Intelligent contract analysis and maintenance method based on image recognition

Abstract

The invention provides a method for realizing contract intelligent analysis and maintenance based on image recognition, and relates to the technical field of image recognition, and the method comprises the steps: dividing contract information into contract basic information, customer information, contract object information and contract term information based on a purchase and sale contract template; key elements in the four types are further subdivided into different types, and attribute custom configuration of the elements is supported; the method comprises the following steps: forming a date format library, automatically identifying date data in various formats, converting the date data into formats required by contract maintenance, forming dictionary item configuration for common attributes in a purchase and sale contract, converting text data into standard dictionary item data, and customizing a data matching and conversion verification rule library; establishing a data verification rule, performing logic rationality verification according to contract terms after image recognition is completed, and providing verification result feedback; and in combination with a UML modeling method, page display elements are dynamically configured according to actual business requirements.

Description

Technical Field

[0001] The present application belongs to the field of image recognition technology, and specifically relates to a method for realizing intelligent contract analysis and maintenance based on image recognition. Background Art

[0002] With the advent of the digital age, the digital transformation of enterprises has penetrated into various industries. As an indispensable part of business activities, contracts are also increasing in number and complexity. Traditional manual contract processing methods, such as manual reading, analysis, and reporting, are not only costly and inefficient, but also prone to errors. Especially in the context of the continuous expansion of enterprise scale, traditional contract management methods have been difficult to adapt to the needs of modern enterprises. Therefore, the use of artificial intelligence technology to realize the automatic parsing and maintenance of contracts has become an inevitable trend. Summary of the invention

[0003] The present application provides a method for intelligent contract analysis and maintenance based on image recognition to solve the technical problems of low efficiency, easy errors and high cost in contract management in the prior art.

[0004] The technical solution adopted in this application is:

[0005] The present application embodiment provides a method for realizing intelligent contract analysis and maintenance based on image recognition, including:

[0006] Based on the purchase and sales contract template, the contract information is divided into four types: basic contract information, customer information, contract subject information and contract terms information;

[0007] The key elements within the four types are further subdivided into different types, and custom configuration of element attributes is supported;

[0008] Form a date format library, automatically identify various formats of date data, and convert them into the format required for contract maintenance, form dictionary item configurations for common attributes in purchase and sales contracts, convert text data into standard dictionary item data, and customize data matching and conversion verification rule libraries;

[0009] Establish data verification rules, perform logical rationality verification according to the contract terms after image recognition is completed, and provide verification result feedback; combine UML modeling methods, and dynamically configure page display elements according to actual business needs.

[0010] The method for realizing intelligent contract analysis and maintenance based on image recognition provided by the present application also includes the following additional technical features: based on the purchase and sale contract template, the contract information is divided into four types: basic contract information, customer information, contract subject information and contract clause information, specifically:

[0011] Based on the purchase and sales contract templates provided by a large number of customers, the objects, elements, their attributes, behaviors and relationships involved in the contract are summarized through business process modeling;

[0012] In general, contract information is divided into four categories: basic contract information, customer information, contract subject information, and contract terms information. These four categories correspond to the four display areas on the system interface.

[0013] According to an embodiment of the present application, the key elements within the four types are further subdivided into different types, and customized configuration of the attributes of the elements is supported, specifically:

[0014] The key elements in each display area are further subdivided into different types, such as input boxes, drop-down boxes, single / multiple selection boxes, help selection boxes, date selection boxes and text fields.

[0015] Supports custom configuration of element display, display order, and required attributes to meet different business needs.

[0016] According to an embodiment of the present application, the date format library is formed to automatically identify date data in various formats and convert them into the format required for contract maintenance, form dictionary item configurations for common attributes in purchase and sales contracts, convert text data into standard dictionary item data, and customize data matching and conversion verification rule libraries, specifically:

[0017] Form a date format library to automatically identify date data in various formats and convert them into the format required for contract maintenance;

[0018] Form dictionary item configuration for common attributes in purchase and sales contracts, and convert text data into standard dictionary item data through exact matching or fuzzy matching;

[0019] Customize the data matching and conversion verification rule library to realize the conversion between uppercase and lowercase amounts, unify the amount measurement unit, and automatically convert thousandths.

[0020] According to an embodiment of the present application, the data verification rules are established, and after the image recognition is completed, the logical rationality verification is performed according to the contract terms, and the verification result feedback is provided; combined with the UML modeling method, the page display elements are dynamically configured according to the actual business needs, specifically:

[0021] Provide data validation rules, such as preventing the appearance of special characters, limiting data length, and verifying the unified social credit code;

[0022] After the image recognition is completed, the system will conduct a logical rationality check according to the contract terms and provide feedback on the verification results;

[0023] Combined with the UML modeling method, page display elements are dynamically configured according to actual business needs to achieve flexible display and efficient operation.

[0024] According to an embodiment of the present application, after the page display elements are dynamically configured according to actual business needs in combination with the UML modeling method to achieve flexible display and efficient operation, it also includes: the contract image recognition interface is divided into two left and right areas, the left area is the image upload and image recognition display area, and the right area is the contract data display editing area;

[0025] Initialize the image recognition area on the left side of the page according to the loaded contract key elements, and transfer the contract key element fields to be recognized to the image recognition component.

[0026] According to an embodiment of the present application, it also includes: in the contract entry interface, the contract data can be uploaded by selecting the local upload method or the mobile phone upload method, and the uploaded contract data will be automatically displayed in the data preview area;

[0027] After uploading the image data, it will be automatically transmitted to the AI ​​server. After receiving the image data, the AI ​​server will perform image detection and image recognition and return the recognized text in the contract text format;

[0028] After the image recognition results are returned, the recognition data is parsed and written back to the contract information maintenance area on the right side of the page according to the data conversion and verification rule model, and the number of filled fields and the number of fields to be filled in are updated;

[0029] When the user positions the mouse on a recognized field, the linked image preview area will highlight the field in the image, helping the user to quickly verify the accuracy of the recognized data;

[0030] For fields that were not recognized or were inaccurately echoed, the user manually copied and pasted the text from the left image area to the right contract maintenance area.

[0031] A computer program product containing instructions, when running on a device, enables the device to execute the steps in a method for intelligent contract analysis and maintenance based on image recognition.

[0032] A computer-readable storage medium stores a program, which, when executed by a processor, implements the steps in a method for intelligent contract analysis and maintenance based on image recognition.

[0033] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in a method for intelligent contract analysis and maintenance based on image recognition are implemented.

[0034] Due to the adoption of the above technical solution, the beneficial effects achieved by this application are as follows:

[0035] This application achieves standardized management of contract information by subdividing purchase and sales contract information into four categories: basic contract information, customer information, contract subject information, and contract clause information, and subdividing and customizing key elements. A date format library is formed to automatically identify and convert date formats, and dictionary items are configured for commonly used contract attributes, so that text data can be accurately converted into standard dictionary item data, greatly enhancing the standardization and readability of contract data. In addition, a customized data matching and conversion verification rule library ensures the consistency and accuracy of important information such as the amount.

[0036] By establishing data verification rules, we conduct logical rationality verification and provide feedback after image recognition is completed, effectively preventing the entry of erroneous data and ensuring the logical rationality of contract information and data reliability. Combining the UML modeling method to dynamically configure page display elements allows the system to flexibly adjust display content according to actual business needs, greatly improving user experience and work efficiency.

[0037] This application not only improves the degree of automation of contract management and reduces manual intervention, but also ensures the accuracy and consistency of contract information through a series of data conversion and verification mechanisms, greatly improving the efficiency and accuracy of contract processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0039] Figure 1 A flowchart of a method for realizing intelligent contract analysis and maintenance based on image recognition provided in an embodiment of the present application;

[0040] Figure 2 A schematic diagram of elements of a method for intelligent contract analysis and maintenance based on image recognition provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to more clearly illustrate the overall concept of the present application, a detailed description is given below in an illustrative manner in conjunction with the accompanying drawings.

[0042] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present application is not limited by the specific embodiments disclosed below. It should be noted that the embodiments of the present application and the features in each embodiment may be combined with each other without conflict.

[0043] In the present application, unless otherwise clearly specified and limited, a first feature "above" or "below" a second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more embodiments or examples.

[0044] Example 1

[0045] like Figure 1 and Figure 2 As shown, a method for realizing intelligent contract analysis and maintenance based on image recognition includes:

[0046] Based on the purchase and sales contract template, the contract information is divided into four types: basic contract information, customer information, contract subject information and contract terms information.

[0047] Specifically, basic contract information: This part covers the basic attributes of the contract, such as the contract number, signing date and other basic information. This type of information usually appears in the header of the contract and is the basis for identifying a contract.

[0048] Customer information: Specific information about both parties to the contract, including but not limited to the name, address, contact information and other identification information of the seller and buyer. This information is crucial to confirming the validity and execution of the contract.

[0049] Contract subject information: refers to the specific details of the transaction items or services in the contract, such as the name, specifications, quantity, price, etc. This is one of the most important parts of the contract and is directly related to the content and conditions of the transaction.

[0050] Contract terms information: includes the contract performance conditions, payment methods, liability for breach of contract, dispute resolution methods, etc. This part of information plays an important role in clarifying the rights and obligations of both parties and preventing legal risks.

[0051] For example, basic contract information: This part usually includes basic information such as contract number, contract name, contract version, signing date, etc. For example, the "Contract Number" field may appear on the page as an input box, where users can enter or view the unique number of the contract.

[0052] Customer information: This part involves the basic information of both parties to the transaction, such as the company name, address, contact information, legal representative, etc. of the buyer and seller. For example, the "seller" may be displayed as "XX Provincial Grain Reserve Management Co., Ltd.", while the "buyer" may be "XX Provincial Heyuan Grain Trade Co., Ltd."

[0053] Information about the subject of the contract: This part of the information covers the details of the goods being traded, such as the name of the goods, specifications, quantity, unit price, amount, etc. For example, if the contract involves the sale of grain, the specific grain variety, grain grade, delivery method (such as car, train, ship), and settlement method (such as wire transfer, check, cash) and other details will be listed here.

[0054] Contract terms information: This section includes the details of the various terms of the contract, such as payment terms, delivery methods, liability for breach of contract, etc. For example, specific terms may involve the payment method of the contract amount, the contract performance period, and any additional legal terms and conditions.

[0055] Furthermore, the classification of contract information can be further refined: in addition to the four main types, specific subcategories such as contract attachment information, payment terms, and breach of contract liability can be further subdivided to manage each part of the contract information in more detail.

[0056] Furthermore, data validation rules can be further enhanced: in addition to existing rules such as date format conversion, data dictionary item matching, amount conversion and validation, more complex validation logic can be added, such as checking the validity of specific terms, or automatically calculating relevant legal risk assessments based on contract terms.

[0057] Furthermore, natural language processing technology can be further introduced: for unstructured contract terms text, natural language processing technology (NLP) can be used to understand the terms content, extract key information in the terms, such as rights and obligations, performance period, etc., and convert it into structured data to facilitate subsequent data analysis and utilization.

[0058] Furthermore, an intelligent early warning system can be further established: by analyzing the contract performance and establishing an early warning mechanism, when potential problems or delays are found in the contract execution process, the system can issue warnings in time to remind relevant personnel to pay attention and deal with them in time.

[0059] Furthermore, the contract life cycle management can be further improved: in addition to data analysis and maintenance during the contract signing stage, it can also be extended to the management of the entire life cycle of contract execution, change, renewal, termination, etc., forming a complete contract management closed loop.

[0060] Furthermore, contract data can be further analyzed and mined: large amounts of contract data can be mined using big data analysis techniques to extract valuable business insights, such as identifying common problems in contract execution and optimization suggestions for contract terms, to provide support for corporate decision-making.

[0061] Furthermore, external data sources can be further integrated: the contract management system can be integrated with other business systems, such as ERP system, CRM system, etc., to achieve data sharing, avoid information islands, and improve the consistency and real-time nature of contract data.

[0062] The key elements within the four types are further subdivided into different types, and custom configuration of element attributes is supported.

[0063] Specifically, subdividing key elements: further subdividing specific key elements in the four categories of basic contract information, customer information, contract subject information and contract clause information. For example, the basic contract information may include the contract number, contract date, effective date, etc.; the customer information may include the customer name, address, contact information, etc.; the contract subject information may include the product name, quantity, unit price, etc.; the contract clause information may include the payment method, delivery conditions, breach of contract liability, etc.

[0064] Element type: These key elements are further subdivided into different types of controls such as input boxes, drop-down boxes, single or multiple selection boxes, text fields, etc. to accommodate different data input requirements. For example, the contract number may be an input box, while the payment method may be a drop-down box.

[0065] Attribute custom configuration: Each element supports a series of attribute custom configurations, such as whether it is required, display order, whether it is displayed, default value, whether it is read-only, etc. In this way, fields can be flexibly configured according to actual business needs, making the system closer to actual usage scenarios.

[0066] Database configuration: All of the above features can be customized in the database, which means that users can adjust the display name, data type and other attributes of the field at any time according to actual needs, so that the page display is more in line with the actual business logic.

[0067] For example, basic contract information

[0068] Input box type: used to enter contract number, signing date, etc.

[0069] Drop-down box type: used to select the contract status (such as pending review, effective, etc.);

[0070] Text field type: used to enter notes;

[0071] Date selection box: used to select the contract signing date, effective date, etc.

[0072] Customer Information

[0073] Input box type: used to enter the customer's name, address, etc.;

[0074] Radio button type: used to select the customer type (such as supplier, distributor, etc.);

[0075] Help selection box: used to enter the customer's contact information;

[0076] Date selection box: used to select the customer's registration date;

[0077] Multiple-select box type: used to select the customer's service scope or cooperation area.

[0078] Information about the subject matter of the contract

[0079] Input box type: used to enter product name, specification model, etc.;

[0080] Drop-down box type: used to select product categories;

[0081] Radio button type: used to select the unit of measurement (such as kilograms, tons, etc.);

[0082] Help selection box: used to enter special requirements or notes for the product;

[0083] Date selection box: used to select the delivery date of the product.

[0084] Contract terms information

[0085] Input box type: used to enter a detailed description of the terms;

[0086] Drop-down box type: used to select the type of terms (such as payment terms, quality terms, etc.);

[0087] Multiple-select box type: used to select applicable laws and regulations;

[0088] Text field type: used to enter additional terms or special instructions;

[0089] Help selection box: used to provide explanations or reference links related to the terms.

[0090] In addition, for each element, the system also supports custom configuration, such as:

[0091] Required or not: Set certain fields as required to ensure that important information is not omitted;

[0092] Display order: adjust the order of fields on the page to make the page layout more reasonable;

[0093] Display or not: Allows you to hide or display fields, and decide which information needs to be displayed to users based on actual needs;

[0094] Default value: Set the default value of the field to simplify user input;

[0095] Read-only: Set certain fields to read-only to prevent accidental modification;

[0096] Unit of measurement: specify the unit of measurement of the field, such as currency symbol, length unit, etc.

[0097] Data format: Specifies the data format of the field, such as numbers, text, dates, etc., and supports automatic matching and formatting.

[0098] This fine-grained custom configuration makes the system more flexible and able to adapt to the personalized needs of different enterprises.

[0099] Furthermore, the flexibility of custom configuration can be enhanced: in addition to the existing attribute configurations such as whether to display, display order, and whether to be required, more custom options can be added, such as data validation rules, logical relationships between fields, contextual help for data input, etc. This will make the system more flexible and more adaptable to different business scenarios.

[0100] Furthermore, machine learning algorithms can be introduced for predictive analysis: using machine learning technology, future contract trends or potential risk points can be predicted based on historical contract data, and preventive measures can be taken in advance. For example, based on past contract performance, possible problems in the future can be predicted and solutions can be proposed.

[0101] Furthermore, the functionality of the data dictionary can be enhanced: in addition to the existing data dictionary item matching conversion, the dictionary content can be further enriched to increase support for new business vocabularies, and even semantic understanding and context awareness capabilities can be added to better understand and process complex contract terms.

[0102] Furthermore, intelligent reminder and early warning functions can be added: after identifying key information such as contract amount, date, terms, etc., if it is found that certain terms are inconsistent with company policies, or certain numbers are inconsistent with expectations, the system can automatically remind the user and give correction suggestions.

[0103] Furthermore, mobile applications can be developed: Considering the convenience of contract processing, special mobile applications can be developed to allow users to handle contract matters anywhere and improve work efficiency.

[0104] Furthermore, third-party services can be integrated: the contract management system can be integrated with third-party payment platforms, logistics systems, etc. to achieve seamless connection of the entire process from contract signing to performance.

[0105] Furthermore, you can also build a knowledge base and help center: establish a knowledge base to collect and organize frequently asked questions, best practice cases and other information in contract processing to help users get started with the system more quickly and solve problems encountered during use.

[0106] Furthermore, a date format library can be formed to automatically identify various formats of date data and convert them into the format required for contract maintenance, form dictionary item configurations for common attributes in purchase and sales contracts, convert text data into standard dictionary item data, and customize data matching and conversion verification rule libraries;

[0107] Furthermore, data verification rules can be established. After image recognition is completed, logical rationality verification can be performed according to the contract terms, and verification result feedback can be provided. Combined with the UML modeling method, page display elements can be dynamically configured according to actual business needs.

[0108] A date format library is formed to automatically identify various formats of date data and convert them into the format required for contract maintenance. Dictionary item configurations are formed for common attributes in purchase and sales contracts, text data is converted into standard dictionary item data, and a data matching and conversion verification rule library is customized.

[0109] Specifically, forming a date format library that automatically identifies various formats of date data and converts them into the format required for contract maintenance means creating a standard library containing multiple date formats, which can identify and convert different forms of date data such as "YYYYMMDD", "YYYY / MM / DD", "YYYY-MM-DD", "YYYY-MM-DD", "MM / DD / YYYY", etc., and convert them uniformly into the date format required by the contract maintenance system. This conversion enables date information from different sources to be processed and stored consistently.

[0110] The configuration of dictionary items for commonly used attributes in purchase and sales contracts means creating a list or database containing attributes that are frequently used in contracts, such as grain varieties, grain grades, shipping methods (cars, trains, ships), bank types, settlement methods (telegraphic transfers, checks, cash), etc. When the contract image is recognized, the system will automatically match the recognized related text data with these dictionary items. Through exact matching or fuzzy matching, the diversified text data is converted into standard dictionary item data, so that the originally scattered and disordered data is standardized according to unified rules, which is convenient for further statistics and analysis.

[0111] Customized data matching and conversion verification rule base refers to a series of rules formulated for contract amounts to achieve conversion between uppercase and lowercase amounts, as well as customized matching conversion of amount measurement units. In addition, the amount data will automatically convert to thousandths, and the converted data will be compared with the original image recognition content for a second time to ensure the accuracy of the data. This series of conversion and verification rules is designed to ensure the consistency and integrity of the amount data.

[0112] In general, by forming a date format library, creating dictionary item configurations, and customizing a data matching and conversion verification rule library, this technical solution aims to convert complex contract text data into structured, easy-to-process information, thereby simplifying the contract maintenance process, reducing manual intervention, and improving the efficiency and accuracy of contract management.

[0113] For example, the date format library and automatic recognition conversion

[0114] Date format library: Create a library that contains multiple common date formats, such as YYYYMMDD, YYYY / MM / DD, YYYY-MM-DD, YYYY-MM-DD, MM / DD / YYYY, etc.

[0115] Automatic recognition and conversion: When the system receives the image recognition results, it automatically recognizes the date data and converts it into the format required for contract maintenance according to the configured business format. For example, if "March 15, 2024" appears in the image, the system will automatically recognize and convert it to "2024-03-15".

[0116] Common attribute dictionary item configuration

[0117] Attribute dictionary item configuration: For common attributes in purchase and sales contracts, such as grain variety, grain grade, shipping method (car, train, ship), bank account opening bank, settlement method (telegraphic transfer, check, cash), etc., create a standard dictionary item configuration.

[0118] Convert text data into standard dictionary item data: After image recognition, the system automatically matches the relevant data in the text to the above dictionary items. For example, if "wheat" is recognized, it will automatically match it to the "wheat" item under "grain varieties".

[0119] Customized data matching and conversion verification rule base

[0120] Contract amount matching conversion: The system has built-in rules that can automatically convert the uppercase amount recognized in the image into the lowercase amount, or vice versa, and perform unified matching conversion of measurement units. For example, "RMB 12,345" is automatically converted to "12,345 yuan".

[0121] Abnormal data verification reminder: When the image recognition result contains special characters that should not be there or the data length exceeds the reasonable range, the system will give a prompt and ask the user to verify or correct the data. For example, if the phone number "123456789012" is recognized, the system will mark the entry because it exceeds the reasonable length of the mobile phone number.

[0122] Data logic comprehensive verification: After the system identifies the contract amount, quantity, and unit price, it will automatically calculate the contract amount based on the quantity and unit price, and compare and confirm it with the image recognition result. In addition, it will also perform logic verification on the signing date, performance date, agreed completion date, etc. to ensure the rationality of the dates. For example, if the identified performance date is earlier than the signing date, the system will mark it as unreasonable and suggest the user to review it.

[0123] Furthermore, the intelligent matching capability of the data dictionary can be enhanced: in addition to the existing exact matching and fuzzy matching, natural language processing technology can be introduced to improve the accuracy of converting text data to standard dictionary item data. In addition, the data dictionary can be continuously updated and improved according to the needs of business development to ensure that it covers the latest business terms and classifications.

[0124] Furthermore, new data conversion rules can be added: in addition to common data types such as date and amount, special data conversion rules can be formulated for other special fields in the contract (such as address, contact information, etc.) to make the contract information more standardized.

[0125] Furthermore, the data verification rules can be optimized: the existing rules already cover multiple data verifications, but new verification requirements may be encountered in actual applications. Therefore, the verification rules can be continuously optimized, such as adding verification for specific business logic, or customizing verification rules according to the characteristics of different industries.

[0126] Furthermore, auxiliary tools or plug-ins can be developed: Auxiliary tools or browser plug-ins can be developed to help users perform preliminary data preprocessing before uploading contract images, such as cleaning up noise data in the text, or automatically marking key information areas in the contract to reduce the difficulty of subsequent image recognition.

[0127] Furthermore, improvements in OCR technology can be integrated: Although existing technologies can handle image recognition well, there may still be some cases where the recognition effect is poor. Therefore, more advanced OCR technology can be further studied and integrated to improve the accuracy and efficiency of recognition.

[0128] Furthermore, a user feedback mechanism can be established: set up user feedback channels, collect problems encountered by users during use, and continuously improve system functions and service quality based on this.

[0129] Furthermore, data security protection can be strengthened: given the importance of contract data, security measures such as data encryption and access control should be strengthened to ensure the security of data during transmission and storage.

[0130] Furthermore, multi-language support can be promoted: with the increase in international business, the system needs to support contract processing in multiple languages, so multi-language versions of data conversion and verification rules can be developed.

[0131] Establish data verification rules, perform logical rationality verification according to the contract terms after image recognition is completed, and provide verification result feedback; combine UML modeling methods, and dynamically configure page display elements according to actual business needs.

[0132] Specifically, establish data verification rules: After the image recognition is completed, the system will verify the recognized data according to the preset rule engine to ensure the logical rationality of the data. Verification rules include but are not limited to:

[0133] Check whether the date data conforms to the preset format;

[0134] Verify the consistency of economic data such as contract amount, quantity, unit price, etc.;

[0135] Verify the rationality of contract terms and information;

[0136] Verify the correctness of the format of unified social credit code, telephone number, postal code, bank account and other information;

[0137] Prompt abnormal data to remind users to make corrections.

[0138] Logical rationality check: The system combines the identified data with the contract terms to perform a logical rationality check. For example, the system will recalculate the contract amount based on the quantity and unit price and compare it with the identified amount; calculate the total contract weight and compare it with the identified total weight; check whether there is an unreasonable time difference between the signing date, performance date, agreed completion date and historical contract data, etc.

[0139] Provide feedback on verification results: When the identified data does not match the preset rules, the system will provide clear feedback to the user, indicating which data has problems and instructing the user on how to correct them.

[0140] Combine UML modeling method to dynamically configure page display elements: Through the UML modeling method, various objects, attributes, and behaviors involved in contract management are abstracted, and then these elements are dynamically rendered on the page. Users can configure the display order, display, and required attributes of page elements according to actual business needs to achieve the display effect that best suits the current business situation. This can flexibly respond to the differences between different contract templates and achieve personalized display.

[0141] For example, contract amount verification: after identifying the contract amount, quantity and unit price, the system recalculates the contract amount based on the quantity and unit price, and performs a second match with the amount recognized by the image. If the two amounts are inconsistent, the user is prompted to check and confirm.

[0142] Date logic check: The identified signing date, performance date, agreed completion date, etc. will be compared with the historical contract data by the system to check whether these dates are in a logical order. For example, the performance date should not be earlier than the signing date, otherwise the system will give a prompt.

[0143] Data format verification: Verify whether the identified data contains illegal characters, exceeds the reasonable data length, and meets the format requirements of specific fields. For example, whether the unified social credit code, contact number, postal code, bank account number and other data meet the format requirements of regular expressions.

[0144] In addition, combined with the UML modeling method, examples of page display elements being dynamically configured according to actual business needs include:

[0145] Dynamic field configuration: Depending on the different business requirements of the contract, the fields on the page (such as input boxes, drop-down boxes, radio buttons, etc.) can be dynamically configured. For example, if a contract does not require the "guarantee information" to be filled in, the field can be set to not be displayed in the configuration.

[0146] Adjust the display order: The fields on the page can be adjusted in the display order according to the needs of the business process. For example, in the process of signing a contract, the basic information is displayed first, then the information of both parties, and finally the contract terms. This order can be pre-set.

[0147] Mandatory field settings: Some fields may be required in certain types of contracts, while others are not. Through configuration, you can specify which fields are required and which are not required, so as to guide users to fill in the contract content correctly.

[0148] Field type selection: Different field types are suitable for different data input methods. For example, date fields can be entered through a date picker, while suppliers can be selected using a drop-down list. Page display elements can be dynamically configured according to actual business needs to adapt to different input scenarios.

[0149] Furthermore, the intelligence of data verification rules can be enhanced: the existing verification rules can already verify the logical rationality of some basic data, such as date, amount, etc. On this basis, higher-level data verification logic can be introduced, such as automatically learning patterns in historical contract data through machine learning algorithms, and more accurately identifying and warning abnormal data. In addition, verification of the interdependence between contract terms can be added to ensure the consistency and logic of the terms.

[0150] Furthermore, natural language processing technology can be introduced: for unstructured text information in the contract, natural language processing technology can be used to extract and understand the content of the terms, further enrich the data verification rules, enable it to understand the semantics of the contract terms, and make more reasonable judgments based on this.

[0151] Furthermore, the user interaction experience can be improved: based on the existing dynamic configuration of the page, the user interface design can be further optimized to provide a more intuitive and easy-to-use operation method. For example, the drag-and-drop editing function can be added to allow users to adjust the position of the field by dragging it directly on the page; the instant preview function can be provided so that users can see the actual effect while configuring.

[0152] Furthermore, mobile applications can be developed: considering that users may need to view and maintain contract information on different occasions, dedicated mobile applications can be developed to allow users to manage contracts anytime and anywhere on smartphones or tablets.

[0153] Furthermore, contract life cycle management can be realized: in addition to the current contract analysis and maintenance functions, it can also be expanded to the full life cycle management of the contract, including each link of contract drafting, approval, execution, change, termination, etc., to form a complete contract management system.

[0154] Furthermore, data security can be strengthened: considering the importance of contract data, data security protection measures should be strengthened, such as using higher-level data encryption algorithms to protect sensitive information and implementing strict access control policies to prevent unauthorized access.

[0155] Furthermore, external systems can be integrated: the contract management system can be integrated with other business systems (such as ERP, CRM, etc.) to achieve data interoperability, reduce information silos, and improve work efficiency.

[0156] Furthermore, a knowledge base can be established: by collecting and organizing common problems and solutions in contract management, a knowledge base can be established to provide users with online help and support to help them solve problems faster.

[0157] The method for realizing intelligent contract analysis and maintenance based on image recognition provided by the present application also includes the following additional technical features: based on the purchase and sale contract template, the contract information is divided into four types: basic contract information, customer information, contract subject information and contract clause information, specifically:

[0158] Based on the purchase and sales contract templates provided by a large number of customers, the objects, elements, their attributes, behaviors and relationships involved in the contract are summarized through business process modeling;

[0159] In general, contract information is divided into four categories: basic contract information, customer information, contract subject information, and contract terms information. These four categories correspond to the four display areas on the system interface.

[0160] Specifically, based on the purchase and sales contract templates provided by a large number of customers: First, by analyzing and summarizing the purchase and sales contract templates of multiple customers, we can understand the common features and differences in the contracts to provide a basis for subsequent modeling.

[0161] The objects, elements, attributes, behaviors and relationships involved in the contract are summarized through business process modeling: Then, the main objects (such as buyers and sellers), elements (such as product names and quantities), attributes (such as prices and quality requirements), behaviors (such as delivery methods and payment terms) and the relationships between these elements (such as the rights and obligations of both parties to the contract) in the contract are abstracted and summarized through business process modeling. This step aims to extract the common structures and patterns in the contract and provide guidance for subsequent data processing and display.

[0162] Generally, contract information is divided into four categories: basic contract information, customer information, contract subject information, and contract clause information, and corresponding display areas are formed on the system interface. Such classification helps users quickly find the information they need and simplifies the contract management process.

[0163] Basic contract information: usually includes basic information such as contract number, signing date, and effective date.

[0164] Customer information: identity information of both parties to the contract, such as name, address, contact information, etc.

[0165] Information on the subject matter of the contract: refers to the specific information on the goods or services agreed upon in the contract, such as specifications, model, quantity, price, etc.

[0166] Contract terms information: including various terms in the contract, such as payment terms, delivery terms, liability for breach of contract, etc.

[0167] These four categories correspond to four display areas on the system interface: On the system interface, special display areas are set up for the above four types of information, and users can view or modify relevant information in the corresponding areas. This design method makes it easy for users to intuitively manage and maintain contract data.

[0168] According to an embodiment of the present application, the key elements within the four types are further subdivided into different types, and customized configuration of the attributes of the elements is supported, specifically:

[0169] The key elements in each display area are further subdivided into different types, such as input boxes, drop-down boxes, single / multiple selection boxes, help selection boxes, date selection boxes and text fields.

[0170] Supports custom configuration of element display, display order, and required attributes to meet different business needs.

[0171] Specifically, the key elements are broken down into

[0172] Input box: used to enter text information, such as contract number, amount, etc.

[0173] Drop-down box: used to select pre-set options, suitable for selecting fixed options, such as province, city, etc.

[0174] Single-select / multiple-select box: suitable for selecting one or more items, such as contract type (long-term / short-term), payment method (one-time / installment), etc.

[0175] Help select boxes: These may be select boxes used to provide additional information or guidance to help users select the correct option.

[0176] Date selection box: specifically used to select dates, such as contract signing date, validity period, etc.

[0177] Text field: suitable for entering longer text information, such as contract terms, notes, etc.

[0178] Custom configuration of properties

[0179] Display or not: determines whether the element is visible on the interface. It can be used to control the visibility of information to adapt to different business scenarios.

[0180] Display order: defines the order in which elements are arranged on the interface, helping to organize the presentation of information and making it easier for users to understand and operate.

[0181] Required: Determine whether this element must be filled in to submit or save the contract information, which helps to ensure data integrity.

[0182] Through the above segmentation and configuration, the input and management of contract information can be made more flexible and efficient. For example, for different types of information, you can choose the most suitable input method (such as using a drop-down box to select a region instead of manual input). At the same time, by customizing element attributes, you can adapt to different business needs, such as hiding certain fields in specific situations or requiring certain key information to be filled in. This can not only ensure the accuracy and consistency of data, but also improve the user experience.

[0183] According to an embodiment of the present application, a date format library is formed to automatically identify date data in various formats and convert them into the format required for contract maintenance. A dictionary item configuration is formed for common attributes in a purchase and sales contract, text data is converted into standard dictionary item data, and a data matching and conversion verification rule library is customized, specifically:

[0184] Form a date format library to automatically identify date data in various formats and convert them into the format required for contract maintenance;

[0185] Form dictionary item configuration for common attributes in purchase and sales contracts, and convert text data into standard dictionary item data through exact matching or fuzzy matching;

[0186] Customize the data matching and conversion verification rule library to realize the conversion between uppercase and lowercase amounts, unify the amount measurement unit, and automatically convert thousandths.

[0187] Specifically, a date format library is formed: a library containing multiple date formats is created, which can identify and process various forms of date data such as "YYYYMMDD", "YYYY / MM / DD", "YYYY-MM-DD", "YYYY year MM month DD day", "MM / DD / YYYY", etc. By configuring the recognition format and business format, the library can automatically complete the conversion of date formats and convert the recognized date data into the standard date format required in the contract maintenance process for unified management.

[0188] Form dictionary item configuration: For common attributes in purchase and sales contracts, such as grain variety, grain grade, shipping method (car, train, ship), bank account opening bank, settlement method (telegraphic transfer, check, cash), etc., a series of standard dictionary item configurations are formed. By exact matching or fuzzy matching, the text data identified in the contract is compared with the dictionary items, and the most appropriate standard dictionary item data is intelligently recommended, thereby converting the diverse business data in the paper contract into statistically measurable data in a unified format.

[0189] Customized data matching and conversion verification rule base: For the contract amount, a data matching and conversion verification rule base is formulated. This rule base can realize the conversion between uppercase and lowercase amounts, and supports customized matching and conversion of amount measurement units. For example, it can automatically convert the recognized amount data to thousandths and perform a secondary comparison with the image recognition content to ensure the accuracy and completeness of the data. In addition, the rule base also includes data logic comprehensive verification, such as calculating the contract amount based on quantity and unit price, and performing a secondary matching verification with the image recognition amount.

[0190] According to an embodiment of the present application, a data verification rule is established. After the image recognition is completed, a logical rationality check is performed according to the contract terms, and feedback on the verification results is provided. Combined with the UML modeling method, the page display elements are dynamically configured according to actual business needs, specifically:

[0191] Provide data validation rules, such as preventing the appearance of special characters, limiting data length, and verifying the unified social credit code;

[0192] After the image recognition is completed, the system will conduct a logical rationality check according to the contract terms and provide feedback on the verification results;

[0193] Combined with the UML modeling method, page display elements are dynamically configured according to actual business needs to achieve flexible display and efficient operation.

[0194] Specifically, provide data verification rules

[0195] Prevent special characters from appearing: Verify whether the text data returned by image recognition contains special characters, such as symbols, control characters, and other characters that should not appear in the contract text, to ensure data cleanliness.

[0196] Limit data length: Check whether the identification data exceeds the reasonable data length of the field to prevent information loss or format errors caused by too long or too short data.

[0197] Verify the unified social credit code: Check whether the unified social credit code in the identification data conforms to the prescribed regular expression rules to ensure the correctness of the code.

[0198] Perform logical rationality check after image recognition is completed

[0199] Amount matching conversion: The system calculates the contract amount based on the identified contract amount, quantity and unit price, and performs a secondary matching check with the image recognition amount to ensure the consistency of the amount.

[0200] Quantity and weight verification: The detailed quantities of the identified contract items are added together to obtain the total contract quantity, and the calculated total contract weight is then matched with the image-recognized total contract weight for a second time to ensure consistency in quantity and weight.

[0201] Date logic verification: The identified signing date, performance date, and agreed completion date will be checked for logical rationality with historical contract data, and suggestions will be provided if major deviations are found.

[0202] Bank information verification: Match and verify the identified bank type, opening bank, and account number to ensure the accuracy of bank information.

[0203] Combine UML modeling method to dynamically configure page display elements

[0204] Dynamically configure page elements: Use UML modeling methods to dynamically configure page display elements, and automatically render page elements according to actual business needs to achieve flexible display and efficient operation.

[0205] Page display element configuration: including but not limited to the field's display name, data type, whether it is required, display format (input box, drop-down box, single choice, multiple choice, date, help selection, text field), whether it is read-only, display order, whether to display, etc.

[0206] Page element initialization: Initialize the image recognition area on the left side of the page according to the loaded contract key elements, and transfer the contract key element fields to be identified to the image recognition component.

[0207] According to an embodiment of the present application, after the page display elements are dynamically configured according to actual business needs in combination with the UML modeling method to achieve flexible display and efficient operation, it also includes: the contract image recognition interface is divided into two left and right areas, the left area is the image upload and image recognition display area, and the right area is the contract data display editing area;

[0208] Initialize the image recognition area on the left side of the page according to the loaded contract key elements, and transfer the contract key element fields to be recognized to the image recognition component.

[0209] Specifically, the contract image recognition interface is designed into two areas, left and right, with the following specific division of labor:

[0210] Left area: Mainly responsible for image upload and image recognition results display. Users can upload contract image data in this area and view the preliminary results after the system recognizes the image data. Image upload supports local upload or mobile phone upload. After the image data is uploaded, it will be automatically displayed in the preview area. Users can switch specific data files through the thumbnails at the bottom of the page.

[0211] Right area: used for displaying and editing contract data. Based on the custom configured contract elements in step 1, the contract information is dynamically rendered in the right area of ​​the page according to the order in which the elements are displayed. Each category supports the folding / expanding function, and the number of data to be filled in and the number of filled in data for the category are displayed to the right of the category title. Users can verify and edit the contract data in this area.

[0212] Dynamically initialize the image recognition area

[0213] When the data display area on the right side of the page is initialized successfully, the image recognition area on the left side of the page will be initialized according to the loaded contract key elements. This means that when the left area is initialized, the system will transfer the contract key element fields that need to be recognized to the image recognition component. The image recognition component supports uploading contract data to the system to complete data preview by uploading locally or by mobile phone.

[0214] With this design, users can view the image recognition results immediately after uploading the contract image data, and quickly locate the key elements in the editing area on the right to perform data verification and calibration, thereby quickly completing contract information maintenance.

[0215] According to an embodiment of the present application, it also includes: in the contract entry interface, the contract data can be uploaded by selecting the local upload method or the mobile phone upload method, and the uploaded contract data will be automatically displayed in the data preview area;

[0216] After uploading the image data, it will be automatically transmitted to the AI ​​server. After receiving the image data, the AI ​​server will perform image detection and image recognition and return the recognized text in the contract text format;

[0217] After the image recognition results are returned, the recognition data is parsed and written back to the contract information maintenance area on the right side of the page according to the data conversion and verification rule model, and the number of filled fields and the number of fields to be filled in are updated;

[0218] When the user positions the mouse on a recognized field, the linked image preview area will highlight the field in the image, helping the user to quickly verify the accuracy of the recognized data;

[0219] For fields that were not recognized or were inaccurately echoed, the user manually copied and pasted the text from the left image area to the right contract maintenance area.

[0220] Specifically, contract information upload and display

[0221] On the contract entry interface, users can choose two ways to upload contract information:

[0222] Local upload: Users can select files from their computers to upload.

[0223] Upload via mobile phone: Users can upload contract image data via mobile phone, such as scanning QR code to upload.

[0224] After the upload is complete, the contract data will be automatically displayed in the data preview area. Users can switch to specific data files through the thumbnails at the bottom of the page to view and confirm the uploaded contract image data.

[0225] Transmission and recognition of image data

[0226] The uploaded image data will be automatically transmitted to the AI ​​server. After receiving the image data, the AI ​​server will perform image detection and image recognition tasks. After the recognition is completed, the recognition results will be returned in the contract text format (including the coordinates of the text and the recognized text content). This process helps to ensure the correspondence between the recognized text data and the original contract text, which facilitates subsequent data processing.

[0227] Parsing and writing back recognition results

[0228] When the image recognition results are returned to the front end, the system will parse the recognized data according to the data conversion and verification rule model, and write the data back to the contract information maintenance area on the right side of the page. At the same time, the system will update the number of filled fields and the number of fields that need to be filled in, so that users can understand the filling progress at any time.

[0229] User verification of identification accuracy

[0230] In order to help users quickly verify the accuracy of the recognized data, the system provides a highlighting function. When the user positions the mouse on a recognized field, the system will highlight the field in the image preview area, making it easier for users to compare the original contract image data with the recognition results to ensure that the data is correct.

[0231] Manually adjust the recognition results

[0232] For fields that are not correctly identified or have inaccurate identification results, users can manually copy the corresponding text content from the image area on the left and paste it into the contract maintenance area on the right to ensure that the final contract information entered is accurate. This manual adjustment method provides flexibility and makes up for the possible errors in automatic identification.

[0233] Example 2

[0234] A computer program product containing instructions, when running on a device, enables the device to execute the steps in a method for intelligent contract analysis and maintenance based on image recognition.

[0235] Example 3

[0236] A computer-readable storage medium stores a program, which, when executed by a processor, implements the steps in a method for intelligent contract analysis and maintenance based on image recognition.

[0237] Example 4

[0238] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in a method for intelligent contract analysis and maintenance based on image recognition are implemented.

[0239] Anything not described in this application can be achieved by adopting or drawing on existing technologies.

[0240] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0241] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for realizing intelligent contract analysis and maintenance based on image recognition, characterized in that: include: Based on the purchase and sales contract template, the contract information is divided into four types: basic contract information, customer information, contract subject information and contract terms information; The key elements within the four types are further subdivided into different types, and custom configuration of element attributes is supported; Form a date format library, automatically identify various formats of date data, and convert them into the format required for contract maintenance. Form dictionary item configuration for common attributes in purchase and sales contracts, convert text data into standard dictionary item data, and customize data matching and conversion verification rule library; Establish data verification rules, perform logical rationality verification according to the contract terms after image recognition is completed, and provide verification result feedback; combine UML modeling methods, and dynamically configure page display elements according to actual business needs.

2. The method according to claim 1, characterized in that Based on the purchase and sale contract template, the contract information is divided into four types: basic contract information, customer information, contract subject information and contract clause information, specifically: Based on the purchase and sales contract templates provided by a large number of customers, the objects, elements, their attributes, behaviors and relationships involved in the contract are summarized through business process modeling; In general, contract information is divided into four categories: basic contract information, customer information, contract subject information, and contract terms information. These four categories correspond to the four display areas on the system interface.

3. The method according to claim 2, characterized in that The key elements within the four types are further subdivided into different types, and support customized configuration of element attributes, specifically: The key elements in each display area are further subdivided into different types, such as input boxes, drop-down boxes, single / multiple selection boxes, help selection boxes, date selection boxes and text fields. Supports custom configuration of element display, display order, and required attributes to meet different business needs.

4. The method according to claim 3, characterized in that The date format library is formed to automatically identify various formats of date data and convert them into the format required for contract maintenance, form dictionary item configurations for common attributes in purchase and sales contracts, convert text data into standard dictionary item data, and customize data matching and conversion verification rule libraries, specifically: Form a date format library to automatically identify date data in various formats and convert them into the format required for contract maintenance; Form dictionary item configuration for common attributes in purchase and sales contracts, and convert text data into standard dictionary item data through exact matching or fuzzy matching; Customize the data matching and conversion verification rule library to realize the conversion between uppercase and lowercase amounts, unify the amount measurement unit, and automatically convert thousandths.

5. The method according to claim 4, characterized in that The data verification rules are established. After the image recognition is completed, the logical rationality verification is carried out according to the contract terms, and the verification result feedback is provided; combined with the UML modeling method, the page display elements are dynamically configured according to the actual business needs, specifically: Provide data validation rules, such as preventing the appearance of special characters, limiting data length, and verifying the unified social credit code; After the image recognition is completed, the system will conduct a logical rationality check according to the contract terms and provide feedback on the verification results; Combined with the UML modeling method, page display elements are dynamically configured according to actual business needs to achieve flexible display and efficient operation.

6. The method according to claim 5, characterized in that After the page display elements are dynamically configured according to actual business needs in the combined UML modeling method to achieve flexible display and efficient operation, it also includes: the contract image recognition interface is divided into two areas, the left area is the image upload and image recognition display area, and the right area is the contract data display editing area; Initialize the image recognition area on the left side of the page according to the loaded contract key elements, and transfer the contract key element fields to be recognized to the image recognition component.

7. The method according to claim 6, characterized in that Also includes: On the contract entry interface, select local upload or mobile upload to upload the contract information. After uploading, it will be automatically displayed in the information preview area; After uploading the image data, it will be automatically transmitted to the AI ​​server. After receiving the image data, the AI ​​server will perform image detection and image recognition and return the recognized text in the contract text format; After the image recognition results are returned, the recognition data is parsed and written back to the contract information maintenance area on the right side of the page according to the data conversion and verification rule model, and the number of filled fields and the number of fields to be filled in are updated; When the user positions the mouse on a recognized field, the linked image preview area will highlight the field in the image, helping the user to quickly verify the accuracy of the recognized data; For fields that were not recognized or were inaccurately echoed, the user manually copied and pasted the text from the left image area to the right contract maintenance area.

8. A computer program product comprising instructions, which, when executed on a device, is characterized in that: The device executes the steps in the method for realizing intelligent contract analysis and maintenance based on image recognition as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the method for intelligent contract analysis and maintenance based on image recognition as described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method for intelligent contract analysis and maintenance based on image recognition as described in any one of claims 1 to 7 are implemented.

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