CN115758025A - Method for supporting multi-platform automatic behavior embedded point acquisition and related device - Google Patents
Method for supporting multi-platform automatic behavior embedded point acquisition and related device Download PDFInfo
- Publication number
- CN115758025A CN115758025A CN202211471531.XA CN202211471531A CN115758025A CN 115758025 A CN115758025 A CN 115758025A CN 202211471531 A CN202211471531 A CN 202211471531A CN 115758025 A CN115758025 A CN 115758025A
- Authority
- CN
- China
- Prior art keywords
- code value
- component
- buried point
- point code
- preset algorithm
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 55
- 238000012545 processing Methods 0.000 claims abstract description 27
- 230000003993 interaction Effects 0.000 claims abstract description 16
- 238000012549 training Methods 0.000 claims description 38
- 230000006399 behavior Effects 0.000 claims description 33
- 230000015654 memory Effects 0.000 claims description 31
- 238000005457 optimization Methods 0.000 claims description 9
- 230000002452 interceptive effect Effects 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 description 10
- 238000010586 diagram Methods 0.000 description 8
- 230000000694 effects Effects 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 238000013480 data collection Methods 0.000 description 3
- 239000000047 product Substances 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000009933 burial Methods 0.000 description 1
- 239000007795 chemical reaction product Substances 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the application discloses a method and a related device for supporting multi-platform automatic behavior buried point acquisition. The method comprises the following steps: acquiring a code; when the code is a component and the component is not a page, processing buried point data; after the buried point data is processed, saving a page buried point code value; splicing the page buried point code value and the buried point code value of the component to generate a primary buried point code value of the component; and when the user triggers interaction on the component, assembling the initial buried point code value and the interaction code value by using a preset algorithm to generate a final buried point code value. By the adoption of the method and the device, efficiency of acquiring the data of the buried points is greatly improved.
Description
Technical Field
The application belongs to the technical field of information processing, and mainly relates to a method and a related device for supporting multi-platform automatic behavior embedded point acquisition.
Background
Currently, a data collection technique for collecting user behavior is known as a point burying technique. By embedding the code value in the front-end product in advance, the user triggers the embedded point when using the product, collects the behavior data of the user, and has important significance for marketing activities, behavior analysis and report generation.
In the prior art, the code is manually written to bury the points, omission and errors of the buried points can occur in the manual point burying process, and the accuracy and the efficiency of the buried points are low.
Disclosure of Invention
An object of the present application is to provide a method and a related apparatus for supporting multi-platform automated behavior embedded point collection, which have the advantages of improving embedded point efficiency and greatly improving efficiency of acquiring embedded point data.
In order to achieve the above object, in a first aspect, an embodiment of the present application provides a method for supporting multi-platform automated behavior embedded point collection, where the method is applied to a user terminal for automated behavior embedded point collection, where the automated behavior embedded point collection system includes the user terminal and a server, and includes:
acquiring a code;
when the code is a component and the component is not a page, processing buried point data;
after the buried point data is processed, saving a page buried point code value;
splicing the page buried point code value and the buried point code value of the component to generate a preliminary buried point code value of the component;
and when the user triggers interaction on the component, assembling the initial buried point code value and the interaction code value by using a preset algorithm to generate a final buried point code value.
The embedded point code value of the page is stored after the embedded point data is processed, the embedded point code value of the page and the embedded point code value of the component are spliced to generate the initial embedded point code value of the component, when a user triggers interaction, the initial embedded point code value and the interaction code value are assembled by using a preset algorithm to generate the final embedded point code value, and the accuracy of acquiring the embedded point data can be improved.
In one possible example, the components include an input information component, a prompt information component, and a guide information component.
It can be understood that the components comprise an information input component, a prompt information component and a guidance information component, and the display efficiency of the component information can be greatly improved.
In one possible example, the component performs processing according to the classification of the component, and adjusts the state, generation form, and generation speed of the component.
It can be understood that the management efficiency of the components can be optimized by processing the components according to the classification of the components and adjusting the state, the generation form and the generation speed of the components.
In one possible example, the adjusting of the generation form and the generation speed of the component comprises the following steps:
adjusting at least one of an appearance, font, and color of a component based on a classification of the component;
and adjusting the generation speed of the component to be a preset generation numerical value based on the classification of the component.
It is understood that adjusting at least one of the appearance, font, and color of the component based on the classification of the component, and adjusting the generation speed of the component to a preset generation value based on the classification of the component can improve the generation process of the component.
In one possible example, the assembling the preliminary buried point code value and the interactive code value by using a preset algorithm to generate a final buried point code value includes the following steps:
and generating a buried point code value representing the buried point definition at the front end in a final buried point code value by using the preset algorithm, and generating a buried point code value representing the operation behavior at the rear end in the final buried point code value by using the preset algorithm.
It can be appreciated that generating the buried point code values representing the buried point definitions at the front end in the final buried point code value using the preset algorithm and generating the buried point code values representing the operation behaviors at the back end in the final buried point code value using the preset algorithm can improve the assembly efficiency of the final buried point code value.
In one possible example, said assembling said preliminary buried point code values and interactive code values using a preset algorithm to generate final buried point code values comprises the steps of:
inputting a preset exercise data set and a corresponding preset final embedded point code value into the preset algorithm, and performing assembly training;
performing parameter optimization on the preset algorithm according to a training result;
and when the accuracy of the training result is greater than a preset value, assembling by using the preset algorithm.
It can be understood that the preset training data set and the corresponding preset final embedded point code value are input into the preset algorithm for assembly training, the preset algorithm is subjected to parameter optimization according to the training result, and when the accuracy of the training result is greater than the preset value, the preset algorithm is used for assembly, so that the training process of the preset algorithm can be optimized.
In one possible example, the performing parameter optimization on the preset algorithm according to the training result includes the following steps:
adjusting at least one of code value identification accuracy, code value assembly accuracy and code value assembly speed in the preset algorithm according to a training result;
and according to the training result, adjusting the weight coefficient of code value identification accuracy, the weight coefficient of code value assembling accuracy and the weight coefficient of code value assembling speed.
It can be understood that, according to the training result, at least one of the code value identification accuracy, the code value assembling accuracy and the code value assembling speed in the preset algorithm is adjusted, and according to the training result, the weight coefficient of the code value identification accuracy, the weight coefficient of the code value assembling accuracy and the weight coefficient of the code value assembling speed are adjusted, so that the optimization efficiency of the preset algorithm can be improved.
In a second aspect, an apparatus supporting multi-platform automated behavior-site acquisition includes means for performing the method provided in the first aspect or any embodiment of the first aspect.
In a third aspect, an apparatus supporting multi-platform automated behavior buried point acquisition includes a processor, a memory, and one or at least one program, where the one or at least one program is stored in the memory and configured to be executed by the processor, and the program includes instructions for performing the method provided by the first aspect or any one of the implementation manners of the first aspect.
In a fourth aspect, a computer-readable storage medium stores a computer program, which causes a computer to execute to implement the method provided by the first aspect or any implementation manner of the first aspect.
The embodiment of the application has the following beneficial effects:
acquiring a code; when the code is a component and the component is not a page, processing buried point data; after the embedded point data is processed, saving a page embedded point code value; splicing the page buried point code value and the buried point code value of the component to generate a primary buried point code value of the component; when the user is in the component triggering interaction, the initial buried point code value and the interaction code value are assembled by using a preset algorithm to generate a final buried point code value, so that the efficiency of acquiring buried point data is greatly improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained based on these drawings without creative efforts. Wherein:
fig. 1 is an application scenario diagram supporting multi-platform automated behavior embedded point collection according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of a main buried point acquisition interface according to an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart illustrating final embedded point code value assembling according to an embodiment of the present disclosure;
fig. 4 is a schematic flowchart of an automated behavior buried point acquisition method supporting multiple platforms according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an automated behavior buried point acquisition device supporting multiple platforms according to an embodiment of the present disclosure;
fig. 6 is a structural diagram of an automated behavior buried point acquisition device supporting multiple platforms according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "1" and "2" and the like in this application are used to distinguish different objects, and are not used to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Referring to fig. 1, fig. 1 is an application scenario diagram supporting multi-platform automated behavior embedded point acquisition according to an embodiment of the present application. As shown in fig. 1, the application scenario diagram includes a user 101, an electronic device 102, and a server 103. It should be noted that the number of devices, the form of the devices, and the number of users in the system shown in fig. 1 are used for example, and do not limit the embodiments of the present application, and one user may use a plurality of electronic devices.
The user 101 is a user who actually operates the electronic device 102 to control the electronic device 102 to perform a corresponding operation. The electronic device 102 may be a notebook computer shown in fig. 1, and may also be a Personal Computer (PC), an all-in-one machine, a palm computer, a tablet computer (pad), a smart phone, a smart television playing terminal, a portable device, and the like. The operating system of the PC-side electronic device, such as a kiosk or the like, may include, but is not limited to, operating systems such as Linux system, unix system, windows series system (e.g., windows xp, windows 7, etc.). The operating system of the electronic device at the mobile end, such as a smart phone, may include, but is not limited to, an operating system such as an android system, an IOS (operating system of an apple mobile phone), a Window system, and the like.
The method for supporting multi-platform automatic behavior embedded point collection provided in the embodiments of the present application is described below, and the method may be executed by an automatic behavior embedded point collection apparatus supporting multiple platforms, where the apparatus may be implemented by software and/or hardware, and may be generally integrated in an electronic device or a server.
Referring to fig. 2, fig. 2 is a schematic view of a main buried point acquisition interface according to an embodiment of the present disclosure. The user of the first electronic device 201 is a user, and the user views the buried point collecting main interface 206 through the first electronic device 201 and displays the following contents: the date 203, the time 204, and the start function 205, at which time the pop-up box 202 of the first electronic device 201 displays "please confirm the above information".
Referring to fig. 3, fig. 3 is a schematic flow chart illustrating final embedded point code value assembling according to an embodiment of the present disclosure. The page buried point code value 301 and the component buried point code value 302 are spliced to generate a preliminary buried point code value 303, and the preliminary buried point code value 303 and the interactive code value 304 are assembled into a final buried point code value 305.
Referring to fig. 4, fig. 4 is a schematic flowchart illustrating an automated behavior buried point collection method supporting multiple platforms according to an embodiment of the present disclosure. The method is exemplified by being applied to an automatic behavior buried point acquisition process supporting multiple platforms, and the automatic behavior buried point acquisition device supporting multiple platforms may include a server or an electronic device. The method comprises the following steps S401-S405, wherein,
s401: a code is obtained.
S402: and when the code is a component and the component is not a page, processing the buried point data.
Three points need to be stated, first, when the code is not a component, then the beacon is acquired from the attribute. And secondly, when the component is a page, storing the embedded point code value of the page. Third, if the result obtained when checking whether a beacon is defined is that a beacon is not defined at present, a prompt pops up.
In one possible example, step S402 includes the following step A1:
the components include an input information component, a prompt information component, and a guidance information component.
As an example, so-called components are just some of the tags that we customize. In the custom tab, some custom specific web page code has been encapsulated. The use of the components can reduce the code of the webpage on one hand and greatly improve the reuse of the code on the other hand. Generally speaking, there are components such as page header, sidebar and content area, each of which contains components such as navigation links, blog articles, etc. through which the page can be more structured and maintained.
In one possible example, step A1 comprises the following steps a11:
and the component carries out processing according to the classification of the component and adjusts the state, the generation form and the generation speed of the component.
In one possible example, step A1 includes the following steps A111-A112:
a111: adjusting at least one of an appearance, font, and color of the component based on the classification of the component.
A112: and adjusting the generation speed of the component to be a preset generation numerical value based on the classification of the component.
S403: and after the buried point data is processed, saving the page buried point code value.
As an example, a buried point is a term of data collection field (especially user behavior data collection field), and refers to a related technology and its implementation process for capturing, processing and transmitting specific user behavior or events. The technical essence of the embedded point is that events in the running process of the software application are monitored, judgment and capture are carried out when the events needing attention occur, then necessary context information is obtained, and finally the information is arranged and sent to the server side. The monitored events are usually provided by platforms such as an operating system, a browser, an application framework and the like, and customization of trigger conditions (such as clicking a certain button) can also be performed on the basis of the events. Typically, the burial point may be programmed by monitoring an analysis tool. The business significance of the buried point is obvious, namely the business data and the accompanying information of the business data are defined and acquired by the aid of the business data. Under different scenes, the information and the angle of interest of business personnel may be different. Typical application scenarios are analysis oriented to the digital marketing field and analysis oriented to the product operation field. The former focuses on source channels and advertising effects, and the latter focuses more on the optimization of the flow and experience of the product itself.
S404: and splicing the page buried point code value and the buried point code value of the component to generate a primary buried point code value of the component.
For example, the pop-up window assembly has two buttons, one is a confirm button and one is a close button, the confirm button defines a "modal. When the user makes a payment, a pop-up window component is required to ensure the user whether to confirm the payment. Since other activities may also use popups, such as by friend authentication, for development, to uniquely mark a confirmed payment activity, a code value "payconfig tips" is required to be passed into the popups component that can specifically mark that activity.
S405: and when the user triggers interaction on the component, assembling the initial buried point code value and the interaction code value by using a preset algorithm to generate a final buried point code value.
For an example, after the popup window component is processed by the collection and embedding tool, when the user pays, the user clicks the confirmation button of the popup window component to finally obtain a' modal.
In one possible example, step S405 comprises the following step B1:
generating a buried point code value representing a buried point definition at a front end in a final buried point code value using the preset algorithm;
and generating the embedded point code value representing the operation behavior at the back end in the final embedded point code value by using the preset algorithm.
In one possible example, step B1 comprises the following steps B11-B13:
b11: and inputting a preset exercise data set and a corresponding preset final embedded point code value into the preset algorithm for assembly training.
B12: and according to the training result, performing parameter optimization on the preset algorithm.
In one possible example, step B12 includes the following steps B121-B122:
b121: and adjusting at least one of code value identification accuracy, code value assembling accuracy and code value assembling speed in the preset algorithm according to the training result.
B122: and according to the training result, adjusting the weight coefficient of code value identification accuracy, the weight coefficient of code value assembling accuracy and the weight coefficient of code value assembling speed.
B13: and when the accuracy of the training result is greater than a preset value, assembling by using the preset algorithm.
As an example, when the preset value is 99.5%, and the accuracy of the calculation training result is 96.3%, at this time, 96.3% is less than 99.5%, continuing to train the preset algorithm; and when the preset numerical value is 99.5%, and the accuracy of the calculation training result is 99.7%, and at the moment, 99.7% is more than 99.5%, stopping training the preset algorithm, and assembling by using the preset algorithm.
Please refer to fig. 5, fig. 5 is a schematic structural diagram of an automatic behavior buried point collecting device supporting multiple platforms according to an embodiment of the present disclosure. Based on the above system architecture, the automated behavior embedded point collecting device 500 supporting multiple platforms may be a server or a unit in the server. This support automatic action of multiple platforms and bury some collection device 500 includes at least: an acquisition unit 501, a determination unit 502, a processing unit 503, and an assembly unit 504, wherein,
the obtaining unit 501 is used for obtaining a code.
The determining unit 502 is used for determining whether the component is a page.
The processing unit 503 is used for processing the buried point data; saving a page buried point code value; and splicing the page buried point code value and the buried point code value of the component to generate a primary buried point code value of the component.
When the user triggers interaction at the component, the assembling unit 504 assembles the preliminary buried point code value and the interaction code value by using a preset algorithm to generate a final buried point code value.
In one possible example, the processing unit 503 is configured to divide the components into an input information component, a prompt information component, and a guidance information component.
In one possible example, the processing unit 503 is configured to process the component according to the classification of the component, and adjust the state, the generation form, and the generation speed of the component.
In one possible example, based on the classification of the component, the processing unit 503 is configured to adjust at least one of an appearance, a font, and a color of the component; based on the classification of the component, the processing unit 503 is configured to adjust the generation speed of the component to a preset generation value.
In one possible example, the processing unit 503 is configured to generate the buried point code value representing the buried point definition at a front end in the final buried point code value using the preset algorithm, and generate the buried point code value representing the operation behavior at a back end in the final buried point code value using the preset algorithm.
In one possible example, the processing unit 503 is configured to input a preset training data set and a corresponding preset final embedded point code value into the preset algorithm for assembly training; according to the training result, the processing unit 503 performs parameter optimization on the preset algorithm; when the accuracy of the training result is greater than the preset value, the processing unit 503 performs assembly using the preset algorithm.
In one possible example, the processing unit 503 is configured to adjust at least one of code value identification accuracy, code value assembling accuracy and code value assembling speed in the preset algorithm according to the training result; according to the result of the training, the processing unit 503 is configured to adjust the weight coefficient of the code value identification accuracy, the weight coefficient of the code value assembling accuracy, and the weight coefficient of the code value assembling speed.
Referring to fig. 6, fig. 6 is a structural diagram of an automatic behavior buried point collecting device supporting multiple platforms according to an embodiment of the present disclosure. As shown in fig. 6, the multi-platform-enabled automated behavior buried point acquisition device 600 includes a processor 601, a memory 602, a communication interface 604, and at least one program 603. The at least one program 603 is stored in the memory 602 and configured to be executed by the processor 601, the at least one program 603 comprising instructions for:
acquiring a code;
when the code is a component and the component is not a page, processing buried point data;
after the buried point data is processed, saving a page buried point code value;
splicing the page buried point code value and the buried point code value of the component to generate a preliminary buried point code value of the component;
and when the user triggers interaction at the component, assembling the initial buried point code value and the interaction code value by using a preset algorithm to generate a final buried point code value.
In one possible example, the at least one program 603 is specifically for executing the instructions of:
the components are divided into an input information component, a prompt information component and a guide information component.
In one possible example, the at least one program 603 is specifically for executing the instructions of:
processing the components according to the classification of the components, and adjusting the state, the generation form and the generation speed of the components;
in one possible example, the at least one program 603 is specifically for executing the instructions of:
adjusting at least one of an appearance, font, and color of a component based on a classification of the component;
and adjusting the generation speed of the component to be a preset generation numerical value based on the classification of the component.
In one possible example, the at least one program 603 is specifically for executing the instructions of:
generating a buried point code value representing a buried point definition at a front end in a final buried point code value using the preset algorithm;
and generating the embedded point code value representing the operation behavior at the back end in the final embedded point code value by using the preset algorithm.
In one possible example, the at least one program 603 is specifically for executing the instructions of:
inputting a preset exercise data set and a corresponding preset final embedded point code value into the preset algorithm, and performing assembly training;
performing parameter optimization on the preset algorithm according to a training result;
and when the accuracy of the training result is greater than a preset value, assembling by using the preset algorithm.
In one possible example, the at least one program 603 is specifically for executing the instructions of:
adjusting at least one of code value identification accuracy, code value assembly accuracy and code value assembly speed in the preset algorithm according to a training result;
and according to the training result, adjusting the weight coefficient of code value identification accuracy, the weight coefficient of code value assembling accuracy and the weight coefficient of code value assembling speed.
Those skilled in the art will appreciate that only one memory 602 and processor 601 are shown in fig. 6 for ease of illustration. In an actual terminal or server, there may be multiple processors and memories. The memory may also be referred to as a storage medium or a storage device, and the like, which is not limited in this application.
It should be understood that, in the embodiment of the present Application, the processor may be a Central Processing Unit (CPU), and the processor may also be other general-purpose processors, digital Signal Processors (DSP), application Specific Integrated Circuits (ASIC), field-Programmable Gate arrays (FPGA) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. The processor may also be a general-purpose microprocessor, a Graphics Processing Unit (GPU), or one or more integrated circuits, and is configured to execute the relevant programs to implement the functions required to be executed in the embodiments of the present application.
The processor 601 may also be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the present application may be implemented by integrated logic circuits in hardware or instructions in software in the processor 601. The processor 601 described above may implement or perform the methods, steps and logic blocks disclosed in the embodiments of the present application. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules may be located in ram, flash and rom, programmable rom or electrically erasable programmable memory, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 602, and the processor 601 reads the information in the memory 602, and in combination with hardware thereof, performs functions required to be performed by units included in the method, apparatus, and storage medium according to the embodiments of the present application.
It will also be appreciated that the memory referred to in the embodiments of the application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. The volatile Memory may be a Random Access Memory (RAM) which serves as an external cache. By way of example and not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and Direct bus RAM (DR RAM). The Memory may also be, but is not limited to, a Compact disk Read-Only Memory (CD-ROM) or other optical disk storage, optical disk storage (including Compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor via a bus. The memory may also be integrated with the processor, and the memory may store a program, which when executed by the processor, performs the steps of the above-described embodiments of the present application.
It should be noted that when the processor is a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, the memory (memory module) is integrated in the processor. It should be noted that the memory described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
It should be understood that the term "and/or" herein is only one kind of association relationship describing the association object, and means that there may be three kinds of relationships, for example, a and/or B, and may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter associated objects are in an "or" relationship.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The steps of a method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in a processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and performs the steps of the method in combination with hardware thereof, which are not described in detail herein to avoid repetition.
Those of ordinary skill in the art will appreciate that the various Illustrative Logical Blocks (ILBs) and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer programmed program product. The computer program product includes one or more computer instructions. When loaded and executed on a processor, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, optical fiber) or wireless (e.g., infrared, wireless, microwave, etc.), or may be transmitted from one website, computer, server, or data center to a mobile phone processor by wire. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk), among others.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application.
Claims (10)
1. A method for supporting multi-platform automatic behavior embedded point acquisition is characterized by comprising the following steps:
acquiring a code;
when the code is a component and the component is not a page, processing buried point data;
after the buried point data is processed, saving a page buried point code value;
splicing the page buried point code value and the buried point code value of the component to generate a preliminary buried point code value of the component;
and when the user triggers interaction at the component, assembling the initial buried point code value and the interaction code value by using a preset algorithm to generate a final buried point code value.
2. The method of claim 1, wherein the components include an input information component, a prompt information component, and a guide information component.
3. The method according to claim 1 or 2, wherein the component is processed according to the classification of the component, and the state, the generation form, and the generation speed of the component are adjusted.
4. The method of claim 3, wherein the adjusting the generation form and the generation speed of the component comprises the steps of:
adjusting at least one of an appearance, font, and color of a component based on a classification of the component;
and adjusting the generation speed of the component to be a preset generation numerical value based on the classification of the component.
5. The method of claim 1, wherein said assembling said preliminary buried point code values and interactive code values using a predetermined algorithm to generate final buried point code values comprises the steps of:
generating a buried point code value representing a buried point definition at a front end in a final buried point code value using the preset algorithm;
and generating the embedded point code value representing the operation behavior at the back end in the final embedded point code value by using the preset algorithm.
6. The method of claim 1, wherein said assembling said preliminary buried point code values and interactive code values using a predetermined algorithm to generate final buried point code values comprises the steps of:
inputting a preset exercise data set and a corresponding preset final embedded point code value into the preset algorithm, and performing assembly training;
performing parameter optimization on the preset algorithm according to a training result;
and when the accuracy of the training result is greater than a preset value, assembling by using the preset algorithm.
7. The method according to claim 6, wherein the optimizing the parameters of the preset algorithm according to the training result comprises the following steps:
adjusting at least one of code value identification accuracy, code value assembly accuracy and code value assembly speed in the preset algorithm according to a training result;
and according to the training result, adjusting the weight coefficient of code value identification accuracy, the weight coefficient of code value assembling accuracy and the weight coefficient of code value assembling speed.
8. An apparatus supporting automated behavior-site acquisition of multiple platforms, for performing the method of any one of claims 1-7.
9. An apparatus supporting multi-platform automated behavior-buried point acquisition, comprising a processor, a memory, and one or at least one program, wherein the one or at least one program is stored in the memory and configured to be executed by the processor, the program comprising instructions for performing the method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, the computer program causing a computer to execute to implement the method of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211471531.XA CN115758025A (en) | 2022-11-23 | 2022-11-23 | Method for supporting multi-platform automatic behavior embedded point acquisition and related device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211471531.XA CN115758025A (en) | 2022-11-23 | 2022-11-23 | Method for supporting multi-platform automatic behavior embedded point acquisition and related device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115758025A true CN115758025A (en) | 2023-03-07 |
Family
ID=85335636
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211471531.XA Pending CN115758025A (en) | 2022-11-23 | 2022-11-23 | Method for supporting multi-platform automatic behavior embedded point acquisition and related device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115758025A (en) |
-
2022
- 2022-11-23 CN CN202211471531.XA patent/CN115758025A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3332320B1 (en) | Model integration tool | |
| CN111552633A (en) | Interface abnormal call testing method and device, computer equipment and storage medium | |
| CN108399565A (en) | Financial product recommendation apparatus, method and computer readable storage medium | |
| CN112597052A (en) | Interface testing method and device and computer readable storage medium | |
| CN106598991A (en) | Web crawler system capable of realizing website interaction and automatic form extraction by conversational mode | |
| US20160292706A1 (en) | Systems and methods for offer selection and reward distribution learning | |
| CN108196829A (en) | A kind of artificial intelligence mobile phone accounting system for analyzing account and method | |
| CN111666298A (en) | Method and device for detecting user service class based on flink, and computer equipment | |
| CN110781427A (en) | Method, device, equipment and storage medium for calculating first screen time | |
| CN111611612A (en) | Block chain-based integrating system, device, storage medium and operation method thereof | |
| CN117522538A (en) | Bid information processing method, device, computer equipment and storage medium | |
| CN117201208A (en) | Malicious mail identification method, malicious mail identification device, electronic equipment and storage medium | |
| CN113361220A (en) | Verification environment construction method and device for automatically cutting integrated circuit design | |
| CN117234505A (en) | Interactive page generation method, device, equipment and storage medium thereof | |
| CN116661936A (en) | Page data processing method and device, computer equipment and storage medium | |
| CN115758025A (en) | Method for supporting multi-platform automatic behavior embedded point acquisition and related device | |
| CN116383787A (en) | Page creation method, page creation device, computer equipment and storage medium | |
| CN111026945A (en) | Multi-platform crawler scheduling method and device and storage medium | |
| CN115809889A (en) | Intelligent passenger group screening method, system, medium and equipment based on marketing effect | |
| CN111832254B (en) | Drawing annotation display processing method and device | |
| CN110262856B (en) | Application program data acquisition method, device, terminal and storage medium | |
| CN102662938A (en) | Treatment controller | |
| CN115495570A (en) | Application user classification method, application user evaluation method, application user classification device, application user evaluation device and application user evaluation equipment | |
| CN106202319B (en) | Abnormal URL (Uniform resource locator) verification method and system | |
| CN111918323B (en) | Data calibration method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Country or region after: China Address after: 518000 18th floor, building A4, Kexing Science Park, Nanshan District, Shenzhen City, Guangdong Province Applicant after: Zhaolian Consumer Finance Co.,Ltd. Address before: 518000 18th floor, building A4, Kexing Science Park, Nanshan District, Shenzhen City, Guangdong Province Applicant before: MERCHANTS UNION CONSUMER FINANCE Co.,Ltd. Country or region before: China |
|
| CB02 | Change of applicant information |