Data query method, intelligent device and storage medium

Abstract

The invention relates to the technical field of automatic driving, particularly provides a data query method, intelligent equipment and a storage medium, and aims to solve the technical problem that an existing ElasticSearch-based tag query method cannot meet actual requirements. In order to achieve the purpose, the data query method comprises the steps that label data is obtained, and the label data at least comprises a label name and an entity identifier; acquiring a first mapping relationship between the entity identifier and the tag name based on the tag data; obtaining a second mapping relationship between the tag name and the bitmap based on a first mapping relationship between the entity identifier and the tag name; and performing data query based on a second mapping relationship between the tag name and the bitmap. Therefore, the storage space of the data is effectively reduced, the query speed is effectively improved, and the data query performance of the equipment is improved.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and specifically provides a data query method, an intelligent device and a storage medium. Background Art

[0002] As autonomous driving evolves from traditional neural network models to end-to-end models, a comprehensive, accurate, and efficient labeling scenario system becomes more important in model iteration, where the end-to-end model pays more attention to the overall process from sensor data input to smart device control command output. At the same time, the continuous increase in the volume and granularity of label data will inevitably bring challenges to data storage, data computing, and analysis, and different business departments have various query requirements for labels.

[0003] Most of the traditional ElasticSearch-based tag query solutions on the market can no longer meet the needs of scenarios such as multiple sources of scene tags, large computing, high storage, and diversified query analysis. Summary of the invention

[0004] In order to overcome the above defects, this application is proposed to provide a solution or at least partially solve the technical problem that the existing tag query method based on ElasticSearch cannot meet the actual needs. This application provides a data query method, an intelligent device and a storage medium.

[0005] In a first aspect, the present application provides a data query method, the method comprising:

[0006] Acquire tag data, where the tag data at least includes a tag name and an entity identifier;

[0007] Acquire a first mapping relationship between the entity identifier and the tag name based on the tag data;

[0008] Acquire a second mapping relationship between the tag name and the bitmap based on the first mapping relationship between the entity identifier and the tag name;

[0009] The second mapping relationship between the tag name and the bitmap is used to perform data query.

[0010] In one embodiment of the present application, the acquiring the second mapping relationship between the tag name and the bitmap based on the first mapping relationship between the entity identifier and the tag name includes:

[0011] Acquire an entity identifier set based on a first mapping relationship between the entity identifier and the tag name;

[0012] Acquire a third mapping relationship between entity identifiers and globally unique identifiers based on the entity identifier set;

[0013] The second mapping relationship is acquired based on the first mapping relationship and the third mapping relationship.

[0014] In one embodiment of the present application, the acquiring of the entity identifier set based on the first mapping relationship between the entity identifier and the tag name includes: deduplicating the entity identifiers in the first mapping relationship to acquire the entity identifier set.

[0015] In one embodiment of the present application, the acquiring a third mapping relationship between an entity identifier and a globally unique identifier based on the entity identifier set includes:

[0016] Sorting the entity identifiers in the entity identifier set to obtain a globally unique identifier corresponding to each of the entity identifiers;

[0017] A third mapping relationship between the entity identifier and the globally unique identifier is obtained according to the entity identifier and the globally unique identifier.

[0018] In one implementation of the present application, the globally unique identifier is of integer type.

[0019] In one implementation of the present application, the acquiring the second mapping relationship based on the first mapping relationship and the third mapping relationship includes:

[0020] Associating the first mapping relationship with the third mapping relationship to obtain a globally unique identifier corresponding to each of the tag names;

[0021] Based on all the tag names and the globally unique identifier corresponding to each of the tag names, the second mapping relationship is constructed in a preset database.

[0022] In one implementation of the present application, constructing the second mapping relationship in a preset database includes:

[0023] Using a bitmap to store a globally unique identifier set corresponding to each tag name, obtaining a bitmap corresponding to each tag name;

[0024] The second mapping relationship is obtained according to each tag name and the bitmap corresponding to each tag name.

[0025] In one implementation of the present application, the preset database is the Doris database.

[0026] In a second aspect, a smart device is provided, comprising:

[0027] at least one processor;

[0028] and, a memory communicatively coupled to the at least one processor;

[0029] Wherein, a computer program is stored in the memory, and the computer program is the aforementioned data query method when executed by the at least one processor.

[0030] In a third aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored in the computer-readable storage medium, wherein the program codes are suitable for being loaded and run by a processor to execute any of the aforementioned data query methods.

[0031] The above one or more technical solutions of this application have at least one or more of the following Beneficial effects:

[0032] The data query method in the present application includes: obtaining tag data, the tag data at least including a tag name and an entity identifier; obtaining a first mapping relationship between the entity identifier and the tag name based on the tag data; obtaining a second mapping relationship between the tag name and a bitmap based on the first mapping relationship between the entity identifier and the tag name; and performing data query based on the second mapping relationship between the tag name and the bitmap. In this way, using a bitmap to represent the mapping relationship between the tag name and the entity identifier effectively reduces the storage space of the data, and greatly saves storage resources compared to the traditional method of recording each entity identifier. When using a bitmap for data query, bitmap operations can be used to achieve efficient tag matching and query operations, effectively improving the query speed and improving the data query performance of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The disclosure of the present application will become more easily understood with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present application. In addition, similar numbers in the drawings are used to represent similar components, among which:

[0034] Figure 1 This is a schematic diagram of the main process of a data query method in one embodiment of the present application;

[0035] Figure 2 is a schematic diagram of a first mapping relationship in an embodiment of the present application;

[0036] Figure 3 is a schematic diagram of a first mapping relationship in another embodiment of the present application;

[0037] Figure 4 is a schematic diagram of an entity identifier set in an embodiment of the present application;

[0038] Figure 5 is a schematic diagram of a third mapping relationship in an embodiment of the present application;

[0039] Figure 6is a schematic diagram of a second mapping relationship in an embodiment of the present application;

[0040] Figure 7 and Figure 8 It is a flowchart of a data query method in one embodiment of the present application;

[0041] Fig. 9 It is a schematic diagram of the main structure of a data query device in one embodiment of the present application;

[0042] Fig.10 It is a schematic diagram of the structure of a smart device in one embodiment of the present application. DETAILED DESCRIPTION

[0043] Some embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.

[0044] In the description of the present application, "module" and "processor" may include hardware, software or a combination of the two. A module may include hardware circuits, various suitable sensors, communication ports, memory, and may also include software parts, such as program code, or a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, hardware or a combination of the two. Non-temporary computer-readable storage media include any suitable medium that can store program code, such as a disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B or A and B. The term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and / or B" and may include only A, only B or A and B. The singular terms "one" and "the" may also include plural forms.

[0045] At present, the traditional tag query solution based on ElasticSearch as the retrieval engine can no longer meet the needs of scenarios such as multiple sources of scene tags, large computing, high storage, and diversified query analysis. To this end, this application proposes a data query method, an intelligent device, and a storage medium.

[0046] See attached Figure 1 , Figure 1 It is a flowchart of the main steps of a data query method according to an embodiment of the present application.

[0047] like Figure 1As shown, the data query method in the embodiment of the present application mainly includes the following steps S10 to S40.

[0048] Step S10: Acquire tag data, where the tag data at least includes a tag name and an entity identifier.

[0049] Labeled data refers to descriptive information attached to an entity or object to classify, identify, annotate, or enhance the meaning of the original data.

[0050] An entity usually refers to an independent, persistent and identifiable element in the environment. It can be static or dynamic. Static entities can be infrastructure such as roads, traffic signs, lane lines, buildings, etc. Dynamic entities can be moving objects such as pedestrians, other vehicles, bicycles, etc.

[0051] An object refers to a specific instance detected by a perception system. It is a specific manifestation of an entity at a certain point in time. An object has a specific position, speed, direction, and other properties. For example, a vehicle object, each car can be regarded as an object. It has its own position, speed, acceleration, steering angle, and other properties, and these properties may be updated over time and with changes in the environment. For example, a pedestrian object, when a sensor detects a pedestrian, the pedestrian is modeled as an object, which contains information such as the pedestrian's position, direction of movement, and walking speed.

[0052] The label data may at least include a label name and an entity identifier. In addition, the label data may also include information such as a timestamp, a source, a weight, and a confidence level, which is not specifically limited.

[0053] The tag name is a mark that describes the characteristics or attributes of an entity and is used to indicate a specific category or characteristic. For example, in a video, the tag name may be pedestrian, vehicle, sunny weather, etc.

[0054] An entity ID is an identifier used to uniquely identify each entity, thereby ensuring that different entities cannot be confused with each other. An entity ID is usually in the form of a string.

[0055] The granularity of entity identification corresponding to different application scenarios is different. For example, in the field of autonomous driving technology, the granularity of entity identification may include clip, frame, and instance.

[0056] A clip generally refers to a continuously recorded video clip, which may be from a few seconds to a few minutes. For example, clip_0001 may represent the unique identifier of the first video clip, and clip_0002 may represent the unique identifier of the second video clip.

[0057] A frame refers to a single image frame in a video, and there are usually multiple image frames per second. For example, clip_0001-frame_0001 may represent the first frame of the first video clip.

[0058] An instance refers to a specific instance object that appears within a specific time and space range. clip_0001-frame_0001-instance_0001 may represent the first object detected in the first frame of the first video clip.

[0059] Exemplarily, the following data can be used as examples of the label data.

[0060] Step S20: Acquire a first mapping relationship between the entity identifier and the tag name based on the tag data.

[0061] The first mapping relationship refers to the corresponding relationship between the entity identifier and the tag name.

[0062] Step S30: acquiring a mapping relationship between a tag name and a bitmap based on a first mapping relationship between an entity identifier and a tag name.

[0063] Bitmap is a classic data structure used for efficient compression storage and fast query of large amounts of binary data. Bitmap can be viewed as an array of bits, with each bit having only two possible states, 0 or 1. When an element needs to be marked, the value of the corresponding bit can be set to 1, otherwise it is 0. When it is necessary to query whether an element appears, it is only necessary to find the binary bit corresponding to the element and determine whether its value is 1, so it is very efficient.

[0064] A bitmap is a collection used to store a set of globally unique identifiers in the Doris database. These globally unique identifiers may represent any entity identifier that needs to be efficiently queried and filtered, so it can also be called a Doris Bitmap.

[0065] The implementation behind Doris Bitmap is based on Roaring Bitmap, which is a data structure used to store aggregated detail data (64-bit non-negative integers). It stores detail data through two-level indexes. In short, it locates the specific container through two-level indexes. The container stores the lower 16 bits of the aggregated detail data. It has three types: pure array type array container (Array container), bitmap type bitmap container (Bitset container) and run-length encoding bitmap container (RunLen container). The use of Roaring Bitmap can speed up complex queries, such as filtering, counting, intersection, union, difference calculation, etc.

[0066] Bitmap can store these identifiers in a very compact and efficient way, and allows fast aggregation operations such as union, intersection, etc. Specifically, the BITMAP_UNION function can be used to calculate the union of two or more bitmaps. The BITMAP_INTERSECT function is used to calculate the intersection of two or more bitmaps. The BITMAP_AND function is used to calculate the intersection and complement of two bitmaps (that is, the part that exists in both bitmaps). The BITMAP_OR function is used to calculate the union of two bitmaps. The BITMAP_XOR function is used to calculate the symmetric difference of two bitmaps. The BITMAP_COUNT function is used to calculate the number of non-zero values ​​in the bitmap, that is, the count. The BITMAP_AND NOT function is used to calculate the part that exists in the first bitmap minus the second bitmap.

[0067] The second mapping relationship refers to the corresponding relationship between the tag name and the bitmap.

[0068] Step S40: performing data query based on the second mapping relationship between the tag name and the bitmap.

[0069] Based on the above steps S10-S40, first obtain the tag data, the tag data at least including the tag name and the entity identifier; obtain the first mapping relationship between the entity identifier and the tag name based on the tag data; obtain the second mapping relationship between the tag name and the bitmap based on the first mapping relationship between the entity identifier and the tag name; perform data query based on the second mapping relationship between the tag name and the bitmap. In this way, using a bitmap to represent the mapping relationship between the tag name and the entity identifier effectively reduces the storage space of the data, and greatly saves storage resources compared to the traditional method of recording each entity identifier. When using a bitmap for data query, bitmap operations can be used to achieve efficient tag matching and query operations, effectively improving the query speed and improving the data query performance of the device.

[0070] The above steps S10 to S40 are further explained below.

[0071] For the above step S10, the label data may be data obtained from different sources, such as from the DA data warehouse, the Safety data warehouse, or the Oceanus platform, etc. The label data of the DA data warehouse, the Safety data warehouse, or the Oceanus platform may be obtained by manual labeling or labeling using a machine learning model or other automated tools.

[0072] DA data warehouse refers to a data warehouse used to store and analyze data related to driving behavior, aiming to collect information from vehicle sensors, GPS, driver interactions, etc.

[0073] Safety Data Warehouse is a data warehouse focused on safety-related data management, aiming to improve road safety by collecting, storing and analyzing various safety incidents and risk factors.

[0074] The Oceanus platform is an integrated data processing and stream computing platform that is particularly suitable for application scenarios that require real-time processing of large amounts of data, such as autonomous driving and intelligent transportation systems.

[0075] The above is a further description of step S10 , and the following is a further description of step S20 .

[0076] Specifically, for the label data obtained from different data sources, big data technologies such as Hadoop and Spark can be used to clean, integrate and process all the label data to remove erroneous or inconsistent data, and store the processed label data in the data warehouse in the form of a mapping relationship between entity identifiers and label names, thereby obtaining a first mapping relationship between entity identifiers and label names. Exemplarily, a label detail table between entity identifiers and label names can be used as an example of the first mapping relationship, and the label detail table contains specific information of each label, such as the label name and the corresponding entity identifier.

[0077] For example, Figure 2 It is a tag list at the clip level, which can be used as an example of the first mapping relationship between the entity identifier and the tag name. In the tag list, the entity identifier is represented by the clip_id field, and the tag name is represented by the tag_name field.

[0078] For example, Figure 3It is a tag list at the instance level, which can be used as another example of the first mapping relationship between the entity identifier and the tag name. In the tag list, the entity identifier is represented by the clip_id-frame_id-instance_id field, and the tag name is represented by the tag_name field.

[0079] The above is a further description of step S20 , and the following is a further description of step S30 .

[0080] Specifically, step S30 can be implemented through the following steps S301 to S303.

[0081] Step S301: Acquire an entity identifier set based on a first mapping relationship between the entity identifier and the tag name.

[0082] An entity identifier set is a data structure containing a set of unique entity identifiers, usually used to represent the identities of all different entities in a dataset. This set ensures that each entity appears only once within its scope.

[0083] In a specific implementation of the present application, the acquiring of the entity identifier set based on the first mapping relationship between the entity identifier and the tag name includes: deduplicating the entity identifiers in the first mapping relationship to acquire the entity identifier set.

[0084] Specifically, all entity identifiers in the first mapping relationship between entity identifiers and tag names are deduplicated to delete repeated entity identifiers, thereby obtaining an entity identifier set.

[0085] For example, Figure 4 The entity table in can be used as an example of an entity identifier set, which is specifically Figure 2 The entity table is obtained by removing duplicates from the tag detail table in , and the entity table includes non-repeated entity identifiers.

[0086] Step S302: acquiring a third mapping relationship between the entity identifier and the globally unique identifier based on the entity identifier set.

[0087] A globally unique identifier is an integer unique identifier that is used to uniquely identify each entity globally.

[0088] The third mapping relationship is the corresponding relationship between the entity identifier and the globally unique identifier.

[0089] Specifically, step S302 can be implemented through the following steps S3021 to S3022.

[0090] Step S3021: sort the entity identifiers in the entity identifier set to obtain a globally unique identifier corresponding to each entity identifier, where the globally unique identifier is an integer type.

[0091] An integer type refers to a data type used to store integer values, where integer types can include short integer, integer, long integer, etc.

[0092] Specifically, the entity identifiers in the entity identifier set may be sorted, specifically in alphabetical order, chronological order, or other logical order, without any specific limitation, as long as a unique identifier is allocated to each entity identifier globally.

[0093] Step S3022: Obtain a third mapping relationship between the entity identifier and the global unique identifier according to the entity identifier and the global unique identifier.

[0094] Specifically, after obtaining the globally unique identifier corresponding to each entity identifier in the entity identifier set, a mapping relationship between the entity identifier and the globally unique identifier, ie, the third mapping relationship, may be established.

[0095] For example, Figure 5 The global dictionary table shown can be used as an example of the third mapping relationship between the entity identifier and the global unique identifier, wherein the entity identifiers in the global dictionary table are sorted in alphabetical order, with the first letter 1 being used as the global unique identifier, and then increasing in order.

[0096] Step S303: Acquire a second mapping relationship based on the first mapping relationship and the third mapping relationship.

[0097] Specifically, step S303 can be implemented through the following steps S3031 to S3032.

[0098] Step S3031: associate the first mapping relationship with the third mapping relationship to obtain a globally unique identifier corresponding to each tag name.

[0099] Specifically, the first mapping relationship is the mapping relationship between the entity identifier and the tag name, and the third mapping relationship is the mapping relationship between the entity identifier and the globally unique identifier. After associating the first mapping relationship with the third mapping relationship, the globally unique identifier corresponding to each tag name can be obtained, that is, the entity identifier in the first mapping relationship is converted from a string to an integer representation.

[0100] Step S3032: construct a second mapping relationship in a preset database based on all tag names and the globally unique identifier corresponding to each tag name.

[0101] Exemplarily, the Doris database can be used as an example of the preset database.

[0102] Doris database is a high-performance distributed SQL database. Doris database supports fast data ingestion and real-time query, suitable for application scenarios that require instant feedback.

[0103] Step S30321: Use a bitmap to store the globally unique identifier set corresponding to each tag name, and obtain a bitmap corresponding to each tag name.

[0104] A bitmap is an efficient data structure for storing a set of values.

[0105] Specifically, for each tag name, all globally unique identifiers related thereto are collected, and all globally unique identifiers corresponding to each tag name are stored through a bitmap, so as to obtain a bitmap corresponding to each tag name.

[0106] Step S30322: Obtain a second mapping relationship according to each tag name and the bitmap corresponding to each tag name.

[0107] Specifically, after obtaining all the tag names and the bitmap corresponding to each tag name, the tag names and the bitmap corresponding to each tag name are imported into the Doris database, and a mapping relationship between the tag names and the bitmaps can be constructed in the Doris database.

[0108] For example, Figure 6 The tag storage table in can be used as an example of the second mapping relationship between the tag name (tag_name) and the bitmap (bitmap).

[0109] By converting the label data into a bitmap in Doris for storage, the storage cost of the label data is greatly reduced and storage resources are saved.

[0110] The above is a further description of step S30 , and the following is a further description of step S40 .

[0111] Specifically, after converting the label data into a bitmap in Doris, the bitmap can be further subjected to logical operations such as intersection, union, complement, and difference, making the query of label data in multiple scenarios and dimensions simple and efficient, thereby solving the bottlenecks in computing, storage, and query that occur in traditional label query solutions using ElasticSearch as the retrieval engine.

[0112] For example, Figure 6For example, the bitmap of the label name "weather.rain" is [1,3], which can be represented as 1010. The bitmap of the label name "weather.snow" is [2,4], which can be represented as 0101. The bitmapintersect function is used to intersect "weather.rain" and "weather.snow", which can be the intersection of 1010 and 0101, and the result is 0000. The bitmap union function is used to intersect "weather.rain" and "weather.snow", which can be the intersection of 1010 and 0101, and the result is 1111.

[0113] Figure 7 and Figure 8 It can be used as a flow chart of the data query method, which can be implemented through the following steps S1 to S3.

[0114] S1.Oceanus data warehouse can obtain label data from DA data warehouse, Safety data warehouse or Oceanus platform.

[0115] S2.Oceanus data warehouse can structure label data from different sources into a label detail table, obtain the global dictionary table based on the label detail table, and associate the label detail table with the global dictionary table and import them into the Doris database to obtain the label storage table.

[0116] Specific as Figure 8 As shown, first, the entity identifiers in the tag list are deduplicated to obtain the entity table. The entity identifiers in the entity table are sorted in alphabetical order to obtain the global unique identifier corresponding to each entity identifier, thereby obtaining the global dictionary table. The tag list and the global dictionary table are associated to obtain the global unique identifier set corresponding to each tag name, and a tag storage table is constructed in the Doris database based on all tag names and the global unique identifier set corresponding to each tag name.

[0117] The S3.Oceanus platform can query label data in the label storage table in the Doris database, such as single label query export, multi-label union query export, multi-label intersection query export, multi-label difference query export, etc.

[0118] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effect of the present application, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present application.

[0119] Furthermore, the present application also provides a data query device.

[0120] See attached Fig. 9 , Fig. 9 It is a main structural block diagram of a data query device according to an embodiment of the present application.

[0121] like Fig. 9 As shown, the data query device in the embodiment of the present application mainly includes a first acquisition module 11, a second acquisition module 12, a third acquisition module 13 and a data query module 14. In some embodiments, one or more of the first acquisition module 11, the second acquisition module 12, the third acquisition module 13 and the data query module 14 can be combined into one module.

[0122] In some embodiments, the first acquisition module 11 may be configured to acquire tag data, where the tag data at least includes a tag name and an entity identifier.

[0123] The second acquisition module 12 may be configured to acquire a first mapping relationship between an entity identifier and a tag name based on the tag data.

[0124] The third acquisition module 13 may be configured to acquire a second mapping relationship between a tag name and a bitmap based on the first mapping relationship between the entity identifier and the tag name.

[0125] The data query module 14 may be configured to perform data query based on the second mapping relationship between the tag name and the bitmap.

[0126] In one implementation, the description of the specific implementation functions can refer to steps S10-S40.

[0127] The data query device is used to execute Figure 1 The data query method embodiment shown in the figure has similar technical principles, technical problems solved and technical effects produced. Technical personnel in this technical field can clearly understand that for the convenience and conciseness of description, the specific working process and related instructions of the data query device can refer to the contents described in the embodiment of the data query method, which will not be repeated here.

[0128] Further, it should be understood that since the setting of each module is only for illustrating the functional units of the device of the present application, the physical devices corresponding to these modules may be the processor itself, or a part of the software in the processor, a part of the hardware, or a part of the combination of software and hardware. Therefore, the number of each module in the figure is only schematic.

[0129] It is understood by those skilled in the art that each module in the device can be adaptively split or merged. Such splitting or merging of specific modules will not cause the technical solution to deviate from the principle of the present application, and therefore, the technical solutions after splitting or merging will fall within the protection scope of the present application.

[0130] It is understood by those skilled in the art that all or part of the processes in the method for implementing the above-mentioned embodiment of the present application can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code.

[0131] Furthermore, the present application also provides an intelligent device, which may include at least one processor; and a memory connected to the at least one processor; wherein a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the data query method described in any of the above embodiments is implemented. Fig.10 As shown, Fig.10 exemplarily shows the structure of a smart device, which includes a processor 100 and a memory 200.

[0132] In some embodiments of the present application, the smart device further includes at least one sensor, which is used to sense information. The sensor is communicatively connected to any type of processor mentioned in the present application. Optionally, the smart device further includes an autonomous driving system, which is used to guide the smart device to drive itself or assist in driving. The processor communicates with the sensor and / or the autonomous driving system to complete the data query method described in any of the above embodiments.

[0133] Furthermore, the present application also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present application, the computer-readable storage medium can be configured to store a program for executing the data query method of the above-mentioned method embodiment, and the program can be loaded and run by the processor to implement the above-mentioned data query method. For ease of explanation, only the parts related to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application. The computer-readable storage medium can be a memory device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present application is a non-temporary computer-readable storage medium.

[0134] The relevant user personal information that may be involved in the various embodiments of this application is strictly in accordance with the requirements of laws and regulations, following the principles of legality, legitimacy and necessity, based on the reasonable purposes of business scenarios, to process the personal information that users actively provide during the use of products / services or generated due to the use of products / services, as well as the personal information obtained with the user's authorization.

[0135] The user personal information processed by this application will vary depending on the specific product / service scenario, and will be based on the specific scenario in which the user uses the product / service. It may involve the user's account information, device information, driving information, vehicle information or other related information. The applicant will treat the user's personal information and its processing with a high degree of diligence.

[0136] This application attaches great importance to the security of user personal information and has taken reasonable and feasible security protection measures that meet industry standards to protect user information and prevent personal information from being accessed, disclosed, used, modified, damaged or lost without authorization.

[0137] So far, the technical solutions of the present application have been described in conjunction with the specific implementations shown in the accompanying drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific implementations. Without departing from the principles of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present application.

Claims

1. A data query method, characterized in that: The method comprises: Acquire tag data, where the tag data at least includes a tag name and an entity identifier; Acquire a first mapping relationship between the entity identifier and the tag name based on the tag data; Acquire a second mapping relationship between the tag name and the bitmap based on the first mapping relationship between the entity identifier and the tag name; The second mapping relationship between the tag name and the bitmap is used to perform data query.

2. The data query method according to claim 1, characterized in that: The acquiring a second mapping relationship between the tag name and the bitmap based on the first mapping relationship between the entity identifier and the tag name includes: Acquire an entity identifier set based on a first mapping relationship between the entity identifier and the tag name; Acquire a third mapping relationship between entity identifiers and globally unique identifiers based on the entity identifier set; The second mapping relationship is acquired based on the first mapping relationship and the third mapping relationship.

3. The data query method according to claim 2, characterized in that: The acquiring of the entity identifier set based on the first mapping relationship between the entity identifier and the tag name includes: deduplicating the entity identifiers in the first mapping relationship to acquire the entity identifier set.

4. The data query method according to claim 2, characterized in that: The acquiring, based on the entity identifier set, a third mapping relationship between the entity identifier and the globally unique identifier comprises: Sorting the entity identifiers in the entity identifier set to obtain a globally unique identifier corresponding to each of the entity identifiers; A third mapping relationship between the entity identifier and the globally unique identifier is obtained according to the entity identifier and the globally unique identifier.

5. The data query method according to claim 4, characterized in that: The globally unique identifier is of integer type.

6. The data query method according to claim 2, characterized in that: The acquiring the second mapping relationship based on the first mapping relationship and the third mapping relationship includes: Associating the first mapping relationship with the third mapping relationship to obtain a globally unique identifier corresponding to each of the tag names; Based on all the tag names and the globally unique identifier corresponding to each of the tag names, the second mapping relationship is constructed in a preset database.

7. The data query method according to claim 6, characterized in that: Constructing the second mapping relationship in a preset database includes: Obtain the bitmap corresponding to each tag name according to the globally unique identifier set corresponding to each tag name; The second mapping relationship is obtained according to each tag name and the bitmap corresponding to each tag name.

8. The data query method according to claim 6, characterized in that: The preset database is the Doris database.

9. A smart device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; Wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the data query method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the data query method according to any one of claims 1 to 8.

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