CN116151532A - Self-service government service handling method and device, computer equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a self-service handling method and device for government affairs, computer equipment and a storage medium. The method comprises the following steps: acquiring a business handling request; prompting file entry according to the business handling request, and obtaining a corresponding picture; inputting the picture into a picture correction model for rotation angle detection to obtain a detection result; correcting the picture according to the detection result and the corresponding correction mode to obtain a correction result; identifying text information in the correction result; the business handling request and the correction result are sent to a background system, so that the background system performs government business handling according to the business handling request and the correction result to obtain a handling result; and feeding back the handling result to a display screen of the intelligent express cabinet. By implementing the method provided by the embodiment of the invention, the government can conveniently transact the government business, the time is saved, and the government business transacting efficiency is improved.
Description
Technical Field
The invention relates to an express cabinet, in particular to a self-service handling method and device for government affairs, computer equipment and storage medium.
Background
The traditional government affair business handling mode is complex in procedure, more in handling staff, low in handling efficiency and long in time, people need to go to a government affair handling hall for queuing to handle, time and labor are wasted, a large amount of time in the working days of people can be occupied, and the working life of people is influenced to a certain extent.
Therefore, a new method is needed to be designed, so that the government affair service handling is convenient for people, the time is saved, and the government affair service handling efficiency is improved.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a self-service handling method, a self-service handling device, computer equipment and a storage medium for government affairs.
In order to achieve the above purpose, the present invention adopts the following technical scheme: the self-service government affair business handling method is applied to an intelligent express cabinet and comprises the following steps:
acquiring a business handling request;
prompting file entry according to the business handling request, and obtaining a corresponding picture;
inputting the picture into a picture correction model for rotation angle detection to obtain a detection result;
correcting the picture according to the detection result and the corresponding correction mode to obtain a correction result;
identifying text information in the correction result;
the business handling request and the correction result are sent to a background system, so that the background system performs government business handling according to the business handling request and the correction result to obtain a handling result;
and feeding back the handling result to a display screen of the intelligent express cabinet.
The further technical scheme is as follows: after the handling result is fed back to the display screen of the intelligent express cabinet, the method further comprises the following steps:
if the handling result is the paper file to be submitted, after the relevant file is copied by a copying system built in the intelligent express cabinet, file registering prompt is carried out on a user, a box door is opened, and after the mail sending information input by the user is obtained, the mail sending information is sent to the background system; if the handling result is unqualified, sending a short message to inform the user of which files need to be modified and submitted, destroying the submitted materials, and waiting for the next submission of the user.
The further technical scheme is as follows: the picture correction model is obtained by training a convolutional neural network by taking a plurality of images with rotation angle labels as a sample set.
The further technical scheme is as follows: the image correction model is obtained by training a convolutional neural network by taking a plurality of images with rotation angle labels as a sample set, and comprises the following steps:
acquiring a plurality of images with rotation angle labels to obtain a sample set;
dividing the sample set to obtain a training set and a testing set;
constructing a structure of a convolutional neural network and setting training parameters;
training the convolutional neural network by using the training set;
testing the trained convolutional neural network by using the test set;
and (5) saving parameters of the tested convolutional neural network to obtain a picture correction model.
The further technical scheme is as follows: the step of correcting the picture according to the detection result and the corresponding correction mode to obtain a correction result comprises the following steps:
determining an included angle between the target text of the picture and a Y axis of a set rectangular coordinate system according to the detection result, and rotating the picture by taking an origin of the rectangular coordinate system as a rotation center to obtain a rotation result;
and carrying out small-angle correction on the rotation result to obtain a correction result.
The further technical scheme is as follows: determining an included angle between the target text of the picture and a Y axis of a set rectangular coordinate system according to the detection result, and rotating the picture by taking an origin of the rectangular coordinate system as a rotation center to obtain a rotation result, wherein the method comprises the following steps of:
establishing a rectangular coordinate system by using the position of the left lower corner vertex of the picture, and taking the left lower corner vertex as the origin of the coordinate system;
determining a preset angle direction of a corresponding preset angle category according to the detection result;
determining an included angle between the target text of the picture and a Y axis of a set rectangular coordinate system according to the preset angle direction and the angle value;
and rotating the picture by a degree corresponding to the included angle according to a preset angle direction by taking the origin as a rotation center to obtain a rotation result.
The invention also provides a self-service handling device for government affairs, which is applied to the intelligent express cabinet and comprises the following components:
the request acquisition unit is used for acquiring a business handling request;
the picture acquisition unit is used for carrying out file entry prompt according to the business handling request to acquire a corresponding picture;
the detection unit is used for inputting the picture into a picture correction model to detect the rotation angle so as to obtain a detection result;
the correction unit is used for correcting the picture according to the detection result and the corresponding correction mode so as to obtain a correction result;
the identification unit is used for identifying the text information in the correction result;
the sending unit is used for sending the business handling request and the correction result to a background system so that the background system performs government business handling according to the business handling request and the correction result to obtain a handling result;
and the feedback unit is used for feeding back the handling result to a display screen of the intelligent express cabinet.
The invention also provides a computer device which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the method when executing the computer program.
The present invention also provides a storage medium storing a computer program which, when executed by a processor, implements the above method.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, the government affairs are transacted by operating on the intelligent express cabinet, after the transacting request is acquired, the corresponding picture is acquired according to the file input prompt, the rotation angle is determined by adopting the neural network, the picture is rotationally aligned in combination with the small-angle correction, the text information is identified, the government affairs can be transacted automatically, and the intelligent express cabinet can be directly utilized for the express of paper files, so that the government affairs are transacted conveniently for people, the time is saved, and the government affairs transacting efficiency is improved.
The invention is further described below with reference to the drawings and specific embodiments.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a self-service handling method for government affairs services according to an embodiment of the present invention;
fig. 2 is a schematic sub-flowchart of a self-service handling method for government affairs services according to an embodiment of the present invention;
fig. 3 is a schematic sub-flowchart of a self-service handling method for government affairs services according to an embodiment of the present invention;
fig. 4 is a schematic sub-flowchart of a self-service handling method for government affairs services according to an embodiment of the present invention;
FIG. 5 is a schematic block diagram of a self-service government service handling device provided by an embodiment of the invention;
FIG. 6 is a schematic block diagram of a correction unit of the self-service government service device provided by the embodiment of the invention;
FIG. 7 is a schematic block diagram of a rotary subunit of the self-service government service device provided by an embodiment of the invention;
fig. 8 is a schematic block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Referring to fig. 1, fig. 1 is a schematic flow chart of a self-service handling method for government affairs service according to an embodiment of the invention. The self-service government affair business handling method is applied to the intelligent express cabinet, the intelligent express cabinet is provided with the scanning and copying system, and government affair business handling operation is carried out on the intelligent express cabinet, so that government affair business handling of people is facilitated, time is saved, and government affair business handling efficiency is improved.
Fig. 1 is a flow chart of a self-service handling method for government affairs services according to an embodiment of the present invention. As shown in fig. 1, the method includes the following steps S110 to S180.
S110, acquiring a service handling request.
In this embodiment, the service transaction request refers to a government service transaction instruction initiated on an interactive interface of the intelligent express cabinet.
S120, performing file entry prompt according to the business handling request, and acquiring a corresponding picture.
In this embodiment, the corresponding pictures include pictures formed after the documents such as the identification card and the application form are scanned.
S130, inputting the picture into a picture correction model for rotation angle detection to obtain a detection result.
In this embodiment, the detection result refers to the angle at which the picture needs to be rotated and aligned.
Specifically, the picture correction model is obtained by training a convolutional neural network by taking a plurality of images with rotation angle labels as a sample set.
In one embodiment, referring to fig. 3, the step S130 may include steps S131 to S136.
S131, acquiring a plurality of images with rotation angle labels to obtain a sample set.
In this embodiment, the sample set refers to a plurality of images such as an identification card with a rotation angle.
S132, dividing the sample set to obtain a training set and a testing set.
In this embodiment, the sample set is divided according to a set ratio, such as a ratio of 8:2, to form a training set for training the network and a test set for testing the trained network.
S133, constructing a structure of the convolutional neural network and setting training parameters.
In this embodiment, the convolutional neural network may be VGG16, but other convolutional neural networks may be used in other embodiments.
S134, training the convolutional neural network by using the training set;
s135, testing the trained convolutional neural network by using the test set;
s136, saving parameters of the convolutional neural network after the test to obtain a picture correction model.
And training the convolutional neural network by adopting a training set, calculating a loss value of a training result by adopting a loss function, and taking the result as a picture correction model if the loss value is stable and unchanged.
S133, namely constructing a convolutional neural network structure based on VGG16, wherein the specific process is as follows:
(1) Taking a sample as a model input, firstly constructing two layers of convolution layers containing 64 convolution kernels, activating a convolution result through an activation function, and finally carrying out maximum pooling operation and outputting characteristics;
(2) Taking the output of the step (1) as input, constructing two layers of convolution layers containing 128 convolution kernels, activating a convolution result through an activation function, and finally performing maximum pooling operation and outputting characteristics;
(3) Taking the output of the step (2) as input, constructing three convolution layers containing 256 convolution kernels, activating a convolution result through an activation function, and finally performing maximum pooling operation and outputting characteristics;
(4) Taking the output of the step (3) as input, constructing three convolution layers containing 512 convolution kernels, activating a convolution result through an activation function, and finally performing maximum pooling operation and outputting characteristics;
(5) And (3) taking the output of the step (4) as input, constructing a classifier, obtaining normalized probability through a Softmax function, and outputting a prediction result for class prediction.
S134, training the convolutional neural network by using the training set;
by minimizing cross entropy loss
The back-propagation error updates the model,
wherein p is the predicted result of S133, y is the sample real label, and M represents the category number.
Specifically, training is divided into 100 epochs, each epochs comprises 100 steps, 32 samples are randomly selected from a training data set to form a batch of training data for each iteration, the learning rate is set to be 0.0001, the problem that the swing amplitude is overlarge in updating of a loss function is optimized by adopting an RMSprop optimization method, and the convergence rate of the function is further accelerated.
And S140, correcting the picture according to the detection result and the corresponding correction mode to obtain a correction result.
In this embodiment, the correction result refers to a new picture formed after the picture is rotationally aligned.
In one embodiment, referring to fig. 4, the step S140 may include steps S141 to S142.
S141, determining an included angle between the target text of the picture and a Y axis of a set rectangular coordinate system according to the detection result, and rotating the picture by taking an origin of the rectangular coordinate system as a rotation center to obtain a rotation result.
In this embodiment, the rotation result refers to a picture after rotation alignment according to the detection result.
In one embodiment, referring to fig. 5, the step S141 may include steps S1411 to S1414.
S1411, establishing a rectangular coordinate system by using the position of the left lower corner vertex of the picture, and taking the left lower corner vertex as the origin of the coordinate system;
s1412, determining a preset angle direction of a corresponding preset angle category according to the detection result;
s1413, determining an included angle between the target text of the picture and a Y axis of a set rectangular coordinate system according to the preset angle direction and the angle value;
s1414, rotating the picture by a degree corresponding to the included angle according to a preset angle direction by taking the origin as a rotation center, so as to obtain a rotation result.
The method comprises the steps of constructing a preset convolutional neural network foundation structure based on a deep learning frame, training the convolutional neural network foundation structure by using an acquired training set to obtain a picture correction model for detecting the inclination angle of an image, further carrying out angle detection on the picture in the picture correction model to obtain an angle value of the inclination angle of the picture, accurately extracting the angle characteristic of the picture by using the trained picture correction model, accurately detecting the inclination angle of the picture, screening out a large number of images containing characters and characters with inclination angles by a manual screening method, reducing the time for correcting the inclination angle of the characters in the image by one-to-one measurement of the characters in the screened images, and carrying out correction processing on the image to be detected to obtain the angle value of the inclination angle of the picture according to a preset correction mode to obtain a corrected target image.
S142, performing small-angle correction on the rotation result to obtain a correction result.
In general, the image can be corrected to an angle error range within (-15, 15) degrees through step S142.
In general, in a text picture with a rotation angle of 0 degree (text line angle of 0 degree greater than 50%), the average value of pixels in each line of the text line picture is greater than that of a non-text region under the condition that the background of the picture is uniform, the average value distribution of pixels in each line of the picture with the rotation angle of 0 degree is more relaxed (the difference between the text region and the non-text region is larger), and the variance of the average value vector of the line pixels is larger. By means of the characteristic, an angle correction range can be set, the small-angle picture to be corrected is rotated by taking the step length as 1 degree, the pixel mean vector and the variance of the vector of each row of the rotated picture are calculated, the rotation angle of the rotated picture with the maximum variance of the pixel mean vector of the row is considered to be 0 degree, and the rotation angle from the picture to be corrected to the picture with the maximum pixel of the row is the rotation angle.
And S150, sending the business handling request and the correction result to a background system so that the background system performs government business handling according to the business handling request and the correction result to obtain a handling result.
In this embodiment, the transacting result refers to whether the transacting of the government service is successful or not according to the service transacting request and the text information.
In this embodiment, the identification of the certificate for the correction result may include the following steps:
reading in a picture; converting the picture into a gray scale picture; binarization processing is carried out on the gray level map, so that the image data quantity is reduced, and the target contour is highlighted; convolving the binarized image with a specified Gaussian kernel, and smoothing the image; the smoothed binarized image is output with topology information of the image; analyzing all the current circumscribed rectangles, and obtaining a region with the largest perimeter; acquiring a rotation transformation matrix of the region, calculating a rotation angle and a rotation far point, performing affine transformation to obtain a rotated and aligned image, and extracting the region image; the output image stream is either saved as a picture.
S150, feeding back the handling result to a display screen of the intelligent express cabinet.
S160, if the handling result is a paper file to be submitted, copying the relevant file by using a copying system built in the intelligent express cabinet, prompting a user for file deposit, opening a box door, acquiring mail information input by the user, and then sending the mail information to the background system; if the handling result is unqualified, sending a short message to inform the user of which files need to be modified and submitted, destroying the submitted materials, and waiting for the next submission of the user.
If the paper file to be submitted by the user cannot be scanned and input through the intelligent express cabinet and needs to be submitted to the government or police service center, the intelligent express cabinet can open a box door for the user to put in the paper file and acquire the mail information of the user, then the intelligent express cabinet informs an express person to collect the file according to the mail information of the user and the position of the government or police service center, and finally the file submitted by the user is posted to the government or police service center.
According to the automatic government affair service handling method, the intelligent express cabinet is operated to handle government affair services, after the handling request is acquired, the corresponding picture is acquired according to the file input prompt, the neural network is adopted to determine the rotation angle, the picture is rotationally aligned in combination with the small-angle correction, the text information is identified, the government affair service handling can be automatically carried out, the intelligent express cabinet can be directly utilized to carry out the express of paper files, the government affair service handling is convenient for people, the time is saved, and the government affair service handling efficiency is improved.
Fig. 5 is a schematic block diagram of a self-service government service handling device 300 according to an embodiment of the present invention. As shown in fig. 5, the present invention further provides a self-service government service handling device 300 corresponding to the self-service government service handling method. The government service self-service transaction apparatus 300 includes a unit for performing the above-mentioned government service self-service transaction method, and may be configured in a server. Specifically, referring to fig. 5, the self-service government service handling device 300 includes a request acquisition unit 301, a picture acquisition unit 302, a detection unit 303, a correction unit 304, a transmission unit 305, a feedback unit 306, and a result processing unit 307.
A request acquiring unit 301, configured to acquire a service handling request; the picture obtaining unit 302 is configured to perform file entry prompting according to the service handling request, and obtain a corresponding picture; a detection unit 303, configured to input the picture into a picture correction model for rotation angle detection, so as to obtain a detection result; the correction unit 304 is configured to correct the picture according to the detection result in combination with a corresponding correction mode, so as to obtain a correction result; a sending unit 305, configured to send the service handling request and the correction result to a background system, so that the background system performs government service handling according to the service handling request and the correction result, to obtain a handling result; and the feedback unit 306 is used for feeding back the handling result to a display screen of the intelligent express cabinet. The result processing unit 307 is configured to, if the handling result is a paper file to be submitted, perform related file copying by using a copying system built in the intelligent express cabinet, perform file registering prompt on a user, open a box door, obtain mail sending information input by the user, and send the mail sending information to the background system; if the handling result is unqualified, sending a short message to inform the user of which files need to be modified and submitted, destroying the submitted materials, and waiting for the next submission of the user.
In an embodiment, the image correction model is obtained by training a convolutional neural network by using a plurality of images with rotation angle labels as a sample set. Specifically, a plurality of images with rotation angle labels are obtained to obtain a sample set; dividing the sample set to obtain a training set and a testing set; constructing a structure of a convolutional neural network and setting training parameters; training the convolutional neural network by using the training set; testing the trained convolutional neural network by using the test set; and (5) saving parameters of the tested convolutional neural network to obtain a picture correction model.
In one embodiment, as shown in fig. 6, the correction unit 304 includes a rotation subunit 3041 and a small angle correction subunit 3042.
The rotation subunit 3041 is configured to determine an included angle between the target text of the picture and the Y axis of the set rectangular coordinate system according to the detection result, and rotate the picture with the origin of the rectangular coordinate system as a rotation center, so as to obtain a rotation result; a small angle correction subunit 3042, configured to perform small angle correction on the rotation result, so as to obtain a correction result.
In an embodiment, as shown in fig. 7, the rotation subunit 3041 includes a coordinate system establishment module 3041, a direction determination module 30112, an included angle determination module 3043, and a picture rotation module 30414.
The coordinate system establishing module 30411 is configured to establish a rectangular coordinate system with a position of a lower left corner vertex of the picture, and use the lower left corner vertex as an origin of the coordinate system; a direction determining module 30212, configured to determine a preset angle direction of the corresponding preset angle category according to the detection result; the included angle determining module 3043 is configured to determine, according to the preset angle direction and the angle value, an included angle between the target text of the picture and a Y axis of a set rectangular coordinate system; the image rotation module 30414 is configured to rotate the image by a degree corresponding to the included angle according to a preset angle direction with the origin as a rotation center, so as to obtain a rotation result.
It should be noted that, as those skilled in the art can clearly understand, the specific implementation process of the self-service government service handling device 300 and each unit may refer to the corresponding description in the foregoing method embodiment, and for convenience and brevity of description, the description is omitted here.
The government service self-service device 300 may be embodied as a computer program that may be run on a computer apparatus as shown in fig. 8.
Referring to fig. 8, fig. 8 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 may be a server, where the server may be a stand-alone server or may be a server cluster formed by a plurality of servers.
With reference to FIG. 8, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.
The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions that, when executed, cause the processor 502 to perform a government service self-service method.
The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the execution of a computer program 5032 in the non-volatile storage medium 503, which computer program 5032, when executed by the processor 502, causes the processor 502 to perform a government service self-service method.
The network interface 505 is used for network communication with other devices. Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of a portion of the architecture in connection with the present application and is not intended to limit the computer device 500 to which the present application is applied, and that a particular computer device 500 may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
Wherein the processor 502 is configured to execute a computer program 5032 stored in a memory to implement the steps of:
acquiring a business handling request; prompting file entry according to the business handling request, and obtaining a corresponding picture; inputting the picture into a picture correction model for rotation angle detection to obtain a detection result; correcting the picture according to the detection result and the corresponding correction mode to obtain a correction result; identifying text information in the correction result; the business handling request and the correction result are sent to a background system, so that the background system performs government business handling according to the business handling request and the correction result to obtain a handling result; and feeding back the handling result to a display screen of the intelligent express cabinet.
The image correction model is obtained by training a convolutional neural network through taking a plurality of images with rotation angle labels as sample sets.
In one embodiment, after implementing the step of feeding back the transaction result to the display screen of the intelligent express cabinet, the processor 502 further implements the following steps:
if the handling result is the paper file to be submitted, after the relevant file is copied by a copying system built in the intelligent express cabinet, file registering prompt is carried out on a user, a box door is opened, and after the mail sending information input by the user is obtained, the mail sending information is sent to the background system; if the handling result is unqualified, sending a short message to inform the user of which files need to be modified and submitted, destroying the submitted materials, and waiting for the next submission of the user.
In one embodiment, when the processor 502 implements the image correction model by using a plurality of images with rotation angle labels as a sample set to train the convolutional neural network, the following steps are specifically implemented:
acquiring a plurality of images with rotation angle labels to obtain a sample set; dividing the sample set to obtain a training set and a testing set; constructing a structure of a convolutional neural network and setting training parameters; training the convolutional neural network by using the training set; testing the trained convolutional neural network by using the test set; and (5) saving parameters of the tested convolutional neural network to obtain a picture correction model.
In an embodiment, when the processor 502 corrects the picture according to the detection result in combination with the corresponding correction mode to obtain the correction result, the following steps are specifically implemented:
determining an included angle between the target text of the picture and a Y axis of a set rectangular coordinate system according to the detection result, and rotating the picture by taking an origin of the rectangular coordinate system as a rotation center to obtain a rotation result; and carrying out small-angle correction on the rotation result to obtain a correction result.
In an embodiment, when the processor 502 determines, according to the detection result, an included angle between the target text of the picture and the Y axis of the set rectangular coordinate system, and rotates the picture with the origin of the rectangular coordinate system as the rotation center, to obtain a rotation result, the following steps are specifically implemented:
establishing a rectangular coordinate system by using the position of the left lower corner vertex of the picture, and taking the left lower corner vertex as the origin of the coordinate system; determining a preset angle direction of a corresponding preset angle category according to the detection result; determining an included angle between the target text of the picture and a Y axis of a set rectangular coordinate system according to the preset angle direction and the angle value; and rotating the picture by a degree corresponding to the included angle according to a preset angle direction by taking the origin as a rotation center to obtain a rotation result.
It should be appreciated that in embodiments of the present application, the processor 502 may be a central processing unit (Central Processing Unit, CPU), the processor 502 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSPs), application specific integrated circuits (Application Specific Integrated Circuit, ASICs), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Those skilled in the art will appreciate that all or part of the flow in a method embodying the above described embodiments may be accomplished by computer programs instructing the relevant hardware. The computer program comprises program instructions, and the computer program can be stored in a storage medium, which is a computer readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.
Accordingly, the present invention also provides a storage medium. The storage medium may be a computer readable storage medium. The storage medium stores a computer program which, when executed by a processor, causes the processor to perform the steps of:
acquiring a business handling request; prompting file entry according to the business handling request, and obtaining a corresponding picture; inputting the picture into a picture correction model for rotation angle detection to obtain a detection result; correcting the picture according to the detection result and the corresponding correction mode to obtain a correction result; identifying text information in the correction result; the business handling request and the correction result are sent to a background system, so that the background system performs government business handling according to the business handling request and the correction result to obtain a handling result; and feeding back the handling result to a display screen of the intelligent express cabinet.
The image correction model is obtained by training a convolutional neural network through taking a plurality of images with rotation angle labels as sample sets.
In one embodiment, after the step of feeding back the transaction result to the display screen of the intelligent express cabinet by executing the computer program, the processor further implements the following steps:
if the handling result is the paper file to be submitted, after the relevant file is copied by a copying system built in the intelligent express cabinet, file registering prompt is carried out on a user, a box door is opened, and after the mail sending information input by the user is obtained, the mail sending information is sent to the background system; if the handling result is unqualified, sending a short message to inform the user of which files need to be modified and submitted, destroying the submitted materials, and waiting for the next submission of the user.
In one embodiment, when the processor executes the computer program to implement the image correction model, the method includes the steps of training the convolutional neural network by using a plurality of images with rotation angle labels as a sample set, and specifically includes the following steps:
acquiring a plurality of images with rotation angle labels to obtain a sample set; dividing the sample set to obtain a training set and a testing set; constructing a structure of a convolutional neural network and setting training parameters; training the convolutional neural network by using the training set; testing the trained convolutional neural network by using the test set; and (5) saving parameters of the tested convolutional neural network to obtain a picture correction model.
In an embodiment, when the processor executes the computer program to implement the step of correcting the picture according to the detection result in combination with the corresponding correction mode to obtain a correction result, the following steps are specifically implemented:
determining an included angle between the target text of the picture and a Y axis of a set rectangular coordinate system according to the detection result, and rotating the picture by taking an origin of the rectangular coordinate system as a rotation center to obtain a rotation result; and carrying out small-angle correction on the rotation result to obtain a correction result.
In an embodiment, when the processor executes the computer program to determine the included angle between the target text of the picture and the Y axis of the set rectangular coordinate system according to the detection result, and rotates the picture with the origin of the rectangular coordinate system as the rotation center, the method specifically includes the following steps:
establishing a rectangular coordinate system by using the position of the left lower corner vertex of the picture, and taking the left lower corner vertex as the origin of the coordinate system; determining a preset angle direction of a corresponding preset angle category according to the detection result; determining an included angle between the target text of the picture and a Y axis of a set rectangular coordinate system according to the preset angle direction and the angle value; and rotating the picture by a degree corresponding to the included angle according to a preset angle direction by taking the origin as a rotation center to obtain a rotation result.
The storage medium may be a U-disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk, or other various computer-readable storage media that can store program codes.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the device embodiments described above are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed.
The steps in the method of the embodiment of the invention can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the invention can be combined, divided and deleted according to actual needs. In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The integrated unit may be stored in a storage medium if implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention is essentially or a part contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a terminal, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (9)
1. The self-service handling method for the government affairs business is characterized by being applied to an intelligent express cabinet and comprising the following steps:
acquiring a business handling request;
prompting file entry according to the business handling request, and obtaining a corresponding picture;
inputting the picture into a picture correction model for rotation angle detection to obtain a detection result;
correcting the picture according to the detection result and the corresponding correction mode to obtain a correction result;
the business handling request and the correction result are sent to a background system, so that the background system performs government business handling according to the business handling request and the correction result to obtain a handling result;
and feeding back the handling result to a display screen of the intelligent express cabinet.
2. The self-service government affairs service handling method according to claim 1, wherein after feeding back the handling result to a display screen of an intelligent express cabinet, further comprising:
if the handling result is the paper file to be submitted, after the relevant file is copied by a copying system built in the intelligent express cabinet, file registering prompt is carried out on a user, a box door is opened, and after the mail sending information input by the user is obtained, the mail sending information is sent to the background system; if the handling result is unqualified, sending a short message to inform the user of which files need to be modified and submitted, destroying the submitted materials, and waiting for the next submission of the user.
3. The self-service transaction method of government affairs business according to claim 1, wherein the picture correction model is obtained by training a convolutional neural network by using a plurality of images with rotation angle labels as a sample set.
4. The self-service government affairs business handling method according to claim 3, wherein the picture correction model is obtained by training a convolutional neural network by using a plurality of images with rotation angle labels as a sample set, and comprises the following steps:
acquiring a plurality of images with rotation angle labels to obtain a sample set;
dividing the sample set to obtain a training set and a testing set;
constructing a structure of a convolutional neural network and setting training parameters;
training the convolutional neural network by using the training set;
testing the trained convolutional neural network by using the test set;
and (5) saving parameters of the tested convolutional neural network to obtain a picture correction model.
5. The self-service handling method of claim 1, wherein the correcting the picture according to the detection result in combination with the corresponding correction mode to obtain a correction result includes:
determining an included angle between the target text of the picture and a Y axis of a set rectangular coordinate system according to the detection result, and rotating the picture by taking an origin of the rectangular coordinate system as a rotation center to obtain a rotation result;
and carrying out small-angle correction on the rotation result to obtain a correction result.
6. The self-service handling method of claim 5, wherein determining an included angle between the target text of the picture and a Y-axis of a set rectangular coordinate system according to the detection result, and rotating the picture with an origin of the rectangular coordinate system as a rotation center to obtain a rotation result, comprises:
establishing a rectangular coordinate system by using the position of the left lower corner vertex of the picture, and taking the left lower corner vertex as the origin of the coordinate system;
determining a preset angle direction of a corresponding preset angle category according to the detection result;
determining an included angle between the target text of the picture and a Y axis of a set rectangular coordinate system according to the preset angle direction and the angle value;
and rotating the picture by a degree corresponding to the included angle according to a preset angle direction by taking the origin as a rotation center to obtain a rotation result.
7. Self-service handling device of government affairs business, its characterized in that is applied to on the intelligent express delivery cabinet, includes:
the request acquisition unit is used for acquiring a business handling request;
the picture acquisition unit is used for carrying out file entry prompt according to the business handling request to acquire a corresponding picture;
the detection unit is used for inputting the picture into a picture correction model to detect the rotation angle so as to obtain a detection result;
the correction unit is used for correcting the picture according to the detection result and the corresponding correction mode so as to obtain a correction result;
the sending unit is used for sending the business handling request and the correction result to a background system so that the background system performs government business handling according to the business handling request and the correction result to obtain a handling result;
and the feedback unit is used for feeding back the handling result to a display screen of the intelligent express cabinet.
8. A computer device, characterized in that it comprises a memory on which a computer program is stored and a processor which, when executing the computer program, implements the method according to any of claims 1-6.
9. A storage medium storing a computer program which, when executed by a processor, implements the method of any one of claims 1 to 6.
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