CN116994279A - Invoice data acquisition method, invoice data acquisition device, invoice data acquisition equipment and invoice data acquisition medium - Google Patents
Invoice data acquisition method, invoice data acquisition device, invoice data acquisition equipment and invoice data acquisition medium Download PDFInfo
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Abstract
The invention relates to the technical field of artificial intelligence and data processing, and provides an invoice data acquisition method, device, equipment and medium.
Description
Technical Field
The invention relates to the technical field of artificial intelligence and data processing, in particular to an invoice data acquisition method, device, equipment and medium.
Background
The business sales data is a certificate for evaluating the loan qualification of enterprises by the fund party, and the invoice information is an important evidence basis of the business sales data.
However, due to the fact that factors such as shaking or paper invoice folds may exist in the invoice shooting process, the shot invoice is not beneficial to identification, and accuracy of invoice identification is affected.
In addition, in the prior art, a full-flow management system for collecting invoice data is not provided, so that unified management of each enterprise invoice is inconvenient.
Disclosure of Invention
In view of the above, it is necessary to provide a method, a device and a medium for collecting invoice data, which aim to solve the problems of difficult invoice collection and inconvenient unified management.
An invoice data collection method, comprising:
responding to an invoice acquisition instruction of a target enterprise, and sending an authorization request to the target enterprise;
when an authorization success response fed back by the target enterprise for the authorization request is received, receiving an initial invoice uploaded by the target enterprise;
performing text recognition on the initial invoice based on a pre-trained invoice data recognition model to obtain invoice data;
checking the invoice data;
and when the invoice data passes the verification, storing the invoice data into a preset database.
According to a preferred embodiment of the invention, the method further comprises:
and adding the target enterprise to an enterprise authorization table when an authorization success response fed back by the target enterprise for the authorization request is received.
According to a preferred embodiment of the invention, the method further comprises:
and prompting authorization failure when an unauthorized response fed back by the target enterprise for the authorization request is received.
According to a preferred embodiment of the present invention, before the text recognition of the initial invoice based on the pre-trained invoice data recognition model, the method further includes:
acquiring historical invoice data to construct a training data set;
randomly acquiring any point as a starting point for each training data in the training data set, and starting to move from the starting point to obtain an intermediate data set; the step length of each movement is randomly generated, and deformation is carried out on the point reached after each movement;
training a text detection model by using the intermediate data set, and acquiring output data of the text detection model as a text line mask;
fusing the intermediate data set and the text line mask to obtain a target data set;
splicing the text detection model with a DocUNet network to obtain a network to be trained;
and training the network to be trained by using the target data set to obtain the invoice data identification model.
According to a preferred embodiment of the present invention, the verifying the invoice data includes:
acquiring an invoice code, an invoice number and an invoicing date of each invoice from the invoice data as invoice information of each invoice;
performing true and false detection and validity detection on each invoice according to invoice information of each invoice;
performing ticket missing detection on the invoice data according to the invoice number;
when the authenticity detection, the validity detection and the ticket missing detection are all passed, determining that the invoice data passes verification; or alternatively
And when the authenticity detection, the validity detection and the ticket missing detection are not passed, determining that the invoice data is not passed through verification.
According to a preferred embodiment of the present invention, after the verification of the invoice data, the method further includes:
when the invoice data passes the verification, prompting that the invoice data is successfully acquired; or alternatively
And when the invoice data does not pass the verification, prompting that the invoice data acquisition fails.
According to a preferred embodiment of the present invention, after the invoice data is stored in the preset database, the method further includes:
acquiring an associated product of the target enterprise;
determining a product demand for each associated product;
calculating based on the invoice data according to the product requirement of each associated product to obtain a processing result;
and storing the processing result to the preset database.
An invoice data collection apparatus, the invoice data collection apparatus comprising:
the sending unit is used for responding to an invoice acquisition instruction of a target enterprise and sending an authorization request to the target enterprise;
the receiving unit is used for receiving an initial invoice uploaded by the target enterprise when receiving an authorization success response fed back by the target enterprise for the authorization request;
the identification unit is used for carrying out text identification on the initial invoice based on a pre-trained invoice data identification model to obtain invoice data;
the verification unit is used for verifying the invoice data;
and the storage unit is used for storing the invoice data to a preset database when the invoice data passes the verification.
A computer device, the computer device comprising:
a memory storing at least one instruction; a kind of electronic device with high-pressure air-conditioning system
And the processor executes the instructions stored in the memory to realize the invoice data acquisition method.
A computer readable storage medium having stored therein at least one instruction for execution by a processor in a computer device to implement the invoice data collection method.
According to the technical scheme, when an authorization success response fed back by a target enterprise for an authorization request is received, the initial invoice uploaded by the target enterprise is received, text recognition is carried out on the initial invoice based on a pre-trained invoice data recognition model to obtain invoice data, then the invoice data can be automatically and accurately recognized based on an artificial intelligent model, further, the invoice data is checked, and when the invoice data passes the check, the invoice data is stored in a preset database, so that unified management of the invoice data is facilitated.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of the invoice data collection method of the present invention.
FIG. 2 is a functional block diagram of a preferred embodiment of the invoice data collection apparatus of the present invention.
FIG. 3 is a schematic diagram of a computer device for implementing a preferred embodiment of the invoice data collection method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a flow chart of a preferred embodiment of the invoice data collection method of the present invention. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs.
The invoice data acquisition method is applied to one or more computer devices, wherein the computer device is a device capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and the hardware of the computer device comprises, but is not limited to, a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, an ASIC), a programmable gate array (Field-Programmable Gate Array, FPGA), a digital processor (Digital Signal Processor, DSP), an embedded device and the like.
The computer device may be any electronic product that can interact with a user in a human-computer manner, such as a personal computer, tablet computer, smart phone, personal digital assistant (Personal Digital Assistant, PDA), game console, interactive internet protocol television (Internet Protocol Television, IPTV), smart wearable device, etc.
The computer device may also include a network device and/or a user device. Wherein the network device includes, but is not limited to, a single network server, a server group composed of a plurality of network servers, or a Cloud based Cloud Computing (Cloud Computing) composed of a large number of hosts or network servers.
The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
The network in which the computer device is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (Virtual Private Network, VPN), and the like.
And S10, responding to an invoice acquisition instruction of a target enterprise, and sending an authorization request to the target enterprise.
In this embodiment, the invoice collection instruction may be automatically triggered when it is detected that an enterprise logs on to a designated platform, or may be periodically triggered, which is not limited by the present invention.
In this embodiment, an authorization request is sent to the target enterprise first, so that invoice collection is performed after authorization is obtained, so as to ensure the security of invoice collection.
And S11, when an authorization success response fed back by the target enterprise for the authorization request is received, receiving an initial invoice uploaded by the target enterprise.
The initial invoice can be of an electronic data type such as a photo.
In this embodiment, the method further includes:
and adding the target enterprise to an enterprise authorization table when an authorization success response fed back by the target enterprise for the authorization request is received.
The enterprise authorization table is a pre-constructed list storing authorized enterprises.
By establishing the enterprise authorization table, unified management of authorized enterprises can be realized.
In this embodiment, the method further includes:
and prompting authorization failure when an unauthorized response fed back by the target enterprise for the authorization request is received.
Through the embodiment, the prompt of authorization failure can be timely sent out when the enterprise authorization is not obtained, so that countermeasures can be timely taken.
And S12, carrying out text recognition on the initial invoice based on a pre-trained invoice data recognition model to obtain invoice data.
In this embodiment, before the text recognition is performed on the initial invoice based on the pre-trained invoice data recognition model, the method further includes:
acquiring historical invoice data to construct a training data set;
randomly acquiring any point as a starting point for each training data in the training data set, and starting to move from the starting point to obtain an intermediate data set; the step length of each movement is randomly generated, and deformation is carried out on the point reached after each movement;
training a text detection model by using the intermediate data set, and acquiring output data of the text detection model as a text line mask;
fusing the intermediate data set and the text line mask to obtain a target data set;
splicing the text detection model with a DocUNet network to obtain a network to be trained;
and training the network to be trained by using the target data set to obtain the invoice data identification model.
The text detection model is any deep learning model with a text detection function.
In the above embodiment, the deformation processing is performed based on the history data to obtain more distorted invoices, and since the number of training samples is increased, the invoice data identification model obtained by training can have higher accuracy. In addition, because the model training is carried out by adopting the distorted sample, even if invoice data is not easy to identify due to shaking or invoice paper deformation in the shooting process, the invoice departure content can be accurately identified by utilizing the model obtained by training, and the accuracy rate of identifying the invoice data is improved.
S13, checking the invoice data.
In this embodiment, the verifying the invoice data includes:
acquiring an invoice code, an invoice number and an invoicing date of each invoice from the invoice data as invoice information of each invoice;
performing true and false detection and validity detection on each invoice according to invoice information of each invoice;
performing ticket missing detection on the invoice data according to the invoice number;
when the authenticity detection, the validity detection and the ticket missing detection are all passed, determining that the invoice data passes verification; or alternatively
And when the authenticity detection, the validity detection and the ticket missing detection are not passed, determining that the invoice data is not passed through verification.
Through the embodiment, the invoice can be checked from multiple dimensions based on the invoice data obtained through recognition, so that the availability and the safety of the invoice data are ensured.
In this embodiment, after the verifying the invoice data, the method further includes:
when the invoice data passes the verification, prompting that the invoice data is successfully acquired; or alternatively
And when the invoice data does not pass the verification, prompting that the invoice data acquisition fails.
Through the embodiment, the targeted prompt can be timely carried out according to different acquisition results of the invoice so as to be checked by related personnel and responded.
And S14, when the invoice data passes the verification, storing the invoice data into a preset database.
In this embodiment, the preset database may be a cloud database or a local database.
The preset database is used for storing invoice data and related operation data of each enterprise so as to uniformly manage the operation and sales data of each enterprise.
In this embodiment, after the invoice data is stored in the preset database, the method further includes:
acquiring an associated product of the target enterprise;
determining a product demand for each associated product;
calculating based on the invoice data according to the product requirement of each associated product to obtain a processing result;
and storing the processing result to the preset database.
For example: the total monthly sales amount, total monthly sales amount and the like corresponding to each product can be calculated, and the method is convenient for subsequent use in the evaluation of the value of each product.
In this embodiment, in order to further improve the security of the data, the preset database may be further deployed on the blockchain node, so as to avoid malicious tampering of the data.
According to the technical scheme, when an authorization success response fed back by a target enterprise for an authorization request is received, the initial invoice uploaded by the target enterprise is received, text recognition is carried out on the initial invoice based on a pre-trained invoice data recognition model to obtain invoice data, then the invoice data can be automatically and accurately recognized based on an artificial intelligent model, further, the invoice data is checked, and when the invoice data passes the check, the invoice data is stored in a preset database, so that unified management of the invoice data is facilitated.
FIG. 2 is a functional block diagram of a preferred embodiment of the invoice data capture device of the present invention. The invoice data acquisition device 11 comprises a sending unit 110, a receiving unit 111, an identification unit 112, a verification unit 113 and a storage unit 114. The module/unit referred to in the present invention refers to a series of computer program segments, which are stored in a memory, capable of being executed by a processor and of performing a fixed function. In the present embodiment, the functions of the respective modules/units will be described in detail in the following embodiments.
The sending unit 110 is configured to send an authorization request to a target enterprise in response to an invoice collection instruction to the target enterprise.
In this embodiment, the invoice collection instruction may be automatically triggered when it is detected that an enterprise logs on to a designated platform, or may be periodically triggered, which is not limited by the present invention.
In this embodiment, an authorization request is sent to the target enterprise first, so that invoice collection is performed after authorization is obtained, so as to ensure the security of invoice collection.
The receiving unit 111 is configured to receive an initial invoice uploaded by the target enterprise when receiving an authorization success response fed back by the target enterprise for the authorization request.
The initial invoice can be of an electronic data type such as a photo.
In this embodiment, when an authorization success response fed back by the target enterprise for the authorization request is received, the target enterprise is added to an enterprise authorization table.
The enterprise authorization table is a pre-constructed list storing authorized enterprises.
By establishing the enterprise authorization table, unified management of authorized enterprises can be realized.
In this embodiment, when an unauthorized response fed back by the target enterprise for the authorization request is received, an authorization failure is prompted.
Through the embodiment, the prompt of authorization failure can be timely sent out when the enterprise authorization is not obtained, so that countermeasures can be timely taken.
The identifying unit 112 is configured to perform text identification on the initial invoice based on a pre-trained invoice data identification model, so as to obtain invoice data.
In this embodiment, before the text recognition is performed on the initial invoice based on the pre-trained invoice data recognition model, historical invoice data is obtained to construct a training data set;
randomly acquiring any point as a starting point for each training data in the training data set, and starting to move from the starting point to obtain an intermediate data set; the step length of each movement is randomly generated, and deformation is carried out on the point reached after each movement;
training a text detection model by using the intermediate data set, and acquiring output data of the text detection model as a text line mask;
fusing the intermediate data set and the text line mask to obtain a target data set;
splicing the text detection model with a DocUNet network to obtain a network to be trained;
and training the network to be trained by using the target data set to obtain the invoice data identification model.
The text detection model is any deep learning model with a text detection function.
In the above embodiment, the deformation processing is performed based on the history data to obtain more distorted invoices, and since the number of training samples is increased, the invoice data identification model obtained by training can have higher accuracy. In addition, because the model training is carried out by adopting the distorted sample, even if invoice data is not easy to identify due to shaking or invoice paper deformation in the shooting process, the invoice departure content can be accurately identified by utilizing the model obtained by training, and the accuracy rate of identifying the invoice data is improved.
The verification unit 113 is configured to verify the invoice data.
In this embodiment, the verifying unit 113 verifies the invoice data includes:
acquiring an invoice code, an invoice number and an invoicing date of each invoice from the invoice data as invoice information of each invoice;
performing true and false detection and validity detection on each invoice according to invoice information of each invoice;
performing ticket missing detection on the invoice data according to the invoice number;
when the authenticity detection, the validity detection and the ticket missing detection are all passed, determining that the invoice data passes verification; or alternatively
And when the authenticity detection, the validity detection and the ticket missing detection are not passed, determining that the invoice data is not passed through verification.
Through the embodiment, the invoice can be checked from multiple dimensions based on the invoice data obtained through recognition, so that the availability and the safety of the invoice data are ensured.
In this embodiment, after the invoice data is checked, when the invoice data passes the check, the invoice data is prompted to be successfully collected; or alternatively
And when the invoice data does not pass the verification, prompting that the invoice data acquisition fails.
Through the embodiment, the targeted prompt can be timely carried out according to different acquisition results of the invoice so as to be checked by related personnel and responded.
The storage unit 114 is configured to store the invoice data to a preset database when the invoice data passes the verification.
In this embodiment, the preset database may be a cloud database or a local database.
The preset database is used for storing invoice data and related operation data of each enterprise so as to uniformly manage the operation and sales data of each enterprise.
In this embodiment, after the invoice data is stored in a preset database, an associated product of the target enterprise is obtained;
determining a product demand for each associated product;
calculating based on the invoice data according to the product requirement of each associated product to obtain a processing result;
and storing the processing result to the preset database.
For example: the total monthly sales amount, total monthly sales amount and the like corresponding to each product can be calculated, and the method is convenient for subsequent use in the evaluation of the value of each product.
In this embodiment, in order to further improve the security of the data, the preset database may be further deployed on the blockchain node, so as to avoid malicious tampering of the data.
According to the technical scheme, when an authorization success response fed back by a target enterprise for an authorization request is received, the initial invoice uploaded by the target enterprise is received, text recognition is carried out on the initial invoice based on a pre-trained invoice data recognition model to obtain invoice data, then the invoice data can be automatically and accurately recognized based on an artificial intelligent model, further, the invoice data is checked, and when the invoice data passes the check, the invoice data is stored in a preset database, so that unified management of the invoice data is facilitated.
Fig. 3 is a schematic structural diagram of a computer device for implementing a preferred embodiment of the invoice data collection method according to the present invention.
The computer device 1 may comprise a memory 12, a processor 13 and a bus, and may further comprise a computer program, such as an invoice data collection program, stored in the memory 12 and executable on the processor 13.
It will be appreciated by those skilled in the art that the schematic diagram is merely an example of the computer device 1 and does not constitute a limitation of the computer device 1, the computer device 1 may be a bus type structure, a star type structure, the computer device 1 may further comprise more or less other hardware or software than illustrated, or a different arrangement of components, for example, the computer device 1 may further comprise an input-output device, a network access device, etc.
It should be noted that the computer device 1 is only used as an example, and other electronic products that may be present in the present invention or may be present in the future are also included in the scope of the present invention by way of reference.
The memory 12 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 12 may in some embodiments be an internal storage unit of the computer device 1, such as a removable hard disk of the computer device 1. The memory 12 may in other embodiments also be an external storage device of the computer device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the computer device 1. Further, the memory 12 may also include both an internal storage unit and an external storage device of the computer device 1. The memory 12 may be used not only for storing application software installed in the computer device 1 and various types of data, such as codes of invoice data collection programs, etc., but also for temporarily storing data that has been output or is to be output.
The processor 13 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, a combination of various control chips, and the like. The processor 13 is a Control Unit (Control Unit) of the computer device 1, connects the respective components of the entire computer device 1 using various interfaces and lines, executes or executes programs or modules stored in the memory 12 (for example, executes an invoice data collection program, etc.), and invokes data stored in the memory 12 to perform various functions of the computer device 1 and process data.
The processor 13 executes the operating system of the computer device 1 and various types of applications installed. The processor 13 executes the application program to implement the steps of the various invoice data collection method embodiments described above, such as the steps shown in fig. 1.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory 12 and executed by the processor 13 to complete the present invention. The one or more modules/units may be a series of computer readable instruction segments capable of performing the specified functions, which instruction segments describe the execution of the computer program in the computer device 1. For example, the computer program may be divided into a transmitting unit 110, a receiving unit 111, an identifying unit 112, a verifying unit 113, and a storing unit 114.
The integrated units implemented in the form of software functional modules described above may be stored in a computer readable storage medium. The software functional module is stored in a storage medium, and includes instructions for causing a computer device (which may be a personal computer, a computer device, or a network device, etc.) or a processor (processor) to execute portions of the invoice data collection method according to the embodiments of the present invention.
The modules/units integrated in the computer device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on this understanding, the present invention may also be implemented by a computer program for instructing a relevant hardware device to implement all or part of the procedures of the above-mentioned embodiment method, where the computer program may be stored in a computer readable storage medium and the computer program may be executed by a processor to implement the steps of each of the above-mentioned method embodiments.
Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory, or the like.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, only one straight line is shown in fig. 3, but not only one bus or one type of bus. The bus is arranged to enable a connection communication between the memory 12 and at least one processor 13 or the like.
Although not shown, the computer device 1 may further comprise a power source (such as a battery) for powering the various components, preferably the power source may be logically connected to the at least one processor 13 via a power management means, whereby the functions of charge management, discharge management, and power consumption management are achieved by the power management means. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The computer device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described in detail herein.
Further, the computer device 1 may also comprise a network interface, optionally comprising a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the computer device 1 and other computer devices.
The computer device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the computer device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
Fig. 3 shows only a computer device 1 with components 12-13, it being understood by those skilled in the art that the structure shown in fig. 3 is not limiting of the computer device 1 and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
In connection with fig. 1, the memory 12 in the computer device 1 stores a plurality of instructions to implement an invoice data collection method, which are executable by the processor 13 to implement:
responding to an invoice acquisition instruction of a target enterprise, and sending an authorization request to the target enterprise;
when an authorization success response fed back by the target enterprise for the authorization request is received, receiving an initial invoice uploaded by the target enterprise;
performing text recognition on the initial invoice based on a pre-trained invoice data recognition model to obtain invoice data;
checking the invoice data;
and when the invoice data passes the verification, storing the invoice data into a preset database.
Specifically, the specific implementation method of the above instructions by the processor 13 may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
The data in this case were obtained legally.
In the several embodiments provided in the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The invention is operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. The units or means stated in the invention may also be implemented by one unit or means, either by software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. The invoice data acquisition method is characterized by comprising the following steps of:
responding to an invoice acquisition instruction of a target enterprise, and sending an authorization request to the target enterprise;
when an authorization success response fed back by the target enterprise for the authorization request is received, receiving an initial invoice uploaded by the target enterprise;
performing text recognition on the initial invoice based on a pre-trained invoice data recognition model to obtain invoice data;
checking the invoice data;
and when the invoice data passes the verification, storing the invoice data into a preset database.
2. The invoice data collection method as claimed in claim 1, wherein the method further comprises:
and adding the target enterprise to an enterprise authorization table when an authorization success response fed back by the target enterprise for the authorization request is received.
3. The invoice data collection method as claimed in claim 1, wherein the method further comprises:
and prompting authorization failure when an unauthorized response fed back by the target enterprise for the authorization request is received.
4. The invoice data collection method as claimed in claim 1, wherein prior to text recognition of the initial invoice based on the pre-trained invoice data recognition model, the method further comprises:
acquiring historical invoice data to construct a training data set;
randomly acquiring any point as a starting point for each training data in the training data set, and starting to move from the starting point to obtain an intermediate data set; the step length of each movement is randomly generated, and deformation is carried out on the point reached after each movement;
training a text detection model by using the intermediate data set, and acquiring output data of the text detection model as a text line mask;
fusing the intermediate data set and the text line mask to obtain a target data set;
splicing the text detection model with a DocUNet network to obtain a network to be trained;
and training the network to be trained by using the target data set to obtain the invoice data identification model.
5. The invoice data collection method as claimed in claim 1, wherein said verifying the invoice data comprises:
acquiring an invoice code, an invoice number and an invoicing date of each invoice from the invoice data as invoice information of each invoice;
performing true and false detection and validity detection on each invoice according to invoice information of each invoice;
performing ticket missing detection on the invoice data according to the invoice number;
when the authenticity detection, the validity detection and the ticket missing detection are all passed, determining that the invoice data passes verification; or alternatively
And when the authenticity detection, the validity detection and the ticket missing detection are not passed, determining that the invoice data is not passed through verification.
6. The invoice data collection method as claimed in claim 5, wherein after said verifying said invoice data, said method further comprises:
when the invoice data passes the verification, prompting that the invoice data is successfully acquired; or alternatively
And when the invoice data does not pass the verification, prompting that the invoice data acquisition fails.
7. The invoice data collection method as claimed in claim 1, wherein after said invoice data is stored in a predetermined database, the method further comprises:
acquiring an associated product of the target enterprise;
determining a product demand for each associated product;
calculating based on the invoice data according to the product requirement of each associated product to obtain a processing result;
and storing the processing result to the preset database.
8. An invoice data collection device, characterized in that, the invoice data collection device includes:
the sending unit is used for responding to an invoice acquisition instruction of a target enterprise and sending an authorization request to the target enterprise;
the receiving unit is used for receiving an initial invoice uploaded by the target enterprise when receiving an authorization success response fed back by the target enterprise for the authorization request;
the identification unit is used for carrying out text identification on the initial invoice based on a pre-trained invoice data identification model to obtain invoice data;
the verification unit is used for verifying the invoice data;
and the storage unit is used for storing the invoice data to a preset database when the invoice data passes the verification.
9. A computer device, the computer device comprising:
a memory storing at least one instruction; a kind of electronic device with high-pressure air-conditioning system
A processor executing instructions stored in the memory to implement the invoice data collection method as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized by: the computer-readable storage medium having stored therein at least one instruction for execution by a processor in a computer device to implement the invoice data collection method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310985209.7A CN116994279A (en) | 2023-08-04 | 2023-08-04 | Invoice data acquisition method, invoice data acquisition device, invoice data acquisition equipment and invoice data acquisition medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310985209.7A CN116994279A (en) | 2023-08-04 | 2023-08-04 | Invoice data acquisition method, invoice data acquisition device, invoice data acquisition equipment and invoice data acquisition medium |
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| Publication Number | Publication Date |
|---|---|
| CN116994279A true CN116994279A (en) | 2023-11-03 |
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310985209.7A Pending CN116994279A (en) | 2023-08-04 | 2023-08-04 | Invoice data acquisition method, invoice data acquisition device, invoice data acquisition equipment and invoice data acquisition medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116994279A (en) |
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2023
- 2023-08-04 CN CN202310985209.7A patent/CN116994279A/en active Pending
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