layer report metrics; The association module is used to associate the first target data link with the application layer report indicators to obtain a second target data link, which is a data link used for data tracking. The dimensional information of the second target data link includes at least one of the following: indicator dimension, data warehouse table dimension, and indicator dictionary dimension; The fourth acquisition module is used to acquire the target report, which is a newly added report in the server or a report already stored in the server; The first parsing module is used to parse the target report in order to construct the indicator dictionary of the target report; The determination module is used to determine the target data based on the indicator dictionary, wherein the target data is the data in the indicator dictionary; A display module is used to display the first target data link; The recall module is used to recall the alarm information corresponding to the target node when there is alarm information in the target node in the first target data link.

5. The apparatus according to claim 4, wherein, The determining module includes: The acquisition submodule is used to acquire supplementary information, which includes at least one of the following: indicator definition information and indicator meaning information; The correction submodule is used to correct the indicator dictionary based on the supplementary information; The determination submodule is used to determine the target data based on the revised indicator dictionary.

6. The apparatus according to claim 4, further comprising: The fifth acquisition module is used to acquire report data included in the target page, wherein the target page is a page of the server; The second parsing module is used to traverse and parse the report data to obtain the target report.

7. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-3.

8. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-3.

9. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-3.

Images