Bank management consultation management method and system based on big data

Abstract

The invention relates to the field of bank consultation data push management, and discloses a bank management consultation management method and system based on big data, and the method comprises the following steps: collecting and preprocessing bank management consultation data, and combining the historical consultation data of a bank target customer and the preparation data of a bank target user, and finally, according to the user portrait of the bank target customer, pushing appropriate bank management consultation data to the bank target customer in the data pushing system, and positioning and trimming defects existing in the pushing process. According to the method and the system, bottlenecks and problems in the business process can be found by utilizing the consultation data, so that optimization and improvement are carried out, the business processing efficiency and the customer satisfaction are improved, and the business data can be accurately pushed to customers.

Description

Technical Field

[0001] The present invention relates to the field of bank consulting data push management, and in particular to a bank management consulting management method and system based on big data. Background Art

[0002] Bank consulting data refers to all kinds of data involved and generated by the bank in the process of consulting business. These data usually cover the bank's business data, market data, customer data and other aspects. The bank consulting data that customers need to handle may vary according to the actual situation of the customer. The customer's identity information, transaction records, credit status, etc., these data are important bases for banks to analyze customers and formulate marketing strategies. The bank consulting data that can be applied for by customer personal information is different, so it is necessary to intelligently push different bank consulting data, propose business decisions based on the different personal information of customers, and thus push different bank consulting data, and discover bottlenecks and problems in the business process during the push process, and then optimize and improve it, improve business processing efficiency and customer satisfaction, and ensure that business data can be accurately pushed to customers. Therefore, this application proposes a bank management consulting management method and system based on big data, which is used for intelligent push processing of bank consulting data. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a bank management consulting management method and system based on big data.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is: The first aspect of the present invention provides a bank management consulting management method based on big data, comprising the following steps: Collecting data related to bank management consulting services, and pre-processing the collected data related to bank management consulting services; Build user profiles of the bank's target customers based on the bank's target customers' historical consultation data and the bank's target users' prepared information data; In the data intelligent push system, qualified bank management consulting data is pushed to the bank's target users based on their user portraits, and push defects are traced and corrected during the push process.

[0005] Furthermore, in a preferred embodiment of the present invention, the data collection of the bank management consulting business related data and the data preprocessing of the collected bank management consulting business related data are specifically as follows: Determine a bank business data collection channel, wherein the bank business data collection channel is a channel for collecting relevant data of the bank's management consulting business, including the bank's internal system and social media; Among them, the associated data of the bank management consulting business is the business data that the bank's target customers need to query, which is marked as bank management consulting data. The bank's target customers are customers who open accounts in the bank; Based on the bank data collection channel, bank management consulting data is collected, and the bank management consulting data is processed in a unified data format to obtain bank management consulting data in a unified data format; Performing data integration on the bank management consulting data in the unified data format, and removing repeated data parts in the bank management consulting data in the unified data format after data integration, to obtain pre-processed bank management consulting data, and marking them as qualified bank management consulting data; A data push system is obtained in the bank, and the qualified bank management consulting data is stored in the data push system, wherein the data push system is capable of pushing the qualified bank management consulting data to target customers of the bank.

[0006] Furthermore, in a preferred embodiment of the present invention, the user profile of the bank's target customer is constructed based on the historical consultation data of the bank's target customer and the prepared data data of the bank's target user, specifically: Obtain a user portrait construction system, and connect the data push system with the user portrait construction system through system configuration to obtain a data intelligent push system; The user portrait construction system can construct user portraits of target bank users, and the data intelligent push system can push qualified bank management consulting data to target bank users based on the user portraits of target bank users; In the user portrait building system, historical consultation data of the bank target user and prepared data of the bank target user are determined, wherein the prepared data of the bank target user is data prepared by the bank target user for handling business; Obtaining a bank master control system, wherein the bank master control system records different businesses of the bank, wherein the types of qualified bank management consulting data and the order of consulting are different in different businesses of the bank; Analyze the historical consultation data of the bank's target users, determine the types and consultation sequence of the bank's users' historical consultation data, and calculate the overlap rate between the types and consultation sequence of the bank's users' historical consultation data and different businesses of the bank; A standard coincidence rate is preset, and the corresponding bank businesses whose coincidence rate between the types and consultation sequences of the bank user's historical consultation data and different bank businesses is greater than the standard coincidence rate are marked as pending businesses; Import the pending business of the bank's target users and the prepared data of the bank's target users into the user portrait building system to build user portraits of the bank's target users.

[0007] Furthermore, in a preferred embodiment of the present invention, in the data intelligent push system, according to the user portrait of the bank target user, the qualified bank management consulting data is pushed to the bank target user, and the push defects are traced and corrected during the push process, specifically: In the data intelligent push system, the user portrait of the bank's target users is analyzed to determine the pending business of the bank's target users and extract the prepared data of the bank's target users; Analyze the prepared data of the bank's target users to determine the current available services for the bank's target users, and analyze whether there is any overlap between the bank's target users' pending services and the current available services; If not, the data intelligent push system is controlled not to push the qualified bank management consulting data to the bank's target users. If yes, the overlapping business is marked as the target handling business, and the qualified bank management consulting data corresponding to the target handling business is marked as the target bank management consulting data; Control the data intelligent push system to push the target bank management consulting data to the bank's target users. At the same time, based on the data intelligent push system, monitor in real time whether there is target bank management consulting data that cannot be pushed to the bank's target users; If not, the data intelligent push system is marked as a qualified data intelligent push system. If not, the data intelligent push system is controlled to restart and the target bank management consulting data is pushed to the target bank users again; If there is still target bank management consulting data that cannot be pushed to the target bank users, the data intelligent push system will be marked as an abnormal data push intelligent push system; Perform potential risk prediction on the intelligent push system for pushing abnormal data, and perform system adjustments on the intelligent push system for pushing abnormal data based on the potential risk prediction results.

[0008] Furthermore, in a preferred embodiment of the present invention, the method of predicting potential risks of the intelligent push system for pushing abnormal data and performing system repair on the intelligent push system for pushing abnormal data based on the potential risk prediction results is as follows: In the intelligent push system for pushing abnormal data, the target bank management consulting data that cannot be pushed to the target bank users will be marked as abnormal bank management consulting data, and the target handling business corresponding to the abnormal bank management consulting data will be marked as abnormal handling business; Analyze the proportion of abnormal handling business in all target handling business, and preset the maximum abnormal proportion. If the proportion of abnormal handling business in all target handling business is less than the maximum abnormal proportion, the abnormal data push intelligent push system is defined as a type of abnormal data push intelligent push system; If the proportion of abnormal handling business in all target handling businesses is not less than the maximum abnormal proportion, the abnormal data push intelligent push system is defined as a second-class abnormal data push intelligent push system; For a type I push abnormal data intelligent push system, obtain the underlying code data of abnormal bank management consulting data in the type I push abnormal data intelligent push system; The underlying code data of the abnormal bank management consulting data is formatted to obtain an abnormal data intelligent push system that does not contain abnormal bank management consulting data, and the target bank management consulting data corresponding to the abnormal bank management consulting data is secondary set in the abnormal data intelligent push system that does not contain abnormal bank management consulting data to obtain a type of data to be analyzed intelligent push system; If there is no abnormal banking management consulting data in a type of data intelligent push system to be analyzed, the type of data intelligent push system to be analyzed is classified as a qualified data intelligent push system; If there are still abnormal bank management consulting data in the first category of data to be analyzed intelligent push system, the first category of data to be analyzed intelligent push system is divided into a second category of abnormal data intelligent push system; Conduct software and hardware collaborative fault analysis on the second-category abnormal data intelligent push system, and perform system repairs on the second-category abnormal data intelligent push system based on the results of the software and hardware collaborative fault analysis.

[0009] Further, in a preferred embodiment of the present invention, the software and hardware collaborative fault analysis is performed on the second type of push abnormal data intelligent push system, and the system is repaired based on the software and hardware collaborative fault analysis results. Specifically, For the second-category push abnormal data intelligent push system, obtain the operation log of the second-category push abnormal data intelligent push system, and based on the operation log of the second-category push abnormal data intelligent push system, obtain the time point when the target bank management consulting data in the second-category push abnormal data intelligent push system cannot be pushed to the bank's target user, and mark it as the abnormality occurrence time point; In the second-type push abnormal data intelligent push system, a network operation state of the second-type push abnormal data intelligent push system is obtained at the time point when the abnormality occurs, wherein the network operation state includes a system server load value and a network signal strength; If at the time point when the anomaly occurs, the server load value and network signal strength in the second-category push abnormal data intelligent push system are not within the preset range, then at the time point when the anomaly occurs, the target bank management consulting data is pushed in a time-sharing manner in the second-category push abnormal data intelligent push system until the server load value is maintained within the preset range, and a signal enhancement device is introduced to adjust the network signal strength so that the network signal strength is maintained within the preset range; If at the time point when the abnormality occurs, when the server load value and network signal strength in the second-category push abnormal data intelligent push system are maintained within the preset range, but the target bank management consulting data still cannot be pushed to the bank's target users, then the underlying code of the system is obtained in the second-category push abnormal data intelligent push system, and the vulnerability analysis of the underlying code of the system is performed to locate the vulnerability code of the second-category push abnormal data intelligent push system; The big data network is introduced to retrieve and output the correction solutions for the vulnerability codes of the second-class abnormal data intelligent push system to obtain a qualified data intelligent push system.

[0010] The second aspect of the present invention further provides a bank management consulting management system based on big data, the bank management consulting management system comprises a memory and a processor, the memory stores a bank management consulting management method, and when the bank management consulting management method is executed by the processor, the following steps are implemented: Collecting data related to bank management consulting services, and pre-processing the collected data related to bank management consulting services; Build user profiles of the bank's target customers based on the bank's target customers' historical consultation data and the bank's target users' prepared information data; In the data intelligent push system, qualified bank management consulting data is pushed to the bank's target users based on their user portraits, and push defects are traced and corrected during the push process.

[0011] The present invention solves the technical defects existing in the background technology, and has the following beneficial effects: collecting and preprocessing bank management consulting data, and combining the historical consulting data of the bank's target customers and the prepared data data of the bank's target users to build a user portrait of the bank's target customers, and finally according to the user portrait of the bank's target customers, push appropriate bank management consulting data to the bank's target users in the data push system, and locate and correct the defects in the push process. The present invention can use consulting data to discover bottlenecks and problems in the business process, and then optimize and improve it, improve business processing efficiency and customer satisfaction, and ensure that business data can be pushed to customers accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0013] Figure 1A flow chart of a bank management consulting management method based on big data is shown; Figure 2 The flowchart of data push and push defect assessment and correction in the data intelligent push system is shown; Figure 3 A program view of a bank management consulting management system based on big data is shown. DETAILED DESCRIPTION

[0014] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0015] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0016] Figure 1 A flowchart of a bank management consulting management method based on big data is shown, comprising the following steps: S102: collecting data related to bank management consulting services, and preprocessing the collected data related to bank management consulting services; S104: constructing a user profile of the bank's target customers based on the historical consultation data of the bank's target customers and the prepared data of the bank's target users; S106: In the data intelligent push system, qualified bank management consulting data is pushed to the bank's target users according to the user portrait of the bank's target users, and push defects are traced and corrected during the push process.

[0017] Furthermore, in a preferred embodiment of the present invention, the data collection of the bank management consulting business related data and the data preprocessing of the collected bank management consulting business related data are specifically as follows: Determine a bank business data collection channel, wherein the bank business data collection channel is a channel for collecting relevant data of the bank's management consulting business, including the bank's internal system and social media; Among them, the associated data of the bank management consulting business is the business data that the bank's target customers need to query, which is marked as bank management consulting data. The bank's target customers are customers who open accounts in the bank; Based on the bank data collection channel, bank management consulting data is collected, and the bank management consulting data is processed in a unified data format to obtain bank management consulting data in a unified data format; Performing data integration on the bank management consulting data in the unified data format, and removing repeated data parts in the bank management consulting data in the unified data format after data integration, to obtain pre-processed bank management consulting data, and marking them as qualified bank management consulting data; A data push system is obtained in the bank, and the qualified bank management consulting data is stored in the data push system, wherein the data push system is capable of pushing the qualified bank management consulting data to target customers of the bank.

[0018] It should be noted that there are various businesses in the bank, and the associated data in different businesses are also different. For example, when applying for a regular deposit business and a credit card, the associated data and preparation materials are also different, and the source channels of the data are also different. Therefore, after determining the channel, all bank management consulting data are obtained, which is the data that can be pushed to the user. The collected data needs to be pre-processed to ensure that the format of the data is consistent, and there is no duplicate and erroneous data, to ensure the integrity and uniformity of the data when it is pushed to the customer. After data pre-processing, qualified bank management consulting data is obtained. The qualified bank management consulting data is stored in the data push system, which can achieve the purpose of pushing data to the bank's target customers.

[0019] Furthermore, in a preferred embodiment of the present invention, the user profile of the bank's target customer is constructed based on the historical consultation data of the bank's target customer and the prepared data data of the bank's target user, specifically: Obtain a user portrait construction system, and connect the data push system with the user portrait construction system through system configuration to obtain a data intelligent push system; The user portrait construction system can construct user portraits of target bank users, and the data intelligent push system can push qualified bank management consulting data to target bank users based on the user portraits of target bank users; In the user portrait building system, historical consultation data of the bank target user and prepared data of the bank target user are determined, wherein the prepared data of the bank target user is data prepared by the bank target user for handling business; Obtaining a bank master control system, wherein the bank master control system records different businesses of the bank, wherein the types of qualified bank management consulting data and the order of consulting are different in different businesses of the bank; Analyze the historical consultation data of the bank's target users, determine the types and consultation sequence of the bank's users' historical consultation data, and calculate the overlap rate between the types and consultation sequence of the bank's users' historical consultation data and different businesses of the bank; A standard coincidence rate is preset, and the corresponding bank businesses whose coincidence rate between the types and consultation sequences of the bank user's historical consultation data and different bank businesses is greater than the standard coincidence rate are marked as pending businesses; Import the pending business of the bank's target users and the prepared data of the bank's target users into the user portrait building system to build user portraits of the bank's target users.

[0020] It should be noted that the user portrait describes the user's characteristic information and the business information that the user may handle. The push system needs to push appropriate bank consulting data to the user based on the user portrait. The user portrait construction system can build the user portrait of the bank's target users. The role of the historical consulting data of the bank's target users and the prepared data data of the bank's target users is that after the user handles a business, all the businesses that the user may need to handle next can be determined based on the user's historical consulting data, and all the businesses that the user may need to handle next can be determined based on the prepared data data. The types and consultation orders of qualified bank management consulting data are different in different bank businesses. According to the types and consultation orders of qualified bank management consulting data, businesses with a coincidence rate higher than the preset value are selected, that is, all the businesses that the user may need to handle next, that is, pending businesses. Finally, combined with the prepared data data, the user portrait of the user is determined, and the user portrait includes the businesses that the user can currently handle.

[0021] Figure 2 The flowchart of data push and push defect assessment and correction in the data intelligent push system is shown, including the following steps: S202: in the data intelligent push system, according to the user portrait of the bank target user, push qualified bank management consulting data to the bank target user, and trace and correct the push defects during the push process; S204: Predicting potential risks for the intelligent push system for pushing abnormal data, and performing system repair on the intelligent push system for pushing abnormal data based on the potential risk prediction results; S206: Performing software and hardware coordinated fault analysis on the second-category push abnormal data intelligent push system, and performing system repair on the second-category push abnormal data intelligent push system based on the software and hardware coordinated fault analysis result.

[0022] Furthermore, in a preferred embodiment of the present invention, in the data intelligent push system, according to the user portrait of the bank target user, the qualified bank management consulting data is pushed to the bank target user, and the push defects are traced and corrected during the push process, specifically: In the data intelligent push system, the user portrait of the bank's target users is analyzed to determine the pending business of the bank's target users and extract the prepared data of the bank's target users; Analyze the prepared data of the bank's target users to determine the current available services for the bank's target users, and analyze whether there is any overlap between the bank's target users' pending services and the current available services; If not, the data intelligent push system is controlled not to push the qualified bank management consulting data to the bank's target users. If yes, the overlapping business is marked as the target handling business, and the qualified bank management consulting data corresponding to the target handling business is marked as the target bank management consulting data; Control the data intelligent push system to push the target bank management consulting data to the bank's target users. At the same time, based on the data intelligent push system, monitor in real time whether there is target bank management consulting data that cannot be pushed to the bank's target users; If not, the data intelligent push system is marked as a qualified data intelligent push system. If not, the data intelligent push system is controlled to restart and the target bank management consulting data is pushed to the target bank users again; If there is still target bank management consulting data that cannot be pushed to the bank's target users, the data intelligent push system will be marked as an abnormal data intelligent push system.

[0023] It should be noted that the preparation data of the bank's target users is analyzed, and it is determined whether there is any overlap between the pending business and the current business that can be handled by the bank's target users. If there is no overlap, it proves that the preparation data brought by the user cannot handle any business, so there is no need to push any qualified bank consulting data to the user. If there is overlap, the overlapping business is the target business, and the corresponding qualified bank management consulting data is the data that can be pushed to the user, marked as the target bank management consulting data. The control push system pushes the target bank management consulting data to the user. If all can be pushed, the data intelligent push system is marked as a qualified data intelligent push system. If there is data that cannot be pushed, it is judged that there may be a problem with the system. Restarting the system can realize the clearing of system occupancy and ensure that the background occupancy is low during the push process. If it still cannot be pushed after restarting, it is necessary to analyze the defects of the system and mark it as an intelligent push system for pushing abnormal data.

[0024] Furthermore, in a preferred embodiment of the present invention, the method of predicting potential risks of the intelligent push system for pushing abnormal data and performing system repair on the intelligent push system for pushing abnormal data based on the potential risk prediction results is as follows: In the intelligent push system for pushing abnormal data, the target bank management consulting data that cannot be pushed to the target bank users will be marked as abnormal bank management consulting data, and the target handling business corresponding to the abnormal bank management consulting data will be marked as abnormal handling business; Analyze the proportion of abnormal handling business in all target handling business, and preset the maximum abnormal proportion. If the proportion of abnormal handling business in all target handling business is less than the maximum abnormal proportion, the abnormal data push intelligent push system is defined as a type of abnormal data push intelligent push system; If the proportion of abnormal handling business in all target handling businesses is not less than the maximum abnormal proportion, the abnormal data push intelligent push system is defined as a second-class abnormal data push intelligent push system; For a type I push abnormal data intelligent push system, obtain the underlying code data of abnormal bank management consulting data in the type I push abnormal data intelligent push system; The underlying code data of the abnormal bank management consulting data is formatted to obtain an abnormal data intelligent push system that does not contain abnormal bank management consulting data, and the target bank management consulting data corresponding to the abnormal bank management consulting data is secondary set in the abnormal data intelligent push system that does not contain abnormal bank management consulting data to obtain a type of data to be analyzed intelligent push system; If there is no abnormal banking management consulting data in a type of data intelligent push system to be analyzed, the type of data intelligent push system to be analyzed is classified as a qualified data intelligent push system; If there are still abnormal bank management consulting data in the first category of intelligent push system for data to be analyzed, the first category of intelligent push system for data to be analyzed will be divided into a second category of intelligent push system for abnormal data.

[0025] It should be noted that, in determining abnormal bank management consulting data and abnormal handling business, if the proportion of abnormal handling business in all target handling business is small and less than the maximum abnormal proportion, it proves that the problem of system data push is an individual phenomenon and not a common phenomenon. On the contrary, if the proportion of abnormal handling business in all target handling business is large and greater than the maximum abnormal proportion, it proves that the problem of system data push is a common phenomenon. According to the proportion, the intelligent push system for pushing abnormal data is classified into a first-class intelligent push system for pushing abnormal data and a second-class intelligent push system for pushing abnormal data. For the first-class intelligent push system for pushing abnormal data, it is judged that there is a problem with the underlying code data of the system, resulting in the system being unable to push individual data. The underlying code data needs to be formatted. After formatting, the underlying code data is cleared and data reconfiguration is required so that the system can continue to push data. After reconfiguration, the system can push all data under normal circumstances. If there is still abnormal bank management consulting data after formatting and reconfiguration, it proves that there may be other reasons that cause the system to be unable to push data. At this time, the first-class intelligent push system for pushing abnormal data is divided into the second-class intelligent push system for pushing abnormal data.

[0026] Further, in a preferred embodiment of the present invention, the software and hardware collaborative fault analysis is performed on the second type of push abnormal data intelligent push system, and the system is repaired based on the software and hardware collaborative fault analysis results. Specifically, For the second-category push abnormal data intelligent push system, obtain the operation log of the second-category push abnormal data intelligent push system, and based on the operation log of the second-category push abnormal data intelligent push system, obtain the time point when the target bank management consulting data in the second-category push abnormal data intelligent push system cannot be pushed to the bank's target user, and mark it as the abnormality occurrence time point; In the second-type push abnormal data intelligent push system, a network operation state of the second-type push abnormal data intelligent push system is obtained at the time point when the abnormality occurs, wherein the network operation state includes a system server load value and a network signal strength; If at the time point when the anomaly occurs, the server load value and network signal strength in the second-category push abnormal data intelligent push system are not within the preset range, then at the time point when the anomaly occurs, the target bank management consulting data is pushed in a time-sharing manner in the second-category push abnormal data intelligent push system until the server load value is maintained within the preset range, and a signal enhancement device is introduced to adjust the network signal strength so that the network signal strength is maintained within the preset range; If at the time point when the abnormality occurs, when the server load value and network signal strength in the second-category push abnormal data intelligent push system are maintained within the preset range, but the target bank management consulting data still cannot be pushed to the bank's target users, then the underlying code of the system is obtained in the second-category push abnormal data intelligent push system, and the vulnerability analysis of the underlying code of the system is performed to locate the vulnerability code of the second-category push abnormal data intelligent push system; The big data network is introduced to retrieve and output the correction solutions for the vulnerability codes of the second-class abnormal data intelligent push system to obtain a qualified data intelligent push system.

[0027] It should be noted that the second-category abnormal data intelligent push system is a data push system that fails to push data on a large scale. First, it is necessary to determine the time point when the abnormality occurs. Because the time point when the abnormality occurs is the time point when the target bank management consulting data cannot be pushed to the bank's target users, the system abnormality is an abnormality that occurs within the time point when the abnormality occurs. During the time point when the abnormality occurs, the cause of the second-category abnormal data intelligent push system may be that the load in the system is too high, causing the system's running memory to prioritize other tasks, resulting in obstructions in the data transmission process and failure to push. It may also be that the network signal strength is poor during the push process. At this time, the purpose of time-sharing push of the target bank management consulting data is to reduce the amount of data pushed at one time, thereby reducing the system load value when pushing data, and introduce a signal enhancement device to adjust the network signal strength so that the network signal strength is maintained within the preset range, so that the data can be pushed to the user smoothly. If the server load value and network signal strength are maintained within the preset range, but the target bank management consulting data still cannot be pushed to the bank's target users, it proves that the underlying code of the system has vulnerabilities even after reconfiguration, and may be attacked from the outside. At this time, the vulnerability needs to be corrected to ensure that all data can be pushed and obtain a qualified data intelligent push system.

[0028] like Figure 3 As shown, the second aspect of the present invention further provides a bank management consulting management system based on big data, the bank management consulting management system includes a memory and a processor, the memory stores a bank management consulting management method, and when the bank management consulting management method is executed by the processor, the following steps are implemented: Collecting data related to bank management consulting services, and pre-processing the collected data related to bank management consulting services; Build user profiles of the bank's target customers based on the bank's target customers' historical consultation data and the bank's target users' prepared information data; In the data intelligent push system, qualified bank management consulting data is pushed to the bank's target users based on their user portraits, and push defects are traced and corrected during the push process.

[0029] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A bank management consulting management method based on big data, characterized in that: The following steps are involved: Collecting data related to bank management consulting services, and pre-processing the collected data related to bank management consulting services; Build user profiles of the bank's target customers based on the bank's target customers' historical consultation data and the bank's target users' prepared information data; In the data intelligent push system, qualified bank management consulting data is pushed to the bank's target users based on their user portraits, and push defects are traced and corrected during the push process.

2. According to the big data-based bank management consulting management method described in claim 1, it is characterized by: The data collection of the bank management consulting business related data and the data preprocessing of the collected bank management consulting business related data are specifically as follows: Determine a bank business data collection channel, wherein the bank business data collection channel is a channel for collecting relevant data of the bank's management consulting business, including the bank's internal system and social media; Among them, the associated data of the bank management consulting business is the business data that the bank's target customers need to query, which is marked as bank management consulting data. The bank's target customers are customers who open accounts in the bank; Based on the bank data collection channel, bank management consulting data is collected, and the bank management consulting data is processed in a unified data format to obtain bank management consulting data in a unified data format; Performing data integration on the bank management consulting data in the unified data format, and removing repeated data parts in the bank management consulting data in the unified data format after data integration, to obtain pre-processed bank management consulting data, and marking them as qualified bank management consulting data; A data push system is obtained in the bank, and the qualified bank management consulting data is stored in the data push system, wherein the data push system is capable of pushing the qualified bank management consulting data to target customers of the bank.

3. According to the big data-based bank management consulting management method described in claim 1, it is characterized by: The user profile of the bank's target customers is constructed based on the historical consultation data of the bank's target customers and the prepared data of the bank's target users, specifically: Obtain a user portrait construction system, and connect the data push system with the user portrait construction system through system configuration to obtain a data intelligent push system; The user portrait construction system can construct user portraits of target bank users, and the data intelligent push system can push qualified bank management consulting data to target bank users based on the user portraits of target bank users; In the user portrait building system, historical consultation data of the bank target user and prepared data of the bank target user are determined, wherein the prepared data of the bank target user is data prepared by the bank target user for handling business; Obtaining a bank master control system, wherein the bank master control system records different businesses of the bank, wherein the types of qualified bank management consulting data and the order of consulting are different in different businesses of the bank; Analyze the historical consultation data of the bank's target users, determine the types and consultation sequence of the bank's users' historical consultation data, and calculate the overlap rate between the types and consultation sequence of the bank's users' historical consultation data and different businesses of the bank; A standard coincidence rate is preset, and the corresponding bank businesses whose coincidence rate between the types and consultation sequences of the bank user's historical consultation data and different bank businesses is greater than the standard coincidence rate are marked as pending businesses; Import the pending business of the bank's target users and the prepared data of the bank's target users into the user portrait building system to build user portraits of the bank's target users.

4. According to the big data-based bank management consulting management method described in claim 1, it is characterized by: The data intelligent push system pushes qualified bank management consulting data to the bank's target users according to the user portrait of the bank's target users, and traces and corrects the push defects during the push process, specifically: In the data intelligent push system, the user portrait of the bank's target users is analyzed to determine the pending business of the bank's target users and extract the prepared data of the bank's target users; Analyze the prepared data of the bank's target users to determine the current available services for the bank's target users, and analyze whether there is any overlap between the bank's target users' pending services and the current available services; If not, the data intelligent push system is controlled not to push the qualified bank management consulting data to the bank's target users. If yes, the overlapping business is marked as the target handling business, and the qualified bank management consulting data corresponding to the target handling business is marked as the target bank management consulting data; Control the data intelligent push system to push the target bank management consulting data to the bank's target users. At the same time, based on the data intelligent push system, monitor in real time whether there is target bank management consulting data that cannot be pushed to the bank's target users; If not, the data intelligent push system is marked as a qualified data intelligent push system. If not, the data intelligent push system is controlled to restart and the target bank management consulting data is pushed to the target bank users again; If there is still target bank management consulting data that cannot be pushed to the target bank users, the data intelligent push system will be marked as an abnormal data push system; Perform potential risk prediction on the intelligent push system for pushing abnormal data, and perform system adjustments on the intelligent push system for pushing abnormal data based on the potential risk prediction results.

5. According to the big data-based bank management consulting management method described in claim 4, it is characterized in that: The method of predicting potential risks of the intelligent push system for pushing abnormal data and performing system adjustment on the intelligent push system for pushing abnormal data based on the potential risk prediction results is specifically as follows: In the intelligent push system for pushing abnormal data, the target bank management consulting data that cannot be pushed to the target bank users will be marked as abnormal bank management consulting data, and the target handling business corresponding to the abnormal bank management consulting data will be marked as abnormal handling business; Analyze the proportion of abnormal handling business in all target handling business, and preset the maximum abnormal proportion. If the proportion of abnormal handling business in all target handling business is less than the maximum abnormal proportion, the abnormal data push intelligent push system is defined as a type of abnormal data push intelligent push system; If the proportion of abnormal handling business in all target handling businesses is not less than the maximum abnormal proportion, the abnormal data push intelligent push system is defined as a second-class abnormal data push intelligent push system; For a type I push abnormal data intelligent push system, obtain the underlying code data of abnormal bank management consulting data in the type I push abnormal data intelligent push system; The underlying code data of the abnormal bank management consulting data is formatted to obtain an abnormal data intelligent push system that does not contain abnormal bank management consulting data, and the target bank management consulting data corresponding to the abnormal bank management consulting data is secondary set in the abnormal data intelligent push system that does not contain abnormal bank management consulting data to obtain a type of data to be analyzed intelligent push system; If there is no abnormal banking management consulting data in a type of data intelligent push system to be analyzed, the type of data intelligent push system to be analyzed is classified as a qualified data intelligent push system; If there are still abnormal bank management consulting data in the first category of data to be analyzed intelligent push system, the first category of data to be analyzed intelligent push system is divided into a second category of abnormal data intelligent push system; Conduct software and hardware collaborative fault analysis on the second-category abnormal data intelligent push system, and perform system repairs on the second-category abnormal data intelligent push system based on the results of the software and hardware collaborative fault analysis.

6. According to the big data-based bank management consulting management method described in claim 5, it is characterized in that: The method of performing a push failure analysis on the intelligent push system for the second type of abnormal data push, and performing a system repair on the intelligent push system for the second type of abnormal data push based on the push failure analysis result, is specifically as follows: For the second-category push abnormal data intelligent push system, obtain the operation log of the second-category push abnormal data intelligent push system, and based on the operation log of the second-category push abnormal data intelligent push system, obtain the time point when the target bank management consulting data in the second-category push abnormal data intelligent push system cannot be pushed to the bank's target user, and mark it as the abnormality occurrence time point; In the second-type push abnormal data intelligent push system, a network operation state of the second-type push abnormal data intelligent push system is obtained at the time point when the abnormality occurs, wherein the network operation state includes a system server load value and a network signal strength; If at the time point when the anomaly occurs, the server load value and network signal strength in the second-category push abnormal data intelligent push system are not within the preset range, then at the time point when the anomaly occurs, the target bank management consulting data is pushed in a time-sharing manner in the second-category push abnormal data intelligent push system until the server load value is maintained within the preset range, and a signal enhancement device is introduced to adjust the network signal strength so that the network signal strength is maintained within the preset range; If at the time point when the abnormality occurs, when the server load value and network signal strength in the second-category push abnormal data intelligent push system are maintained within the preset range, but the target bank management consulting data still cannot be pushed to the bank's target users, then the underlying code of the system is obtained in the second-category push abnormal data intelligent push system, and the vulnerability analysis of the underlying code of the system is performed to locate the vulnerability code of the second-category push abnormal data intelligent push system; The big data network is introduced to retrieve and output the correction solutions for the vulnerability codes of the second-class abnormal data intelligent push system to obtain a qualified data intelligent push system.

7. A bank management consulting management system based on big data, characterized in that: The bank management consulting management system includes a memory and a processor, wherein the memory stores a bank management consulting management method program, and when the bank management consulting management method program is executed by the processor, the steps of the bank management consulting management method according to any one of claims 1 to 6 are implemented: Collecting data related to bank management consulting services, and pre-processing the collected data related to bank management consulting services; Build user profiles of the bank's target customers based on the bank's target customers' historical consultation data and the bank's target users' prepared information data; In the data intelligent push system, qualified bank management consulting data is pushed to the bank's target users based on their user portraits, and push defects are traced and corrected during the push process.

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