CN117633027A

CN117633027A - Data processing method and device, electronic equipment and storage medium

Info

Publication number: CN117633027A
Application number: CN202210986050.6A
Authority: CN
Inventors: 曾泓浩
Original assignee: Beijing Zitiao Network Technology Co Ltd
Current assignee: Beijing Zitiao Network Technology Co Ltd
Priority date: 2022-08-16
Filing date: 2022-08-16
Publication date: 2024-03-01

Abstract

The embodiment of the disclosure provides a data processing method, a data processing device, electronic equipment and a storage medium. Determining a value corresponding to an attribute included in a buried point model, wherein the attribute comprises an attribute of an event to be processed; converting the determined value and the corresponding attribute into intermediate data in the form of a key value pair, wherein the determined value is used as a value in the key value pair, and the attribute name of the attribute corresponding to the determined value is used as a key in the key value pair; and processing the intermediate data by adopting a set rule to obtain the buried point information of the event to be processed. According to the method and the device, the value corresponding to the buried point model attribute is determined, so that buried point information conforming to the set rule is determined, the accuracy of the attribute and the value determined based on the buried point model is improved, and the accuracy of the buried point information determination is further improved.

Description

Data processing method and device, electronic equipment and storage medium

Technical Field

The embodiment of the disclosure relates to computer technology, in particular to a data processing method, a data processing device, electronic equipment and a storage medium.

Background

The data analysis refers to analyzing, summarizing, understanding and digesting the collected data by a proper statistical analysis method so as to develop the data maximally and play the role of the data. Wherein data collection is the basis for data analysis.

Data collection is currently generally performed by adopting an event tracking mode. When data is collected in an event tracking mode, the event tracking is realized in a character string dictionary mode in most cases. However, when the event tracking is performed in the character string dictionary mode, the accuracy of key values in the character string dictionary model cannot be ensured, so that the accuracy of data acquired based on the character string dictionary model is affected.

Disclosure of Invention

The disclosure provides a data processing method, a device, electronic equipment and a storage medium, wherein data determination is realized through a buried point model, and the accuracy of the determined data is improved.

In a first aspect, an embodiment of the present disclosure provides a data processing method, including:

determining a value corresponding to an attribute included in the buried point model, wherein the attribute comprises an attribute of an event to be processed;

converting the determined value and the corresponding attribute into intermediate data in the form of a key value pair, wherein the determined value is used as a value in the key value pair, and the attribute name of the attribute corresponding to the determined value is used as a key in the key value pair;

and processing the intermediate data by adopting a set rule to obtain the buried point information of the event to be processed.

In a second aspect, an embodiment of the present disclosure further provides a data processing apparatus, including:

The determining module is used for determining a value corresponding to an attribute included in the buried point model, wherein the attribute comprises an attribute of an event to be processed;

a conversion module, configured to convert a determined value and a corresponding attribute into intermediate data in the form of a key value pair, where the determined value is used as a value in the key value pair, and an attribute name of the attribute corresponding to the determined value is used as a key in the key value pair;

and the processing module is used for processing the intermediate data by adopting a set rule to obtain the buried point information of the event to be processed.

In a third aspect, embodiments of the present disclosure further provide an electronic device, including:

one or more processing devices;

storage means for storing one or more programs,

the one or more programs, when executed by the one or more processing devices, cause the one or more processing devices to implement the data processing methods as provided by the embodiments of the present disclosure.

In a fourth aspect, the disclosed embodiments also provide a storage medium containing computer executable instructions which, when executed by a computer processor, are used to perform a data processing method as provided by the disclosed embodiments.

According to the embodiment of the disclosure, the value corresponding to the attribute associated with the event to be processed is acquired through the embedded point model, then the determined value and the corresponding attribute are converted into the intermediate data in the form of the key value pair, and further the intermediate data is processed according to the set rule to obtain the embedded point information, so that the event to be processed is conveniently analyzed based on the embedded point information, the problem of accuracy of the data acquired based on the character string dictionary model is solved, the accuracy of the attribute and the value determined based on the embedded point model is improved, and the accuracy of the determined embedded point information is further improved.

Drawings

The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.

FIG. 1 is a flow chart of a data processing method according to an embodiment of the present disclosure;

FIG. 2 is a flow chart of yet another data processing method provided by an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of a pipeline system provided by an embodiment of the present disclosure;

FIG. 4 is a schematic diagram of a data processing apparatus according to an embodiment of the present disclosure;

fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.

It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.

The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below.

It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.

It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.

The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

It will be appreciated that prior to using the technical solutions disclosed in the embodiments of the present disclosure, the user should be informed and authorized of the type, usage range, usage scenario, etc. of the personal information related to the present disclosure in an appropriate manner according to the relevant legal regulations.

For example, in response to receiving an active request from a user, a prompt is sent to the user to explicitly prompt the user that the operation it is requesting to perform will require personal information to be obtained and used with the user. Thus, the user can autonomously select whether to provide personal information to software or hardware such as an electronic device, an application program, a server or a storage medium for executing the operation of the technical scheme of the present disclosure according to the prompt information.

As an alternative but non-limiting implementation, in response to receiving an active request from a user, the manner in which the prompt information is sent to the user may be, for example, a popup, in which the prompt information may be presented in a text manner. In addition, a selection control for the user to select to provide personal information to the electronic device in a 'consent' or 'disagreement' manner can be carried in the popup window.

It will be appreciated that the above-described notification and user authorization process is merely illustrative and not limiting of the implementations of the present disclosure, and that other ways of satisfying relevant legal regulations may be applied to the implementations of the present disclosure.

It will be appreciated that the data (including but not limited to the data itself, the acquisition or use of the data) involved in the present technical solution should comply with the corresponding legal regulations and the requirements of the relevant regulations.

The buried point technology is a data acquisition technology, also called event tracking, and is to monitor events in the running process of software applications first, and judge and capture the events when the events needing to be concerned occur.

In one embodiment, the character string dictionary may be used as a buried point model, and key value pairs in the dictionary are buried point data. When the embedded point is reported, the character string dictionary needs to be traversed to construct data in a specific format, such as JSON format, and the data is uploaded to a server for storage.

When the character string dictionary is used as the embedded point model, if a temporary character string embedded point key is constructed, specifically, when the temporary character string embedded point key is constructed and put into the dictionary, a compiler will not warn and report errors, and may cause data setting or acquisition errors, for example, when the temporary character string embedded point key is written or the temporary character string embedded point key is empty, the data setting or acquisition errors will be caused.

To solve this technical problem, the present disclosure performs data acquisition through a buried point model including attributes. Specifically, fig. 1 is a schematic flow chart of a data processing method provided by an embodiment of the present disclosure, where the embodiment of the present disclosure is applicable to a situation where buried points are collected and processed, for example, a situation where a business party performs collection and processing of buried points. The method may be performed by a data processing device, which may be implemented in the form of software and/or hardware, optionally by an electronic device, which may be a mobile terminal, a PC-side or a server, etc.

As shown in fig. 1, the method includes:

s110, determining a value corresponding to an attribute included in the buried point model, wherein the attribute comprises an attribute of the event to be processed.

The buried point model can complete the collection of the attribute of each event to be processed, and forms a snapshot for the occurrence of the event. Different business scenarios may correspond to different events to be processed, one business scenario may correspond to at least one buried point model, and one buried point model may include attributes of at least one event to be processed. The acquisition of the buried points can be realized based on the buried point model.

In one embodiment, each business scenario may correspond to a class, i.e., a buried point model, which may include multiple buried point classifications. If the business can expand different buried point classifications according to business needs, there are two built-in buried point classifications: dvetrackevent+monitor: the method comprises the steps of monitoring the used buried point class classification and DVETrackEvent+TypeConversion by the terminal: and the embedded point module converter and the analyzer are configured as required. The required attributes under different scenes can be defined under different buried point classifications. When a parameter under a certain scene needs to be determined, the parameter can be determined based on the corresponding buried point classification under the scene, such as a starting time scene, and a value corresponding to a predefined attribute is determined based on DVETrackEvent+monitor, so that the starting time is determined.

The event to be processed can be understood as an event to be processed, and the event to be processed can be processed based on the attribute and the corresponding value determined by the buried point model. The buried point model can comprise the attribute of the event to be processed, and the value corresponding to the attribute can be determined through buried point acquisition. The attribute may be an attribute associated with the event to be processed, for example, an attribute required for processing the event to be processed, and the event to be processed may be processed based on a value corresponding to the attribute.

The attribute may be an attribute in a property of the OC syntax, which is used to encapsulate data in the object, or may be a member variable in the OC or java. The data processing method provided by the disclosure can obtain the value corresponding to the attribute when the application program runs. For example, when the programming language is OC, the value corresponding to the attribute may be determined by a runtime resolution mechanism. When the programming language is java, the value corresponding to the attribute can be determined through a reflection mechanism. The Java reflection (reflection) mechanism refers to that in the running state of a program, an object of any one class can be constructed, the class to which any one object belongs can be known, the member variable and method of any one class can be known, and the attribute and method of any one object can be invoked. This function of dynamically acquiring program information and dynamically calling an object is called a reflection mechanism of the Java language. Reflection is considered critical to dynamic language.

In one embodiment, the event to be processed may be the determination of the first frame data, and the attributes of the event to be processed may include the first frame duration and the software version. The acquisition of the buried point, such as a specific duration value of the first frame duration and a version number of the software version, can be realized by determining the value corresponding to the attribute. The event to be processed may be processed, such as by analyzing first frame data, based on the attribute and corresponding value of the event to be processed. The first frame data may be a homepage of the application.

The data processing method provided by the present disclosure may be integrated in an application program, which may be an application program provided by a service party, where the service party may be considered as a party providing a service, such as an application program, e.g. may be a client terminal on which the application program is installed. In the step, the buried point model can define the attribute to be assigned, and in the running process of the program corresponding to the data processing method, the attribute included in the buried point model can be assigned to determine the value corresponding to the attribute included in the buried point model.

The attributes of the desired assignment defined in the buried point model in this step may be fixed by detecting the running of the application to match the values for the attributes. The application program is not limited to the video application program, and the video application program can be a short video application program, namely an application program for online social connection based on short videos.

S120, converting the determined value and the corresponding attribute into intermediate data in the form of key value pairs.

After determining the values of the attributes included in the buried point model, the attributes and the corresponding values may be converted into the form of key-value pairs for transmission, the determined values being values in the key-value pairs, and the attribute names of the attributes corresponding to the determined values being keys in the key-value pairs. The technical means of conversion is not limited here, for example, the name of the attribute is used as a key, and the analysis of the attribute name into the character string key value of the buried point is realized. And taking the value corresponding to the attribute as the value in the key value pair, namely the value. The attribute name may be considered a parameter of the buried point. The buried point model can determine which attributes are assigned when being initialized, and can determine the values corresponding to the attributes when an application program runs.

S130, processing the intermediate data by adopting a set rule to obtain buried point information of the event to be processed.

The buried point information may be information of buried points characterized in a dictionary form. The set rules may be rules for processing intermediate data, and the set rules may include fixed rules and custom rules. Different business parties may correspond to the same fixed rule or different fixed rules. Different business parties may correspond to different custom rules.

The fixed rule may be understood as a rule for processing intermediate data, and different service parties may correspond to the same fixed rule or may correspond to different fixed rules. Custom rules can be understood as rules that the business party defines to handle intermediate data. In one embodiment, the fixed rule may be removing a fixed prefix of the attribute, removing a fixed suffix of the attribute, and/or filtering intermediate data that does not need to be transmitted, which may be intermediate data of a specified type and/or intermediate data of a specified attribute name. The custom rule may be a character string custom by the service party instead of the attribute, or may be a custom rule by the service party.

The setting rules may include at least one fixed rule and at least one custom rule, the execution order of the rules included in the setting rules is not limited, and may be determined by the writing order of the rules, or the developer sets an order array based on the actual requirement, and the order array sets the execution order among the rules included in the setting rules.

In processing intermediate data, rules included in the set rule may be sequentially applied based on the execution order. For example, each time a rule is applied, the object processed by the rule may be a single piece of data processed by the last rule, and the rule is applied followed by the data processed by the applied rule. If the rule applied is the first rule applied, the object processed is intermediate data.

The buried point information of the event to be processed can be obtained after the intermediate data is processed by adopting the set rule.

The process of obtaining buried point information is exemplarily described as follows:

firstly, a defined buried point model is obtained, wherein the attribute of the buried point model comprises a character string type user identification, a floating point type duration and an integer version number. The attribute in the buried point model may be a key to be reported. Because the embedded point model comprises the attribute, the compiler can automatically prompt the attribute of the embedded point model for a user to select under the condition that the target character such as the point number is detected, if the attribute has a problem, the compiler can prompt if judging that the attribute does not exist after analyzing, and the probability of data errors is reduced.

And after the buried point model is obtained, assigning values to all the attributes included in the buried point model through a run-time resolution mechanism, namely determining the corresponding values of the attributes.

To facilitate transmission, the attributes and corresponding values in the buried point model may be converted to a dictionary. And e.g. calling a method for reporting the buried point model when an application program is run by using an OC dynamic run-time mechanism, analyzing the attribute in the buried point model when the application program is reported, and constructing a dictionary by using the run-time mechanism so as to represent the user identification and the corresponding value in a dictionary form. After representing the attributes and corresponding values in dictionary form, a set rule process may be performed.

If the attribute includes a fixed prefix, such as dve in dve _tracktype, dve may be a general identifier of the provider, and may be removed when the service party processes the data, so as to implement application of the fixed rule, that is, change the attribute dve _tracktype including the fixed prefix to trackType.

If the attribute comprises a general attribute, such as version, analysis can be performed based on a set rule, and if the version is the general attribute, the version is replaced by a corresponding special character string iOS version, so that application of the custom rule is realized. Wherein the mapping relationship between version and iOS version may be stored in a string mapping table.

According to the technical scheme, the values corresponding to the attributes associated with the event to be processed are collected through the embedded point model, then the determined values and the corresponding attributes are converted into intermediate data in the form of key value pairs, and further the intermediate data are processed according to the set rules to obtain embedded point information, so that the event to be processed is conveniently analyzed based on the embedded point information, the problem of accuracy of the collected data based on the character string dictionary model is solved, the accuracy of the attributes and the values determined based on the embedded point model is improved, and the accuracy of the determined embedded point information is further improved.

Fig. 2 is a flow chart of yet another data processing method according to an embodiment of the disclosure, as shown in fig. 2, where the method includes:

s210, determining a value corresponding to the attribute included in the buried point model by adopting a runtime resolution mechanism.

In the step, when determining the value corresponding to the attribute in the buried point model, a runtime resolution mechanism can be adopted, for example, in the running process of the application program corresponding to the buried point, the value corresponding to the attribute is collected, so as to realize the collection of the buried point.

S220, converting the determined value and the corresponding attribute into intermediate data in the form of key value pairs.

S230, processing the intermediate data by adopting fixed rules and/or custom rules in the set rules to obtain the buried point information of the event to be processed.

In processing the intermediate data, one or more of the fixed rules and the custom rules may be employed to process the intermediate data.

The rules used may be determined based on actual conditions and are not limited herein. The intermediate data may be processed based on fixed rules and/or custom rules, such as when the intermediate data is processed as required by rules specified by the fixed rules and/or custom rules. For example, the fixed rule may be to remove the prefix that is fixed and included in the attribute, and if the intermediate data includes a key corresponding to the attribute that includes the fixed prefix, the fixed rule is used to remove the prefix. The custom rule may be to convert the general attribute included in the intermediate data into a specific string based on the string mapping table, and correspondingly, when the general attribute is included in the intermediate data, the general attribute may be converted into the specific string.

When the intermediate data needs to be processed based on the fixed rule and the custom rule, the application sequence of the fixed rule and the custom rule is not limited, and the intermediate data can be processed based on the fixed rule first and then processed based on the custom rule. Or processing the intermediate data based on the custom rules and then based on the fixed rules. Such as defining the execution order of fixed rules and custom rules by an order array. The order array may be defined by the business party.

According to the data processing method provided by the embodiment of the disclosure, the corresponding value of the attribute is determined through the runtime resolution mechanism, and the intermediate data is processed through the fixed rule and/or the custom rule, so that the buried point information is obtained, the accuracy of the buried point information is improved, and the safety of the data is ensured.

In one embodiment, the custom rule includes a string mapping table, the string mapping table includes a correspondence between a general attribute and a special string in the embedded point model, the intermediate data includes a general attribute in the string mapping table, and the embedded point information includes a special string corresponding to the general attribute included in the intermediate data.

The universal attribute can be regarded as an attribute which is universal for different service parties, the attribute can be shared by the different service parties through the universal attribute, so that the attribute can be multiplexed among the different service parties, and the buried point data can be multiplexed by maintaining a character string mapping table.

The private string may be considered a string that is private to the business party. The custom rule may be to replace a general attribute included in the intermediate data with a corresponding special character string to obtain buried point information.

In one embodiment, the attribute included within the buried point model is read-only.

The attribute included in the buried point model is read-only. Modification of the attributes only takes effect upon initialization of the buried point model. Thereby improving the security of the data.

In one embodiment, converting the determined values and corresponding attributes into intermediate data in the form of key-value pairs includes:

the determined value and the corresponding attribute are converted into intermediate data in the form of key value pairs based on the type of the determined value through a converter, wherein a dictionary for caching class information of the embedded point model is stored in the converter, and the class information comprises a set formed by attribute names of the attributes in the embedded point model.

A converter may be considered to be an implementation form conversion. Such as converting the determined value and corresponding attribute into the form of key-value pairs.

When the converter converts the key value pairs, the conversion of the key value pairs can be realized based on the type of the determined value, for example, an attribute name character string corresponding to the attribute is used as a key of the key value pairs, and the value of the key value pairs is decided to take the value according to different types. The correspondence between the type and the value is not limited.

The buffer in the buried model may be present in the converter. When creating the translator, the translator may hold a dictionary for caching class information and a buried thread. When the translator is destroyed, the cache and threads are destroyed accordingly.

The class information can comprise an array or a set of attribute names in the embedded point model, the converter can acquire and cache the attribute of the embedded point model when the application program runs, and the converter can directly process based on the cached attribute when the embedded point model is applied next time. When caching the attribute, the character string corresponding to the attribute name can be cached into an array or a set stored in the form of the character string. There is an order among attribute names stored in the array. The value of each attribute name may be determined based on the order in the array. The sequence of the character strings corresponding to the attribute names in the array can be determined according to the sequence of inserting the character strings into the array, and the character strings corresponding to the attribute names are orderly, for example, the sequence of inserting the array elements into the array is determined as the sequence of the character strings corresponding to the attribute names. The attribute name strings stored in aggregate form are unordered.

The timing of the class information buffering by the converter can be controlled by a developer, or can be the time of class conversion by the converter, such as the time of converting the value and the corresponding attribute into intermediate data. The translator cache class information may be cached asynchronously.

When the class is converted by the converter, whether the class is cached or not, namely whether the attribute and the value of the buried point model corresponding to the class are converted into intermediate data or not can be firstly determined, if yes, the character string array of the cached attribute name is traversed, whether the attribute assigned to the class is found or not, and if yes, the attribute name character string and the value are associated, so that the intermediate data is obtained.

In one embodiment, the operations of converting intermediate data into a key-value pair form and processing intermediate data using set rules are performed by a sub-thread, the life cycle of which is equal to the life cycle of the converter.

The operation of converting the determined value and the corresponding attribute into the intermediate data in the form of key value pairs and adopting the set rule to process the intermediate data to obtain the embedded point information of the event to be processed is realized through the sub-threads, and the independent thread model is adopted to realize the data processing method so as to reduce the influence of the sub-threads (also called embedded point threads) for embedded point processing on the embedded point acquisition threads. The buried acquisition thread can be a main thread or other threads. The thread for buried point acquisition and the thread for buried point processing are different threads.

In one embodiment, the converting, by the converter, the determined value and the corresponding attribute into intermediate data in the form of key-value pairs based on the type of the determined value, includes:

taking the attribute name character string of the corresponding attribute as a key of the key value pair;

when the type of the determined value is the basic data type or the enumerated type, taking the determined value of the package type as the value of the key value pair;

When the type of the determined value is a character string type, a class type or a number type, taking the determined value as a value of a key value pair;

when the type of the determined value is dictionary, array, set or class object type, recursively expanding the element required to be expanded in the determined value, converting the expanded element into a key value pair form, and taking the converted element as the value of the key value pair.

When the type of the determined value is a character string, a class type or a number type, the determined value and the corresponding attribute may be directly written into the dictionary, wherein the number type includes: NSNumber, NSDecimalNumber. Class type nature can be thought of as a pointer to a structure, with the corresponding value representing the object type, without expansion. The generic class object type is usually inherited to NSObject, and the corresponding value needs to be expanded.

The base data types may include integer, floating point, boolean, etc. The basic data types can be converted into package types when written into the dictionary. The package type may be of the type packaged by NSValue, NSNumber, among others. Wherein, NSValue can store any type of object, NSNumber packages basic data types.

When the attribute belongs to the type of dictionary type, array type, set type or class object type, expanding the dictionary, array, set or class object included in the determined value into a key value pair form, namely recursively expanding the elements required to be expanded in the determined value. The type of element to be expanded may be a dictionary type, an array type, a collection type, or a class object type.

In one embodiment, the determined value is a dictionary type, the dictionary may be traversed and each element in the dictionary converted into the form of a key-value pair as a value.

In one embodiment, the determined value is an array type or a set type, and then each element in the array or set is expanded to be the value.

In one embodiment, the attribute is dvalue and the value of the corresponding dictionary type is: { "anotherobject": anotherObject, "anotherValue": @ {300.0}, wherein the anotherObject includes a plurality of attributes, such as integer value, string type svalue, and dictionary type dvalue, when converting into a key-value pair form, the attribute is used as a key, and the value is used as a value after being expanded, such as: "dvalue": { "anotherObject": { "value": @ (300), svalue: "anotherValue" }, "anotherValue": @ (300) }, i.e., the value is in the form of a key-value pair after the anotherObject in the value is expanded.

In one embodiment, the attribute is avalue, and the value of the corresponding set type is: { (anotherObject, @ (300)) } converting to key-value pair form, using the attribute as a key, and using the value after expansion as a value, such as "avalue": { ("value": @ (300), svalue: "anothervalue" }, @ (300)) }. That is, each element in the value of the collection type is converted to the form of a key-value pair, including the anotherObject.

Illustratively, the converter will use the attribute name string as a key of a key-value pair, the value of which will be determined to be valued according to different types:

1. basic data types, enumeration types: the value of the key-value pair is the value of the write-package type (i.e., when the type of the determined value is the base data type or the enumerated type, the determined value of the package type is taken as the value of the key-value pair);

2. string type (e.g., NSString, NSMutableString, etc.), number type (e.g., NSNumber, NSDecimalNumber, etc.), class type: the value of the key-value pair is the original value (i.e., when the type of the determined value is a string type, a class type, or a numeric type, the determined value is taken as the value of the key-value pair);

3. structure, association, pointer, block type: unsupported (logic of business party can be defined according to attribute names by custom rules);

4. Dictionary, array, collection, generic class object types: the elements which can be converted are converted into a dictionary in the form of key value pairs by using a converter after recursion expansion, and the converted dictionary exists as the value of the key value pairs (namely, when the type of the determined value is dictionary, array, set or class object type, after recursion expansion of the elements which need to be expanded in the determined value, the elements after expansion are converted into the form of the key value pairs, and the converted elements are taken as the values of the key value pairs).

If the value of a key pair is a null pointer, this key pair will be discarded directly.

The type of value is a container type (dictionary, array, set) and is divided into the following two cases:

(1) Dictionary for dictionary

The type of the value in the key value pair is obtained, the key type must be a character string type, otherwise the key value pair is discarded. The type of the value is converted by using four conversion rules of the converter; after the conversion is completed, the new dictionary value is written into the value of the key-value pair.

(2) Array and set

The types of the values are converted by using four conversion rules of the converter; after the conversion is completed, the new array or set value is written into the value of the key-value pair.

The present disclosure is described below as an exemplary embodiment, and the data processing method provided by the present disclosure may be considered as a data processing method implemented based on the embedded point model and pipeline system of the Objective-C runtime. The data processing method provided by the present disclosure can solve the following technical problems: the construction of the temporary character string embedded point key has the security problem and different services cannot share embedded point data. The present disclosure may use only one step in constructing the string embedded point key. Without requiring two steps to be declared and defined.

The prior art cannot share buried point data, specifically, different strings may be used as buried point keys by different services, but the values corresponding to the keys may be the same, for example, a service uses a trackType as a key, B service uses a track_type as a key, and their values may be @ "video".

The embedded point pipeline system based on the OC operation is realized based on the embedded point model DVETrackEvent. Fig. 3 is a schematic diagram of a pipeline system according to an embodiment of the disclosure, referring to fig. 3, the pipeline system determines buried point information based on a buried point model and a buried point process, and performs buried point reporting based on the buried point information.

The buried point pipeline system of the present disclosure is divided into three stages: a conversion stage, an application rule stage and a reporting stage.

The conversion stage converts the buried point model into intermediate data, namely, the converter outputs a dictionary and specific parameters (namely, intermediate data) based on the attribute included in the input buried point model, namely, the object attribute, and realizes the conversion of the attribute and the corresponding value into the form of key value pairs.

The buried point model is characterized by DVETrackEvent, and is shown in the form of a uml class diagram in FIG. 3, wherein the name of the key is left on the colon of the buried point model, and the type of the key is right on the colon. Three attributes in the embedded point model are eventName for representing the name of the embedded point, and the type of the eventName is a character string; eventFlags characterizing the parameters of the runtime resolution, the type of which is integer; dve _tracktype, which characterizes the track type, is a string. Values corresponding to the attributes in the buried point model may be stored in a memory of the electronic device.

The parameter representing the resolution in operation can be a variable defined by the service party, the parameter is suitable for rule logic of the service party, and the value corresponding to the parameter representing the resolution in operation can obtain the represented information through bit operation. The value of the information may be YES or No. For example, a value corresponding to the needledcommonparam is obtained through bit operation, and is used for indicating whether the general parameters of the service party need to be added.

Illustratively, the business party defines an integer (temporary name Value) that can be assigned to it. If the low first digit of the binary number represents the needled CommonParam, the service party performs an AND operation with Value and binary 1 to obtain the Value of the low first digit. This value is only two possible, 0 or 1. When the AND operation result is 1, the needled CommonParam is YES, and the universal embedded point parameters added by the business side are required to be added by using the custom rules. When the AND operation result is 0, the needled CommonParam is No, and the universal embedded point parameters added by the service side do not need to be added by using a custom rule.

Illustratively, the value corresponding to the integer type eventFlags may be 1, 2, or 3 of the integer type. The eventflag value can determine the value of different bits through bit operation, for example, the first low bit indicates whether a general parameter is needed, the second low bit indicates whether a prefix filtering rule needs to be applied, and the third low bit indicates whether a prefix needs to be added. If the value is AND-operated with 1, the value of the first low-order bit can be judged, and if the value is AND-operated with 2, the value of the second low-order bit can be judged.

The application rule stage processes the intermediate data of the conversion stage and outputs dictionaries applying different rules, namely buried point information. The embedded point information may be derived based on default rules (i.e., fixed rules) and business custom rules (i.e., custom rules) in a particular set of parameters and rules. The specific parameters may be considered as parameters to be used when applying custom rules. Intermediate data between the first and second steps as in fig. 3, wherein the key "needledcommonparameters" and the corresponding value "yes" are specific parameters (the characterization requires the addition of a common parameter, where the common parameter may be considered as a common parameter, and the common parameter may include identification information of the electronic device for identifying the electronic device). The rule set may be a buried point rule customized by a user, for example, adding a common parameter, mapping a model attribute to a customized character string through a character string mapping table, etc., so that different services can share the same buried point data, and the buried point data can be multiplexed.

Wherein the specific parameter may be used to generate a corresponding plurality of key-value pairs, which may be common parameters added, and the generated key-value pairs may be added to the dictionary. The dictionary may be a dictionary to which a setting rule is applied, and may include key value pairs corresponding to specific parameters, or key value pairs corresponding to buried point model attributes and values.

The specific parameters may be included in the intermediate data or may be stored in the setting rule.

As can be seen from fig. 3, the key "needledcommonparameters" and the corresponding value "yes" represent that a common parameter needs to be added, and after the application rule set is applied, the dictionary includes "[ universal buried point parameter dictionary ]", that is, the dictionary between the second step and the third step in fig. 3 includes "[ universal buried point parameter dictionary ]", where the universal buried point parameter dictionary may be composed of a key value pair generated by the universal buried point parameter, that is, the universal parameter.

In fig. 3, dve _tracktype, after the rule set is applied, the fixed prefix dve is first removed based on the fixed rule, and then the custom rule is applied to replace it with the special string track_type. the trackType may be a general attribute corresponding to the special string track_type, or dve _track_type may be a general attribute corresponding to the special string track_type. After the fixed prefix is removed through the fixed rule, the size of the transmitted data can be reduced, and the bandwidth occupancy rate is reduced. The transmitted data may be buried point information or processed buried point information. The means of processing is not limited and may be determined based on actual conditions.

The dedicated string, after being transmitted to the service party, may enable the service party to determine which service party is the service party. The server may be a user, such as a server, that provides data analysis.

The reporting node may convert the dictionary into reporting data and report the reporting data, that is, convert the embedded point information into real embedded point data (such as a json format character string). In fig. 3, the data processing method is executed through an independent buried point process, so that the influence of the pipeline on the buried point acquisition thread is reduced.

Business parties may report embedded points frequently, and use asynchronous pipelines but use heavy data synchronization behaviors, such as locking, copying and the like, so that thread security is guaranteed to seriously affect performance. For the buried point model, each reported buried point model is destroyed. Therefore, after each buried point model is built, the attribute of the buried point model can be considered as read-only, and concurrent data read-write does not exist and the thread is safe. The properties of dvetrackavent are read-only, so in this scenario thread security can be considered.

Fig. 4 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present disclosure, as shown in fig. 4, where the apparatus includes: a determination module 410, a conversion module 420, and a processing module 430.

A determining module 410, configured to determine a value corresponding to an attribute included in the buried point model, where the attribute includes an attribute of the event to be processed;

a conversion module 420, configured to convert the determined value and the corresponding attribute into intermediate data in the form of a key-value pair, where the determined value is used as a value in the key-value pair, and an attribute name of the attribute corresponding to the determined value is used as a key in the key-value pair;

And the processing module 430 is configured to process the intermediate data by using a set rule to obtain buried point information of the event to be processed.

According to the technical scheme provided by the embodiment of the disclosure, the value corresponding to the attribute related to the event to be processed is acquired through the embedded point model, then the determined value and the corresponding attribute are converted into the intermediate data in the form of the key value pair, and further the intermediate data is processed according to the set rule to obtain the embedded point information, so that the event to be processed is conveniently analyzed based on the embedded point information, the problem of accuracy of the acquired data based on the character string dictionary model is solved, the accuracy of the attribute and the value determined based on the embedded point model is improved, and the accuracy of the determined embedded point information is further improved.

In one embodiment, the determining module 410 is specifically configured to:

and determining the value corresponding to the attribute included in the buried point model by adopting a runtime resolution mechanism.

In one embodiment, the processing module 430 is specifically configured to:

and processing the intermediate data by adopting fixed rules and/or custom rules in the set rules to obtain the buried point information of the event to be processed.

In one embodiment, the conversion module 420 is specifically configured to:

The data processing device provided by the embodiment of the disclosure can execute the data processing method provided by any embodiment of the disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

It should be noted that each unit and module included in the above apparatus are only divided according to the functional logic, but not limited to the above division, so long as the corresponding functions can be implemented; in addition, the specific names of the functional units are also only for convenience of distinguishing from each other, and are not used to limit the protection scope of the embodiments of the present disclosure.

Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure. Referring now to fig. 5, a schematic diagram of an electronic device (e.g., a terminal device or server in fig. 5) 500 suitable for use in implementing embodiments of the present disclosure is shown.

The electronic device 500 of the present disclosure includes:

one or more processing devices 501;

storage 508, for storing one or more programs,

the one or more programs, when executed by the one or more processing devices 501, cause the one or more processing devices 501 to implement the data processing methods as provided by the present disclosure.

The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 5 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.

As shown in fig. 5, the electronic device 500 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 501, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An edit/output (I/O) interface 505 is also connected to bus 504.

In general, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 507 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 508 including, for example, magnetic tape, hard disk, etc.; and communication means 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 shows an electronic device 500 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or from the storage means 508, or from the ROM 502. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing device 501.

The electronic device provided by the embodiment of the present disclosure and the data processing method provided by the foregoing embodiment belong to the same inventive concept, and technical details not described in detail in the present embodiment may be referred to the foregoing embodiment, and the present embodiment has the same beneficial effects as the foregoing embodiment.

The present disclosure provides a computer storage medium having stored thereon a computer program which, when executed by a processor, implements the data processing method provided by the above embodiments.

It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two.

The computer storage medium may be a storage medium of computer executable instructions, which when executed by a computer processor, are for performing a method as provided by the present disclosure.

The computer readable storage medium may be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.

The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.

The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to:

the computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: determining a value corresponding to an attribute included in the buried point model, wherein the attribute comprises an attribute of an event to be processed;

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, or combinations thereof, including, but not limited to, languages with the ability to determine values corresponding to attributes at the time of application execution, such as object oriented programming languages, such as Java (determining values via a reflection mechanism), and languages with the ability to execute, such as Objective-C (requiring values of object attributes to be obtained at the time of execution via specified symbols, such as attribute names), or similar programming languages.

The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules or units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. Where the name of a module or unit does not in some way constitute a limitation of the unit itself, for example a processing module may also be described as an "intermediate data processing module".

The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

According to one or more embodiments of the present disclosure, there is provided a data processing method [ example 1 ] including determining a value corresponding to an attribute included in a buried point model, the attribute including an attribute of an event to be processed;

According to one or more embodiments of the present disclosure, there is provided the method of example 1, the determining a value corresponding to an attribute included in a buried point model, including:

According to one or more embodiments of the present disclosure, there is provided the method of example 1, wherein the processing the intermediate data using a set rule to obtain buried point information of the event to be processed includes:

According to one or more embodiments of the present disclosure, the method of example 3 is provided [ example 4 ], where the custom rule includes a string mapping table, the string mapping table includes a correspondence between general attributes and special strings in the buried point model, the intermediate data includes general attributes in the string mapping table, and the buried point information includes special strings corresponding to the general attributes included in the intermediate data.

According to one or more embodiments of the present disclosure, there is provided the method of example 1 [ example 5 ], the attribute included within the buried point model being read-only.

According to one or more embodiments of the present disclosure, there is provided the method of example 1, converting the determined values and corresponding attributes into intermediate data in the form of key-value pairs, comprising:

and converting the determined value and the corresponding attribute into intermediate data in the form of key value pairs based on the type of the corresponding attribute by a converter, wherein a dictionary for caching class information of the embedded point model is stored in the converter, and the class information comprises a set formed by attribute names of the attributes in the embedded point model.

According to one or more embodiments of the present disclosure, there is provided the method of example 6 [ example 7 ], the operations of converting to intermediate data in the form of key-value pairs and processing the intermediate data with set rules being performed by a sub-thread, the life cycle of the sub-thread being equal to the life cycle of the converter.

According to one or more embodiments of the present disclosure, there is provided the method of example 6, the converting, by the converter, the determined values and the corresponding attributes into intermediate data in the form of key-value pairs based on the type of the determined values, comprising:

According to one or more embodiments of the present disclosure, there is provided a data processing apparatus [ example 9 ], comprising:

According to one or more embodiments of the present disclosure, there is provided an electronic device [ example 10 ], the electronic device comprising:

one or more processing devices;

storage means for storing one or more programs,

when the one or more programs are executed by the one or more processing devices, the one or more processing devices are caused to implement the data processing method as described in any of examples 1-8.

According to one or more embodiments of the present disclosure, there is provided a storage medium containing computer executable instructions for performing the data processing method as described in any one of examples 1-5 when executed by a computer processor.

The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this disclosure is not limited to the specific combinations of features described above, but also covers other embodiments which may be formed by any combination of features described above or equivalents thereof without departing from the spirit of the disclosure. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).

Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.

Claims

1. A method of data processing, comprising:

2. The method of claim 1, wherein determining the value corresponding to the attribute included in the buried point model comprises:

3. The method according to claim 1, wherein the processing the intermediate data using the set rule to obtain the buried point information of the event to be processed includes:

4. The method of claim 3, wherein the custom rule includes a string mapping table, the string mapping table includes a correspondence between general attributes and special strings in the embedded point model, the intermediate data includes general attributes in the string mapping table, and the embedded point information includes special strings corresponding to the general attributes included in the intermediate data.

5. The method of claim 1, wherein the attribute included in the buried point model is read-only.

6. The method of claim 1, wherein converting the determined values and corresponding attributes into intermediate data in the form of key-value pairs comprises:

7. The method of claim 6, wherein converting intermediate data in the form of key-value pairs and processing intermediate data using set rules are performed by a sub-thread, the sub-thread having a lifecycle equal to the lifecycle of the converter.

8. The method of claim 6, wherein converting, by the converter, the determined values and the corresponding attributes into intermediate data in the form of key-value pairs based on the type of the determined values, comprises:

9. A data processing apparatus, comprising:

10. An electronic device, the electronic device comprising:

One or more processing devices;

storage means for storing one or more programs,

when the one or more programs are executed by the one or more processing devices, the one or more processing devices are caused to implement the data processing method of any of claims 1-8.

11. A storage medium containing computer executable instructions for performing the data processing method of any of claims 1-8 when executed by a computer processor.