Quality detection of map data production chain and map rendering method and device

Abstract

Embodiments of the present application provide a map data production link quality detection and map rendering method and device. In the embodiments of the present application, for a map data production link comprising a plurality of service nodes, map data generated by the map data production link processing road source data is obtained; and feature extraction is performed on the map data to determine data features of the map data and road event features reflected by the map data; then, according to the data features and the road event features, service quality detection is performed on the map data production link, full-link quality monitoring of the map data production link is achieved, and the defect that single-service-node data monitoring cannot find full-link quality problems can be made up.

Description

Technical Field

[0001] This application relates to the field of Internet technology, and in particular to a method and device for quality inspection and map rendering in the map data production chain. Background Technology

[0002] In today's information age, the application and research of information data has become a trend. The process of transforming source data into data required for downstream applications involves a data production process. This process can be achieved through a data production chain comprised of service nodes such as structured data acquisition, strategy mining, data format conversion, data cleaning, and data disambiguation and fusion, which processes the source data.

[0003] In existing technologies, problems in the data production chain are discovered by testing individual services along the chain. However, this monitoring method can only cover single-point functions and cannot achieve full-chain quality monitoring. For example, in the map data production chain, quality monitoring of individual service nodes is not possible, as it cannot achieve quality monitoring of the entire map data production chain. Summary of the Invention

[0004] This application provides a quality inspection and map rendering method for map data production links, a quality inspection method and device for data production links, to achieve full-link quality monitoring of multi-service data production links.

[0005] This application provides a quality inspection method for map data production chain, including:

[0006] The map data is generated by processing the first road source data through a map data production chain that includes multiple service nodes.

[0007] Feature extraction is performed on the map data to determine the data characteristics of the map data and the road event characteristics reflected by the map data;

[0008] Based on the data characteristics and the road event characteristics, service quality detection is performed on the map data production link to determine the service quality of the map data production link.

[0009] This application provides a method for quality inspection of a data production chain, including:

[0010] Acquire event production data generated by processing the first event source data through multiple service links;

[0011] Feature extraction is performed on the event production data to determine the data characteristics of the event production data and the event characteristics reflected by the event production data;

[0012] Based on the data characteristics and event characteristics, service quality detection is performed on the multi-service links to determine the service quality of the multi-service links.

[0013] This application embodiment also provides a computer device, which includes: a memory and a processor; wherein the memory is used to store computer programs;

[0014] The processor is coupled to the memory and is used to execute the computer program to perform the steps in the above-described quality inspection method for the data production chain and / or the quality inspection method for the map data production chain.

[0015] This application also provides a computer-readable storage medium storing computer instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps in the above-described quality inspection method for the data production chain and / or the quality inspection method for the map data production chain.

[0016] This application also provides a computer program product, including: a computer program; the computer program is executed by a processor to implement the above-described quality detection method for the data production chain and / or the quality detection method for the map data production chain.

[0017] In this embodiment, for a map data production link containing multiple service nodes, map data generated by processing road source data in the map data production link is obtained; features are extracted from the map data to determine the data features of the map data and the road event features reflected by the map data; then, based on the data features and road event features, service quality detection is performed on the map data production link, realizing full-link quality monitoring of the map data production link, which can make up for the deficiency that single service node data monitoring cannot detect full-link quality problems. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 A flowchart illustrating the quality inspection method for the data production chain provided in this application embodiment;

[0020] Figure 2 A flowchart illustrating another data production chain quality inspection method provided in this application embodiment;

[0021] Figure 3 A flowchart illustrating the quality inspection method for the map data production chain provided in this application embodiment;

[0022] Figure 4 A flowchart illustrating the map rendering method provided in this application embodiment;

[0023] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] In existing technologies, link issues are discovered by testing individual services along the data production chain. However, this monitoring method only covers single-point functions and cannot achieve end-to-end quality monitoring. To achieve end-to-end quality monitoring, existing technologies for link quality detection based on single-point function testing require deploying test cases for each individual service. These test cases are complex and have many dependencies, resulting in lengthy end-to-end quality monitoring and failing to promptly identify quality issues throughout the entire data production chain.

[0026] To address the technical problem that existing technologies cannot achieve end-to-end quality monitoring, in some embodiments of this application, for a data production link containing multiple service nodes, i.e., a multi-service link, event production data generated by the processing of event source data by the multi-service production link is obtained; feature extraction is performed on the event production data to determine the data characteristics of the event production data and the event characteristics reflected by the event production data; then, based on the data characteristics and event characteristics, service quality detection is performed on the multi-service link, thereby realizing end-to-end quality monitoring of the data production link, which can make up for the deficiency that single-node data monitoring cannot detect end-to-end quality problems.

[0027] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0028] It should be noted that the same reference numerals denote the same object in the following figures and embodiments. Therefore, once an object is defined in one figure or embodiment, it does not need to be discussed further in subsequent figures and embodiments.

[0029] Figure 1 This is a flowchart illustrating the quality inspection method for the data production chain provided in this application embodiment. Figure 1 As shown, the method includes:

[0030] 101. Obtain event production data generated by processing event source data through multiple service links.

[0031] 102. Perform feature extraction on event production data to determine the data characteristics of the event production data and the event characteristics reflected by the event production data.

[0032] 103. Based on data characteristics and event characteristics, perform service quality testing on multiple service links to determine the service quality of multiple service links.

[0033] In this embodiment, a multi-service link refers to a data production link that includes multiple service nodes. "Multiple" means two or more. A service node is a logical node that processes the source data input into the data production link. In this embodiment, a logical node that performs the same data processing is called a service node. Different service nodes can be deployed on the same physical machine or on different physical machines. Different service nodes have different data processing methods or functions. For example, ... Figure 2 As shown, the multi-service link may include: access service, standardization processing service, fusion service, and other policy processing services. The access service is used to access event source data into the data production link; the standardization processing service is used to convert event source data into data in a defined data organization format, such as converting event source data into data in the format required by downstream nodes. The fusion service is used to perform fusion processing on events. Event fusion processing will be described in detail in the following embodiments and will not be repeated here.

[0034] Event source data refers to the source data of an event, specifically the source data provided to the data production chain for data production. An event is a real-world event that actually occurs. The implementation form of events differs in different application scenarios. In the field of electronic maps, events can be implemented as events affecting electronic map data, such as road events. Road events can include road closures, traffic congestion, or traffic accidents. In the field of e-commerce, events can be implemented as order-related events, such as order placement events, order cancellation events, or order logistics events. In the field of cloud computing, events can be implemented as virtual instance-related events, such as instance creation events, instance release events, or instance failure events. Virtual instances can be virtual machines, containers, or container groups (such as pods).

[0035] In this embodiment, the data output by the service nodes after processing the data is collectively referred to as event production data. Event production data refers to the data output by each service node in a multi-service link, and may include event output data from the multi-service link and output data from each intermediate service node. Considering that event production data can reflect, to some extent, whether the service node outputting the event production data is functioning correctly, in this embodiment, to achieve end-to-end quality inspection of the data production link including multiple service nodes (i.e., the multi-service link), in step 101, event production data generated by the multi-service link processing the event source data can be obtained; and in step 102, feature extraction is performed on the event production data to determine the data characteristics of the event production data and the event characteristics reflected by the event production data.

[0036] Data characteristics refer to the inherent attributes of the data itself, unrelated to the data content, such as data organization format and data field attributes. Event characteristics reflected in event-generated data refer to the characteristics of the event corresponding to the event-generated data, such as event information or information about events related to the event corresponding to the event-generated data. For example, in the field of electronic maps, if multiple users report the same road event, publishing each user's report would result in duplicate publication of the same road event, affecting user experience. Therefore, event fusion can be performed on road events reported by multiple users, and the fused event can include the correlation information of the road events reported by multiple users. As another example, if multiple users report road events of the same nature, the electronic map server can aggregate these reports and publish the aggregated event information. The aggregated event information is a new event, which to some extent reflects the correlation information of the road events of the same nature reported by multiple users.

[0037] The data characteristics of event production data and the event characteristics reflected by the event production data can reflect the service quality of service nodes in a multi-service link to a certain extent. Therefore, in step 103, the service quality of the multi-service link can be detected based on the data characteristics of event production data and the event characteristics reflected by the event production data to determine the service quality of the multi-service link.

[0038] In this embodiment, when performing service quality detection on multiple service links based on the data characteristics of event production data and the event characteristics reflected by the event production data, service quality detection can be performed on the service nodes used for data attribute processing in the multiple service links based on the data characteristics of the event production data to determine the service quality of the service nodes used for data attribute processing; and service quality detection can be performed on the service nodes used for event processing in the multiple service links based on the event characteristics of the event processing in the multiple service links to determine the service quality of the service nodes used for event processing.

[0039] In a multi-service chain, different service nodes have different data processing functions, and therefore require different feature information for quality inspection. Based on this, the data processing function of service nodes in a multi-service chain can be determined. For some service nodes whose data processing function is data attribute processing, data features can be extracted from the event production data generated by the data production chain to determine the data characteristics of the event production data. For service nodes whose data processing function is event processing, event features are extracted from the event production data generated by the data production chain to determine the event characteristics reflected in the event production data. Accordingly, for service nodes used for data attribute processing, service quality inspection can be performed based on the data characteristics of the event production data to determine the service quality of the service nodes used for data attribute processing; similarly, for service nodes used for event processing, service quality inspection can be performed based on the event characteristics reflected in the event production data to determine the service quality of the service nodes used for event processing.

[0040] The data processing functions of service nodes differ, resulting in different characteristic information for service quality detection and different detection methods. The following provides illustrative examples of service quality detection methods for service nodes with different data processing functions.

[0041] In some embodiments, the quality of the event output data from the data production link, i.e., the final output data of the data production link, can reflect the service quality of some service nodes in the data production link to a certain extent. Based on this, event output data from multiple service links within the detection period can be obtained; and event source data input to multiple service links within the detection period can be obtained. In this embodiment, for ease of description and distinction, the event source data corresponding to event production data in step 101 above is defined as the first event source data; and the event source data input to multiple service links within the detection period, i.e., the entry data of multiple service links within the detection period, is defined as the second event source data. The second event source data may include the first event source data.

[0042] In this embodiment, the multi-service link may or may not output event exit data. Correspondingly, all second event source data may correspond to event exit data. In this case, all second event source data are first event source data. In other embodiments, some second event source data may correspond to event exit data, while others may not. In this case, the second event source data may include first event source data, or it may include event source events without corresponding event exit data (defined as third event source data). Of course, all second event source data may also have no corresponding event exit data. In this case, all second event source data are third event source data.

[0043] In cases where no event output data is output, it can be determined that some or all service nodes in the multi-service link are abnormal, but it is impossible to determine which specific service nodes are abnormal. In practical applications, among the service nodes in the multi-service link, some service nodes may output event production data, while others may not; the service nodes that do not output event production data are the abnormal service nodes. Based on this, in order to determine the specific abnormal service nodes, for embodiments where the second event source data contains third event source data, the event identifier of the third event source data can also be used to match the event production data generated by the multi-service link during the detection time; from the multi-service link, it can be determined that the service node that does not have event production data corresponding to the event identifier of the third event source data is abnormal. Further, as... Figure 2 As shown, for abnormal service nodes, a service node interruption prompt message can also be output.

[0044] Optionally, event production data generated by multiple service links during the detection time can be obtained; and based on the event identifiers corresponding to the event production data, the event production data corresponding to each event identifier can be obtained; then, according to the chronological order of the event history identifiers of the event production data corresponding to the event identifiers of the third event source data, the event identifiers of the third event source data can be sequentially matched with the event production data corresponding to the current event history identifier. If the event production data corresponding to the current event history identifier does not contain event production data corresponding to the event identifier of the third event source data, then it is determined that the service node to which the event production data corresponding to the current event history identifier belongs is abnormal. Here, the event history identifiers of the event production data can represent the chronological order of data processing during the data production process.

[0045] For example, such as Figure 2As shown, the data processing sequence can be as follows: the access service node connects the event source data to the data production chain; the persistence service node performs persistence processing on the event source data; the fusion service performs fusion processing on the same event reported by different users; the exposure service exposes the exposure fields of the data corresponding to the fused event; the packaging service packages the data to be published, and so on. Correspondingly, for third-party event source data, the event identifier of the third-party event source data can be matched against the exit data of the access service node; if the exit data of the access service contains exit data corresponding to the event identifier of the third-party event source data, the access service node is considered normal; if the exit data of the access service does not contain exit data corresponding to the event identifier of the third-party event source data, the access service node is considered abnormal; and an access service node interruption prompt can be output. Furthermore, if the access service node is functioning normally, the event identifier of the third event source data can be matched against the egress data of the persistent service node. If the egress data of the persistent service contains egress data corresponding to the event identifier of the third event source data, then the access service node is determined to be normal. If the egress data of the persistent service does not contain egress data corresponding to the event identifier of the third event source data, then the access service node is determined to be abnormal, and an interruption prompt for the persistent service node can be output. This process can be repeated until quality checks are performed on the packaging service node, etc.

[0046] In an embodiment where the second event source data contains the first event source data, the second event source data with corresponding event exit data can be determined as the first event source data based on the event identifier of the second event source data input into the multi-service link within the detection time and the event identifier of the event exit data output by the multi-service link within the detection time; accordingly, the event exit data corresponding to the first event source data can be determined as at least part of the event production data.

[0047] In this embodiment of the application, when performing service quality detection on multiple service links based on the data characteristics of event production data and the event characteristics reflected by the event production data, service quality detection can be performed on the service nodes used for data attribute processing in the multiple service links based on the data characteristics of the event production data, so as to determine the service quality of the service nodes used for data attribute processing. The data characteristics of the event production data may include: data organization format, data field attributes, or the relationships between data fields, etc.

[0048] Accordingly, in some embodiments, for service nodes used for data standardization processing, one optional implementation of the above-mentioned data feature extraction of event exit data to determine the data characteristics of the event exit data is: the data organization format of the event exit data can be determined based on the field identifiers of the event exit data, which serves as the data characteristics of the event exit data. Accordingly, based on the data characteristics of the event exit data, service quality detection can be performed on service nodes used for data attribute processing in a multi-service link, which can be achieved as follows: Figure 2 The standardization detection is performed as follows: The system determines whether the data organization format conforms to the set format requirements; if the determination result is yes, the service node used for data standardization conversion in the multi-service link is determined to be normal. If the determination result is no, the service node used for data standardization conversion in the multi-service link is determined to be abnormal. Furthermore, a prompt message indicating that the service node used for data standardization conversion is abnormal can be output.

[0049] In other embodiments, some data fields need to be transparently processed. Based on this, for the service node used for data transparent processing, Figure 2 One optional implementation for extracting features from event egress data to determine its data characteristics is as follows: Based on the field identifiers of the event egress data, determine the transparent fields of the event egress data, using these fields as its data characteristics. Correspondingly, based on these data characteristics, service quality detection of service nodes used for data attribute processing in a multi-service link can also be implemented as follows: Figure 2 The pass-through detection can be implemented as follows: It is determined whether the data in the pass-through field of the event exit data is consistent with the data in the pass-through field of the first event source data. If the determination result is yes, the service node used for data pass-through in the multi-service link is determined to be normal. If the determination result is no, the service node used for data pass-through in the multi-service link is determined to be abnormal. Furthermore, a prompt message indicating an abnormal service node used for data pass-through can be output.

[0050] In some embodiments, some data fields are calculated from other fields. Based on this, another optional implementation for feature extraction of event exit data to determine the data characteristics of event exit data is: determining the associated fields in the event exit data based on the field identifiers of the event exit data, and using these as the data characteristics of the event exit data. Correspondingly, based on the data characteristics of the event exit data, an optional implementation for service quality detection of service nodes used for data attribute processing in a multi-service link is: calculating the association relationship between associated fields based on the data of the associated fields; determining whether the association relationship between the associated fields meets the set association relationship; if the determination result is yes, then determining that the service node used for data association calculation in the multi-service link is normal. If the determination result is no, then determining that the service node used for data association calculation in the multi-service link is abnormal. Further, a prompt message indicating that the service node used for data association calculation is abnormal can be output.

[0051] The above embodiments only take the data characteristics of event production data as data organization format, data field attributes and the relationship between data fields as examples to illustrate the optional implementation of steps 102 and 103. It does not mean that the data characteristics of event production data must include all the data organization format, data field attributes and data fields, nor does it mean that the data characteristics of event production data only include data organization format, data field attributes and data fields.

[0052] In some embodiments, in addition to service nodes that process data attributes, the multi-service link also includes service nodes that process events, such as the service nodes for event fusion and event aggregation mentioned above. When performing service quality detection on the multi-service link, the service nodes used for event processing can be tested based on the event characteristics within the multi-service link to determine their service quality. The optional implementations of steps 102 and 103 are illustrated below using service quality detection of service nodes used for event fusion and event aggregation as examples.

[0053] In some embodiments, obtaining event production data generated by processing event source data through multiple service links can also be implemented as follows: determining a field for recording event identifiers associated with event exit data based on the field identifiers of event exit data; obtaining the event identifier associated with event exit data from the field for recording event identifiers associated with event exit data; and obtaining the first entry data corresponding to the event identifier associated with event exit data from the entry data of the service node used for event processing, based on the event identifier associated with event exit data, as at least part of the event production data.

[0054] Furthermore, for service nodes used for event fusion processing, one implementation method for extracting event features from event production data and determining the event features reflected in the event production data can be achieved by: obtaining the association information of the fused events associated with the event exit data from the first entry data based on the field identifier of the first entry data; and obtaining the association information of the fused events from the event exit data based on the field identifier of the event exit data. Correspondingly, based on the event features used for event processing in the multi-service link, service quality detection of service nodes used for event processing in the multi-service link can be implemented as follows: Figure 2 The event fusion detection shown can be implemented as follows: It determines whether the association information of the fused event is consistent with the association information of the fused event; if the determination result is yes, then the service node used for event fusion in the multi-service link is determined to be normal. Conversely, if the determination result indicates that the association information of the fused event is inconsistent with the association information of the fused event, then the service node used for event fusion in the multi-service link is determined to be abnormal. Furthermore, a prompt message indicating that the service node used for event fusion is abnormal can be output.

[0055] For example, for road events, the associated information of the merged event, such as user identifier, lane information, traffic light information, and road sign information, can be obtained from the associated information of the merged event. Accordingly, it can be determined whether the user identifier, lane information, traffic light information, and road sign information of the merged event are consistent with those of the merged event. If they are completely consistent, the service node used for event fusion in the multi-service link is determined to be normal. Conversely, if the determination result shows inconsistencies between the associated information of the merged event and the associated information of the merged event, the service node used for event fusion in the multi-service link is determined to be abnormal. Furthermore, a prompt message indicating the abnormality of the service node used for event fusion can be output.

[0056] In some embodiments, when multiple users report events of the same nature, these events can be aggregated into a new event. The association information of the aggregated event can, to some extent, reflect the association information of the events reported by multiple users. In practical applications, when aggregating events, the sources of the aggregated events differ, resulting in varying levels of credibility and accuracy. To improve the credibility and accuracy of published events, certain fields of the data corresponding to the aggregated event need to originate from users with high credibility. Based on this, the field used to record the event identifier of the aggregated event can be determined according to the field identifier of the event exit data; the event identifier of the aggregated event can be obtained from the field used to record the event identifier of the aggregated event; and the first entry data corresponding to the aggregated event can be obtained from the entry data of the service node used for event aggregation based on the event identifier of the aggregated event. Accordingly, one implementation method for extracting event features from event production data and determining the event features reflected in the event production data can be as follows: based on the field identifier of the first entry data corresponding to the aggregated event, obtain the source of the aggregated event associated with the event exit data from the first entry data, as the event feature reflected in the first entry data; and based on the field identifier of the event exit data, determine a specified field; obtain the source of the data recorded in the specified field from the specified field, as the event feature reflected in the event exit data.

[0057] Accordingly, based on the event characteristics used for event processing in a multi-service link, service quality detection of the service nodes used for event processing in the multi-service link can be implemented as follows: Figure 2 The event aggregation detection shown is implemented as follows: if the source of the associated information of the aggregated event includes a specified source, determine whether the source of the data recorded in the specified field is the specified source; if the determination result is no, then determine that the service node used for event aggregation in the multi-service link is abnormal.

[0058] In the field of electronic maps, events can be implemented as road events. Road events have location information. To ensure the accuracy of information dissemination, the distance between the location of the aggregated event and the location of the aggregated event should not be too large. Based on this, in some embodiments, another implementation of step 102, which extracts features from the event production data to determine the event features reflected by the event production data, can be implemented as follows: based on the field identifier of the event exit data, obtain the geographic location information associated with the aggregated event from the event exit data; obtain the geographic location information associated with the aggregated event from the association information of the aggregated event obtained from the first entry data.

[0059] Accordingly, based on the event characteristics used for event processing in the multi-service link, the service quality detection of the service nodes used for event processing in the multi-service link can be implemented as spatial update detection. The specific implementation method is as follows: based on the geographical location information associated with the aggregated event and the geographical location information associated with the aggregated event, calculate the distance between the location of the aggregated event and the location of the aggregated event; determine whether the distance between the location of the aggregated event and the location of the aggregated event is less than or equal to a set distance threshold; if the determination result is not, then determine that the service node used for event aggregation in the multi-service link is abnormal.

[0060] The above-described implementation methods for quality inspection of service nodes used for event aggregation can be implemented individually or in combination. In the combined implementation, another implementation method for extracting features from event production data and determining the event features reflected in the event production data in step 102 can be implemented as follows: based on the field identifier of the first entry data corresponding to the aggregated event, obtain the source of the aggregated event associated with the event exit data from the first entry data as the event feature reflected in the first entry data; and based on the field identifier of the event exit data, determine a specified field; obtain the source of the data recorded in the specified field as the event feature reflected in the event exit data; and based on the field identifier of the event exit data, obtain the geographic location information associated with the aggregated event from the event exit data; and obtain the geographic location information associated with the aggregated event from the association information of the aggregated event obtained from the first entry data.

[0061] Accordingly, based on the event characteristics used for event processing in a multi-service link, the implementation method for service quality detection of service nodes used for event processing in a multi-service link is as follows: If the source of the associated information of the aggregated event includes a specified source, determine whether the source of the data recorded in the specified field is the specified source; and, based on the geographical location information associated with the aggregated event and the geographical location information associated with the aggregated event, calculate the distance between the location of the aggregated event and the location of the aggregated event; determine whether the distance between the location of the aggregated event and the location of the aggregated event is less than or equal to a set distance threshold; if the determination result is negative, then the service node used for event aggregation in the multi-service link is determined to be abnormal. Conversely, if the determination results are all positive, then the service node used for event aggregation in the multi-service link is determined to be normal.

[0062] The above embodiments are merely examples of event processing, specifically event fusion and event aggregation, to illustrate the optional implementation methods of steps 102 and 103. They do not imply that event processing must include event fusion and event aggregation, nor do they imply that the data characteristics of event production data only include event fusion and event aggregation.

[0063] The data production link quality detection method provided in this embodiment targets a data production link containing multiple service nodes, i.e., a multi-service link. It acquires event production data generated by the processing of event source data by the multi-service production link; extracts features from the event production data to determine the data characteristics of the event production data and the event characteristics reflected by the event production data; and then performs service quality detection on the multi-service link based on the data characteristics and event characteristics. This achieves full-link quality monitoring of the data production link, which can make up for the deficiency that single-node data monitoring cannot detect full-link quality problems.

[0064] On the other hand, the data production link quality detection method provided in this embodiment links the processing processes in the entire link through information such as event identifiers and event history identifiers, which can simplify test cases and improve the timeliness of detection of multi-service data production links.

[0065] like Figure 2 As shown, under normal service quality conditions across multiple service links, expiration and deletion detection can be performed on event egress data. The specific implementation is as follows: Based on the field identifiers of the event egress data, the event lifecycle and the event reporting time associated with the event egress data are obtained from the event egress data; based on the event reporting time and event lifecycle, it is determined whether the event associated with the event egress data has expired; if the determination result is no, the event egress data is published. If the determination result is that the event associated with the event egress data has expired, the event egress data is discarded. For example, the event reporting time and event lifecycle can be added to obtain the event's duration; if the event's duration is earlier than the current time, it is determined that the event associated with the event egress data has expired; if the event's duration is later than the current time, it is determined that the event associated with the event egress data has not expired, and the event egress data can be published.

[0066] The data production chain quality inspection method provided in this application is applicable to data production chain quality inspection in various application scenarios. The following uses a map data production chain as an example to illustrate the map data production chain quality inspection method provided in this application.

[0067] Figure 3 A quality inspection method for the map data production chain provided in this application embodiment. For example... Figure 3 As shown, the method includes:

[0068] 301. Obtain map data generated by processing the first road source data in the map data production chain containing multiple service nodes.

[0069] 302. Perform feature extraction on the map data to determine the data characteristics of the map data and the characteristics of road events reflected by the map data.

[0070] 303. Based on data characteristics and road event characteristics, perform service quality testing on the map data production chain to determine the service quality of the map data production chain.

[0071] In this embodiment, the map data production chain includes multiple service nodes. Each service node is a logical node used to process the input data to the service node to obtain the output data for the next service node. Under normal service quality conditions, the map data production chain can process the road source data input to the map data production chain to obtain output map data that meets the needs of map applications.

[0072] Road source data refers to the source data of road events obtained by the server of a map application. This source data of road events can be collected by map data acquisition equipment (such as data collection vehicles), reported by map application users, or reported by map application maintenance personnel, etc. Road events can be defined as events that affect electronic map data, such as road closures, traffic congestion, or traffic accidents.

[0073] In this embodiment, the data output by each service node in the map data production chain is collectively referred to as map data, which may include: the output map data of the map data production chain and the output data of each intermediate service node. Considering that map data can reflect the normality of the service node that outputs the map data to a certain extent, in order to realize the full-link quality detection of the map data production chain including multiple service nodes, in step 301, map data generated by processing road source data in the map data production chain is used; and in step 302, feature extraction is performed on the map data to determine the data characteristics of the map data and the road event characteristics reflected by the map data.

[0074] The description of data characteristics can be found in the relevant content of the above embodiments, and will not be repeated here. The road event characteristics reflected in the map data refer to the characteristics of the road events corresponding to the map data, such as road event information or information about road events associated with the road events corresponding to the map data. The description of road event characteristics can be found in the description of event characteristics in the field of electronic maps in the above embodiments, and will not be repeated here.

[0075] The data characteristics of map data and the road event characteristics reflected in map data can, to some extent, reflect the service quality of service nodes in the map service production chain. Therefore, in step 303, the service quality of the map data production chain can be detected based on the data characteristics of map data and the road event characteristics reflected in map data to determine the service quality of the map data production chain.

[0076] In this embodiment, when performing service quality detection on the map data production chain based on the data characteristics of the map data and the road event characteristics reflected in the map data, the service quality of the service nodes used for data attribute processing in the map data production chain can be detected based on the data characteristics of the map data to determine the service quality of the service nodes used for data attribute processing; and the service quality of the service nodes used for event processing in the map data production chain can be detected based on the road event characteristics in the map data production chain to determine the service quality of the service nodes used for event processing.

[0077] In the map data production chain, different service nodes have different data processing functions, and therefore, the feature information required for quality inspection of service nodes with different data processing functions also differs. Based on this, the data processing functions of service nodes in the map data production chain can be determined. For some service nodes whose data processing function is data attribute processing, data features can be extracted from the map data generated by the data production chain to determine the data characteristics of the map data. For service nodes whose data processing function is event processing, road event features can be extracted from the map data generated by the data production chain to determine the road event characteristics reflected in the map data. Accordingly, for service nodes used for data attribute processing, service quality inspection can be performed based on the data characteristics of the map data to determine the service quality of the service nodes used for data attribute processing; similarly, for service nodes used for event processing, service quality inspection can be performed based on the road event characteristics reflected in the map data to determine the service quality of the service nodes used for event processing.

[0078] The data processing functions of service nodes differ, resulting in different characteristic information for service quality detection and different detection methods. The following provides illustrative examples of service quality detection methods for service nodes with different data processing functions.

[0079] In some embodiments, the quality of the output map data of the data production link, i.e., the final output data of the data production link, can reflect the service quality of some service nodes in the data production link to a certain extent. Based on this, the output map data of the map data production link during the detection period can be obtained; and the road source data input to the map data production link during the detection period can be obtained. In this embodiment, for ease of description and distinction, the road source data corresponding to map data in step 301 above is defined as the first road source data; the road source data input to the map data production link during the detection period, i.e., the input data of the map data production link during the detection period, is defined as the second road source data. The second road source data may include the first road source data.

[0080] In this embodiment, the map data production chain may or may not output export map data. Correspondingly, all of the second road source data may correspond to export map data. In this case, all of the second road source data is first road source data. In other embodiments, the second road source data may partially correspond to export map data and partially not. In this case, the second road source data may include first road source data, or it may include road source data without corresponding export map data (defined as third road source data). Of course, the second road source data may also have no corresponding export map data at all. In this case, all of the second road source data is third road source data.

[0081] In cases where there is no output map data, it can be determined that some or all service nodes in the map data production chain are abnormal, but it is impossible to determine which specific service nodes are abnormal. In practical applications, some service nodes in the map data production chain may output map data, while others may not; the service nodes that do not output map data are the abnormal service nodes. Based on this, in order to determine the specific abnormal service nodes, for embodiments where the second road source data contains third road source data, the event identifier of the third road source data can also be used to match the map data generated in the map data production chain during the detection time; from the map data production chain, it can be determined that the service node that does not contain map data corresponding to the event identifier of the third road source data is abnormal. Furthermore, for the abnormal service node, a service node interruption prompt message can also be output.

[0082] Optionally, map data generated during the detection time in the map data production chain can be obtained; and map data corresponding to each event identifier can be obtained according to the event identifiers corresponding to the map data; then, according to the chronological order of the event history identifiers of the map data corresponding to the event identifiers of the third road source data, the event identifiers of the third road source data can be used to match the map data corresponding to the current event history identifier. If the map data corresponding to the current event history identifier does not contain map data corresponding to the event identifier of the third road source data, then it is determined that the service node to which the map data corresponding to the current event history identifier belongs is abnormal. Here, the event history identifiers of the map data can represent the chronological order of data processing during the data production process.

[0083] In an embodiment where the second road source data contains the first road source data, the second road source data with the corresponding exit map data can be determined as the first road source data based on the event identifier of the second road source data input into the map data production link within the detection time and the event identifier of the exit map data output by the map data production link within the detection time; accordingly, the exit map data corresponding to the first road source data can be determined as at least a portion of the map data.

[0084] In this embodiment, correspondingly, for the service node used for data attribute processing, data attribute features can be extracted from the export map data to determine the data attribute features of the export map data. Furthermore, when performing service quality detection on the service node used for data attribute processing in the map data production chain based on the data features of the map data, it can be determined whether the data attribute features of the export map data meet the set data attribute requirements; if the determination result is yes, then it is determined that the service node used for data attribute processing in the map data production chain is normal.

[0085] The data attribute characteristics of map data may include: data organization format, data field attributes, or relationships between data fields.

[0086] In some embodiments, for service nodes used for data standardization processing, an optional implementation of the above-mentioned data feature extraction of export map data to determine the data characteristics of export map data is as follows: the data organization format of the export map data can be determined based on the field identifiers of the export map data, serving as the data attribute characteristics of the export map data. Correspondingly, based on the data attribute characteristics of the export map data, service quality detection is performed on the service nodes used for data attribute processing in the map data production chain. This can be achieved by: determining whether the data organization format conforms to the set format requirements; if the determination result is yes, then the service node used for data standardization conversion in the map data production chain is determined to be normal. If the determination result is no, then the service node used for data standardization conversion in the map data production chain is determined to be abnormal. Further, a prompt message indicating that the service node used for data standardization conversion is abnormal can be output.

[0087] In other embodiments, some data fields require pass-through processing. Based on this, for service nodes used for data pass-through processing, an optional implementation method for extracting attribute features from the exit map data to determine the data attribute features of the exit map data is: determining the pass-through fields of the exit map data based on the field identifiers of the exit map data, using these as the data attribute features of the exit map data. Correspondingly, based on the data attribute features of the exit map data, service quality detection of service nodes used for data attribute processing in the map data production chain can also be implemented as follows: determining whether the data in the pass-through fields of the exit map data is consistent with the data in the pass-through fields of the first road source data; if the determination result is yes, then the service node used for data pass-through in the map data production chain is determined to be normal. If the determination result is no, then the service node used for data pass-through in the map data production chain is determined to be abnormal. Furthermore, a prompt message indicating an abnormal service node used for data pass-through can be output.

[0088] In some embodiments, some data fields are calculated from other fields. Based on this, another optional implementation for feature extraction of export map data to determine its data characteristics is: determining related fields in the export map data based on the field identifiers, as data attribute features of the export map data. Correspondingly, based on the data attribute features of the export map data, an optional implementation for service quality detection of service nodes used for data attribute processing in the map data production chain is: calculating the association relationship between related fields based on the data of the related fields; determining whether the association relationship between the related fields meets the set association relationship; if the determination result is yes, then determining that the service node used for data association calculation in the map data production chain is normal. If the determination result is no, then determining that the service node used for data association calculation in the map data production chain is abnormal. Further, a prompt message indicating that the service node used for data association calculation is abnormal can be output.

[0089] The above embodiments only take the data characteristics of map data, such as data organization format, data field attributes, and the relationship between data fields, as examples, but do not constitute a limitation.

[0090] In some embodiments, in addition to service nodes that process data attributes, the map data production link also includes service nodes that process road events, such as the service nodes for road event fusion and road event aggregation mentioned above. When performing service quality detection on the map data production link for service nodes that process road events, the service quality of these service nodes can be determined based on the road event characteristics in the map data production link used for crossing event processing. The optional implementations of steps 302 and 303 are illustrated below using service quality detection of service nodes used for event fusion and event aggregation as examples.

[0091] In some embodiments, obtaining map data generated by processing road source data through a map data production link can also be implemented as follows: obtaining a road event identifier associated with the exit map data from the exit map data of the map data production link; and, based on the road event identifier associated with the exit map data, obtaining first entry data corresponding to the event identifier associated with the exit map data from the entry data of the service node used for event processing, as at least a portion of the map data generated by the aforementioned map data production link. Specifically, a field for recording road event identifiers associated with the exit map data can be determined based on the field identifier of the exit map data; the road event identifier associated with the exit map data can be obtained from the field for recording road event identifiers associated with the exit map data; and, based on the event identifier associated with the exit map data, obtaining first entry data corresponding to the event identifier associated with the exit map data from the entry data of the service node used for event processing, as at least a portion of the map data generated by the aforementioned map data production link.

[0092] Furthermore, for the service nodes used for event fusion processing, one implementation method for extracting road event features from map data and determining the road event features reflected in the map data can be achieved by: obtaining road information associated with the fused road events from the first entry data and the exit map data; and obtaining road information associated with the fused road events from the exit map data. Specifically, based on the field identifier of the first entry data, road information associated with the fused road events associated with the exit map data can be obtained from the first entry data; and based on the field identifier of the exit map data, road information associated with the fused road events can be obtained from the exit map data.

[0093] Accordingly, based on the road event characteristics reflected in the map data, service quality detection of the service nodes used for event processing in the map data production chain can be implemented as follows: Determine whether the road information associated with the fused road event is consistent with the road information associated with the fused event; if the determination result is yes, then the service node used for event fusion in the map data production chain is determined to be normal. Conversely, if the determination result is that the road information associated with the fused event is inconsistent with the road information associated with the fused event, then the service node used for event fusion in the map data production chain is determined to be abnormal. Furthermore, a prompt message indicating that the service node used for event fusion is abnormal can be output. For example, the road information associated with the road event can be the road information reported by the road event, which may include: lane information, traffic light information, road sign information, etc. of the road reported by the road event.

[0094] In some embodiments, multiple users report road events of the same nature. For example, multiple users report traffic accidents on the same road segment. In this embodiment, road events of the same nature can be aggregated into a new road event, and the association information of the aggregated road event can, to some extent, reflect the association information of road events reported by multiple users. In practical applications, when aggregating events, the sources of the aggregated road events differ, and their credibility and accuracy also differ. To improve the credibility and accuracy of published events, certain fields of the data corresponding to the aggregated road events need to originate from users with high credibility. Based on this, the event identifier of the aggregated event can be obtained from the field identifiers of the exit map data; and according to the event identifier of the aggregated event, the first entry data corresponding to the aggregated event can be obtained from the entry data of the service node used for event aggregation.

[0095] Specifically, the field for recording the event identifier of the aggregated road events can be determined based on the field identifier of the exit map data; the event identifier of the aggregated event can be obtained from the field for recording the event identifier of the aggregated road events corresponding to the exit map data; and the first entry data corresponding to the aggregated event can be obtained from the entry data of the service node used for event aggregation based on the event identifier of the aggregated road events.

[0096] Accordingly, one implementation method for extracting road event features from map data and determining the road event features reflected in the map data can be: obtaining the source of aggregated road events associated with exit map data from the first entry data as the road event features reflected in the first entry data; and obtaining the source of data recorded in a specified field from the exit map data as the road event features reflected in the exit map data.

[0097] Specifically, a specified field can be determined based on the field identifier of the export map data; the source of the data recorded in the specified field can be obtained from the specified field as the road event characteristics reflected in the export map data.

[0098] Accordingly, based on the characteristics of road events used for event processing in the map data production chain, the service quality detection of service nodes used for event processing in the map data production chain can be implemented as follows: if the source of the associated information of the aggregated road event includes a specified source, determine whether the source of the data recorded in the specified field is the specified source; if the determination result is no, then determine that the service node used for event aggregation in the map data production chain is abnormal.

[0099] Road events contain location information. To ensure the accuracy of information dissemination, the distance between the location of the aggregated road event and the location of the aggregated road event should not be too large. Based on this, in some embodiments, another implementation of step 302, which extracts features from the map data to determine the road event features reflected in the map data, can be: obtaining the geographic location information associated with the aggregated road event from the exit map data; and obtaining the geographic location information associated with the aggregated road event from the association information of the aggregated road event obtained from the first entrance data.

[0100] Specifically, based on the field identifiers of the exit map data, the geographic location information associated with the aggregated road events can be obtained from the exit map data; and the geographic location information associated with the aggregated road events can be obtained from the association information of the aggregated road events obtained from the first entry data.

[0101] Accordingly, based on the road event characteristics reflected in the map data, service quality detection of service nodes used for event processing in the map data production chain can be implemented as spatial update detection. The specific implementation method is as follows: based on the geographic location information associated with the aggregated road events and the geographic location information associated with the road events of the aggregated events, calculate the distance between the location of the aggregated road events and the location of the aggregated road events; determine whether the distance between the location of the aggregated road events and the location of the aggregated road events is less than or equal to a set distance threshold; if the determination result is negative, then determine that the service node used for event aggregation in the map data production chain is abnormal.

[0102] The above-described methods for quality detection of service nodes used for event aggregation can be implemented individually or in combination. When implemented in combination, if any of the judgment results are negative, the service node used for event aggregation in the map data production chain is determined to be abnormal. Conversely, if all judgment results are positive, the service node used for event aggregation in the map data production chain is determined to be normal.

[0103] The above embodiments are merely examples of event processing, specifically event fusion and event aggregation, to illustrate the optional implementations of steps 302 and 303. They do not imply that event processing must include event fusion and event aggregation, nor do they imply that the data features of map data only include event fusion and event aggregation.

[0104] The quality detection method for the map data production chain provided in this embodiment targets a map data production chain containing multiple service nodes. It acquires map data generated by processing road source data in the map data production chain; extracts features from the map data to determine the data characteristics of the map data and the road event characteristics reflected by the map data; and then performs service quality detection on the map data production chain based on the data characteristics and road event characteristics. This achieves full-chain quality monitoring of the data production chain and can make up for the deficiency that single-service node data monitoring cannot detect full-chain quality problems.

[0105] On the other hand, the quality detection method for the map data production chain provided in this embodiment links the processing processes in the entire chain through information such as event identifiers and event history identifiers, which can simplify test cases and improve the timeliness of detection of the map data production chain with multiple service nodes.

[0106] Under normal service quality conditions in the map data production chain, expiration and deletion detection can be performed on exported map data. The specific implementation is as follows: Obtain the lifecycle of road events and the reporting time of road events associated with the exported map data from the exported map data; based on the reporting time and lifecycle of the road events, determine whether the road events associated with the exported map data have expired; if the result is no, then publish the exported map data. If the result is that the road events associated with the exported map data have expired, discard the exported map data. For example, the event reporting time and event lifecycle can be added to obtain the event's duration; if the event's duration is earlier than the current time, then the event associated with the exported map data has expired; if the event's duration is later than the current time, then the event associated with the exported map data has not expired, and the exported map data can be published.

[0107] For clients with map applications installed, they can obtain the output map data from the map data production chain for map rendering, etc. Correspondingly, such as Figure 4 As shown, the map rendering method provided in this application embodiment may include:

[0108] 401. Obtain export map data released from the production chain that has passed quality inspection.

[0109] 402. Render the export map data on the electronic map.

[0110] In this embodiment, the map data production chain includes multiple service nodes. A description of the map data production chain can be found in the relevant content of the above embodiments, and will not be repeated here. In this embodiment, the map data production chain performs service quality detection based on the data characteristics of the map data generated by the map data production chain and the road event characteristics reflected in the map data, and the map data production chain passes the aforementioned service quality detection. The implementation method for performing service quality detection on the map data production chain can be found in the relevant content of the above embodiments, and will not be repeated here.

[0111] In this embodiment, the export map data published in the map data production chain can be rendered on the electronic map, thereby displaying the road events associated with the export map data on the terminal, so that users of map applications can promptly learn about road events.

[0112] It should be noted that the execution subject of each step of the method provided in the above embodiments can be the same device, or the method can be executed by different devices. For example, the execution subject of steps 101 and 102 can be device A; or the execution subject of step 101 can be device A, and the execution subject of step 102 can be device B; and so on.

[0113] Furthermore, some processes described in the above embodiments and accompanying drawings include multiple operations that appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or they may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel.

[0114] Accordingly, embodiments of this application also provide a computer-readable storage medium storing computer instructions, which, when executed by one or more processors, cause one or more processors to perform the steps in the above-described quality inspection method for the data production chain and / or the quality inspection method for the map data production chain.

[0115] This application also provides a computer program product, including: a computer program; the computer program is executed by a processor to implement the above-described quality detection method for the data production chain and / or the quality detection method for the map data production chain. This computer program product can be implemented as data production application software or a plug-in; it can also be implemented as a SaaS service product, etc.

[0116] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of this application. For example... Figure 5As shown, the computer device includes: a memory 50a and a processor 50b; wherein, the memory 50a is used to store computer programs;

[0117] The processor 50b is coupled to the memory 50a and is used to execute a computer program for: acquiring event production data generated by processing first event source data through multiple service links; extracting features from the event production data to determine the data features of the event production data and the event features reflected by the event production data; and performing service quality detection on the multiple service links based on the data features and event features to determine the service quality of the multiple service links.

[0118] In some embodiments, the processor 50b is further configured to: determine the data processing function of a service node in a multi-service link. Optionally, when extracting features from event production data, the processor 50b is specifically configured to: if the data processing function of the service node is data attribute processing, extract data features from the event production data to determine the data features of the event production data; if the data processing function of the service node is event processing, extract event features from the event production data to determine the event features reflected by the event production data.

[0119] Optionally, when performing service quality detection on multiple service links, the processor 50b specifically performs the following: for service nodes used for data attribute processing, based on the data characteristics of the event production data, to determine the service quality of the service nodes used for data attribute processing; and for service nodes used for event processing, based on the event characteristics of the event production data, to determine the service quality of the service nodes used for event processing.

[0120] In some embodiments, when the processor 50b acquires event production data generated by processing the first event source data by the multi-service link, it is specifically used to: acquire event exit data output by the multi-service link within a detection period; determine, based on the event identifier of the second event source data input to the multi-service link within the detection period and the event identifier of the event exit data, that the second event source data corresponding to the event exit data is the first event source data; and determine that the event exit data corresponding to the first event source data is at least part of the event production data.

[0121] Accordingly, when extracting data features from event production data, processor 50b specifically determines the data organization format of the event output data based on the field identifiers of the event output data for the service nodes used for data standardization processing. Similarly, when performing service quality checks on service nodes used for data attribute processing in a multi-service link, processor 50b specifically determines whether the data organization format conforms to the set format requirements; if the determination result is yes, then it is determined that the service node used for data standardization conversion in the multi-service link is normal.

[0122] In other embodiments, when the processor 50b extracts data features from the event egress data, it specifically performs the following: for the service node used for data pass-through processing, it determines the pass-through field of the event egress data based on the field identifier of the event egress data. Correspondingly, when the processor 50b performs service quality detection on the service node used for data attribute processing in the multi-service link, it specifically performs the following: it determines whether the data of the pass-through field of the event egress data is consistent with the data of the pass-through field of the first event source data; if the determination result is yes, it determines that the service node used for data pass-through in the multi-service link is normal.

[0123] In some other embodiments, the processor 50b, in acquiring event production data generated by processing the first event source data through multiple service links, is further configured to: determine a field for recording event identifiers associated with the event exit data based on the field identifiers of the event exit data; obtain the event identifier associated with the event exit data from the field for recording the event identifier associated with the event exit data; and obtain the first entry data corresponding to the event identifier associated with the event exit data from the entry data of the service node used for event processing, based on the event identifier associated with the event exit data, as at least part of the event production data.

[0124] Optionally, when extracting event features from event production data, the processor 50b specifically performs the following: for the service node used for event fusion processing, based on the field identifier of the first entry data, obtains the association information of the fused event associated with the event exit data from the first entry data; and based on the field identifier of the event exit data, obtains the association information of the fused event from the event exit data. Correspondingly, when performing service quality detection on the service node used for event processing in the multi-service link, the processor 50b specifically performs the following: determines whether the association information of the fused event is consistent with the association information of the fused event; if the determination result is yes, it determines that the service node used for event fusion processing in the multi-service link is normal.

[0125] Optionally, when the processor 50b extracts event features from the event production data, it specifically performs the following: for the service node used for event aggregation processing, based on the field identifier of the first entry data, obtains the source of the aggregated event associated with the event exit data from the first entry data; determines a specified field based on the field identifier of the event exit data; obtains the source of the data recorded in the specified field from the specified field; and / or, based on the field identifier of the event exit data, obtains the geographic location information associated with the aggregated event from the event exit data; and obtains the geographic location information associated with the aggregated event from the associated information of the aggregated event obtained from the first entry data.

[0126] Accordingly, processor 50b performs service quality checks on the service nodes used for event processing in the multi-service link, specifically by performing at least one of the following judgment operations:

[0127] If the source of the associated information of the aggregated event includes a specified source, determine whether the source of the data recorded in the specified field is the specified source;

[0128] Based on the geographic location information associated with the aggregated event and the geographic location information associated with the aggregated event, calculate the distance between the location of the aggregated event and the location of the aggregated event; determine whether the distance between the location of the aggregated event and the location of the aggregated event is less than or equal to a set distance threshold.

[0129] If at least one judgment operation results in a negative outcome, the service node used for event aggregation processing in the multi-service link is determined to be abnormal.

[0130] In some other embodiments, the processor 50b is further configured to: for third event source data in which there is no corresponding event exit data in the second event source data, match the event identifier of the third event source data in the event production data generated by the multi-service link during the detection time; and determine from the multi-service link that there is no service node abnormality that does not have event production data corresponding to the event identifier of the third event source data.

[0131] Optionally, the processor 50b is also configured to: when the service quality of the multi-service link is normal, obtain the event lifecycle and the event reporting time associated with the event egress data from the event egress data according to the field identifier of the event egress data; determine whether the event associated with the event egress data has expired according to the event reporting time and the event lifecycle; if the determination result is no, publish the event egress data.

[0132] In this embodiment, the data production link can be implemented as a map data production link. Accordingly, the processor 50b is further configured to: acquire map data generated by processing the first road source data through the map data production link, which includes multiple service nodes; extract features from the map data to determine the data features of the map data and the road event features reflected by the map data; and perform service quality detection on the map data production link based on the data features and the road event features to determine the service quality of the map data production link.

[0133] Optionally, when performing service quality detection on the map data production link, the processor 50b is specifically used to: perform service quality detection on the service nodes used for data attribute processing in the map data production link according to data characteristics, so as to determine the service quality of the service nodes used for data attribute processing; and perform service quality detection on the service nodes used for road event processing in the map data production link according to road event characteristics, so as to determine the service quality of the service nodes used for road event processing.

[0134] In some embodiments, when the processor 50b obtains map data generated by processing the first road source data through a map data production link containing multiple service nodes, it is specifically configured to: obtain the exit map data output by the map data production link within a detection time; obtain the event identifier associated with the exit map data based on the field identifier of the exit map data; and obtain the first entrance map data corresponding to the event identifier associated with the exit map data from the entrance map data of the service node used for road event processing, based on the event identifier associated with the exit map data, as at least part of the map data.

[0135] Accordingly, when extracting features from map data, processor 50b is specifically used to: for the service node in the map data production chain used for event fusion processing, obtain the road information associated with the fused road event corresponding to the event exit data from the first entry data; and obtain the road information associated with the fused road event from the exit map data.

[0136] Accordingly, when processor 50b performs service quality detection on the service nodes used for road event processing in the map data production chain, it specifically determines whether the road information associated with the fused road event is consistent with the road information associated with the fused road event; if the determination result is yes, it determines that the service node used for event fusion processing is normal.

[0137] Optionally, when extracting features from map data, the processor 50b specifically performs the following: for the service node in the map data production chain used for event aggregation processing, it obtains the source of the aggregated road events associated with the exit map data from the first entry map data; obtains the data source of a specified field record in the exit map data from the exit map data; and / or obtains the geographic location information associated with the aggregated road events from the exit map data; and obtains the geographic location information associated with the aggregated road events from the association information of the aggregated road events obtained from the first entry data.

[0138] Accordingly, when processor 50b performs service quality detection on service nodes used for road event processing in the map data production chain, it specifically performs at least one of the following judgment operations: if the source of the associated information of the aggregated road event includes a specified source, it determines whether the source of the data recorded in the specified field is the specified source; and / or, based on the geographic location information associated with the aggregated road event and the geographic location information associated with the aggregated road event, it calculates the distance between the location of the aggregated road event and the location of the aggregated road event; and determines whether the distance between the location of the aggregated road event and the location of the aggregated road event is less than or equal to a set distance threshold.

[0139] If at least one judgment operation results in a negative outcome, the service node used for event aggregation processing is determined to be abnormal.

[0140] In other embodiments, when the processor 50b acquires map data generated by processing the first road source data through a map data production link containing multiple service nodes, it is specifically used to: acquire the exit map data output by the map data production link within a detection time; determine, based on the event identifier of the second road source data input to the map data production link within the detection time and the event identifier of the exit map data, that the second road source data corresponding to the exit map data is the first road source data; and determine that the exit map data corresponding to the first road source data is at least a portion of the map data.

[0141] Accordingly, when extracting features from map data, processor 50b is specifically used to: extract data attribute features from exit map data for service nodes that process data attributes, so as to determine the data attribute features of exit map data.

[0142] Accordingly, when processor 50b performs service quality detection on the service nodes used for data attribute processing in the map data production chain, it specifically determines whether the data attribute characteristics of the exported map data meet the set data attribute requirements; if the determination result is yes, it determines that the service nodes used for data attribute processing are normal.

[0143] Optionally, the processor 50b is further configured to: for third road source data in which there is no corresponding map exit data in the second road source data, use the event identifier of the third road source data to match the map data generated by the multi-service link during the detection time; and determine from the map data production link that there is no service node abnormality in the map data corresponding to the event identifier of the third road source data.

[0144] Optionally, the processor 50b is also configured to: obtain the lifecycle of road events and the reporting time of road events associated with the export map data from the export map data when the map data production chain is normal; determine whether the road events associated with the export map data have expired based on the road event reporting time and the lifecycle of the road events; and publish the export map data if the determination result is negative.

[0145] In some alternative implementations, such as Figure 5 As shown, the computer device may also include components such as a communication component 50c and a power supply component 50d. If the computer device is a terminal device such as a computer, it may also include components such as a display component 50e and an audio component 50f. Figure 5 The diagram only shows some components and does not mean that the computer device must contain them. Figure 5 The inclusion of all components does not imply that a computer device can only include... Figure 5 The components shown.

[0146] The computer device provided in this embodiment acquires event production data generated by processing event source data in a data production link containing multiple service nodes, i.e., a multi-service link; it then extracts features from the event production data to determine the data characteristics and event characteristics reflected by the event production data; subsequently, based on the data characteristics and event characteristics, it performs service quality detection on the multi-service link, thereby realizing full-link quality monitoring of the data production link and making up for the deficiency that single-node data monitoring cannot detect full-link quality problems.

[0147] In some embodiments, the computer device provided in this application can be implemented as a terminal device. Accordingly, the processor 50b can also be used to: acquire export map data published through a map data production link that has passed quality inspection; render the export map data on an electronic map; wherein the map data production link includes multiple service nodes; and perform service quality inspection based on the data characteristics of the map data generated by the map data production link and the road event characteristics reflected in the map data.

[0148] In this embodiment, the memory is used to store computer programs and can be configured to store various other data to support operation on its host device. The processor can execute the computer programs stored in the memory to implement corresponding control logic. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0149] In the embodiments of this application, the processor can be any hardware processing device capable of executing the above-described method logic. Optionally, the processor can be a central processing unit (CPU), a graphics processing unit (GPU), or a microcontroller unit (MCU); it can also be a field-programmable gate array (FPGA), a programmable array logic (PAL), a general array logic (GAL), a complex programmable logic device (CPLD), or other programmable devices; or it can be an advanced reduced instruction set (RISC) processor (ARM) or a system on chip (SOC), etc., but is not limited thereto.

[0150] In this embodiment, the communication component is configured to facilitate wired or wireless communication between its host device and other devices. The device housing the communication component can access wireless networks based on communication standards, such as WiFi, 2G or 3G, 4G, 5G, or combinations thereof. In one exemplary embodiment, the communication component receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In another exemplary embodiment, the communication component may also be implemented based on Near Field Communication (NFC), Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wideband (UWB), Bluetooth (BT), or other technologies.

[0151] In embodiments of this application, the display component may include a liquid crystal display (LCD) and a touch panel (TP). If the display component includes a touch panel, the display component may be implemented as a touchscreen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may not only sense the boundaries of touch or swipe actions but also detect the duration and pressure associated with the touch or swipe operation.

[0152] In this embodiment, a power supply component is configured to provide power to various components of the device in which it resides. The power supply component may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device in which the power supply component resides.

[0153] In embodiments of this application, the audio component can be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC), which is configured to receive external audio signals when the device containing the audio component is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals can be further stored in memory or transmitted via a communication component. In some embodiments, the audio component also includes a speaker for outputting audio signals. For example, in devices with voice interaction capabilities, voice interaction with the user can be achieved through the audio component.

[0154] It should be noted that the terms "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0155] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0156] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0157] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0158] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0159] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0160] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0161] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0162] It should also be noted that 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 limitation, 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.

[0163] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A quality inspection method for map data production chain, wherein, include: Map data is generated by processing the first road source data through a map data production link containing multiple service nodes; wherein, the map data includes the output map data of the map data production link and the output data of each intermediate service node; Feature extraction is performed on the map data to determine the data characteristics of the map data and the road event characteristics reflected by the map data; Based on the data characteristics and the road event characteristics, the service quality of the map data production link is tested to determine the service quality of the map data production link; The step of obtaining map data generated by processing the first road source data through a map data production chain containing multiple service nodes includes: Obtain the output map data from the map data production chain within the detection time period; Based on the field identifiers of the export map data, obtain the event identifiers associated with the export map data; Based on the event identifier associated with the exit map data, the first entrance map data corresponding to the event identifier associated with the exit map data is obtained from the entrance map data of the service node used for road event processing, as at least part of the map data.

2. The method according to claim 1, wherein, The step of performing service quality detection on the map data production chain based on the data features and the road event features includes: Based on the data characteristics, service quality detection is performed on the service nodes used for data attribute processing in the map data production chain to determine the service quality of the service nodes used for data attribute processing. Based on the road event characteristics, service quality detection is performed on the service nodes used for road event processing in the map data production chain to determine the service quality of the service nodes used for road event processing.

3. The method according to claim 1, wherein, The feature extraction of the map data includes: For the service node used for event fusion processing in the map data production chain, obtain the road information associated with the fused road event corresponding to the event exit data from the first entry map data; Obtain road information associated with the merged road events from the export map data; The step of performing service quality detection on the service nodes used for road event processing in the map data production chain based on the road event characteristics includes: Determine whether the road information associated with the fused road event is consistent with the road information associated with the fused road event; If the judgment result is yes, it is determined that the service node used for event fusion processing is normal.

4. The method according to claim 1, wherein, The feature extraction of the map data includes: For the service node used for event aggregation processing in the map data production chain, the source of the aggregated road event associated with the exit map data is obtained from the first entry map data; Obtain the data source of the specified field record from the export map data; And / or, Obtain the aggregated road event-related geographic location information from the exit map data; obtain the aggregated road event-related geographic location information from the first entrance map data; The step of performing service quality detection on the service nodes used for road event processing in the map data production chain based on the road event characteristics includes performing at least one of the following judgment operations: If the source of the associated information of the aggregated road event includes a specified source, determine whether the source of the data recorded in the specified field is the specified source; Based on the geographic location information associated with the aggregated road events and the geographic location information associated with the aggregated road events, calculate the distance between the location of the aggregated road events and the location of the aggregated road events; determine whether the distance between the location of the aggregated road events and the location of the aggregated road events is less than or equal to a set distance threshold. If the result of at least one of the judgment operations is negative, the service node used for event aggregation processing is determined to be abnormal.

5. The method according to claim 2, wherein, The map data generated by processing the first road source data through the map data production chain, which includes multiple service nodes, includes: Obtain the output map data from the map data production chain within the detection time period; Based on the event identifier of the second road source data input into the map data production link and the event identifier of the exit map data within the detection time, the second road source data with corresponding exit map data is determined to be the first road source data; The exit map data corresponding to the first road source data is determined to be at least a portion of the map data.

6. The method according to claim 5, wherein, The feature extraction of the map data includes: For the service node that processes the data attributes, data attribute features are extracted from the export map data to determine the data attribute features of the export map data; The step of performing service quality detection on the service nodes used for data attribute processing in the map data production chain based on the data characteristics includes: Determine whether the data attribute characteristics of the export map data meet the set data attribute requirements; If the judgment result is yes, it is determined that the service node used for data attribute processing is normal.

7. The method according to claim 6, wherein, Also includes: For third road source data that does not have corresponding map exit data in the second road source data, the event identifier of the third road source data is used to match it in the map data generated in the detection time in the map data production chain; From the map data production chain, it is determined that there are no abnormal service nodes for map data corresponding to the event identifier of the third road source data.

8. The method according to any one of claims 1-7, wherein, Also includes: Under normal map data production conditions, the lifecycle of road events and the reporting time of road events associated with the export map data are obtained from the export map data. Based on the road event reporting time and the lifecycle of the road event, determine whether the road event associated with the exit map data has expired; If the judgment result is negative, then the export map data will be published.

9. A map rendering method, wherein, include: Acquire export map data released from the map data production chain that has passed quality inspection; Render the export map data on the electronic map; The map data production chain includes multiple service nodes; and service quality is detected using the quality detection method described in any one of claims 1-8, based on the data characteristics of the map data generated by the map data production chain and the road event characteristics reflected by the map data.

10. A quality inspection method for a data production chain, wherein, include: The event production data generated by processing the first event source data through multiple service links is obtained; wherein, the data includes the exit map data of the map data production link and the exit data of each intermediate service node; Feature extraction is performed on the event production data to determine the data characteristics of the event production data and the event characteristics reflected by the event production data; Based on the data characteristics and the event characteristics, service quality detection is performed on the multi-service links to determine the service quality of the multi-service links; This includes acquiring event production data generated by processing the first event source data through multiple service links, including: Obtain the output map data from the map data production chain within the detection time period; Based on the field identifiers of the export map data, obtain the event identifiers associated with the export map data; Based on the event identifier associated with the exit map data, the first entrance map data corresponding to the event identifier associated with the exit map data is obtained from the entrance map data of the service node used for road event processing, as at least part of the map data.

11. A computer device, wherein, include: A memory and a processor; wherein the memory is used to store computer programs; The processor is coupled to the memory for executing the computer program to perform the steps of the method according to any one of claims 1-10.

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