CN113065940A - Invoice reimbursement method, device, equipment and storage medium based on artificial intelligence - Google Patents
Invoice reimbursement method, device, equipment and storage medium based on artificial intelligence Download PDFInfo
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Abstract
The application relates to the field of financial science and technology, and discloses an invoice reimbursement method based on artificial intelligence, which comprises the following steps: acquiring the information type of the reimbursement invoice selected by a user, wherein the information type comprises a picture invoice and a text invoice; calling a first information acquisition mode corresponding to the picture invoice to acquire the specified information in the picture invoice, and calling a second information acquisition mode corresponding to the text invoice to acquire the specified information in the text invoice; checking the duplicate in a background database, and judging whether a historical reimbursement record corresponding to the specified information exists; if not, assigning the specified information as a dynamic variable of the page through a specified function; combining the assigned dynamic variable and a preset static variable into an invoice reimbursement note; and starting invoice audit and invoice reimbursement according to the invoice reimbursement bill. By using the key information of the invoice as a dynamic parameter and using other information as a static parameter, the invoice reimbursement bill is quickly formed by combining the key information and the static information of the invoice, and the electronization of invoice reimbursement data is realized.
Description
Technical Field
The application relates to the field of financial science and technology, in particular to an invoice reimbursement method, an invoice reimbursement device, invoice reimbursement equipment and an invoice reimbursement storage medium based on artificial intelligence.
Background
Various invoices of different purposes need to be checked and reimbursed in different levels in daily management of a company, corresponding invoice details and invoice list documents are manually filled by staff according to the actual conditions of the invoices of the company in the conventional invoice reimbursement process, and financial staff verify and establish an invoice document library.
Disclosure of Invention
The main purpose of this application is to provide the method of invoice reimbursement based on artificial intelligence, aims at solving current invoice reimbursement and can not regard as the electronic data processing, leads to wasting time and energy, easily make mistakes and inconvenient statistics's that gathers technical problem.
The application provides an invoice reimbursement method based on artificial intelligence, which comprises the following steps:
acquiring an information type of a reimbursement invoice selected by a user, wherein the information type comprises a picture invoice and a text invoice;
calling a first information acquisition mode corresponding to the picture invoice to acquire the specified information in the picture invoice, and calling a second information acquisition mode corresponding to the text invoice to acquire the specified information in the text invoice;
checking the duplicate in a background database, and judging whether a historical reimbursement record corresponding to the specified information exists in the background database;
if not, assigning the specified information as a dynamic variable of the page through a specified function;
combining the assigned dynamic variable and a preset static variable into an invoice reimbursement note;
and starting invoice audit and invoice reimbursement according to the invoice reimbursement bill.
The application also provides a device of invoice reimbursement based on artificial intelligence, includes:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring the information type of the reimbursement invoice selected by a user, and the information type comprises a picture invoice and a text invoice;
the calling module is used for calling a first information acquisition mode corresponding to the picture invoice to acquire the specified information in the picture invoice, and calling a second information acquisition mode corresponding to the text invoice to acquire the specified information in the text invoice;
the judging module is used for checking duplicate in a background database and judging whether a historical reimbursement record corresponding to the specified information exists in the background database;
the assignment module is used for assigning the specified information to a dynamic variable of a page through a specified function if the historical reimbursement record corresponding to the specified information does not exist;
the combination module is used for combining the assigned dynamic variable and the preset static variable into an invoice reimbursement note;
and the starting module is used for starting invoice audit and invoice reimbursement according to the invoice reimbursement bill.
The present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method as described above.
This application is through regarding as the dynamic parameter with the key information of newly uploading the invoice, and the invoice reimbursement note is as static parameter except that the information of key information, then accurately acquires the key information of invoice through the invoice type to combine with static information, form the invoice reimbursement note fast, realize the electronization of invoice reimbursement data, completion invoice reimbursement affairs that can be quick, and reduce the probability of makeing mistakes, save time improves the treatment effeciency.
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FIG. 1 is a schematic flow chart illustrating a method for artificial intelligence based invoice reimbursement according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a system for artificial intelligence based invoice reimbursement according to an embodiment of the present application;
fig. 3 is a schematic diagram of an internal structure of a computer device according to an embodiment of the present application.
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.
Referring to fig. 1, a method for invoice reimbursement based on artificial intelligence according to an embodiment of the present application includes:
s1: acquiring an information type of a reimbursement invoice selected by a user, wherein the information type comprises a picture invoice and a text invoice;
s2: calling a first information acquisition mode corresponding to the picture invoice to acquire the specified information in the picture invoice, and calling a second information acquisition mode corresponding to the text invoice to acquire the specified information in the text invoice;
s3: checking the duplicate in a background database, and judging whether a historical reimbursement record corresponding to the specified information exists in the background database;
s4: if not, assigning the specified information as a dynamic variable of the page through a specified function;
s5: combining the assigned dynamic variable and a preset static variable into an invoice reimbursement note;
s6: and starting invoice audit and invoice reimbursement according to the invoice reimbursement bill.
The invoice reimbursement system is composed of HTML pages, can support multiple persons to submit invoices at the same time, and can store invoice information submitted by different users at the same time. And when the user updates the invoice information every time, the loaded page is dynamically refreshed and loaded through the javascript technology, so that the page loading speed is increased. For example, when only the invoice information content of the invoice reported this time in the HTML page is refreshed, other contents of the HTML page are not changed and are used as static variables, and the static variables are assigned only once when the system is initially started. And for the part to be refreshed, using a jquery framework of a javascript library, and importing the part into the current code through tag matching to be used as a dynamic variable. When a user clicks a submission button to submit an invoice, data of a client is transmitted to a background through a location and reload () function, and after the data are combined to form an invoice reimbursement bill, the client submitting the invoice can confirm whether the invoice amount is wrong or not in the invoice reimbursement system after receiving the related amount, and clicks and confirms without errors, and then subsequent auditing and reimbursement are started. And if the invoice amount is wrong, describing specific problem content, feeding the content back to the invoice reimbursement system, and using the content as an optimization basis of a model algorithm in subsequent information identification and acquisition of the invoice reimbursement system.
The specific information includes but is not limited to invoice date, invoice code, invoice head-up, invoice tax number, invoice type, invoice amount and the like. The method for acquiring the designated information comprises the steps that the designated information is acquired in different modes for picture invoices and text invoices, the picture invoices are paperboard invoices, a user needs to scan the picture invoices and then upload the scanned picture invoices, and the text invoices are electronic invoices and can be acquired through a website linked with the electronic invoices. The specified information of the picture invoice is obtained by combining OCR (Optical Character Recognition) and a deep neural network. The specified information of the text invoice is acquired in a python crawler mode. The picture invoice and the text invoice are distinguished by reading a file suffix of the invoice uploaded by a user. According to the method and the device, data duplication checking is performed in the background database, so that repeated invoices are prevented from being found in time, the follow-up auditing and calculating processes are borrowed, and the invoice reimbursement accuracy is improved.
This application is through regarding the key information who newly uploads the invoice as dynamic parameter, the information beyond the key information of invoice reimbursement note is as static parameter, then through the information acquisition mode that the different invoice types that picture invoice and text invoice correspond, the key information who accurately acquires the invoice respectively is as dynamic parameter, and combine with static information, form invoice reimbursement note fast, completion invoice reimbursement affairs that can be quick, and reduce the probability of makeing mistakes, and the time saving improves the treatment effeciency.
Further, the step S6 of starting invoice audit and invoice reimbursement according to the invoice reimbursement sheet includes:
s61: acquiring a designated department to which the user belongs;
s62: selecting a designated auditing path corresponding to the designated department;
s63: judging whether the audit result of the appointed audit path is approved;
s64: if yes, linking a national tax official network, and judging whether the invoice information of the invoice reimbursement bill is valid or not;
s65: and if so, distributing the invoice amount of the invoice reimbursement bill to a bank account corresponding to the user.
The user of the application is a registered user of the system, and when the user registers, the user can fill out information of the department, including but not limited to name, telephone, mailbox, bank card number and the like, and clicks a storage button, the information of the department is stored in a background database. The designated audit path and the department information of the designated department are stored in the background database in an associated mode, and the preset audit path corresponding to the department information can be selected conveniently and timely according to the department information. The appointed auditing path consists of a plurality of auditing nodes, and the arrangement sequence of the auditing nodes is sequentially arranged from low to high according to the auditing grades.
After the nodes in the audit path are approved, the nodes automatically request to access the national tax official network, and determine whether the information of the current invoice is accurate from the national tax official network so as to verify the validity of the invoice. For example, after the page is judged to be loaded, the code requests the database to inquire invoice information, and information comparison is carried out on the invoice information and the invoice information in the tax official website page, if the information is consistent, the invoice is valid, and if the information is not consistent, the invoice is invalid. The invoice reimbursement system is bound with the financial system, after invoice is filled and audited, the financial system enters a unified account checking system, after the amount of money is confirmed to be correct, the amount of money of the reimbursed invoice is automatically credited to a bank account corresponding to a user through the financial system, and the personal account is credited according to the actual reimbursement amount of the user counted by the background database through bank authorization service. Compared with the existing manual auditing process, the digital circulation of the information is realized, the auditing process time is saved, the data is fixed, stored and analyzed through the digitization of the information, the data checking and the global analysis are facilitated, and the information accuracy is improved.
Further, after the step S65 of dispatching the invoice amount of the invoice reimbursement slip to the bank account corresponding to the user, the method includes:
s66: acquiring label information selected by a summary invoice initiated by a request end;
s67: performing multi-table association query in the background database according to the label information to acquire all invoice reimbursement notes associated with the label information;
s68: and displaying all invoice reimbursement sheets on the request terminal display page according to a preset summary table.
In the embodiment of the application, the tag information includes an initiator tag, a department tag, a time tag, and the like, and summarized data with different requirements can be realized according to the tag information. In the summarizing process, contents meeting the label information in the multiple tables are intensively copied into the summarizing table in a multi-table correlation query mode to form all invoice reimbursement bill data corresponding to the label information, so that data summarizing and displaying are realized, an invoice total list and an invoice personal list are automatically generated, and paper invoices and electronic invoices are distinguished through source marks. Before data is written into the database, a link is established with the database in the written code, and a link field is established in a database table. And when the electronic invoice information is written into the database, the acquired electronic invoice information is stored into the database in a timestamp label field by using an sql language to form label information. A source label is set for distinguishing the paper invoice and the electronic invoice, for example, the label flag of the electronic invoice is written as 1, the label flag of the paper invoice is 0 to mark different sources, so that information summarization is facilitated according to different label information, corresponding data can be rapidly and conveniently obtained according to needs and displayed in a centralized manner, and convenience in data processing and data application is improved.
Further, the step S2 of calling the first information obtaining manner corresponding to the picture invoice and obtaining the specified information in the picture invoice includes:
s21: starting a designated optical character recognition algorithm;
s22: scanning the picture invoice through the optical character recognition process to obtain scanning data;
s23: inputting the scanning data into a preset recurrent neural network model to obtain a recognition result;
s24: and taking the identification result as the specified information in the picture invoice.
In the application, in the process of translating the paper invoice into computer characters after scanning the invoice by an OCR character recognition technology, the recognition accuracy needs to be improved by an RNN recurrent neural network model algorithm, and the model weight with the highest accuracy is trained by a training set, so that the information recognition accuracy in the paper invoice is improved.
Further, the step S23 of inputting the scan data into a preset recurrent neural network model to obtain a recognition result includes:
s231: converting the scanning data into a designated vector;
s232: classifying and identifying the designated vectors through a preset clustering algorithm to obtain clustering probabilities of the designated vectors corresponding to the clustering clusters;
s233: and inputting the clustering probability and the designated vector into the preset recurrent neural network model to obtain an identification result corresponding to the scanning data.
The preset recurrent neural network model is an RNN recurrent neural network model, the weighting value closest to the actual result is confirmed through the current input and the previous input weighting and the function result, and the last calculation output of the RNN recurrent neural network model is used as the input to realize the circulation. Training is carried out according to the time sequence, the output of the previous hidden layer is used as the input of the next hidden layer, the output result is obtained by combining the weight of each hidden layer, the training loss of each step of the RNN neural network model is recorded through a loss function, and the weight value of each step is adjusted until the accurate recognition result is obtained. Hidden layer at time t: h (t) ═ Z (Ux + Wh (t-1+ b)), Z () is a tanh activation function, Z (x) ═ (e (x) -e (-x))/(e (x)) + e (-x)), and time t the output layer: o (t) ═ G (vh (t) + c), G () is the softmax function, G (x) ═ 1/(1+ e (-x)), where x denotes input, h denotes hidden layer unit, o is output, b and c are preset modified parameters, and U/V/W/is weight.
In order to improve the identification accuracy of the RNN cyclic neural network model to scanning data, the RNN cyclic neural network model is subjected to instructive intervention through an LSTM long-short term gate control technology and a K-means clustering algorithm, namely data are input through a double input gate, and simultaneously the neural cell state of the RNN cyclic neural network model is updated through a sigmoid activation function of the K-means clustering algorithm and a tanh function of the LSTM long-short term gate control technology, so that the similarity of an invoice and each cluster is obtained according to the classification information of an invoice vector, and the character identification accuracy of the RNN cyclic neural network model is improved. The trained RNN recurrent neural network model can lock the identification range of the input information according to the classification information of the input data, and the identification accuracy of the invoice information is improved.
Further, the step S2 of calling a second information obtaining manner corresponding to the text invoice and obtaining the specified information in the text invoice includes:
s201: starting a python crawler to download webpage information corresponding to the text invoice from an electronic invoice website set to be crawled;
s202: storing the webpage information in a temporary database;
s203: and cleaning the data in the temporary data to obtain the designated information corresponding to the text invoice.
In the embodiment of the application, the invoice information of the electronic-version text invoice is actively captured in a mode of acquiring the information by a python crawler, and the invoice core information is popped up after the invoice information is identified, wherein the invoice core information comprises invoice date, invoice code, invoice number, head raising, tax number and the like, and a user submitting the invoice only needs to input the actual reimbursement amount and click to confirm. The URL manager manages the website set of the electronic invoices to be crawled, a webpage downloader is used for downloading webpage content to the local, a request is sent to a target site server through an HTTP request, webpage link information of all the electronic invoices is obtained, and the webpage link information is stored in a temporary database to facilitate subsequent calling. The obtained webpage link information is stored in a csv file or a database, the invoice information content in the webpage link information is cleaned, irrelevant content is removed, and the main invoice information can be directly obtained, so that the accuracy of obtaining the information in the electronic text invoice is ensured.
Further, the step S3 of performing duplicate checking in the background database and determining whether the historical reimbursement record corresponding to the specified information exists in the background database includes:
s31: acquiring a first vector corresponding to the designated information and a second vector corresponding to a designated invoice in the background database, wherein the designated invoice is any one invoice in the background database;
s32: calculating the vector similarity value of the first vector and the second vector by a specified formula, wherein the specified formula is
X represents a vector similarity value, M is the first vector, N is the second vector, Mi is a component corresponding to the ith information dimension of the first vector, Ni is a component corresponding to the ith information dimension of the second vector, and the first vector and the second vector both have p information dimensions with the same arrangement times;
s33: judging whether the vector similarity value is larger than a preset threshold value or not;
s34: if yes, judging that the historical reimbursement record corresponding to the specified information exists in the background database, otherwise, judging that the historical reimbursement record does not exist.
In the application, the invoice information is processed into multi-dimensional vectors according to different information types, and then duplication is checked by comparing the similarity between the multi-dimensional vectors. The duplicate checking is carried out by referring to all the invoice main information, and the duplicate checking precision is improved.
Referring to fig. 2, an apparatus for invoice reimbursement based on artificial intelligence according to an embodiment of the present application includes:
the system comprises an acquisition module 1, a processing module and a display module, wherein the acquisition module is used for acquiring the information type of a reimbursement invoice selected by a user, and the information type comprises a picture invoice and a text invoice;
the calling module 2 is used for calling a first information acquisition mode corresponding to the picture invoice to acquire the specified information in the picture invoice, and calling a second information acquisition mode corresponding to the text invoice to acquire the specified information in the text invoice;
the judging module 3 is used for performing duplicate checking in a background database and judging whether a historical reimbursement record corresponding to the specified information exists in the background database;
the assignment module 4 is configured to assign the specified information to a dynamic variable of a page through a specified function if no historical reimbursement record corresponding to the specified information exists;
the combination module 5 is used for combining the assigned dynamic variables and preset static variables into an invoice reimbursement note;
and the starting module 6 is used for starting invoice audit and invoice reimbursement according to the invoice reimbursement bill.
The relevant explanation of the embodiments of the present application, the explanation of the corresponding parts of the applicable method, are not repeated.
Further, the starting module 6 includes:
a first acquisition unit configured to acquire a designated department to which the user belongs;
the selection unit is used for selecting a designated auditing path corresponding to the designated department;
the first judging unit is used for judging whether the auditing result of the specified auditing path is approved;
the link unit is used for linking a national tax official network if the invoice passes the audit, and judging whether the invoice information of the invoice reimbursement note is valid or not;
and the dispatching unit is used for dispatching the invoice amount of the invoice reimbursement bill to the bank account corresponding to the user if the invoice amount is valid.
Further, the starting module 6 includes:
the second acquisition unit is used for acquiring the label information selected by the summary invoice initiated by the request end;
a third obtaining unit, configured to perform multi-table association query in the background database according to the tag information, and obtain all invoice reimbursement notes associated with the tag information;
and the display unit is used for displaying all invoice reimbursement sheets on the request terminal display page according to a preset summary table.
Further, the calling module 2 includes:
a first starting unit for starting a designated optical character recognition algorithm;
the scanning unit is used for scanning the picture invoice through the optical character recognition process to obtain scanning data;
the input unit is used for inputting the scanning data into a preset recurrent neural network model to obtain a recognition result;
and the unit is used for taking the identification result as the specified information in the picture invoice.
Further, an input unit includes:
the transformation unit is used for transforming the scanning data into a specified vector;
the identification subunit is used for carrying out classification identification on the designated vector through a preset clustering algorithm to obtain the clustering probability of the designated vector corresponding to each clustering cluster;
and the input subunit is used for inputting the clustering probability and the specified vector into the preset recurrent neural network model to obtain an identification result corresponding to the scanning data.
Further, the calling module 2 includes:
the second starting unit is used for starting a python crawler to download webpage information corresponding to the text invoice from the electronic invoice website set to be crawled;
the storage unit is used for storing the webpage information in a temporary database;
and the cleaning unit is used for cleaning the data in the temporary data to obtain the specified information corresponding to the text invoice.
Further, the judging module 3 includes:
a fourth obtaining unit, configured to obtain a first vector corresponding to the specified information and a second vector corresponding to a specified invoice in the background database, where the specified invoice is any one invoice in the background database;
a calculating unit, configured to calculate a vector similarity value between the first vector and the second vector according to a given formula
X represents a vector similarity value, M is the first vector, N is the second vector, Mi is a component corresponding to the ith information dimension of the first vector, Ni is a component corresponding to the ith information dimension of the second vector, and the first vector and the second vector both have p information dimensions with the same arrangement times;
the second judgment unit is used for judging whether the vector similarity value is larger than a preset threshold value or not;
and the judging unit is used for judging that the historical reimbursement record corresponding to the specified information exists in the background database if the historical reimbursement record is larger than a preset threshold value, and otherwise, the historical reimbursement record does not exist.
Referring to fig. 3, a computer device, which may be a server and whose internal structure may be as shown in fig. 3, is also provided in the embodiment of the present application. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the computer designed processor is used to provide computational 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 memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store all the data required for the artificial intelligence based invoice reimbursement process. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method for artificial intelligence based invoice reimbursement.
The processor executes the method for invoice reimbursement based on artificial intelligence, and the method comprises the following steps: acquiring an information type of a reimbursement invoice selected by a user, wherein the information type comprises a picture invoice and a text invoice; calling a first information acquisition mode corresponding to the picture invoice to acquire the specified information in the picture invoice, and calling a second information acquisition mode corresponding to the text invoice to acquire the specified information in the text invoice; checking the duplicate in a background database, and judging whether a historical reimbursement record corresponding to the specified information exists in the background database; if not, assigning the specified information as a dynamic variable of the page through a specified function; combining the assigned dynamic variable and a preset static variable into an invoice reimbursement note; and starting invoice audit and invoice reimbursement according to the invoice reimbursement bill.
Above-mentioned computer equipment, through the key information as the dynamic parameter with the invoice newly uploaded, the invoice reimbursement note is as static parameter except that the information of key information, then accurately obtains the key information of invoice through the invoice type to combine with static information, form the invoice reimbursement note fast, realize the electronization of invoice reimbursement data, completion invoice reimbursement affairs that can be quick, and reduce the probability of makeing mistakes, save time improves the treatment effeciency.
In one embodiment, the step of initiating invoice audit and invoice reimbursement by the processor according to the invoice reimbursement bill includes: acquiring a designated department to which the user belongs; selecting a designated auditing path corresponding to the designated department; judging whether the audit result of the appointed audit path is approved; if yes, linking a national tax official network, and judging whether the invoice information of the invoice reimbursement bill is valid or not; and if so, distributing the invoice amount of the invoice reimbursement bill to a bank account corresponding to the user.
In one embodiment, after the step of dispatching the invoice amount of the invoice reimbursement bill to the bank account corresponding to the user, the processor further comprises: acquiring label information selected by a summary invoice initiated by a request end; performing multi-table association query in the background database according to the label information to acquire all invoice reimbursement notes associated with the label information; and displaying all invoice reimbursement sheets on the request terminal display page according to a preset summary table.
In an embodiment, the step of acquiring the specified information in the picture invoice by calling the first information acquisition manner corresponding to the picture invoice by the processor includes: starting a designated optical character recognition algorithm; scanning the picture invoice through the optical character recognition process to obtain scanning data; inputting the scanning data into a preset recurrent neural network model to obtain a recognition result; and taking the identification result as the specified information in the picture invoice.
In an embodiment, the step of inputting the scan data into a preset recurrent neural network model by the processor to obtain the recognition result includes: converting the scanning data into a designated vector; classifying and identifying the designated vectors through a preset clustering algorithm to obtain clustering probabilities of the designated vectors corresponding to the clustering clusters; and inputting the clustering probability and the designated vector into the preset recurrent neural network model to obtain an identification result corresponding to the scanning data.
In an embodiment, the step of acquiring the specified information in the text invoice by invoking a second information acquisition manner corresponding to the text invoice by the processor includes: starting a python crawler to download webpage information corresponding to the text invoice from an electronic invoice website set to be crawled; storing the webpage information in a temporary database; and cleaning the data in the temporary data to obtain the designated information corresponding to the text invoice.
In an embodiment, the step of the processor performing duplicate checking in a background database and determining whether a historical reimbursement record corresponding to the specified information exists in the background database includes: acquisition instituteA first vector corresponding to the specified information and a second vector corresponding to a specified invoice in the background database, wherein the specified invoice is any one invoice in the background database; calculating the vector similarity value of the first vector and the second vector by a specified formula, wherein the specified formula is
X represents a vector similarity value, M is the first vector, N is the second vector, Mi is a component corresponding to the ith information dimension of the first vector, Ni is a component corresponding to the ith information dimension of the second vector, and the first vector and the second vector both have p information dimensions with the same arrangement times; judging whether the vector similarity value is larger than a preset threshold value or not; if yes, judging that the historical reimbursement record corresponding to the specified information exists in the background database, otherwise, judging that the historical reimbursement record does not exist.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is only 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 may be applied.
An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, the computer program, when executed by a processor, implementing a method for artificial intelligence based invoice reimbursement, including: acquiring an information type of a reimbursement invoice selected by a user, wherein the information type comprises a picture invoice and a text invoice; calling a first information acquisition mode corresponding to the picture invoice to acquire the specified information in the picture invoice, and calling a second information acquisition mode corresponding to the text invoice to acquire the specified information in the text invoice; checking the duplicate in a background database, and judging whether a historical reimbursement record corresponding to the specified information exists in the background database; if not, assigning the specified information as a dynamic variable of the page through a specified function; combining the assigned dynamic variable and a preset static variable into an invoice reimbursement note; and starting invoice audit and invoice reimbursement according to the invoice reimbursement bill.
According to the computer-readable storage medium, the key information of the newly uploaded invoice is used as the dynamic parameter, the information of the invoice reimbursement bill except the key information is used as the static parameter, then the key information of the invoice is accurately obtained through the invoice type, and the invoice reimbursement bill is quickly formed by combining with the static information, so that the electronization of invoice reimbursement data is realized, the invoice reimbursement affairs can be quickly completed, the error probability is reduced, the time is saved, and the processing efficiency is improved.
In one embodiment, the step of initiating invoice audit and invoice reimbursement by the processor according to the invoice reimbursement bill includes: acquiring a designated department to which the user belongs; selecting a designated auditing path corresponding to the designated department; judging whether the audit result of the appointed audit path is approved; if yes, linking a national tax official network, and judging whether the invoice information of the invoice reimbursement bill is valid or not; and if so, distributing the invoice amount of the invoice reimbursement bill to a bank account corresponding to the user.
In one embodiment, after the step of dispatching the invoice amount of the invoice reimbursement bill to the bank account corresponding to the user, the processor further comprises: acquiring label information selected by a summary invoice initiated by a request end; performing multi-table association query in the background database according to the label information to acquire all invoice reimbursement notes associated with the label information; and displaying all invoice reimbursement sheets on the request terminal display page according to a preset summary table.
In an embodiment, the step of acquiring the specified information in the picture invoice by calling the first information acquisition manner corresponding to the picture invoice by the processor includes: starting a designated optical character recognition algorithm; scanning the picture invoice through the optical character recognition process to obtain scanning data; inputting the scanning data into a preset recurrent neural network model to obtain a recognition result; and taking the identification result as the specified information in the picture invoice.
In an embodiment, the step of inputting the scan data into a preset recurrent neural network model by the processor to obtain the recognition result includes: converting the scanning data into a designated vector; classifying and identifying the designated vectors through a preset clustering algorithm to obtain clustering probabilities of the designated vectors corresponding to the clustering clusters; and inputting the clustering probability and the designated vector into the preset recurrent neural network model to obtain an identification result corresponding to the scanning data.
In an embodiment, the step of acquiring the specified information in the text invoice by invoking a second information acquisition manner corresponding to the text invoice by the processor includes: starting a python crawler to download webpage information corresponding to the text invoice from an electronic invoice website set to be crawled; storing the webpage information in a temporary database; and cleaning the data in the temporary data to obtain the designated information corresponding to the text invoice.
In an embodiment, the step of the processor performing duplicate checking in a background database and determining whether a historical reimbursement record corresponding to the specified information exists in the background database includes: acquiring a first vector corresponding to the designated information and a second vector corresponding to a designated invoice in the background database, wherein the designated invoice is any one invoice in the background database; calculating the vector similarity value of the first vector and the second vector by a specified formula, wherein the specified formula is
X represents a vector similarity value, M is the first vector, N is the second vector, Mi is a component corresponding to the ith information dimension of the first vector, Ni is a component corresponding to the ith information dimension of the second vector, and the first vector and the second vector both have p information dimensions with the same arrangement times; judging whether the vector similarity value is larger than a preset threshold value or not; if yes, judging that the historical reimbursement record corresponding to the specified information exists in the background database, otherwise, judging that the historical reimbursement record does not exist.
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 provided herein and used in the examples may include non-volatile and/or volatile memory. 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-rate SDRAM (SSRSDRAM), Enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and bus dynamic RAM (RDRAM).
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that includes the element.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.
Claims (10)
1. An artificial intelligence based invoice reimbursement method, comprising:
acquiring an information type of a reimbursement invoice selected by a user, wherein the information type comprises a picture invoice and a text invoice;
calling a first information acquisition mode corresponding to the picture invoice to acquire the specified information in the picture invoice, and calling a second information acquisition mode corresponding to the text invoice to acquire the specified information in the text invoice;
checking the duplicate in a background database, and judging whether a historical reimbursement record corresponding to the specified information exists in the background database;
if not, assigning the specified information as a dynamic variable of the page through a specified function;
combining the assigned dynamic variable and a preset static variable into an invoice reimbursement note;
and starting invoice audit and invoice reimbursement according to the invoice reimbursement bill.
2. The artificial intelligence based invoice reimbursement method of claim 1, wherein said step of initiating invoice audit and invoice reimbursement in accordance with said invoice reimbursement sheet comprises:
acquiring a designated department to which the user belongs;
selecting a designated auditing path corresponding to the designated department;
judging whether the audit result of the appointed audit path is approved;
if yes, linking a national tax official network, and judging whether the invoice information of the invoice reimbursement bill is valid or not;
and if so, distributing the invoice amount of the invoice reimbursement bill to a bank account corresponding to the user.
3. The artificial intelligence based invoice reimbursement method according to claim 2, wherein said step of dispatching the invoice amount of the invoice reimbursement slip to the user's corresponding bank account is followed by:
acquiring label information selected by a summary invoice initiated by a request end;
performing multi-table association query in the background database according to the label information to acquire all invoice reimbursement notes associated with the label information;
and displaying all invoice reimbursement sheets on the request terminal display page according to a preset summary table.
4. The method for invoice reimbursement based on artificial intelligence as claimed in claim 1, wherein said step of calling the first information acquisition mode corresponding to said picture invoice to obtain the specified information in said picture invoice comprises:
starting a designated optical character recognition algorithm;
scanning the picture invoice through the optical character recognition process to obtain scanning data;
inputting the scanning data into a preset recurrent neural network model to obtain a recognition result;
and taking the identification result as the specified information in the picture invoice.
5. The method for artificial intelligence based invoice reimbursement as claimed in claim 4, wherein said step of inputting said scan data into a predetermined recurrent neural network model to obtain recognition results comprises:
converting the scanning data into a designated vector;
classifying and identifying the designated vectors through a preset clustering algorithm to obtain clustering probabilities of the designated vectors corresponding to the clustering clusters;
and inputting the clustering probability and the designated vector into the preset recurrent neural network model to obtain an identification result corresponding to the scanning data.
6. The method for invoice reimbursement based on artificial intelligence as claimed in claim 1, wherein said step of invoking a second information acquisition mode corresponding to said text invoice to obtain the specified information in said text invoice comprises:
starting a python crawler to download webpage information corresponding to the text invoice from an electronic invoice website set to be crawled;
storing the webpage information in a temporary database;
and cleaning the data in the temporary data to obtain the designated information corresponding to the text invoice.
7. The method for invoice reimbursement based on artificial intelligence as claimed in claim 1, wherein said step of checking duplicate in background database and determining whether there is historical reimbursement record corresponding to said designated information in said background database comprises:
acquiring a first vector corresponding to the designated information and a second vector corresponding to a designated invoice in the background database, wherein the designated invoice is any one invoice in the background database;
calculating the vector similarity value of the first vector and the second vector by a specified formula, wherein the specified formula is
X represents a vector similarity value, M is the first vector, N is the second vector, Mi is a component corresponding to the ith information dimension of the first vector, Ni is a component corresponding to the ith information dimension of the second vector, and the first vector and the second vector both have p information dimensions with the same arrangement times;
judging whether the vector similarity value is larger than a preset threshold value or not;
if yes, judging that the historical reimbursement record corresponding to the specified information exists in the background database, otherwise, judging that the historical reimbursement record does not exist.
8. An invoice reimbursement device based on artificial intelligence, comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring the information type of the reimbursement invoice selected by a user, and the information type comprises a picture invoice and a text invoice;
the calling module is used for calling a first information acquisition mode corresponding to the picture invoice to acquire the specified information in the picture invoice, and calling a second information acquisition mode corresponding to the text invoice to acquire the specified information in the text invoice;
the judging module is used for checking duplicate in a background database and judging whether a historical reimbursement record corresponding to the specified information exists in the background database;
the assignment module is used for assigning the specified information to a dynamic variable of a page through a specified function if the historical reimbursement record corresponding to the specified information does not exist;
the combination module is used for combining the assigned dynamic variable and the preset static variable into an invoice reimbursement note;
and the starting module is used for starting invoice audit and invoice reimbursement according to the invoice reimbursement bill.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
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 of any one of claims 1 to 7.
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