CN115953168A - Big data-based information technology consultation management system and method - Google Patents
Big data-based information technology consultation management system and method Download PDFInfo
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Abstract
The invention discloses an information technology consultation management system and method based on big data, and belongs to the field of information technology consultation management. According to the invention, the images are sequentially preprocessed by acquiring the intelligent household images, the three-dimensional model is constructed according to the processed images, the images are displayed to relevant technicians, and voice interaction is carried out, so that the efficiency of information technology consultation management of the user is improved, the time cost is reduced, and the user experience is improved.
Description
Technical Field
The invention relates to the field of information technology consultation management, in particular to an information technology consultation management system and method based on big data.
Background
With the development of science and technology, smart home gradually becomes the inevitable trend in the home field, and the smart home is embodied in an internet of things under the influence of the internet of things. The intelligent home is connected with various devices in the home through the Internet of things technology, multiple functions and means such as home appliance control, illumination control and telephone remote control are provided, compared with the common home, the intelligent home not only has the traditional living function, but also has a building function, network communication, information home appliances and equipment automation, the system, the structure, service and management are integrated, comfort and safety are realized, convenience and environmental protection living environment are realized, the omnibearing information interaction function is provided, the home is helped to keep smooth information communication with the outside, the life style of people is optimized, the time is effectively arranged for people, the safety of home life is enhanced, and even funds are saved for various energy expenses.
When the smart home is in failure, a user often searches for customer service to perform information technology consultation, and the problem is fed back by taking photos, however, some special situations often occur in the process of uploading images, for example, when uploading is performed, the photos are randomly sent or the photos are different in size without paying attention to the arrangement sequence of the photos, network problems cause that later-sent images are successfully transmitted first, and when the customer service asks related technical personnel, the sending of the images may also cause disorder of the image sequence, so that the process of information technology consultation is too complicated, and the related technical personnel cannot directly know specific failure problems only by means of description, sometimes only one small problem, and possibly go to home for overhaul by the related technical personnel, thereby wasting time cost of a large number of users and the related technical personnel, and the efficiency of information technology consultation is low.
Therefore, how to sequentially display the image information uploaded by the user and directly display the image information to related technicians through the three-dimensional model is necessary to realize direct interaction and improve the efficiency of information technology consultation management. Therefore, a big data-based information technology consultation management system and method are needed.
Disclosure of Invention
The invention aims to provide an information technology consultation management system and method based on big data.
In order to solve the technical problems, the invention provides the following technical scheme: an information technology consultation management system based on big data, comprising: the system comprises a data acquisition module, a database, a model construction module and a consultation feedback module;
the data acquisition module is connected with a database, the database is connected with a model construction module, and the model construction module is connected with the consultation feedback module; the system comprises a data acquisition module, a database, a model construction module and a consultation feedback module, wherein the data acquisition module is used for acquiring basic data information and image information, the database is used for carrying out sequencing pretreatment on acquired images and carrying out encryption storage on acquired data and analysis results, the model construction module is used for establishing a three-dimensional model according to the processed images, and the consultation feedback module is used for displaying the established three-dimensional model to related technicians through display equipment and carrying out remote assistance and management according to the three-dimensional model.
Furthermore, the data acquisition module comprises a basic data entry unit and an image acquisition unit, the basic data entry unit is used for acquiring basic data information of a user, such as a purchase account number of the user, the type of the smart home and the like, so that relevant technicians can know the basic information of the smart home in advance, a foundation is laid for subsequent problem solving, and the image acquisition unit is used for acquiring image information of the smart home through camera equipment, such as a mobile phone or a household camera.
Furthermore, the database comprises a data encryption unit, a data storage unit and a data preprocessing unit, the data encryption unit performs data encryption transmission on acquired data and analysis results through an SM2 elliptic curve algorithm, the safety of user data is guaranteed, and privacy information of a user is prevented from being leaked in the process of transmitting pictures, the SM2 elliptic curve algorithm is a national secret standard elliptic curve encryption algorithm, belongs to an asymmetric encryption mode, and is realized in a prime domain mode and a binary domain expansion mode, for the discrete logarithm problem of a general elliptic curve, only an exponential calculation complexity solving method exists at present, compared with a large number decomposition problem and the discrete logarithm problem on a finite domain, the solving difficulty of the elliptic curve discrete logarithm problem is much larger, and therefore, under the requirement of the same safety degree, the elliptic curve password is much smaller than the public key required by other public keys; the data storage unit stores data through Hadoop, the Hadoop is a distributed system infrastructure, a user can develop a distributed program under the condition that distributed bottom-layer details are not known, the power of a cluster is fully utilized to carry out high-speed operation and storage, a main node and a sub-node mode is adopted, the data are distributed on a plurality of sub-nodes, the main node coordinates operation, the data are inquired and operated to quickly inquire various types of data, whether structured data or unstructured data have high reliability, high expansibility, high fault tolerance and high efficiency, the data preprocessing unit is used for carrying out sequencing preprocessing on collected image information and automatically adjusting the image sequence, the user often has some special conditions in the process of uploading images, for example, pictures are randomly sent to technical personnel without paying attention to the arrangement sequence of the pictures, the pictures are disordered due to network problems or the naming modes of the two images are inconsistent, the pictures are disordered and the like, and the pictures are arranged in advance according to the preprocessing, so that the technical personnel can better know the specific conditions of faults.
Furthermore, the model building module comprises a data analysis unit and a building unit, the data processing unit is used for analyzing and processing data according to the collected image information, the intelligent home in the image is placed in a coordinate system, and key point coordinates on the intelligent furniture are determined, and the building unit is used for building an intelligent home three-dimensional model according to the determined key point coordinates, so that related technicians can remotely assist users and manage the intelligent home according to the three-dimensional model.
Further, the consultation feedback module comprises a screen display unit and a voice response unit, the screen display unit displays the constructed three-dimensional model to related technical personnel through a screen, such as a three-dimensional display screen, so that the technical personnel can visually know the problems of the intelligent home according to the three-dimensional model, the voice response unit is used for directly interacting the related technical personnel with the user through voice after the user authorizes the intelligent home, remotely assisting the user to manage the intelligent home, avoiding the problem that when the intelligent home is small, such as the problem does not occur to the intelligent home or the problem is easy to solve because the user only has operation errors, the related technical personnel go to the home to overhaul, improving the work efficiency of the related technical personnel, saving the time cost of the technical personnel, improving the overhaul efficiency of the intelligent home, improving the use experience of the intelligent home by the user, and being capable of directly communicating with the user when the related technical personnel judge that the major problem of the intelligent home needs to be repaired, the step of customer service consultation is omitted, the time cost of the user is saved, and the efficiency of information technology is improved.
An information technology consultation management method based on big data comprises the following steps:
s1, acquiring basic data information of a user and an intelligent home, acquiring image information of the intelligent home through a camera device, and carrying out encryption transmission and storage on the basic data information and the image information;
s2, intelligently sequencing and preprocessing the image information according to the acquired image information;
s3, constructing an intelligent home three-dimensional model according to the preprocessed image information;
and S4, displaying the constructed three-dimensional model to related technicians through display equipment, and interacting with a user through voice.
Further, in step S2, according to the acquired image information, analyzing an arrangement order of the images, for example, a user uses a mobile phone or a home camera to acquire image information of the smart home;
placing the image in a two-dimensional coordinate system, collecting pixel coordinates of the image, and settingTwo acquired images X 1 (X, y) and X 2 (x, y), where x and y represent time domain variations of pixel coordinates of the images, and there is a translation relationship between the images, where a pattern moves from one position to another position in a certain direction without changing shape or size, in a plane, then:
f 1 (x，y)＝f 2 (x-x 0 ，y-y 0 )；
wherein x is 0 For relative horizontal translation, y 0 Relative vertical translation;
performing Fourier transform to a frequency domain:
wherein u and v are expressed as frequency domain variables of pixel coordinates of the image, and j is expressed as an imaginary number;
the normalization process is performed by the following formula:
wherein, F 2 ' (u, v) is F 2 And (u, v) normalizing to obtain an exponential function, and obtaining an impulse function delta (·) by the following formula:
according to the obtained impulse function, finding out the corresponding peak position in the space domain, expressing as the correlation of the two images, and simultaneously determining the relative horizontal translation amount x 0 And relative vertical translation y 0 ；
Besides the translation relationship, the images also have the processes of rotation and scale scaling, for example, the shooting angles of the same object are not consistent, the details of the whole equipment for shooting a reduced picture or the shooting of a magnified picture are specific, and if the rotation angle between the two images is theta and the scaling factor is alpha, then:
f 1 (x，y)＝f 2 (axcosθ+aysinθ-x 0 ，-axsinθ+αycosθ-y 0 )；
performing Fourier transform to obtain:
let F 1 Has a mode of E 1 ，F 2 Has a mode of E 2 ρ represents the polar diameter of the coordinate point, τ represents the polar angle to be marked, u = ρ cos τ, v = ρ sin τ, then:
substituting the obtained rotation angle theta and the scale factor alpha into the obtained impulse function to determine the relative horizontal translation amount x 0 And relative vertical translation y 0 。
Constructing a two-dimensional maximum correlation degree array for all the collected images, wherein the maximum peak value corresponding to the image impulse function is the maximum correlation degree, and if the maximum correlation degree is obviously greater than other correlation degrees, if the relative horizontal translation amount x is larger than other correlation degrees 0 <0, which is represented as a head image, i.e. the first image, and vice versa as a tail image, i.e. the last image; the whole image chain can be determined in turn according to the translation amount of the maximum correlation degree, if the translation amount x is horizontal 0 <0, indicates that the image is in front of the image and vice versa.
Further, in step S3, the model of the device is analyzed according to the sorted images and the images recorded by the system;
forming a vector set P = { P) by pixel point coordinates of the collected images 1 ，P 2 ，…，P n And (5) recording pixel points of the image by a system to form a vector set Q = { Q = 1 ，Q 2 ,，…，Q n N is the number, and the Euclidean distance between the acquired image and the input image is calculated by the following formula:
when D = D min In time, the intelligent home model representing the acquisition is consistent with the basic data model entered in the database, D min Represents a minimum euclidean distance;
constructing a three-dimensional model by combining basic data information with the images after sequencing; the method comprises the steps of establishing a camera equipment coordinate system by taking camera equipment as an origin, describing the position of an object from the angle of the camera equipment, and establishing a three-dimensional coordinate system by taking a certain point of a three-dimensional world as the origin, for example, taking a corner of the three-dimensional world as the origin; acquiring a pixel characteristic point on the image through an OpenCV technology according to the color on the image, and representing two-dimensional coordinates of the characteristic point as A = { (x) 1 ，y 1 )，(x 2 ，y 2 )，…，(x m ，y m ) Represents a corresponding three-dimensional coordinate as a' = { (X) 1 ，Y 1 ，Z 1 )，(X 2 ，Y 2 ，Z 2 )，…，(X m ，Y m ，Z m ) And m is the number of key points, and a two-dimensional image coordinate system and a three-dimensional model coordinate system are analyzed through the following formulas:
wherein, f x Expressed as the focal length of the camera device in the x-axis, f y Expressed as the focal length of the image pickup apparatus on the y-axis, R as a rotation matrix which is a matrix of the effect of changing the direction of a vector without changing the size when multiplied by one vector, T as an offset vector which is a graphDirection and magnitude of image shift (a) 0 ，b 0 ) The intersection point of the camera optical axis and the imaging plane is represented by Zc, corresponding Z-axis coordinates of the feature points in a three-dimensional world are represented by dx and dy, and two-dimensional coordinates (x, y) of the feature points of the pixels are differentiated; and mapping the points of the pixel coordinate system into a three-dimensional world through a formula, thereby constructing a three-dimensional model of the acquired image.
Further, in step S4, the constructed three-dimensional model and the image uploaded by the user are displayed to the relevant technical staff through the screen, so that the relevant technical staff can conveniently and quickly know the user requirements in a remote manner to assist the user, and the information technology consultation is efficiently managed.
Compared with the prior art, the invention has the following beneficial effects:
according to the intelligent home information processing method and device, the image of the intelligent home is acquired through the camera equipment, the acquired image is preprocessed, image sequence arrangement is achieved, the problem situation of the intelligent home is conveniently and specifically known by related technical personnel, the situation that judgment errors of the related technical personnel are caused by disordered image sequences is avoided, the three-dimensional model is built according to the processed image, the whole damaged equipment is restored, the situation is conveniently and specifically known by the technical personnel in a remote mode, a user is assisted and managed in a remote mode according to the three-dimensional model, the image is displayed to the related technical personnel through the display equipment, voice interaction is carried out, the efficiency of information technology consultation of the user is improved, the management of the information technology consultation is facilitated, the time cost is reduced, and the user experience is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of a big data-based information technology consultation management system according to the present invention;
FIG. 2 is a schematic structural diagram of an information technology consulting management method based on big data according to the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1-2, the present invention provides a technical solution: an information technology consultation management system based on big data, the information technology consultation management system including: the system comprises a data acquisition module, a database, a model construction module and a consultation feedback module;
the data acquisition module is connected with a database, the database is connected with a model construction module, and the model construction module is connected with the consultation feedback module;
the data acquisition module is used for gathering basic data information and image information, and the data acquisition module includes basic data entry unit and image acquisition unit, basic data entry unit is used for gathering user's basic data information, for example user's purchase account number and the type of intelligent house etc. be convenient for relevant technical staff to know the basic information of intelligent house in advance, lay the basis for subsequent problem solution, the image acquisition unit is used for gathering intelligent house image information through camera equipment, for example uses cell-phone or domestic camera etc..
The database is used for carrying out sequencing preprocessing on the acquired images and carrying out encryption storage on the acquired data and the analysis results, the database comprises a data encryption unit, a data storage unit and a data preprocessing unit, the data encryption unit carries out data encryption transmission on the acquired data and the analysis results through an SM2 elliptic curve algorithm, the safety of user data is guaranteed, and privacy information of a user in the process of transmitting the images is prevented from being leaked, the SM2 elliptic curve algorithm is a national secret standard elliptic curve encryption algorithm and belongs to an asymmetric encryption mode, an implementation mode has two modes of a prime field and a binary domain expansion, for the discrete logarithm problem of a general elliptic curve, only an exponential calculation complexity solving method exists at present, compared with a large number decomposition problem and the discrete logarithm problem on a finite field, the solving difficulty of the elliptic curve discrete logarithm problem is much higher, and therefore under the requirement of the same safety degree, the elliptic curve password is much smaller than the secret key required by other public key passwords; the data storage unit stores data through Hadoop, the Hadoop is a distributed system infrastructure, a user can develop a distributed program under the condition that distributed bottom-layer details are not known, the power of a cluster is fully utilized to carry out high-speed operation and storage, a main node and a sub-node mode is adopted, the data are distributed on a plurality of sub-nodes, the main node coordinates operation, the data are inquired and operated to quickly inquire various types of data, whether structured data or unstructured data have high reliability, high expansibility, high fault tolerance and high efficiency, the data preprocessing unit is used for carrying out sequencing preprocessing on collected image information and automatically adjusting the image sequence, the user often has some special conditions in the process of uploading images, for example, pictures are randomly sent to technical personnel without paying attention to the arrangement sequence of the pictures, the pictures are disordered due to network problems or the naming modes of the two images are inconsistent, the pictures are disordered and the like, and the pictures are arranged in advance according to the preprocessing, so that the technical personnel can better know the specific conditions of faults.
The intelligent home furnishing three-dimensional model building method comprises the steps that a model building module is used for building a three-dimensional model according to processed images, the model building module comprises a data analysis unit and a building unit, the data processing unit is used for analyzing and processing data according to collected image information, an intelligent home furnishing in the images is placed in a coordinate system, and key point coordinates on the intelligent home furnishing are determined, the building unit is used for building the intelligent home furnishing three-dimensional model according to the determined key point coordinates, and therefore relevant technical staff can conveniently assist users and manage the intelligent home furnishing in a remote mode according to the three-dimensional model.
The consultation feedback module is used for displaying the constructed three-dimensional model to relevant technicians through the display equipment and performing remote assistance and management according to the three-dimensional model. The consultation feedback module comprises a screen display unit and a voice response unit, the screen display unit is used for displaying the constructed three-dimensional model to related technical personnel through a screen, for example, a three-dimensional display screen and the like, the technical personnel can visually know the problems of the intelligent home according to the three-dimensional model, the voice response unit is used for directly interacting the related technical personnel with a user through voice after the user authorizes the intelligent home, and remotely assisting the user to manage the intelligent home, so that when the problem of the intelligent home is solved, for example, the problem of the intelligent home is not caused by misoperation of the user or is easily solved, and the like, the related technical personnel go to the home to overhaul, the working efficiency of the related technical personnel is improved, the time cost of the technical personnel is saved, the overhaul efficiency of the intelligent home is improved, the use experience of the user on the intelligent home is improved, when the related technical personnel judge that the intelligent home has major problems and need to go to repair, the related technical personnel can directly communicate with the user, the step of customer service consultation is omitted, the time cost of the user is saved, and the efficiency of information technology is improved.
An information technology consultation management method based on big data comprises the following steps:
s1, acquiring basic data information of a user and an intelligent home, acquiring image information of the intelligent home through a camera device, and carrying out encryption transmission and storage on the basic data information and the image information;
s2, intelligently sequencing and preprocessing the image information according to the acquired image information;
in step S2, analyzing an arrangement order of the images according to the acquired image information, for example, a user uses a mobile phone or a home camera to acquire the image information of the smart home;
arranging the images in a two-dimensional coordinate system, collecting pixel coordinates of the images, and setting two collected imagesImage X 1 (X, y) and X 2 (x, y), where x and y represent time domain variables of pixel coordinates of images, and there is a translation relationship between the images, where the translation relationship means that a figure moves from one position to another position in a certain direction without changing the shape and size of the figure in a plane, then:
f 1 (x，y)＝f 2 (x-x 0 ，y-y 0 )；
wherein x is 0 For relative horizontal translation, y 0 Relative vertical translation;
performing Fourier transform to a frequency domain:
where u and v are expressed as frequency domain variables of pixel coordinates of the image and j is expressed as an imaginary number.
The normalization process is performed by the following formula:
wherein, F 2 ' (u, v) is F 2 And (u, v) normalizing to obtain an exponential function, and obtaining an impulse function delta (·) by the following formula:
according to the obtained impulse function, finding out the corresponding peak position in the space domain, expressing as the correlation of the two images, and simultaneously determining the relative horizontal translation amount x 0 And relative vertical translation y 0 ；
Besides the translation relationship, the images also have the processes of rotation and scale scaling, for example, the shooting angles of the same object are not consistent, the details of the whole equipment for shooting a reduced picture or the shooting of a magnified picture are specific, and if the rotation angle between the two images is theta and the scaling factor is alpha, then:
f 1 (x，y)＝f 2 (αxcosθ+αysinθ-x 0 ，-αxsinθ+αycosθ-y 0 )；
performing Fourier transform to obtain:
let F 1 Is a mode of E 1 ，F 2 Has a mode of E 2 ρ represents the polar diameter of the coordinate point, τ represents the polar angle to be marked, u = ρ cos τ, v = ρ sin τ, then:
substituting the obtained rotation angle theta and the scale factor alpha into the obtained impulse function to determine the relative horizontal translation amount x 0 And relative vertical translation y 0 。
Constructing a two-dimensional maximum correlation array for all the acquired images, wherein the maximum peak value corresponding to the image impulse function is the maximum correlation, and if the maximum correlation is obviously greater than other correlations, if the relative horizontal translation x is greater than the other correlations 0 <0, which is represented as a head image, i.e. the first image, and vice versa as a tail image, i.e. the last image; the whole image chain can be determined in turn according to the translation amount of the maximum correlation degree, if the translation amount x is horizontal 0 <0, indicating that the image is in front of the image and vice versa.
S3, constructing an intelligent home three-dimensional model according to the preprocessed image information;
in step S3, analyzing the model of the equipment according to the images after the sorting processing and the images input by the system;
the coordinates of the pixel points of the collected images form a vector set P = { P = { (P) } 1 ，P 2 ，…，P n And (5) recording pixel points of an image by a system to form a vector set Q = { Q = } 1 ，Q 2 ,，…，Q n N is the number, and the Euclidean distance between the acquired image and the input image is calculated by the following formula:
when D = D min In time, the intelligent home model representing the acquisition is consistent with the basic data model entered in the database, D min Represents a minimum euclidean distance;
constructing a three-dimensional model by combining basic data information with the images subjected to sequencing processing; the method comprises the steps of establishing a camera equipment coordinate system by taking camera equipment as an origin, describing the position of an object from the angle of the camera equipment, and establishing a three-dimensional coordinate system by taking a certain point of a three-dimensional world as the origin, for example, taking a corner of the three-dimensional world as the origin; acquiring a pixel characteristic point on the image through an OpenCV technology according to the color on the image, and representing two-dimensional coordinates of the characteristic point as A = { (x) 1 ，y 1 )，(x 2 ，y 2 )，…，(x m ，y m ) Represents a corresponding three-dimensional coordinate as a' = { (X) 1 ，Y 1 ，Z 1 )，(X 2 ，Y 2 ，Z 2 )，…，(X m ，Y m ，Z m ) And (5) analyzing a two-dimensional image coordinate system and a three-dimensional model coordinate system by using the following formulas, wherein m is the number of key points:
wherein, f x Expressed as the focal length of the camera device in the x-axis, f y Expressed as the focal length of the image pickup apparatus on the y-axis, and R as a rotation matrix which changes the direction of a vector when multiplied by the vector but does not change the direction of the vectorA matrix of the effect of changing size, T being denoted as an offset vector, which refers to the direction and magnitude of the image offset, (a) 0 ，b 0 ) The intersection point of the camera optical axis and the imaging plane is represented by Zc, corresponding Z-axis coordinates of the feature points in a three-dimensional world are represented by dx and dy, and two-dimensional coordinates (x, y) of the feature points of the pixels are differentiated; and mapping the points of the pixel coordinate system into a three-dimensional world through a formula, thereby constructing a three-dimensional model of the acquired image.
And S4, displaying the constructed three-dimensional model to related technicians through display equipment, and interacting with a user through voice.
In the step S4, the constructed three-dimensional model and the images uploaded by the user are displayed to relevant technical personnel through the screen, so that the relevant technical personnel can conveniently and quickly know the user requirements in a long-distance mode, assist the user and efficiently manage the information technology consultation, after the user authorizes the information technology consultation, direct interaction between the information technology consultation system and the user is realized through voice, the technical personnel do not need to be inquired through customer service, the consultation efficiency caused by unclear expression is avoided, the use experience of the user is improved, the time cost of the user and the relevant technical personnel is saved, and the efficiency of the information technology consultation is improved.
The first embodiment is as follows:
if the user uploads 6 images, the impulse function δ (-) is obtained by the following formula:
the corresponding impulse function peak is obtained as: delta 1 ＝15，δ 2 ＝8，δ 3 ＝31，δ 4 ＝12，δ 5 ＝36，δ 6 =19, then δ 5 >δ 3 >δ 6 >δ 1 >δ 4 >δ 2 At this time, the peak values of the third and fifth graphs are obviously larger than other peak values if the relative horizontal translation amount x of the fifth graph is larger than that of the other peak values 0 ＝-1<0, relative horizontal translation x of the third graph 0 ＝2>0, then the fifthThe image is a head image, the third image is a tail image, the whole image chain can be determined in turn according to the translation amount of the maximum correlation degree, and if the translation amount x is horizontal 0 <0, indicates that the image is in front of the image and vice versa.
If the Euclidean distance between the acquired image and the system input image is as follows:
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. An information technology consultation management system based on big data is characterized in that: the information technology consultation management system comprises: the system comprises a data acquisition module, a database, a model construction module and a consultation feedback module;
the data acquisition module is connected with a database, the database is connected with a model construction module, and the model construction module is connected with the consultation feedback module; the system comprises a data acquisition module, a database, a model construction module and an advisory feedback module, wherein the data acquisition module is used for acquiring basic data information and image information, the database is used for sequencing and preprocessing acquired images and encrypting and storing acquired data and analysis results, the model construction module is used for establishing a three-dimensional model according to the processed images, and the advisory feedback module is used for displaying the established three-dimensional model to related technicians through display equipment and performing remote assistance and management according to the three-dimensional model.
2. The big-data-based information technology consultation management system according to claim 1, wherein: the data acquisition module comprises a basic data input unit and an image acquisition unit, the basic data input unit is used for acquiring basic data information of a user, and the image acquisition unit is used for acquiring image information of the smart home through camera equipment.
3. The big data-based information technology consultation management system according to claim 2, wherein: the database comprises a data encryption unit, a data storage unit and a data preprocessing unit, wherein the data encryption unit carries out data encryption transmission on the acquired data and the analysis result through an SM2 elliptic curve algorithm; the data storage unit stores data through Hadoop, and the data preprocessing unit is used for sequencing and preprocessing acquired image information and automatically adjusting the image sequence.
4. The big-data-based information technology consultation management system according to claim 3, wherein: the model building module comprises a data analysis unit and a building unit, the data processing unit is used for analyzing and processing data according to collected image information, the intelligent home in the image is placed in a coordinate system, key point coordinates on the intelligent home are determined, and the building unit is used for building an intelligent home three-dimensional model according to the determined key point coordinates.
5. The big data-based information technology consultation management system according to claim 4, wherein: the consultation feedback module comprises a screen display unit and a voice response unit, the screen display unit displays the constructed three-dimensional model to relevant technical personnel through a screen, and the voice response unit is used for enabling the relevant technical personnel to directly interact with the user through voice after the user authorizes the consultation feedback module.
6. An information technology consultation management method based on big data is characterized in that: comprises the following steps:
s1, acquiring basic data information of a user and an intelligent home, acquiring image information of the intelligent home through a camera device, and carrying out encryption transmission and storage on the basic data information and the image information;
s2, intelligently sequencing and preprocessing the image information according to the acquired image information;
s3, constructing an intelligent home three-dimensional model according to the preprocessed image information;
and S4, displaying the constructed three-dimensional model on related technicians through display equipment, and interacting with a user through voice.
7. The big data-based information technology consultation management method according to claim 6, wherein: in step S2, analyzing the arrangement sequence of the images according to the acquired image information;
arranging the images in a two-dimensional coordinate system, collecting pixel coordinates of the images, and setting two collected images X 1 (X, y) and X 2 (x，y)，x and y represent the time domain variables of the pixel coordinates of the images, and a translation relationship exists between the images, then:
f 1 (x，y)＝f 2 (x-x 0 ，y-y 0 )；
wherein x is 0 For relative horizontal translation, y 0 Relative vertical translation;
performing Fourier transform to a frequency domain:
wherein u and v are expressed as frequency domain variables of pixel coordinates of the image, and j is expressed as an imaginary number; />
The normalization process is performed by the following formula:
wherein, F' 2 (u, v) is F 2 And (u, v) normalizing to obtain an exponential function, and obtaining an impulse function delta (·) by the following formula:
according to the obtained impulse function, finding out the corresponding peak position in the space domain, expressing as the correlation of the two images, and simultaneously determining the relative horizontal translation amount x 0 And relative vertical translation y 0 ；
Assuming that the rotation angle between two images is θ and the scaling factor is α, then:
f 1 (x，y)＝f 2 (αxcosθ+αysinθ-x 0 ，-αxsinθ+αycosθ-y 0 )；
performing Fourier transform to obtain:
let F 1 Is a mode of E 1 ，F 2 Is a mode of E 2 ρ represents the polar diameter of the coordinate point, τ represents the polar angle to be marked, u = ρ cos τ, v = ρ sin τ, then:
substituting the obtained rotation angle theta and the scale factor alpha into the obtained impulse function to determine the relative horizontal translation amount x 0 And relative vertical translation y 0 。
Constructing a two-dimensional maximum correlation degree array for all the collected images, wherein the maximum peak value corresponding to the image impulse function is the maximum correlation degree, and if the maximum correlation degree is obviously greater than other correlation degrees, if the relative horizontal translation amount x is larger than other correlation degrees 0 <0, representing as a head image, and conversely representing as a tail image; the whole image chain can be determined in turn according to the translation amount of the maximum correlation degree, if the translation amount x is horizontal 0 <0, indicates that the image is in front of the image and vice versa.
8. The big data-based information technology consultation management method according to claim 7, wherein: in step S3, analyzing the model of the equipment according to the images after the sorting processing and the images input by the system;
forming a vector set P = { P) by pixel point coordinates of the collected images 1 ，P 2 ，…，P n And (5) recording pixel points of an image by a system to form a vector set Q = { Q = } 1 ，Q 2, ，…，Q n And n is the number of formed vectors, and the Euclidean distance between the acquired image and the recorded image is calculated by the following formula:
when D = D min Representing acquired intelligent home model and entry in a databaseConsistent model of base data, D min Represents a minimum euclidean distance;
constructing a three-dimensional model by combining basic data information with the images after sequencing; acquiring a pixel characteristic point on the image through an OpenCV (open circuit vehicle) technology according to the color on the image, and representing the two-dimensional coordinate of the characteristic point as A = { (x) 1 ，y 1 )，(x 2 ，y 2 )，…，(x m ，y m ) Represents a corresponding three-dimensional coordinate as a' = { (X) 1 ，Y 1 ，Z 1 )，(X 2 ，Y 2 ，Z 2 )，…，(X m ，Y m ，Z m ) And (5) analyzing a two-dimensional image coordinate system and a three-dimensional model coordinate system by using the following formulas, wherein m is the number of key points:
wherein, f x Expressed as the focal length of the camera device in the x-axis, f y Expressed as the focal length of the image pickup apparatus on the y-axis, R as the rotation matrix, T as the offset vector, (a) 0 ，b 0 ) Is the intersection of the camera's optical axis and the imaging plane, Z c Representing the corresponding Z-axis coordinate of the characteristic point in the three-dimensional world, and dx and dy representing differentiating the two-dimensional coordinate (x, y) of the pixel characteristic point; and mapping the points of the pixel coordinate system into a three-dimensional world through a formula, thereby constructing a three-dimensional model of the acquired image.
9. The big data-based information technology consultation management method according to claim 8, wherein: in step S4, the constructed three-dimensional model and the image uploaded by the user are displayed to the relevant technical staff through the screen, and after the user is authorized, direct interaction with the user is realized through voice.
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