Data collection method, device, storage medium and program product

Abstract

This application provides a data acquisition method, device, storage medium, and program product. The method includes acquiring a acquisition target, parsing the acquisition target to obtain acquisition trigger condition information and acquisition object information corresponding to the acquisition target, invoking at least one target rule component from multiple basic rule components based on the acquisition trigger condition information, determining the target data source and target policy group corresponding to the at least one target rule component, and performing matching processing on the data of the target data source based on the target policy group through a rule engine. If the matching is successful, data is acquired based on the acquisition object information to obtain target data. This application embodiment can realize dynamic changes in acquisition strategies during the data acquisition process, reduce the threshold for strategy configuration and manpower input, and improve the intelligence level of acquisition equipment.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of autonomous driving technology, and in particular to a data collection method, device, storage medium and program product. Background Art

[0002] Autonomous driving technology is currently developing rapidly. As an important verification and testing method for autonomous driving, autonomous driving scene simulation plays an important role in the entire life cycle of autonomous driving technology. The autonomous driving scene simulation requires a large amount of real driving data as the data basis, so data collection has become an important part of autonomous driving scene simulation that cannot be ignored.

[0003] In the prior art, real driving data is usually collected by buried meter reading and fixed full collection.

[0004] However, in the process of implementing this application, the inventors found that there are at least the following problems in the prior art: the above-mentioned data collection method requires manual setting of tracking points, the operation is complicated, the collection content is fixed, and the cost of changing the collection strategy is high. Summary of the invention

[0005] The embodiments of the present application provide a data collection method, device, storage medium and program product to enable dynamic changes in collection strategies during data collection, reduce the threshold for policy configuration and manpower investment, and improve the intelligence level of the collection equipment.

[0006] In a first aspect, an embodiment of the present application provides a data collection method, including:

[0007] Acquire a collection target, and analyze the collection target to obtain collection trigger condition information and collection object information corresponding to the collection target;

[0008] According to the acquisition trigger condition information, at least one target rule component is called from a plurality of basic rule components, and a target data source and a target policy group corresponding to the at least one target rule component are determined;

[0009] Based on the target policy group, matching processing is performed on the data of the target data source through a rule engine;

[0010] If the match is successful, data collection is performed according to the collection object information to obtain the target data.

[0011] In a possible design, calling at least one target rule component from a plurality of basic rule components according to the acquisition trigger condition information, and determining a target data source and a target policy group corresponding to the at least one target rule component, includes:

[0012] Parsing the collection trigger condition information to obtain at least one collection item and data requirement information corresponding to each collection item;

[0013] For each collection item, calling a target rule component corresponding to the collection item from a plurality of basic rule components, and determining a data source and a target strategy corresponding to the target rule component according to the data requirement information;

[0014] Determine the data source corresponding to each target rule component as the target data source;

[0015] According to the logical relationship between each target strategy, each target strategy is processed to obtain a target strategy group corresponding to the acquisition target.

[0016] In a possible design, determining the target strategy corresponding to the target rule component according to the data requirement information includes:

[0017] Obtaining status information of a collection component corresponding to a data source of the target rule component;

[0018] If the status information indicates that the acquisition component is in an available state, the target strategy corresponding to the target rule component is determined according to the data requirement information.

[0019] In a possible design, the target strategies are processed according to the logical relationship between the target strategies to obtain the target strategy group corresponding to the acquisition target, including:

[0020] Get the type identifier of each target rule component;

[0021] For target rule components with the same type identifier, each target strategy is merged according to the logical relationship between the target strategies to obtain a target strategy group corresponding to the acquisition target.

[0022] In a possible design, determining the data sources corresponding to each target rule component as the target data source includes:

[0023] Determine the target fields corresponding to the data sources corresponding to the target rule components from the target database; the target database is used to store the data of the data sources of the plurality of basic rule components;

[0024] The data of each of the target fields is pre-read into a time series database, and the time series database is determined as the target data source.

[0025] In a possible design, the matching process of the data of the target data source by a rule engine based on the target policy group includes:

[0026] In response to writing new data into the time series database, comparing the generation time of the new data with the generation time of existing data in the time series database;

[0027] If the generation time of the newly added data is later than the generation time of the existing data in the time series database, the rule engine detects whether the newly added data meets the conditions of the target policy group. If so, the match is successful.

[0028] In one possible design, the method further includes:

[0029] Obtaining function information of the acquisition device and parameter information corresponding to the function information;

[0030] According to the function information and the parameter information, a plurality of the basic rule components are constructed; the basic rule components include a data source, an output value range and an available operator; the output value range and the available operator are determined according to the parameter information.

[0031] In a second aspect, an embodiment of the present application provides a data acquisition device, including:

[0032] The parsing module is used to obtain the acquisition target and parse the acquisition target to obtain the acquisition trigger condition information and acquisition object information corresponding to the acquisition target;

[0033] A determination module, configured to call at least one target rule component from a plurality of basic rule components according to the acquisition trigger condition information, and determine a target data source and a target policy group corresponding to the at least one target rule component;

[0034] A matching module, used for performing matching processing on the data of the target data source through a rule engine based on the target policy group;

[0035] The acquisition module is used to acquire target data based on the acquisition object information if the match is successful.

[0036] In a third aspect, an embodiment of the present application provides a data acquisition device, including: at least one processor and a memory;

[0037] The memory stores computer-executable instructions;

[0038] The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the method described in the first aspect and various possible designs of the first aspect.

[0039] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the method described in the first aspect and various possible designs of the first aspect are implemented.

[0040] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the method described in the first aspect and various possible designs of the first aspect.

[0041] The data collection method, device, storage medium and program product provided in this embodiment first obtain the collection target, and parse the collection target to obtain the collection trigger condition information and collection object information corresponding to the collection target, and then call at least one target rule component from multiple basic rule components according to the collection trigger condition information, and determine the target data source and target policy group corresponding to the at least one target rule component, and then match the data of the target data source through the rule engine based on the target policy group. If the match is successful, data collection is performed according to the collection object information to obtain the target data. The data collection method provided in this embodiment parses the collection target, calls the relevant rule components from multiple pre-created basic rule components to form a policy group, and determines whether the collection trigger condition is met through the rule engine based on the policy group. If it is met, collection is performed, which can realize the dynamic change of the collection strategy during the data collection process, reduce the threshold of policy configuration and manpower investment, and improve the intelligence level of the collection equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0043] Figure 1 A schematic diagram of an application scenario of the data collection method provided in an embodiment of the present application;

[0044] Figure 2 Schematic diagram of the data collection method provided in the embodiment of the present application Figure 1 ;

[0045] Figure 3 A schematic diagram of a collection target setting interface provided in an embodiment of the present application;

[0046] Figure 4 Schematic diagram of the data collection method provided in the embodiment of the present application Figure 2 ;

[0047] Figure 5 A schematic diagram of the structure of a data acquisition device provided in an embodiment of the present application;

[0048] Figure 6 This is a structural block diagram of the data acquisition device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] Autonomous driving technology is currently developing rapidly. As an important verification and testing method for autonomous driving, autonomous driving scene simulation plays an important role in the entire life cycle of autonomous driving technology. The autonomous driving scene simulation requires a large amount of real driving data as material for construction. Therefore, data collection capability has become an important part of autonomous driving scene simulation that cannot be ignored.

[0051] The current traditional implementation method mainly uses point-to-point meter reading and fixed full collection to realize the data collection of autonomous driving scene simulation. Specifically, point-to-point meter reading is carried out based on the demand list provided by the user. The content of the demand list may include information such as the B signal of device A that needs to be collected. However, the implementation method of point-to-point meter reading has many inconveniences such as rigid collection trigger conditions, fixed collection content, the need to update the content of the demand list by wireless download technology (Over The Air, OTA) firmware upgrade, and difficulty in adjusting the collection strategy. With the evolution of business, a large number of corner cases BAD CASE (data of scenes with a relatively low probability of occurrence, for example, data of a scene where a cat jumps out of the roadside), and the targeted collection requirements of high-fresh scene data have put forward requirements for high availability, flexible changes, and atomic configuration for the collection capabilities of autonomous driving scene simulation data. As the scope of collection expands, the traditional model that relies on developers to configure collection strategies in code development mode has become increasingly difficult to implement.

[0052] In order to solve the above technical problems, the inventors of this application have found that by parsing the collection target, relevant rule components can be called from multiple pre-created basic rule components to form a policy group. Based on the policy group, the rule engine determines whether the collection trigger conditions are met, and if they are met, the collection is carried out. By using the rule engine technology, the collection strategy can be dynamically changed during the collection process, and the policy group can be automatically created and executed. Users only need to set the collection target to complete data collection, which reduces the policy configuration threshold and manpower investment in the collection process and improves the intelligence level of data collection equipment.

[0053] Figure 1 Schematic diagram of application scenarios of the data collection method provided in the embodiment of the present application. Figure 1 As shown, the client is connected to the collection device, and the collection device is connected to the cloud server. The client is used to generate collection content and send the collection content to the collection device in response to the user's touch operation on the collection content setting interface. The collection device is used to parse the collection content, obtain the collection trigger condition information and collection object information corresponding to the collection target, call at least one target rule component from multiple basic rule components according to the collection trigger condition information, and determine the target data source and target policy group corresponding to the at least one target rule component. Based on the target policy group, the data of the target data source is matched and processed by the rule engine. If the match is successful, data collection is performed according to the collection object information to obtain the target data. The cloud server is used to store the various data collected by the collection device and provide the target data source of each target rule component. The collection device may include a radar for collecting point cloud data, a camera for collecting push data, a GPS module for collecting positioning data, and other different devices for collecting different data.

[0054] In the specific implementation process, the client generates the collected content in response to the user's touch operation on the collection content setting interface and sends the collected content to the collection device. The collection device is used to parse the collected content, obtain the collection trigger condition information and collection object information corresponding to the collection target, call at least one target rule component from multiple basic rule components according to the collection trigger condition information, and determine the target data source and target policy group corresponding to the at least one target rule component. Based on the target policy group, the data of the target data source is matched and processed by the rule engine. If the match is successful, data collection is performed according to the collection object information to obtain the target data. The cloud server stores the various data collected by the collection device and provides the target data source of each target rule component. The data collection method provided in this embodiment parses the collection target, calls the relevant rule components from multiple pre-created basic rule components, forms a policy group, and determines whether the collection trigger condition is met through the rule engine based on the policy group. If it is met, the collection is performed, which can realize the dynamic change of the collection strategy during the data collection process, reduce the threshold of policy configuration and manpower investment, and improve the intelligence level of the collection device.

[0055] It should be noted that Figure 1 The scenario diagram shown is only an example. The data collection method and scenario described in the embodiment of the present application are intended to more clearly illustrate the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided in the embodiment of the present application. Ordinary technicians in this field can know that with the evolution of the system and the emergence of new business scenarios, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.

[0056] The technical solution of the present application is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0057] Figure 2 Schematic diagram of the data collection method provided in the embodiment of the present application Figure 1 .like Figure 2 As shown, the method includes:

[0058] 201. Acquire a collection target, and parse the collection target to obtain collection trigger condition information and collection object information corresponding to the collection target.

[0059] The execution subject of this embodiment is a collection device, for example, Figure 1 The acquisition equipment shown.

[0060] Specifically, after receiving the collection target set by the user through the interface or the front-end interface (for example, collecting road sign information within an X time range and a Y geographical range), the collection target can be parsed to obtain the collection trigger condition information corresponding to the collection target (for example, the X time range and the Y geographical range) and the collection object information (for example, the point cloud and image of the road sign and the corresponding collection requirements, such as the point cloud collection frequency, the image resolution, etc.).

[0061] Figure 3 Schematic diagram of the acquisition target setting interface provided in the embodiment of the present application. Figure 3 As shown, the acquisition target setting interface may include geographic rule components, time rule components, attribute rule components, environment rule components, and event rule components. The user can select components from the corresponding types of components. For example, in actual operation, in response to the user clicking the button next to the environment rule component, the specific options of this type of component are displayed in the interface, including the weather rule component, the temperature rule component, the humidity rule component, and the brightness rule component. In response to the user's touch operation on a specific component, such as dragging the weather rule component to the editing interface, the user can fill in the specific limit value, such as limiting the weather to cloudy.

[0062] 202. According to the collection trigger condition information, call at least one target rule component from a plurality of basic rule components, and determine a target data source and a target policy group corresponding to the at least one target rule component.

[0063] In this embodiment, the target data source can be provided by a cloud database, and can also be provided by a specific collection device in the collection device. For example, the data of the collection device is usually reported to the cloud database for storage, and some data is not reported. For the data reported to the cloud database, the data source of the target rule component can be obtained from the cloud database, and for the data not reported to the cloud database, it can be directly obtained from the collection device, for example, from the Electronic Control Unit (ECU).

[0064] The target policy group refers to a collection of multiple policies of multiple target rule components, which can be a simple collection or the result of a logical operation. For example, based on the acquisition trigger condition information, two time rule components are called, where the policy corresponding to the first time rule component is within the time period of every Tuesday, and the policy corresponding to the second time rule component is between 6:00 and 18:00 every day. Then, the two policies can be directly used as the target policy group, or the two policies can be logically operated to obtain the target policy group, for example, the two policies can be ANDed or ORed.

[0065] In addition, since there is a dependency relationship between the basic rule components, for example, the execution of component A is a prerequisite for the execution of component B, then if component B is determined based on the collection target, component A can also be called based on the association relationship between components, and the strategy of component A can also be included in the target strategy group.

[0066] In some embodiments, in order to save computing resources, the target policies of each target rule component can be logically merged to obtain a target policy group, so that the subsequent matching process reduces the amount of calculation, saves computing resources, and improves processing efficiency. The method of calling at least one target rule component from multiple basic rule components according to the acquisition trigger condition information and determining the target data source and target policy group corresponding to the at least one target rule component may include: parsing the acquisition trigger condition information to obtain at least one acquisition item and data requirement information corresponding to each acquisition item; for each acquisition item, calling the target rule component corresponding to the acquisition item from multiple basic rule components, and determining the data source and target policy corresponding to the target rule component according to the data requirement information; determining the data source corresponding to each target rule component as the target data source; processing each target policy according to the logical relationship between each target policy to obtain the target policy group corresponding to the acquisition target.

[0067] For example, taking the collection trigger condition as X time range and Y geographic range, two collection items, time collection item and location collection item, can be obtained by analysis. The corresponding data requirement information is the range requirement corresponding to each collection item, for example, the data requirement information of the time collection item is the time range.

[0068] In this embodiment, each collection item corresponds to a target rule component, and the data source and target strategy of each target rule component can be determined by the data requirements of the corresponding collection item. For example, the data requirement of the time collection item is 6:00 to 18:00 every day. Based on this data requirement, the target strategy can be determined as a time greater than 6:00 and less than 18:00. The data source can be selected from the data collected by the collection device that can record this period of time. For example, if multiple collection devices can perform collection during this period of time, then one of them can be selected.

[0069] In some embodiments, due to problems such as failure of the collection device, the target data source cannot obtain the newly collected data of the collection device. In this case, in order to save computing resources, the target policy may not be configured for the corresponding target rule component, thereby reducing the resource occupation of the subsequent rule engine for matching processing. Specifically, determining the target policy corresponding to the target rule component according to the data requirement information may include: obtaining the status information of the collection component corresponding to the data source of the target rule component; if the status information indicates that the collection component is in an available state, determining the target policy corresponding to the target rule component according to the data requirement information.

[0070] In some embodiments, in order to save computing resources, logical operations can be performed on the target policies of target rule components of the same type, while operations between different types are not required. Specifically, the processing of each target policy according to the logical relationship between each target policy to obtain the target policy group corresponding to the acquisition target may include: obtaining the type identification of each target rule component; for target rule components with the same type identification, merging each target policy according to the logical relationship between each target policy to obtain the target policy group corresponding to the acquisition target.

[0071] Exemplarily, each basic rule component is divided into multiple types of components such as geographic components, time components, and environment components. When performing logical operations between target strategies, logical operations can be performed on target strategies of the same type. If there is no logical relationship between types, logical operations may not be performed.

[0072] In some embodiments, the target data source may come from a cloud database. Specifically, determining the data source corresponding to each target rule component as the target data source may include: determining the target field corresponding to the data source corresponding to each target rule component from the target database; the target database is used to store data of the data sources of multiple basic rule components; pre-reading the data of each target field into a time series database, and determining the time series database as the target data source.

[0073] The implementation process of step 202 is illustrated below. Specifically, after the basic rule component is determined and used to set the collection target, the system can call the involved rule components based on the collection target and generate each component instance. Users can set their data collection targets through the interface or front-end interface without worrying about the collection strategy. After setting the data collection targets, the system calls the rule components that meet the conditions in the component library composed of multiple basic rule components based on the dependency relationship between the components, the association relationship between the collection trigger conditions and the collection devices (for example, the trigger condition is that the location is within the area range C, and there are multiple collection devices that can collect location information. Based on the association relationship, multiple collection devices can be determined, and further existing data requirements, such as accuracy greater than W, select the basic rule components corresponding to the collection devices that meet the data requirements). The data processing part (each data requirement information) in each rule component instance can be regarded as a query statement with an association relationship. The system will pre-read data from different target data sources to the time series database based on the target data sources of each rule component involved in the query statement (for example, the field content of the corresponding field in the cloud database), and form a rule component instance that can be directly parsed by the rule engine. Based on the rule component instance, the data pre-read into the time series database is matched and processed.

[0074] After confirming the instance and target data source of each target rule component, the system will determine the threshold and operator of the rule instance based on the current state of the corresponding collection device (whether it is available, that is, whether the target data source will obtain new collection data) and the data requirement information of each collection item of the collection trigger condition in the collection target, and form a single target strategy corresponding to each target rule component to form the final target strategy group. Specifically, based on the current state of the collection device and the data requirement information of the collection trigger condition of the collection target input by the user, the system will determine the operator and threshold of the collection rule instance. Its logic is mainly based on the two principles of "mapping the quality parameters of the collection target data" and "circling and controlling the device through the state". The system automatically adjusts the parsing threshold and operator according to the business input, and generates the final target strategy group based on a single target strategy.

[0075] In the process of generating a target policy group based on multiple single target policies, the system determines the AND / OR relationship between policies according to the dependency and opposition relationship between different policies to form a target policy group. Specifically, after obtaining multiple independent policies, due to the dependency and opposition relationship between policies (for example, two time policies can be merged), the system will confirm the AND / OR relationship between policies based on the acquisition target and the usage logic of the policy itself, and integrate them into a dynamic cache, which is connected one by one with the time series data in the time series database to form an engine data source for use in rule matching.

[0076] 203. Based on the target policy group, match the data of the target data source through a rule engine.

[0077] 204. If the match is successful, data collection is performed according to the collection object information to obtain target data.

[0078] Specifically, the matching module continuously obtains data from the target data source, and outputs the result to the execution module after the match is successful. The execution module notifies the acquisition device to complete the data acquisition based on the matching result. Whenever new data is sent from the target data source in a time sequence, the rule engine will be activated to continuously match the new data with the set target strategy group. This process will stop after the match is successful or manually terminated. If the match is successful in the middle, a matching result will be output in real time for the subsequent execution module to execute instructions. When the matching conditions of the rule engine are met, the execution module will notify the acquisition device through the communication link based on the acquisition object information (for example, notify the radar to perform point cloud acquisition based on the acquisition requirements in the acquisition object information, or perform image acquisition through the camera based on the acquisition requirements in the acquisition object information) to perform an acquisition action and obtain the set target data. Then the matching engine continues to match the next trigger data, and the cycle continues until the autonomous driving scene simulation data acquisition is completed.

[0079] In some embodiments, if the user adjusts the collection target or data quality requirements for some collected Corner Cases after acquiring data, the system will dynamically adjust the collection strategy group based on the updated collection target, and reform the target data source to complete the iterative collection of data.

[0080] In some embodiments, the matching processing of the data of the target data source through a rule engine based on the target policy group may include: in response to the writing of new data into the time series database, comparing the generation time of the new data with the generation time of the existing data in the time series database; if the generation time of the new data is later than the generation time of the existing data in the time series database, detecting through a rule engine whether the new data meets the conditions of the target policy group, and if so, the match is successful.

[0081] From the above description, it can be seen that the data collection method provided in this embodiment analyzes the collection target, calls relevant rule components from multiple pre-created basic rule components, forms a policy group, and determines whether the collection trigger conditions are met through the rule engine based on the policy group. If so, collection is performed. This can realize dynamic changes in collection strategies during data collection, reduce the threshold for policy configuration and manpower investment, and improve the intelligence level of collection equipment.

[0082] Figure 4 Schematic diagram of the data collection method provided in the embodiment of the present application Figure 2 .like Figure 4 As shown, based on the above embodiment, for example Figure 2 On the basis of the illustrated embodiment, in this embodiment, an example is given for constructing a basic rule component, and the method includes:

[0083] 401. Obtain function information of an acquisition device and parameter information corresponding to the function information, and construct a plurality of basic rule components according to the function information and the parameter information; the basic rule components include a data source, an output value range and available operators; the output value range and the available operators are determined according to the parameter information.

[0084] Specifically, the acquisition equipment may include multiple acquisition devices, each of which may have multiple functions. For example, the acquisition equipment may include acquisition devices such as radar, camera, vehicle network terminal (Telematics Box, TBOX), digital video recorder (Digital Video Recorder, DVR), etc., wherein TBOX has various acquisition functions such as GPS data acquisition function and time data acquisition function, and DVR has various acquisition functions such as image acquisition function and audio acquisition function, wherein each acquisition function of the acquisition device corresponds to a parameter, such as the resolution of image acquisition, the accuracy of GPS data acquisition, etc. According to the above functional information and the corresponding parameter information, multiple basic rule components can be constructed, each of which includes a data source, an output value range and an available operator. Among them, the output value range refers to the range that the acquisition data corresponding to the component can reach, for example, the range that the data resolution can reach, up to 720p. Available operators refer to operators such as greater than and less than. That is, the resolution-related output value range and available operators can be determined based on the resolution parameters. For example, there are multiple image acquisition devices in the acquisition device that can acquire images, some with high resolution and some with low resolution. By comprehensively considering each resolution, the output value range and available operators of the image acquisition rule component of the acquisition device can be determined.

[0085] like Figure 3 As shown, multiple basic rule components can be classified. For example, a type identifier can be configured for each component when constructing the basic rule components. Through classification, it is convenient to find components when setting acquisition targets. In addition, when generating a target policy group based on the target policies of each basic rule component through logical operations, it can be completed by performing logical operations on the target policies corresponding to each basic rule component in the type. There is no logical relationship between types, and logical operations can be omitted to improve processing efficiency and save computing resources. Exemplarily, it can be classified into geographic rule components, time rule components, attribute rule components, environment rule components, and event rule components. The meaning of each type of rule component can be referred to the following Table 1.

[0086] Table 1

[0087]

[0088] 402. Acquire a collection target, and parse the collection target to obtain collection trigger condition information and collection object information corresponding to the collection target.

[0089] 403. According to the collection trigger condition information, call at least one target rule component from a plurality of basic rule components, and determine a target data source and a target policy group corresponding to the at least one target rule component.

[0090] 404. Based on the target policy group, match the data of the target data source through a rule engine.

[0091] 405. If the match is successful, data collection is performed according to the collection object information to obtain target data.

[0092] In this embodiment, steps 402 to 405 are similar to steps 201 to 204 in the above embodiment, and are not described again here.

[0093] The data collection method provided in this embodiment constructs multiple basic rule components based on the various functions and corresponding parameters of the collection equipment in advance, so that the collection target can be analyzed, and the relevant rule components are called from the multiple pre-created basic rule components to form a policy group. Based on the policy group, the rule engine determines whether the collection trigger condition is met. If it is met, collection is performed. It can realize dynamic change of collection strategy during data collection, reduce the threshold of policy configuration and manpower investment, and improve the intelligence level of collection equipment.

[0094] Figure 5 This is a schematic diagram of the structure of the data acquisition device provided in the embodiment of the present application. Figure 5 As shown, the data collection device 50 includes: a parsing module 501 , a determining module 502 , a matching module 503 and a collection module 504 .

[0095] The parsing module 501 is used to obtain a collection target, and parse the collection target to obtain collection trigger condition information and collection object information corresponding to the collection target;

[0096] A determination module 502 is used to call at least one target rule component from a plurality of basic rule components according to the acquisition trigger condition information, and determine a target data source and a target policy group corresponding to the at least one target rule component;

[0097] A matching module 503 is used to perform matching processing on the data of the target data source through a rule engine based on the target policy group;

[0098] The acquisition module 504 is used to acquire target data according to the acquisition object information if the match is successful.

[0099] The data acquisition device provided in the embodiment of the present application analyzes the acquisition target, calls relevant rule components from multiple pre-created basic rule components, forms a policy group, and determines whether the acquisition trigger conditions are met through the rule engine based on the policy group. If met, acquisition is performed. This can realize dynamic change of acquisition strategies during data acquisition, reduce the threshold for policy configuration and manpower investment, and improve the intelligence level of the acquisition equipment.

[0100] The data acquisition device provided in the embodiment of the present application can be used to execute the above-mentioned method embodiment. Its implementation principle and technical effects are similar, and this embodiment will not be repeated here.

[0101] Figure 6 This is a structural block diagram of a data acquisition device provided in an embodiment of the present application. The device may be a data acquisition vehicle, a data acquisition terminal installed on a vehicle, etc.

[0102] The device 60 may include one or more of the following components: a processing component 601 , a memory 602 , a power component 603 , a multimedia component 604 , an audio component 605 , an input / output (I / O) interface 606 , a sensor component 607 , and a communication component 608 .

[0103] The processing component 601 generally controls the overall operation of the device 60, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 601 may include one or more processors 609 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 601 may include one or more modules to facilitate the interaction between the processing component 601 and other components. For example, the processing component 601 may include a multimedia module to facilitate the interaction between the multimedia component 604 and the processing component 601.

[0104] The memory 602 is configured to store various types of data to support operations on the device 60. Examples of such data include instructions for any application or method operating on the device 60, contact data, phone book data, messages, pictures, videos, etc. The memory 602 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 memory, flash memory, magnetic disk or optical disk.

[0105] The power supply component 603 provides power to the various components of the device 60. The power supply component 603 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device 60.

[0106] The multimedia component 604 includes a screen that provides an output interface between the device 60 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 604 includes a front camera and / or a rear camera. When the device 60 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and the rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0107] The audio component 605 is configured to output and / or input audio signals. For example, the audio component 605 includes a microphone (MIC), and when the device 60 is in an operation mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 602 or sent via the communication component 608. In some embodiments, the audio component 605 also includes a speaker for outputting audio signals.

[0108] I / O interface 606 provides an interface between processing component 601 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

[0109] The sensor assembly 607 includes one or more sensors for providing various aspects of status assessment for the device 60. For example, the sensor assembly 607 can detect the open / closed state of the device 60, the relative positioning of components, such as the display and keypad of the device 60, and the sensor assembly 607 can also detect the position change of the device 60 or a component of the device 60, the presence or absence of user contact with the device 60, the orientation or acceleration / deceleration of the device 60, and the temperature change of the device 60. The sensor assembly 607 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 607 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 607 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0110] The communication component 608 is configured to facilitate wired or wireless communication between the device 60 and other devices. The device 60 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 608 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 608 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0111] In an exemplary embodiment, the device 60 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0112] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 602 including instructions, and the instructions can be executed by a processor 609 of the device 60 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0113] The computer-readable storage medium mentioned above 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 memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0114] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0115] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0116] An embodiment of the present application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the data acquisition method performed by the above data acquisition device is implemented.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A data collection method, characterized in that: Applied to a collection device, the method comprises: Acquire a collection target, and analyze the collection target to obtain collection trigger condition information and collection object information corresponding to the collection target; Parsing the collection trigger condition information to obtain at least one collection item and data requirement information corresponding to each collection item; Calling a target rule component corresponding to the collection item from a plurality of basic rule components, and determining a target data source and a target policy group corresponding to the target rule component according to the data requirement information; the target policy group includes a target policy corresponding to the target rule component determined according to the data requirement information; Based on the target policy group, matching processing is performed on the data of the target data source through a rule engine; If the match is successful, data collection is performed according to the collection object information to obtain the target data.

2. The method according to claim 1, characterized in that The determining, according to the data requirement information, a target data source and a target policy group corresponding to the target rule component includes: Determine the data source and target strategy corresponding to the target rule component according to the data requirement information; Determine the data source corresponding to each target rule component as the target data source; According to the logical relationship between each target strategy, each target strategy is processed to obtain a target strategy group corresponding to the acquisition target.

3. The method according to claim 2, characterized in that The determining, according to the data requirement information, a target strategy corresponding to the target rule component includes: Obtaining status information of a collection component corresponding to a data source of the target rule component; If the status information indicates that the acquisition component is in an available state, the target policy corresponding to the target rule component is determined according to the data requirement information.

4. The method according to claim 2, characterized in that: The step of processing each target strategy according to the logical relationship between the target strategies to obtain a target strategy group corresponding to the acquisition target includes: Get the type identifier of each target rule component; For target rule components with the same type identifier, each target strategy is merged according to the logical relationship between the target strategies to obtain a target strategy group corresponding to the acquisition target.

5. The method according to claim 2, characterized in that: The step of determining the data sources corresponding to the target rule components as the target data sources includes: Determine the target fields corresponding to the data sources corresponding to the target rule components from the target database; the target database is used to store the data of the data sources of the plurality of basic rule components; The data of each of the target fields is pre-read into a time series database, and the time series database is determined as the target data source.

6. The method according to claim 5, characterized in that The matching process of the data of the target data source by a rule engine based on the target policy group includes: In response to writing new data into the time series database, comparing the generation time of the new data with the generation time of existing data in the time series database; If the generation time of the newly added data is later than the generation time of the existing data in the time series database, the rule engine detects whether the newly added data meets the conditions of the target policy group. If so, the match is successful.

7. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: Obtaining function information of the acquisition device and parameter information corresponding to the function information; According to the function information and the parameter information, a plurality of the basic rule components are constructed; the basic rule components include a data source, an output value range and an available operator; the output value range and the available operator are determined according to the parameter information.

8. A data acquisition device, characterized in that: include: The parsing module is used to obtain the acquisition target and parse the acquisition target to obtain the acquisition trigger condition information and acquisition object information corresponding to the acquisition target; A determination module, configured to parse the collection trigger condition information to obtain at least one collection item and data requirement information corresponding to each collection item; Calling a target rule component corresponding to the collection item from a plurality of basic rule components, and determining a target data source and a target policy group corresponding to the target rule component according to the data requirement information; the target policy group includes a target policy corresponding to the target rule component determined according to the data requirement information; A matching module, used for performing matching processing on the data of the target data source through a rule engine based on the target policy group; The acquisition module is used to acquire target data based on the acquisition object information if the match is successful.

9. An electronic device, characterized in that: include: at least one processor and memory; The memory stores computer-executable instructions; The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor performs the data collection method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the processor executes the computer-executable instructions, the data collection method according to any one of claims 1 to 7 is implemented.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the data collection method according to any one of claims 1 to 7 is implemented.

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