CN111160091A - Supply chain intelligent image comparison system and method - Google Patents
Supply chain intelligent image comparison system and method Download PDFInfo
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- CN111160091A CN111160091A CN201911156893.8A CN201911156893A CN111160091A CN 111160091 A CN111160091 A CN 111160091A CN 201911156893 A CN201911156893 A CN 201911156893A CN 111160091 A CN111160091 A CN 111160091A
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Abstract
The invention provides a system and a method for comparing intelligent images of a supply chain, wherein a sample library, a project information list and an authority-confirming material list are collected through a first platform and are pushed to an open platform; pushing the back-end service of the open platform to a set path of a supply chain server; the method comprises the steps that a supply chain server scans by adopting a timing service, stores a file list obtained by scanning into a database, and initiates an image obtaining request, wherein the image obtaining request downloads an image piece from a cloud server of a first platform through an open platform gateway; the supply chain server receives the image piece, calls the intelligent image identification service through the ESB enterprise bus to perform image comparison, obtains a comparison result and returns the comparison result to the supply chain server, and the supply chain server stores the comparison result and transmits the comparison result to the first platform through the open platform. By adopting OCR intelligent recognition comparison, the flow requirement of checking the face video and signing the photo material under the manual line is eliminated, and the labor cost and the time cost are greatly reduced.
Description
Technical Field
The invention relates to the technical field of intelligent identification, in particular to a system and a method for comparing intelligent images of a supply chain.
Background
Image recognition
The OCR technology is an automatic input mode for Chinese character manuscript, it uses the cooperation of optical scanner and computer, and uses OCR software to make operation and classification of image data, then converts the image data into computer internal code, so that it can greatly reduce intensity of data input work and raise data input speed. The research work in the aspect of OCR technology in China starts late, the traditional text type OCR software mainly comprises Qinghua Wen Tong TH-OCR, Beijing BI-OCR, Zhongyuan ICR, Shenyang Automation institute SY-OCR, Beijing eosin company NI-OCR and the like, and the matched scanner uses a flat-panel scanner on the market.
Traditional OCR image recognition is firstly limited to recognition scenes, and the current technology is more limited to recognition of scanning pieces; the scene shot by the mobile phone in the existing service cannot be processed well, and a special camera is matched with the scene to ensure the definition and format of an input image; secondly, the method is limited by the number of captured features, compared with the feature capture of a neural network, the traditional technology has poor precision, and the recognition precision of non-standard files with complex interference, handwriting and the like is low; moreover, the method is limited by the problems of format, direction and the like of the image, the traditional technology can only process text type identification of a fixed format, and the method is very limited for scenes with various formats.
For example, patent document CN109543614A discloses a full text difference comparison method, which includes the following steps: the comparison piece and the original piece recognize characters through an OCR recognition engine and generate a recognized text, and the recognized text comprises coordinates corresponding to the characters; identifying the text, comparing the text with a text comparison algorithm to obtain a difference character, and acquiring the coordinate of the difference character; the difference words are located and then marked in the comparison.
Disclosure of Invention
In view of the defects in the prior art, the present invention provides a system and a method for comparing intelligent images in a supply chain.
The invention provides a supply chain intelligent image comparison system, which comprises:
a sample module: collecting a sample library, a project information list and an authority-confirming material list through a first platform, pushing the sample library, the project information list and the authority-confirming material list to an open platform, and storing the sample library, the project information list and the authority-confirming material list as a file list;
a pushing module: pushing the file list to a set path of a supply chain server by using a back-end service of the open platform;
a request module: the method comprises the steps that a supply chain server scans a file list by adopting a timing service, stores the scanned file list into a database, and initiates an image acquisition request according to an address in the file list, wherein the image acquisition request downloads an image piece from a cloud server of a first platform through an open platform gateway;
a comparison module: the supply chain server receives the image piece, calls the intelligent image identification service through the ESB enterprise bus to perform image comparison, obtains a comparison result and returns the comparison result to the supply chain server, and the supply chain server stores the comparison result and transmits the comparison result to the first platform through the open platform.
Preferably, the supply chain intelligent image comparison system further comprises a display module, which displays the comparison result for each transaction in the file list, displays the comparison result in a list mode, and provides detailed information of the comparison result viewed by the secondary menu.
Preferably, the alignment module comprises:
accessing a comparison module: receiving an image piece, downloading an authentication image to an image platform according to the ID of the file to be authenticated of the image piece, and associating an image sample in the image piece with the authentication image;
a task scheduling module: decoupling the dependence degree of a comparison algorithm by an RPC (remote procedure call) serial scheduling comparison algorithm, and realizing synchronous or asynchronous interface calling as required;
a preprocessing module: processing the image with the right determination based on image region detection, rotation and classification of deep learning, and providing a first RPC interface call;
a signature detection module: performing signature detection on the image with the right confirmation based on the handwritten signature area detection of the deep learning, returning the detected signature and the corresponding position information, and providing a second RPC interface call;
a signature comparison module: performing text region recognition on the weight-confirming image based on text recognition of deep learning to obtain text content, and providing a third RPC interface call;
a result output module: and outputting the comparison result obtained by detection in a structured data form, and providing a fourth RPC interface call.
Preferably, the image received by the supply chain server is uploaded to the image platform in a load balancing manner, and the image platform returns an image ID to the supply chain server after storing the image ID, where the image ID is a unique identification of the image.
Preferably, after receiving the image, the supply chain server provides version management for the image sample in the image, wherein the version management includes addition, modification, deletion and detail check; the comparison results include consistency, inconsistency and need to be checked manually.
The invention provides an intelligent image comparison method for a supply chain, which comprises the following steps:
a sample step: collecting a sample library, a project information list and an authority-confirming material list through a first platform, pushing the sample library, the project information list and the authority-confirming material list to an open platform, and storing the sample library, the project information list and the authority-confirming material list as a file list;
a pushing step: pushing the file list to a set path of a supply chain server by using a back-end service of the open platform;
a request step: the method comprises the steps that a supply chain server scans a file list by adopting a timing service, stores the scanned file list into a database, and initiates an image acquisition request according to an address in the file list, wherein the image acquisition request downloads an image piece from a cloud server of a first platform through an open platform gateway;
and (3) comparison: the supply chain server receives the image piece, calls the intelligent image identification service through the ESB enterprise bus to perform image comparison, obtains a comparison result and returns the comparison result to the supply chain server, and the supply chain server stores the comparison result and transmits the comparison result to the first platform through the open platform.
Preferably, the method for comparing the supply chain intelligent images further comprises a display step of displaying the comparison result for each transaction in the file list in a list mode and providing detailed information of the comparison result viewed by the secondary menu.
Preferably, the aligning step comprises:
an access comparison step: receiving an image piece, downloading an authentication image to an image platform according to the ID of the file to be authenticated of the image piece, and associating an image sample in the image piece with the authentication image;
and task scheduling step: decoupling the dependence degree of a comparison algorithm by an RPC (remote procedure call) serial scheduling comparison algorithm, and realizing synchronous or asynchronous interface calling as required;
a pretreatment step: processing the image with the right determination based on image region detection, rotation and classification of deep learning, and providing a first RPC interface call;
signature detection: performing signature detection on the image with the right confirmation based on the handwritten signature area detection of the deep learning, returning the detected signature and the corresponding position information, and providing a second RPC interface call;
signature comparison: performing text region recognition on the weight-confirming image based on text recognition of deep learning to obtain text content, and providing a third RPC interface call;
and a result output step: and outputting the comparison result obtained by detection in a structured data form, and providing a fourth RPC interface call.
Compared with the prior art, the invention has the following beneficial effects:
1. by adopting OCR intelligent recognition, 360-degree rotation recognition can be carried out, and or relation can be set for a sample, correlation comparison is carried out according to the corresponding relation, a frame capture technology is adopted for video comparison to screen a human face and compare the human face, the flow requirement of examining human face videos and signing photo materials under manual lines is eliminated, and the labor cost and the time cost are greatly reduced.
2. The problem of sample change management is solved by adopting a supply chain sample information management function, and the problem comprises the addition, modification, deletion and detail check of image samples.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a system framework diagram of the present invention;
FIG. 2 is a diagram of a data architecture according to the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
The invention aims at the project sample library and the video written and signed by the on-site trustee of the project, captures the photo and signature of the trustee from the on-site video, compares the photo and signature of the trustee with the photo and signature of the trustee in the sample library based on the AI algorithm, and returns the comparison results which are consistent, inconsistent and need to be checked manually. The processing process of the AI algorithm mainly involves three modules: detecting and comparing handwritten signatures, detecting and comparing dynamic faces, identifying and structurally outputting handwritten receipt lists: developing a handwritten signature of a receipt and a detection and comparison model of a handwritten text based on Fast R-CNN, performing special training respectively for a signature, a human face and a handwritten font through a specific data set, and accurately completing signature area positioning through three processes of feature extraction, interest point retrieval, classification and regression; therefore, the comparison of characteristic values and consistency check are carried out through the ArcFace algorithm, and a service judgment result is output.
The method comprises the steps that a sample library, a project information list and a right-confirming material list are collected through a first platform, and are pushed to an open platform to be stored as a file list; pushing the file list to a set path of a supply chain server by using a back-end service of the open platform; the method comprises the steps that a supply chain server scans a file list by adopting a timing service, stores the scanned file list into a database, and initiates an image acquisition request according to an address in the file list, wherein the image acquisition request downloads an image piece from a cloud server of a first platform through an open platform gateway; the supply chain server receives the image piece, calls the intelligent image identification service through the ESB enterprise bus to perform image comparison, obtains a comparison result and returns the comparison result to the supply chain server, and the supply chain server stores the comparison result and transmits the comparison result to the first platform through the open platform.
In specific use, the first platform adopts a partner platform, a cloud server of the first platform adopts an Aliskiun cloud, as shown in FIG. 1, the partner platform pushes a sample library, an item information list and an authority-confirming material list to an open platform, a back-end server of the open platform pushes a file list containing image item information to be compared to a specific path of a supply chain, the item information comprises an item ID, a payment order number, an invoice number and file URL information, the supply chain adopts a timed task to scan files and stores the files in a database of the own party, meanwhile, an image acquisition request is initiated according to an address in the list, and the request downloads an image piece from the Aliskiun cloud through an open platform gateway.
The downloaded samples and the right confirming images are uploaded by the supply chain, the samples belong to one or more sample libraries, the samples are regarded as reference standards for comparison, the right confirming images are images to be compared, the image pieces downloaded from the Aliskiu comprise the samples and the right confirming images, and the samples and the right confirming images have an association relation. The supply chain uploads the image to the server through load balance of the image platform and returns the image ID information, specifically, the supply chain uploads the image to the image platform and obtains the generated image ID, and the supply chain database stores the image ID but does not store the image. The image ID information is used for a supply chain to provide the image ID information for an image intelligent recognition service, the image intelligent recognition service downloads images from an image platform according to the image ID information and then is used for comparison, the image intelligent recognition service adopts an OCR service, and the OCR service is specifically applied to the aspect of character and video face recognition.
The supply chain calls an intelligent image identification service through an ESB (enterprise service bus) to perform image comparison, as shown in fig. 2, in a service layer, the intelligent image identification service transmits a request to a service layer scheduling module through service flow access, then the intelligent image identification service performs comparison processing on a face video and a signature photo by adopting signature detection and comparison and face detection and comparison, during the period, the image is obtained according to an image ID (identity), an actual matching result, an AI (Artificial intelligence) matching result and intermediate/result data are obtained and stored in a database, and the database adopts a MySQL database. In the service layer, the task scheduling module schedules each algorithm submodule in series through RPC, decouples the submodules from each other, improves the utilization rate of computing resources, and realizes a synchronous or asynchronous interface calling mode as required. The preprocessing module comprises functions of image region detection, rotation, classification and the like based on a deep learning model and provides RPC interface calling. The hand-written signature/face detection is based on the hand-written signature area detection of deep learning, returns the detected signature and the corresponding position information, and provides RPC interface calling. The signature comparison module identifies text content aiming at a given text region based on a text identification submodule of deep learning and provides RPC interface calling. And the structured output module outputs structured data based on the results of detecting the handwritten signature and comparing the characteristics, provides RPC interface call, and adopts JSON character strings for the structured data. The data layer comprises an algorithm model, a knowledge base and a database. The base layer is configured with a network, a server, and hardware storage.
The image intelligent identification service returns the comparison result (whether match) to the supply chain through the enterprise service bus ESB, and the supply chain stores the result information and transmits the image comparison result to the partner through the open platform. The supply chain can display the comparison result aiming at each transaction in the file list, and the comparison result is displayed in a list mode, so that detailed information of the comparison result is checked through a secondary menu.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. A supply chain intelligent image comparison system is characterized by comprising:
a sample module: collecting a sample library, a project information list and an authority-confirming material list through a first platform, pushing the sample library, the project information list and the authority-confirming material list to an open platform, and storing the sample library, the project information list and the authority-confirming material list as a file list;
a pushing module: pushing the file list to a set path of a supply chain server by using a back-end service of the open platform;
a request module: the method comprises the steps that a supply chain server scans a file list by adopting a timing service, stores the scanned file list into a database, and initiates an image acquisition request according to an address in the file list, wherein the image acquisition request downloads an image piece from a cloud server of a first platform through an open platform gateway;
a comparison module: the supply chain server receives the image piece, calls the intelligent image identification service through the ESB enterprise bus to perform image comparison, obtains a comparison result and returns the comparison result to the supply chain server, and the supply chain server stores the comparison result and transmits the comparison result to the first platform through the open platform.
2. The system of claim 1, further comprising a display module for displaying the comparison result for each transaction in the document list, wherein the display module is configured to display the comparison result in a list manner and provide a second level menu for viewing detailed information of the comparison result.
3. The supply chain intelligent image matching system of claim 1, wherein the matching module comprises:
accessing a comparison module: receiving an image piece, downloading an authentication image to an image platform according to the ID of the file to be authenticated of the image piece, and associating an image sample in the image piece with the authentication image;
a task scheduling module: decoupling the dependence degree of a comparison algorithm by an RPC (remote procedure call) serial scheduling comparison algorithm, and realizing synchronous or asynchronous interface calling as required;
a preprocessing module: processing the image with the right determination based on image region detection, rotation and classification of deep learning, and providing a first RPC interface call;
a signature detection module: performing signature detection on the image with the right confirmation based on the handwritten signature area detection of the deep learning, returning the detected signature and the corresponding position information, and providing a second RPC interface call;
a signature comparison module: performing text region recognition on the weight-confirming image based on text recognition of deep learning to obtain text content, and providing a third RPC interface call;
a result output module: and outputting the comparison result obtained by detection in a structured data form, and providing a fourth RPC interface call.
4. The system according to claim 1, wherein the image received by the supply chain server is uploaded to the image platform in a load balancing manner, and the image platform stores the image ID, which is a unique identification of the image, and returns the image ID to the supply chain server.
5. The system according to claim 1, wherein the supply chain server provides version management for the image samples in the image pieces after receiving the image pieces, the version management including addition, modification, deletion and detail check; the comparison results include consistency, inconsistency and need to be checked manually.
6. An intelligent image comparison method for a supply chain is characterized by comprising the following steps:
a sample step: collecting a sample library, a project information list and an authority-confirming material list through a first platform, pushing the sample library, the project information list and the authority-confirming material list to an open platform, and storing the sample library, the project information list and the authority-confirming material list as a file list;
a pushing step: pushing the file list to a set path of a supply chain server by using a back-end service of the open platform;
a request step: the method comprises the steps that a supply chain server scans a file list by adopting a timing service, stores the scanned file list into a database, and initiates an image acquisition request according to an address in the file list, wherein the image acquisition request downloads an image piece from a cloud server of a first platform through an open platform gateway;
and (3) comparison: the supply chain server receives the image piece, calls the intelligent image identification service through the ESB enterprise bus to perform image comparison, obtains a comparison result and returns the comparison result to the supply chain server, and the supply chain server stores the comparison result and transmits the comparison result to the first platform through the open platform.
7. The supply chain intelligent image comparison method as claimed in claim 6, further comprising a display step of displaying the comparison result for each transaction in the document list in a list manner, and providing a secondary menu to view detailed information of the comparison result.
8. The method of claim 6, wherein the comparing step comprises:
an access comparison step: receiving an image piece, downloading an authentication image to an image platform according to the ID of the file to be authenticated of the image piece, and associating an image sample in the image piece with the authentication image;
and task scheduling step: decoupling the dependence degree of a comparison algorithm by an RPC (remote procedure call) serial scheduling comparison algorithm, and realizing synchronous or asynchronous interface calling as required;
a pretreatment step: processing the image with the right determination based on image region detection, rotation and classification of deep learning, and providing a first RPC interface call;
signature detection: performing signature detection on the image with the right confirmation based on the handwritten signature area detection of the deep learning, returning the detected signature and the corresponding position information, and providing a second RPC interface call;
signature comparison: performing text region recognition on the weight-confirming image based on text recognition of deep learning to obtain text content, and providing a third RPC interface call;
and a result output step: and outputting the comparison result obtained by detection in a structured data form, and providing a fourth RPC interface call.
9. The method according to claim 6, wherein the image received by the supply chain server is uploaded to the image platform in a load balancing manner, and the image platform returns an image ID to the supply chain server after storing the image ID, wherein the image ID is a unique identification of the image.
10. The method according to claim 6, wherein the supply chain server provides version management for the image samples in the image after receiving the image, wherein the version management includes adding, modifying, deleting and checking details; the comparison results include consistency, inconsistency and need to be checked manually.
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