Financial service business guiding method and device, equipment, medium and product

Abstract

The invention discloses a financial service business guiding method and device, equipment, a medium and a product. The method comprises the following steps: analyzing a business problem input by a user by adopting a financial service large language model to obtain a to-be-handled target business item; determining a basic selection probability of each candidate implement according to the target business item, historical behavior data of the user and attribute information of each candidate implement; correcting the basic selection probability according to the current business handling condition of each candidate machine tool, the position of the candidate machine tool and the current position of the user to obtain a target selection probability of each candidate machine tool, and sorting each candidate machine tool according to the target selection probability to obtain a sorting result of each candidate machine tool; a user can select a target machine tool from the candidate machine tools; and guiding a user to go to the target machine tool to handle the target business item. According to the embodiment of the invention, the financial business handling efficiency and experience of the user can be improved.

Description

Technical Field

[0001] The present invention relates to the fields of artificial intelligence technology and financial technology, and in particular to a method, device, equipment, medium and product for guiding financial service business. Background Art

[0002] Nowadays, many banking services have been realized online, and users can complete part of the business online. However, due to the wide variety of banking services, a wide range of users, and different authority requirements, offline outlets are still an indispensable way for users to conduct banking business. Summary of the invention

[0003] The present invention provides a method, device, equipment, medium and product for guiding financial service business, so as to solve the problem that it is difficult for users to select suitable business handling outlets, so as to save the time cost of users in handling business.

[0004] According to one aspect of the present invention, a method for guiding a financial service business is provided, comprising:

[0005] Use the financial services big language model to analyze the business questions input by users and obtain the target business items to be processed;

[0006] Determine the basic selection probability of each candidate machine according to the target business item, the user's historical behavior data and the attribute information of each candidate machine;

[0007] The basic selection probability is modified according to the current business handling status of each candidate machine, the location of the candidate machine and the current location of the user to obtain the target selection probability of each candidate machine, and the candidate machines are sorted according to the target selection probability so that the user can select the target machine from the candidate machines;

[0008] Guide the user to go to the target machine to handle the target business item.

[0009] According to another aspect of the present invention, there is provided a device for guiding a financial service service, comprising:

[0010] The target business item module is used to analyze the business problems input by users using the financial services big language model to obtain the target business items to be processed;

[0011] A basic selection probability module, used to determine the basic selection probability of each candidate machine according to the target business item, the user's historical behavior data and the attribute information of each candidate machine;

[0012] A target selection probability module is used to modify the basic selection probability according to the current business handling status of each candidate machine, the location of the candidate machine and the current location of the user, obtain the target selection probability of each candidate machine, and sort the candidate machines according to the target selection probability so that the user can select the target machine from the candidate machines;

[0013] The guidance module is used to guide the user to go to the target machine to handle the target business item.

[0014] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method for guiding a financial service service according to any embodiment of the present invention.

[0015] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0016] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for guiding the financial services business described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, wherein the computer instructions are used to enable a processor to implement the method for guiding a financial service business described in any embodiment of the present invention when executed.

[0018] According to another aspect of the present invention, a computer program product is provided, comprising a computer program / instruction, wherein when the computer program / instruction is executed by a processor, the computer program / instruction implements the method for guiding a financial service service as described in any embodiment of the present invention.

[0019] The embodiment of the present invention analyzes the business problems input by the user through the financial service language model, obtains the professional financial service business items in the banking system to be handled by the user as the target business items, clarifies the business needs of the user, facilitates accurate machine recommendation for the user based on the clear business needs, and improves the efficiency and accuracy of confirming the business to be handled by the user. The basic selection probability of each candidate machine is determined according to the target business item, the user's historical behavior data and the attribute information of each candidate machine; the basic selection probability is corrected according to the current business handling situation of each candidate machine, the location of the candidate machine and the current location of the user, and the target selection probability of each candidate machine is obtained, and the candidate machines are sorted according to the target selection probability, so that the user can select the target machine from each candidate machine. Avoid the time waste and inconvenience caused by the user's selection of inappropriate machines, and improve the efficiency and satisfaction of users in handling financial business. When selecting the target machine, refer to the target business item, the user's historical behavior data, the attribute information of the candidate machine, and the real-time business handling situation and location information to further improve the accuracy of the target machine selection. The comprehensive intelligent navigation system builds an intelligent navigation system through the combination of digital twin technology and dynamic real-scene technology, providing point-to-point navigation guidance throughout the entire process from the user's current location to handling business, thereby improving the convenience and experience of users in handling financial business.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 This is a first flow chart of a method for guiding a financial service business provided by an embodiment of the present invention;

[0023] Figure 2 is a second flow chart of a method for guiding a financial service business provided by an embodiment of the present invention;

[0024] Figure 3 It is a structural schematic diagram of a guiding device for a financial service business provided by an embodiment of the present invention;

[0025] Figure 4 It is a schematic diagram of the structure of an electronic device implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data comply with the relevant laws, regulations and standards of the relevant regions.

[0028] Figure 1 This is the first flow chart of a method for guiding financial services provided by an embodiment of the present invention. This embodiment can be applied to clarify the business needs of users, select appropriate business processing outlets according to the business needs of users, guide users to the business processing outlets, and save the time cost of users before processing business. This method can be executed by a guidance device for financial services, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device with corresponding data processing capabilities. Figure 1 As shown, the method includes:

[0029] S110. Use the financial services big language model to analyze the business questions input by the user to obtain target business items to be processed.

[0030] When users need to handle financial services, it is often difficult to know exactly what specific business items need to be handled. The financial service big language model analyzes the business problems entered by the user and obtains the target business items to be handled. Specifically, the user logs in to the digital twin financial service platform and enters the user name and password. The digital twin financial service platform authenticates the user and optionally performs two-factor authentication (such as SMS verification code verification plus biometric authentication) to ensure account security. After successful identity authentication, the platform allocates corresponding resources to the user based on the user's permissions, including the user's accessible data sets, available tools and services, and personalized resource configuration for the current user. The resource allocation process is completed by the permission management system in the digital twin financial service platform to ensure that each user can only access resources within their authority, thereby ensuring the security and efficiency of the platform.

[0031] In the digital twin financial service platform, users input business questions based on fuzzy financial business needs. The digital twin financial service platform uses the financial service big language model to conduct in-depth analysis of the fuzzy financial business needs input by users, and obtains professional financial service business items in the banking system as target business items. Specifically, the digital twin financial service platform uses natural language processing technology to identify the keywords and intentions in the business questions input by users, and conducts comprehensive semantic analysis in combination with the context, thereby inferring the fuzzy financial service business items that users need to handle. Through the financial service big language model, a dialogue is conducted with the user to further clarify and infer the user's specific needs. The financial service big language model uses natural language processing technology to parse user input, including grammatical analysis and named entity recognition. Through context management technology, the financial service big language model can analyze and record the content of the dialogue to ensure coherence. The dialogue state tracking technology records and updates the dialogue state, assisting the financial service big language model to understand the evolution of needs. Through reinforcement learning and supervised learning, the financial service big language model continuously optimizes the dialogue strategy to ensure the provision of accurate and efficient financial services, thereby inferring the accurate target business items that users actually need to handle.

[0032] The financial services big language model accurately understands the natural language input by users through learning massive financial data and user historical behaviors, and converts it into executable business requirements. The financial services big language model analyzes the business problems input by users, obtains the professional financial service business items in the banking system that users are waiting to handle as target business items, clarifies the business needs of users, and facilitates accurate machine tool recommendations for users based on clear business needs.

[0033] S120: Determine a basic selection probability of each candidate machine according to the target service item, the user's historical behavior data, and the attribute information of each candidate machine.

[0034] All machines within a certain range from the user's current location are considered candidate machines, including self-service machines at self-service outlets and machines at business outlets, such as ATMs (Automated Teller Machines), automatic deposit and withdrawal machines, manual counters, and intelligent teller machines. Each candidate machine can handle banking services, but different candidate machines have different permissions and can handle different business items. For example, ATMs and automatic deposit and withdrawal machines do not have the permission to open accounts; the permissions of machines at different business outlets are also different.

[0035] According to the target business item, the user's historical behavior data and the attribute information of each candidate machine, the compatibility of each candidate machine with the target business item is determined, and the compatibility of each candidate machine with the target business item is used as the basic selection probability of the candidate machine. The basic selection probability is used to characterize whether the candidate machine can handle the target business item. Among them, the user's historical behavior data refers to multi-dimensional data such as the user's historical operation records, historical business handling records, historical feedback evaluations, etc. in the digital twin financial service platform. The attribute information of the candidate machine includes the professional financial service business items under the banking system that the candidate machine can execute and the authority of the candidate machine.

[0036] S130. The basic selection probability is modified according to the current business handling status of each candidate machine, the location of the candidate machine and the current location of the user to obtain the target selection probability of each candidate machine, and the candidate machines are sorted according to the target selection probability so that the user can select the target machine from the candidate machines.

[0037] The distance between the location of the candidate machine and the user's current location and the current business handling status of the candidate machine will affect the processing efficiency of the target business item. For example, if the distance between the location of the candidate machine and the user's current location is very far, the user needs to waste a lot of time to reach the candidate machine, or the candidate machine is currently busy with business handling, and the user needs to wait for a long time to handle the target business item when arriving at the candidate machine, which will lead to low processing efficiency of the target business item. According to the current business handling status of each candidate machine, the location of the candidate machine and the current location of the user, the basic selection probability of the candidate machine is corrected to obtain the target selection probability of each candidate machine, and each candidate machine is sorted according to the target selection probability of each candidate machine, and the sorted candidate machine list is displayed to the user for selection. The candidate machine selected by the user is used as the target machine.

[0038] S140, guiding the user to go to the target machine to handle the target business item.

[0039] Based on dynamic real-life technology, users are intelligently guided to the target machine to handle the target business items. Specifically, the camera is started to capture the real-life scene around the user in real time, and the navigation information is superimposed on the real scene through AR (Augmented Reality) technology. According to the user's current location and the location of the target machine, an optimal path is generated and displayed to the user in the real-life scene with virtual arrows, lines or other visual elements. During the navigation process, the path is dynamically adjusted according to the user's actual movement trajectory and environmental changes (such as obstacles, changes in crowd density) to ensure the accuracy and efficiency of the guidance. In addition to visual guidance, voice navigation is also provided to help users easily follow the guidance in complex environments. Guiding users to the target machine through AR technology can help users quickly and accurately find the target machine to handle the target business. By combining digital twin technology and dynamic real-life technology, an intelligent navigation system is built, which can provide point-to-point navigation guidance from the user's current location to the entire process of handling business, greatly improving the convenience and experience of users in handling financial business.

[0040] In the prior art, when a user decides to go to a branch to handle a business, he or she usually chooses the nearest corresponding bank branch that he or she can find to handle the business. However, this method often has the following disadvantages:

[0041] If a user wants to handle a business item, he can do so at the self-service machines (such as ATMs (Automated Teller Machines), automatic deposit and withdrawal machines, etc.) at 24-hour self-service outlets, without having to go to the machines (manual counters, intelligent teller machines, etc.) at manual business outlets to complete the business. However, this information is not known to the user before arriving at the manual business outlets.

[0042] After the user arrives at a manual business outlet, he or she finds that this outlet does not have the authority to handle the business the user wants to handle, so the user needs to spend time going to another outlet again.

[0043] After entering a manual business outlet, users need to consult repeatedly to find out whether they need to queue up at a manual counter or at an intelligent ATM to handle the business they want to handle. In addition, intelligent ATMs are divided into different types, which need to be distinguished before handling business at the intelligent ATM, increasing the time cost for users before handling business.

[0044] The embodiment of the present invention analyzes the business problems input by the user through the financial service language model, obtains the professional financial service business items in the banking system to be handled by the user as the target business items, clarifies the business needs of the user, facilitates accurate machine recommendation for the user based on the clear business needs, and improves the efficiency and accuracy of confirming the business to be handled by the user. The basic selection probability of each candidate machine is determined according to the target business item, the user's historical behavior data and the attribute information of each candidate machine; the basic selection probability is corrected according to the current business handling situation of each candidate machine, the location of the candidate machine and the current location of the user, and the target selection probability of each candidate machine is obtained, and the candidate machines are sorted according to the target selection probability, so that the user can select the target machine from each candidate machine. Avoid the time waste and inconvenience caused by the user's selection of inappropriate machines, and improve the efficiency and satisfaction of users in handling financial business. When selecting the target machine, refer to the target business item, the user's historical behavior data, the attribute information of the candidate machine, and the real-time business handling situation and location information to further improve the accuracy of the target machine selection. The comprehensive intelligent navigation system builds an intelligent navigation system through the combination of digital twin technology and dynamic real-scene technology, providing point-to-point navigation guidance throughout the entire process from the user's current location to handling business, thereby improving the convenience and experience of users in handling financial business.

[0045] In an optional implementation, the method of using a financial services large language model to analyze a business problem input by a user to obtain a target business item to be processed includes: using the financial services large language model to process the business problem input by the user to obtain a fuzzy business item to be processed, and obtaining a target sub-model associated with the fuzzy business item from each candidate sub-model in the financial services large language model; and calling the target sub-model to interact with the user to obtain the target business item to be processed.

[0046] Specifically, in the training process of the financial services big language model, a dataset specifically for the banking industry is required, including a large number of banking terms, frequently asked questions, and user conversation records. The training dataset of the financial services big language model needs to cover a variety of banking business scenarios such as account opening, transfer, loan, and credit card application. The financial services big language model is first pre-trained on the basis of the general language model, and then fine-tuned on the banking business dataset to make the financial services big language model have both high versatility and professionalism, ensuring that the financial services big language model can better understand and process natural language input related to banking business.

[0047] As banking business develops, banking business data sets are also enriched. In order to maintain the accuracy of the financial services big language model, it is necessary to regularly perform incremental training on the financial services big language model. Using modular component design, the financial services big language model is designed into modular components so that only the parameters of specific sub-modules need to be updated during incremental learning, rather than the entire big language model. The financial services big language model includes candidate sub-models, each of which is responsible for specific tasks and functions. At the same time, a plug-in architecture design is used to allow the insertion of new modules or functions to expand the capabilities of the big language model without changing the existing architecture.

[0048] The financial service big language model is used to identify the keywords and intentions in the business questions input by the user, and a comprehensive semantic analysis is performed in combination with the context, so as to infer the fuzzy financial service business items that the user needs to handle. The target sub-model associated with the fuzzy business item is obtained from each candidate sub-model in the financial service big language model; the target sub-model is called to interact with the user to obtain the target business item to be handled. Exemplarily, the financial service big language model includes at least one of an account management sub-model, a transfer and payment sub-model, a loan and credit sub-model, a credit card business sub-model, an investment and financial management sub-model, and a risk management sub-model. The account management sub-model is used to guide at least one of an account opening business, an account query business, an account change business, and an account cancellation business. The transfer and payment sub-model is used to guide at least one of an intra-bank transfer business, an inter-bank transfer business, a cross-border payment business, and a mobile payment business. The loan and credit sub-model is used to guide at least one of a loan application business, a loan approval business, a repayment management business, and an expectation management business. The credit card business sub-model is used to guide at least one of a credit card application business, a credit card activation business, a credit card consumption business, a credit card repayment business, and a credit card points management business. The investment and financial management sub-model is used to guide at least one of the financial product recommendation business, fund trading business, stock trading business and insurance product business. The risk management sub-model is used to guide at least one of the anti-fraud business, credit assessment business, market risk business and operational risk business.

[0049] The big language model for banking business scenarios is modularly designed, the incremental training process is optimized, the banking business scenario modules are subdivided, and bank-specific data sets are used for training. This enables the big language model for financial services to accurately understand users' financial needs and make personalized business recommendations, greatly improving the efficiency and accuracy of users' confirmation of business operations.

[0050] In an optional implementation, the basic selection probability of each candidate machine is determined based on the target business item, the user's historical behavior data and the attribute information of each candidate machine, including: feature encoding the target business item, and the historical operation records, historical business handling records, and historical feedback evaluations in the historical behavior data to obtain a user feature vector; encoding the attribute information of each candidate machine to obtain candidate machine features; wherein the attribute information includes at least one of a business scope, a transaction limit range, or a user authority level; and performing similarity calculations on the user feature vector and the candidate machine features to obtain a basic selection probability of each candidate machine.

[0051] The similarity between the user feature vector and the candidate tool feature can be calculated by matrix decomposition method, such as alternating least squares method, as shown in the following formulas (1.1)-(1.2).

[0052] R≈PQ T (1.1)

[0053]

[0054] Among them, R is the user-candidate machine rating matrix, P is the user feature matrix with a size of m×k, where m is the number of users and k is the number of implicit features, and Q is the candidate machine feature matrix with a size of n×k, where n is the number of candidate machines and k is the number of implicit features. is the prediction result of user u for candidate machine j, that is, S j,pref , which is the basic selection probability of candidate machine j.

[0055] By constructing the user feature vector and the features of each candidate machine, the similarity between the user feature vector and the candidate machine features is calculated to obtain the basic selection probability of each candidate machine. Accurately matching user needs with machine characteristics, providing users with more suitable candidate machine selection, improving business processing efficiency and user satisfaction.

[0056] Figure 2 FIG. 2 is a second flow chart of a method for guiding a financial service business provided by an embodiment of the present invention. This embodiment is optimized and improved on the basis of the above embodiment. Figure 2 As shown, the method includes:

[0057] S210: Use the financial services big language model to analyze the business questions input by the user to obtain target business items to be processed.

[0058] S220: Determine a basic selection probability of each candidate machine according to the target business item, the user's historical behavior data, and the attribute information of each candidate machine.

[0059] S230: Determine the shortest path length and estimated arrival time between the candidate machine and the current position according to the position of the candidate machine, and determine the position correction probability of the candidate machine according to the shortest path length and the estimated arrival time.

[0060] Specifically, after obtaining the user's consent, the user's current location is obtained through intelligent positioning technology, such as GPS positioning, IP address positioning or WiFi positioning. The location of the candidate machine is obtained from the bank's database or map service provider, and the location of the candidate machine is integrated into the machine information database. The distance between the user and each candidate machine is calculated through Geographic Information System (GIS) technology or the user and the candidate machine are grouped through a geographic location clustering algorithm to determine the shortest path length between the user and the candidate machine and the estimated time for the user to arrive at the candidate machine. The location correction probability of the candidate machine is determined based on the shortest path length and the estimated arrival time, as shown in the following formula (2).

[0061]

[0062] Among them, d shortest represents the shortest path length from the user's current location to the candidate machine j, t expected is the estimated arrival time of the user from the current location to the candidate machine j, w1 and w2 are preset weights, S j,geo represents the position correction probability of candidate tool j.

[0063] S240: Determine the queuing correction probability of the candidate equipment according to the current business handling status of the candidate equipment.

[0064] The current business handling status of the candidate machine includes the queuing status of the candidate machine and the duration of each business to be processed by the candidate machine. The current business handling status of the candidate machine will affect the waiting time when the user arrives at the candidate machine. Based on the current business handling status of the candidate machine, the queuing correction probability of the candidate machine is determined.

[0065] In an optional implementation, the queuing correction probability of the candidate machine is determined based on the current business processing status of the candidate machine, including: analyzing the current business processing status of the candidate machine to obtain the number of existing queuers of the candidate machine, the estimated business processing time of the queuers, the total processing time of the currently being processed business, and the processed time of the currently being processed business; and determining the queuing correction probability of the candidate machine based on the number of existing queuers, the estimated business processing time, the total processing time, and the processed time.

[0066] The current business status of the machine is collected in real time through monitoring equipment or sensors installed on the candidate machine. These devices include at least one of cameras, infrared sensors and pressure sensors to capture key information such as the machine's usage status, user behavior, and queue status. The collected data is transmitted to the digital twin financial service platform via wired or wireless means. Digital twin technology is used to create a virtual model corresponding to the physical machine in order to simulate and analyze the machine's operating status in a virtual environment. Clean, integrate and format the data to ensure data accuracy and consistency.

[0067] The database is retrieved to obtain the current business handling status of each candidate machine in real time. Machine learning, deep learning and / or statistical analysis algorithms and technologies are used to mine patterns and trends in the data. The current business handling status of the candidate machine is analyzed to obtain the number of existing queues of the candidate machine, the estimated business processing time of the queues, the total processing time of the current business being processed, and the processing time of the current business being processed, so as to evaluate the busyness of the candidate machine. The queue status of each candidate machine is predicted by analyzing historical data and real-time monitoring. The queue correction probability S of candidate machine j j,queue The calculation process of is shown in the following formula (3).

[0068]

[0069] Where N is the number of people in the queue, t i,estimated is the estimated service processing time of the ith queuer, t current_total is the total processing time of the business currently being processed, t current_processed is the processing time of the business currently being processed (counted from the start of processing), ∈ is a very small positive number used to prevent the denominator from being zero.

[0070] S250: Use the position correction probability and the queue correction probability to correct the basic selection probability to obtain the target selection probability of the candidate machine.

[0071] S260 , sorting the candidate machines according to the target selection probability, so that the user can select a target machine from the candidate machines.

[0072] By determining the shortest path length between the user's current location and the candidate machines and the user's estimated arrival time at each candidate machine, the position correction probability of the candidate machines can be determined, which can more reasonably evaluate the accessibility of the candidate machines and improve the accuracy of machine recommendations and user convenience. The queue correction probability of the candidate machines is determined by the current business handling status of the candidate machines, and the basic selection probability of each candidate machine is corrected by the queue modification positive probability, which can more accurately reflect the actual use efficiency and user experience of the candidate machines, thereby optimizing resource allocation, reducing user waiting time, and improving overall service quality and user satisfaction.

[0073] S270, guiding the user to go to the target machine to handle the target business item.

[0074] The embodiment of the present invention uses the location correction probability and the queue correction probability to double correct the basic selection probability, comprehensively considering the convenience of users reaching the candidate machines and the current busyness of the machines, thereby obtaining a more accurate target selection probability. The candidate machines are sorted according to the target selection probability, providing users with machine selection suggestions that are closer to actual needs, thereby improving user satisfaction and selection efficiency.

[0075] In an optional implementation, it also includes: extracting the user's business handling habits, time preferences and frequented locations from the historical behavior data; determining the behavior habit correction probability of the candidate machine based on the current time period, the business handling habits, the time preferences and the frequented locations; and using the behavior habit correction probability to correct the target selection probability to obtain a corrected target selection probability.

[0076] Analyze the business handling habits of users in different time periods, such as the differences in behavior on weekdays and weekends, and the selection habits during morning and evening peaks. Identify the outlets or machine locations that users often visit, as well as the activity patterns of users in specific areas. Based on the user's historical behavior data, determine the behavior habit correction probability of the candidate machine; use the behavior habit correction probability to correct the target selection probability to obtain the corrected target selection probability. Specifically, use an autoregressive model (AR model) to determine the behavior habit correction probability of the candidate machine. The calculation process of the behavior habit correction probability is shown in the following formula (4).

[0077]

[0078] Among them, x t is the behavior characteristic value of the user at time t, and also the probability of behavior habit correction S j,habit , c is a constant term, φ i is the autoregressive coefficient, p is the lag order, v t is the noise error.

[0079] Combined with multiple factors such as geographic location, queuing situation, user preferences and behavioral habits, the target selection probability of each candidate machine is determined, and the candidate machines are ranked and recommended to the user according to their target selection probabilities. According to the user's real-time location and current needs, the recommendation results are dynamically adjusted to ensure that the recommended machines meet both user preferences and current business needs. In the recommendation process, multiple optimization goals are considered at the same time, such as minimizing user waiting time, maximizing user satisfaction, and balancing the utilization rate of each machine. The weighted average method is used to calculate the target selection probability of each candidate machine. The specific calculation is shown in the following formula (5).

[0080] S j =w1S j,geo +w2S j,queue +w3S j,pref +w4S j,habit (5)

[0081] Among them, S j is the corrected target selection probability of candidate machine j, S j,geo is the position correction probability of candidate tool j, S j,queue is the queue correction probability of candidate machine j, S j,pref is the basic selection probability of candidate machine j, S j,habit is the behavior habit correction probability of candidate machine j, w1, w2, w3, w4 are the weights of each probability, and w1+w2+w3+w4=1. The candidate machines are sorted according to the corrected target selection probability so that the user can select the target machine from the candidate machines.

[0082] By correcting the target selection probability of each candidate machine through the user's historical behavior data, the ranking results of candidate machines can be optimized more personalized and accurately according to user preferences, helping users to quickly lock in the target machine and improve service experience and efficiency.

[0083] Figure 3 Schematic diagram of a structure of a guiding device for a financial service business provided by an embodiment of the present invention. Figure 3 As shown, the device comprises:

[0084] The target business item module 310 is used to analyze the business problem input by the user using the financial service big language model to obtain the target business item to be processed;

[0085] A basic selection probability module 320 is used to determine the basic selection probability of each candidate machine according to the target business item, the user's historical behavior data and the attribute information of each candidate machine;

[0086] The target selection probability module 330 is used to modify the basic selection probability according to the current business handling status of each candidate machine, the location of the candidate machine and the current location of the user, obtain the target selection probability of each candidate machine, and sort the candidate machines according to the target selection probability so that the user can select the target machine from the candidate machines;

[0087] The guidance module 340 is used to guide the user to go to the target machine to handle the target business item.

[0088] The device for guiding financial service services provided in the embodiment of the present invention can execute the method for guiding financial service services provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0089] Optionally, the target business item module is specifically used to: use the financial service big language model to process the business problem input by the user to obtain the fuzzy business item to be processed, and obtain the target sub-model associated with the fuzzy business item from each candidate sub-model in the financial service big language model; call the target sub-model to interact with the user to obtain the target business item to be processed.

[0090] Optionally, the basic selection probability module is specifically used to: perform feature encoding on the target business item, as well as historical operation records, historical business handling records, and historical feedback evaluations in the historical behavior data to obtain a user feature vector; encode the attribute information of each candidate machine to obtain candidate machine features; wherein the attribute information includes at least one of a business scope, a transaction limit range, or a user authority level; perform similarity calculation on the user feature vector and the candidate machine features to obtain a basic selection probability for each candidate machine.

[0091] Optionally, the target selection probability module includes:

[0092] a position correction unit, configured to determine the shortest path length and the estimated arrival time between the candidate machine and the current position of the candidate machine, and determine the position correction probability of the candidate machine according to the shortest path length and the estimated arrival time;

[0093] A queue correction unit, used to determine the queue correction probability of the candidate equipment according to the current business handling status of the candidate equipment;

[0094] The target selection probability unit is used to correct the basic selection probability by using the position correction probability and the queue correction probability to obtain the target selection probability of the candidate machine.

[0095] Optionally, the queue correction unit is specifically used to: analyze the current business processing status of the candidate machine to obtain the number of existing queuers of the candidate machine, the estimated business processing time of the queuers, the total processing time of the currently being processed business and the processed time of the currently being processed business; determine the queue correction probability of the candidate machine based on the number of existing queuers, the estimated business processing time, the total processing time and the processed time.

[0096] Optionally, it also includes a behavior habit correction module, which is used to extract the user's business handling habits, time preferences and frequent locations from the historical behavior data; determine the behavior habit correction probability of the candidate machine based on the current time period, the business handling habits, the time preferences and the frequent locations; use the behavior habit correction probability to correct the target selection probability to obtain a corrected target selection probability.

[0097] The financial service business guidance device further described can also execute the financial service business guidance method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0098] According to an embodiment of the present invention, the present invention also provides an electronic device, a readable storage medium and a computer program product.

[0099] Figure 4 A schematic diagram of the structure of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0100] like Figure 4As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0101] A number of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0102] The processor 41 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as the boot method of the financial services business.

[0103] In some embodiments, the boot method of the financial services service may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the boot method of the financial services service described above may be performed. Alternatively, in other embodiments, the processor 41 may be configured to execute the boot method of the financial services service in any other appropriate manner (e.g., by means of firmware).

[0104] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0105] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0106] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0107] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0108] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0109] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0110] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0111] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for guiding financial services, characterized in that: include: Use the financial services big language model to analyze the business questions input by users and obtain the target business items to be processed; Determine the basic selection probability of each candidate machine according to the target business item, the user's historical behavior data and the attribute information of each candidate machine; The basic selection probability is modified according to the current business handling status of each candidate machine, the location of the candidate machine and the current location of the user to obtain the target selection probability of each candidate machine, and the candidate machines are sorted according to the target selection probability so that the user can select the target machine from the candidate machines; Guide the user to go to the target machine to handle the target business item.

2. The method according to claim 1, characterized in that: The financial service big language model is used to analyze the business problem input by the user to obtain the target business items to be processed, including: The financial service large language model is used to process the business question input by the user to obtain the fuzzy business item to be processed, and the target sub-model associated with the fuzzy business item is obtained from each candidate sub-model in the financial service large language model; The target sub-model is called to interact with the user to obtain the target business item to be processed.

3. The method according to claim 1, characterized in that According to the target service item, the user's historical behavior data and the attribute information of each candidate machine, a basic selection probability of each candidate machine is determined, including: Performing feature encoding on the target business item, and the historical operation records, historical business handling records, and historical feedback evaluations in the historical behavior data to obtain a user feature vector; Encoding the attribute information of each candidate machine to obtain the characteristics of the candidate machine; wherein the attribute information includes at least one of the business scope, transaction limit range or user authority level; The similarity between the user feature vector and the candidate machine features is calculated to obtain a basic selection probability of each candidate machine.

4. The method according to claim 1, characterized in that: The modifying of the basic selection probability according to the current business handling status of each candidate machine, the location of the candidate machine and the current location of the user includes: Determine the shortest path length and estimated arrival time between the candidate machine and the candidate machine according to the position of the candidate machine and the current position, and determine the position correction probability of the candidate machine according to the shortest path length and the estimated arrival time; Determine the queue correction probability of the candidate equipment according to the current business handling status of the candidate equipment; The basic selection probability is corrected using the position correction probability and the queue correction probability to obtain the target selection probability of the candidate machine.

5. The method according to claim 4, characterized in that The determining of the queuing correction probability of the candidate tool mode according to the current business handling status of the candidate tool comprises: Analyze the current business processing status of the candidate equipment to obtain the number of existing queuers of the candidate equipment, the estimated business processing time of the queuers, the total processing time of the currently processed business and the processed time of the currently processed business; The queue correction probability of the candidate machine is determined according to the number of existing queuers, the estimated business processing time, the total processing time and the processed time.

6. The method according to claim 4, characterized in that Also includes: Extracting the user's business handling habits, time preferences and frequently visited locations from the historical behavior data; Determine the behavior habit modification probability of the candidate device according to the current time period, the business handling habit, the time preference and the frequently visited location; The target selection probability is corrected using the behavior habit correction probability to obtain a corrected target selection probability.

7. A guiding device for financial service business, characterized in that: The device comprises: The target business item module is used to analyze the business problems input by users using the financial services big language model to obtain the target business items to be processed; A basic selection probability module, used to determine the basic selection probability of each candidate machine according to the target business item, the user's historical behavior data and the attribute information of each candidate machine; A target selection probability module is used to modify the basic selection probability according to the current business handling status of each candidate machine, the location of the candidate machine and the current location of the user, obtain the target selection probability of each candidate machine, and sort the candidate machines according to the target selection probability so that the user can select the target machine from the candidate machines; The guidance module is used to guide the user to go to the target machine to handle the target business item.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for guiding the financial service business according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for guiding the financial service business according to any one of claims 1 to 6 when executed.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method for guiding a financial service service according to any one of claims 1 to 6.

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