CN111709718A - Intelligent warranty asset service platform, method and storage medium based on artificial intelligence - Google Patents

Abstract

The application relates to artificial intelligence, and provides an intelligent warranty asset service platform, a method and a storage medium based on artificial intelligence, which comprises the following steps: the enterprise auditing module is used for identifying enterprise files through an optical character recognition technology, reading enterprise data and determining an enterprise access result; the asset auditing module is used for receiving an asset order, inquiring the registration information of a supplier for each asset, obtaining the mid-registration information corresponding to the supplier, generating an auditing task, distributing the auditing task to the corresponding intelligent robot equipment, completing the verification of the mid-registration asset to obtain a verification result, completing the auditing and obtaining an asset admission result; the contract generation module is used for performing mid-registration, triggering and generating an electronic contract, pushing an online signing task corresponding to the electronic contract to the terminal and finishing online signing of the electronic contract; and the block chain module is used for writing the electronic contract into the contract sub-block chain and writing the asset into the asset sub-block chain, so that the handling efficiency and quality of the warranty service are improved.

Description

Intelligent warranty asset service platform, method and storage medium based on artificial intelligence

Technical Field

The present application relates to artificial intelligence, and more particularly, to an intelligent warranty asset service platform, method and storage medium based on artificial intelligence.

Background

The warranty service is the largest potential scale type in the supply chain financial market, and is an accounts receivable and debt right transfer service based on the real trade background, namely, the warranty provider provides a commercial warranty financial scheme to a financing party, wherein the commercial warranty financial scheme comprises a financing scheme, an asset management scheme, a risk management scheme, a collection promotion scheme and the like.

With the accelerated development of warranty business, the modes of a large number of manual operations and manual audits result in long process, low efficiency, high labor consumption and high risk in the asset service stage, and directly hinder the business scale from increasing. For example, the traditional warranty asset service platform has the following problems: the manual verification of enterprises and the authenticity of assets consumes long time and is easy to be overlooked, and the logging efficiency in the manual inspection and registration is low.

Disclosure of Invention

Therefore, it is necessary to provide an intelligent warranty asset service platform, method and storage medium based on artificial intelligence, so as to reduce the examination order and manpower input of the warranty in the warranty business as a whole, improve the management capability of the warranty asset, and improve the handling efficiency and quality of the warranty business.

An intelligent warranty asset service platform based on artificial intelligence, comprising:

the enterprise auditing module is used for identifying enterprise files through an optical character recognition technology, reading enterprise data, wherein the enterprise data comprises enterprise complaint information, and determining an enterprise admission result according to the enterprise data;

the asset auditing module is used for receiving an asset receipt, inquiring provider registration information for each asset, inquiring to obtain mid-registration information corresponding to the provider, taking the mid-registration information as a matching sample pool for machine learning, generating a corresponding examination task for each asset, wherein the examination task comprises mid-registration information corresponding to the asset, distributing the examination task to corresponding intelligent robot equipment, acquiring assignment information related to the asset by learning the mid-registration information in the examination task, completing verification of the mid-registration asset according to the assignment information to obtain a verification result, and completing the examination according to the verification result to obtain an asset admission result;

the contract generation module is used for performing mid-registration after enterprise access and asset access are completed, automatically triggering and generating an electronic contract after registration, pushing an online signing task corresponding to the electronic contract to a terminal to complete online signing of the electronic contract, and entering the block chain module;

and the block chain module is used for automatically triggering the electronic contract to be written into the contract sub-block chain after the electronic contract is signed on line, and automatically triggering the asset to be written into the asset sub-block chain after the middle registration is successful.

In one embodiment, the enterprise audit module comprises:

and the enterprise complaint information auditing unit is used for identifying the complaint attribute state of the enterprise according to the enterprise complaint information, acquiring the association relation between the enterprise in the original state and a preset contract type when the complaint attribute state is in a reported state, and determining the enterprise admission result according to the association relation and the enterprise complaint information.

In one embodiment, the enterprise audit module comprises:

and the enterprise personnel auditing unit is used for identifying the qualification certificate of the enterprise personnel, reading the key information, sending an identity identification request carrying the key information to an authority, and obtaining the auditing result of the enterprise personnel according to the identification result corresponding to the identity identification request.

In one embodiment, the asset auditing module comprises:

the invoice checking unit is used for identifying the invoice electronic parts through an optical character recognition technology to obtain invoice element information, sending an invoice checking request carrying the invoice element information to an authority, and obtaining a corresponding asset admission result according to a checking result corresponding to the invoice checking request.

In one embodiment, the asset auditing module comprises:

and the machine learning judgment unit is used for identifying the invoice identifier, the contract identifier, the transaction contract name and the company name of the project corresponding to the asset in the registered information by an optical character recognition technology, analyzing the identification results in sequence according to a preset field sequence to obtain each sub-analysis result, and obtaining the verification result according to each sub-analysis result.

In one embodiment, the contract generation module includes:

and the face identification unit is used for receiving a face-to-face identification request sent by the terminal, identifying the face according to the face characteristic data carried in the face-to-face identification request to obtain an identification result, and initiating a formal signing request to the corresponding terminal when the identification result is that the face identification passes the authentication.

In one embodiment, the contract generation module includes:

and the electronic signature unit is used for acquiring the characteristic information of the electronic signer, making special identification data according to the characteristic information and generating a signature carrying the special identification data.

In one embodiment, the blockchain module includes:

the main chain and sub chain structure unit is used for providing global account book maintenance through a main chain, selecting a corresponding consensus mechanism through the sub chain according to a scene identifier, and determining the incidence relation between the main chain and other sub chains according to the scene identifier, wherein the incidence relation comprises cross-chain transaction and independent operation.

An artificial intelligence based intelligent warranty asset service method based on the artificial intelligence based intelligent warranty asset service platform described in any one of the above embodiments, the method comprising:

identifying enterprise files through an optical character recognition technology, reading enterprise data, wherein the enterprise data comprises enterprise complaint information, and determining an enterprise admission result according to the enterprise data;

the method comprises the steps of receiving an order for assets, inquiring provider registration information for each asset, inquiring to obtain mid-registration information corresponding to a provider, using the mid-registration information as a matching sample pool for machine learning, generating corresponding examination tasks for each asset, wherein the examination tasks comprise mid-registration information corresponding to the assets, distributing the examination tasks to corresponding intelligent robot equipment, acquiring assignment information related to the assets by learning the mid-registration information in the examination tasks by the intelligent robot equipment, completing verification of the mid-registration assets according to the assignment information to obtain a verification result, and completing the examination according to the verification result to obtain an asset admission result;

after enterprise access and asset access are completed, mid-registration is carried out, after registration, an electronic contract is automatically triggered and generated, and an online signing task corresponding to the electronic contract is pushed to a terminal so as to complete online signing of the electronic contract;

and after the online signing of the electronic contract is completed, automatically triggering to write the electronic contract into the contract sub-block chain, and after the register is successful, automatically triggering to write the assets into the asset sub-block chain.

A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:

identifying enterprise files through an optical character recognition technology, reading enterprise data, wherein the enterprise data comprises enterprise complaint information, and determining an enterprise admission result according to the enterprise data;

the method comprises the steps of receiving an order for assets, inquiring provider registration information for each asset, inquiring to obtain mid-registration information corresponding to a provider, using the mid-registration information as a matching sample pool for machine learning, generating corresponding examination tasks for each asset, wherein the examination tasks comprise mid-registration information corresponding to the assets, distributing the examination tasks to corresponding intelligent robot equipment, acquiring assignment information related to the assets by learning the mid-registration information in the examination tasks by the intelligent robot equipment, obtaining a verification result according to the verification result of the completion of the mid-registration assets verification of the assignment information, and completing the examination according to the verification result to obtain an asset admission result;

after enterprise access and asset access are completed, mid-registration is carried out, after registration, an electronic contract is automatically triggered and generated, and an online signing task corresponding to the electronic contract is pushed to a terminal so as to complete online signing of the electronic contract;

and after the online signing of the electronic contract is completed, automatically triggering to write the electronic contract into the contract sub-block chain, and after the register is successful, automatically triggering to write the assets into the asset sub-block chain.

The intelligent warranty asset service platform, the method and the storage medium based on artificial intelligence identify enterprise files through an optical character recognition technology through an enterprise auditing module, read enterprise data, wherein the enterprise data comprises complaint information, and determine an enterprise admission result according to the enterprise data; the asset auditing module is used for receiving an asset receipt, inquiring provider registration information for each asset, inquiring to obtain mid-registration information corresponding to the provider, taking the mid-registration information as a matching sample pool for machine learning, generating a corresponding examination task for each asset, wherein the examination task comprises mid-registration information corresponding to the asset, distributing the examination task to corresponding intelligent robot equipment, acquiring assignment information related to the asset by the intelligent robot equipment through learning the mid-registration information in the examination task, completing authentication of the mid-registration asset according to the assignment information to obtain a verification result, and completing the examination according to the verification result to obtain an asset admission result; the contract generation module is used for performing mid-registration after enterprise access and asset access are completed, automatically triggering and generating an electronic contract after registration, pushing an online signing task corresponding to the electronic contract to a terminal to complete online signing of the electronic contract, and entering the block chain module; the block chain module is used for automatically triggering the electronic contract to be written into a contract sub-block chain after the electronic contract is signed on line, automatically triggering the asset to be written into an asset sub-block chain after the middle registration is successful, realizing intelligent examination of the enterprise and the asset through the mutual cooperation of an enterprise examination module, an asset examination module, a contract generation module and a block chain module, applying OCR (optical character Recognition) intelligent identification, big data, intelligent examination and the like, realizing intelligent examination, machine learning intelligent judgment and automatic registration, and guaranteeing the safety by applying block chain encryption and storage technology, thereby reducing the examination order and labor input of a maintainer on a management service on the whole, improving the management capability of the insurance asset, and improving the handling efficiency and quality of the insurance service.

Drawings

FIG. 1 is an architecture diagram of an intelligent warranty asset service platform in one embodiment;

FIG. 2 is a diagram of an application environment in which the intelligent warranty asset service platform operates according to one embodiment;

FIG. 3 is a block diagram of an intelligent warranty asset service platform in one embodiment;

FIG. 4 is an interaction diagram of an audit flow for enterprise admission in one embodiment;

FIG. 5 is an interaction diagram of an audit flow for enterprise admission in another embodiment;

FIG. 6 is an architecture diagram of AI in combination with RPA in one embodiment;

FIG. 7 is an interaction flow diagram of asset admission auditing in one embodiment;

FIG. 8 is an interaction flow diagram of invoice validation in one embodiment;

FIG. 9 is a diagram of machine learning logic in one embodiment;

FIG. 10 is an interaction diagram illustrating the process of uplink in a blockchain in one embodiment;

FIG. 11 is a schematic flow diagram that illustrates a method for intelligent warranty asset service based on artificial intelligence, according to one embodiment;

FIG. 12 is a schematic flow chart illustrating an implementation of extreme quick payout, according to one embodiment;

FIG. 13 is a diagram illustrating an internal structure of a computer device according to an embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

The utility model provides an intelligence warranty asset service platform's based on artificial intelligence framework is shown in fig. 1, can get into service platform through unified entry, unified entry includes computer end entry, remove end applet entry and standard butt joint entry, service platform is applied to a plurality of different business scenes, as shown in the figure, to different objects, there are corresponding different functions, whole service platform has used the advanced technology of multiple difference, including robot, artificial intelligence, block chain, biological identification, OCR (Optical Character Recognition ) technique, enterprise big data, electron signing, invoice verification etc.. FIG. 2 is a diagram of an application environment in which an artificial intelligence based intelligent warranty asset service platform operates, according to one embodiment. As shown in fig. 2, the application environment includes a terminal 110, a server 120, and an organization center 130. The terminals, the servers and the organization center 130 communicate with each other through a network, and the communication network may be a wireless or wired communication network, such as an IP network, a cellular mobile communication network, etc., wherein the number of the terminals, the servers and the organization center is not limited. Wherein the organization center may include, but is not limited to, a large data center for industry and commerce, a large data center for public security and public security, a middle login, a large data center for national tax, and the like.

The terminal 110 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices. The server may be implemented as a stand-alone server or as a server cluster consisting of a plurality of servers. An artificial intelligence based intelligent warranty asset service platform may be deployed on one or more different servers.

In one embodiment, as shown in FIG. 3, an artificial intelligence based intelligent warranty asset service platform is provided and includes an enterprise audit module 210, an asset audit module 220, a contract generation module 230, and a blockchain module 240.

The enterprise auditing module 210 is configured to identify an enterprise file through an optical character recognition technology, read enterprise data, determine an enterprise admission result according to the enterprise data, where the enterprise data includes enterprise complaint information.

In particular, the enterprise file is a qualification related file of the enterprise, such as a license electronic part and the like. Enterprise data includes, but is not limited to, enterprise name, enterprise type, legal representative of the enterprise, enterprise registration address, business start and end date, whether the enterprise is listed in an abnormal business directory, enterprise complaint information, and the like. The enterprise complaint information can be obtained from a service interface provided by an external data service provider, and the specific information provided in the interface is subjected to keyword identification to obtain the enterprise complaint information.

The method comprises the following steps of intelligently identifying electronic parts such as qualification certificates of enterprises through an OCR (optical character recognition), completing key information reading, completing intelligent auditing of the enterprises by combining large data of industrial and commercial companies, complaints and blacklists, realizing zero-manual entry and intervention auditing, and obtaining an enterprise admission result, wherein the specific steps are as shown in the following figure 4 and comprise the following steps:

step 301: after an enterprise name is input into a terminal and an enterprise business license image is submitted, a business license image identification request is sent to an intelligent management asset service platform, and an enterprise access flow is started;

step 302, an enterprise auditing module in the intelligent management asset service platform performs OCR image recognition on the submitted business license, reads certificate information, verifies the certificate information with the input enterprise name, and performs the next step if the two data are matched;

step 303, adding 305, the enterprise auditing module sends an enterprise registration information inquiring request to the industrial and commercial data center according to the enterprise name by borrowing the technical means such as enterprise inquiry and the like, calls the industrial and commercial data to verify the detailed information and the operation condition of the enterprise, receives an enterprise registration information response message, stores and verifies the enterprise information, and enters the next step if the enterprise access condition is met;

step 306, step 308, further inquiring the enterprise complaint information, sending an enterprise complaint information inquiry request to the large data center of the industry and commerce, providing data basis for enterprise admission, receiving an enterprise complaint information response message, analyzing the enterprise complaint information, if complaint information is too much or the complaint information contains transaction asset information, the enterprise admission is not passed, otherwise, the enterprise admission is passed, and storing the enterprise complaint information.

In one embodiment, enterprise audit module 210 includes:

the enterprise complaint information auditing unit 211 is configured to identify a complaint attribute state of an enterprise according to enterprise complaint information, acquire an association relationship between the enterprise in an original reporting state and a preset contract type when the complaint attribute state is a reported state, and determine an enterprise admission result according to the association relationship and the enterprise complaint information.

Specifically, the complaint attribute state comprises a complaint state and a source state, for example, the enterprise A has complaint information, whether the enterprise A is the source state or the complaint state is identified, if the enterprise A is the source state, the violation risk is low, and the enterprise is allowed to admit. If the status is the reported status, further judging whether the original report is the project company corresponding to the trade contract, if the original report is not the project company corresponding to the trade contract, then judging whether disputes caused by the contract are not needed, if the original report is the project company corresponding to the trade contract, then deeply judging the elements such as the contract, the invoice and the like, obtaining the involved amount, counting the involved information quantity, and when the involved amount exceeds a preset threshold value or the involved information quantity exceeds a preset threshold value, determining that the enterprise is not allowed to pass the admission. In one embodiment, the identified valid information is presented on an enterprise audit information page.

In this embodiment, the incidence relation between the enterprise in the original state and the preset contract type is further obtained through the concern attribute state, and the enterprise admission result is determined according to the incidence relation and the enterprise concern information, so that the validity of the enterprise admission result judgment can be improved.

In one embodiment, enterprise audit module 210 includes:

and the enterprise personnel auditing unit 212 is used for identifying the qualification certificate of the enterprise personnel, reading the key information, sending an identity identification request carrying the key information to an authority, and obtaining the auditing result of the enterprise personnel according to the identification result corresponding to the identity identification request.

Specifically, electronic components such as personal qualification certificates and the like are intelligently identified through OCR, key information reading is completed, intelligent auditing of legal representatives and dealers is completed by combining public security, complaints and blacklist big data, and zero-manual entry and intervention auditing are realized. The specific steps are shown in fig. 5, and include the following steps:

step 401, submitting a legal identity card image at a terminal, sending a legal identity card identification request to an intelligent management asset service platform, and starting an identification process;

402, an enterprise personnel auditing unit in an enterprise auditing module in the intelligent management asset service platform identifies the submitted legal personnel identity card image by using an OCR technology;

step 403, adding 405, the enterprise personnel auditing unit submits the identified information to the public security big data through the legal real name authentication request for real name authentication to obtain real name authentication response information, if the real name authentication response information indicates that the authentication is passed, the auditing result is that the enterprise personnel are approved and the enterprise is allowed to access, and the legal information is stored, otherwise, the auditing result is that the enterprise personnel are not approved and the enterprise is not allowed to access.

In the embodiment, the qualification license of the enterprise personnel is quickly identified, the key information is read, the identification request carrying the key information is sent to the authority, the auditing result of the enterprise personnel is obtained according to the identification result corresponding to the identification request, the auditing result of the enterprise personnel can be quickly and efficiently obtained, and the enterprise admission passing result is determined according to the auditing result of the enterprise personnel.

The asset auditing module 220 is configured to receive an asset invoice, query provider registration information for each asset, query to obtain mid-registration information corresponding to the provider, use the mid-registration information as a matching sample pool for machine learning, generate a corresponding audit task for each asset, the audit task includes mid-registration information corresponding to the asset, allocate the audit task to corresponding intelligent robot equipment, the intelligent robot equipment obtains assignment information related to the asset by learning the mid-registration information in the audit task, obtain a verification result by completing mid-registration asset verification according to the assignment information, and complete the audit according to the verification result to obtain an asset admission result.

The property transfer description in the registration file in the asset has no official standard filling example, is completely different from the property transfer description registered according to the language and the context of the asset transferee, consists of some unstructured data, has no rule, and cannot be identified through conventional computer programming application. The asset-related transfer information includes an invoice number, a transaction contract name, a company name of the asset-corresponding project, and the like.

Specifically, the asset auditing module enables the AI robot to know different languages and contexts in registration by machine learning and natural language processing, so that the robot is helped to accurately identify asset hit information in the document auditing process, an intelligent document auditing effect is achieved, document auditing and registration efficiency is fully improved, and the labor cost is saved. And the registration information of the supplier can be used as a matching sample pool for machine learning and used as a sample for subsequent machine learning, so that the learning effect is continuously improved.

Ai (artificial intelligence), also known as artificial intelligence, is an advanced technology for studying and developing theories, methods, techniques and application systems for simulating, extending and expanding human intelligence. RPA (robotic process automation) is also called robot process automation, can simulate human operation, is the solution with the lowest cost and the quickest solution for replacing manual operation, and is suitable for data-intensive and repetitive operation and regular fixable triggering process.

By the aid of the leading-edge AI technology, Machine Learning (Machine Learning) and natural language Processing (natural language Processing) enable RPA (robot Process Automation), so that the RPA has the capability of optimizing the Process, manual work is replaced by a Machine, and a large amount of manual debugging work is avoided. In one embodiment, the architecture diagram of AI in conjunction with RPA is shown in FIG. 6, which is low cost, safe, flexible to deploy and is self-developed.

In one embodiment, the specific steps of the asset audit module for performing asset admission audit are shown in fig. 7, and include the following steps:

step 501: an asset auditing module in the intelligent management asset service platform receives an asset bill through various channels;

step 502: the asset auditing module inquires supplier registration information for each asset, inquires all the registration information of the suppliers and uses the information as a matching sample pool for subsequent machine learning;

step 503: the asset auditing module generates an auditing task for each asset;

step 504: the asset auditing module carries out system assignment auditing tasks according to the number of the robots and the state of each robot, and can also carry out manual adjustment on task assignment conditions, and the process can synchronously push asset related information including mid-registration information to the intelligent robots;

step 505: the intelligent robot starts to work after receiving the examination order task, learns each mid-registration file of a supplier corresponding to the asset, and checks whether transfer information related to the asset, such as invoice numbers, transaction contract names, project company names corresponding to the asset and the like exists or not, so that the examination order action is finished;

step 5061-1: if the examination order of the robot does not pass, generating corresponding result information and outputting the corresponding result information to the asset examination module, and generating a corresponding examination order follow-up task by the asset examination module;

step 5061-2: the asset auditing module is used for dispatching a bill to a customer service call center personnel according to the bill examining result, and the customer service completes offline communication with a project company or a supplier to supplement or replace data;

step 5062-: if the robot passes the examination order, the asset examination module generates a manual review task according to the examination result, and the operator performs review and then completes asset admission, so that the step can be selected, and the follow-up action of the personnel review can be gradually cancelled along with the increasing intelligence of machine learning and the improvement of learning capacity.

And the contract generation module 230 is configured to perform mid-registration after enterprise admission and asset admission are completed, automatically trigger generation of an electronic contract after registration, and push an online signing task corresponding to the electronic contract to the terminal to complete online signing of the electronic contract, so as to enter the block chain module.

Specifically, after the electronic contract is generated, an online signing task can be pushed to the terminal to sign online for related maintainers, core enterprises, project companies and suppliers, face recognition can be performed before signing, the terminal sends an AI face recognition request to a contract generation module of the intelligent maintenance asset service platform, and the intelligent maintenance asset service platform can initiate formal signing actions to the terminal after face recognition authentication is passed, so that online signing of the electronic contract is completed.

And the block chain module 240 is used for automatically triggering the electronic contract to be written into the contract sub-block chain after the electronic contract is signed on line, and automatically triggering the asset to be written into the asset sub-block chain after the electronic contract is successfully registered.

Specifically, the blockchain is a decentralized distributed shared ledger, and the value of the blockchain is that a distributed consensus mechanism is established by constructing an ad hoc network and using a string of data blocks generated by cryptography association, wherein the data blocks are ordered in time and cannot be tampered, so that a decentralized trust system is realized. The block chain module of the intelligent warranty asset service platform carries out electronic signature on an electronic contract jointly signed by a warranty and enterprises of all parties, various electronic files and even collection records finally filed by the warranty, and writes the electronic signature into a contract sub-block chain through a alliance block chain certificate, so that the intelligent warranty asset service platform has the advantages of being tamper-proof and easy to obtain the certificate. When the hastening period is exceeded and the final litigation measure can be adopted, the block chain technology can ensure that the evidence chain is complete and effective. After the mid-check registration is successful, the assets are automatically triggered to be written into the asset sub-block chain, and the safety of the assets can also be guaranteed. The chains of contracts and asset sub-blocks may form cross-chain transactions or run independently. It should be emphasized that the electronic contract is written into the contract sub-block chain, and the asset is written into the asset sub-block chain, so that the privacy and the security of the information are ensured, and the information can be stored in a node of the block chain.

In the embodiment, the enterprise auditing module, the asset auditing module, the contract generating module and the block chain module are mutually matched to apply OCR intelligent recognition, big data, intelligent inspection and the like to realize intelligent auditing of enterprises and assets, realize intelligent examination, intelligent judgment of machine learning and automatic registration, apply block chain encryption and guarantee technology to guarantee safety, reduce examination orders and manpower input of a security manager on a security service as a whole, improve the management capability of the security assets, and improve the handling efficiency and quality of the security service.

In one embodiment, the asset audit module 220 includes:

the invoice checking unit 221 is configured to recognize an invoice electronic part through an optical character recognition technology to obtain invoice element information, send an invoice checking request carrying the invoice element information to an authority, and obtain a corresponding asset admission result according to a checking result corresponding to the invoice checking request.

Specifically, electronic parts such as invoices are intelligently identified through an OCR (optical character recognition), invoice element information is obtained through identification, the invoice element information comprises invoice numbers, invoice amount, invoice top-up information and the like, intelligent invoice inspection is carried out, tax bureau information is obtained, whether the invoices are real, are paid off or are overdue is judged, if the invoices are true, automatic audit is rejected, and manual intervention is not needed. The specific steps are shown in fig. 8, and include the following steps:

step 601, submitting an invoice image at a terminal, sending an invoice image identification request to an intelligent warranty asset service platform, and starting an identification process;

step 602, adding 603, identifying invoice information from an invoice image by an invoice inspection unit of an asset auditing module in the intelligent management asset service platform through an OCR technology, avoiding a manual input link, and storing the identified invoice information;

and step 604, submitting the identified invoice element information to the national tax big data for checking through an invoice information query request by the invoice checking unit, and calling the national tax big data to check the authenticity of the invoice by using the identified invoice element information, such as invoice number, code, tax-containing amount, tax-free amount, invoice type, check code and the like.

Step 605 and 606, obtaining an invoice information response message, if the invoice verification of the invoice information response message passes, the identification result is that the invoice verification passes, otherwise, the identification result is that the invoice verification fails, and storing the invoice verification information.

In one embodiment, the asset audit module 220 includes:

and the machine learning judgment unit 222 is configured to recognize the invoice identifier, the contract identifier, the transaction contract name, and the company name of the project corresponding to the asset in the registered information by using an optical character recognition technology, sequentially analyze the recognition results according to a preset field sequence to obtain sub-analysis results, and obtain a verification result according to the sub-analysis results.

Specifically, if the inquiry certificate of enterprise a checks the invoice number 00010001 to be currently assigned to the first company, the invoice number is already assigned to the organization B, which means that the right assigned to the first company is defective, and is a type of hit. And (4) intelligently judging whether the registered bottom-layer assets are compared in the process of completion and accurately identifying whether the asset information has the defect conditions of the rights such as public registration or transfer and the like by using machine learning. And identifying invoice identification, contract identification, trade contract name and asset corresponding project company name information in the registration information in the comparison process, sequentially analyzing according to a preset field sequence to obtain sub-analysis results, wherein the sub-analysis results comprise results obtained after sequential comparison, and determining a final verification result according to the sub-analysis results obtained by sequential comparison.

In an embodiment, a schematic diagram of the machine learning logic is shown in fig. 9, and the comparison results are obtained in sequence according to the logic sequence shown in fig. 9, and finally the verification result is obtained.

In the embodiment, the invoice identifier, the contract identifier, the transaction contract name and the name information of the project company corresponding to the asset in the registered information are identified, and are sequentially analyzed in sequence to obtain each sub-analysis result, and the verification result is obtained according to each sub-analysis result, so that the reliability of the verification result is improved.

In one embodiment, the contract generation module 230 includes:

the face recognition unit 231 is configured to receive a face-to-face recognition request sent by the terminal, recognize a face according to face feature data carried in the face-to-face recognition request to obtain a recognition result, and initiate a formal signing request to the corresponding terminal when the recognition result is that face recognition authentication passes.

The face recognition is a living organism recognition technology for identity recognition based on face feature information of a person. The face recognition authentication is a technology that a camera or a camera is used for collecting images or video streams containing faces, the faces are automatically detected and tracked in the images, the detected faces are accurately recognized, and the detected faces are compared with face data in a database to achieve identity determination.

Specifically, the intelligent management asset service platform performs living body detection and identification on legal representatives and dealers by applying a face recognition authentication technology, and ensures that the legal representatives and the dealers perform personal operations when performing business operations and signing contracts. The later time that the other party proposes not self-operated is avoided, so the behavior of the other party has no credit risk of effectiveness.

In this embodiment, when the identification result is that the face identification authentication passes, the formal signing request is initiated to the corresponding terminal, so that the signing reliability is further ensured.

In one embodiment, the contract generation module 230 includes:

and the electronic signature unit 232 is used for acquiring the characteristic information of the electronic signer, making the special identification data according to the characteristic information, and generating the signature carrying the special identification data.

Specifically, the electronic signature is a signature in a proprietary data text format which is made for the electronic signer based on the proprietary information provided by the electronic signer under the authentication of real will. There are four core elements: the data used when making the electronic signature belongs to the exclusive of the signer, the electronic signature making data is only controlled by the signer when signing, any change to the electronic signature after signing can be found, and any change to the content and form of the data message after signing can be found.

The intelligent management asset service platform completes real wish authentication by applying a face recognition authentication technology. And making proprietary identification data according to the characteristic information, generating a signature carrying the proprietary identification data, and ensuring that any change of the electronic signature after signing can be found by technical means such as a Hash algorithm, a trusted timestamp and the like. And the integrity and authenticity of the certificate information are ensured by using a block chain certificate storage technology in combination with a third-party notarization department and a judicial authentication center.

The specific application scenarios are as follows: by intelligent identity recognition and online electronic signature, the safety, effectiveness and convenience of contract signing are ensured, the electronic signature supports one-key seal falling, flexible seal falling, perforation seal falling and the like, the seal type covers enterprise seals, legal representative seals, office seals and the like, and different service scenes are fully met.

In one embodiment, the blockchain module 240 includes:

the main chain sub-chain structure unit 241 is configured to provide global account book maintenance through a main chain, select a corresponding consensus mechanism according to a scene identifier through a sub-chain, and determine an association relationship between the main chain and other sub-chains according to the scene identifier, where the association relationship includes cross-chain transaction and independent operation.

Specifically, the intelligent warranty alliance block chain adopts a structure with one main chain and a plurality of sub-chains, the main chain does not perform a large amount of data synchronization, only provides a global account book maintenance function, and guarantees safety and decentralization. The sub-chains select a proper consensus mechanism according to an actual scene, and form bidirectional anchoring with the main chain through the cross-chain link points, so that cross-chain transaction or independent operation with other sub-chains can be realized according to the actual service scene, and the sub-chains can be helped to obtain the final consistency provided by the main chain on the premise of improving the performance. Through the layered structure, the safety and the stability are put into the main chain, the efficiency is put into the sub-chain to independently set a scene to do, and therefore the contradiction that no triangle is possible among decentralization, safety and efficiency is solved.

The intelligent warranty asset service platform carries out electronic signature on electronic contracts signed by the warranty and enterprises of all parties, various electronic files and collection urging records finally filed by the warranty and stores evidence through a alliance block chain, and has the advantages of being tamper-proof and easy to obtain evidence. When the hastening period is exceeded and the final litigation measure can be adopted, the block chain technology can ensure that the evidence chain is complete and effective.

In one embodiment, as shown in fig. 10, a specific interaction flow example is as follows:

step 701, after the intelligent examination order finishes the asset admission, performing mid-check registration, and automatically triggering to generate an electronic contract after the mid-check registration;

step 702, automatically triggering the asset uplink after successful registration;

step 703, after the electronic contract is generated, the electronic contract is manually checked and pushed to the terminal to perform online signing for the related warranty, the core enterprise, the project company and the supplier;

step 704, face recognition is carried out before signing a contract, and an AI face recognition request is sent;

step 705, initiating a formal signing operation after the face recognition authentication is passed;

step 706, completing the online signing of the electronic contract;

and step 707, after the contract signing of each party of the contract is completed, the transaction is completed, and the contract is automatically linked.

In one embodiment, as shown in fig. 11, based on the intelligent management asset service platform based on artificial intelligence described in any of the above embodiments, there is provided an intelligent management asset service method based on artificial intelligence, including:

step 802, identifying enterprise files through an optical character recognition technology, reading enterprise data, wherein the enterprise data comprises enterprise complaint information, and determining an enterprise admission result according to the enterprise data.

And 804, receiving an asset order, inquiring provider registration information for each asset, inquiring to obtain corresponding mid-registration information of the provider, using the mid-registration information as a matching sample pool for machine learning, and generating a corresponding examination task for each asset, wherein the examination task comprises the mid-registration information corresponding to the asset. And the examination order task is distributed to the corresponding intelligent robot equipment, the intelligent robot equipment acquires the transfer information related to the assets through learning the mid-registration information in the examination order task, completes the verification of the mid-registration assets according to the transfer information to obtain a verification result, and completes the examination order according to the verification result to obtain an asset access result.

Step 806, after the enterprise access and the asset access are completed, performing mid-registration, automatically triggering to generate an electronic contract after registration, pushing an online signing task corresponding to the electronic contract to the terminal to complete online signing of the electronic contract, and entering step 808.

And 808, automatically triggering to write the electronic contract into the contract sub-block chain after the online signing of the electronic contract is completed, and automatically triggering to write the asset into the asset sub-block chain after the middle registration is successful.

Fig. 12 is a schematic flow chart illustrating how to realize the extreme fast deposit through the intelligent management asset service method according to the specific embodiment.

It should be understood that although the various steps in the flowcharts of fig. 4-5 and 7-11 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 4-5 and 7-11 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least some of the sub-steps or stages of other steps.

For specific limitations of the intelligent management asset service method based on artificial intelligence, reference may be made to the above limitations of the intelligent management asset service platform based on artificial intelligence, and details are not repeated here. All or part of each module in the artificial intelligence intelligent management asset service platform can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the computer equipment, or can be stored in a memory in the computer equipment in a software form, so that the processor can call and execute operations corresponding to the modules, and the operations can be distributed in a plurality of different servers.

In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 13. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data tables. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement an artificial intelligence based intelligent warranty asset service method.

Those skilled in the art will appreciate that the architecture shown in fig. 13 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: identifying enterprise files through an optical character recognition technology, reading enterprise data, wherein the enterprise data comprises enterprise complaint information, determining an enterprise admission result according to the enterprise data, performing asset receipt, inquiring supplier registration information for each asset, inquiring to obtain corresponding mid-registration information of the supplier, taking the mid-registration information as a matching sample pool for machine learning, generating corresponding examination tasks for each asset, and enabling the examination tasks to comprise the mid-registration information corresponding to the asset. The examination order task is distributed to the corresponding intelligent robot equipment, the intelligent robot equipment acquires transfer information related to assets through learning the middle registration information in the examination order task, the middle registration asset verification is completed according to the transfer information to obtain a verification result, the examination order is completed according to the verification result to obtain an asset admission result, the middle registration is performed after the enterprise admission and the asset admission are completed, an electronic contract is automatically triggered and generated after the registration, the online signing task corresponding to the electronic contract is pushed to the terminal to complete the online signing of the electronic contract, the electronic contract is automatically triggered to be written into the contract sub-area chain after the online signing of the electronic contract is completed, and the assets are automatically triggered to be written into the asset sub-area chain after the middle registration is successful.

In one embodiment, the computer program when executed by the processor further performs the steps of: identifying the complaint attribute state of the enterprise according to the complaint information of the enterprise, acquiring the association relation between the enterprise in the original state and the preset contract type when the complaint attribute state is in the reported state, and determining the admission result of the enterprise according to the association relation and the complaint information of the enterprise.

In one embodiment, the computer program when executed by the processor further performs the steps of: and identifying the qualification license of the enterprise personnel, reading the key information, sending an identity identification request carrying the key information to an authority, and obtaining the auditing result of the enterprise personnel according to the identification result corresponding to the identity identification request.

In one embodiment, the computer program when executed by the processor further performs the steps of: identifying the electronic invoice parts through an optical character recognition technology to obtain invoice element information, sending an invoice inspection request carrying the invoice element information to an authority, and obtaining a corresponding asset access result according to an inspection result corresponding to the invoice inspection request.

In one embodiment, the computer program when executed by the processor further performs the steps of: and identifying the invoice identification, the contract identification, the transaction contract name and the project company name corresponding to the asset in the registered information by an optical character identification technology, analyzing the identification results in sequence according to a preset field sequence to obtain each sub-analysis result, and obtaining a verification result according to each sub-analysis result.

In one embodiment, the computer program when executed by the processor further performs the steps of: and receiving a face-to-face identification request sent by the terminal, identifying the face according to face characteristic data carried in the face-to-face identification request to obtain an identification result, and initiating a formal signing request to the corresponding terminal when the identification result is that the face identification passes the authentication.

In one embodiment, the computer program when executed by the processor further performs the steps of: and acquiring the characteristic information of the electronic signer, making special identification data according to the characteristic information, and generating a signature carrying the special identification data.

In one embodiment, the computer program when executed by the processor further performs the steps of: the method comprises the steps of providing global account book maintenance through a main chain, selecting a corresponding consensus mechanism through a sub-chain according to a scene identifier, and determining the incidence relation between the main chain and other sub-chains according to the scene identifier, wherein the incidence relation comprises cross-chain transaction and independent operation.

The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

This application can be applied to in the wisdom government affairs to promote the construction in wisdom city.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. An intelligent management asset service platform based on artificial intelligence, comprising:

the enterprise auditing module is used for identifying enterprise files through an optical character recognition technology, reading enterprise data, wherein the enterprise data comprises enterprise complaint information, and determining an enterprise admission result according to the enterprise data;

the asset auditing module is used for receiving an asset receipt, inquiring provider registration information for each asset, inquiring to obtain mid-registration information corresponding to the provider, taking the mid-registration information as a matching sample pool for machine learning, generating a corresponding examination task for each asset, wherein the examination task comprises mid-registration information corresponding to the asset, distributing the examination task to corresponding intelligent robot equipment, acquiring assignment information related to the asset by learning the mid-registration information in the examination task, completing verification of the mid-registration asset according to the assignment information to obtain a verification result, and completing the examination according to the verification result to obtain an asset admission result;

the contract generation module is used for performing mid-registration after enterprise access and asset access are completed, automatically triggering and generating an electronic contract after registration, pushing an online signing task corresponding to the electronic contract to a terminal to complete online signing of the electronic contract, and entering the block chain module;

and the block chain module is used for automatically triggering the electronic contract to be written into the contract sub-block chain after the electronic contract is signed on line, and automatically triggering the asset to be written into the asset sub-block chain after the middle registration is successful.

2. The platform of claim 1, wherein the enterprise audit module comprises:

and the enterprise complaint information auditing unit is used for identifying the complaint attribute state of the enterprise according to the enterprise complaint information, acquiring the association relation between the enterprise in the original state and a preset contract type when the complaint attribute state is in a reported state, and determining the enterprise admission result according to the association relation and the enterprise complaint information.

3. The platform of claim 1, wherein the enterprise audit module comprises:

and the enterprise personnel auditing unit is used for identifying the qualification certificate of the enterprise personnel, reading the key information, sending an identity identification request carrying the key information to an authority, and obtaining the auditing result of the enterprise personnel according to the identification result corresponding to the identity identification request.

4. The platform of claim 1, wherein the asset audit module comprises:

the invoice checking unit is used for identifying the invoice electronic parts through an optical character recognition technology to obtain invoice element information, sending an invoice checking request carrying the invoice element information to an authority, and obtaining a corresponding asset admission result according to a checking result corresponding to the invoice checking request.

5. The platform of claim 1, wherein the asset audit module comprises:

and the machine learning judgment unit is used for identifying the invoice identifier, the contract identifier, the transaction contract name and the company name of the project corresponding to the asset in the registered information by an optical character recognition technology, analyzing the identification results in sequence according to a preset field sequence to obtain each sub-analysis result, and obtaining the verification result according to each sub-analysis result.

6. The platform of claim 1, wherein the contract generation module comprises:

and the face identification unit is used for receiving a face-to-face identification request sent by the terminal, identifying the face according to the face characteristic data carried in the face-to-face identification request to obtain an identification result, and initiating a formal signing request to the corresponding terminal when the identification result is that the face identification passes the authentication.

7. The platform of claim 1, wherein the contract generation module comprises:

and the electronic signature unit is used for acquiring the characteristic information of the electronic signer, making special identification data according to the characteristic information and generating a signature carrying the special identification data.

8. The platform of claim 1, wherein the blockchain module comprises:

the main chain and sub chain structure unit is used for providing global account book maintenance through a main chain, selecting a corresponding consensus mechanism through the sub chain according to a scene identifier, and determining the incidence relation between the main chain and other sub chains according to the scene identifier, wherein the incidence relation comprises cross-chain transaction and independent operation.

9. An artificial intelligence based intelligent warranty asset service method based on the artificial intelligence based intelligent warranty asset service platform of any one of claims 1 to 8, the method comprising:

identifying enterprise files through an optical character recognition technology, reading enterprise data, wherein the enterprise data comprises enterprise complaint information, and determining an enterprise admission result according to the enterprise data;

the method comprises the steps of receiving an order for assets, inquiring provider registration information for each asset, inquiring to obtain mid-registration information corresponding to a provider, using the mid-registration information as a matching sample pool for machine learning, generating corresponding examination tasks for each asset, wherein the examination tasks comprise mid-registration information corresponding to the assets, distributing the examination tasks to corresponding intelligent robot equipment, acquiring assignment information related to the assets by learning the mid-registration information in the examination tasks by the intelligent robot equipment, completing verification of the mid-registration assets according to the assignment information to obtain a verification result, and completing the examination according to the verification result to obtain an asset admission result;

after enterprise access and asset access are completed, mid-registration is carried out, after registration, an electronic contract is automatically triggered and generated, and an online signing task corresponding to the electronic contract is pushed to a terminal so as to complete online signing of the electronic contract;

and after the online signing of the electronic contract is completed, automatically triggering to write the electronic contract into the contract sub-block chain, and after the register is successful, automatically triggering to write the assets into the asset sub-block chain.

10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method as claimed in claim 9.

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