Business data processing method, device, electronic device and computer storage medium

Abstract

This disclosure relates to a business data processing method, apparatus, electronic device, and computer-readable storage medium. The method includes: determining target reported information to be verified from reported information; the reported information being generated based on a business request for an online recommendation service; generating statistical data for detection based on the target reported information; and detecting online data generated by the online recommendation service based on the statistical data to obtain a detection result. In this way, before the Browse Set component receives user feedback, the accuracy of the Browse Set can be quickly and accurately detected using the above method. This solves the problem of slow and difficult accuracy detection of the Browse Set based on user feedback, greatly improving the timeliness, stability, and accuracy of detection, thereby enhancing the user experience.

Description

Technical Field

[0001] The present disclosure relates to the field of Internet technology, and in particular to a business data processing method, device, electronic device, and computer-readable storage medium. Background Art

[0002] In recent years, with the development of the Internet content ecosystem, the push of duplicate content in the recommendation system will greatly reduce the user experience. The recommendation system will naturally choose to push similar content in the user's browsing history, which is very likely to cause duplicate content. Therefore, it is a common practice for most recommendation systems to record user browsing history data and then remove it from the recommendation results. This stored user history data is called Browse Set.

[0003] However, as the recommended content pool and the number of users continue to grow, the amount of Browse Set data will grow rapidly, and in order to enable efficient query, the data structure will gradually become more complex. In this context, the accuracy of Browse Set cannot be effectively guaranteed.

[0004] Currently, the above problems can be detected through the general Browse Set component. However, most Browse Set components can only find problems with the component itself when users report bad cases. Moreover, when users report bad cases, many users have already been affected, so the timeliness is very low. In addition, since the amount of data in the Browse Set itself is large, it is difficult to quickly locate the problem through the bad cases reported by users, and the accuracy is poor. Summary of the invention

[0005] In order to overcome the problems existing in the related art, the present disclosure provides a business data processing method, device, electronic device and storage medium. The technical solution of the present disclosure is as follows:

[0006] According to a first aspect of an embodiment of the present disclosure, a service data processing method is provided, including:

[0007] Determining target reported information to be verified from the reported information; the reported information is generated based on a business request for an online recommendation service;

[0008] Generating statistical data for detection based on the target reporting information;

[0009] The online data generated by the online recommendation service is detected based on the statistical data to obtain a detection result.

[0010] Optionally, the generating statistical data for detection based on the target reporting information includes:

[0011] Convert the target reporting information into a format to obtain reporting data;

[0012] Verifying the reported data to obtain a verification result;

[0013] Statistical data for detection is generated based on the reported data and the verification result.

[0014] Optionally, determining the target reported information to be verified from the reported information includes:

[0015] Acquire first reporting information from the reporting information according to a preset sampling rate;

[0016] Acquire second reporting information from the reporting information according to a preset whitelist;

[0017] The first reported information and the second reported information are used as target reported information to be verified.

[0018] Optionally, the format conversion of the target reporting information to obtain reporting data includes:

[0019] The first reporting information and the second reporting information in the target reporting information are formatted respectively using a preset data structure to obtain reporting data having the preset data structure; the preset data structure includes a standard data structure and / or a custom data structure.

[0020] Optionally, the reported data includes at least one sub-data;

[0021] The verifying the reported data to obtain a verification result includes:

[0022] Acquire corresponding business data from online data based on a first preset field in the reported data; the first preset field is used to record location information of the business data;

[0023] It is detected whether each sub-data in the at least one sub-data belongs to the business data, and a detection result corresponding to each sub-data in the at least one sub-data is obtained.

[0024] Optionally, any sub-data in the at least one sub-data has a corresponding sub-data identifier; the business data includes an identifier list, and the identifier list includes at least one sub-data identifier;

[0025] The detecting whether each sub-data in the at least one sub-data belongs to the business data, and obtaining a detection result corresponding to each sub-data in the at least one sub-data, includes:

[0026] Matching the sub-data identifier of any sub-data in the at least one sub-data with the identifier list;

[0027] If there is a match, determining that any of the sub-data belongs to the business data, and generating a first test result of successful verification;

[0028] If there is no matching item, it is determined that any of the sub-data does not belong to the business data, and a second detection result of verification failure is generated.

[0029] Optionally, the generating statistical data for detection based on the reported data and the verification result includes:

[0030] splicing the verification result and the reported data based on a second preset field to obtain at least one set of target reported data; any set of target reported data in the at least one set of target reported data includes at least one sub-data; the second preset field is a repeated field in the verification result and the reported data;

[0031] Respectively counting the first number of sub-data with a detection result of successful detection and the second number of sub-data with a detection result of failed detection in each group of target reported data;

[0032] The first quantity and the second quantity corresponding to each group of target reported data are used as statistical data.

[0033] Optionally, the detecting the online data generated by the online recommendation service based on the statistical data to obtain the detection result includes:

[0034] Calculate analytical data of at least one dimension corresponding to each preset feature of at least one preset feature using the statistical data;

[0035] Detecting whether the analysis data of any dimension of the at least one dimension exceeds the analysis data threshold of the any dimension;

[0036] If the analysis data of any dimension of the at least one dimension does not exceed the analysis data threshold of any dimension, it is determined that the online data generated by the online recommendation service is normal.

[0037] Optionally, also include:

[0038] In response to the access request, the online data is accessed through a preset data interface.

[0039] According to a second aspect of an embodiment of the present disclosure, a service data processing device is provided, including:

[0040] A data acquisition unit, configured to determine target reported information to be verified from the reported information; the reported information is obtained based on a business request for an online recommendation service;

[0041] a statistical data generating unit, configured to generate statistical data for detection based on the target reporting information;

[0042] The monitoring unit is configured to detect the online data generated by the online recommendation service based on the statistical data to obtain a detection result.

[0043] Optionally, the statistical data generating unit includes:

[0044] The formatting subunit is configured to perform format conversion on the target reporting information to obtain reporting data;

[0045] The calling subunit is configured to verify the reported data and obtain a verification result;

[0046] The multi-dimensional statistical subunit is configured to generate statistical data for detection based on the reported data and the verification results.

[0047] Optionally, the data acquisition unit includes:

[0048] An online data sampling subunit, configured to obtain first reporting information from the reporting information according to a preset sampling rate;

[0049] A whitelist data subunit is configured to obtain second reporting information from the reporting information according to a preset whitelist;

[0050] The first processing subunit is configured to use the first reporting information and the second reporting information as target reporting information to be verified.

[0051] Optionally, the formatting unit is specifically configured as follows:

[0052] The first reporting information and the second reporting information in the target reporting information are formatted respectively using a preset data structure to obtain reporting data having the preset data structure; the preset data structure includes a standard data structure and / or a custom data structure.

[0053] Optionally, the reported data includes at least one sub-data;

[0054] The calling unit comprises:

[0055] A business data subunit is configured to obtain corresponding business data from online data based on a first preset field in the reported data; the first preset field is used to record location information of the business data;

[0056] The first detection subunit is configured to detect whether each sub-data in the at least one sub-data belongs to the business data, and obtain a detection result corresponding to each sub-data in the at least one sub-data.

[0057] Optionally, any sub-data in the at least one sub-data has a corresponding sub-data identifier; the business data includes an identifier list, and the identifier list includes at least one sub-data identifier;

[0058] The first detection subunit is specifically configured as follows:

[0059] The sub-data identifier of any sub-data in the at least one sub-data is matched with the identifier list; if there is a match, it is determined that any sub-data belongs to the business data, and a first test result of successful verification is generated; if there is no match, it is determined that any sub-data does not belong to the business data, and a second test result of failed verification is generated.

[0060] Optionally, the multi-dimensional statistical unit includes:

[0061] A splicing subunit is configured to splice the verification result with the reported data based on a second preset field to obtain at least one set of target reported data; any set of target reported data in the at least one set of target reported data includes at least one sub-data; the second preset field is a repeated field in the verification result and the reported data;

[0062] A counting subunit is configured to respectively count a first number of sub-data with a detection result of successful detection and a second number of sub-data with a detection result of failed detection in each group of target reported data;

[0063] The second processing sub-unit is configured to use the first quantity and the second quantity corresponding to each group of target reported data as statistical data.

[0064] Optionally, the monitoring unit includes:

[0065] A calculation subunit, configured to calculate analysis data of at least one dimension corresponding to each preset feature of at least one preset feature using the statistical data;

[0066] A second detection subunit is configured to detect whether the analysis data of any dimension of the at least one dimension exceeds the analysis data threshold of the any dimension;

[0067] The determination subunit is configured to determine that the online data generated by the online recommendation service is normal if the analysis data of any dimension of the at least one dimension does not exceed the analysis data threshold of any dimension.

[0068] Optionally, also include:

[0069] The access unit is configured to access the online data through a preset data interface.

[0070] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, including:

[0071] processor;

[0072] a memory for storing processor-executable instructions;

[0073] Wherein, the processor is configured to execute the instructions to implement the business data processing method as in the first aspect.

[0074] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided. When instructions in the storage medium are executed by a processor of a mobile terminal, the electronic device can execute the business data processing method of the first aspect.

[0075] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, which includes a computer program or computer instructions, and when the computer program or computer instructions are executed by a processor, the business data processing method of the first aspect is implemented.

[0076] The technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: the Browse Set verification system determines the target reported information to be verified from the reported information; the reported information is generated based on the business request for the online recommendation service; then statistical data for detection is generated based on the target reported information, and the online data generated by the online recommendation service is detected based on the statistical data to obtain a detection result of whether the online data generated by the online recommendation service is abnormal. In this way, before the Browse Set component receives the user's problem feedback, the accuracy of the Browse Set can be quickly and accurately detected through the above method, solving the problem of slow and difficult detection of the accuracy of the Browse Set based on the user's problem feedback, greatly improving the timeliness, stability and accuracy of the detection, thereby improving the user experience.

[0077] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0079] Figure 1 The present invention is a flowchart of a method for processing business data according to an exemplary embodiment.

[0080] Figure 2It is a schematic diagram of a system framework according to an exemplary embodiment.

[0081] Figure 3 FIG. 1 is a schematic diagram of a Browse Set verification system according to an exemplary embodiment. Figure 1 .

[0082] Figure 4 FIG. 1 is a schematic diagram of a Browse Set verification system according to an exemplary embodiment. Figure 2 .

[0083] Figure 5 FIG. 1 is a schematic diagram of a Browse Set verification system according to an exemplary embodiment. Figure 3 .

[0084] Figure 6 FIG. 1 is a schematic diagram of a Browse Set verification system according to an exemplary embodiment. Figure 4 .

[0085] Figure 7 It is a block diagram of a business data processing device according to an exemplary embodiment.

[0086] Figure 8 It is a block diagram of a device for processing business data according to an exemplary embodiment. DETAILED DESCRIPTION

[0087] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0088] Figure 1 is a flow chart of a method for processing business data according to an exemplary embodiment. Figure 1 As shown, the business data processing method is used in the Browse Set verification system and includes the following steps.

[0089] In step S11, target reported information to be verified is determined from the reported information; the reported information is generated based on a business request for an online recommendation service.

[0090] Reference Figure 2, shows a schematic diagram of the system framework of this embodiment, which includes an online recommendation service, a Browse Set verification system, and a Browse Set component. The online recommendation service is used to perform online traffic sampling according to the user's business request and report the sampling to the Browse Set verification system; the Browse Set verification system is used to verify the accuracy of the Browse Set; the Browse Set component is used to report relevant information of whitelist users, provide Browse SetSDK (Software Development Kit, Software Development Kit) verification, and store the complete Browse Set.

[0091] Specifically, users can initiate service requests to online recommendation services through terminal devices (such as mobile phones and tablet computers), so an APP for initiating service requests can be installed in the terminal device. In this way, when the user initiates a service request through the APP in the terminal device, the service request can carry information such as user ID, device ID, requested service, requested platform, and APP version.

[0092] After receiving the business request, the online recommendation service generates reporting information (such as server show information) based on the above information in the business request. The reporting information includes but is not limited to the user ID (Identity document) of the business request, the device ID, the requested business, the requesting platform, and the APP (Application) version, and then sends the reporting information to the Browse Set verification system.

[0093] In actual applications, since the amount of reported information reported by the online recommendation service is huge, in order to reduce the load of the Browse Set verification system, a part of the reported information can be selected from all the reported information for verification. For the convenience of description, the selected reported information is recorded as the target reported information to be verified.

[0094] In this embodiment, determining the target reported information to be verified from the reported information includes:

[0095] Acquire first reporting information from the reporting information according to a preset sampling rate;

[0096] Acquire second reporting information from the reporting information according to a preset whitelist;

[0097] The first reported information and the second reported information are used as target reported information to be verified.

[0098] Reference Figure 3, showing the structural framework of the Browse Set verification system of this embodiment Figure 1 , where the Browse Set verification system includes a data acquisition module and a verification center, and the data acquisition module includes an online data sampling module and a whitelist data module.

[0099] Specifically, the online data sampling module can obtain a part of the reported information from all the reported information according to the preset sampling rate, which is recorded as the "first reported information". For example, it can be obtained at a sampling rate of 0.1%, that is, 1 reported information is obtained from every 1000 reported information as the first reported information. Of course, it can also be obtained according to other acquisition rates, such as 1%, 10%, etc. In practical applications, the sampling rate can be adjusted inversely according to the amount of reported information. This embodiment does not limit the specific value of the sampling rate.

[0100] Furthermore, in addition to reporting the reported information to the Browse Set verification system, the online recommendation service will also report the reported information to the Browse Set component. The Browse Set component can store all the reported information, and then the whitelist data module obtains part of the reported information from the Browse Set component according to the whitelist, which is recorded as "second reported information".

[0101] Then, the acquired first reporting information and second reporting information are used as final target reporting information.

[0102] For example, the online recommendation service generates 100 reports, the preset sampling rate is 1%, and the number of whitelists is 10. Then, the online data sampling module obtains 1 first report from the 100 reports, and the whitelist data module obtains 10 second reports from the 100 reports. Finally, the target report to be verified is 11 reports.

[0103] Furthermore, after obtaining the first reported information, the online data sampling module can send the first reported information to a preset message system, such as kafka, according to a preset data format, such as proto data format, so that the BrowseSet verification center obtains the first reported information from kafka. Similarly, after the whitelist data module obtains the second reported information, it can also send the second reported information to kafka according to a preset data format, such as json data format, so that the Browse Set verification center obtains the second reported information from kafka.

[0104] It should be noted that the Browse Set component can store all the reported information obtained from it into a preset database management system, such as Clickhouse, and then the whitelist data module obtains the second reported information from the database management system according to the whitelist. Moreover, in addition to storing the reported information, the database management system can also store all requests of whitelist users as a full request link log of whitelist users for subsequent troubleshooting, thereby realizing real-time sampling of online data and storage of full whitelist data, overcoming the singleness of the verification data source, and allowing the verification service to cover all logic.

[0105] Furthermore, in addition to Kafka as the message system and Clickhouse as the database management system in this embodiment, other message systems and database management systems are also applicable to this embodiment. In actual applications, they can be set according to actual needs, and this embodiment does not impose any restrictions on this.

[0106] In step S12, statistical data for detection is generated based on the target reporting information.

[0107] After the target reporting information is obtained, statistical data may be generated according to the target reporting information, and the statistical data is used to detect the online data generated by the online recommendation service.

[0108] In this embodiment, generating statistical data for detection based on the target reporting information includes:

[0109] Convert the target reporting information into a format to obtain reporting data;

[0110] Verifying the reported data to obtain a verification result;

[0111] Statistical data for detection is generated based on the reported data and the verification result.

[0112] Specifically, after the verification center obtains the target reporting information, it can convert the format of the target reporting information to obtain reporting data in a unified format to facilitate subsequent data verification.

[0113] In this embodiment, the format conversion of the target reporting information to obtain the reporting data includes:

[0114] The first reporting information and the second reporting information in the target reporting information are formatted respectively using a preset data structure to obtain reporting data having the preset data structure; the preset data structure includes a standard data structure and / or a custom data structure.

[0115] Reference Figure 4, showing the structural framework of the Browse Set verification system of this embodiment Figure 2 Among them, the verification center includes a formatting module, a Browse Set verification module and a monitoring and alarm module.

[0116] Specifically, since the target reporting information includes the first reporting information and the second reporting information, the formatting module may use a preset data structure, wherein the preset data structure may include but is not limited to a standard data structure and a custom data structure. For example, if the preset data structure is a proto data structure, the first reporting information and the second reporting information are formatted respectively to obtain reporting data having the data structure, so that the Browse Set verification module verifies the reporting data.

[0117] Among them, the definition of proto data structure is as follows:

[0118] browsed_photo_list: includes the identification list of photo IDs that need to be verified. For online data sampling, each sampled request is recorded as a group. For whitelist users, in order to reduce the service pressure of the online Browse Set, user ID can be aggregated on a daily basis. For example, a user's one-day reporting information will be aggregated into one proto. In other words, a proto (reported data) is a user's one-day reporting data. A proto includes multiple groups of data, each group of data includes multiple photos (sub-data), and each photo has a unique identifier, recorded as a "sub-data identifier" (photoID).

[0119] extension_info: An extended information field that stores unique information about the data source. For online data sampling, a configurable extended information field has been added. In addition, server show is a proto format data that contains caller information. The system supports adding configurable extended information fields. In this way, by adding the field path of server show to the configuration file, the system can read the value of the corresponding field from proto through the proto reflection mechanism and write it into extension_info, so that statistics can be performed based on extension_info in subsequent verification.

[0120] It should be noted that, in addition to converting the reported information into the reported data of the proto data structure, it can also be converted into other custom data structures, for example, the administrator can pre-set the data structure to be converted and the corresponding conversion method. Therefore, this embodiment provides a general data structure and a custom data structure, which ensures the freedom of expansion while being compatible with the personalized data of various different businesses.

[0121] Furthermore, after the verification center formats the target reported information to obtain the reported data, it can verify the reported data to obtain the verification result.

[0122] In this embodiment, the verifying the reported data to obtain a verification result includes:

[0123] Acquire corresponding business data from the generated online data based on the first preset field in the reported data;

[0124] It is detected whether each sub-data in the at least one sub-data belongs to the business data, and a detection result corresponding to each sub-data in the at least one sub-data is obtained.

[0125] Specifically, the verification center can obtain the business data of the business requested by the user from the generated online data according to the first preset field in the reported data. The generated online data can be all the generated BrowseSets; the first preset field records the location information of the business data, so that the verification center can obtain the business data of the business requested by the user from the Browse Set according to the location information.

[0126] Furthermore, since the reported data includes at least one sub-data, it is possible to detect whether each sub-data belongs to the service data, thereby obtaining a detection result corresponding to each sub-data.

[0127] Wherein, the business data includes an identification list, and the identification list includes at least one sub-data identification;

[0128] The detecting whether each sub-data in the at least one sub-data belongs to the business data, and obtaining a detection result corresponding to each sub-data in the at least one sub-data, includes:

[0129] Matching the sub-data identifier of any sub-data in the at least one sub-data with the identifier list;

[0130] If there is a match, determining that any of the sub-data belongs to the business data, and generating a first test result of successful verification;

[0131] If there is no matching item, it is determined that any of the sub-data does not belong to the business data, and a second detection result of verification failure is generated.

[0132] Specifically, the business data includes an identification list, and the identification list includes at least one sub-data identification. Therefore, for any sub-data in the reported data, the sub-data identification of the any sub-data can be matched with all the sub-data identifications in the identification list. If there is a match, it is determined that the any sub-data belongs to the business data, and a first test result of successful verification is generated; if there is no match, it is determined that the any sub-data does not belong to the business data, and a second test result of failed verification is generated. By analogy, the test result of each sub-data in the reported data can be obtained, and the test result of each sub-data is used as the final verification result of the reported data.

[0133] Further, refer to Figure 5 , showing the structural framework of the Browse Set verification system of this embodiment Figure 3 , where the Browse Set verification module includes the Browse Set calling module, the multi-dimensional statistics module and the dot reporting module. Moreover, as mentioned above, the Browse Set component provides Browse Set SDK verification, because the Browse Set SDK has judgment logic, and the Browse Set component can include the Browse Set SDK. Therefore, when the verification center detects the sub-data, the Browse Set calling module can call the Browse Set SDK in the Browse Set component to detect each sub-data.

[0134] Furthermore, after obtaining the verification result of the reported data, the reported data is combined with the verification result to determine the number of successfully verified sub-data and the number of failed verified sub-data in the reported data, thereby generating statistical data for detection based on the two.

[0135] In this embodiment, generating statistical data based on the reported data and the verification result includes:

[0136] Combining the verification result with the reported data based on the second preset field to obtain at least one set of target reported data; any set of target reported data in the at least one set of target reported data includes at least one sub-data;

[0137] Respectively counting the first number of sub-data with a detection result of successful detection and the second number of sub-data with a detection result of failed detection in each group of target reported data;

[0138] The first quantity and the second quantity corresponding to each group of target reported data are used as statistical data.

[0139] Specifically, the verification result and the reported data each include corresponding fields, and the fields of the verification result and the fields of the reported data may be partially repeated, so the multidimensional statistical module can combine the verification result with the reported data based on the repeated fields (recorded as "second preset fields") to obtain at least one set of target reported data. Among them, a set of target reported data includes at least one sub-data, and each sub-data includes the second preset field.

[0140] Then, for any group of target reported data, the multidimensional statistical module counts the number of sub-data whose detection results are successful detection, recorded as the "first number", and the number of sub-data whose detection results are failed detection, recorded as the "second number", and so on. The first number and second number corresponding to each group of target reported data can be counted, and all the first numbers and second numbers are used as the final statistical data.

[0141] Furthermore, after obtaining the statistical data, the statistical data can be reported using the reporting module. Specifically, the reporting module supports the configuration of custom multi-dimensional reporting. The administrator can pre-configure the field names that need to be reported. When reporting, the reporting module can obtain the corresponding fields from extension_info according to the field names. For non-direct fields, the info stored in the extension_info field can be automatically parsed to obtain the fields that need to be reported from json. After determining the fields, the statistical data can be reported to the preset database management system, such as clickhouse, according to the determined fields, for subsequent use by the monitoring and alarm module.

[0142] In step S13, the online data generated by the online recommendation service is detected based on the statistical data to obtain a detection result.

[0143] After the reporting module reports the statistical data, the monitoring and alarm module can obtain the statistical data from the database management system, and perform accuracy detection on the business based on the statistical data to obtain a business detection result of whether there is any abnormality in the accuracy of the business.

[0144] In this embodiment, the detecting the service based on the statistical data to obtain the service detection result includes:

[0145] Calculate analytical data of at least one dimension corresponding to each preset feature of at least one preset feature using the statistical data;

[0146] Detecting whether the analysis data of any dimension of the at least one dimension exceeds the analysis data threshold of the any dimension;

[0147] If the analysis data of any dimension of the at least one dimension does not exceed the analysis data threshold of any dimension, it is determined that the online data generated by the online recommendation service is normal.

[0148] Specifically, the administrator can pre-set multiple features that need to be detected, including but not limited to the requested business, request platform, APP version, etc., and the dimensions of the analysis data corresponding to each feature, including but not limited to the average value, maximum value, year-on-year growth value, month-on-month growth value, etc. In this way, when detecting, the monitoring alarm module can use statistical data to calculate the analysis data of each feature in each dimension. For example, statistical data can be used to calculate the analysis data of four dimensions, such as the average value, maximum value, year-on-year growth value and month-on-month growth value of the sub-data verification failure in the reported data under the APP version.

[0149] Then, check whether the analysis data of each dimension exceeds the corresponding analysis data threshold. For example, check whether the average value of sub-data verification failures in the reported data exceeds the average value threshold, whether the maximum value exceeds the maximum value threshold, whether the year-on-year growth value exceeds the year-on-year growth value threshold, and whether the month-on-month growth value exceeds the month-on-month growth value threshold. If the analysis data of each dimension under each feature does not exceed the analysis data threshold of each dimension, it is determined that the business requested by the user is normal; otherwise, it is determined that the business requested by the user is abnormal, and the monitoring and alarm module performs alarm processing. In this way, through detailed monitoring and alarming in multiple dimensions, problems can be quickly discovered and alarms can be issued, greatly improving the timeliness of detection.

[0150] Furthermore, after the analysis data of at least one dimension corresponding to each feature is calculated, the analysis data can be visualized, such as being drawn into a curve chart, a bar chart, etc., for easy viewing.

[0151] In this embodiment, the method further includes:

[0152] In response to the access request, the online data is accessed through a preset data interface.

[0153] Specifically, refer to Figure 6 , showing the structural framework of the Browse Set verification system of this embodiment Figure 4, where the verification center also includes a storage review module, and the Browse Set also includes underlying storage. The storage review module has a preset data interface, such as a web interface. The underlying storage stores all Browse Sets (that is, the generated online data). When an alarm occurs, the administrator can directly access all Browse Sets through the web interface to locate the anomaly, so there is no need to access through the Browse Set SDK, avoiding the problem of low accuracy of anomaly location due to bugs in the Browse Set SDK interfering with access.

[0154] In this embodiment, the Browse Set verification system determines the target reported information to be verified from the reported information; the reported information is generated based on the business request for the online recommendation service; then, based on the target reported information, statistical data for detection is generated, and based on the statistical data, the online data generated by the online recommendation service is detected to obtain a detection result of whether the online data generated by the online recommendation service is abnormal. In this way, before the Browse Set component receives the user's problem feedback, the accuracy of the Browse Set can be quickly and accurately detected through the above method, which solves the problem of slow and difficult detection of the accuracy of the Browse Set based on the user's problem feedback, greatly improves the timeliness, stability and accuracy of the detection, and thus improves the user experience.

[0155] Figure 7 FIG. 1 is a block diagram of a business data processing device according to an exemplary embodiment. Figure 7 The device includes a data acquisition unit 71, a statistical data generation unit 72 and a monitoring unit 73.

[0156] The data acquisition unit 71 is configured to determine target reported information to be verified from the reported information; the reported information is generated based on a business request for an online recommendation service;

[0157] A statistical data generating unit 72, configured to generate statistical data for detection based on the target reporting information;

[0158] The monitoring unit 73 is configured to detect the online data generated by the online recommendation service based on the statistical data to obtain a detection result.

[0159] Optionally, the statistical data generating unit includes:

[0160] The formatting subunit is configured to perform format conversion on the target reporting information to obtain reporting data;

[0161] The calling subunit is configured to verify the reported data and obtain a verification result;

[0162] The multi-dimensional statistical subunit is configured to generate statistical data for detection based on the reported data and the verification results.

[0163] Optionally, the data acquisition unit includes:

[0164] An online data sampling subunit, configured to obtain first reporting information from the reporting information according to a preset sampling rate;

[0165] A whitelist data subunit is configured to obtain second reporting information from the reporting information according to a preset whitelist;

[0166] The first processing subunit is configured to use the first reporting information and the second reporting information as target reporting information to be verified.

[0167] Optionally, the formatting unit is specifically configured as follows:

[0168] The first reporting information and the second reporting information in the target reporting information are formatted respectively using a preset data structure to obtain reporting data having the preset data structure; the preset data structure includes a standard data structure and / or a custom data structure.

[0169] Optionally, the reported data includes at least one sub-data;

[0170] The calling unit comprises:

[0171] A business data subunit is configured to obtain corresponding business data from online data based on a first preset field in the reported data; the first preset field is used to record location information of the business data;

[0172] The first detection subunit is configured to detect whether each sub-data in the at least one sub-data belongs to the business data, and obtain a detection result corresponding to each sub-data in the at least one sub-data.

[0173] Optionally, any sub-data in the at least one sub-data has a corresponding sub-data identifier; the business data includes an identifier list, and the identifier list includes at least one sub-data identifier;

[0174] The first detection subunit is specifically configured as follows:

[0175] The sub-data identifier of any sub-data in the at least one sub-data is matched with the identifier list; if there is a match, it is determined that any sub-data belongs to the business data, and a first test result of successful verification is generated; if there is no match, it is determined that any sub-data does not belong to the business data, and a second test result of failed verification is generated.

[0176] Optionally, the multi-dimensional statistical unit includes:

[0177] A splicing subunit is configured to splice the verification result with the reported data based on a second preset field to obtain at least one set of target reported data; any set of target reported data in the at least one set of target reported data includes at least one sub-data; the second preset field is a repeated field in the verification result and the reported data;

[0178] A counting subunit is configured to respectively count a first number of sub-data with a detection result of successful detection and a second number of sub-data with a detection result of failed detection in each group of target reported data;

[0179] The second processing sub-unit is configured to use the first quantity and the second quantity corresponding to each group of target reported data as statistical data.

[0180] Optionally, the monitoring unit includes:

[0181] A calculation subunit, configured to calculate analysis data of at least one dimension corresponding to each preset feature of at least one preset feature using the statistical data;

[0182] A second detection subunit is configured to detect whether the analysis data of any dimension of the at least one dimension exceeds the analysis data threshold of the any dimension;

[0183] The determination subunit is configured to determine that the online data generated by the online recommendation service is normal if the analysis data of any dimension of the at least one dimension does not exceed the analysis data threshold of any dimension.

[0184] Optionally, also include:

[0185] The access unit is configured to access the online data through a preset data interface.

[0186] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0187] Figure 88 is a block diagram of a service data processing device 800 according to an exemplary embodiment. For example, the device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0188] Reference Figure 8 , the device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0189] The processing component 802 generally controls the overall operation of the device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0190] The memory 804 is configured to store various types of data to support operations on the device 800. Examples of such data include instructions for any application or method operating on the device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 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.

[0191] The power supply component 806 provides power to the various components of the device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 800.

[0192] The multimedia component 808 includes a screen that provides an output interface between the device 800 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 808 includes a front camera and / or a rear camera. When the device 800 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 rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0193] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the device 800 is in an operating 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 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0194] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

[0195] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the device 800. For example, the sensor assembly 814 can detect the open / closed state of the device 800, the relative positioning of components, such as the display and keypad of the device 800, and the sensor assembly 814 can also detect the position change of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, the orientation or acceleration / deceleration of the device 800, and the temperature change of the device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 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 814 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0196] The communication component 816 is configured to facilitate wired or wireless communication between the device 800 and other devices. The device 800 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 816 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 816 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.

[0197] In an exemplary embodiment, the apparatus 800 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 method.

[0198] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, and the instructions can be executed by the processor 820 of the device 800 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.

[0199] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed in this disclosure. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the following claims.

[0200] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A business data processing method, characterized in that: include: Determine the target reported information to be verified from the reported information; The reported information is generated based on a business request for an online recommendation service; Convert the target reporting information into a format to obtain reporting data; Verifying the reported data to obtain a verification result; splicing the verification result and the reported data based on a second preset field to obtain at least one set of target reported data; any set of target reported data in the at least one set of target reported data includes at least one sub-data; the second preset field is a repeated field in the verification result and the reported data; Respectively counting the first number of sub-data with a detection result of successful detection and the second number of sub-data with a detection result of failed detection in each group of target reported data; Taking the first quantity and the second quantity corresponding to each group of target reported data as statistical data; The online data generated by the online recommendation service is detected based on the statistical data to obtain a detection result.

2. The business data processing method according to claim 1, characterized in that: The step of determining the target reported information to be verified from the reported information includes: Acquire first reporting information from the reporting information according to a preset sampling rate; Acquire second reporting information from the reporting information according to a preset whitelist; The first reported information and the second reported information are used as target reported information to be verified.

3. The business data processing method according to claim 1, characterized in that: The format conversion of the target reported information to obtain the reported data includes: The first reporting information and the second reporting information in the target reporting information are formatted respectively using a preset data structure to obtain reporting data having the preset data structure; the preset data structure includes a standard data structure and / or a custom data structure.

4. The business data processing method according to claim 1, characterized in that: The reported data includes at least one sub-data; The verifying the reported data to obtain a verification result includes: Acquire corresponding business data from online data based on a first preset field in the reported data; the first preset field is used to record location information of the business data; It is detected whether each sub-data in the at least one sub-data belongs to the business data, and a detection result corresponding to each sub-data in the at least one sub-data is obtained.

5. The business data processing method according to claim 4, characterized in that: Any sub-data in the at least one sub-data has a corresponding sub-data identifier; the business data includes an identifier list, and the identifier list includes at least one sub-data identifier; The detecting whether each sub-data in the at least one sub-data belongs to the business data, and obtaining a detection result corresponding to each sub-data in the at least one sub-data, includes: Matching the sub-data identifier of any sub-data in the at least one sub-data with the identifier list; If there is a match, determining that any of the sub-data belongs to the business data, and generating a first test result of successful verification; If there is no matching item, it is determined that any of the sub-data does not belong to the business data, and a second detection result of verification failure is generated.

6. The business data processing method according to claim 1, characterized in that: The detecting the online data generated by the online recommendation service based on the statistical data to obtain the detection result includes: Calculate analytical data of at least one dimension corresponding to each preset feature of at least one preset feature using the statistical data; Detecting whether the analysis data of any dimension of the at least one dimension exceeds the analysis data threshold of the any dimension; If the analysis data of any dimension of the at least one dimension does not exceed the analysis data threshold of any dimension, it is determined that the online data generated by the online recommendation service is normal.

7. The business data processing method according to claim 1, characterized in that: Also includes: In response to the access request, the online data is accessed through a preset data interface.

8. A business data processing device, characterized in that: include: A data acquisition unit, configured to determine target reported information to be verified from the reported information; The reported information is generated based on a business request for an online recommendation service; a statistical data generating unit, configured to generate statistical data for detection based on the target reporting information; A monitoring unit configured to detect the online data generated by the online recommendation service based on the statistical data to obtain a detection result; Wherein, the statistical data generating unit includes: The formatting subunit is configured to perform format conversion on the target reporting information to obtain reporting data; The calling subunit is configured to verify the reported data and obtain a verification result; a multi-dimensional statistical subunit, configured to generate statistical data for detection based on the reported data and the verification result; The multi-dimensional statistical subunit includes: A splicing subunit is configured to splice the verification result with the reported data based on a second preset field to obtain at least one set of target reported data; any set of target reported data in the at least one set of target reported data includes at least one sub-data; the second preset field is a repeated field in the verification result and the reported data; A counting subunit is configured to respectively count a first number of sub-data with a detection result of successful detection and a second number of sub-data with a detection result of failed detection in each group of target reported data; The second processing sub-unit is configured to use the first quantity and the second quantity corresponding to each group of target reported data as statistical data.

9. The service data processing device according to claim 8, characterized in that: The data acquisition unit comprises: An online data sampling subunit, configured to obtain first reporting information from the reporting information according to a preset sampling rate; A whitelist data subunit is configured to obtain second reporting information from the reporting information according to a preset whitelist; The first processing subunit is configured to use the first reporting information and the second reporting information as target reporting information to be verified.

10. The service data processing device according to claim 8, characterized in that: The formatting subunit is specifically configured as follows: The first reporting information and the second reporting information in the target reporting information are formatted respectively using a preset data structure to obtain reporting data having the preset data structure; the preset data structure includes a standard data structure and / or a custom data structure.

11. The service data processing device according to claim 8, characterized in that: The reported data includes at least one sub-data; The calling subunit comprises: A business data subunit is configured to obtain corresponding business data from online data based on a first preset field in the reported data; the first preset field is used to record location information of the business data; The first detection subunit is configured to detect whether each sub-data in the at least one sub-data belongs to the business data, and obtain a detection result corresponding to each sub-data in the at least one sub-data.

12. The service data processing device according to claim 11, characterized in that: Any sub-data in the at least one sub-data has a corresponding sub-data identifier; the business data includes an identifier list, and the identifier list includes at least one sub-data identifier; The first detection subunit is specifically configured as follows: Matching the sub-data identifier of any sub-data in the at least one sub-data with the identifier list; if there is a match, determining that the any sub-data belongs to the business data, and generating a first detection result of successful verification; If there is no matching item, it is determined that any of the sub-data does not belong to the business data, and a second detection result of verification failure is generated.

13. The service data processing device according to claim 8, characterized in that: The monitoring unit comprises: A calculation subunit, configured to calculate analysis data of at least one dimension corresponding to each preset feature of at least one preset feature using the statistical data; A second detection subunit is configured to detect whether the analysis data of any dimension of the at least one dimension exceeds the analysis data threshold of the any dimension; The determination subunit is configured to determine that the online data generated by the online recommendation service is normal if the analysis data of any dimension of the at least one dimension does not exceed the analysis data threshold of any dimension.

14. The service data processing device according to claim 8, characterized in that: Also includes: The access unit is configured to access the online data through a preset data interface.

15. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to execute the instructions to implement the business data processing method as described in any one of claims 1 to 7.

16. A computer-readable storage medium, when instructions in the storage medium are executed by a processor of a mobile terminal, the mobile terminal is enabled to execute the service data processing method according to any one of claims 1 to 7.

17. A computer program product comprising a computer program or computer instructions, characterized in that When the computer program or computer instruction is executed by a processor, the business data processing method according to any one of claims 1 to 7 is implemented.

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