CN113239927A - Handwriting data acquisition method and system based on big data - Google Patents
Handwriting data acquisition method and system based on big data Download PDFInfo
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- CN113239927A CN113239927A CN202110378009.6A CN202110378009A CN113239927A CN 113239927 A CN113239927 A CN 113239927A CN 202110378009 A CN202110378009 A CN 202110378009A CN 113239927 A CN113239927 A CN 113239927A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/14—Image acquisition
- G06V30/142—Image acquisition using hand-held instruments; Constructional details of the instruments
- G06V30/1423—Image acquisition using hand-held instruments; Constructional details of the instruments the instrument generating sequences of position coordinates corresponding to handwriting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3438—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment monitoring of user actions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0484—Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
- G06F3/04847—Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0487—Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser
- G06F3/0488—Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures
- G06F3/04883—Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures for inputting data by handwriting, e.g. gesture or text
Abstract
The invention provides a handwriting data acquisition method and system based on big data, wherein the method comprises the following steps: step S1: receiving a handwriting data acquisition request of a user; step S2: analyzing the handwriting data acquisition request to acquire the identity information and/or the authority information of the user; step S3: verifying the identity information and/or the authority information, and acquiring first parameter information of equipment used by a user when the verification is passed; step S4: configuring a data acquisition node for the equipment based on the first parameter information; step S5: and acquiring handwriting data input by the equipment based on the data acquisition node. According to the handwriting data acquisition method based on the big data, before handwriting acquisition equipment is used, the identity of a user is verified, and when the user passes the verification, the handwriting data can be acquired through the equipment, so that the safety of handwriting data acquisition is guaranteed.
Description
Technical Field
The invention relates to the technical field of data acquisition, in particular to a handwriting data acquisition method and system based on big data.
Background
At present, big data is emerging IT technique, big data application layer is mainly based on big data platform's ability and magnanimity data, carries out the processing of full life cycle to data, including collection, management, visualization, analysis and application etc. everyone's handwriting is all different, regards the recognition and the verification of handwriting as user's identity, can guarantee the accuracy of discernment and verification, how to guarantee the accuracy and the validity of handwriting data acquisition, the urgent problem that needs to solve.
Disclosure of Invention
The invention aims to provide a handwriting data acquisition method based on big data.
The embodiment of the invention provides a handwriting data acquisition method based on big data, which comprises the following steps:
step S1: receiving a handwriting data acquisition request of a user;
step S2: analyzing the handwriting data acquisition request to acquire the identity information and/or the authority information of the user;
step S3: verifying the identity information and/or the authority information, and acquiring first parameter information of equipment used by a user when the verification is passed;
step S4: configuring a data acquisition node for the equipment based on the first parameter information;
step S5: and acquiring handwriting data input by the equipment based on the data acquisition node.
Preferably, step S4: configuring a data acquisition node for the device based on the first parameter information, comprising:
step S401: acquiring a history verification record of a user;
step S402: acquiring position information of the position of the equipment;
step S403: screening the records based on the position information and the first parameter information to obtain data acquisition nodes corresponding to the position information and the first parameter information in the records; configuring a data acquisition node as a data acquisition node of the equipment;
step S404: when the data node corresponding to the position information and the first parameter information does not exist in the record, analyzing the position information to obtain a first position;
step S405: acquiring all data acquisition nodes of big data within a preset range from a first position, and constructing a preselection list;
step S406: configuring a data acquisition node for the device from the preselected list.
Preferably, step S406: configuring a data acquisition node for a device from a preselected list, comprising:
step S4061: constructing a first parameter vector based on the first parameter information;
step S4062: acquiring a connection list of devices which are allowed to be connected by a data acquisition node in a pre-list;
step S4063: acquiring second parameter information of each device allowing connection in the connection list;
step S4064: constructing a second parameter vector based on the second parameter information;
step S4065: calculating a first fitness of the first parameter vector and the second parameter vector; the calculation formula is as follows:
wherein Q is a first fitness of the first parameter vector and the second parameter vector; n is the total number of data of the first parameter vector or the total number of data of the second parameter vector; a isiIs the value of the ith data of the first parameter vector; biIs the value of the ith data of the second parameter vector; a iskIs the value of the kth data of the first parameter vector; bkIs the value of the kth data of the second parameter vector;
step S4066: acquiring second position information of the equipment in the connection list;
step S4067: determining a second fitness based on the first position information and the second position information, wherein the second fitness formula is as follows:
q＝α(X1-X2)2+β(Y1-Y2)2+γ(Z1-Z2)2；
wherein q is a second degree of adaptation, X1、Y1、Z1Longitude, latitude and altitude which are first position information; x2、Y2、Z2Longitude, latitude, and altitude which are the second location information; alpha, beta and gamma are preset weights;
step S4068: determining the matching degree of the equipment and the data acquisition node based on the first matching degree and the second matching degree, wherein the calculation formula is as follows:
wherein T is the matching degree;
a first correlation coefficient corresponding to a preset first fitness;
a second correlation coefficient corresponding to a preset second fitness;
step S4069: and configuring the data acquisition node with the highest matching degree as the data acquisition node of the equipment.
Preferably, in step S1, the handwriting data collection request is obtained through the following steps;
step S101: acquiring a first handwriting input by a user through handwriting input equipment;
step S102: identifying the first handwriting, and determining first identification information;
step S103: when a trigger command word for identifying handwriting data acquisition exists in the first identification information, outputting first prompt information for prompting a user to input identity information and authority information;
step S104: acquiring a second handwriting input by a user through handwriting input equipment;
step S105: taking the second handwriting as identity information and/or authority information of the user;
step S106: and constructing a handwriting data acquisition request based on the trigger command word and the second handwriting.
Preferably, step S2: analyzing the handwriting data acquisition request to acquire the identity information and/or the authority information of the user; the method comprises the following steps:
step S201: performing ORC recognition on the second handwriting to acquire second recognition information;
step S202: sampling the second handwriting, and acquiring stroke order information and writing strength and writing time of each stroke in the stroke order information;
step S203: and taking the second identification information as verification information of the identity information, and taking the stroke order information and the writing strength and the writing time of each stroke in the stroke order information as verification information of the authority information.
Preferably, the method for determining the writing force is as follows:
in the formula, FhWriting strength for writing the h-th stroke; n is the total number of stroke sampling points; f. ofθ,hForce of theta sampling point, f, for h strokeω,hThe strength of the w sampling point of the h stroke; when the force of writing the theta sampling point of the h stroke of the user falls on
Has a probability greater than
If so, M takes a value of 1, otherwise, M takes a value of 0; sigma is a preset first correction coefficient.
Preferably, between step S3 and step S4, the method further comprises:
step S31: acquiring historical handwriting data acquisition records of equipment;
step S32: analyzing the historical handwriting data acquisition record, and determining the number of data acquisition nodes configured for the equipment;
wherein, step S32: analyzing historical handwriting data acquisition records, and determining the number of data acquisition nodes configured for the equipment, wherein the method comprises the following steps:
step S321A: extracting the characteristics of the historical handwriting data acquisition record to obtain a plurality of characteristic values;
step S322A: bringing the plurality of characteristic values into a preset neural network model to obtain configuration factors;
step S323A: inquiring a preset configuration table based on the configuration factor to obtain the number of data acquisition nodes configured for the equipment;
or the like, or, alternatively,
step S322B: constructing a feature vector based on the plurality of feature values;
step S323B: acquiring a preset configuration library; the configuration vector in the configuration library is associated with a configuration number;
step S324B: calculating the similarity between the configuration vector and the feature vector;
step S325B: acquiring the configuration number corresponding to the configuration vector with the maximum similarity, and configuring a corresponding number of data acquisition nodes for the equipment based on the configuration number;
wherein the characteristic values include: one or more of the maximum packet loss rate, the average packet loss rate and the data acquisition delay are combined.
The invention also provides a handwriting data acquisition system based on big data, which comprises:
the request receiving module is used for receiving a handwriting data acquisition request of a user;
the analysis module is used for analyzing the handwriting data acquisition request to acquire the identity information and/or the authority information of the user;
the verification module is used for verifying the identity information and/or the authority information, and acquiring first parameter information of equipment used by a user when the verification is passed;
the configuration module is used for configuring data acquisition nodes for the equipment based on the first parameter information;
and the acquisition module is used for acquiring handwriting data input by the equipment based on the data acquisition node.
Preferably, the configuration module performs the following operations:
acquiring a history verification record of a user;
acquiring position information of the position of the equipment;
screening the records based on the position information and the first parameter information to obtain data acquisition nodes corresponding to the position information and the first parameter information in the records; configuring a data acquisition node as a data acquisition node of the equipment;
when the data node corresponding to the position information and the first parameter information does not exist in the record, analyzing the position information to obtain a first position;
acquiring all data acquisition nodes of big data within a preset range from a first position, and constructing a preselection list;
configuring a data acquisition node for the device from the preselected list.
Preferably, configuring a data acquisition node for a device from a preselected list comprises:
constructing a first parameter vector based on the first parameter information;
acquiring a connection list of devices which are allowed to be connected by a data acquisition node in a pre-list;
acquiring second parameter information of each device allowing connection in the connection list;
constructing a second parameter vector based on the second parameter information;
calculating a first fitness of the first parameter vector and the second parameter vector; the calculation formula is as follows:
wherein Q is a first fitness of the first parameter vector and the second parameter vector; n is the total number of data of the first parameter vector or the total number of data of the second parameter vector; a isiIs the value of the ith data of the first parameter vector; biIs the value of the ith data of the second parameter vector; a iskIs the value of the kth data of the first parameter vector; bkIs the value of the kth data of the second parameter vector;
acquiring second position information of the equipment in the connection list;
determining a second fitness based on the first position information and the second position information, wherein the second fitness formula is as follows:
q＝α(X1-X2)2+β(Y1-Y2)2+γ(Z1-Z2)2；
wherein q is a second degree of adaptation, X1、Y1、Z1Longitude, latitude and altitude which are first position information; x2、Y2、Z2Longitude, latitude, and altitude which are the second location information; alpha, beta and gamma are preset weights;
determining the matching degree of the equipment and the data acquisition node based on the first matching degree and the second matching degree, wherein the calculation formula is as follows:
wherein T is the matching degree;
a first correlation coefficient corresponding to a preset first fitness;
a second correlation coefficient corresponding to a preset second fitness;
and configuring the data acquisition node with the highest matching degree as the data acquisition node of the equipment.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
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 handwriting data acquisition method based on big data according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of another handwriting data collection method based on big data according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a handwriting data acquisition system based on big data according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The embodiment of the invention provides a handwriting data acquisition method based on big data, as shown in FIG. 1, comprising the following steps:
step S1: receiving a handwriting data acquisition request of a user;
step S2: analyzing the handwriting data acquisition request to acquire the identity information and/or the authority information of the user;
step S3: verifying the identity information and/or the authority information, and acquiring first parameter information of equipment used by a user when the verification is passed;
step S4: configuring a data acquisition node for the equipment based on the first parameter information;
step S5: and acquiring handwriting data input by the equipment based on the data acquisition node.
The working principle and the beneficial effects of the technical scheme are as follows:
firstly, a user sends a handwriting data acquisition request through equipment connected with big data, and when the big data platform receives the handwriting data acquisition request of the user, the handwriting data acquisition request is analyzed to obtain identity information and/or authority information of the user; verifying the identity information and/or the authority information, and acquiring first parameter information of equipment used by a user when the verification is passed; configuring a data acquisition node for the equipment based on the first parameter information; and acquiring handwriting data input by the equipment based on the data acquisition node. And configuring corresponding data acquisition nodes for the equipment through the first parameter information of the equipment, so that the accuracy of handwriting data acquisition is improved. The identity of the user is verified, so that the safety of handwriting collection is improved; the method can be mainly applied to a new user account registration stage, such as: the large platform of the education data can be used for verifying a new student by a teacher when the new student is registered with an account, and after verification is passed, the student collects handwriting data through equipment and uses the collected handwriting data as verification information used when the subsequent student logs in, so that the safety of account registration is improved.
In one embodiment, as shown in FIG. 2, step S4: configuring a data acquisition node for the device based on the first parameter information, comprising:
step S401: acquiring a history verification record of a user;
step S402: acquiring position information of the position of the equipment;
step S403: screening the records based on the position information and the first parameter information to obtain data acquisition nodes corresponding to the position information and the first parameter information in the records; configuring a data acquisition node as a data acquisition node of the equipment;
step S404: when the data node corresponding to the position information and the first parameter information does not exist in the record, analyzing the position information to obtain a first position;
step S405: acquiring all data acquisition nodes of big data within a preset range from a first position, and constructing a preselection list;
step S406: configuring a data acquisition node for the device from the preselected list.
The working principle and the beneficial effects of the technical scheme are as follows:
the historical verification records of the user can realize the rapid configuration of the data acquisition node; and the data acquisition nodes of the big data are screened by adopting the distance, so that the efficiency and the accuracy of data transmission are ensured.
In one embodiment, step S406: configuring a data acquisition node for a device from a preselected list, comprising:
step S4061: constructing a first parameter vector based on the first parameter information;
step S4062: acquiring a connection list of devices which are allowed to be connected by a data acquisition node in a pre-list;
step S4063: acquiring second parameter information of each device allowing connection in the connection list;
step S4064: constructing a second parameter vector based on the second parameter information;
step S4065: calculating a first fitness of the first parameter vector and the second parameter vector; the calculation formula is as follows:
wherein Q is a first fitness of the first parameter vector and the second parameter vector; n is the total number of data of the first parameter vector or the total number of data of the second parameter vector; a isiIs the value of the ith data of the first parameter vector; biIs the value of the ith data of the second parameter vector; a iskIs the value of the kth data of the first parameter vector; bkIs the value of the kth data of the second parameter vector;
step S4066: acquiring second position information of the equipment in the connection list;
step S4067: determining a second fitness based on the first position information and the second position information, wherein the second fitness formula is as follows:
q＝α(X1-X2)2+β(Y1-Y2)2+γ(Z1-Z2)2；
wherein q is a second degree of adaptation, X1、Y1、Z1Longitude, latitude and altitude which are first position information; x2、Y2、Z2Longitude, latitude, and altitude which are the second location information; alpha, beta,Gamma is a preset weight;
step S4068: determining the matching degree of the equipment and the data acquisition node based on the first matching degree and the second matching degree, wherein the calculation formula is as follows:
wherein T is the matching degree;
a first correlation coefficient corresponding to a preset first fitness;
a second correlation coefficient corresponding to a preset second fitness;
step S4069: and configuring the data acquisition node with the highest matching degree as the data acquisition node of the equipment.
The working principle and the beneficial effects of the technical scheme are as follows:
the first adaptation degree of the data acquisition node and the equipment and the second adaptation degree of the position of the data acquisition node and the position of the equipment are comprehensively considered, the corresponding data acquisition node is configured for the equipment, the accuracy of configuring the data acquisition node is improved, and the accuracy of acquiring the handwriting data is ensured.
In one embodiment, in step S1, a handwriting data collection request is obtained by;
step S101: acquiring a first handwriting input by a user through handwriting input equipment;
step S102: identifying the first handwriting, and determining first identification information;
step S103: when a trigger command word for identifying handwriting data acquisition exists in the first identification information, outputting first prompt information for prompting a user to input identity information and authority information;
step S104: acquiring a second handwriting input by a user through handwriting input equipment;
step S105: taking the second handwriting as identity information and/or authority information of the user;
step S106: and constructing a handwriting data acquisition request based on the trigger command word and the second handwriting.
The working principle and the beneficial effects of the technical scheme are as follows:
when the handwriting input equipment is used, a user inputs a first handwriting for triggering handwriting data acquisition, the first handwriting is triggered to enter a handwriting data acquisition mode, a second handwriting is data for identity verification input by the user, and the second handwriting is used for realizing verification of entering the handwriting data acquisition mode; the method and the device realize acquisition of the handwriting data acquisition request based on the handwriting input equipment, and ensure efficient and convenient acquisition of the data request.
In one embodiment, step S2: analyzing the handwriting data acquisition request to acquire the identity information and/or the authority information of the user; the method comprises the following steps:
step S201: performing ORC recognition on the second handwriting to acquire second recognition information;
step S202: sampling the second handwriting, and acquiring stroke order information and writing strength and writing time of each stroke in the stroke order information;
step S203: and taking the second identification information as verification information of the identity information, and taking the stroke order information and the writing strength and the writing time of each stroke in the stroke order information as verification information of the authority information.
The working principle and the beneficial effects of the technical scheme are as follows:
the second identification information is recognized characters or patterns and is used as identity information for distinguishing the identity of the user; the stroke order information, the writing force and the writing time are used as verification information for verifying the authority of the user identity, so that the user identity can be accurately recognized, and the safety of user recognition is improved.
In one embodiment, the writing power is determined as follows:
in the formula, FhWriting strength for writing the h-th stroke; n is the total number of stroke sampling points; f. ofθ，hForce of theta sampling point, f, for h strokeω，hThe strength of the w sampling point of the h stroke; when the force of writing the theta sampling point of the h stroke of the user falls on
Has a probability greater than
If so, M takes a value of 1, otherwise, M takes a value of 0; sigma is a preset first correction coefficient.
The working principle and the beneficial effects of the technical scheme are as follows:
by performing point-based power-track acquisition on a stroke, a determination of the writing power of the stroke is determined based on the acquired power track.
In one embodiment, between step S3 and step S4, further comprising:
step S31: acquiring historical handwriting data acquisition records of equipment;
step S32: analyzing the historical handwriting data acquisition record, and determining the number of data acquisition nodes configured for the equipment;
wherein, step S32: analyzing historical handwriting data acquisition records, and determining the number of data acquisition nodes configured for the equipment, wherein the method comprises the following steps:
step S321A: extracting the characteristics of the historical handwriting data acquisition record to obtain a plurality of characteristic values;
step S322A: bringing the plurality of characteristic values into a preset neural network model to obtain configuration factors;
step S323A: inquiring a preset configuration table based on the configuration factor to obtain the number of data acquisition nodes configured for the equipment;
or the like, or, alternatively,
step S322B: constructing a feature vector based on the plurality of feature values;
step S323B: acquiring a preset configuration library; the configuration vector in the configuration library is associated with a configuration number;
step S324B: calculating the similarity between the configuration vector and the feature vector;
step S325B: acquiring the configuration number corresponding to the configuration vector with the maximum similarity, and configuring a corresponding number of data acquisition nodes for the equipment based on the configuration number;
wherein the characteristic values include: one or more of the maximum packet loss rate, the average packet loss rate and the data acquisition delay are combined.
The working principle and the beneficial effects of the technical scheme are as follows:
the historical handwriting data acquisition records of the equipment are analyzed, the historical handwriting data acquisition condition of the equipment can be clearly obtained, data acquisition nodes are configured according to the data acquisition condition of the equipment, and the large data platform can accurately acquire handwriting data through the data acquisition nodes. The data acquisition method has the advantages that the data transmission loss between the equipment and the data acquisition nodes is prevented, the number of the data acquisition nodes configured for the equipment is determined by analyzing historical handwriting data acquisition records, and the safe and effective data acquisition is guaranteed.
The invention also provides a handwriting data acquisition system based on big data, as shown in fig. 3, comprising:
the request receiving module 11 is used for receiving a handwriting data acquisition request of a user;
the analysis module 12 is used for analyzing the handwriting data acquisition request to acquire the identity information and/or the authority information of the user;
the verification module 13 is configured to verify the identity information and/or the authority information, and when the verification passes, obtain first parameter information of a device used by a user;
a configuration module 14, configured to configure a data acquisition node for the device based on the first parameter information;
and the acquisition module 15 is used for acquiring handwriting data input by the equipment based on the data acquisition node.
The working principle and the beneficial effects of the technical scheme are as follows:
firstly, a user sends a handwriting data acquisition request through equipment connected with big data, and when the big data platform receives the handwriting data acquisition request of the user, the handwriting data acquisition request is analyzed to obtain identity information and/or authority information of the user; verifying the identity information and/or the authority information, and acquiring first parameter information of equipment used by a user when the verification is passed; configuring a data acquisition node for the equipment based on the first parameter information; and acquiring handwriting data input by the equipment based on the data acquisition node. And configuring corresponding data acquisition nodes for the equipment through the first parameter information of the equipment, so that the accuracy of handwriting data acquisition is improved. The identity of the user is verified, so that the safety of handwriting collection is improved; the method can be mainly applied to a new user account registration stage, such as: the large platform of the education data can be used for verifying a new student by a teacher when the new student is registered with an account, and after verification is passed, the student collects handwriting data through equipment and uses the collected handwriting data as verification information used when the subsequent student logs in, so that the safety of account registration is improved.
In one embodiment, the configuration module performs the following operations:
acquiring a history verification record of a user;
acquiring position information of the position of the equipment;
screening the records based on the position information and the first parameter information to obtain data acquisition nodes corresponding to the position information and the first parameter information in the records; configuring a data acquisition node as a data acquisition node of the equipment;
when the data node corresponding to the position information and the first parameter information does not exist in the record, analyzing the position information to obtain a first position;
acquiring all data acquisition nodes of big data within a preset range from a first position, and constructing a preselection list;
configuring a data acquisition node for the device from the preselected list.
The working principle and the beneficial effects of the technical scheme are as follows:
the historical verification records of the user can realize the rapid configuration of the data acquisition node; and the data acquisition nodes of the big data are screened by adopting the distance, so that the efficiency and the accuracy of data transmission are ensured.
In one embodiment, configuring a data collection node for a device from a preselected list comprises:
constructing a first parameter vector based on the first parameter information;
acquiring a connection list of devices which are allowed to be connected by a data acquisition node in a pre-list;
acquiring second parameter information of each device allowing connection in the connection list;
constructing a second parameter vector based on the second parameter information;
calculating a first fitness of the first parameter vector and the second parameter vector; the calculation formula is as follows:
wherein Q is a first fitness of the first parameter vector and the second parameter vector; n is the total number of data of the first parameter vector or the total number of data of the second parameter vector; a isiIs the value of the ith data of the first parameter vector; biIs the value of the ith data of the second parameter vector; a iskIs the value of the kth data of the first parameter vector; bkIs the value of the kth data of the second parameter vector;
acquiring second position information of the equipment in the connection list;
determining a second fitness based on the first position information and the second position information, wherein the second fitness formula is as follows:
q＝α(X1-X2)2+β(Y1-Y2)2+γ(Z1-Z2)2；
wherein q is a second degree of adaptation, X1、Y1、Z1Longitude, latitude and altitude which are first position information; x2、Y2、Z2Longitude, latitude, and altitude which are the second location information; alpha, beta and gamma are preset weights;
determining the matching degree of the equipment and the data acquisition node based on the first matching degree and the second matching degree, wherein the calculation formula is as follows:
wherein T is the matching degree;
a first correlation coefficient corresponding to a preset first fitness;
a second correlation coefficient corresponding to a preset second fitness;
and configuring the data acquisition node with the highest matching degree as the data acquisition node of the equipment.
The working principle and the beneficial effects of the technical scheme are as follows:
the first adaptation degree of the data acquisition node and the equipment and the second adaptation degree of the position of the data acquisition node and the position of the equipment are comprehensively considered, the corresponding data acquisition node is configured for the equipment, the accuracy of configuring the data acquisition node is improved, and the accuracy of acquiring the handwriting data is ensured.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A handwriting data acquisition method based on big data is characterized by comprising the following steps:
step S1: receiving a handwriting data acquisition request of a user;
step S2: analyzing the handwriting data acquisition request to acquire the identity information and/or the authority information of the user;
step S3: verifying the identity information and/or the authority information, and acquiring first parameter information of equipment used by the user when the verification is passed;
step S4: configuring a data acquisition node for the equipment based on the first parameter information;
step S5: and acquiring handwriting data input by the equipment based on the data acquisition node.
2. A method for acquiring handwriting data based on big data according to claim 1, wherein said step S4: configuring a data acquisition node for the device based on the first parameter information, comprising:
step S401: acquiring a record of the user history verification;
step S402: acquiring position information of the position of the equipment;
step S403: screening the record based on the position information and the first parameter information to obtain a data acquisition node corresponding to the position information and the first parameter information in the record; configuring the data acquisition node as a data acquisition node of the device;
step S404: when the data node corresponding to the position information and the first parameter information does not exist in the record, analyzing the position information to obtain a first position;
step S405: acquiring all data acquisition nodes of the big data within a preset range from the first position, and constructing a preselection list;
step S406: configuring the data collection node for the device from the preselected list.
3. A handwriting data collection method based on big data according to claim 2, characterized in that said step S406: configuring the data collection node for the device from the preselected list, comprising:
step S4061: constructing a first parameter vector based on the first parameter information;
step S4062: acquiring a connection list of the devices which are allowed to be connected by the data acquisition node in the pre-list;
step S4063: acquiring second parameter information of each device allowing connection in the connection list;
step S4064: constructing a second parameter vector based on the second parameter information;
step S4065: calculating a first fitness of the first parameter vector and the second parameter vector; the calculation formula is as follows:
wherein Q is the first fitness of the first parameter vector and the second parameter vector; n is the total number of data of the first parameter vector or the total number of data of the second parameter vector; a isiIs the value of the ith data of the first parameter vector; biIs the value of the ith data of the second parameter vector; a iskIs the value of the kth data of the first parameter vector; bkIs the value of the kth data of the second parameter vector;
step S4066: acquiring second position information of the equipment in the connection list;
step S4067: determining a second degree of adaptation based on the first location information and the second location information, the second degree of adaptation being formulated as follows:
q＝α(X1-X2)2+β(Y1-Y2)2+γ(Z1-Z2)2；
wherein q is the second degree of adaptation, X1、Y1、Z1Longitude, latitude and altitude of the first location information; x2、Y2、Z2Longitude, latitude and altitude of the second location information; alpha, beta and gamma are preset weights;
step S4068: determining the matching degree of the equipment and the data acquisition node based on the first adaptation degree and the second adaptation degree, wherein the calculation formula is as follows:
wherein T is the matching degree;
a first correlation coefficient corresponding to the preset first adaptation degree;
a second correlation coefficient corresponding to the preset second fitness;
step S4069: and configuring the data acquisition node with the highest matching degree as a data acquisition node of the equipment.
4. A handwriting data collection method based on big data according to claim 1, characterized in that in said step S1, said handwriting data collection request is obtained by the following steps;
step S101: acquiring a first handwriting input by a user through handwriting input equipment;
step S102: identifying the first handwriting, and determining first identification information;
step S103: when a trigger command word for marking the handwriting data acquisition exists in the first identification information, outputting first prompt information for prompting a user to input identity information and authority information;
step S104: acquiring a second handwriting input by the user through the handwriting input equipment;
step S105: taking the second handwriting as the identity information and/or the authority information of the user;
step S106: and constructing the handwriting data acquisition request based on the trigger command word and the second handwriting.
5. A handwriting data collection method based on big data according to claim 4, characterized in that said step S2: analyzing the handwriting data acquisition request to acquire the identity information and/or the authority information of the user; the method comprises the following steps:
step S201: performing ORC recognition on the second handwriting to acquire second recognition information;
step S202: sampling the second handwriting, and acquiring stroke order information and writing strength and writing time of each stroke in the stroke order information;
step S203: and taking the second identification information as verification information of the identity information, and taking the writing force and the writing time of each stroke in the stroke order information and the stroke order information as verification information of the authority information.
6. A handwriting data collection method based on big data according to claim 5, characterized in that said writing strength determination method is as follows:
in the formula, FhWriting strength for writing the h-th stroke; n is the total number of stroke sampling points; f. ofθ，hForce of theta sampling point, f, for h strokeω，hThe strength of the w sampling point of the h stroke; when the force of writing the theta sampling point of the h stroke of the user falls on
Has a probability greater than
If so, M takes a value of 1, otherwise, M takes a value of 0; sigma is a preset first correction coefficient.
7. A method for acquiring handwriting data based on big data according to claim 1, further comprising between said step S3 and said step S4:
step S31: acquiring historical handwriting data acquisition records of the equipment;
step S32: analyzing the historical handwriting data acquisition record, and determining the number of the data acquisition nodes configured for the equipment;
wherein the step S32: analyzing the historical handwriting data acquisition record, and determining the number of the data acquisition nodes configured for the equipment, wherein the steps comprise:
step S321A: extracting the characteristics of the historical handwriting data acquisition record to obtain a plurality of characteristic values;
step S322A: bringing a plurality of characteristic values into a preset neural network model to obtain configuration factors;
step S323A: inquiring a preset configuration table based on the configuration factor to obtain the number of the data acquisition nodes configured for the equipment;
or the like, or, alternatively,
step S322B: constructing a feature vector based on a plurality of the feature values;
step S323B: acquiring a preset configuration library; the configuration vector in the configuration library is associated with a configuration number;
step S324B: calculating the similarity between the configuration vector and the feature vector;
step S325B: acquiring the configuration number corresponding to the configuration vector with the maximum similarity, and configuring a corresponding number of data acquisition nodes for the equipment based on the configuration number;
wherein the characteristic values include: one or more of the maximum packet loss rate, the average packet loss rate and the data acquisition delay are combined.
8. A handwriting data acquisition system based on big data is characterized by comprising:
the request receiving module is used for receiving a handwriting data acquisition request of a user;
the analysis module is used for analyzing the handwriting data acquisition request to acquire the identity information and/or the authority information of the user;
the verification module is used for verifying the identity information and/or the authority information, and acquiring first parameter information of equipment used by the user when the verification is passed;
the configuration module is used for configuring a data acquisition node for the equipment based on the first parameter information;
and the acquisition module is used for acquiring handwriting data input by the equipment based on the data acquisition node.
9. The big-data based handwriting data acquisition system of claim 8 wherein said configuration module performs the following operations:
acquiring a record of the user history verification;
acquiring position information of the position of the equipment;
screening the record based on the position information and the first parameter information to obtain a data acquisition node corresponding to the position information and the first parameter information in the record; configuring the data acquisition node as a data acquisition node of the device;
when the data node corresponding to the position information and the first parameter information does not exist in the record, analyzing the position information to obtain a first position;
acquiring all data acquisition nodes of the big data within a preset range from the first position, and constructing a preselection list;
configuring the data collection node for the device from the preselected list.
10. A big-data based handwriting data acquisition system according to claim 9 and wherein said configuring said data acquisition node for said device from said preselected list comprises:
constructing a first parameter vector based on the first parameter information;
acquiring a connection list of the devices which are allowed to be connected by the data acquisition node in the pre-list;
acquiring second parameter information of each device allowing connection in the connection list;
constructing a second parameter vector based on the second parameter information;
calculating a first fitness of the first parameter vector and the second parameter vector; the calculation formula is as follows:
wherein Q is the first fitness of the first parameter vector and the second parameter vector; n is the total number of data of the first parameter vector or the total number of data of the second parameter vector; a isiIs the value of the ith data of the first parameter vector; biIs the value of the ith data of the second parameter vector; a iskIs the value of the kth data of the first parameter vector; bkIs the value of the kth data of the second parameter vector;
acquiring second position information of the equipment in the connection list;
determining a second degree of adaptation based on the first location information and the second location information, the second degree of adaptation being formulated as follows:
q＝α(X1-X2)2+β(Y1-Y2)2+γ(Z1-Z2)2；
wherein q is the second degree of adaptation, X1、Y1、Z1Longitude, latitude and altitude of the first location information; x2、Y2、Z2Longitude, latitude and altitude of the second location information; alpha, beta and gamma are preset weights;
determining the matching degree of the equipment and the data acquisition node based on the first adaptation degree and the second adaptation degree, wherein the calculation formula is as follows:
wherein T is the matching degree；
A first correlation coefficient corresponding to the preset first adaptation degree;
a second correlation coefficient corresponding to the preset second fitness;
and configuring the data acquisition node with the highest matching degree as a data acquisition node of the equipment.
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