Banking business intelligent circulation distribution method and system based on big data analysis

Abstract

The invention discloses a banking business intelligent circulation distribution method and system based on big data analysis, and relates to the field of data processing methods specially suitable for finance, in the method, the fund business safety coefficient and the actual available transaction limit of each network point are calculated, and each network point is divided into a first-level network point, a second-level network point and a third-level network point; splitting the business amount into a plurality of sub-business packets; determining candidate websites, and determining a fund flow path set; calculating a risk value, selecting a path combination with the minimum risk value, and generating an initial fund allocation scheme; calculating the fund processing pressure of each network point; and when the fund processing pressure of any network point exceeds a preset threshold value, selecting a standby path from the fund flow path set of the network points, and redistributing the sub-service packets corresponding to the network points until the fund processing pressure of all network points is lower than the preset threshold value. According to the method and the device, optimal allocation of cross-regional resources is realized, so that the business processing efficiency and the service quality of banks are improved.

Description

Technical Field

[0001] This application belongs to the field of data processing methods specifically applicable to finance, and particularly relates to an intelligent transfer and allocation method and system for banking operations based on big data analysis. Background Art

[0002] With the continuous increase in the types of banking operations and the rapid growth of the business volume, the traditional manual allocation mode has been difficult to meet the requirements of the intelligent and efficient transfer of banking operations. In the traditional mode, business allocation mainly relies on manual experience judgment and fixed allocation rules. This method is not only inefficient but also prone to problems such as uneven task allocation and waste of professional resources, reducing the processing efficiency of banking operations.

[0003] In related technologies, the incoming banking operations can be automatically classified and allocated through preset business rules. The system allocates the operations to the corresponding processing personnel according to factors such as business type, priority, and the professional skills of the processing personnel, improving the automation level and accuracy of business allocation.

[0004] However, when processing a large-amount fund operation, it may require the collaboration of personnel from multiple branches and different departments. The above-mentioned related technologies are difficult to comprehensively consider multi-dimensional factors such as the real-time business volume of each branch, the matching degree of personnel professionalism, and geographical location distribution, thus making it difficult to achieve the optimal allocation of cross-regional resources and reducing the processing efficiency and service quality of the bank. Summary of the Invention

[0005] This application provides an intelligent transfer and allocation method and system for banking operations based on big data analysis, which is used to achieve the optimal allocation of cross-regional resources and thereby improve the processing efficiency and service quality of the bank.

[0006] In the first aspect, this application provides an intelligent transfer and allocation method for banking operations based on big data analysis. The fund operation safety coefficient of each branch is calculated according to the historical handling data of large-amount fund operations of each branch. The historical handling data of large-amount fund operations includes the maximum limit, historical transaction success rate, and large-amount fund error rate. The fund operation safety coefficient is obtained by weighted calculation of the historical transaction success rate and the large-amount fund error rate. Based on the maximum limit and the fund operation safety coefficient of each branch, the actual available transaction limit of each branch is calculated, and each branch is divided into a first-level branch, a second-level branch, and a third-level branch according to the actual available transaction limit of each branch. The actual available transaction limit of the first-level branch is greater than that of the second-level branch, and the actual available transaction limit of the second-level branch is greater than that of the third-level branch. Receive and extract the business amount, customer level, and original branch information in the large-amount fund operation request. Split the business amount into multiple sub - business packages, and the amount of each sub - business package is less than the actual available transaction limit of the first - level network points; Determine candidate network points within a preset radius centered on the original network point, and calculate the risk paths of fund transfer between candidate network points based on the fund business security coefficients of each candidate network point to obtain a set of fund transfer paths; Calculate the risk values of each sub - business package for each path in the set of fund transfer paths, select the path combination with the minimum risk value, and generate an initial fund allocation plan. The initial fund allocation plan includes the network points corresponding to each sub - business package and the fund transfer paths; According to the initial fund allocation plan, calculate the fund processing pressure of each network point. The fund processing pressure is determined according to the ratio of the amount of the sub - business package to the actual available transaction limit of the network point; When the fund processing pressure of any network point exceeds the preset threshold, select a backup path from the set of fund transfer paths of the network point, and re - allocate the sub - business package corresponding to the network point until the fund processing pressure of all network points is lower than the preset threshold.

[0007] By adopting the above technical solutions, the fund business security coefficient is calculated by analyzing the historical transaction success rate and large - amount fund error rate of network points, the actual available transaction limit is determined in combination with the maximum limit, and a scientific network point grading mechanism is established. When dealing with large - amount fund business, the business amount is split into sub - business packages suitable for the processing capabilities of different - level network points, and the optimal fund transfer plan is selected based on the risk paths between candidate network points. The fund processing pressure index is introduced for dynamic adjustment. When the pressure of a network point is too high, the backup path is automatically switched to re - allocate the business, improving the security and efficiency of large - amount fund business processing. Through business splitting and multi - path diversion, the system can make full use of network point resources and avoid excessive single - point pressure; through risk path optimization and dynamic adjustment mechanism, it can achieve efficient business flow on the premise of ensuring fund security, reducing the operational risk and systemic risk in the process of large - amount fund business processing.

[0008] Combined with some embodiments of the first aspect, in some embodiments, calculating the actual available transaction limit of each network point based on the maximum limit and the fund business security coefficient of each network point specifically includes: Calculate the transaction reliability of each network point according to the ratio of the historical transaction success rate of each network point to the preset benchmark success rate; Take the ratio of the large - amount fund error rate of each network point to the preset benchmark error rate as the risk attenuation coefficient of each network point; Take the product of the maximum limit of each network point and the corresponding transaction reliability and risk attenuation coefficient as the actual available transaction limit of each network point.

[0009] By adopting the above technical solution, the ratio of the historical transaction success rate of the network point to the preset benchmark success rate is used as the transaction reliability, and the ratio of the large-amount fund error rate to the preset benchmark error rate is used as the risk attenuation coefficient. The maximum limit is taken as the product of the transaction reliability and the risk attenuation coefficient to obtain the actual available transaction limit, so that the transaction amount of the network point matches its actual business level, enabling the system to more accurately evaluate and control the business carrying capacity of each network point. By introducing the considerations of two dimensions, namely transaction reliability and risk attenuation coefficient, it not only reflects the transaction success ability of the network point but also takes into account the risk control level, so that the obtained actual available transaction limit is more reasonable and practical, and the risk brought by the network point undertaking business beyond its own ability can be reduced.

[0010] Combined with some embodiments of the first aspect, in some embodiments, based on the fund business security coefficients of each candidate network point, the risk paths of fund transfer between each candidate network point are calculated to obtain a fund transfer path set, which specifically includes: Calculate the difference in the fund business security coefficients between each pair of candidate network points, and take the difference in the fund business security coefficients as the basic risk value for the fund transfer between network points; Based on the basic risk value, construct a risk-weighted graph between candidate network points, where the network points in the risk-weighted graph are nodes and the basic risk value is the weight of the edge; Establish a network point path mapping relationship according to the risk-weighted graph, and the network point path mapping relationship includes a starting network point, intermediate network points, and a target network point; Use the improved Dijkstra algorithm to search for multiple paths with the minimum risk weight in the risk-weighted graph, and convert the node sequence of the path with the minimum risk weight into the corresponding network point sequence; Combine the shortest physical path between adjacent network points with the network point path mapping relationship according to the network point sequence to generate the actual path of fund transfer; Classify and organize the actual paths to obtain a fund transfer path set, and the overlap degree between any two paths in the fund transfer path set does not exceed a preset ratio.

[0011] By adopting the above technical solution, a risk-weighted graph is constructed by calculating the difference in the fund business security coefficients between candidate network points, multiple paths with the minimum risk weight are searched using the improved Dijkstra algorithm, and the actual fund transfer path set is generated in combination with the physical path. The difference in business security between network points is used as the path weight, which not only ensures the minimization of path risk but also takes into account the actual physical distance constraint. By controlling the overlap degree between any two paths in the path set not to exceed a preset ratio, the anti-risk ability of the system is improved, the security of fund transfer is optimized, and the feasibility and diversity of the path are ensured.

[0012] In some embodiments in combination with some embodiments of the first aspect, calculating the risk value of each sub-business package for each path in the capital flow path set specifically includes: Calculating the sum of the reciprocals of the capital business safety coefficients of each network point on each path in the capital flow path set to obtain the path basic risk coefficient; Multiplying the amount of each sub-business package by the basic risk coefficient of the corresponding optional path to obtain the initial risk value; Calculating the business congestion probability based on the historical business data of each network point, and taking the product of the business congestion probability and the initial risk value as the risk value of each final path.

[0013] By adopting the above technical solution, the basic risk coefficient is obtained by calculating the sum of the reciprocals of the capital business safety coefficients of each network point on the path, multiplying it by the amount of the sub-business package to obtain the initial risk value, and then combining the business congestion probability to obtain the final path risk value, considering the security of the path itself, the impact of the business scale, and the actual operation status of the network points. By combining the static path risk and the dynamic business pressure for comprehensive evaluation, the risk assessment result is more objective and accurate. It can help the system select the optimal solution among multiple optional paths and improve the security of capital flow.

[0014] In some embodiments in combination with some embodiments of the first aspect, calculating the business congestion probability based on the historical business data of each network point specifically includes: Counting the business handling volume of each network point in different time periods, and calculating the ratio of the business handling volume per unit time to the maximum processing capacity of the network point to obtain the business saturation; Extracting the peak business volume and the average business volume from the historical business data of each network point, and calculating the business volatility, where the business volatility is the ratio of the peak business volume to the average business volume; Calculating the business backlog risk value according to the business saturation and the business volatility, and the business backlog risk value increases as the business saturation and the business volatility increase; Dividing the business backlog risk value by the standard processing capacity of the network point to obtain the standardized congestion index; Setting the congestion level division standard based on the standardized congestion index, and taking the probability value corresponding to the current congestion level as the business congestion probability.

[0015] By adopting the above technical solution, by counting the business handling volumes of each network point in different time periods and calculating the business saturation degree, and calculating the business volatility by combining the peak business volume and the average business volume in the historical business data, the business processing status of the network point can be comprehensively reflected. By comprehensively considering the business saturation degree and the business volatility to calculate the business backlog risk value, the current business processing pressure of the network point can be accurately evaluated. By dividing the business backlog risk value by the standard processing capacity of the network point to obtain the standardized congestion index, the influence of differences between network points of different scales is reduced. Based on the standardized congestion index, setting the congestion level division standard and determining the business congestion probability makes the evaluation of the business congestion status of the network point more objective and accurate, and can dynamically reflect the actual business processing capacity and pressure status of the network point.

[0016] Combined with some embodiments of the first aspect, in some embodiments, after redistributing the sub-business packages corresponding to the network points until the capital processing pressures of all network points are lower than the preset threshold, the method further includes: Calculating the capital utilization rate of each network point, where the capital utilization rate is the ratio of the average daily business capital volume of the network point to the capital reserve volume of the network point; Setting the capital warning level based on the capital utilization rate. When the capital utilization rate is greater than the first preset value, it is the first warning. When the capital utilization rate is greater than the second preset value and less than the first preset value, it is the second warning. When the capital utilization rate is less than the second preset value, it is the third warning; Establishing a capital mutual assistance group according to the geographical distances between the network points, and each capital mutual assistance group includes at least three network points with adjacent geographical locations; When any network point is in the first warning, select the network point with the lowest capital utilization rate from the network points in the third warning in the capital mutual assistance group to which the network point belongs for capital allocation.

[0017] By adopting the above technical solution, by calculating the capital utilization rate of the network point and setting the three-level capital warning level, a real-time monitoring mechanism for the capital status of the network point is established. Based on the geographical distance, a capital mutual assistance group is established. When it is found that a certain network point is in the first warning state, the network point with the lowest capital utilization rate can be selected from the network points in the third warning state in the same mutual assistance group for capital allocation, realizing the dynamic balance of the capital surplus between the network points and improving the overall capital utilization efficiency. Since the capital allocation is only carried out between network points with adjacent geographical locations, the time cost and safety risk in the capital allocation process are reduced. By selecting the network point with the lowest capital utilization rate as the allocation source, not only the feasibility of the capital allocation is ensured, but also the normal business of the allocated network point is avoided, improving the overall capital use efficiency of the banking system.

[0018] Combined with some embodiments of the first aspect, in some embodiments, selecting the network point with the lowest capital utilization rate from the network points in the third warning in the capital mutual assistance group to which the network point belongs for capital allocation specifically includes: Calculate the capital utilization rate of each network point according to the average daily business capital volume and the capital reserve volume of each network point within the capital mutual assistance group; Compare the capital utilization rate with the first preset value and the second preset value to identify the network points in the first warning state and the network points in the third warning state; Calculate the distances between each network point based on the geographical locations of each network point, and determine at least three adjacent network points in the same capital mutual assistance group; Screen out the network points in the third warning state from the capital mutual assistance group and sort them in ascending order of capital utilization rate; Select the network point with the lowest capital utilization rate as the source network point for capital allocation, and allocate the capital to the target network point in the first warning state.

[0019] By adopting the above technical solution, by specifying in detail the method for selecting the network points for capital allocation, the standardization and automation of the capital allocation process are realized. Classifying the network points according to the capital utilization rate and establishing a capital mutual assistance group based on geographical location ensure the accuracy and operability of capital allocation. When screening the source network points for capital allocation, the optimal network point is selected through the capital utilization rate ranking, ensuring the rationality of capital allocation. This precise capital allocation mechanism can not only quickly respond to the capital needs of network points, but also reduce the impact of capital allocation on the normal business of network points. Since the geographical distance between network points is considered in the allocation process, the actual execution efficiency of capital allocation is improved, and at the same time, the risk of capital in transit is reduced, improving the efficiency and accuracy of capital allocation, and making the capital use of the entire bank network system more reasonable and efficient.

[0020] In a second aspect, an embodiment of the present application provides a banking business intelligent transfer and allocation system based on big data analysis. The banking business intelligent transfer and allocation system based on big data analysis includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code. The computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, including instructions, which when running on the system, enable the system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer program product, which when running on the system, enables the system to execute the method described in any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The present application provides an intelligent transfer and allocation method for banking services based on big data analysis. By analyzing the historical transaction success rate and large-amount fund error rate of outlets to calculate the fund business security coefficient, and combining the maximum limit to determine the actual available transaction limit, a scientific outlet grading mechanism is established. When processing large-amount fund business, the business amount is split into sub-business packages suitable for the processing capabilities of different-level outlets, and the optimal fund transfer plan is selected based on the risk paths among candidate outlets. The fund processing pressure index is introduced for dynamic adjustment. When the pressure on an outlet is too high, the standby path is automatically switched to reallocate the business, improving the security and efficiency of processing large-amount fund business. Through business splitting and multi-path diversion, the system can make full use of outlet resources and avoid excessive single-point pressure; through risk path optimization and dynamic adjustment mechanism, the efficient transfer of business can be achieved on the premise of ensuring fund security, reducing the operational risk and systematic risk in the process of processing large-amount fund business.

[0024] 2. The present application provides an intelligent transfer and allocation method for banking services based on big data analysis. By counting the business handling volume of each outlet at different time periods and calculating the business saturation, and combining the peak business volume and average business volume in the historical business data to calculate the business volatility, the business processing status of the outlet can be comprehensively reflected. Considering the business saturation and business volatility comprehensively to calculate the business backlog risk value can accurately evaluate the current business processing pressure of the outlet. By dividing the business backlog risk value by the standard processing capacity of the outlet to obtain the standardized congestion index, the influence of differences between outlets of different scales is reduced. Based on the standardized congestion index, the congestion level division standard is set and the business congestion probability is determined, making the evaluation of the business congestion status of the outlet more objective and accurate, and dynamically reflecting the actual business processing capacity and pressure status of the outlet.

[0025] 3. The present application provides an intelligent transfer and allocation method for banking services based on big data analysis. By calculating the fund utilization rate of outlets and setting a three-level fund early warning level, a real-time monitoring mechanism for the fund status of outlets is established. Based on the geographical distance, a fund mutual assistance group is established. When it is found that an outlet is in the first early warning state, the outlet with the lowest fund utilization rate can be selected from the outlets in the third early warning state within the same mutual assistance group for fund allocation, realizing the dynamic balance of the fund surplus among outlets and improving the overall fund utilization efficiency. Since the fund allocation is only carried out among adjacent outlets in terms of geographical location, the time cost and security risk in the process of fund allocation are reduced. By selecting the outlet with the lowest fund utilization rate as the allocation source, not only the feasibility of fund allocation is ensured, but also the normal business of the allocated outlet is avoided, improving the overall fund use efficiency of the banking system. Description of the Drawings

[0026] Figure 1 It is a flowchart of a method for intelligent transfer and allocation of banking operations based on big data analysis in an embodiment of the present application.

[0027] Figure 2 It is a flowchart of a method for capital early warning and allocation in an embodiment of the present application.

[0028] Figure 3 It is a schematic structural diagram of an entity device of a system for intelligent transfer and allocation of banking operations based on big data analysis provided in an embodiment of the present application. Detailed implementation manners

[0029] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular forms "a", "an", "the", "above-mentioned", "said", and "this" are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term " / and / " used in the present application refers to any or all possible combinations including one or more of the listed items.

[0030] Hereinafter, the terms "first" and "second" are only used for descriptive purposes, and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0031] Next, an embodiment is used and combined with Figure 1 to describe a method for intelligent transfer and allocation of banking operations based on big data analysis in an embodiment of the present application: Please refer to Figure 1 which is a flowchart of a method for intelligent transfer and allocation of banking operations based on big data analysis in an embodiment of the present application.

[0032] S101. Calculate the capital business safety factor of each network point according to the historical handling data of large-amount capital business of each network point; The system calculates the fund business safety factor for each network point based on the historical handling data of large-amount fund transactions at each network point. The historical handling data of large-amount fund transactions includes the maximum limit, historical transaction success rate, and large-amount fund error rate. The fund business safety factor is obtained by weighted calculation of the historical transaction success rate and the large-amount fund error rate. This step calculates the fund business safety factor for each network point. The system obtains the historical handling data of large-amount fund transactions at each network point, including data such as the maximum limit, historical transaction success rate, and large-amount fund error rate, and uses this data to calculate the fund business safety factor for each network point. The purpose of this step is to evaluate the security and reliability of each network point when handling large-amount fund transactions, and to provide a basis for subsequent network point grading and fund allocation. In addition to the maximum limit, historical transaction success rate, and large-amount fund error rate mentioned in the text, other relevant historical data, such as business processing time, customer complaint rate, etc., can also be considered to more comprehensively evaluate the fund business security of the network point.

[0033] In specific implementation, the system can use the weighted calculation method to calculate the fund business safety factor. First, the system can assign different weights to the historical transaction success rate and the large-amount fund error rate respectively, and then sum them up by weighting to obtain the final safety factor. The setting of the weight can be determined according to the actual situation and business requirements. For example, a higher weight can be assigned to the historical transaction success rate because it directly reflects the success rate of the network point in handling business; or a higher weight can be assigned to the large-amount fund error rate because a network point with a high error rate may pose a greater risk. In addition, the system can also introduce other factors, such as business volume, customer satisfaction, etc., and incorporate them into the weighted calculation to obtain a more comprehensive and accurate safety factor.

[0034] S102. Calculate the actual available transaction limit for each network point based on the maximum limit and the fund business safety factor of each network point, and divide each network point into a first-level network point, a second-level network point, and a third-level network point according to the actual available transaction limit of each network point; The system calculates the actual available transaction limit for each network point based on the maximum limit and the fund business safety factor of each network point. Specifically, according to the ratio of the historical transaction success rate of each network point to the preset benchmark success rate, calculate the transaction reliability of each network point; use the ratio of the large-amount fund error rate of each network point to the preset benchmark error rate as the risk attenuation coefficient of each network point; multiply the maximum limit of each network point by the corresponding transaction reliability and risk attenuation coefficient respectively as the actual available transaction limit of each network point. And divide each network point into a first-level network point, a second-level network point, and a third-level network point according to the actual available transaction limit of each network point. The actual available transaction limit of the first-level network point is greater than that of the second-level network point, and the actual available transaction limit of the second-level network point is greater than that of the third-level network point.

[0035] This step calculates the actual available transaction limit based on the maximum limit of each network point and the capital business security factor, and classifies the network points accordingly. First, the system calculates the transaction reliability by the ratio of the historical transaction success rate of each network point to the preset benchmark success rate, which is used to evaluate the actual transaction success ability of the network point. Then, the ratio of the large-amount fund error rate to the preset benchmark error rate is used as the risk attenuation coefficient to evaluate the error risk of the network point. Finally, the maximum limit is multiplied by the transaction reliability and the risk attenuation coefficient to obtain the actual available transaction limit. This limit comprehensively considers the transaction ability and risk level of the network point and can more accurately reflect the actual transaction ability of the network point. According to the size of the actual available transaction limit, the system divides the network points into three levels. The higher the level, the larger the actual available transaction limit, and the larger the amount of business that can be processed. In addition to the maximum limit, transaction success rate and error rate, other factors such as the business volume and personnel configuration of the network point can also be considered when calculating the actual available transaction limit to more comprehensively evaluate the actual transaction ability of the network point.

[0036] In specific implementation, the system can set different thresholds to classify the network point levels. For example, network points with actual available transaction limits in the top 20% can be classified as the first level, those in the range of 20%-50% as the second level, and the rest as the third level. The setting of the threshold can be determined according to the actual situation and business requirements, or algorithms such as clustering can be used to automatically classify the levels. In addition, the system can regularly update the actual available transaction limit and level of the network point to adapt to business changes and changes in the capabilities of the network points. The classification of levels can also be more refined, for example, divided into five levels, to better distinguish the transaction capabilities of the network points.

[0037] S103. Receive and extract the business amount, customer level, and original network point information in the large-amount fund business request, and split the business amount into multiple sub-business packages; The system receives and extracts the business amount, customer level, and original network point information in the large-amount fund business request, and splits the business amount into multiple sub-business packages, and the amount of each sub-business package is less than the actual available transaction limit of the first-level network point.

[0038] This step is that the system receives a large-amount fund business request and performs necessary information extraction and business splitting. When a customer submits a large-amount fund business request, the system first receives the request and extracts key information such as the business amount, customer level, and original network point from the request. These information are the basis for subsequent business processing. Since the requested business amount is large, in order to reduce the risk of fund processing, the system will split the business amount into multiple sub-business packages with smaller amounts, and the amount of each sub-business package does not exceed the actual available transaction limit of the first-level network point. This can distribute large-amount business to multiple network points for processing, reducing the fund pressure and risk of a single network point. In addition to amount splitting, the system can also split the business request according to other factors such as customer level and business type to achieve more fine-grained business distribution and processing.

[0039] In specific implementation, the system can split the business amount into sub-business packages according to preset splitting rules. For example, the business amount can be split according to a fixed ratio or a fixed amount, or the splitting plan can be dynamically adjusted according to the actual available transaction limit of the network point. When splitting, it is necessary to ensure that the sum of the amounts of the sub-business packages is equal to the original business amount to avoid problems of amount mismatch. At the same time, in order to improve the efficiency of splitting, the system can preset a splitting plan template and automatically select a suitable splitting plan according to the business amount. After splitting, the system will generate multiple sub-business packages, and each sub-business package contains relevant information of the original business request, such as customer level, original network point, etc., for subsequent business distribution and processing.

[0040] S104. Determine candidate network points within a preset radius centered on the original network point, and calculate the risk paths of fund transfer between candidate network points based on the fund business security factor of each candidate network point to obtain a set of fund transfer paths; The system determines candidate outlets within a preset radius centered on the original outlet, and calculates the risk paths of fund transfer between candidate outlets based on the fund business security factors of each candidate outlet, obtaining a set of fund transfer paths. Specifically, it calculates the difference in fund business security factors between every two candidate outlets, and uses this difference as the basic risk value for fund transfer between outlets; constructs a risk-weighted graph of candidate outlets based on the basic risk value, where the outlets in the graph are nodes and the basic risk value is the weight of the edge; establishes an outlet path mapping relationship according to the risk-weighted graph, which includes the starting outlet, intermediate outlets, and target outlet; uses an improved Dijkstra algorithm to search for multiple paths with the smallest risk weight in the risk-weighted graph, and converts the node sequence of the path with the smallest risk weight into the corresponding outlet sequence; combines the shortest physical path between adjacent outlets with the outlet path mapping relationship according to the outlet sequence to generate the actual path of fund transfer; classifies and organizes the actual paths to obtain a set of fund transfer paths, and the overlap degree between any two paths in the set of fund transfer paths does not exceed a preset ratio.

[0041] This step is to determine candidate outlets based on the original outlet and calculate the risk paths of fund transfer between candidate outlets. The system takes the original outlet as the center and determines candidate outlets within its preset radius. These candidate outlets are the outlets that may participate in fund transfer. Then, the system calculates the risk paths of fund transfer between them based on the fund business security factors of each candidate outlet. Specifically, the system first calculates the difference in fund business security factors between every two candidate outlets as the basic risk value, and then constructs a risk-weighted graph of candidate outlets based on the basic risk value, where the outlets in the graph are nodes and the basic risk value is the weight of the edge. Next, the system establishes an outlet path mapping relationship according to the risk-weighted graph, including the starting outlet, intermediate outlets, and target outlet. Finally, the system uses an improved Dijkstra algorithm to search for multiple paths with the smallest risk weight in the risk-weighted graph, converts the node sequence of the path with the smallest risk weight into the corresponding outlet sequence, combines it with the shortest physical path between outlets to generate the actual path of fund transfer, and classifies and organizes the actual paths to obtain a set of fund transfer paths, and the overlap degree between any two paths in the set does not exceed a preset ratio. The purpose of this step is to find the fund transfer path with the lowest risk and provide a basis for subsequent fund allocation. In addition to the method mentioned in the text, other algorithms can also be used to calculate the risk path, such as the Floyd algorithm, A* algorithm, etc.

[0042] In specific implementation, the system can flexibly set the selection range of candidate outlets and the generation method of paths. The size of the preset radius can be determined according to business requirements and the distribution of outlets. The larger the radius, the more candidate outlets and the more paths are generated. When constructing the risk-weighted graph, in addition to using the difference in the safety factor of the capital business as the weight of the edge, other factors such as the distance between outlets and the business volume can also be comprehensively considered to more comprehensively evaluate the risk. When searching for the path with the minimum risk weight, the system can set limit conditions such as the number and length of paths according to actual needs to control the scale of the path set. When generating the actual path, the system can consider factors such as the physical distance and traffic conditions between outlets to select the optimal physical path. When classifying and sorting the actual paths, the system can adopt clustering algorithms such as K-means and DBSCAN to automatically classify similar paths into one category.

[0043] When generating the set of capital transfer paths, problems such as excessive number of paths and high duplication may occur, resulting in an increase in the complexity of subsequent capital allocation and affecting system performance. To solve this problem, the system can set the upper limits of the number of paths and the duplication. When the number of generated paths or the duplication exceeds the upper limit, the system can automatically screen and optimize the paths. When screening, the quality of the paths can be evaluated according to indicators such as the risk weight, length, and duplication of the paths, and the paths with higher quality are preferentially retained. When optimizing, the paths with high duplication can be merged, or some redundant nodes and edges can be removed to reduce the number and complexity of the paths. Through the screening and optimization of the paths, a set of capital transfer paths with appropriate scale and high quality can be obtained, improving the efficiency and accuracy of subsequent capital allocation.

[0044] S105. Calculate the risk values of each sub-business package for each path in the set of capital transfer paths, select the path combination with the minimum risk value, and generate an initial capital allocation plan; The system calculates the risk value of each sub-business package for each path in the capital transfer path set, specifically including: calculating the sum of the reciprocals of the capital business safety coefficients of each network point on each path in the capital transfer path set to obtain the path basic risk coefficient; multiplying the amount of each sub-business package by the corresponding basic risk coefficient of the optional path to obtain the initial risk value; calculating the business congestion probability based on the historical business data of each network point, and taking the product of the business congestion probability and the initial risk value as the final risk value of each path. Calculating the business congestion probability based on the historical business data of each network point specifically includes: counting the business handling volume of each network point in different time periods, and calculating the ratio of the business handling volume per unit time to the maximum processing capacity of the network point to obtain the business saturation, which reflects the business busy degree of the network point; extracting the peak business volume and average business volume from the historical business data of each network point, and calculating the business volatility, where the business volatility is the ratio of the peak business volume to the average business volume; calculating the business backlog risk value according to the business saturation and business volatility, and the business backlog risk value increases with the increase of the business saturation and business volatility; dividing the business backlog risk value by the standard processing capacity of the network point to obtain the standardized congestion index; setting the congestion level division standard based on the standardized congestion index, and taking the probability value corresponding to the current congestion level as the business congestion probability. And select the path combination with the smallest risk value, and generate an initial capital allocation plan, where the initial capital allocation plan includes the network points corresponding to each sub-business package and the capital transfer path.

[0045] This step further details how to calculate the risk value of each sub-business package for each path in the capital transfer path set. Among them, the calculation of the business congestion probability is an important detail. The system first counts the business handling volume of each network point in different time periods, calculates the ratio of the business handling volume per unit time to the maximum processing capacity of the network point to obtain the business saturation, which reflects the business busy degree of the network point. Then, the system extracts the peak business volume and average business volume from the historical business data of each network point, and calculates the business volatility, that is, the ratio of the peak business volume to the average business volume, which reflects the fluctuation of the business volume of the network point. Next, the system calculates the business backlog risk value according to the business saturation and business volatility, and the business backlog risk value increases with the increase of the business saturation and business volatility, indicating that the busier the network point and the greater the business volume fluctuation, the higher the backlog risk. To facilitate the comparison of the backlog risks of different network points, the system divides the business backlog risk value by the standard processing capacity of the network point to obtain the standardized congestion index. Finally, the system sets the congestion level division standard based on the standardized congestion index, and takes the probability value corresponding to the current congestion level as the business congestion probability. This calculation method fully considers the dynamic changes of the business volume of the network point and the backlog risk, and can more accurately evaluate the congestion situation of the path.

[0046] In the process of calculating the business congestion probability, the system can adopt a variety of data processing and analysis techniques. When counting the business handling volume, the system can use a sliding time window method to dynamically calculate the business volume within a certain time range to reflect the real-time changes in the business volume. When calculating the business saturation, the system can set different maximum processing capacity thresholds according to the business types and processing capabilities of the outlets to more accurately evaluate the saturation degree of the outlets. When calculating the business volatility, the system can adopt more complex statistical indicators such as standard deviation and coefficient of variation to more comprehensively describe the fluctuation characteristics of the business volume. When calculating the business backlog risk value, the system can adopt different risk functions such as linear functions and exponential functions to adapt to different risk preferences. When setting the congestion level division criteria, the system can use clustering algorithms to automatically divide the standardized congestion index into different levels and count the historical congestion probabilities of each level. In addition, the system can also introduce machine learning algorithms such as decision trees and random forests to automatically learn and optimize the calculation model of the congestion probability based on historical data to improve the calculation accuracy and adaptability.

[0047] S106. Calculate the fund processing pressure of each outlet according to the initial fund allocation plan; The system calculates the fund processing pressure of each outlet according to the initial fund allocation plan, and the fund processing pressure is determined according to the ratio of the amount of the sub-business package to the actual available transaction limit of the outlet.

[0048] This step is to calculate the fund processing pressure of each outlet according to the initial fund allocation plan. The fund processing pressure reflects the difficulty and risk of the outlet in handling the allocated fund business. The greater the pressure, the tighter the processing capacity of the outlet and the higher the risk. The system calculates the fund processing pressure by dividing the sum of the amounts of the sub-business packages allocated to each outlet by the actual available transaction limit of the outlet. The larger this ratio, the greater the fund processing pressure of the outlet. The purpose of calculating the fund processing pressure is to evaluate the feasibility and balance of the initial fund allocation plan and provide a basis for subsequent fund reallocation. In addition to using the ratio of the sub-business package amount to the actual available transaction limit to measure the pressure, other factors such as the historical business volume and personnel configuration of the outlet can also be comprehensively considered to more comprehensively evaluate the processing capacity of the outlet.

[0049] In specific implementation, the system can compare the fund processing pressure with a preset pressure threshold to determine whether a network point can bear the allocated fund business. The pressure threshold can be set according to business requirements and risk preferences. The lower the threshold, the higher the requirement for the processing ability of the network point and the lower the risk tolerance. When the fund processing pressure of a network point exceeds the threshold, the fund allocation plan needs to be adjusted to reduce the pressure on the network point. The adjustment method can be to reallocate some sub-business packages to other network points or adjust the actual available transaction limit of the network point. The system can determine the adjustment priority and amplitude according to the magnitude of the fund processing pressure. The greater the pressure on a network point, the higher the adjustment priority and the greater the amplitude.

[0050] When calculating the fund processing pressure, it may occur that the pressure of some individual network points is much higher than that of other network points, resulting in unbalanced fund allocation and affecting the overall processing efficiency and risk control. To solve this problem, the system can introduce a pressure balance mechanism to make the pressures of all network points as close as possible on the premise that the fund processing pressure of each network point is within the threshold. Specifically, the system can calculate the mean and variance of the network point pressures. When the variance exceeds a certain range, key adjustments are made to the network points with higher pressures to reduce their pressures to a level close to the mean. The adjustment method can be to transfer the sub-business packages allocated to them to network points with lower pressures or increase their actual available transaction limits. Through the pressure balance mechanism, the fund allocation can be made more balanced, the overall processing efficiency of the network points can be improved, and the single-point risk can also be reduced.

[0051] S107. When the fund processing pressure of any network point exceeds the preset threshold, select an alternative path from the set of fund transfer paths of the network point, and reallocate the sub-business packages corresponding to the network point until the fund processing pressures of all network points are lower than the preset threshold.

[0052] This step dynamically adjusts the fund allocation plan according to the fund processing pressure of the network point. When the system finds that the fund processing pressure of a certain network point exceeds the preset threshold, it indicates that the current fund allocation plan places too high a demand on the processing ability of this network point and needs to be adjusted. The adjustment method is to select an alternative path from the set of fund transfer paths of this network point, and then reallocate some of the sub-business packages allocated to this network point to these alternative paths to reduce the fund processing pressure of this network point. The quantity and amount of the reallocated sub-business packages can be determined according to the degree of exceeding the threshold. The more it exceeds, the more sub-business packages need to be reallocated. The adjustment will continue until the fund processing pressures of all network points are reduced below the threshold. The purpose of this step is to dynamically optimize the fund allocation plan to ensure that the processing pressures of all network points are within an acceptable range. In addition to alternative paths, other adjustment methods can also be considered, such as increasing the actual available transaction limit of the network point, deploying personnel between network points, etc., to improve the processing ability of the network point.

[0053] In specific implementation, the system needs to pre-generate a set of fund transfer paths for each network point as the source of alternative paths. The alternative paths can be the paths not selected in the initial fund allocation plan or new paths dynamically generated according to the real-time network conditions and business requirements. When selecting alternative paths, the system needs to comprehensively consider factors such as the risk value of the path, the processing capacity of the network point, and the timeliness of the business, and preferentially select paths with lower risks, stronger processing capacities of network points, and lower timeliness requirements. After selecting the alternative path, the system sends the information of the sub-business package to the relevant network points on the path to notify them for processing. At the same time, the system also needs to update the fund processing pressure of the network point to promptly detect the situation where the pressure exceeds the standard.

[0054] In the above embodiments, by analyzing the historical transaction success rate and large-amount fund error rate of network points to calculate the fund business safety coefficient, combining with the maximum limit to determine the actual available transaction limit, a scientific network point grading mechanism is established. When processing large-amount fund business, the business amount is split into sub-business packages suitable for the processing capacities of different-level network points, and the optimal fund transfer plan is selected based on the risk paths among candidate network points. The fund processing pressure index is introduced for dynamic adjustment. When the pressure of a network point is too high, the alternative path is automatically switched to reallocate the business, improving the security and efficiency of processing large-amount fund business. Through business splitting and multi-path diversion, the system can make full use of network point resources and avoid excessive single-point pressure; through risk path optimization and dynamic adjustment mechanism, it can achieve efficient business transfer on the premise of ensuring fund safety, reducing the operational risk and systematic risk in the process of processing large-amount fund business.

[0055] The above embodiments describe the basic process of an intelligent transfer and allocation method for banking business based on big data analysis. This method realizes the safe and efficient processing of large-amount fund business through means such as calculating the fund business safety coefficient of network points, business splitting, and risk path optimization. However, in order to further improve the operation efficiency of the system and ensure the reasonable utilization of the fund reserves of each network point, this application also provides a fund warning and allocation method. This method maintains the fund balance of the entire bank network point system by monitoring the fund utilization status of network points in real time and promptly allocating funds when there is fund pressure. The following combines Figure 2 to describe a fund warning and allocation method in the embodiments of this application: Please refer to Figure 2 which is a schematic flowchart of a fund warning and allocation method in the embodiments of this application.

[0056] S201. Calculate the fund utilization rate of each network point; The system calculates the fund utilization rate of each network point, and the fund utilization rate is the ratio of the average daily business fund amount of the network point to the fund reserve amount of the network point.

[0057] In this step, the system grasps the usage of funds at each network point by calculating the fund utilization rate of each network point. The fund utilization rate reflects the utilization efficiency of the funds at the network point and is an important indicator for measuring the fund operation status of the network point. The system can calculate the fund utilization rate in various ways, such as using indicators like the ratio of the average daily business funds to the fund reserve at the network point, the turnover days, etc. In addition, the system can also analyze and compare the fund utilization rate according to different dimensions such as different periods and different business types to comprehensively evaluate the fund utilization situation at the network point.

[0058] Specifically, the system can calculate the fund utilization rate in the following way: First, obtain the data of the average daily business funds and the fund reserve at each network point; then, divide the average daily business funds by the fund reserve at the network point to get the fund utilization rate. The average daily business funds can be obtained by statistically calculating the total business funds at the network point within a certain period (such as one month) and then dividing by the number of days in the time period. The fund reserve at the network point refers to the total amount of funds available for business operation, including cash in stock, funds in transit, etc.

[0059] S202. Set the fund warning level based on the fund utilization rate; The system sets the fund warning level based on the fund utilization rate. When the fund utilization rate is greater than the first preset value, it is the first warning; when the fund utilization rate is greater than the second preset value and less than the first preset value, it is the second warning; when the fund utilization rate is less than the second preset value, it is the third warning.

[0060] In this step, the system sets different fund warning levels according to the calculated fund utilization rate to reflect the health degree of the fund status at the network point. The setting of the warning level can help managers timely discover problems in fund utilization and take corresponding measures for adjustment and optimization. The system can set multiple warning levels according to the actual situation, such as normal, attention, warning, high risk, etc., and different levels correspond to different fund utilization rate thresholds.

[0061] Specifically, when implementing, the system can preset two fund utilization rate thresholds: the first preset value and the second preset value. When the fund utilization rate is greater than the first preset value, it is regarded that the fund utilization rate is too high, and the funds at the network point face greater pressure, and the warning level is set to the first warning; when the fund utilization rate is between the first preset value and the second preset value, it is regarded that the fund utilization rate is relatively high, and the funds at the network point face certain pressure, and the warning level is set to the second warning; when the fund utilization rate is lower than the second preset value, it is regarded that the fund utilization rate is normal, and the funds at the network point are relatively abundant, and the warning level is set to the third warning.

[0062] When setting the warning level, the system may face the problem of unreasonable preset values, resulting in distorted warning levels and being unable to accurately reflect the capital status of the outlets. Therefore, the system can adopt a dynamic adjustment mechanism to automatically optimize the preset values according to the historical data and changing trends of the capital utilization rate of the outlets. For example, the system can calculate the average value and standard deviation of the capital utilization rate of all outlets within a certain period, and use the average value plus or minus different multiples of the standard deviation as the preset value. At the same time, the system can also set a floating range for the preset value, and adjust the preset value in a timely manner when the actual situation changes to maintain the effectiveness of the warning level.

[0063] S203. Establish a capital mutual assistance group based on the geographical distances between the outlets; The system establishes a capital mutual assistance group based on the geographical distances between the outlets, and each capital mutual assistance group includes at least three adjacent outlets in terms of geographical location.

[0064] In this step, the system forms adjacent outlets into a capital mutual assistance group according to the geographical location relationship between the outlets, providing a basis for subsequent capital allocation. The establishment of the capital mutual assistance group can promote the capital flow between the outlets and improve the capital utilization efficiency. The system can adopt various ways to divide the capital mutual assistance groups, such as grouping according to factors such as the administrative regions where the outlets are located and economic circles.

[0065] In specific implementation, the system can obtain the geographical coordinate information of each outlet, calculate the distances between the outlets, and divide the outlets with distances less than or equal to a certain threshold (such as 50 kilometers) into the same capital mutual assistance group. To ensure the feasibility and effectiveness of capital mutual assistance, the system can require that each capital mutual assistance group contains at least three outlets. When calculating the distances between the outlets, the system can adopt various distance measurement methods, such as Euclidean distance, Manhattan distance, etc., and can also consider factors such as road distance and driving time.

[0066] When establishing the capital mutual assistance group, the system may face problems such as uneven distribution of outlets and large differences in group sizes, which affect the effect of capital mutual assistance. Therefore, the system can adopt a dynamic adjustment mechanism to timely adjust the grouping method and parameter settings according to the changes in the outlet distribution. For example, when new outlets are added in a certain area, the system can recalculate the distances between the outlets and divide the new outlets into the group with the closest distance; when the number of outlets in a group is too large or too small, the system can split or merge the group by adjusting the distance threshold to keep the group size relatively balanced.

[0067] S204. When any outlet is in the first warning, select the outlet with the lowest capital utilization rate from the outlets in the third warning within the capital mutual assistance group to which the outlet belongs for capital allocation.

[0068] When any network point is in the first-level warning, select the network point with the lowest capital utilization rate from the network points in the third-level warning within the capital mutual assistance group to which the network point belongs. Specifically, calculate the capital utilization rate of each network point according to the average daily business capital volume and the network point capital reserve volume of each network point within the capital mutual assistance group; compare the capital utilization rate with the first preset value and the second preset value to identify the network points in the first-level warning state and the network points in the third-level warning state; calculate the distances between each network point based on the geographical locations of each network point, and determine at least three geographically adjacent network points within the same capital mutual assistance group; screen out the network points in the third-level warning state from within the capital mutual assistance group, and sort them in ascending order according to the capital utilization rate; select the network point with the lowest capital utilization rate as the source network point for capital allocation, and allocate the capital to the target network point in the first-level warning state.

[0069] In this step, the system implements capital allocation according to the capital warning level and the affiliated capital mutual assistance group of the network point to relieve the capital pressure. When a certain network point triggers the first-level warning, it indicates that the capital of this network point is tight and it is necessary to transfer in capital from other network points. The system first screens out the network points with the third-level warning level within the capital mutual assistance group to which this network point belongs. These network points have relatively abundant capital and are capable of providing capital support. Then, the system selects the network point with the lowest capital utilization rate among them as the capital supplier and allocates the capital to the network point with tight capital. This allocation method can minimize the impact on the capital supplier while ensuring sufficient capital supply.

[0070] When specifically implemented, the system can execute the following steps: First, obtain the real-time capital utilization rate and warning level of each network point within the capital mutual assistance group; then, identify the network points with the first-level warning level as the target network points for capital allocation; next, within the group to which this network point belongs, select the network points with the third-level warning level as the alternative capital suppliers; finally, sort the alternative network points in ascending order according to the capital utilization rate, select the network point with the lowest capital utilization rate as the final capital supplier, and calculate the amount of allocated capital to complete the capital allocation. The amount of allocated capital can be comprehensively determined according to factors such as the capital gap of the target network point and the surplus capital volume of the supplier.

[0071] In the above embodiments, a real-time monitoring mechanism for the fund status of outlets is established by calculating the fund utilization rate of outlets and setting three levels of fund early warning. A fund mutual assistance group is established based on geographical distance. When it is found that a certain outlet is in the first early warning state, the outlet with the lowest fund utilization rate can be selected from the outlets in the third early warning state within the same mutual assistance group for fund allocation, achieving a dynamic balance of the remaining funds between outlets and improving the overall fund utilization efficiency. Since the fund allocation is only carried out between adjacent outlets in terms of geographical location, the time cost and safety risk in the fund allocation process are reduced. By selecting the outlet with the lowest fund utilization rate as the allocation source, not only the feasibility of the fund allocation is ensured, but also the normal business of the allocated outlet is avoided, improving the overall fund usage efficiency of the banking system.

[0072] The following describes the system in the embodiments of the present invention application from the perspective of hardware processing. Please refer to Figure 3 , which is a schematic structural diagram of an entity device of an intelligent banking business transfer and allocation system based on big data analysis provided by the embodiments of the present application.

[0073] It should be noted that Figure 3 the structure of the system shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0074] As Figure 3 shown, the system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage section 308 into the random access memory (RAM) 303, such as executing the methods in the above embodiments. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other through a bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0075] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a liquid crystal display (LCD), a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as required. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is mounted on the drive 310 as required so that a computer program read therefrom is installed into the storage section 308 as required.

[0076] Specifically, according to an embodiment of the present invention, the processes described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by a central processing unit (CPU) 301, various functions defined in the present invention are executed.

[0077] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above.

[0078] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, as well as the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0079] As another aspect, the present invention also provides a computer-readable storage medium, which may be included in the system described in the above embodiments; or may exist separately without being assembled into the system. The above storage medium carries one or more computer programs, and when the one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiments.

[0080] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application.

[0081] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as "if...", or "after...", or "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" can be interpreted as "if determining...", or "in response to determining...", or "when detecting (the stated condition or event)", or "in response to detecting (the stated condition or event)".

[0082] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid-state drive), etc.

[0083] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by relevant hardware instructed by a computer program. This program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The aforementioned storage medium includes various media that can store program codes, such as ROM or random access memory RAM, magnetic disks, or optical discs.

Claims

1. A method for intelligent transfer and allocation of banking services based on big data analysis, characterized in that, Including: Calculating the fund business security coefficient of each network point according to the historical handling data of large-amount fund business of each network point. The historical handling data of large-amount fund business includes the maximum limit, historical transaction success rate and large-amount fund error rate. The fund business security coefficient is calculated by weighted calculation of the historical transaction success rate and the large-amount fund error rate; Calculating the actual available transaction limit of each network point based on the maximum limit and the fund business security coefficient of each network point, and dividing each network point into a first-level network point, a second-level network point and a third-level network point according to the actual available transaction limit of each network point. The actual available transaction limit of the first-level network point is greater than the actual available transaction limit of the second-level network point, and the actual available transaction limit of the second-level network point is greater than the actual available transaction limit of the third-level network point; Receiving and extracting the business amount, customer level and original network point information in the large-amount fund business request; Splitting the business amount into multiple sub-business packages, and the amount of each sub-business package is less than the actual available transaction limit of the first-level network point; Determining candidate network points within a preset radius centered on the original network point, and calculating the risk paths of fund transfer between each candidate network point based on the fund business security coefficient of each candidate network point to obtain a fund transfer path set; Calculating the risk value of each sub-business package on each path in the fund transfer path set, selecting the path combination with the smallest risk value, and generating an initial fund allocation plan. The initial fund allocation plan includes the network point corresponding to each sub-business package and the fund transfer path; Calculating the fund processing pressure of each network point according to the initial fund allocation plan. The fund processing pressure is determined according to the ratio of the amount of the sub-business package to the actual available transaction limit of the network point; When the fund processing pressure of any network point exceeds the preset threshold, selecting an alternative path from the fund transfer path set of the network point and reallocating the sub-business package corresponding to the network point until the fund processing pressure of all network points is lower than the preset threshold.

2. The method according to claim 1, wherein The calculating the actual available transaction limit of each network point based on the maximum limit and the fund business security coefficient of each network point specifically includes: Calculating the transaction reliability of each network point according to the ratio of the historical transaction success rate of each network point to the preset benchmark success rate; Taking the ratio of the large-amount fund error rate of each network point to the preset benchmark error rate as the risk attenuation coefficient of each network point; Taking the product of the maximum limit of each network point and the corresponding transaction reliability and risk attenuation coefficient as the actual available transaction limit of each network point.

3. The method according to claim 1, wherein The calculating the risk paths of fund transfer between each candidate network point based on the fund business security coefficient of each candidate network point to obtain a fund transfer path set specifically includes: Calculating the difference in the fund business security coefficient between each pair of candidate network points, and taking the difference in the fund business security coefficient as the basic risk value of fund transfer between the network points; Constructing a risk weighted graph between candidate network points based on the basic risk value. In the risk weighted graph, the network points are nodes and the basic risk value is the weight of the edge; Establish a mapping relationship between network paths according to the risk-weighted graph, where the mapping relationship between network paths includes a starting network point, intermediate network points, and a target network point; Use the improved Dijkstra algorithm to search for multiple paths with the minimum risk weight in the risk-weighted graph, and convert the node sequence of the path with the minimum risk weight into the corresponding network point sequence; Combine the shortest physical path between adjacent network points with the mapping relationship between network paths according to the network point sequence to generate the actual path of fund transfer; Classify and organize the actual paths to obtain a set of fund transfer paths, and the overlap degree between any two paths in the set of fund transfer paths does not exceed a preset ratio.

4. The method according to claim 1, wherein The calculation of the risk value of each sub-service package on each path in the set of fund transfer paths specifically includes: Calculate the sum of the reciprocals of the fund business safety coefficients of each network point on each path in the set of fund transfer paths to obtain the basic risk coefficient of the path; Multiply the amount of each sub-service package by the basic risk coefficient of the corresponding optional path to obtain the initial risk value; Calculate the business congestion probability based on the historical business data of each network point, and use the product of the business congestion probability and the initial risk value as the risk value of each path finally.

5. The method according to claim 4, characterized in that, The calculation of the business congestion probability based on the historical business data of each network point specifically includes: Count the business handling volume of each network point in different time periods, and calculate the ratio of the business handling volume to the maximum processing capacity of the network point per unit time to obtain the business saturation; Extract the peak business volume and average business volume from the historical business data of each network point, and calculate the business volatility, where the business volatility is the ratio of the peak business volume to the average business volume; Calculate the business backlog risk value according to the business saturation and the business volatility, and the business backlog risk value increases with the increase of the business saturation and the business volatility; Divide the business backlog risk value by the standard processing capacity of the network point to obtain the standardized congestion index; Set the congestion level division standard based on the standardized congestion index, and use the probability value corresponding to the current congestion level as the business congestion probability.

6. The method according to claim 1, wherein After reallocating the sub-service packages corresponding to the network points until the fund processing pressure of all network points is lower than the preset threshold, the method further includes: Calculate the fund utilization rate of each network point, where the fund utilization rate is the ratio of the average daily business fund volume of the network point to the fund reserve volume of the network point; Set the fund warning level based on the fund utilization rate. When the fund utilization rate is greater than the first preset value, it is the first warning. When the fund utilization rate is greater than the second preset value and less than the first preset value, it is the second warning. When the fund utilization rate is less than the second preset value, it is the third warning; Establish a fund mutual assistance group according to the geographical distance between each network point, and each fund mutual assistance group includes at least three geographically adjacent network points; When any network point is in the first warning, select the network point with the lowest fund utilization rate from the network points in the third warning in the fund mutual assistance group to which the network point belongs for fund allocation.

7. The method according to claim 6, wherein Select the outlet with the lowest capital utilization rate from the outlets in the third warning in the capital mutual assistance group to which the outlet belongs for capital allocation, specifically including: Calculate the capital utilization rate of each outlet according to the daily average business capital volume and the outlet capital reserve volume of each outlet in the capital mutual assistance group; Compare the capital utilization rate with the first preset value and the second preset value to identify the outlets in the first warning state and the outlets in the third warning state; Calculate the distances between the outlets based on the geographical locations of the outlets, and determine at least three adjacent outlets in the same capital mutual assistance group; Screen out the outlets in the third warning state from the capital mutual assistance group and sort them in ascending order of capital utilization rate; Select the outlet with the lowest capital utilization rate as the source outlet for capital allocation, and allocate the capital to the target outlet in the first warning state.

8. An intelligent transfer and allocation system for banking operations based on big data analysis, characterized in that, The system includes: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the system to execute the method according to any one of claims 1-7.

9. A computer-readable storage medium, comprising instructions, characterized in that, When the instruction runs on the system, it enables the system to execute the method according to any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product runs on the system, it enables the system to execute the method according to any one of claims 1-7.

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