Financial deployment intelligent management system using neural network

Abstract

The invention relates to a financial deployment intelligent management system using a neural network. The financial deployment intelligent management system comprises a content construction component, a first capture component, a second capture component, an intelligent analysis device and a financial deployment device. The AI analysis model of a customized structure can be adopted to improve the utilization rate of effective fund resources.

Description

Technical Field

[0001] The present invention relates to the field of financial management, and in particular to a financial allocation intelligent management system using a neural network. Background Art

[0002] Financial management, as an important part of the modern economic system, refers to a series of activities in the financial market environment, such as the raising, use, management and investment decision-making of funds by enterprises or individuals. The goal of financial management is to maximize the value of enterprises or maintain and increase the value of personal assets. Through effective financial management, enterprises can reduce capital costs and improve the efficiency of capital use, thereby achieving profit goals; individuals can achieve steady growth of wealth through scientific and reasonable investment decisions. The technologies involved in financial management are as follows: CN119088830A discloses a data processing system suitable for accounting and financial management, which relates to the technical field of financial data processing. By communicating with a user end, the accounting and financial management demand tasks are obtained, and the real-time data stream is screened according to the correlation matching information between the real-time data stream and the accounting and financial management demand tasks to obtain the first-level task data; the historical call data of the accounting and financial management demand tasks are retrieved, and the historical call data of the accounting and financial management demand tasks are screened according to the timeliness and availability standards of the data to obtain the second-level task data; the third-level task data is called based on the degree of relevance to the second-level task data; the first-level task data, the second-level task data and the third-level task data are integrated to obtain the task demand data set, and then the task demand data set is fed back to the user end. The optimization of the data screening process is realized, the real-time data processing capability and data analysis accuracy are improved, and the historical call data are efficiently utilized.

[0003] CN118940994A discloses a transformation finance management system, including: a pre-loan transformation finance management subsystem, which is used to establish a user access evaluation model based on a comprehensive evaluation method before the loan, and assist commercial banks in screening users with transformation potential based on the user access evaluation model; a mid-loan transformation process management subsystem, which is used to track and manage the entire transformation process; and a post-loan risk management and transformation performance evaluation subsystem, which is used to perform dynamic risk monitoring and early warning and intelligent evaluation of transformation performance; based on big data, AI and BI technologies, a large visual data screen and data cockpit for transformation finance projects are constructed, and the performance of transformation projects is dynamically evaluated and monitored through a business-driven multi-dimensional indicator visualization display, effectively monitoring corporate transformation risks and ESG risks, and evaluating transformation performance; used to achieve monitoring visualization and autonomous performance analysis, effectively improving the risk prevention and performance evaluation capabilities of commercial banks' transformation finance projects.

[0004] CN118907903A discloses an Internet financial management platform based on big data, including: a base, a support frame fixedly mounted on the base, a conveyor belt mounted on the support frame, two driving rollers rotatably connected to the support frame, a motor mounted on the support frame, the output end of the motor is connected and fixed to one of the driving rollers, a mounting frame fixedly mounted on one side of the base, and a scanning device mounted on the mounting frame. The technical solution sets a driving roller, a conveyor belt and a motor, so that the staff can place the financial data to be scanned on the conveyor belt. Under the action of the motor, the paper data will automatically move to one side, so that the scanning device can upload the data completely to the computer without the staff having to manually move the data, which reduces the difficulty of scanning and improves the efficiency of data scanning. Summary of the invention

[0005] In order to solve the technical problems in the prior art, the present invention provides a financial allocation intelligent management system using a neural network, which uses a content construction component to generate an AI analysis model with a customized structure. The AI ​​analysis model is a deep neural network obtained by multiple learning of the deep neural network. The number of times the deep neural network is learned is positively correlated with the geographical area of ​​a set block. The cash withdrawal amount values ​​corresponding to each historical time segment of a fixed number of the set block are obtained. The single cash withdrawal amount value corresponding to each historical time segment of the set block is the total cash withdrawal amount of the target bank ATM machine arranged in the set block within the historical time segment, and multiple configuration data of the set block are obtained. The multiple configuration data of the set block are the number of registered users of the target bank in the set block, the geographical area of ​​the set block, and other banks in the set block. The number of ATMs is determined, thereby providing a number of sufficient and comprehensive basic information for subsequent intelligent analysis, and an AI analysis model with customized structural design is used to intelligently analyze the predicted value of the withdrawal amount of the set block in the future time segment according to the cash withdrawal amount values ​​corresponding to each historical time segment of a fixed number of historical time segments in the set block and multiple configuration data of the set block, and before the arrival of the future time segment, the target bank ATMs in the set block are pre-configured with cash required to serve the withdrawals in the future time segment, the cash required for withdrawals matches the predicted value of the withdrawal amount of the set block in the future time segment obtained by intelligent analysis, the cash required for withdrawal is a multiple of the predicted value of the withdrawal amount of the set block in the future time segment obtained by intelligent analysis, and the value of the multiple is greater than 1, thereby avoiding the use of excessive cash to meet the cash withdrawal needs of each ATM of the target bank.

[0006] According to the present invention, a financial allocation intelligent management system using a neural network is provided, the system comprising: A content construction component for generating an AI analysis model, wherein the AI ​​analysis model is a deep neural network obtained by performing multiple learning of the deep neural network, and the number of times the deep neural network is learned is positively correlated with the geographical area of ​​the set block; The first capture component is used to obtain the cash withdrawal amount values ​​corresponding to each of the fixed number of historical time segments in the set block, and the single cash withdrawal amount value corresponding to each historical time segment in the set block is the total cash withdrawal amount of the target bank ATM machine arranged in the set block within the historical time segment; A second capture component is used to obtain multiple configuration data of a set block, wherein the multiple configuration data of the set block are the number of registered users of the target bank in the set block, the geographical area of ​​the set block, and the number of ATM machines of other banks in the set block; An intelligent analysis device, connected to the content construction component, the first capture component and the second capture component respectively, for using the AI ​​analysis model to intelligently analyze the predicted value of the cash withdrawal amount value of the set block in the future time segment according to the respective cash withdrawal amount values ​​corresponding to each historical time segment of a fixed number of the set block and multiple configuration data of the set block; A financial allocation device, connected to the intelligent analysis device, is used to configure the target bank ATM in the set block with cash required for withdrawals in the future time segment in advance before the future time segment arrives, wherein the cash required for withdrawals matches the predicted value of the cash withdrawal amount in the set block in the future time segment obtained by intelligent analysis; Wherein, the financial allocation device is connected to the intelligent analysis device, and is used to configure the cash required for the withdrawal of the target bank ATM in the set block in advance before the future time segment arrives, and the cash required for the withdrawal is matched with the predicted value of the cash withdrawal amount of the set block in the future time segment obtained by intelligent analysis, including: the cash required for the withdrawal is a multiple of the predicted value of the cash withdrawal amount of the set block in the future time segment obtained by intelligent analysis, and the value of the multiple is greater than 1; Among them, the AI ​​analysis model is used to intelligently analyze the predicted value of the cash withdrawal amount value of the set block in the future time segment according to the respective cash withdrawal amount values ​​corresponding to the fixed number of historical time segments of the set block and multiple configuration data of the set block, including: the future time segment and the fixed number of historical time segments form a complete time interval; Among them, the AI ​​analysis model is used to intelligently analyze the cash withdrawal amount values ​​corresponding to each fixed number of historical time segments of the set block and multiple configuration data of the set block, and the predicted value of the cash withdrawal amount value of the set block in the future time segment includes: the future time segment has the same duration as each time segment in the fixed number of historical time segments.

[0007] The present invention has at least the following important invention points: Firstly: a content building component is used to generate an AI analysis model of a customized structure, wherein the AI ​​analysis model is a deep neural network obtained by performing multiple learning on the deep neural network, and the number of times the deep neural network is learned is positively correlated with the geographical area of ​​the set block; Secondly: obtaining the cash withdrawal amount values ​​corresponding to each historical time segment of a fixed number of set blocks, the single cash withdrawal amount value corresponding to each historical time segment of the set block is the total cash withdrawal amount of the target bank ATM arranged in the set block within the historical time segment, and obtaining multiple configuration data of the set block, the multiple configuration data of the set block is the number of registered users of the target bank in the set block, the geographical area of ​​the set block and the number of ATMs of other banks in the set block, so as to provide multiple sufficient and comprehensive basic information for subsequent intelligent analysis; Finally: an AI analysis model with customized structure design is used to intelligently analyze the predicted value of the cash withdrawal amount of the set block in the future time segment according to the cash withdrawal amount values ​​corresponding to each fixed number of historical time segments of the set block and multiple configuration data of the set block, and before the arrival of the future time segment, the target bank ATM in the set block is pre-configured with cash required to serve the withdrawal in the future time segment, the cash required for withdrawal matches the predicted value of the cash withdrawal amount of the set block in the future time segment obtained by intelligent analysis, the cash required for withdrawal is a multiple of the predicted value of the cash withdrawal amount of the set block in the future time segment obtained by intelligent analysis, and the value of the multiple is greater than 1, thereby avoiding the use of excessive cash to meet the cash withdrawal needs of each ATM of the target bank. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein:

[0009] Figure 1 Schematic diagram of the internal structure of a financial allocation intelligent management system using a neural network according to implementation scheme A of the present invention.

[0010] Figure 2 Schematic diagram of the internal structure of a financial allocation intelligent management system using a neural network according to implementation scheme B of the present invention.

[0011] Figure 3 Schematic diagram of the internal structure of a financial allocation intelligent management system using a neural network according to implementation scheme C of the present invention. DETAILED DESCRIPTION

[0012] At present, as one of the operating entities of financial management, bank ATMs are an important place for financial activities. With the increasing development of electronic payment, the pre-deposit of cash in bank ATMs requires a certain amount of cash resources and human resources. How to make full use of limited cash resources and human resources is one of the technical problems that need to be solved at present. However, since the withdrawal amount of the set block associated with each bank ATM in the future time segment is difficult to predict, it is difficult to configure the cash required for withdrawal in the future time segment for the bank ATMs serving the set block in advance.

[0013] The implementation scheme of the financial allocation intelligent management system using neural network of the present invention will be described in detail below with reference to the accompanying drawings.

[0014] Figure 1 This is a schematic diagram of the internal structure of a financial allocation intelligent management system using a neural network according to Embodiment A of the present invention, wherein the system comprises: A content construction component for generating an AI analysis model, wherein the AI ​​analysis model is a deep neural network obtained by performing multiple learning of the deep neural network, and the number of times the deep neural network is learned is positively correlated with the geographical area of ​​the set block; Specifically, a SOC chip may be selected to implement the content construction component, which is used to generate an AI analysis model, wherein the AI ​​analysis model is a deep neural network obtained by performing multiple learning on the deep neural network, and the number of times the deep neural network is learned is positively correlated with the geographical area of ​​the set block; The first capture component is used to obtain the cash withdrawal amount values ​​corresponding to each of the fixed number of historical time segments in the set block, and the single cash withdrawal amount value corresponding to each historical time segment in the set block is the total cash withdrawal amount of the target bank ATM machine arranged in the set block within the historical time segment; A second capture component is used to obtain multiple configuration data of a set block, wherein the multiple configuration data of the set block are the number of registered users of the target bank in the set block, the geographical area of ​​the set block, and the number of ATM machines of other banks in the set block; An intelligent analysis device, connected to the content construction component, the first capture component and the second capture component respectively, for using the AI ​​analysis model to intelligently analyze the predicted value of the cash withdrawal amount value of the set block in the future time segment according to the respective cash withdrawal amount values ​​corresponding to each historical time segment of a fixed number of the set block and multiple configuration data of the set block; A financial allocation device, connected to the intelligent analysis device, is used to configure the target bank ATM in the set block with cash required for withdrawals in the future time segment in advance before the future time segment arrives, wherein the cash required for withdrawals matches the predicted value of the cash withdrawal amount in the set block in the future time segment obtained by intelligent analysis; Wherein, the financial allocation device is connected to the intelligent analysis device, and is used to configure the cash required for the withdrawal of the target bank ATM in the set block in advance before the future time segment arrives, and the cash required for the withdrawal is matched with the predicted value of the cash withdrawal amount of the set block in the future time segment obtained by intelligent analysis, including: the cash required for the withdrawal is a multiple of the predicted value of the cash withdrawal amount of the set block in the future time segment obtained by intelligent analysis, and the value of the multiple is greater than 1; Among them, the AI ​​analysis model is used to intelligently analyze the predicted value of the cash withdrawal amount value of the set block in the future time segment according to the respective cash withdrawal amount values ​​corresponding to the fixed number of historical time segments of the set block and multiple configuration data of the set block, including: the future time segment and the fixed number of historical time segments form a complete time interval; Among them, the AI ​​analysis model is used to intelligently analyze the predicted value of the cash withdrawal amount value of the set block in the future time segment according to the respective cash withdrawal amount values ​​corresponding to the fixed number of historical time segments of the set block and multiple configuration data of the set block, including: the future time segment is the same as the duration of each time segment in the fixed number of historical time segments; The second capture component is used to obtain multiple configuration data of a set block, wherein the multiple configuration data of the set block are the number of registered users of the target bank in the set block, the geographical area of ​​the set block, and the number of ATM machines of other banks in the set block, including: other banks are banks other than the target bank; The content construction component is used to generate an AI analysis model, wherein the AI ​​analysis model is a deep neural network obtained by performing multiple learning on the deep neural network, and the number of times the deep neural network is learned is positively correlated with the geographical area of ​​the set block, including: using a numerical conversion formula to express the numerical conversion relationship of the number of times the deep neural network is learned and the geographical area of ​​the set block; The cash required for withdrawal is a multiple of the predicted value of the withdrawal amount of the set block in the future time segment obtained by intelligent analysis, and the value of the multiple is greater than 1, including: the value of the multiple is between 1.02-1.1; And wherein, the AI ​​analysis model is used to intelligently analyze the predicted value of the cash withdrawal amount of the set block in the future time segment according to the respective cash withdrawal amount values ​​corresponding to each fixed number of historical time segments of the set block and multiple configuration data of the set block, including: selecting a numerical simulation mode to realize the simulation and testing of the data processing process of using the AI ​​analysis model to intelligently analyze the predicted value of the cash withdrawal amount of the set block in the future time segment according to the respective cash withdrawal amount values ​​corresponding to each fixed number of historical time segments of the set block and multiple configuration data of the set block.

[0015] Figure 2 Schematic diagram of the internal structure of a financial allocation intelligent management system using a neural network according to implementation scheme B of the present invention.

[0016] Compared with implementation scheme A, the financial allocation intelligent management system using neural network shown in implementation scheme B may also include the following components: The frequency monitoring component is used to measure the number of unit time operations of the step of using the AI ​​analysis model to intelligently analyze the predicted value of the cash withdrawal amount value of the set block in the future time segment according to the various historical time segments of a fixed number of set blocks and multiple configuration data of the set blocks.

[0017] Figure 3 Schematic diagram of the internal structure of a financial allocation intelligent management system using a neural network according to implementation scheme C of the present invention.

[0018] Compared with implementation scheme A, the financial allocation intelligent management system using neural network shown in implementation scheme C may also include the following components: A status alarm component is connected to the frequency monitoring component, and is used to perform a corresponding alarm action when the number of unit time operations of the step of using the AI ​​analysis model to intelligently analyze the predicted value of the cash withdrawal amount value of the set block in the future time segment according to each historical time segment of a fixed number of set blocks and multiple configuration data of the set blocks exceeds the limit; The number monitoring component has a built-in computing sensor circuit for measuring the number of unit time operations of the step of using the AI ​​analysis model to intelligently analyze the predicted value of the cash withdrawal amount value of the set block in the future time segment according to the values ​​of each cash withdrawal amount corresponding to each historical time segment of a fixed number of set blocks and multiple configuration data of the set blocks; Among them, the state alarm component is connected to the number monitoring component, and is used to perform corresponding alarm actions when the number of unit time operations of the step of using the AI ​​analysis model to intelligently analyze the predicted value of the cash withdrawal amount value of the set block in the future time segment according to each historical time segment of a fixed number of blocks and multiple configuration data of the set block exceeds the limit: the state alarm component is a vibration alarm unit; Among them, the state alarm component is connected to the number monitoring component, and is used to perform corresponding alarm actions when the number of unit time operations of the step of using the AI ​​analysis model to intelligently analyze the predicted value of the cash withdrawal amount value of the set block in the future time segment according to the various historical time segments of the set block and the multiple configuration data of the set block exceeds the limit: the state alarm component is an acoustic alarm unit; The state alarm component is connected to the number monitoring component and is used to perform corresponding alarm actions when the number of unit time operations of the step of using the AI ​​analysis model to intelligently analyze the predicted value of the cash withdrawal amount value of the set block in the future time segment according to the various historical time segments of the set block and the multiple configuration data of the set block exceeds the limit: the state alarm component is an optical alarm unit; And wherein, the status alarm component is connected to the number monitoring component, and is used to execute corresponding alarm actions when the number of unit time operations of the step of using the AI ​​analysis model to intelligently analyze the predicted value of the cash withdrawal amount of the set block in the future time segment according to each historical time segment of a fixed number of blocks and multiple configuration data of the set blocks exceeds the limit: the status alarm component has a built-in comparison device.

[0019] In addition, in the financial allocation intelligent management system using neural networks, a numerical conversion formula is used to express the numerical conversion relationship in which the number of times the deep neural network learns is positively correlated with the geographical area of ​​a set block, including: in the numerical conversion formula, the geographical area of ​​the set block is the input content of the numerical conversion formula, and the number of times the deep neural network learns corresponding to the geographical area of ​​the set block is the output content of the numerical conversion formula.

[0020] The financial allocation intelligent management system using neural network of the present invention is used to address the technical problem in the prior art that it is difficult to configure in advance the cash required for withdrawals in future time segments for target bank ATMs in a set block. By adopting an AI analysis model with a customized structure, the predicted value of the cash withdrawal amount in the set block in the future time segment is intelligently analyzed, and the cash required for withdrawals in the future time segment is configured in advance for the target bank ATMs in the set block, thereby improving the utilization rate of effective cash resources and solving the above-mentioned technical problems.

[0021] Although the present invention has been disclosed in detail with various preferred embodiments as above, the present invention should not be interpreted as only covering the above embodiments, which are only exemplary embodiments. Any person skilled in the art may make some changes and improvements without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the claims and their equivalents.

Claims

1. A financial allocation intelligent management system using neural network, characterized in that: The system comprises: A content construction component for generating an AI analysis model, wherein the AI ​​analysis model is a deep neural network obtained by performing multiple learning of the deep neural network, and the number of times the deep neural network is learned is positively correlated with the geographical area of ​​the set block; The first capture component is used to obtain the cash withdrawal amount values ​​corresponding to each of the fixed number of historical time segments in the set block, and the single cash withdrawal amount value corresponding to each historical time segment in the set block is the total cash withdrawal amount of the target bank ATM machine arranged in the set block within the historical time segment; A second capture component is used to obtain multiple configuration data of a set block, wherein the multiple configuration data of the set block are the number of registered users of the target bank in the set block, the geographical area of ​​the set block, and the number of ATM machines of other banks in the set block; An intelligent analysis device, connected to the content construction component, the first capture component and the second capture component respectively, for using the AI ​​analysis model to intelligently analyze the predicted value of the cash withdrawal amount value of the set block in the future time segment according to the respective cash withdrawal amount values ​​corresponding to each historical time segment of a fixed number of the set block and multiple configuration data of the set block; A financial allocation device, connected to the intelligent analysis device, is used to configure the target bank ATM in the set block with cash required for withdrawals in the future time segment in advance before the future time segment arrives, wherein the cash required for withdrawals matches the predicted value of the cash withdrawal amount in the set block in the future time segment obtained by intelligent analysis; Wherein, the financial allocation device is connected to the intelligent analysis device, and is used to configure the cash required for the withdrawal of the target bank ATM in the set block in advance before the future time segment arrives, and the cash required for the withdrawal is matched with the predicted value of the cash withdrawal amount of the set block in the future time segment obtained by intelligent analysis, including: the cash required for the withdrawal is a multiple of the predicted value of the cash withdrawal amount of the set block in the future time segment obtained by intelligent analysis, and the value of the multiple is greater than 1; Among them, the AI ​​analysis model is used to intelligently analyze the predicted value of the cash withdrawal amount value of the set block in the future time segment according to the respective cash withdrawal amount values ​​corresponding to the fixed number of historical time segments of the set block and multiple configuration data of the set block, including: the future time segment and the fixed number of historical time segments form a complete time interval; Among them, the AI ​​analysis model is used to intelligently analyze the cash withdrawal amount values ​​corresponding to each fixed number of historical time segments of the set block and multiple configuration data of the set block, and the predicted value of the cash withdrawal amount value of the set block in the future time segment includes: the future time segment has the same duration as each time segment in the fixed number of historical time segments.

2. The financial allocation intelligent management system using neural network as claimed in claim 1, characterized in that: A second capture component is used to obtain multiple configuration data of a set block, wherein the multiple configuration data of the set block are the number of registered users of the target bank in the set block, the geographical area of ​​the set block, and the number of ATM machines of other banks in the set block, including: other banks are banks other than the target bank; The content construction component is used to generate an AI analysis model, wherein the AI ​​analysis model is a deep neural network obtained by performing multiple learning on the deep neural network, and the number of times the deep neural network is learned is positively correlated with the geographical area of ​​the set block, including: using a numerical conversion formula to express the numerical conversion relationship of the number of times the deep neural network is learned and the geographical area of ​​the set block; The cash required for withdrawal is a multiple of the predicted value of the withdrawal amount of the set block in the future time segment obtained by intelligent analysis, and the value of the multiple is greater than 1, including: the value of the multiple is between 1.02-1.1; Among them, the AI ​​analysis model is used to intelligently analyze the predicted value of the cash withdrawal amount of the set block in the future time segment according to the respective cash withdrawal amount values ​​corresponding to each fixed number of historical time segments of the set block and multiple configuration data of the set block, including: selecting a numerical simulation mode to realize the simulation and testing of the data processing process of using the AI ​​analysis model to intelligently analyze the predicted value of the cash withdrawal amount of the set block in the future time segment according to the respective cash withdrawal amount values ​​corresponding to each fixed number of historical time segments of the set block and multiple configuration data of the set block.

3. The financial allocation intelligent management system using neural network as claimed in claim 2, characterized in that: The system further comprises: The frequency monitoring component is used to measure the number of unit time operations of the step of using the AI ​​analysis model to intelligently analyze the predicted value of the cash withdrawal amount value of the set block in the future time segment according to the various historical time segments of a fixed number of set blocks and multiple configuration data of the set blocks.

4. The financial allocation intelligent management system using neural network as claimed in claim 3, characterized in that: The system further comprises: The status alarm component is connected to the frequency monitoring component and is used to execute a corresponding alarm action when the number of unit time operations of the step of using the AI ​​analysis model to intelligently analyze the predicted value of the cash withdrawal amount of the set block in the future time segment according to each historical time segment of a fixed number of set blocks and multiple configuration data of the set blocks exceeds the limit.

5. The financial allocation intelligent management system using neural network as claimed in claim 4, characterized in that: The number monitoring component has a built-in computing sensor circuit for measuring the number of operations per unit time of the step of using the AI ​​analysis model to intelligently analyze the predicted value of the cash withdrawal amount of the set block in the future time segment based on the values ​​of each cash withdrawal amount corresponding to each historical time segment of a fixed number of blocks and multiple configuration data of the set blocks.

6. The financial allocation intelligent management system using neural network as claimed in claim 5, characterized in that: The status alarm component is connected to the number monitoring component and is used to execute corresponding alarm actions when the number of unit time operations of the step of using the AI ​​analysis model to intelligently analyze the predicted value of the cash withdrawal amount of the set block in the future time segment according to each historical time segment of a fixed number of set blocks and multiple configuration data of the set blocks exceeds the limit: the status alarm component is a vibration alarm unit.

7. The financial allocation intelligent management system using neural network as claimed in claim 5, characterized in that: The status alarm component is connected to the number monitoring component and is used to execute corresponding alarm actions when the number of unit time operations of the step of using the AI ​​analysis model to intelligently analyze the predicted value of the cash withdrawal amount of the set block in the future time segment according to each historical time segment of a fixed number of set blocks and multiple configuration data of the set blocks exceeds the limit. The status alarm component is an acoustic alarm unit.

8. The financial allocation intelligent management system using neural network as claimed in claim 5, characterized in that: The status alarm component is connected to the number monitoring component and is used to execute corresponding alarm actions when the number of unit time operations of the step of using the AI ​​analysis model to intelligently analyze the predicted value of the cash withdrawal amount of the set block in the future time segment according to each historical time segment of a fixed number of set blocks and multiple configuration data of the set blocks exceeds the limit: the status alarm component is an optical alarm unit.

9. The financial allocation intelligent management system using neural network as claimed in claim 5, characterized in that: The status alarm component is connected to the number monitoring component and is used to execute corresponding alarm actions when the number of unit time operations of the step of using the AI ​​analysis model to intelligently analyze the predicted value of the cash withdrawal amount of the set block in the future time segment according to each historical time segment of a fixed number of blocks and multiple configuration data of the set blocks exceeds the limit: the status alarm component has a built-in comparison device.

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