CN111597450A

CN111597450A - Intelligent analysis system and method for big data

Info

Publication number: CN111597450A
Application number: CN202010434583.4A
Authority: CN
Inventors: 杨登峰
Original assignee: Shenzhen Brilliant Tomorrow Technology Co ltd
Current assignee: Shenzhen Brilliant Tomorrow Technology Co ltd
Priority date: 2020-05-21
Filing date: 2020-05-21
Publication date: 2020-08-28

Abstract

The application provides a big data intelligent analysis method and a big data intelligent analysis system, which comprise the following steps: the terminal extracts big data information within a set time and generates an input vector of a target object according to the big data information; the terminal compares each element value of the input vector with a first threshold value to determine x positions of x element values larger than the first threshold value; the terminal obtains n weight vectors corresponding to the n advertisements, and filters the n weight vectors to obtain w weight vectors, wherein element values of positions corresponding to the x positions in the w weight vectors are all larger than a second threshold; and the terminal performs vector multiplication operation on the input vector and the w weight vectors to obtain w operation results, and performs descending order arrangement on the w operation results to obtain recommendation sequences corresponding to the w advertisements. The technical scheme provided by the application has the advantage of low power consumption.

Description

Intelligent analysis system and method for big data

Technical Field

The application relates to the field of big data, in particular to an intelligent analysis system and method for big data.

Background

Such a definition is given for the "Big data" (Big data) research institute Gartner. The big data is information assets which need a new processing mode and have stronger decision-making power, insight discovery power and flow optimization capability to adapt to mass, high growth rate and diversification. A data set with large scale which greatly exceeds the capability range of the traditional database software tools in the aspects of acquisition, storage, management and analysis has the four characteristics of large data scale, rapid data circulation, various data types and low value density.

The strategic significance of big data technology is not to grasp huge data information, but to specialize the data containing significance. In other words, if big data is compared to an industry, the key to realizing profitability in the industry is to improve the "processing ability" of the data and realize the "value-added" of the data through the "processing".

The existing big data has a large calculation amount for matching data, so that the matching complexity is increased, and the power consumption is increased.

Disclosure of Invention

The invention aims to provide an intelligent analysis system and method for big data.

In a first aspect, a big data intelligent analysis method is provided, the method includes the following steps:

the terminal extracts big data information within a set time and generates an input vector of a target object according to the big data information;

the terminal compares each element value of the input vector with a first threshold value to determine x positions of x element values larger than the first threshold value;

the terminal obtains n weight vectors corresponding to the n advertisements, and filters the n weight vectors to obtain w weight vectors, wherein element values of positions corresponding to the x positions in the w weight vectors are all larger than a second threshold;

the terminal performs vector multiplication operation on the input vector and the w weight vectors to obtain w operation results, and performs descending order arrangement on the w operation results to obtain recommendation sequences corresponding to the w advertisements;

n, w and x are integers, and n is more than w.

In a second aspect, a big data intelligent analysis system is provided, the system comprising:

the acquisition unit is used for extracting big data information in set time and generating an input vector of a target object according to the big data information;

a processing unit for comparing each element value of the input vector with a first threshold value to determine x positions of x element values greater than the first threshold value;

the acquiring unit is further configured to acquire n weight vectors corresponding to the n advertisements, and filter the n weight vectors to obtain w weight vectors, where element values of positions corresponding to the x positions in the w weight vectors are all greater than a second threshold;

the processing unit is also used for performing vector multiplication operation on the input vector and the w weight vectors to obtain w operation results, and performing descending order arrangement on the w operation results to obtain recommendation orders corresponding to the w advertisements;

n, w and x are integers, and n is more than w.

In a third aspect, a computer-readable storage medium storing a computer program for electronic data exchange is provided, wherein the computer program causes a computer to perform the method provided in the first aspect.

The technical scheme provided by the application generates an input vector of a target object according to big data information, then x positions of larger element values are inquired from the input vector, weight vectors of n advertisements are selected according to the x positions to obtain w weight vectors, then product operation is executed between the w weight vectors and the input vector to obtain w operation results, and then descending order arrangement is executed according to the w operation results to obtain the recommendation sequence of the advertisements.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic structural diagram of a terminal according to the present invention;

FIG. 2 is a schematic flow chart of a big data intelligent analysis method provided by the present invention;

fig. 3 is a schematic structural diagram of a big data intelligent analysis system provided by the present invention.

Detailed Description

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 some, but not all, embodiments of the present application. 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.

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.

The embodiments of the present application will be described below with reference to the drawings.

The term "and/or" in this application is only one kind of association relationship describing the associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" in this document indicates that the former and latter related objects are in an "or" relationship.

The "plurality" appearing in the embodiments of the present application means two or more. The descriptions of the first, second, etc. appearing in the embodiments of the present application are only for illustrating and differentiating the objects, and do not represent the order or the particular limitation of the number of the devices in the embodiments of the present application, and do not constitute any limitation to the embodiments of the present application. The term "connect" in the embodiments of the present application refers to various connection manners, such as direct connection or indirect connection, to implement communication between devices, which is not limited in this embodiment of the present application.

A terminal in the embodiments of the present application may refer to various forms of UE, access terminal, subscriber unit, subscriber station, mobile station, MS (mobile station), remote station, remote terminal, mobile device, user terminal, terminal device (terminal equipment), wireless communication device, user agent, or user equipment. The terminal device may also be a cellular phone, a cordless phone, an SIP (session initiation protocol) phone, a WLL (wireless local loop) station, a PDA (personal digital assistant), a handheld device with wireless communication function, a computing device or other processing device connected to a wireless modem, a vehicle-mounted device, a wearable device, a terminal device in a future 5G network or a terminal device in a future evolved PLMN (public land mobile network, chinese), and the like, which are not limited in this embodiment.

Referring to fig. 1, fig. 1 is a schematic structural diagram of a terminal disclosed in an embodiment of the present application, the terminal 100 includes a storage and processing circuit 110, and a sensor 170 connected to the storage and processing circuit 110, where the sensor 170 may include a camera, a distance sensor, a gravity sensor, and the like, the electronic device may include two transparent display screens, the transparent display screens are disposed on a back side and a front side of the electronic device, and part or all of components between the two transparent display screens may also be transparent, so that the electronic device may be a transparent electronic device in terms of visual effect, and if part of the components are transparent, the electronic device may be a hollow electronic device. Wherein:

the terminal 100 may include control circuitry, which may include storage and processing circuitry 110. The storage and processing circuitry 110 may be a memory, such as a hard drive memory, a non-volatile memory (e.g., flash memory or other electronically programmable read-only memory used to form a solid state drive, etc.), a volatile memory (e.g., static or dynamic random access memory, etc.), etc., and the embodiments of the present application are not limited thereto. Processing circuitry in the storage and processing circuitry 110 may be used to control the operation of the terminal 100. The processing circuitry may be implemented based on one or more microprocessors, microcontrollers, digital signal processors, baseband processors, power management units, audio codec chips, application specific integrated circuits, display driver integrated circuits, and the like.

The storage and processing circuitry 110 may be used to run software in the terminal 100, such as an Internet browsing application, a Voice Over Internet Protocol (VOIP) telephone call application, an email application, a media playing application, operating system functions, and so forth. Such software may be used to perform control operations such as camera-based image capture, ambient light measurement based on an ambient light sensor, proximity sensor measurement based on a proximity sensor, information display functionality based on status indicators such as status indicator lights of light emitting diodes, touch event detection based on a touch sensor, functionality associated with displaying information on multiple (e.g., layered) display screens, operations associated with performing wireless communication functionality, operations associated with collecting and generating audio signals, control operations associated with collecting and processing button press event data, and other functions in the terminal 100, to name a few, embodiments of the present application are not limited.

The terminal 100 may include an input-output circuit 150. The input-output circuit 150 may be used to enable the terminal 100 to input and output data, i.e., to allow the terminal 100 to receive data from external devices and also to allow the terminal 100 to output data from the terminal 100 to external devices. The input-output circuit 150 may further include a sensor 170. Sensor 170 vein identification module, can also include ambient light sensor, proximity sensor based on light and electric capacity, fingerprint identification module, touch sensor (for example, based on light touch sensor and/or capacitanc touch sensor, wherein, touch sensor can be touch-control display screen's partly, also can regard as a touch sensor structure independent utility), acceleration sensor, the camera, and other sensors etc. the camera can be leading camera or rear camera, the fingerprint identification module can integrate in the display screen below, be used for gathering the fingerprint image, the fingerprint identification module can be: optical fingerprint module, etc., and is not limited herein. The front camera can be arranged below the front display screen, and the rear camera can be arranged below the rear display screen. Of course, the front camera or the rear camera may not be integrated with the display screen, and certainly in practical applications, the front camera or the rear camera may also be a lifting structure.

Input-output circuit 150 may also include one or more display screens, and when multiple display screens are provided, such as 2 display screens, one display screen may be provided on the front of the electronic device and another display screen may be provided on the back of the electronic device, such as display screen 130. The display 130 may include one or a combination of liquid crystal display, transparent display, organic light emitting diode display, electronic ink display, plasma display, and display using other display technologies. The display screen 130 may include an array of touch sensors (i.e., the display screen 130 may be a touch display screen). The touch sensor may be a capacitive touch sensor formed by a transparent touch sensor electrode (e.g., an Indium Tin Oxide (ITO) electrode) array, or may be a touch sensor formed using other touch technologies, such as acoustic wave touch, pressure sensitive touch, resistive touch, optical touch, and the like, and the embodiments of the present application are not limited thereto.

The terminal 100 can also include an audio component 140. Audio component 140 may be used to provide audio input and output functionality for terminal 100. The audio components 140 in the terminal 100 may include a speaker, a microphone, a buzzer, a tone generator, and other components for generating and detecting sound.

The communication circuit 120 can be used to provide the terminal 100 with the capability to communicate with external devices. The communication circuit 120 may include analog and digital input-output interface circuits, and wireless communication circuits based on radio frequency signals and/or optical signals. The wireless communication circuitry in communication circuitry 120 may include radio-frequency transceiver circuitry, power amplifier circuitry, low noise amplifiers, switches, filters, and antennas. For example, the wireless Communication circuitry in Communication circuitry 120 may include circuitry to support Near Field Communication (NFC) by transmitting and receiving Near Field coupled electromagnetic signals. For example, the communication circuit 120 may include a near field communication antenna and a near field communication transceiver. The communications circuitry 120 may also include a cellular telephone transceiver and antenna, a wireless local area network transceiver circuitry and antenna, and so forth.

The terminal 100 may further include a battery, a power management circuit, and other input-output units 160. The input-output unit 160 may include buttons, joysticks, click wheels, scroll wheels, touch pads, keypads, keyboards, cameras, light emitting diodes and other status indicators, and the like.

A user may input commands through input-output circuitry 150 to control operation of terminal 100 and may use output data of input-output circuitry 150 to enable receipt of status information and other outputs from terminal 100.

Big data includes structured, semi-structured, and unstructured data, with unstructured data becoming an increasingly dominant part of the data. Survey reports by IDC show: 80% of the data in a business is unstructured and the data grows exponentially by 60% each year. The big data is a representation or a characteristic of the internet which is developed to the present stage, and does not need to be worried or worried about the big data, under the setback of a technical innovation large screen represented by cloud computing, the data which is originally hard to collect and use is easy to utilize, and the big data can gradually create more value for human beings through continuous innovation of various industries.

Secondly, to acquire the cognitive big data of the system, it must be decomposed comprehensively and finely, and the development is started from three layers: the first level is theory, which is the necessary path for cognition and is the baseline for widespread acceptance and dissemination. The overall description and qualification of the industry on the big data are understood from the characteristic definition of the big data; deeply analyzing the rarity of the big data from the discussion of the value of the big data; the development trend of big data is known; the long game between the person and the data is reviewed from this particular and important perspective of big data privacy. The second level is technology, which is a means and advancing foundation for large data value embodiment. The whole process of big data acquisition, processing, storage and result formation is described herein from the development of cloud computing, distributed processing technology, storage technology and perception technology, respectively. The third level is practice, which is the ultimate value embodiment of big data. The beautiful scene that the big data already shows and the blueprint to be realized are depicted from the big data of the internet, the big data of the government, the big data of the enterprise and the big data of the individual respectively.

The big data intelligent analysis method and system mainly aim at a matching method based on big data advertisements, and for advertisement matching, such as jitter advertisement promotion, AI calculation is performed based on big data, so that the corresponding matching degree is calculated, and recommendation is performed.

Referring to fig. 2, fig. 2 provides a big data intelligent analysis method, which is implemented by using the terminal shown in fig. 1, and the method shown in fig. 2 includes the following steps:

step S201, the terminal extracts big data information within a set time and generates an input vector of a target object according to the big data information;

step S202, the terminal compares each element value of the input vector with a first threshold value to determine x positions of x element values larger than the first threshold value;

step S203, the terminal obtains n weight vectors corresponding to the n advertisements, and filters the n weight vectors to obtain w weight vectors, wherein element values of positions corresponding to the x positions in the w weight vectors are all larger than a second threshold;

and step S204, the terminal performs vector multiplication operation on the input vector and the w weight vectors to obtain w operation results, and performs descending order arrangement on the w operation results to obtain recommendation orders corresponding to the w advertisements.

In an optional scheme, the implementation method of step S201 may specifically include:

extracting alpha elements of an input vector, acquiring alpha categories corresponding to the alpha elements, acquiring alpha residence average time corresponding to the alpha categories from big data information, and determining the alpha residence average time as a corresponding value of the alpha elements of the input vector.

The average residence time may be specifically an average of residence (e.g., viewing) times of the same webpage or advertisement in the big data information as the category, for example, if one category of the α categories is motion, in the big data, the viewing calculation advertisement is 10s, the viewing motion is introduced as 30s, and then the average residence time is 20s, that is, the element value of the position corresponding to the one category is 20.

For the operation, the key to determine the operation result is the product of the corresponding positions, for example, the input vector is [ a1, a2 … ai ], and the weight vector is [ b1, b2 … bi ]; the operation result is the result of the a1 corresponding to the b1 corresponding to the position, because if the position does not correspond to, for example, b1=0, the calculation is meaningless even if the value of the input vector is large with the element of the b1 corresponding to the position a1, and similarly, if a1=0, the value of b1 of the weight vector corresponding to the advertisement is meaningless even if the input vector is fixed, once the large data information is fixed, the input vector cannot be changed, and the input vector cannot be filtered.

In an optional scheme, the position corresponding to the x positions may specifically include:

the same position as the x position corner mark in the weight vector. For example, the x positions are a1, a4, a6 and a8, and the positions corresponding to the x positions are b1, b4, b6 and b8 in the weight vector.

Referring to fig. 3, fig. 3 provides a big data intelligent analysis system, which includes:

an obtaining unit 301, configured to extract big data information within a set time, and generate an input vector of a target object according to the big data information;

a processing unit 302, configured to compare each element value of the input vector with a first threshold to determine x positions of x element values greater than the first threshold;

the obtaining unit 301 is further configured to obtain n weight vectors corresponding to the n advertisements, and filter the n weight vectors to obtain w weight vectors, where element values of positions corresponding to x positions in the w weight vectors are all greater than a second threshold;

the processing unit 302 is further configured to perform vector product operation on the input vector and the w weight vectors to obtain w operation results, and perform descending order arrangement on the w operation results to obtain recommendation orders corresponding to the w advertisements;

n, w and x are integers, and n is more than w.

The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims

1. An intelligent big data analysis method is characterized by comprising the following steps:

n, w and x are integers, and n is more than w.

2. The method according to claim 1, wherein the terminal extracts big data information within a set time, and generating the input vector of the target object according to the big data information specifically comprises:

extracting alpha elements of an input vector, acquiring alpha categories corresponding to the alpha elements, acquiring alpha residence average time corresponding to the alpha categories from big data information, and determining the alpha residence average time as a corresponding value of the alpha elements of the input vector;

alpha is an integer and alpha > x.

3. The method of claim 2,

the residence time average is the residence time average of the webpages or advertisements in the big data information, wherein the webpages or advertisements are the same as the big data information in category.

4. The method of claim 1,

the position corresponding to the x positions is the position in the weight vector which is the same as the x position corner mark.

5. An intelligent big data analysis system, comprising:

n, w and x are integers, and n is more than w.

6. A computer-readable storage medium, characterized in that a computer program for electronic data exchange is stored, wherein the computer program causes a computer to perform the method according to any one of claims 1-4.