CN116502054A - Flow data analysis method, system, medium and electronic equipment - Google Patents
Flow data analysis method, system, medium and electronic equipment Download PDFInfo
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Classifications
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- G06F11/302—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a software system
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Abstract
The invention provides a flow data analysis method, a flow data analysis system, a flow data analysis medium and electronic equipment; the method comprises the following steps: acquiring a plurality of flow data by adopting buried points; verifying the integrity of the flow data, and extracting the flow data with the integrity to generate an analysis data set; selecting and calling an analysis model based on the analysis target; the analysis model comprises an event table, an attribute table and an object table; inputting the analysis data set into an analysis model; associating the event in the event table with the user attribute in the attribute table and the service carrier in the object table to generate a data analysis table; invoking a preset label for classifying each user in the analysis data set based on the data analysis table to obtain an analysis result; according to the method, the analysis targets can be defined and the analysis models are preset, so that the corresponding analysis models can be called for flow data analysis according to different analysis targets, the universality of flow data analysis is improved, and the burden of small and medium enterprises is greatly reduced.
Description
Technical Field
The present invention relates to the field of communications, and in particular, to a flow data analysis technique, and in particular, to a flow data analysis method, system, medium, and electronic device.
Background
The flow data is mainly flow data generated by a series of processes from starting to using the product when the user accesses the product/page.
In the current e-commerce environment, the acquisition cost is high, and a new user may run off by only opening the app once. Therefore, it is necessary to diagnose abnormality of data by monitoring traffic data so as to improve business and promote product profits. When a user accesses a product/page, a series of processes from start-up to use of the product may generate a lot of traffic data. By analyzing the client behavior through the flow data, the client demand perception capability and the client service capability can be greatly improved.
However, the flow data analysis in the prior art is designed for the specific purpose of a specific product, and has no universal flow analysis tool, which makes it difficult for small enterprises to analyze flow data.
Disclosure of Invention
The invention aims to provide a flow data analysis method, a system, a medium and electronic equipment, which are used for solving the problem that a flow analysis tool which lacks universality in the prior art causes difficulty in analyzing flow data of a small enterprise.
To achieve the above and other related objects, the present invention provides a flow data analysis method, including the following steps: acquiring a plurality of flow data by adopting buried points; verifying the integrity of each flow data, and extracting the flow data with the integrity to generate an analysis data set; acquiring an analysis target, and selecting and calling an analysis model based on the analysis target; the analysis model at least comprises an event table, an attribute table and an object table; inputting the analysis dataset into the analysis model; associating the event in the event table with the user attribute in the attribute table and the service carrier in the object table to generate a data analysis table; and calling a preset label for classifying each user in the analysis data set based on the data analysis table to obtain an analysis result.
In an embodiment of the present invention, said verifying the integrity of each of said traffic data comprises: calling a buried point setting table; the embedded point setting table at least comprises the equipment ID, the operation time, the operation place, the operation mode and the operation behavior of the user; and comparing the integrity of the flow data based on the buried point setting table.
In an embodiment of the invention, the method further comprises: and caching the data analysis table.
In an embodiment of the present invention, the preset tag includes: event tags and model tags; the event tag at least comprises a new user, a short-time user and an old user; the model tag includes at least a low-consumption user, a medium-consumption user, and a high-consumption user.
In an embodiment of the invention, the method further comprises: and acquiring a historical result corresponding to the analysis result based on the analysis result, and comparing the analysis result with the historical result to obtain an optimized result.
The invention also provides a flow data analysis system, which comprises: the device comprises an acquisition module, a verification module, a calling module, an input module, a generation module and a classification module; the acquisition module is used for acquiring a plurality of flow data by adopting buried points; the verification module is used for verifying the integrity of each flow data, extracting the flow data with the integrity, and generating an analysis data set; the calling module is used for acquiring an analysis target, selecting and calling an analysis model based on the analysis target; the analysis model at least comprises an event table, an attribute table and an object table; the input module is used for inputting the analysis data set into the analysis model; the generation module is used for associating the event in the event table with the user attribute in the attribute table and the service carrier in the object table to generate a data analysis table; the classification module is used for retrieving a preset label for classifying each user in the analysis data set based on the data analysis table to obtain an analysis result.
The present invention also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described flow data analysis method.
The present invention also provides an electronic device including: a processor and a memory; the memory is used for storing a computer program; the processor is configured to execute the computer program stored in the memory, so that the electronic device executes the flow data analysis method.
As described above, the flow data analysis method, system, medium and electronic device of the present invention have the following beneficial effects:
compared with the prior art, the method can be used for calling the corresponding analysis model for flow data analysis according to different analysis purposes by defining the analysis targets and presetting the analysis model, so that the universality of flow data analysis is improved, and the burden of small and medium enterprises is greatly reduced.
Drawings
FIG. 1 is a flow chart of a flow data analysis method according to an embodiment of the invention.
FIG. 2 is a flow chart illustrating the verification of the integrity of each flow data according to an embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a terminal according to an embodiment of the invention.
Fig. 4 is a schematic structural diagram of a flow data analysis system according to an embodiment of the invention.
Detailed Description
The following specific examples are presented to illustrate the present invention, and those skilled in the art will readily appreciate the additional advantages and capabilities of the present invention as disclosed herein. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the illustrations, not according to the number, shape and size of the components in actual implementation, and the form, number and proportion of each component in actual implementation may be arbitrarily changed, and the layout of the components may be more complex.
Compared with the prior art, the flow data analysis method, the system, the medium and the electronic equipment can call the corresponding analysis model for flow data analysis aiming at different analysis purposes by defining the analysis targets and presetting the analysis model, so that the universality of flow data analysis is improved, and the burden is greatly reduced for small and medium enterprises.
As shown in fig. 1, in an embodiment, the flow data analysis method of the present invention is applied to an electronic device; specifically, the flow data analysis method includes the following steps:
and S1, acquiring a plurality of flow data by using buried points.
It should be noted that, the embedded point refers to a tool for collecting operation behavior data of a user when the user accesses the web page, the product or the application program, and the embedded point may be implemented by using an existing statistical SDK (statistical SDK refers to a software development kit for collecting data (not just front-end behavior data)).
Taking the example of recording starting data in the android device, three situations usually occur in one starting process, wherein the first is that after a user starts an application, the application is closed after the processing is finished for a preset purpose; secondly, after the user starts the application, the user needs to start other applications midway, and the started application is switched to background operation; third, after the user starts the application, the screen is blacked out due to long-term non-operation.
At this time, the statistical SDK will send application start data once at the time of application start, continuously monitor whether the above three cases occur, and send data once again when the above cases occur.
Meanwhile, the SDK has a statistical function, namely, data are counted according to a preset rule, and the data are sent according to a preset time interval.
In this embodiment, each complete flow data at least includes an equipment ID, an operation time, an operation location, an operation mode and an operation behavior; wherein the device ID is used to tag the source of the traffic data; the operation data is used for marking the generation time of the flow data; the operation mode is used for marking the generation mode of the user flow data; the operational behavior is used to mark what the user did with the traffic data.
And S2, verifying the integrity of each flow data, and extracting the flow data with the integrity to generate an analysis data set.
It should be noted that the integrity mainly includes the time sequence and the accuracy of the data record.
The sequence refers to whether the detected flow data is acquired by installing a preset operation flow, wherein the flow is sequential in time, and the front flow data is the premise and the basis of the rear flow data; the accuracy refers to whether the data record is abnormal, and the data without abnormality is the accuracy in this embodiment.
In particular, a subset of the flow data is acquired based on the integrity, an analysis dataset is generated, the analysis dataset being used for analysis of the flow data.
As shown in fig. 2, in one embodiment, the verifying the integrity of each of the traffic data includes:
step S21, a buried point setting table is called.
In one embodiment, there are pre-selected set alternative analysis targets, each of which is pre-set with a buried point mode and a buried point range, and recorded in a buried point setting table.
In an embodiment, the buried point setting table at least includes an equipment ID, an operation time, an operation place, an operation mode, and an operation behavior of the user.
And S22, comparing the integrity of the flow data based on the buried point setting table.
Specifically, by comparing the flow data with the buried point setting table, judging whether the flow data contains the equipment ID, the operation time, the operation place, the operation mode and the operation behavior of the user, and if so, indicating that the flow data has integrity; otherwise, if one does not, it means that the traffic data has no integrity.
And S3, acquiring an analysis target, and selecting and calling an analysis model based on the analysis target.
It should be noted that the analysis targets refer to options to be analyzed by a user, and are selectable preset options, and each analysis target corresponds to a corresponding analysis model.
In one embodiment, the analysis targets include, but are not limited to: user behavior analysis, user attribute analysis, and the like.
In one embodiment, the analysis model includes at least an event table, an attribute table, and an object table.
Specifically, the analysis model comprises an event table, an attribute table and an object table; the event table records information such as time, event and the like executed by a user in a product, a webpage or an application program, and the event refers to an action controlled by the user to be executed; the attribute table is used for recording attribute information of a user, and the attribute information at least comprises platform information of the user login, such as information of a device model, a user position, a user member level and the like; the object table contains a service carrier that accepts execution of the event, such as executable items present in a web page.
And S4, inputting the analysis data set into the analysis model.
And S5, associating the event in the event table with the user attribute in the attribute table and the service carrier in the object table to generate a data analysis table.
In one embodiment, the method further comprises: and caching the data analysis table.
Buffering in the point-of-burial device may allow for the possibility of preprocessing the traffic data at the point-of-burial device.
The data analysis table may record a plurality of data analysis purposes, and each analysis purpose is, for example: XX, a user with XXX properties has performed XXX events on XX.
Specifically, the data included in the analysis data set in step S2 is the data to be filled in the event table, the attribute table and the object table, and is divided into different description segments to record different data, where the data analysis table is an analysis table preset according to the user behavior or the user attribute.
For example, the XX attribute described above refers to one of the user attributes, and the performing of an XXX event on XX refers to performing an event on a service carrier. The data analysis table is generated based on a plurality of the analysis purposes. The description section of the data analysis table is preset.
In this embodiment, the associating includes, when the buried point detects an event, recording a user attribute and a service carrier corresponding to the event, and recording the user attribute and the service carrier to the data analysis table to implement associating.
And S6, retrieving a preset label for classifying each user in the analysis data set based on the data analysis table to obtain an analysis result.
The data analysis table at least comprises a time description section, a user attribute description section, an event carrier description section and an event description section, statistics is respectively carried out on each description section, the data is recorded and filled according to the preset description sections, father-son nodes are generated according to the relation of each data, and the data are ordered according to the father-son relation to generate an analysis result.
The analysis results have both sequential data relationships and data content.
Specifically, in this embodiment, the user attribute description is adopted as a father node, the event carrier is adopted as a child node, the event description is counted as a secondary child node, a final analysis result is obtained, and the analysis result is scored, so that an analysis result description is obtained.
The analysis result description may be expressed by the following expression:
wherein, P refers to the score of each data analysis item; the A is i Is a preset score for each node; the S is i The score of each node affects the parameters; the Y is i Is a scoring warning line for each node; i.e. when said A i ≤Y i A ticket overrules the other node score.
Marking each data analysis item based on the scores, including event tags and model tags.
In one embodiment, the preset tag includes: event tags and model tags; the event tag at least comprises a new user, a short-time user and an old user; the model tag includes at least a low-consumption user, a medium-consumption user, and a high-consumption user.
In one embodiment, the method further comprises: and acquiring a historical result corresponding to the analysis result based on the analysis result, and comparing the analysis result with the historical result to obtain an optimized result.
It should be noted that the historical result is historical data that is kept by the back end, that is, the server end, when analyzing a specific target or a specific crowd.
When a historical result corresponding to an analysis result is obtained, the buried point sampling data is not analyzed at all, but is subjected to purposeful long-time tracking analysis or one-time large-range census analysis according to requirements; according to the requirement of data integrity, most buried point data can be in one-to-one correspondence.
Specifically, the analysis results are compared with the historical results, and not the specific sampling results are compared with the historical sampling results.
The optimization of the analysis result is realized by specific data processing means such as label definition, data mining and the like on the sampled data according to the specific requirements of the actual service, and the optimization result is changed from single data or single analysis data.
The number of burial points is large, the sampled data is the amount of days, the data cannot be transmitted to the server side in full, most incomplete data need to be discarded, the required business data are transmitted according to the requirement of the server side, the server side analyzes and processes the data, and business behavior analysis is carried out by combining historical data, so that decision is affected.
The invention provides a flow data analysis method, which can provide a flow data analysis system construction thought and provide a basic technology realization path for realizing universal flow analysis tools; the invention aims to construct a universal flow analysis tool, so that the analysis cost of B-end enterprises to C-end client behaviors is greatly reduced, and the C-end client demand perception capability and the B-end enterprise client service capability are improved in a crossing manner.
It should be noted that, the protection scope of the flow data analysis method of the present invention is not limited to the execution sequence of the steps listed in the embodiment, and all the schemes implemented by adding or removing steps and replacing steps according to the prior art made by the principles of the present invention are included in the protection scope of the present invention.
The storage medium of the present invention stores a computer program which, when executed by a processor, implements the flow data analysis method described above. The storage medium includes: read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disks, U-discs, memory cards, or optical discs, and the like, which can store program codes.
Any combination of one or more storage media may be employed. The storage medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a RAM, a 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 this document, 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.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. 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 wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ or the like and conventional procedural programming languages, such as the "C" programming language 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 present invention is described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks (article of manufacture).
The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The electronic device of the invention comprises a processor and a memory.
The memory is used for storing a computer program; preferably, the memory includes: various media capable of storing program codes, such as ROM, RAM, magnetic disk, U-disk, memory card, or optical disk.
The processor is connected with the memory and is used for executing the computer program stored in the memory so as to enable the electronic equipment to execute the flow data analysis method.
Preferably, the processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, abbreviated as CPU), a network processor (Network Processor, abbreviated as NP), etc.; but also digital signal processors (Digital Signal Processor, DSP for short), application specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), field programmable gate arrays (Field Programmable Gate Array, FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
In an embodiment, the electronic device comprises a terminal and/or a server.
Fig. 3 shows a block diagram of an exemplary terminal 3 suitable for use in implementing embodiments of the invention.
The terminal 3 shown in fig. 3 is only an example and should not be construed as limiting the function and scope of use of the embodiment of the present invention.
As shown in fig. 3, the terminal 3 is in the form of a general purpose computing device. The components of the terminal 3 may include, but are not limited to: one or more processors or processing units 31, a memory 32, a bus 33 connecting the various system components, including the memory 32 and the processing unit 31.
Bus 33 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include industry Standard architecture (Industry Standard Architecture, ISA) bus, micro channel architecture (Micro Channel Architecture, MCA) bus, enhanced ISA bus, video electronics standards Association (Video Electronics Standards Association, VESA) local bus, and peripheral component interconnect (Peripheral Component Interconnect, PCI) bus.
The terminal 3 typically includes a variety of computer system readable media. Such media can be any available media that can be accessed by the terminal 3 and includes both volatile and nonvolatile media, removable and non-removable media.
Memory 32 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 321 and/or cache memory 322. The terminal 3 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 323 may be used to read from and write to non-removable, non-volatile magnetic media (not shown in FIG. 3, commonly referred to as a "hard disk drive"). Although not shown in fig. 3, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be coupled to bus 33 through one or more data medium interfaces. Memory 32 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention.
A program/utility 324 having a set (at least one) of program modules 3241 can be stored, for example, in memory 32, such program modules 3241 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which can include an implementation of a network environment. Program modules 3241 typically carry out the functions and/or methods of the embodiments described herein.
The terminal 3 may also communicate with one or more external devices 4 (e.g., keyboard, pointing device, display 5, etc.), one or more devices that enable a user to interact with the terminal 3, and/or any devices (e.g., network card, modem, etc.) that enable the terminal 3 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 34. And, the terminal 3 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the internet, via the network adapter 35. As shown in fig. 3, the network adapter 35 communicates with other modules of the terminal 3 via the bus 33. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in connection with the terminal 3, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
As shown in fig. 4, in an embodiment, the flow data analysis system of the present invention includes an acquisition module 41, a verification module 42, a calling module 43, an input module 44, a generation module 45 and a classification module 46.
The acquiring module 41 is configured to acquire a plurality of flow data by using a buried point.
The verification module 42 is configured to verify the integrity of each of the flow data and extract the flow data with the integrity to generate an analysis data set.
In one embodiment, the verification module 42 includes a retrieval unit (not shown) and a comparison unit (not shown).
Specifically, the calling unit is used for calling the buried point setting table; and the comparison unit is used for comparing the integrity of the flow data based on the buried point setting table.
It should be noted that the structure and principle of the retrieving unit correspond to the above step S21, and the structure and principle of the comparing unit correspond to the above step S22, so that detailed descriptions thereof are omitted herein.
The calling module 43 is configured to obtain an analysis target, and select and call an analysis model based on the analysis target; the analysis model at least comprises an event table, an attribute table and an object table.
The input module 44 is configured to input the analysis dataset to the analysis model.
The generating module 45 is configured to associate an event in the event table with a user attribute in the attribute table and a service carrier in the object table, and generate a data analysis table.
In one embodiment, the system further comprises: a cache module (not shown in the figures).
Specifically, the buffer module is used for buffering the data analysis table.
The classification module 46 is configured to retrieve a preset label for classifying each user in the analysis dataset based on the data analysis table, so as to obtain an analysis result.
In one embodiment, the system further comprises: a review module (not shown in the figures).
Specifically, the review module is used for acquiring a historical result corresponding to the analysis result based on the analysis result, and comparing the analysis result with the historical result to obtain an optimized result.
The structures and principles of the obtaining module 41, the verifying module 42, the calling module 43, the input module 44, the generating module 45, and the classifying module 46 are in one-to-one correspondence with the steps (step S1 to step S6) in the flow data analysis method, and therefore will not be described in detail herein.
It should be noted that, it should be understood that the division of the modules of the above system is merely a division of a logic function, and may be fully or partially integrated into a physical entity or may be physically separated. And these modules may all be implemented in software in the form of calls by the processing element; or can be realized in hardware; the method can also be realized in a form of calling software by a processing element, and the method can be realized in a form of hardware by a part of modules. For example, the x module may be a processing element that is set up separately, may be implemented in a chip of the system, or may be stored in a memory of the system in the form of program code, and the function of the x module may be called and executed by a processing element of the system. The implementation of the other modules is similar. In addition, all or part of the modules can be integrated together or can be independently implemented. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in a software form.
For example, the modules above may be one or more integrated circuits configured to implement the methods above, such as: one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), or one or more digital signal processors (Digital Signal Processor, abbreviated as DSP), or one or more field programmable gate arrays (Field Programmable Gate Array, abbreviated as FPGA), etc. For another example, when a module above is implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a central processing unit (Central Processing Unit, CPU) or other processor that may invoke the program code. For another example, the modules may be integrated together and implemented in the form of a System-On-a-Chip (SOC).
It should be noted that, the flow data analysis system of the present invention may implement the flow data analysis method of the present invention, but the implementation device of the flow data analysis method of the present invention includes, but is not limited to, the structure of the flow data analysis system listed in this embodiment, and all structural modifications and substitutions made according to the principles of the present invention in the prior art are included in the protection scope of the present invention.
In summary, compared with the prior art, the flow data analysis method, the system, the medium and the electronic equipment can call the corresponding analysis model for flow data analysis aiming at different analysis purposes by defining the analysis targets and presetting the analysis model, so that the universality of flow data analysis is improved, and the burden is greatly reduced for small and medium enterprises; therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
Claims (8)
1. A method of flow data analysis, comprising the steps of:
acquiring a plurality of flow data by adopting buried points;
verifying the integrity of each flow data, and extracting the flow data with the integrity to generate an analysis data set;
acquiring an analysis target, and selecting and calling an analysis model based on the analysis target; the analysis model at least comprises an event table, an attribute table and an object table;
inputting the analysis dataset into the analysis model;
associating the event in the event table with the user attribute in the attribute table and the service carrier in the object table to generate a data analysis table;
and calling a preset label for classifying each user in the analysis data set based on the data analysis table to obtain an analysis result.
2. The flow data analysis method of claim 1, wherein said verifying the integrity of each of said flow data comprises:
calling a buried point setting table; the embedded point setting table at least comprises the equipment ID, the operation time, the operation place, the operation mode and the operation behavior of the user;
and comparing the integrity of the flow data based on the buried point setting table.
3. The flow data analysis method of claim 1, wherein the method further comprises: and caching the data analysis table.
4. The flow data analysis method according to claim 1, wherein the preset tag includes: event tags and model tags; wherein, the liquid crystal display device comprises a liquid crystal display device,
the event label at least comprises a new user, a short-time user and an old user;
the model tag includes at least a low-consumption user, a medium-consumption user, and a high-consumption user.
5. The flow data analysis method of claim 1, wherein the method further comprises: and acquiring a historical result corresponding to the analysis result based on the analysis result, and comparing the analysis result with the historical result to obtain an optimized result.
6. A flow data analysis system, comprising: the device comprises an acquisition module, a verification module, a calling module, an input module, a generation module and a classification module;
the acquisition module is used for acquiring a plurality of flow data by adopting buried points;
the verification module is used for verifying the integrity of each flow data, extracting the flow data with the integrity, and generating an analysis data set;
the calling module is used for acquiring an analysis target, selecting and calling an analysis model based on the analysis target; the analysis model at least comprises an event table, an attribute table and an object table;
the input module is used for inputting the analysis data set into the analysis model;
the generation module is used for associating the event in the event table with the user attribute in the attribute table and the service carrier in the object table to generate a data analysis table;
the classification module is used for retrieving a preset label for classifying each user in the analysis data set based on the data analysis table to obtain an analysis result.
7. A storage medium having stored thereon a computer program, which when executed by a processor implements the flow data analysis method of any of claims 1 to 5.
8. An electronic device, comprising: a processor and a memory;
the memory is used for storing a computer program;
the processor is configured to execute the computer program stored in the memory, so that the electronic device performs the flow data analysis method according to any one of claims 1 to 5.
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