CN116994253A - Risk point identification method, device, equipment and medium for project contract - Google Patents
Risk point identification method, device, equipment and medium for project contract Download PDFInfo
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Abstract
The application discloses a risk point identification method, a device, equipment and a medium for project contracts, which are used for acquiring target project contract documents to be identified and recording typesetting layout information of the target project contract documents; performing feature recognition on the target project contract document, and extracting feature point information in the target project contract document; determining the contract type of the target project contract document according to the feature point information; invoking a risk point detection model corresponding to the contract type to perform risk point detection on the target project contract document to obtain risk point information corresponding to the target project contract document; and determining the position information of each risk point in the target project contract document according to the risk point information and the typesetting layout information. The method can effectively identify the risk point information in the project contract document, and mark the risk point information to provide auditing guide for the user, so that auditing efficiency of the contract can be conveniently improved. The application can be widely applied to the technical field of artificial intelligence.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a risk point identification method, device, equipment and medium for project contracts.
Background
ICT service refers to a business activity associated with information communication technology (Information and Communication Technology). It covers various services in the fields of information technology, telecommunications, the internet, etc. In recent years, ICT business has been rapidly developed by means of networks, and has penetrated various fields of social life.
In the related technology, ICT is often applied to project contract planning, in the process of carrying out ICT project contract planning, the image-text quantity is overlarge, the main information is comprehensive, the planning contents corresponding to different projects are different, and the examination is carried out manually, so that a plurality of identical data and image-text are required to be repeatedly confirmed, and the contract examination efficiency is influenced; moreover, during manual auditing, due to the fact that the content is more, key content cannot be grasped quickly, auditing omission and other problems are easy to occur, and the risk of project contract planning is high.
In view of the above, there is a need to solve the problems in the related art.
Disclosure of Invention
The present application aims to solve at least one of the technical problems existing in the related art to a certain extent.
Therefore, an object of the embodiments of the present application is to provide a risk point identification method for a project contract, which can effectively identify risk point information in a project contract document, and is convenient for providing auditing guidance for a user.
Another object of an embodiment of the present application is to provide a risk point identification device for a project contract.
In order to achieve the technical purpose, the technical scheme adopted by the embodiment of the application comprises the following steps:
in a first aspect, an embodiment of the present application provides a risk point identification method for a project contract, including the following steps:
acquiring a target project contract document to be identified, and recording typesetting layout information of the target project contract document;
performing feature recognition on the target project contract document, and extracting feature point information in the target project contract document;
determining the contract type of the target project contract document according to the characteristic point information;
invoking a risk point detection model corresponding to the contract type to detect risk points of the target project contract document, and obtaining risk point information corresponding to the target project contract document;
and determining the position information of each risk point in the target project contract document according to the risk point information and the typesetting layout information.
In addition, the risk point identification method for the project contract according to the above embodiment of the present application may further have the following additional technical features:
Further, in one embodiment of the present application, after the step of obtaining the target project contract document to be identified, the method further includes:
acquiring a standard project contract document;
calculating the similarity between the target project contract document and the standard project contract document;
if the similarity is smaller than or equal to a preset threshold value, returning the target project contract document and outputting prompt information; the prompt message is used for informing the user that the document format of the target project contract document is not satisfactory.
Further, in one embodiment of the present application, the calculating the similarity between the target project contract document and the standard project contract document includes:
detecting first pixel values of all pixel points in the target project contract document and second pixel values of all pixel points in the standard project contract document;
calculating a pixel difference between the first pixel value and the second pixel value;
and determining the similarity between the target project contract document and the standard project contract document according to the pixel difference value.
Further, in an embodiment of the present application, the feature recognition of the target project contract document, extracting feature point information in the target project contract document, includes:
Performing feature recognition on the target project contract document through an image segmentation technology or a text detection technology, and determining a target area corresponding to the feature point information;
and extracting the text content in the target area based on an optical character recognition technology to obtain the characteristic point information.
Further, in an embodiment of the present application, the determining, according to the feature point information, a contract type to which the target project contract document belongs includes:
acquiring an industry specification database; the industry standard database comprises industry standard information of project contract documents;
and matching the characteristic point information through the industry specification database, and determining the contract type of the target project contract document.
Further, in an embodiment of the present application, the invoking the risk point detection model corresponding to the contract type to perform risk point detection on the target project contract document to obtain risk point information corresponding to the target project contract document includes:
determining a variation range corresponding to the target project contract document according to the industry standard information;
invoking a risk point detection model corresponding to the contract type, and inputting the target project contract document into the risk point detection model to obtain a first output variable corresponding to the target project contract document;
Calculating a variable difference between the first output variable and a standard output variable;
and obtaining risk point information corresponding to the target project contract document according to the variable difference value and the variable range. Further, in an embodiment of the present application, the determining, according to the risk point information and the typesetting layout information, the location information of each risk point in the target project contract document includes:
searching the risk point information in the target project contract document;
and determining page number information, image-text distribution information or annotation distribution information of each risk point according to the typesetting layout information.
In a second aspect, an embodiment of the present application provides a risk point identifying apparatus for a project contract, including:
the acquisition unit is used for acquiring a target project contract document to be identified and recording typesetting layout information of the target project contract document;
the identification unit is used for carrying out feature identification on the target project contract document and extracting feature point information in the target project contract document;
the matching unit is used for determining the contract type of the target project contract document according to the characteristic point information;
The detection unit is used for calling a risk point detection model corresponding to the contract type to detect risk points of the target project contract document, so as to obtain risk point information corresponding to the target project contract document;
and the processing unit is used for determining the position information of each risk point in the target project contract document according to the risk point information and the typesetting layout information.
In a third aspect, an embodiment of the present application provides an electronic device, including:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement the risk point identification method of the project contract of the first aspect.
In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, in which a program executable by a processor is stored, the program executable by the processor being configured to implement the risk point identification method of the project contract according to the first aspect.
The advantages and benefits of the application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.
The embodiment of the application provides a risk point identification method, device, equipment and medium for project contracts, which are used for acquiring target project contract documents to be identified and recording typesetting layout information of the target project contract documents; performing feature recognition on the target project contract document, and extracting feature point information in the target project contract document; determining the contract type of the target project contract document according to the characteristic point information; invoking a risk point detection model corresponding to the contract type to detect risk points of the target project contract document, and obtaining risk point information corresponding to the target project contract document; and determining the position information of each risk point in the target project contract document according to the risk point information and the typesetting layout information. The method can effectively identify the risk point information in the project contract document, and mark the risk point information to provide auditing guide for the user, so that the auditing efficiency of the contract can be conveniently improved, the probability of auditing omission of key content is reduced, and the risk of project contract planning is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description is made with reference to the accompanying drawings of the embodiments of the present application or the related technical solutions in the prior art, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solutions of the present application, and other drawings may be obtained according to these drawings without the need of inventive labor for those skilled in the art.
FIG. 1 is a schematic diagram of an implementation environment of a risk point identification method for a project contract according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a risk point identification method for a project contract according to an embodiment of the present application;
FIG. 3 is a schematic flow chart for determining whether the document format of the target project contract document meets the requirements according to the embodiment of the application;
FIG. 4 is a flow chart of step 320 provided in an embodiment of the present application;
FIG. 5 is a schematic flow chart of step 220 provided in an embodiment of the present application;
FIG. 6 is a flow chart of step 230 provided in an embodiment of the present application;
FIG. 7 is a schematic flow chart of step 240 provided in an embodiment of the present application;
FIG. 8 is a schematic flow chart of step 250 provided in an embodiment of the present application;
FIG. 9 is a schematic diagram of a risk point identification device for a project contract according to an embodiment of the present application;
fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the application. The step numbers in the following embodiments are set for convenience of illustration only, and the order between the steps is not limited in any way, and the execution order of the steps in the embodiments may be adaptively adjusted according to the understanding of those skilled in the art.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the application only and is not intended to be limiting of the application.
1) ICT service refers to business activities associated with information communication technology (Information and Communication Technology). It covers various services in the fields of information technology, telecommunications, the internet, etc.
2) Artificial intelligence (artificial intelligence, AI): is a new technical science for researching and developing theories, methods, technologies and application systems for simulating, extending and expanding the intelligence of people; artificial intelligence is a branch of computer science that attempts to understand the nature of intelligence and to produce a new intelligent machine that can react in a manner similar to human intelligence, research in this field including robotics, language recognition, image recognition, natural language processing, and expert systems. Artificial intelligence can simulate the information process of consciousness and thinking of people. Artificial intelligence is also a theory, method, technique, and application system that utilizes a digital computer or digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.
3) Machine Learning (ML): the method is a multi-field intersection subject, relates to a plurality of subjects such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and the like, and specially researches how a computer simulates or realizes the learning behavior of human beings so as to acquire new knowledge or skills, and reorganizes the existing knowledge structure to continuously improve the performance of the computer. Machine learning is the core of artificial intelligence and is the fundamental approach to make computers have intelligence, which is applied throughout various fields of artificial intelligence, and machine learning generally includes techniques of artificial neural networks, confidence networks, reinforcement learning, transfer learning, induction learning, teaching learning, and the like.
4) Image segmentation techniques refer to techniques that divide pixels in an image into different regions or objects. The image segmentation technology is one of important technologies in the field of computer vision, and is widely applied to the fields of medical image analysis, target detection and identification, image editing and the like.
5) Text detection technology refers to technology that automatically recognizes and locates words in an image or video. It has wide application in many fields of application, such as autopilot, video subtitle generation, image search, etc.
6) OCR (optical character recognition) character recognition, which is a process in which an electronic device (e.g., a scanner or a digital camera) checks characters printed on paper and then translates the shape into computer characters by a character recognition method; the text data is scanned, and then the image file is analyzed and processed to obtain text and layout information.
ICT service refers to a business activity associated with information communication technology (Information and Communication Technology). It covers various services in the fields of information technology, telecommunications, the internet, etc. In recent years, ICT business has been rapidly developed by means of networks, and has penetrated various fields of social life.
In the related technology, ICT is often applied to project contract planning, in the process of carrying out ICT project contract planning, the image-text quantity is overlarge, the main information is comprehensive, the planning contents corresponding to different projects are different, and the examination is carried out manually, so that a plurality of identical data and image-text are required to be repeatedly confirmed, and the contract examination efficiency is influenced; moreover, during manual auditing, due to the fact that the content is more, key content cannot be grasped quickly, auditing omission and other problems are easy to occur, and the risk of project contract planning is high.
In view of this, in the embodiment of the present application, a method, an apparatus, a device, and a medium for identifying risk points of a project contract are provided, where the method in the embodiment of the present application obtains a target project contract document to be identified, and records typesetting layout information of the target project contract document; performing feature recognition on the target project contract document, and extracting feature point information in the target project contract document; determining the contract type of the target project contract document according to the characteristic point information; invoking a risk point detection model corresponding to the contract type to detect risk points of the target project contract document, and obtaining risk point information corresponding to the target project contract document; and determining the position information of each risk point in the target project contract document according to the risk point information and the typesetting layout information. The method can effectively identify the risk point information in the project contract document, and mark the risk point information to provide auditing guide for the user, so that the auditing efficiency of the contract can be conveniently improved, the probability of auditing omission of key content is reduced, and the risk of project contract planning is reduced.
Referring to fig. 1, fig. 1 is a schematic diagram illustrating an implementation environment of a risk point identification method for a project contract according to an embodiment of the present application. In this implementation environment, the main hardware and software body includes a terminal device 110 and a background server 120.
Specifically, the terminal device 110 may be provided with a related application, and the background server 120 is a background server of the application. The terminal device 110 and the background server 120 are in communication connection. The risk point identification method of the project contract provided in the embodiment of the application can be independently executed on the side of the terminal equipment 110 or the side of the background server 120, and can also be executed through data interaction between the terminal equipment 110 and the background server 120.
The terminal device 110 of the above embodiment may include, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart watch, and a vehicle-mounted terminal.
The background server 120 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs (Content Delivery Network, content delivery networks), basic cloud computing services such as big data and artificial intelligence platforms, and the like.
A communication connection may be established between the terminal device 110 and the background server 120 through a wireless network or a wired network. The wireless network or wired network may be configured as the internet, using standard communication techniques and/or protocols, or any other network including, for example, but not limited to, a local area network (Local Area Network, LAN), metropolitan area network (Metropolitan Area Network, MAN), wide area network (Wide Area Network, WAN), mobile, wired or wireless network, a private network, or any combination of virtual private networks. The software and hardware main bodies can adopt the same communication connection mode or different communication connection modes, and the application is not particularly limited.
Of course, it can be understood that the implementation environment in fig. 1 is only some optional application scenarios of the risk point identification method of the project contract provided in the embodiment of the present application, and the actual application is not fixed to the software and hardware environment shown in fig. 1.
The risk point identification method of the project contract provided in the embodiment of the application is described and illustrated below in conjunction with the description of the implementation environment.
Referring to fig. 2, fig. 2 is a schematic diagram of a risk point identification method of a project contract according to an embodiment of the present application, where the risk point identification method of the project contract includes, but is not limited to:
step 210, acquiring a target project contract document to be identified, and recording typesetting layout information of the target project contract document;
in this step, the project contract document for which the risk point identification is required is recorded as the target project contract document. Specifically, in the embodiment of the application, a data reading platform can be established, the information content of each ICT project contract is read through the data reading platform, the information content of the ICT project contract is generated into a document format which can be identified by a computer, namely, an ICT project contract document is generated, and then the ICT project contract document can be used as a target project contract document to carry out the operation of risk point identification.
In this step, after the target project contract document is acquired, the typesetting layout information of the target project contract document may be recorded. Illustratively, in some embodiments, the typesetting layout information of the target project contract document may include, but is not limited to, page numbers, corresponding graphic information distribution positions and various annotation distribution positions in the document, and it is understood that recording the typesetting layout information of the target project contract document may facilitate overall layout grasp of the information content of the target project contract document, so as to perform positioning processing on the risk point information at a later stage.
220, carrying out feature recognition on the target project contract document, and extracting feature point information in the target project contract document;
in the step, after the target project contract document is obtained, the characteristic identification can be carried out on the target project contract document, so that the characteristic point information in the target project contract document is extracted. Specifically, in the embodiment of the present application, the feature point information in the target project contract document may be important information in the project contract document, for example, may be some terms, amounts, contract constraint information, etc. For example, in some embodiments, the feature point information may include information related to predetermined regulations or content, for example, if a legal annotation regulation is referenced in the target project contract document, the legal annotation regulation (or its corresponding label, etc.) may be used as the feature point information in the target project contract document. In the embodiment of the application, the characteristic recognition is carried out on the target project contract document, the characteristic points in the target project contract document can be determined, and the content is extracted according to the recognized characteristic points, so that the corresponding characteristic point information can be obtained.
Step 230, determining the contract type of the target project contract document according to the characteristic point information;
in this step, after the feature point information in the target project contract document is extracted, the contract type to which the target project contract document belongs may be determined according to the feature point information. Here, the contract type to which the target project contract document belongs refers to a legal contract category to which the contract relates, such as a sales contract, a service contract, a lease contract, a collaboration contract, and the like, to which the present application is not limited.
According to the embodiment of the application, the contract type of the target project contract document can be determined according to the characteristic point information. For example, in some embodiments, some text information in the feature point information may be extracted, where the text information may include contract titles, contract terms, contract contents, and the like, and then the extracted text information may be classified using techniques such as natural language processing and machine learning, for example, a pre-trained classification model may be used or a custom model may be built to determine which contract type the text information belongs to. In some embodiments, in the target project contract document, a particular keyword or phrase may suggest the type of contract. Thus, by analyzing the feature point information, the frequency of occurrence, the context, etc. of these keywords or phrases can be determined, thereby deducing the type of contract.
Of course, it should be noted that some contract types may not be fully determined by automated methods, where manual assistance may be introduced to make the final determination. Specifically, in the embodiment of the application, the characteristic point information can be sent to related staff, and the staff can judge the undetermined part based on the characteristic point information and combined with the expertise and experience, so that the contract type of the target project contract document is obtained.
Step 240, invoking a risk point detection model corresponding to the contract type to perform risk point detection on the target project contract document to obtain risk point information corresponding to the target project contract document;
in this step, after the contract type is determined, a risk point detection model corresponding to the contract type may be invoked to perform risk point detection on the target project contract document, so as to obtain risk point information corresponding to the target project contract document. In the embodiment of the application, the risk point detection model is a model for automatically identifying and extracting the risk point information in the target project contract document, and can help a user to quickly discover possible legal, economic or other risks in the contract. The risk point detection of the target project contract document by the risk point detection model can be generally carried out according to the following steps: first, the target project contract document can be preprocessed, including operations such as word recognition, text cleaning, word segmentation, and the like, so that the contract document can be converted into data for model recognition. Before using the risk point detection model, the risk point detection model needs to be trained, specifically, the risk point detection model can be trained by using the already marked contract document data set.
After the risk point detection model is trained, the risk point detection model is applied to a target project contract document, and by inputting the target project contract document, the model can detect the risk point of each sentence, picture or paragraph in the contract, and the detection result can be binary classification (with risk points/without risk points) or multi-class classification (with different types of risk points).
It should be noted that, in the embodiment of the present application, the risk point information refers to a potential risk or compliance problem that may exist in the contract document. Illustratively, the risk point information may include vulnerabilities of legal terms, economic risks, ambiguities of contractual terms, and the like.
And 250, determining the position information of each risk point in the target project contract document according to the risk point information and the typesetting layout information.
In this step, after risk point information corresponding to the target project contract document is detected, risk point position information may be determined according to the risk point information and layout information, and then each risk point information may be accurately located and marked in the target project contract document. In this way, the user can be helped to more intuitively understand the distribution of risk points so as to better understand the potential risks existing in the contract.
It can be understood that, in the risk point identification method of the project contract provided by the embodiment of the application, a target project contract document to be identified is obtained, and typesetting layout information of the target project contract document is recorded; performing feature recognition on the target project contract document, and extracting feature point information in the target project contract document; determining the contract type of the target project contract document according to the characteristic point information; invoking a risk point detection model corresponding to the contract type to detect risk points of the target project contract document, and obtaining risk point information corresponding to the target project contract document; and determining the position information of each risk point in the target project contract document according to the risk point information and the typesetting layout information. The method can effectively identify the risk point information in the project contract document, and mark the risk point information to provide auditing guide for the user, so that the auditing efficiency of the contract can be conveniently improved, the probability of auditing omission of key content is reduced, and the risk of project contract planning is reduced.
Specifically, in some embodiments, referring to fig. 3, after the step of obtaining the target project contract document to be identified, the method further includes:
Step 310, obtaining a standard project contract document;
step 320, calculating the similarity between the target project contract document and the standard project contract document;
step 330, if the similarity is smaller than or equal to a preset threshold, returning the target project contract document and outputting prompt information; the prompt message is used for informing the user that the document format of the target project contract document is not satisfactory.
In the embodiment of the application, it can be understood that various scenes exist in the format of the target project contract document possibly submitted due to different business requirements, processing levels of personnel and the like, so that in order to improve the processing accuracy when the target project contract document is read, the document format conversion of the target project contract document is possibly needed. On the one hand, the format conversion of the target project contract document needs to take more time, and the efficiency of risk point identification is reduced. On the other hand, various target project contract documents are converted into a uniform format, and the functional requirements of the system are large, which may result in high construction cost of the system. Therefore, in the embodiment of the application, the limitation at the data source can be considered, and the user is restrained to process according to a fixed format when editing and uploading the target project contract document. In this way, the efficiency of risk point identification can be improved, and the implementation cost of the system can be reduced.
Specifically, in the embodiment of the present application, some standard project contract documents may be obtained, where a standard project contract document refers to a project contract document meeting a predetermined format requirement, and the specific format requirement may be determined according to the requirement, which is not limited in the present application. Upon acquisition of these standard project contract documents, they may be, in some embodiments, self-editing by the relevant staff member according to predetermined format requirements; in other embodiments, the selection from existing project contract documents may be made by the associated staff member according to predetermined format requirements. It may be understood that the number of standard project contract documents acquired in the embodiment of the present application may be one or more, and the present application is not limited to the number.
In the embodiment of the application, after the standard project contract document is obtained, the similarity between the target project contract document and the standard project contract document can be calculated. Here, the similarity is used to characterize the degree of similarity between two given terms, which are, for the embodiment of the present application, the target project contract document and the standard project contract document. The higher the value of the similarity, the more similar the target project contract document and the standard project contract document are. Therefore, it can be understood that in the embodiment of the application, the similarity degree of the target project contract document and the standard project contract document can be constrained through the similarity degree, so that the document format of the target project contract document is as close as possible to the standard project contract document, and the subsequent processing of the target project contract document is convenient. Specifically, in the embodiment of the present application, a threshold value of similarity may be set correspondingly, and is recorded as a preset threshold value. The similarity between the constraint target project contract document and the standard project contract document is greater than or equal to a preset threshold value, so that the constraint target project contract document and the standard project contract document are in a similar range, and the recognition efficiency and accuracy of the target project contract document are improved.
Here, it should be noted that, in the embodiment of the present application, the numerical expression of the similarity is not limited. For example, in some embodiments, the size of the similarity may be characterized by a percentage, 100% may be set to the maximum value of the similarity, when the similarity between the target project contract document and the standard project contract document reaches 100%, indicating that the two are completely identical, the size of the preset threshold may be set to 60%, and when the similarity between the target project contract document and the standard project contract document is less than or equal to 60%, the two may be considered dissimilar. Of course, the magnitude of the preset threshold is not limited in this embodiment, and may be flexibly set according to actual requirements.
In the embodiment of the application, if the similarity between the target project contract document and the standard project contract document is larger than the preset threshold value, the format of the target project contract document to be identified is basically accordant with the requirement, and the next risk point identification processing can be performed. In contrast, if the similarity between the target project contract document and the standard project contract document is smaller than or equal to the preset threshold, the format of the target project contract document to be identified is not in accordance with the requirement, at this time, the target project contract document can be returned and a prompt message can be output, and the prompt message can be used for informing the user that the document format of the target project contract document is not in accordance with the requirement, and conversion adjustment is required.
Generally, the common similarity algorithm includes cosine similarity algorithm, jacquard similarity algorithm, hamming distance algorithm, and the like, and in the embodiment of the present application, the specific algorithm is not limited.
Specifically, in some embodiments, referring to fig. 4, the calculating the similarity between the target project contract document and the standard project contract document includes:
step 410, detecting a first pixel value of each pixel point in the target project contract document and a second pixel value of each pixel point in the standard project contract document;
step 420, calculating a pixel difference between the first pixel value and the second pixel value;
step 430, determining the similarity between the target project contract document and the standard project contract document according to the pixel difference value.
In the embodiment of the application, a method for calculating the similarity between a target project contract document and a standard project contract document is provided, specifically, in the embodiment of the application, the pixel value of each pixel point in the target project contract document can be obtained and marked as a first pixel value, and the pixel value of each pixel point in the standard project contract document is marked as a second pixel value. Then, a pixel difference value between the first pixel value and the second pixel value may be calculated, and a similarity between the target project contract document and the standard project contract document may be determined from the pixel difference value. For example, in some embodiments, the calculation formula for the similarity may be:
Wherein i is the number of the pixel, similarity is the Similarity, and P i For the first pixel value, T i For the second pixel value, n is the total number of pixel points, and i and n are positive integers.
Specifically, in some embodiments, referring to fig. 5, the feature recognition of the target project contract document, extracting feature point information in the target project contract document, includes:
step 510, performing feature recognition on the target project contract document through an image segmentation technology or a text detection technology, and determining a target area corresponding to the feature point information;
and step 520, extracting text contents in the target area based on an optical character recognition technology to obtain the characteristic point information.
In the embodiment of the application, when the characteristic recognition is carried out on the target project contract document and the characteristic point information in the target project contract document is extracted, an image segmentation technology, a text detection technology and an optical character recognition technology can be used.
Specifically, taking a practical example as an example, it is assumed that there is an ICT project contract in which information such as the cost, delivery date, warranty period, and the like of the project is contained, and these information can be automatically extracted as feature point information by a feature recognition method. First, for fee information and the like, the position of the monetary digits may be located using image segmentation techniques; text detection techniques may be used to locate and extract date-related literal content for delivery dates, warranty periods, and the like. Once these feature points are located, OCR (optical character recognition) technology may be used to extract text content corresponding to the feature points, and thus feature point information may be obtained. For monetary digits, etc., OCR can convert it to recognizable digits; for date information, OCR may convert it to a standard date format.
Specifically, in the embodiment of the application, when an image segmentation technology is used, a semantic segmentation model based on deep learning can be used for segmenting different information areas in a target project contract document. Having obtained the segmented region in which the fee information resides, the following method may be used to locate the position of the monetary digits: firstly, converting a segmentation area into a gray image; applying a binarization method, such as an Otsu algorithm or an adaptive thresholding method, to the gray scale image to convert the image into a binary image; carrying out connected region analysis on the binary image, and detecting each connected region; for each connected region, its bounding box or minimum bounding rectangle is calculated to determine the location of the monetary digits. In locating the delivery date and warranty period (date information), date-dependent text content may be located and extracted using text detection techniques, such as deep learning based text detection models and OCR techniques. Having obtained the area where the date text is located, the following method can be used to locate the delivery date and the location of the warranty period: text detection is carried out on the area where the date characters are located, and a text detection algorithm, such as EAST or textboxes++, is used for detecting the boundary boxes of the character areas; transmitting the detected text region to an OCR engine, such as Tesseact or CRNN, and performing text recognition to obtain text content related to date; for the date text content, it may be parsed into a standard date format using a date parsing algorithm and the location of the delivery date and warranty period determined.
In the embodiment of the application, when the character content corresponding to the feature point is extracted by using the OCR technology, the specific mode can be as follows: identification of the monetary digits: locating characteristic points of the monetary digits, and converting the monetary digits into identifiable digits by using an OCR technology; the pre-trained CNN model can also be used for identifying the monetary digits; in the identification process, an image of the monetary figure is used as input, and forward propagation is carried out through a CNN model, so that a corresponding digital result is obtained. Identification of date information: feature points of the date information may be located and the date information converted to a standard date format using OCR technology. In some embodiments, a Recurrent Neural Network (RNN) based approach, such as Long Short Term Memory (LSTM) or two-way LSTM, may also be used, which is capable of processing sequence data, suitable for text recognition tasks.
In general, in the embodiment of the present application, the feature point information is extracted by using an image segmentation technique, a text detection technique and an optical character recognition technique, which can help to determine the contract type to which the target project contract document belongs.
Specifically, in some embodiments, referring to fig. 6, the determining, according to the feature point information, a contract type to which the target project contract document belongs includes:
Step 610, acquiring an industry specification database; the industry standard database comprises industry standard information of project contract documents;
and 620, matching the characteristic point information through the industry specification database, and determining the contract type of the target project contract document.
In the embodiment of the application, in the process of confirming the contract type of the target project contract document, the cooperative project and the signing rule in the target project contract document can be determined first, and an industry standard database can be acquired and can comprise the industry standard information of the project contract document as a reference standard for later comparison. Then, the characteristic point information in the current target project contract document can be compared with an industry specification database to determine the type of the target project contract document. For example, in some embodiments, the feature point information in the target project contract document may include contents such as "service", "transfer" and "collaboration", which represent different types of project contract documents, and the type of the target project contract document may be determined according to the information.
Specifically, in some embodiments, referring to fig. 7, the invoking the risk point detection model corresponding to the contract type to perform risk point detection on the target project contract document to obtain risk point information corresponding to the target project contract document includes:
Step 710, determining a variation range corresponding to the target project contract document according to the industry standard information;
step 720, calling a risk point detection model corresponding to the contract type, and inputting the target project contract document into the risk point detection model to obtain a first output variable corresponding to the target project contract document;
step 730, calculating a variable difference between the first output variable and a standard output variable;
and 740, obtaining risk point information corresponding to the target project contract document according to the variable difference value and the variable range.
In the embodiment of the application, the risk point detection model can be built by using a convolutional neural network. Specifically, when the risk point detection is performed on the target project contract document according to the risk point detection model, the variation range corresponding to the target project contract document can be obtained.
The convolutional neural network may include:
input layer: collecting project contract standard standards of various industries, creating a training set, selecting a plurality of marked standard project contract documents, marking contents in a contract according to industry standard information, determining a variation range corresponding to a target project contract document, and converting the target project contract document into characteristic data comprising texts, numbers and charts so as to be input into a convolutional neural network;
Convolution layer: adopting a ResNet or acceptance model structure, and selecting proper parameters and super parameters for training by referring to related researches in the field of the application; the project contract standard input convolution layer can obtain a standard output variable, and the target project contract document input convolution layer can obtain a first output variable;
output layer: comparing the standard output variable with the first output variable to obtain a variable difference value between the standard output variable and the first output variable, judging whether the variable difference value is in a corresponding variation range, marking the variable difference value in the corresponding variation range as a normal value to indicate that the content corresponding to the first output variable in the target project contract document accords with the industry specification, marking the variable difference value not in the variation range as an abnormal value to indicate that the content corresponding to the first output variable in the target project contract document does not accord with the industry specification.
Specifically, in the embodiment of the present application, the convolutional neural network may employ a threshold planning algorithm, where the algorithm formula is as follows:
W μ ＝λ|W variation -W standard |
wherein W is μ As the difference of the variables, W Variation For the first output variable, W, corresponding to the target project contract document standard Lambda is a wide variable corresponding to each variable, and is a constant value, and F (W) μ ) The function is programmed for a threshold value,for the variable difference threshold value, when the variable difference value W μ Below the output variable difference threshold +.>At this time, the threshold planning function F (W μ ) Output is 0, which indicates that the content corresponding to the first output variable in the target project contract document accords with the industry specification, when the variable difference value W μ Not lower than the variable difference threshold +.>At this time, the threshold planning function F (W μ ) And the output is 1, which indicates that the content corresponding to the first output variable in the target project contract document does not accord with the industry specification.
Specifically, in some embodiments, referring to fig. 8, determining location information of each risk point in the target project contract document according to the risk point information and the typesetting layout information includes:
step 810, searching the risk point information in the target project contract document;
step 820, determining page number information, graphic-text distribution information or annotation distribution information of each risk point according to the typesetting layout information.
Specifically, in the embodiment of the application, the typesetting layout information can represent the typesetting layout condition of the target project contract document, and according to the typesetting layout information, the position information of the chapter, the page number, the line number and the like of each sentence, picture or paragraph can be obtained. The position information of each risk point can be determined by combining the risk point information and the typesetting layout information, so that the position information of each risk point can be marked on a contract document, namely page number information, image-text distribution information or annotation distribution information of each risk point is determined.
In the embodiment of the application, different marking modes, such as highlighting, underlining, numbering and the like, can be used, so that a user can clearly identify the position of each risk point. Through determining the position information of the risk points and marking, the user can intuitively know the distribution situation of the risk points in the contract, is helpful for the user to evaluate the importance and influence of the risk points, and takes corresponding measures when necessary, such as modifying contract terms, negotiating with the counterpart and the like, so as to reduce the risk of the contract and protect the benefit of the user.
The following describes a risk point identification apparatus of a project contract proposed according to an embodiment of the present application with reference to the accompanying drawings.
Referring to fig. 9, a risk point identification device for a project contract according to an embodiment of the present application includes:
an obtaining unit 910, configured to obtain a target project contract document to be identified, and record typesetting layout information of the target project contract document;
an identifying unit 920, configured to perform feature identification on the target project contract document, and extract feature point information in the target project contract document;
a matching unit 930, configured to determine, according to the feature point information, a contract type to which the target project contract document belongs;
The detection unit 940 is configured to invoke a risk point detection model corresponding to the contract type to perform risk point detection on the target project contract document, so as to obtain risk point information corresponding to the target project contract document;
and a processing unit 950, configured to determine location information of each risk point in the target project contract document according to the risk point information and the typesetting layout information.
It can be understood that the content in the above method embodiment is applicable to the embodiment of the present device, and the specific functions implemented by the embodiment of the present device are the same as those of the embodiment of the above method, and the achieved beneficial effects are the same as those of the embodiment of the above method.
Referring to fig. 10, an embodiment of the present application provides an electronic device including:
at least one processor 1010;
at least one memory 1020 for storing at least one program;
the at least one program, when executed by the at least one processor 1010, causes the at least one processor 1010 to implement a risk point identification method for a project contract.
Similarly, the content in the above method embodiment is applicable to the present electronic device embodiment, and the functions specifically implemented by the present electronic device embodiment are the same as those of the above method embodiment, and the beneficial effects achieved by the present electronic device embodiment are the same as those achieved by the above method embodiment.
The embodiment of the present application also provides a computer-readable storage medium in which a program executable by the processor 1010 is stored, the program executable by the processor 1010 being for performing the above-described risk point identification method of the project contract when executed by the processor 1010.
Similarly, the content in the above method embodiment is applicable to the present computer-readable storage medium embodiment, and the functions specifically implemented by the present computer-readable storage medium embodiment are the same as those of the above method embodiment, and the beneficial effects achieved by the above method embodiment are the same as those achieved by the above method embodiment.
In some alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present application are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed, and in which sub-operations described as part of a larger operation are performed independently.
Furthermore, while the application is described in the context of functional modules, it should be appreciated that, unless otherwise indicated, one or more of the functions and/or features may be integrated in a single physical device and/or software module or may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary to an understanding of the present application. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be apparent to those skilled in the art from consideration of their attributes, functions and internal relationships. Accordingly, one of ordinary skill in the art can implement the application as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative and are not intended to be limiting upon the scope of the application, which is to be defined in the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium upon which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the foregoing description of the present specification, reference has been made to the terms "one embodiment/example", "another embodiment/example", "certain embodiments/examples", and the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the application, the scope of which is defined by the claims and their equivalents.
While the preferred embodiment of the present application has been described in detail, the present application is not limited to the embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present application, and the equivalent modifications or substitutions are intended to be included in the scope of the present application as defined in the appended claims.
Claims (10)
1. A risk point identification method for a project contract, comprising:
acquiring a target project contract document to be identified, and recording typesetting layout information of the target project contract document;
performing feature recognition on the target project contract document, and extracting feature point information in the target project contract document;
determining the contract type of the target project contract document according to the characteristic point information;
invoking a risk point detection model corresponding to the contract type to detect risk points of the target project contract document, and obtaining risk point information corresponding to the target project contract document;
And determining the position information of each risk point in the target project contract document according to the risk point information and the typesetting layout information.
2. The method for risk point identification of a project contract according to claim 1, characterized in that after the step of acquiring a target project contract document to be identified, the method further comprises:
acquiring a standard project contract document;
calculating the similarity between the target project contract document and the standard project contract document;
if the similarity is smaller than or equal to a preset threshold value, returning the target project contract document and outputting prompt information; the prompt message is used for informing the user that the document format of the target project contract document is not satisfactory.
3. The method of claim 2, wherein said calculating a similarity between said target project contract document and said standard project contract document comprises:
detecting first pixel values of all pixel points in the target project contract document and second pixel values of all pixel points in the standard project contract document;
calculating a pixel difference between the first pixel value and the second pixel value;
And determining the similarity between the target project contract document and the standard project contract document according to the pixel difference value.
4. The method for identifying risk points of a project contract according to claim 1, wherein the feature identifying the target project contract document, extracting feature point information in the target project contract document, comprises:
performing feature recognition on the target project contract document through an image segmentation technology or a text detection technology, and determining a target area corresponding to the feature point information;
and extracting the text content in the target area based on an optical character recognition technology to obtain the characteristic point information.
5. The risk point identification method of a project contract according to any one of claims 1 to 4, characterized in that the determining, based on the feature point information, a contract type to which the target project contract document belongs includes:
acquiring an industry specification database; the industry standard database comprises industry standard information of project contract documents;
and matching the characteristic point information through the industry specification database, and determining the contract type of the target project contract document.
6. The method for identifying risk points of a project contract according to claim 5, wherein the invoking the risk point detection model corresponding to the contract type to perform risk point detection on the target project contract document to obtain risk point information corresponding to the target project contract document includes:
determining a variation range corresponding to the target project contract document according to the industry standard information;
invoking a risk point detection model corresponding to the contract type, and inputting the target project contract document into the risk point detection model to obtain a first output variable corresponding to the target project contract document;
calculating a variable difference between the first output variable and a standard output variable;
and obtaining risk point information corresponding to the target project contract document according to the variable difference value and the variable range.
7. The method for identifying risk points of project contracts according to claim 1, wherein said determining location information of each risk point in said target project contract document based on said risk point information and said layout information includes:
searching the risk point information in the target project contract document;
And determining page number information, image-text distribution information or annotation distribution information of each risk point according to the typesetting layout information.
8. A risk point identification device for a project contract, comprising:
the acquisition unit is used for acquiring a target project contract document to be identified and recording typesetting layout information of the target project contract document;
the identification unit is used for carrying out feature identification on the target project contract document and extracting feature point information in the target project contract document;
the matching unit is used for determining the contract type of the target project contract document according to the characteristic point information;
the detection unit is used for calling a risk point detection model corresponding to the contract type to detect risk points of the target project contract document, so as to obtain risk point information corresponding to the target project contract document;
and the processing unit is used for determining the position information of each risk point in the target project contract document according to the risk point information and the typesetting layout information.
9. An electronic device, comprising:
at least one processor;
at least one memory for storing at least one program;
The at least one program, when executed by the at least one processor, causes the at least one processor to implement the risk point identification method of the project contract of any of claims 1-7.
10. A computer-readable storage medium having stored therein a program executable by a processor, characterized in that: the processor-executable program when executed by a processor is for implementing the risk point identification method of a project contract according to any one of claims 1-7.
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