Traffic data identification method and device, storage medium and computer device

Abstract

Embodiments of the present application disclose a traffic data identification method and device, a storage medium and a computer device. The method comprises: collecting traffic data through a traffic data recording platform; querying whether a target configuration rule path corresponding to the traffic data exists in a preset configuration rule path table based on attribute information carried by the traffic data; if the target path corresponding to the attribute information exists in the preset path rule table, performing feature extraction on the out parameters and the in parameters corresponding to the traffic data based on the target path to obtain a feature identifier; and updating the feature identifier to the traffic data to obtain identified traffic data, so as to query the identified traffic data based on the feature identifier. The out parameters and the in parameters of the traffic data are subjected to feature extraction, so that the extracted feature identifier is identified in the traffic data, and then the traffic data can be quickly queried according to the feature identifier in subsequent queries, thereby improving the query efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the computer field, in particular to a traffic data identification method and device, computer readable storage medium and computer equipment. BACKGROUND

[0002] In the test field, the traffic generated in the production environment or each version test stage is a very valuable resource for the entire test link, and with the continuous improvement of the current system complexity, the number of traffic data is becoming more and more huge, and the data structure is becoming more and more complex, which increases the difficulty of traffic query accuracy and identification.

[0003] The traditional method is to retrieve the traffic directly according to the request parameters, however, in most cases, different interface request parameters may have information duplication, and the information entity is generally too large, and if the parameters are directly queried, the query efficiency will be low. SUMMARY

[0004] The embodiments of the present application provide a traffic data identification method and device, which can improve the query efficiency.

[0005] To solve the above technical problems, the embodiments of the present application provide the following technical solutions:

[0006] A traffic data identification method comprises:

[0007] Collecting traffic data through a traffic data recording platform;

[0008] Based on the attribute information carried by the traffic data, it is queried whether there is a target configuration rule path corresponding to the traffic data in a preset configuration rule path table;

[0009] If the target path corresponding to the attribute information exists in the preset path rule table, the feature extraction is performed on the out parameters and the in parameters corresponding to the traffic data based on the target path, and the feature identification is obtained;

[0010] The feature identification is updated to the traffic data, and the identified traffic data is obtained, so that the identified traffic data is queried based on the feature identification.

[0011] A traffic data identification device comprises:

[0012] The collecting module is configured to collect traffic data through a traffic data recording platform;

[0013] The query module is configured to query whether there is a target configuration rule path corresponding to the traffic data in a preset configuration rule path table based on the attribute information carried by the traffic data;

[0014] extracting a feature identifier based on the target path if the target path corresponding to the attribute information exists in the preset path rule table;

[0015] updating the feature identifier into the traffic data to obtain identified traffic data, so that the identified traffic data is queried based on the feature identifier.

[0016] In some embodiments, the apparatus further comprises:

[0017] parsing the out-parameter and the in-parameter corresponding to the traffic data to obtain data in a preset syntax format if the target path corresponding to the attribute information does not exist in the preset path rule table;

[0018] rendering the data in the preset syntax format into a feature item.

[0019] In some embodiments, the apparatus further comprises:

[0020] recording the selected target feature item;

[0021] generating a target configuration rule path for the traffic data according to the target feature item;

[0022] storing the target configuration rule path into the preset path rule table.

[0023] In some embodiments, the apparatus further comprises:

[0024] determining a target request type of the traffic data;

[0025] screening a target parser corresponding to the target request type from a plurality of candidate parsers;

[0026] the parsing module comprises:

[0027] parsing the out-parameter and the in-parameter corresponding to the traffic data by the target parser to obtain data in a preset syntax format.

[0028] In some embodiments, the apparatus further comprises:

[0029] screening target traffic data with the same attribute information as the traffic data from other stored traffic data based on the attribute information;

[0030] An updating module is configured to update the feature identifier into the target traffic data to obtain identified target traffic data.

[0031] In some embodiments, the apparatus further comprises:

[0032] A second generating module is configured to generate a corresponding query condition based on the feature identifier, so as to query the traffic data according to the query condition.

[0033] A computer readable storage medium stores a plurality of instructions, which are adapted to be loaded by a processor to execute the steps in the traffic data identification method.

[0034] A computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the traffic data identification method when executing the program.

[0035] Embodiments of the present application collect traffic data through a traffic data recording platform; based on attribute information carried by the traffic data, it is determined whether a target configuration rule path corresponding to the traffic data exists in a preset configuration rule path table; if the target path corresponding to the attribute information exists in the preset path rule table, feature extraction is performed on out parameters and in parameters corresponding to the traffic data based on the target path to obtain a feature identifier; the feature identifier is updated into the traffic data to obtain identified traffic data, so that the identified traffic data is queried based on the feature identifier. In this way, the out and in parameters of the traffic data are subjected to feature extraction, the extracted feature identifier is identified in the traffic data, and the traffic data can be quickly queried according to the feature identifier in subsequent queries, thereby improving the query efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0037] Figure 1a The system schematic diagram of the traffic data identification method provided by the embodiments of the present application.

[0038] Figure 1b The flowchart of the traffic data identification method provided by the embodiments of the present application.

[0039] Figure 2 The structure schematic diagram of the traffic data identification apparatus provided by the embodiments of the present application.

[0040] Figure 3 The structural schematic diagram of the computer device provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person skilled in the art without creative work fall within the protection scope of the present application.

[0042] Please refer to Figure 1a , Figure 1a The system schematic diagram of the traffic data identification method provided by the embodiment of the present application can include at least one client 1000, at least one computer device 2000, at least one database 3000, and a network 4000. The client 1000 can be a terminal device such as a mobile phone, a computer, or a personal digital assistant, and the computer device 2000 can be a server or the like for providing a traffic recording platform. The network 4000 can be a wireless network or a wired network, such as a wireless local area network (WLAN), a local area network (LAN), a cellular network, a 2G network, a 3G network, a 4G network, a 5G network, and the like. In addition, the system can include a database 3000, which can be used to store traffic data generated by a user.

[0043] The embodiment of the present application provides a traffic data identification method, which can be executed by a computer device. As shown in the figure, Figure 1a the computer device 2000 collects traffic data through a traffic data recording platform; based on attribute information carried by the traffic data, it is determined whether there is a target configuration rule path corresponding to the traffic data in a preset configuration rule path table; if there is a target path corresponding to the attribute information in the preset path rule table, feature extraction is performed on the out-parameters and the in-parameters corresponding to the traffic data based on the target path to obtain a feature identifier; the feature identifier is updated to the traffic data to obtain identified traffic data, so that the identified traffic data can be queried based on the feature identifier. In this way, the out-parameters and the in-parameters of the traffic data are extracted for feature extraction, so that the extracted feature identifier is identified in the traffic data, and then the traffic data can be quickly queried according to the feature identifier in subsequent queries, thereby improving the query efficiency.

[0044] It should be noted that, Figure 1aThe scenario schematic diagram of the flow data identification method shown is only an example. The flow data identification system and scenario described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. It can be known by a person of ordinary skill in the art that, as the flow data identification system evolves and new service scenarios appear, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0045] In the embodiment, the flow data identification device is described from the perspective of the flow data identification device, which can be specifically integrated in a computer device with a storage unit and a microprocessor installed and having computing capability.

[0046] Referring to Figure 1b , Figure 1b A flow chart of the flow data identification method provided by the embodiments of the present application is shown. The flow data identification method comprises the following steps.

[0047] In step 101, flow data is collected by a flow data recording platform.

[0048] The flow data is generated when a user opens an application through a client and performs a networking operation in the application, and the flow data is recorded by the flow recording platform. Therefore, in a test environment, the flow data can be collected by the flow recording platform. The collection manner can be intermittent collection, for example, collection once every 30 minutes, or real-time collection, for example, the flow data is collected after being recorded by the flow recording platform, and the like, which is not limited herein.

[0049] In step 102, whether a target configuration rule path corresponding to the flow data exists in a preset configuration rule path table is queried based on attribute information carried by the flow data.

[0050] The flow data carries attribute information, for example, application attribute, for example, in which application the flow data is generated, name of the application, version information of the application, and the like, or interface attribute, for example, which interface of the background is called to generate the flow data, and the like.

[0051] Specifically, a corresponding relationship between the attribute information and the configuration rule path can be established in advance, so that the corresponding configuration rule path can be found according to the attribute information carried by the flow data when the flow data is queried subsequently. However, due to the large number of flow data, it cannot be guaranteed that each flow data has a corresponding path in the configuration rule path table. Therefore, whether a target configuration rule path corresponding to the flow data exists in a preset configuration rule path table needs to be queried.

[0052] The configuration rule path refers to which interfaces are called in sequence according to time and which applications are experienced by the traffic data, thereby forming a specific path.

[0053] In step 103, if the target path corresponding to the attribute information exists in the preset path rule table, the feature extraction is performed on the out parameters and the in parameters corresponding to the traffic data based on the target path, and the feature identifier is obtained.

[0054] In the case that the target path corresponding to the attribute information exists in the preset path rule table, the in parameters and the out parameters required for forming the traffic data can be obtained according to each application or each interface configured under the target path. The feature extraction is performed on the out parameters and the in parameters corresponding to the traffic data, and the feature identifier is obtained.

[0055] Specifically, the feature extraction can be customized according to the requirements of the business. For example, in the current test environment, it is more concerned whether there is a case of purchasing a specified commodity in the user's historical purchase record, so the specified commodity in the traffic data can be extracted to obtain the feature identifier: purchased, or the commodity name of the specified commodity is directly extracted.

[0056] In some embodiments, after the step of performing the feature extraction on the out parameters and the in parameters corresponding to the traffic data based on the target path to obtain the feature identifier, the method further includes:

[0057] (1) filtering, based on the attribute information, target traffic data with the same attribute information as the traffic data from other stored traffic data;

[0058] (2) updating the feature identifier to the target traffic data to obtain the identified target traffic data.

[0059] In order to facilitate the subsequent query and classification of the traffic data, after the feature identifier is extracted, the target traffic data with the same attribute information as the traffic data can be filtered from other stored traffic data according to the attribute information, and the feature identifier is updated to the target traffic data, so that the target traffic data with the same attribute information as the previously stored traffic data is also identified by the feature identifier.

[0060] For example, in the current test environment, it is more concerned whether there is a case of purchasing a specified commodity in the user's historical purchase record, so the traffic data with the behavior of purchasing the specified commodity in the other stored traffic data can also be identified as purchased, or the commodity name of the specified commodity.

[0061] In some embodiments, after the step of performing feature extraction on the out-parameters and in-parameters corresponding to the traffic data based on the target path to obtain feature identifiers, the method further comprises:

[0062] generating corresponding query conditions based on the feature identifiers, so as to query the traffic data according to the query conditions.

[0063] In this way, when the traffic data is provided with feature identifiers, the query conditions can be dynamically generated at the front end, and the tester only needs to query according to the extracted dynamic feature characters, so that the required traffic data can be retrieved without querying the entire parameters as in the traditional method, and the traffic can be directly identified through the extracted dynamic features, thereby avoiding irrelevant information interference.

[0064] In some embodiments, the method further comprises:

[0065] (1) If the target path corresponding to the attribute information does not exist in the preset path rule table, the out-parameters and in-parameters corresponding to the traffic data are parsed to obtain data in a preset syntax format;

[0066] (2) The data in the preset syntax format is rendered into feature items.

[0067] In this way, if the corresponding configuration rule path does not exist in the rule table, the out-parameters and in-parameters corresponding to the traffic data are parsed, and the rules are dynamically configured.

[0068] Specifically, the manner of parsing the out-parameters and in-parameters can be to parse the out-parameters and in-parameters into a standard json format, and then render these json format strings into feature items with hierarchical structure and capable of being selected by users through front-end technology, so as to facilitate the tester to select the required feature items.

[0069] In some embodiments, after the step of rendering the data in the preset syntax format into feature items, the method further comprises:

[0070] (1) recording the selected target feature items;

[0071] (2) generating a target configuration rule path for the traffic data according to the target feature items;

[0072] (3) storing the target configuration rule path in the preset path rule table.

[0073] In this way, when the tester selects the required feature items, the selected target feature items are recorded, a target configuration rule path for the traffic data is generated according to the target feature items, and the target configuration rule path is stored in the preset path rule table. The target configuration rule path is the configuration rule path corresponding to the attribute information of the traffic data.

[0074] In some embodiments, before the step of parsing the out-parameters and the in-parameters corresponding to the traffic data to obtain data in a preset syntax format, the method further comprises:

[0075] (1.1) determining a target request type of the traffic data;

[0076] (1.2) selecting a target parser corresponding to the target request type from a plurality of candidate parsers;

[0077] The step of parsing the out-parameters and the in-parameters corresponding to the traffic data to obtain data in a preset syntax format comprises:

[0078] The out-parameters and the in-parameters corresponding to the traffic data are parsed by the target parser to obtain data in a preset syntax format.

[0079] In the above, since the out-parameters and the in-parameters are generally standard http requests and entity objects, they correspond to different request types and have different parsing processes. Therefore, before parsing, the request type (such as http, java, etc.) of the traffic is determined, and then different parsers designed in advance are selected according to the type for parsing.

[0080] In step 104, the feature identifier is updated to the traffic data to obtain identified traffic data, so that the identified traffic data is queried based on the feature identifier.

[0081] After the feature identifier is extracted, the traffic data can be identified, that is, the feature identifier is updated to the traffic data to obtain identified traffic data, so that the traffic data with the feature identifier can be directly queried through the feature identifier in the subsequent traffic data query process.

[0082] As known from the above, the embodiments of the present application collect traffic data through a traffic data recording platform; based on attribute information carried by the traffic data, it is queried whether a target configuration rule path corresponding to the traffic data exists in a preset configuration rule path table; if the target path corresponding to the attribute information exists in the preset path rule table, feature extraction is performed on the out-parameters and the in-parameters corresponding to the traffic data based on the target path to obtain a feature identifier; the feature identifier is updated to the traffic data to obtain identified traffic data, so that the identified traffic data is queried based on the feature identifier. In this way, the out-parameters and the in-parameters of the traffic data are extracted for feature extraction, so that the extracted feature identifier is identified in the traffic data, and then the traffic data can be quickly queried according to the feature identifier in the subsequent query, improving the query efficiency.

[0083] In some embodiments, when a user opens an application through a client and performs a networking operation inside the application, traffic data is generated, and the generated traffic data is recorded by a traffic recording platform. Therefore, in a test environment, the traffic data can be collected by the traffic recording platform. The collection method can be intermittent collection, such as collecting once every 30 minutes, or real-time collection, such as collecting traffic data as soon as the traffic recording platform records it. The traffic data carries attribute information, such as application attributes, the application on which the traffic data is generated, the name of the application, the version information of the application, and the like; or interface attributes, the interface of the background through which the traffic data is generated, and the like. Specifically, a correspondence between the attribute information and the configuration rule path can be established in advance, so that when the traffic data is queried subsequently, the corresponding configuration rule path can be found according to the attribute information carried by the traffic data. However, due to the large number of traffic data, it is not possible to guarantee that each traffic data has a corresponding path in the configuration rule path table. Therefore, it is necessary to query whether the target configuration rule path corresponding to the traffic data exists in the preset configuration rule path table.

[0084] For the case where the target path corresponding to the attribute information exists in the preset path rule table, the input parameters and the output parameters required for forming the traffic data can be obtained according to each application or each interface under the target path configuration. The output parameters and the input parameters corresponding to the traffic data are extracted to obtain a feature identifier. Specifically, the feature extraction can be customized according to the needs of the business. For example, in the current test environment, it is more concerned whether there is a case of purchasing a specified product in the user's historical purchase record. Therefore, the specified product in the traffic data can be extracted to obtain a feature identifier: purchased, or the product name of the specified product is directly extracted.

[0085] In order to facilitate the subsequent query and classification of traffic data, after the feature identifier is extracted, the target traffic data with the same attribute information as the traffic data can be screened out from the other stored traffic data according to the attribute information, and the feature identifier is updated to the target traffic data, so that the target traffic data with the same attribute information in the previously stored traffic data is also identified with the feature identifier. For example, in the current test environment, it is more concerned whether there is a case of purchasing a specified product in the user's historical purchase record. Therefore, the traffic data in which the behavior of purchasing the specified product exists in the other stored traffic data can also be identified as purchased, or the product name of the specified product, and the like.

[0086] The corresponding query condition is generated based on the feature identifier, so as to query the traffic data according to the query condition. Wherein, when the traffic data is provided with the feature identifier, the query condition can be dynamically generated in the front end, and the tester only needs to query according to the extracted dynamic feature character, so as to retrieve the required traffic data, without querying the entire parameter as in the traditional method, and meanwhile, the traffic can be directly identified through the extracted dynamic feature, avoiding irrelevant information interference.

[0087] Wherein, if the corresponding configuration rule path does not exist in the rule table, the in-out parameters corresponding to the traffic data are parsed, and the rule is dynamically configured. Specifically, the in-out parameters can be parsed into a standard json format, and then the json format string is rendered into a feature item with a hierarchical structure and can be selected by the user through the front-end technology, so as to facilitate the tester to select the required feature item.

[0088] Wherein, when the tester selects the required feature item, the selected target feature item is recorded, and a target configuration rule path for the traffic data is generated according to the target feature item, and stored in a preset path rule table. The target configuration rule path is the configuration rule path corresponding to the attribute information of the traffic data.

[0089] Wherein, since the general in-out parameters are standard http requests and entity objects, they correspond to different request types and have different parsing processes, therefore, before parsing, the request type (such as http, java type) of the traffic is determined, and then different parsers designed in advance are selected according to the type for parsing.

[0090] After the feature identifier is extracted, the traffic data can be identified, that is, the feature identifier is updated to the traffic data, to obtain the identified traffic data, so that the traffic data with the feature identifier can be directly queried through the feature identifier in the subsequent traffic data query process.

[0091] In order to better implement the traffic data identification method provided by the embodiment of the application, the embodiment of the application further provides a device based on the above traffic data identification method. The meanings of the terms are the same as those in the above traffic data identification method, and the specific implementation details can be referred to the description in the method embodiment.

[0092] Please refer to Figure 2 , Figure 2 The structure diagram of the traffic data identification device provided by the embodiment of the application is shown in the figure, wherein the traffic data identification device can include a collection module 201, a query module 202, an extraction module 203 and an identification module 204.

[0093] The collection module 201 is used for collecting traffic data through a traffic data recording platform.

[0094] The query module 202 is configured to query whether a target configuration rule path corresponding to the traffic data exists in a preset configuration rule path table based on attribute information carried by the traffic data.

[0095] The extraction module 203 is configured to perform feature extraction on an out-parameter and an in-parameter corresponding to the traffic data based on a target path corresponding to the attribute information if the target path exists in the preset path rule table, to obtain a feature identifier.

[0096] The identification module 204 is configured to update the feature identifier into the traffic data to obtain identified traffic data, so that the identified traffic data is queried based on the feature identifier.

[0097] In some embodiments, the apparatus further includes:

[0098] The analysis module is configured to analyze the out-parameter and the in-parameter corresponding to the traffic data to obtain data in a preset syntax format if the target path corresponding to the attribute information does not exist in the preset path rule table.

[0099] The rendering module is configured to render the data in the preset syntax format into a feature item.

[0100] In some embodiments, the apparatus further includes:

[0101] The recording module is configured to record the selected target feature item.

[0102] The first generation module is configured to generate a target configuration rule path for the traffic data according to the target feature item.

[0103] The storage module is configured to store the target configuration rule path into the preset path rule table.

[0104] In some embodiments, the apparatus further includes:

[0105] The determination module is configured to determine a target request type of the traffic data.

[0106] The first screening module is configured to screen a target parser corresponding to the target request type from a plurality of candidate parsers.

[0107] The analysis module includes:

[0108] The analysis submodule is configured to analyze the out-parameter and the in-parameter corresponding to the traffic data by using the target parser to obtain data in a preset syntax format.

[0109] In some embodiments, the apparatus further includes:

[0110] a second screening module configured to screen target traffic data with the same attribute information as the attribute information of the traffic data from other stored traffic data based on the attribute information;

[0111] an updating module configured to update the feature identifier into the target traffic data to obtain identified target traffic data.

[0112] In some embodiments, the apparatus further comprises:

[0113] a second generating module configured to generate a corresponding query condition based on the feature identifier, so as to query the traffic data according to the query condition.

[0114] As known from the above, the embodiments of the present application collect traffic data through a traffic data recording platform by the collecting module 201; the query module 202 queries whether there is a target configuration rule path corresponding to the traffic data in a preset configuration rule path table based on attribute information carried by the traffic data; the extracting module 20 extracts features of out parameters and in parameters corresponding to the traffic data based on a target path corresponding to the attribute information in the preset path rule table, to obtain a feature identifier; the identifying module 204 updates the feature identifier into the traffic data to obtain identified traffic data, so as to query the identified traffic data based on the feature identifier. In this way, the out and in parameters of the traffic data are extracted for features, the extracted feature identifier is identified in the traffic data, and then the traffic data can be quickly queried according to the feature identifier in subsequent queries, thereby improving the query efficiency.

[0115] The specific implementation of each operation can refer to the foregoing embodiments, which will not be described here.

[0116] Correspondingly, the embodiments of the present application also provide a computer device, which can be a terminal or a server. The terminal can be a terminal device such as a smart phone, a tablet computer, a notebook computer, a touch screen, a game console, a personal computer (PC, Personal Computer), a personal digital assistant (PDA), etc. As shown in Figure 3 Figure 3 ​A structural schematic diagram of a computer device provided in an embodiment of the present application. The computer device 2000 includes a processor 401 having one or more processing cores, a memory 402 having one or more computer readable storage media, and a computer program stored on the memory 402 and executable on the processor. The processor 401 is electrically connected to the memory 402. Those skilled in the art can understand that the computer device structure shown in the figure does not constitute a limitation on the computer device, and can include more or fewer components than shown in the figure, or combine certain components, or different component arrangements.

[0117] The processor 401 is the control center of the computer device 2000, and connects various parts of the entire computer device 2000 through various interfaces and lines, executes various functions and processes data of the computer device 2000 by running or loading software programs and / or modules stored in the memory 402 and calling data stored in the memory 402, thereby overall monitoring the computer device 2000.

[0118] In an embodiment of the present application, the processor 401 in the computer device 2000 will load the instructions corresponding to the processes of one or more application programs into the memory 402, and run the application programs stored in the memory 402 by the processor 401, thereby realizing various functions according to the following steps:

[0119] acquiring traffic data through a traffic data recording platform; querying whether a target configuration rule path corresponding to the traffic data exists in a preset configuration rule path table based on attribute information carried by the traffic data; if a target path corresponding to the attribute information exists in the preset path rule table, performing feature extraction on an out-parameter and an in-parameter corresponding to the traffic data based on the target path to obtain a feature identifier; updating the feature identifier to the traffic data to obtain identified traffic data, so as to query the identified traffic data based on the feature identifier.

[0120] In some embodiments, the method further comprises:

[0121] if the target path corresponding to the attribute information does not exist in the preset path rule table, parsing the out-parameter and the in-parameter corresponding to the traffic data to obtain data in a preset syntax format;

[0122] rendering the data in the preset syntax format into a feature item.

[0123] In some embodiments, after the step of rendering the data in the preset syntax format into a feature item, the method further comprises:

[0124] recording the selected target feature item;

[0125] Generate a target configuration rule path for the traffic data based on the target feature items;

[0126] The target configuration rule path is stored in the preset path rule table.

[0127] In some embodiments, before the step of parsing the output and input parameters corresponding to the traffic data to obtain data with a preset syntax format, the method further includes:

[0128] Determine the target request type of the traffic data;

[0129] Select the target parser that corresponds to the target request type from among multiple candidate parsers;

[0130] The step of parsing the output and input parameters corresponding to the traffic data to obtain data in a preset syntax format includes:

[0131] The target parser parses the output and input parameters corresponding to the traffic data to obtain data in a preset syntax format.

[0132] In some embodiments, after the step of extracting features from the outgoing and incoming parameters corresponding to the traffic data based on the target path to obtain feature identifiers, the method further includes:

[0133] Based on the attribute information, target traffic data with the same attribute information as the traffic data is filtered out from other stored traffic data;

[0134] The feature identifier is updated in the target traffic data to obtain the identified target traffic data.

[0135] In some embodiments, after the step of extracting features from the outgoing and incoming parameters corresponding to the traffic data based on the target path to obtain feature identifiers, the method further includes:

[0136] Based on the feature identifier, corresponding query conditions are generated so that the traffic data can be queried according to the query conditions.

[0137] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0138] Optional, such as Figure 3 As shown, the computer device 2000 also includes a touch screen display 403, an input unit 404, and a power supply 405. The processor 401 is electrically connected to the touch screen display 403, the input unit 404, and the power supply 405. Those skilled in the art will understand that... Figure 3The computer device structure shown in the figure is not a limitation of the computer device, and can include more or fewer components than shown, or combine certain components, or arrange different components.

[0139] The touch display screen 403 can be used to display a graphical user interface and receive operation instructions generated by a user acting on the graphical user interface. The touch display screen 403 can include a display panel and a touch panel. The display panel can be used to display information input by a user or information provided to a user and various graphical user interfaces of the computer device, which can be composed of graphics, text, icons, videos, and any combination thereof. Optionally, the display panel can be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like. The touch panel can be used to collect touch operations (such as operations of a user using a finger, a stylus, or any suitable object or accessory on or near the touch panel) of a user thereon or therearound, and generate corresponding operation instructions, and the operation instructions execute corresponding programs. Optionally, the touch panel can include two parts, a touch detection device and a touch controller. The touch detection device detects the touch position of a user and detects signals generated by touch operations, and transmits the signals to the touch controller; the touch controller receives touch information from the touch detection device, and converts the touch information into touch coordinates, and sends the touch coordinates to the processor 401, and can receive commands from the processor 401 and execute the commands. The touch panel can cover the display panel, and when the touch panel detects a touch operation thereon or therearound, the touch panel transmits the touch operation to the processor 401 to determine the type of the touch event, and then the processor 401 provides corresponding visual output on the display panel according to the type of the touch event. In the embodiments of the present application, the touch panel and the display panel can be integrated into the touch display screen 403 to realize input and output functions. However, in some embodiments, the touch panel and the touch panel can realize input and output functions as two independent components. That is, the touch display screen 403 can also realize input functions as part of the input unit 404.

[0140] In the embodiments of the present application, the processor 401 executes a game application to generate a graphical user interface on the touch display screen 403, and the virtual scene on the graphical user interface includes at least one skill control area, and the skill control area includes at least one skill control. The touch display screen 403 is used to present the graphical user interface and receive operation instructions generated by a user acting on the graphical user interface.

[0141] The input unit 404 can be used to receive inputted digital, character information or user feature information (e.g. fingerprint, iris, face information, etc.), and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0142] The power supply 405 is used to supply power to various components of the computer device 2000. Optionally, the power supply 405 can be logically connected to the processor 401 through a power management system, so as to realize functions such as management of charging, discharging and power consumption management through the power management system. The power supply 405 can also include one or more direct current or alternating current power supplies, recharging systems, power failure detection circuits, power converters or inverters, power status indicators and any other components.

[0143] Although Figure 3 The computer device 2000 can also include a wireless fidelity module, a Bluetooth module, etc., which are not shown in the embodiment.

[0144] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0145] As can be seen from the above, the computer device provided by the embodiment collects traffic data through a traffic data recording platform; based on attribute information carried by the traffic data, it is queried whether a target configuration rule path corresponding to the traffic data exists in a preset configuration rule path table; if a target path corresponding to the attribute information exists in the preset path rule table, feature extraction is performed on out parameters and in parameters corresponding to the traffic data based on the target path to obtain a feature identifier; the feature identifier is updated to the traffic data to obtain identified traffic data, so as to query the identified traffic data based on the feature identifier. In this way, the out and in parameters of the traffic data are subjected to feature extraction, so that the extracted feature identifier is identified in the traffic data, and then the traffic data can be quickly queried according to the feature identifier in subsequent queries, thereby improving the query efficiency.

[0146] Those skilled in the art can understand that all or part of the steps of the various methods of the above embodiments can be completed by instructions, or by instructions controlling related hardware, which can be stored in a computer readable storage medium and loaded and executed by a processor.

[0147] Therefore, the embodiment of the present application provides a computer readable storage medium, which stores a plurality of computer programs. The computer programs can be loaded by a processor to execute the steps in the control method of any one of the skills provided by the embodiment of the present application. For example, the computer program can execute the following steps:

[0148] Collecting traffic data through a traffic data recording platform; querying whether a target configuration rule path corresponding to the traffic data exists in a preset configuration rule path table based on attribute information carried by the traffic data; if the target path corresponding to the attribute information exists in the preset path rule table, performing feature extraction on out parameters and in parameters corresponding to the traffic data based on the target path to obtain a feature identifier; updating the feature identifier to the traffic data to obtain identified traffic data, so as to query the identified traffic data based on the feature identifier.

[0149] In some embodiments, the method further comprises:

[0150] If the target path corresponding to the attribute information does not exist in the preset path rule table, parsing the out parameters and the in parameters corresponding to the traffic data to obtain data in a preset syntax format;

[0151] Rendering the data in the preset syntax format into a feature item.

[0152] In some embodiments, after the step of rendering the data in the preset syntax format into a feature item, the method further comprises:

[0153] Recording the selected target feature item;

[0154] Generating a target configuration rule path for the traffic data according to the target feature item;

[0155] Storing the target configuration rule path in the preset path rule table.

[0156] In some embodiments, before the step of parsing the out parameters and the in parameters corresponding to the traffic data to obtain data in a preset syntax format, the method further comprises:

[0157] Determining a target request type of the traffic data;

[0158] Filtering a target parser corresponding to the target request type from a plurality of candidate parsers;

[0159] The step of parsing the out parameters and the in parameters corresponding to the traffic data to obtain data in a preset syntax format comprises:

[0160] Parsing the out parameters and the in parameters corresponding to the traffic data through the target parser to obtain data in a preset syntax format.

[0161] In some embodiments, after the step of performing feature extraction on the out parameters and the in parameters corresponding to the traffic data based on the target path to obtain a feature identifier, the method further comprises:

[0162] filtering, from other stored traffic data, target traffic data identical to the attribute information of the traffic data based on the attribute information;

[0163] updating the feature identifier into the target traffic data to obtain identified target traffic data.

[0164] In some embodiments, after the step of performing feature extraction on the out-parameters and in-parameters corresponding to the traffic data based on the target path to obtain a feature identifier, the method further comprises:

[0165] generating a corresponding query condition based on the feature identifier, so as to query the traffic data according to the query condition.

[0166] The specific implementation of each operation can refer to the foregoing embodiments, which will not be described here again.

[0167] The storage medium can include a read-only memory (ROM), a random access memory (RAM), a magnetic disk, an optical disk, and the like.

[0168] Since the computer program stored in the storage medium can execute the steps of any traffic data identification method provided by the embodiments of the present application, the beneficial effects of any traffic data identification method provided by the embodiments of the present application can be achieved. For details, refer to the foregoing embodiments, which will not be described here again.

[0169] The above describes in detail a traffic data identification method, device, storage medium, and computer device provided by the embodiments of the present application. The principle and implementation manner of the present application are described by applying specific examples. The above embodiment description is only used to help understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manner and application range will be changed. In summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A traffic data identification method characterized by, Comprising: acquiring traffic data through a traffic data recording platform; querying whether a target configuration rule path corresponding to the traffic data exists in a preset configuration rule path table based on attribute information carried by the traffic data; if the target configuration rule path corresponding to the attribute information does not exist in the preset configuration rule path table, the following steps are executed: determining a target request type of the traffic data, and screening a target parser corresponding to the target request type from a plurality of candidate parsers; parsing out parameters and input parameters corresponding to the traffic data through the target parser to obtain data in a preset syntax format; rendering the data in the preset syntax format into feature items, and recording a target feature item selected; generating a target configuration rule path for the traffic data according to the target feature item, and storing it in the preset configuration rule path table; if the target configuration rule path corresponding to the attribute information exists in the preset configuration rule path table, feature extraction is performed on the out parameters and the input parameters corresponding to the traffic data based on the target configuration rule path to obtain a feature identifier; updating the feature identifier to the traffic data to obtain identified traffic data, so as to query the identified traffic data based on the feature identifier.

2. The traffic data identifying method according to claim 1, characterized by, After the step of performing feature extraction on the out parameters and the input parameters corresponding to the traffic data based on the target configuration rule path to obtain a feature identifier, the following steps are further included: screening target traffic data with the same attribute information as the traffic data from other stored traffic data based on the attribute information; updating the feature identifier to the target traffic data to obtain identified target traffic data.

3. The traffic data identifying method according to claim 1, characterized by, After the step of performing feature extraction on the out parameters and the input parameters corresponding to the traffic data based on the target configuration rule path to obtain a feature identifier, the following steps are further included: generating a corresponding query condition based on the feature identifier, so as to query the traffic data according to the query condition.

4. A traffic data identifying apparatus characterized by comprising: Comprising: an acquisition module for acquiring traffic data through a traffic data recording platform; a query module for querying whether a target configuration rule path corresponding to the traffic data exists in a preset configuration rule path table based on attribute information carried by the traffic data; an analysis module for determining a target request type of the traffic data, and screening a target parser corresponding to the target request type from a plurality of candidate parsers if the target configuration rule path corresponding to the attribute information does not exist in the preset configuration rule path table; parsing out parameters and input parameters corresponding to the traffic data through the target parser to obtain data in a preset syntax format; a rendering module for rendering the data in the preset syntax format into feature items, and recording a target feature item selected; generating a target configuration rule path for the traffic data according to the target feature item, and storing it in the preset configuration rule path table; An extraction module is configured to, if a target configuration rule path corresponding to the attribute information exists in the preset configuration rule path table, perform feature extraction on the out-parameters and the in-parameters corresponding to the traffic data based on the target configuration rule path, and obtain a feature identifier. An identification module is configured to update the feature identifier into the traffic data, and obtain identified traffic data, so as to query the identified traffic data based on the feature identifier.

5. A computer readable storage medium, characterized in that, The computer readable storage medium stores a plurality of instructions, which are adapted to be loaded by the processor to execute the steps in the traffic data identification method of any one of claims 1 to 3.

6. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the steps in the traffic data identification method of any one of claims 1 to 3. The processor executes the program to implement the steps in the traffic data identification method of any one of claims 1 to 3.

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