Financial payment starting system based on artificial intelligence

Abstract

The invention relates to a financial payment starting system based on artificial intelligence. The system comprises an object establishing component, a first acquisition component, a second acquisition component, a data authentication mechanism and a starting control mechanism. According to the invention, an artificial intelligence mechanism can be adopted to intelligently predict key data.

Description

Technical Field

[0001] The present invention relates to the financial field, and in particular to an artificial intelligence-based financial payment activation system. Background Art

[0002] The essence of finance is the circulation of value. There are many types of financial products, including banks, securities, insurance, trusts, etc. The academic fields involved in finance are very broad, including accounting, finance, investment, banking, securities, insurance, trusts, etc. Finance is a trading activity. Financial transactions themselves do not create value. Financial transactions are a way to realize future income, that is, to spend tomorrow's money today. If more money is spent tomorrow, will it cause inflation? Simply put, the frequency of financial transactions is an important indicator of the economic prosperity of a region, area, or even a country. The concept of traditional finance is a discipline that studies the circulation of monetary funds. The essence of modern finance is the capitalization process of business activities.

[0003] CN118916295A discloses a payment channel automation testing method, system and device based on JS MOCK, which solves the barrier problem that payment channels are difficult to automate. The key points of its technical solution are: configure channel templates, run automated test cases, and initiate channel requests; parse payment request messages to obtain channel names, channel transaction types and payment request keywords; match channel templates for payment request keywords; if the corresponding keyword template is matched, the corresponding response keyword is obtained, and the response message is reconstructed through the message template; if the keyword template is not matched, the response message is generated by initializing the channel message template; core accounting is performed according to the response message, and the accounting results are simulated. The technical solution can improve the coverage of automated use cases and can adapt to high-efficiency and fast-paced version iteration cycles.

[0004] CN118815243A provides a security protection device and system for a financial payment system. The security protection device includes a privacy safety cabin, an equipment detection mechanism is provided on the outside of the privacy safety cabin, a safety seat mechanism, a ground keyboard and a storage box are provided in the privacy safety cabin, a lifting control mechanism is provided in the storage box, a safety mobile phone holder and a table mouse key are provided on the lifting control mechanism, and a physiological detection mechanism is provided on the storage box; the security protection system includes a privacy safety cabin subsystem, a safety linkage door subsystem, an equipment detection subsystem, a safety seat subsystem, a lifting control subsystem, a financial computer subsystem, a physiological state detection subsystem, a safety mobile phone holder subsystem, a dual password input subsystem, a data aggregation processing subsystem and a payment protection subsystem. This technology uses technical prevention and physical protection in combination to build an all-round and multi-level security protection system.

[0005] CN118071345A discloses a payment account selection method, device, terminal equipment and storage medium, which relates to the field of financial payment technology. The payment account selection method includes: receiving a payment request from a client, wherein the payment request includes client information and information to be paid; determining a list of payable accounts according to the client information, and determining the payment priority of each payable account in the payable account list according to the information to be paid; arranging each of the payable accounts according to the payment priority, taking the first payable account as the payment account, and generating a payment strategy explanation report of the payable account list after receiving a query request and transmitting it to the client for display. The use of this technical solution can help users better understand and trust payment account selection decisions and improve the flexibility of user payment account selection. Summary of the invention

[0006] In order to solve the technical problems in the related fields, the present invention provides a financial payment opening system based on artificial intelligence. The artificial intelligence mechanism is used to intelligently predict the number of withdrawal personnel and the number of deposit personnel corresponding to the fixed time segments of each day in the history of the target self-service bank and multiple related data of the target self-service bank, thereby providing key data for the subsequent ATM configuration strategy of the fixed time segments of the target self-service bank on the same day, and then a deep belief network model is used to implement the intelligent prediction of the number of withdrawal personnel and the number of deposit personnel corresponding to the fixed time segments of the target self-service bank on the same day. The deep belief network model is used to complete each learning action. A deep belief network is formed after the deep belief network is formed, and the number of learning actions completed by the deep belief network is positively correlated with the number of permanent residents in the block where the target self-service bank is located, so as to design deep belief network models with different structures for different self-service banks, and dynamically correct the number of ATM machines opened at the fixed time segments of the target self-service bank on the same day based on the intelligently predicted number of withdrawal personnel corresponding to the fixed time segments of the target self-service bank on the same day, and dynamically correct the number of ATM deposit machines opened at the fixed time segments of the target self-service bank on the same day based on the intelligently predicted number of deposit personnel corresponding to the fixed time segments of the target self-service bank on the same day, so as to avoid meaningless waste of electricity resources of ATM machines while ensuring that the needs of self-service withdrawal and deposit are met.

[0007] According to the present invention, there is provided a financial payment opening system based on artificial intelligence, the system comprising: An object building component is used to sequentially perform each learning action on the deep belief network to obtain the deep belief network after completing each learning action, and output the deep belief network after completing each learning action as a deep belief network model; The first collection component is used to obtain multiple sets of related data of the target self-service bank, wherein the multiple sets of related data of the target self-service bank are the total number of intersection lanes at the nearest traffic intersection near the target self-service bank, the number of permanent residents in the block where the target self-service bank is located, the distance from the nearest other self-service bank near the target self-service bank to the target self-service bank, and the operating area of ​​the target self-service bank; The second collection component is used to obtain the number of withdrawal personnel and the number of deposit personnel corresponding to the fixed time segments of each day in the history of the target self-service bank, and the fixed time segments of each day in the history have the same position on the time axis of that day; A data identification mechanism is connected to the object establishment component, the first collection component and the second collection component respectively, and is used to intelligently identify the number of withdrawal personnel and the number of deposit personnel corresponding to the fixed time segment of the target self-service bank on the day of the day by using a deep belief network model based on the duration of the fixed time segment, multiple sets of related data of the target self-service bank, and the number of withdrawal personnel and the number of deposit personnel corresponding to the fixed time segment of each day in the history of the target self-service bank; An opening control mechanism is connected to the data identification mechanism, and is used to correct the opening number of ATM cash dispensers corresponding to the fixed time segment of the target self-service bank on the same day based on the received number of cash withdrawal personnel corresponding to the fixed time segment of the target self-service bank on the same day, and also correct the opening number of ATM deposit machines corresponding to the fixed time segment of the target self-service bank on the same day based on the received number of deposit personnel corresponding to the fixed time segment of the target self-service bank on the same day; The method includes: the smaller the number of withdrawal personnel corresponding to the fixed time segment of the target self-service bank on the same day, the smaller the number of ATM cash withdrawal machines corresponding to the fixed time segment of the target self-service bank on the same day after the correction; and the smaller the number of deposit personnel corresponding to the fixed time segment of the target self-service bank on the same day after the correction. Among them, based on the received number of withdrawal personnel corresponding to the fixed time segments of the target self-service bank on the same day, the opened number of ATM cash machines corresponding to the fixed time segments of the target self-service bank on the same day is corrected, and based on the received number of deposit personnel corresponding to the fixed time segments of the target self-service bank on the same day, the opened number of ATM deposit machines corresponding to the fixed time segments of the target self-service bank on the same day is corrected, including: the fewer the received number of deposit personnel corresponding to the fixed time segments of the target self-service bank on the same day, the fewer the opened number of ATM deposit machines corresponding to the fixed time segments of the target self-service bank on the same day after correction.

[0008] It can be seen that the present invention has at least the following three important inventive concepts: Firstly: artificial intelligence mechanism is used to intelligently predict the number of withdrawal personnel and deposit personnel corresponding to the fixed time segment of the target self-service bank on the same day according to the number of withdrawal personnel and deposit personnel corresponding to the fixed time segment of each day in the history of the target self-service bank and multiple related data of the target self-service bank, so as to provide key data for the subsequent ATM configuration strategy of the target self-service bank at the fixed time segment of the same day; Secondly: a deep belief network model is used to implement intelligent prediction of the number of cash withdrawal personnel and the number of deposit personnel corresponding to fixed time segments of the target self-service bank on the same day. The deep belief network model is a deep belief network after completing each learning action, and the number of each learning action completed by the deep belief network is positively correlated with the number of permanent residents in the block where the target self-service bank is located, so as to design deep belief network models with different structures for different self-service banks; Again: based on the intelligent prediction of the number of withdrawal personnel corresponding to the fixed time segments of the target self-service bank on the same day, the number of ATM cash machines opened corresponding to the fixed time segments of the target self-service bank on the same day is dynamically corrected; based on the intelligent prediction of the number of deposit personnel corresponding to the fixed time segments of the target self-service bank on the same day, the number of ATM deposit machines opened corresponding to the fixed time segments of the target self-service bank on the same day is dynamically corrected, thereby ensuring that the self-service withdrawal and deposit needs are met while avoiding meaningless waste of ATM machines' electricity resources. DETAILED DESCRIPTION

[0009] For a bank that manages multiple self-service banks, how to allocate limited cash resources to the ATMs of each self-service bank in a targeted manner so as to minimize the number of open ATMs while ensuring that the withdrawal needs of residents near each self-service bank are met, thereby taking into account both the self-service withdrawal and deposit needs and the ATM power resource saving. This is one of the technical challenges that need to be solved at present.

[0010] The following is a detailed description of an embodiment of the artificial intelligence-based financial payment activation system of the present invention.

[0011] The financial payment opening system based on artificial intelligence according to the first embodiment of the present invention includes: An object building component is used to sequentially perform each learning action on the deep belief network to obtain the deep belief network after completing each learning action, and output the deep belief network after completing each learning action as a deep belief network model; Specifically, a PAL device may be selected to implement the object establishment component, which is used to sequentially perform each learning action on the deep belief network to obtain a deep belief network after each learning action is completed, and output the deep belief network after each learning action is completed as a deep belief network model; The first collection component is used to obtain multiple sets of related data of the target self-service bank, wherein the multiple sets of related data of the target self-service bank are the total number of intersection lanes at the nearest traffic intersection near the target self-service bank, the number of permanent residents in the block where the target self-service bank is located, the distance from the nearest other self-service bank near the target self-service bank to the target self-service bank, and the operating area of ​​the target self-service bank; The second collection component is used to obtain the number of withdrawal personnel and the number of deposit personnel corresponding to the fixed time segments of each day in the history of the target self-service bank, and the fixed time segments of each day in the history have the same position on the time axis of that day; A data identification mechanism is connected to the object establishment component, the first collection component and the second collection component respectively, and is used to intelligently identify the number of withdrawal personnel and the number of deposit personnel corresponding to the fixed time segment of the target self-service bank on the day of the day by using a deep belief network model based on the duration of the fixed time segment, multiple sets of related data of the target self-service bank, and the number of withdrawal personnel and the number of deposit personnel corresponding to the fixed time segment of each day in the history of the target self-service bank; An opening control mechanism is connected to the data identification mechanism, and is used to correct the opening number of ATM cash dispensers corresponding to the fixed time segment of the target self-service bank on the same day based on the received number of cash withdrawal personnel corresponding to the fixed time segment of the target self-service bank on the same day, and also correct the opening number of ATM deposit machines corresponding to the fixed time segment of the target self-service bank on the same day based on the received number of deposit personnel corresponding to the fixed time segment of the target self-service bank on the same day; The method includes: the smaller the number of withdrawal personnel corresponding to the fixed time segment of the target self-service bank on the same day, the smaller the number of ATM cash withdrawal machines corresponding to the fixed time segment of the target self-service bank on the same day after the correction; and the smaller the number of deposit personnel corresponding to the fixed time segment of the target self-service bank on the same day after the correction. The method includes: the smaller the number of depositors corresponding to the fixed time segment of the target self-service bank on the same day, the smaller the number of ATM depositors corresponding to the fixed time segment of the target self-service bank on the same day after the correction; Among them, each learning action is sequentially executed on the deep belief network to obtain the deep belief network after completing each learning action, and the deep belief network after completing each learning action is used as the deep belief network model output, including: the number of each learning action completed by the deep belief network is positively correlated with the number of permanent residents in the block where the target self-service bank is located.

[0012] Different from the first embodiment of the present invention, the artificial intelligence-based financial payment opening system shown in the second embodiment of the present invention may also include the following components: a content mapping device, connected to the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component, respectively, for performing signal optimization processing on the respective output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component, so as to obtain respective optimized processed images corresponding to the respective output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component; The signal optimization processing is performed on the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component respectively to obtain the optimized processed images corresponding to the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component respectively, including: the first step of the optimization processing is to perform edge sharpening operation on the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component respectively; The signal optimization processing is performed on the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component respectively to obtain the optimized processed images corresponding to the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component respectively, and the second optimization processing also includes: the second step of optimizing the processing is to perform content enhancement operation on the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component respectively; The signal optimization processing is performed on the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component respectively to obtain the optimized processed images corresponding to the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component respectively, and the third step of optimization processing is to perform filtering processing operations on the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component respectively; Among them, signal optimization processing is performed on the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component respectively to obtain optimized processed images corresponding to the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component respectively, and also includes: the fourth step of optimization processing performs interpolation processing operations on the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component respectively.

[0013] Different from the first embodiment of the present invention, the artificial intelligence-based financial payment opening system shown in the third embodiment of the present invention may also include the following components: An instant playback component is connected to the data identification mechanism, the object creation component, the first acquisition component and the second acquisition component respectively, and is used to perform instant display of each optimized processed image corresponding to the output image signals of the data identification mechanism, the object creation component, the first acquisition component and the second acquisition component respectively; Wherein, the instant playback component is connected to the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component respectively, and is used to perform instant display of each optimized processed image corresponding to the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component respectively, including: the instant playback component is a liquid crystal display device; And wherein, the instant playback component is respectively connected to the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component, and is used to perform instant display of each optimized processed image corresponding to the respective output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component, including: the instant playback component is a split-screen display device.

[0014] In addition, in the financial payment opening system based on artificial intelligence, the deep belief network model is used to intelligently identify the number of withdrawal personnel and the number of deposit personnel corresponding to the fixed time segment of the target self-service bank on the same day based on the duration of the fixed time segment, multiple sets of associated data of the target self-service bank, and the number of withdrawal personnel and the number of deposit personnel corresponding to the fixed time segment of each day in the history of the target self-service bank. The number of withdrawal personnel and the number of deposit personnel corresponding to the fixed time segment of each day in the history of the target self-service bank include: performing octal value conversion processing on the duration of the fixed time segment, multiple sets of associated data of the target self-service bank, and the number of withdrawal personnel and the number of deposit personnel corresponding to the fixed time segment of each day in the history of the target self-service bank, and then synchronously inputting them into the deep belief network model; And wherein, the deep belief network model is used to intelligently identify the number of withdrawal personnel and the number of deposit personnel corresponding to the fixed time segments of the target self-service bank on that day based on the duration of the fixed time segments, multiple related data of the target self-service bank, and the number of withdrawal personnel and the number of deposit personnel corresponding to the fixed time segments of each day in the history of the target self-service bank. It also includes: the number of withdrawal personnel and the number of deposit personnel corresponding to the fixed time segments of the target self-service bank on that day that are intelligently identified are both in octal numerical form.

[0015] The artificial intelligence-based financial payment opening system of the present invention aims to solve the technical problem in the prior art that it is difficult to take into account both the self-service withdrawal and deposit needs and the power resource saving of ATM machines. By adopting an artificial intelligence mechanism, the number of withdrawal personnel and the number of deposit personnel corresponding to the fixed time segments of the target self-service bank on the same day are intelligently predicted, thereby providing key data for the subsequent ATM configuration strategy of the fixed time segments of the target self-service bank on the same day, thereby ensuring that the self-service withdrawal and deposit needs are met while avoiding the meaningless waste of power resources of ATM machines, thereby solving the above technical problems.

[0016] The present invention is not limited to the above embodiments. According to the description of the above embodiments, a person skilled in the art may make some obvious changes, but these changes should fall within the protection scope of the claims of the present invention.

Claims

1. A financial payment opening system based on artificial intelligence, characterized in that: The system comprises: An object building component is used to sequentially perform each learning action on the deep belief network to obtain the deep belief network after completing each learning action, and output the deep belief network after completing each learning action as a deep belief network model; The first collection component is used to obtain multiple sets of related data of the target self-service bank, wherein the multiple sets of related data of the target self-service bank are the total number of intersection lanes at the nearest traffic intersection near the target self-service bank, the number of permanent residents in the block where the target self-service bank is located, the distance from the nearest other self-service bank near the target self-service bank to the target self-service bank, and the operating area of ​​the target self-service bank; The second collection component is used to obtain the number of withdrawal personnel and the number of deposit personnel corresponding to the fixed time segments of each day in the history of the target self-service bank, and the fixed time segments of each day in the history have the same position on the time axis of that day; A data identification mechanism is connected to the object establishment component, the first collection component and the second collection component respectively, and is used to intelligently identify the number of withdrawal personnel and the number of deposit personnel corresponding to the fixed time segment of the target self-service bank on the day of the day by using a deep belief network model based on the duration of the fixed time segment, multiple sets of related data of the target self-service bank, and the number of withdrawal personnel and the number of deposit personnel corresponding to the fixed time segment of each day in the history of the target self-service bank; An opening control mechanism is connected to the data identification mechanism, and is used to correct the opening number of ATM cash dispensers corresponding to the fixed time segment of the target self-service bank on the same day based on the received number of cash withdrawal personnel corresponding to the fixed time segment of the target self-service bank on the same day, and also correct the opening number of ATM deposit machines corresponding to the fixed time segment of the target self-service bank on the same day based on the received number of deposit personnel corresponding to the fixed time segment of the target self-service bank on the same day; The method includes: the smaller the number of withdrawal personnel corresponding to the fixed time segment of the target self-service bank on the same day, the smaller the number of ATM cash withdrawal machines corresponding to the fixed time segment of the target self-service bank on the same day after the correction; and the smaller the number of deposit personnel corresponding to the fixed time segment of the target self-service bank on the same day after the correction. Among them, based on the received number of withdrawal personnel corresponding to the fixed time segments of the target self-service bank on the same day, the opened number of ATM cash machines corresponding to the fixed time segments of the target self-service bank on the same day is corrected, and based on the received number of deposit personnel corresponding to the fixed time segments of the target self-service bank on the same day, the opened number of ATM deposit machines corresponding to the fixed time segments of the target self-service bank on the same day is corrected, including: the fewer the received number of deposit personnel corresponding to the fixed time segments of the target self-service bank on the same day, the fewer the opened number of ATM deposit machines corresponding to the fixed time segments of the target self-service bank on the same day after correction.

2. The artificial intelligence-based financial payment opening system according to claim 1, characterized in that: The deep belief network is sequentially executed with each learning action to obtain the deep belief network after each learning action is completed, and the deep belief network after each learning action is completed is used as the deep belief network model output, including: the number of each learning action completed by the deep belief network is positively correlated with the number of permanent residents in the block where the target self-service bank is located.

3. The artificial intelligence-based financial payment opening system as claimed in claim 2, characterized in that: The system further comprises: a content mapping device, connected to the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component, respectively, for performing signal optimization processing on the respective output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component, so as to obtain respective optimized processed images corresponding to the respective output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component; Among them, signal optimization processing is performed on the respective output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component to obtain respective optimized processed images corresponding to the respective output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component, including: the first step of optimization processing is to perform edge sharpening operation on the respective output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component.

4. The artificial intelligence-based financial payment opening system as claimed in claim 3, characterized in that: Signal optimization processing is performed on the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component respectively to obtain optimized processed images corresponding to the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component respectively, and the second step of optimization processing also includes: content enhancement operation is performed on the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component respectively.

5. The artificial intelligence-based financial payment opening system according to claim 4, characterized in that: The signal optimization processing is respectively performed on the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component to obtain the optimized processed images corresponding to the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component respectively, and also includes: the third step of optimization processing is respectively performed filtering processing operations on the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component.

6. The artificial intelligence-based financial payment opening system according to claim 5, characterized in that: The signal optimization processing is respectively performed on the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component to obtain the optimized processed images corresponding to the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component respectively, and also includes: the fourth step of optimization processing is respectively performed with interpolation processing operations on the output image signals of the data identification mechanism, the object establishment component, the first acquisition component and the second acquisition component.

7. The artificial intelligence-based financial payment opening system as claimed in claim 3, characterized in that: The system further comprises: The instant playback component is respectively connected to the data identification mechanism, the object creation component, the first acquisition component and the second acquisition component, and is used to perform instant display of each optimized processed image corresponding to the respective output image signals of the data identification mechanism, the object creation component, the first acquisition component and the second acquisition component.

8. The artificial intelligence-based financial payment opening system according to claim 7, characterized in that: An instant playback component is respectively connected to the data identification mechanism, the object creation component, the first acquisition component and the second acquisition component, and is used to perform instant display of each optimized processed image corresponding to the respective output image signals of the data identification mechanism, the object creation component, the first acquisition component and the second acquisition component, including: the instant playback component is a liquid crystal display device.

9. The artificial intelligence-based financial payment opening system according to claim 7, characterized in that: An instant playback component is respectively connected to the data identification mechanism, the object creation component, the first acquisition component and the second acquisition component, and is used to perform instant display of each optimized processed image corresponding to the respective output image signals of the data identification mechanism, the object creation component, the first acquisition component and the second acquisition component, including: the instant playback component is a split-screen display device.