Consultation service optimization system and method

Abstract

The invention discloses a consultation service optimization system and method, and relates to the technical field of financial consultation services, in the system, a data acquisition module is used for acquiring operation log data and transaction log data, and sending the operation log data and the transaction log data to a data analysis module; the data analysis module is used for filtering and analyzing the running log data and the transaction log data to obtain target transaction data; and the service optimization module is used for generating an optimized consultation service according to the preset consultation service and the target transaction data, and sending the optimized consultation service to the target service node. According to the invention, the optimal consultation service with the optimal data is obtained based on the transaction log data and the operation log data related to the real-time transaction of the user and diversified preset consultation services and is sent to the target service node of the consultation service system, so that the consultation service system has a self-adaptive optimization capability during operation; therefore, the diversified consultation service requirements of the user are met.

Description

Technical Field

[0001] The present application relates to the technical field of financial consulting services, and in particular to a consulting service optimization system and method. Background Art

[0002] As the "commission war" among securities firms becomes increasingly fierce, securities firms have transformed from their initial model of mainly focusing on brokerage channel business to wealth management business, and have begun to gradually increase their investment advisory consulting business for investors.

[0003] However, the current investment consulting service system is too single to meet the diversified needs of users and adapt to the changing large-scale trading market, and it is impossible for users and brokers to achieve the maximum profit. Therefore, how to solve this problem has become an urgent problem to be solved.

[0004] In order to better serve users and make them profitable, and in the process of making profits for users, also to obtain better returns for securities firms, proposing a diversified consulting service model is an issue that urgently needs to be addressed. Summary of the invention

[0005] The main purpose of this application is to provide a consulting service optimization system and method, aiming to solve the technical problem of how to improve the safety of CNC machining.

[0006] To achieve the above objectives, the present application proposes a consulting service optimization system, which includes: a data collection module, a data analysis module and a service optimization module;

[0007] The data collection module is used to collect operation log data and transaction log data, and send the operation log data and transaction log data to the data analysis module;

[0008] The data analysis module is used to filter and analyze the operation log data and the transaction log data to obtain target transaction data;

[0009] The service optimization module is used to generate an optimized consulting service according to the preset consulting service and the target transaction data, and send the optimized consulting service to the target service node.

[0010] In one embodiment, the data analysis module includes: a transaction analysis submodule and a fee benchmark submodule;

[0011] The transaction analysis submodule is used to filter the transaction log data, obtain target transaction instruction data, and send the target transaction instruction data to the service optimization module;

[0012] The fee benchmark submodule is used to determine target commission data based on the operation log data and send the target commission data to the service optimization module.

[0013] In one embodiment, the transaction analysis submodule includes: a filter and a behavior analyzer;

[0014] The filter is used to filter the transaction log data according to the preset tag data to obtain the processed transaction data;

[0015] The behavior analyzer is used to perform preset behavior marking on the processed transaction data, obtain target transaction instruction data, and send the target transaction instruction data to the service optimization module.

[0016] In one embodiment, the fee benchmark submodule includes: a commission analysis unit and a price setting unit;

[0017] The commission analysis unit is used to perform pre-design fee analysis based on the operation log data, obtain a target commission model, and send the target commission model to the service optimization module;

[0018] The price setting unit is used to receive the expected transaction level input by the user and send the expected transaction level to the service optimization module.

[0019] In one embodiment, the commission analysis unit includes: an instruction recognition subunit and a model analyzer;

[0020] The instruction recognition subunit is used to perform instruction recognition on the operation log data to obtain a target instruction set;

[0021] The model analyzer is used to match the target instruction set according to a preset commission model, obtain the target commission model, and send the target commission model to the service optimization module.

[0022] In one embodiment, the service optimization module includes: a revenue and expenditure analysis submodule and a service distribution submodule;

[0023] The income and expenditure analysis submodule is used to determine target consulting data according to the target commission model, the expected transaction gear and the target transaction instruction data;

[0024] The service distribution submodule is used to generate an optimized consulting service according to the preset consulting service and the target consulting data, and send the optimized consulting service to the target service node.

[0025] In one embodiment, the revenue and expenditure analysis submodule includes: a cost accrual unit and a revenue planning unit;

[0026] The fee charging unit is used to determine the target service fee according to the target commission model and the expected transaction level;

[0027] The revenue planning unit is used to perform instruction matching on the target service fee and the target transaction instruction data to obtain the target transaction revenue.

[0028] In addition, to achieve the above purpose, the present application also proposes a consulting service optimization method based on the above consulting service optimization system, the method comprising:

[0029] The data collection module collects operation log data and transaction log data, and sends the operation log data and the transaction log data to the data analysis module;

[0030] The data analysis module filters and analyzes the operation log data and the transaction log data to obtain target transaction data;

[0031] The service optimization module generates an optimized consulting service according to the preset consulting service and the target transaction data, and sends the optimized consulting service to a target service node.

[0032] In one embodiment, the data analysis module includes: a filter, a behavior analyzer, an instruction recognition subunit, a model analyzer, and a price setting unit;

[0033] The data analysis module filters and analyzes the operation log data and the transaction log data to obtain target transaction data, including:

[0034] The filter filters the transaction log data according to the preset tag data to obtain the processed transaction data;

[0035] The behavior analyzer performs preset behavior marking on the processed transaction data, obtains target transaction instruction data, and sends the target transaction instruction data to the service optimization module;

[0036] The instruction recognition subunit performs instruction recognition on the operation log data to obtain a target instruction set;

[0037] The model analyzer matches the target instruction set according to the preset commission model, obtains the target commission model, and sends the target commission model to the service optimization module;

[0038] The price setting unit is used to receive the expected transaction level input by the user and send the expected transaction level to the service optimization module.

[0039] In one embodiment, the service optimization module includes: a fee accrual unit, a revenue planning unit, and a service distribution submodule;

[0040] The step of the service optimization module generating an optimized consulting service according to the preset consulting service and the target transaction data, and sending the optimized consulting service to the target service node includes:

[0041] The fee calculation unit determines a target service fee according to the target commission model and the expected transaction level;

[0042] The revenue planning unit performs instruction matching on the target service fee and the target transaction instruction data to obtain the target transaction revenue;

[0043] The service distribution submodule generates an optimized consulting service according to the preset consulting service and the target consulting data, and sends the optimized consulting service to the target service node.

[0044] The present application provides a consulting service optimization system and method, the system includes: a data collection module, a data analysis module and a service optimization module; the data collection module is used to collect operation log data and transaction log data, and send the operation log data and transaction log data to the data analysis module; the data analysis module is used to filter and analyze the operation log data and transaction log data to obtain target transaction data; the service optimization module is used to generate optimized consulting services according to preset consulting services and target transaction data, and send the optimized consulting services to the target service node. The present application can first collect transaction log data and operation log data from the service nodes of the operating transaction system and the consulting service system, and then filter and analyze the transaction log data and operation log data through the data analysis module, and finally obtain the optimized consulting service with the best data based on the target transaction data and preset consulting service obtained after analysis and send it to the target service node of the consulting service system, so that the consulting service system has the ability of adaptive optimization at runtime, thereby meeting the diversified consulting service needs of users. Therefore, the present application can enable the consulting service system located at the target service node to dynamically adjust the consulting strategy according to the current user transaction data, thereby improving the efficiency of user contract consulting services. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0046] In order to more clearly illustrate the embodiments of the present application 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, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0047] Figure 1 It is a structural block diagram of the first embodiment of the consulting service optimization system of the present invention;

[0048] Figure 2 A schematic diagram of the physical structure of the first embodiment of the consulting service optimization system of the present invention;

[0049] Figure 3 It is a structural block diagram of the second embodiment of the consulting service optimization system of the present invention;

[0050] Figure 4 This is a flowchart of an embodiment of the application consulting service optimization method;

[0051] Figure 5 Schematic diagram of the process of optimizing the consulting service for this application.

[0052] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0053] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0054] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0055] The main solution of this application is: to propose an integrated consulting service optimization system, which includes a data acquisition module, a data analysis module and a service optimization module; the data acquisition module is used to collect operation log data and transaction log data, and send the operation log data and transaction log data to the data analysis module; the data analysis module is used to filter and analyze the operation log data and transaction log data to obtain target transaction data; the service optimization module is used to generate optimized consulting services according to preset consulting services and target transaction data, and send the optimized consulting services to the target service node.

[0056] At present, the investment consulting service process only allows users to choose one service model. The consulting service system is single, which makes it impossible to meet the diversified needs of users and adapt to the changing large-scale trading market, and it is impossible for users and brokers to achieve the maximum profit. Therefore, how to solve this problem has become an urgent problem to be solved.

[0057] In order to solve the above problems, the present application can introduce a runtime adaptive optimization strategy for diversified consulting service needs. Specifically, the present application can first collect transaction log data and operation log data from the service nodes running the transaction system and the consulting service system, and then filter and analyze the transaction log data and operation log data through the data analysis module. Finally, based on the target transaction data obtained after analysis and the preset consulting service, the optimized consulting service with the best data is obtained and sent to the target service node of the consulting service system, so that the consulting service system has the ability of runtime adaptive optimization, thereby meeting the diversified consulting service needs of users. Therefore, the present application can enable the consulting service system located at the target service node to dynamically adjust the consulting strategy according to the current user transaction data, thereby improving the efficiency of the user's contract consulting service.

[0058] The embodiment of the present invention provides a consulting service optimization system, referring to Figure 1 , Figure 1 FIG. 1 is a structural block diagram of the first embodiment of the consulting service optimization system of the present invention. Figure 1 As shown, in this embodiment, the consulting service optimization system includes: a data collection module, a data analysis module and a service optimization module;

[0059] The data collection module is used to collect operation log data and transaction log data, and send the operation log data and transaction log data to the data analysis module;

[0060] The data analysis module is used to filter and analyze the operation log data and the transaction log data to obtain target transaction data;

[0061] The service optimization module is used to generate an optimized consulting service according to the preset consulting service and the target transaction data, and send the optimized consulting service to the target service node.

[0062] It should be understood that the consulting service optimization system proposed in this embodiment can be deployed on a computer that can run independently, and the above-mentioned data collection module can be composed of a number of collection agents distributed and deployed on the computers of the centralized trading system and the consulting service system. That is, this embodiment can distribute and deploy collection agents on service nodes of the deployed trading system and consulting service system at different geographical locations, so that the trading system has the ability to be collected, and information is transmitted between each collection agent node through the network. In the consulting service optimization system, the data analysis module and the collection agents in the data collection module can communicate with each other through the network. The specific distribution relationship diagram can be shown as follows: Figure 2 As shown, Figure 2 This is a physical structure diagram of the first embodiment of the consulting service optimization system of the present invention. Figure 2The consulting fee system is the consulting service system mentioned above, and the consulting fee analysis system is the consulting service optimization system proposed in this embodiment.

[0063] It is understandable that the centralized trading system can be a system deployed at server nodes in different geographical locations to provide real-time trading services to end users. When it is running, it will process the real-time transactions of users and generate log data, which is the above-mentioned transaction log data. At the same time, the consulting service system can be a system deployed at service nodes in different geographical locations to provide consulting services to users. The user contract consulting service data such as buy and sell instructions for providing consulting services to users can be collected from it, which is the above-mentioned operation log data. The buy and sell instructions can be initiated by the consulting service system for a certain stock. The user can initiate the corresponding transaction order by manually selecting the instruction.

[0064] It is easy to understand that, combined with the above analysis, this embodiment can obtain transaction data related to the buying and selling factors running in real time in the current consulting service system, that is, the above-mentioned target transaction data, by filtering the operation log data contained in the consulting service system and the real-time transaction data of users collected when the centralized trading system is running, thereby providing an adaptive optimization means for improving the efficiency of consulting service contract signing. The buying and selling factor is a set consisting of multiple buying and selling instructions.

[0065] It is understandable that the above-mentioned preset consulting services can be a variety of buying and selling factors pre-configured in the consulting service system. In order to meet the user's diversified consulting service needs, this embodiment can screen and recombine the preset consulting services according to the target transaction data to obtain a new optimized consulting service containing at least one consulting service, and send the optimized consulting service to the server node corresponding to the consulting service system, that is, the above-mentioned target service node.

[0066] In this embodiment, after the optimized consulting service is sent to the target service node, the user can simultaneously sign up for multiple consulting services based on the consulting service system located at the target service node. Each consulting service provides services to the user according to a different consulting model, thereby meeting the user's diversified consulting service needs.

[0067] In summary, for diversified consulting services, this embodiment proposes a runtime optimization strategy. This embodiment can first collect transaction log data and operation log data from the service nodes running the transaction system and the consulting service system, and then filter and analyze the transaction log data and operation log data through the data analysis module. Finally, based on the target transaction data and preset consulting services obtained after analysis, the optimized consulting service with the best data is obtained and sent to the target service node of the consulting service system, so that the consulting service system has the ability of runtime adaptive optimization, thereby meeting the diversified consulting service needs of users. Therefore, this embodiment can enable the consulting service system located at the target service node to dynamically adjust the consulting strategy according to the current user transaction data, thereby improving the efficiency of the user's contract consulting service.

[0068] The present embodiment discloses a consulting service optimization system, which includes: a data collection module, a data analysis module and a service optimization module; the data collection module is used to collect operation log data and transaction log data, and send the operation log data and transaction log data to the data analysis module; the data analysis module is used to filter and analyze the operation log data and transaction log data to obtain target transaction data; the service optimization module is used to generate optimized consulting services according to preset consulting services and target transaction data, and send the optimized consulting services to the target service node. For diversified consulting services, the present embodiment proposes a runtime optimization strategy. The present embodiment can first collect transaction log data and operation log data from the service nodes of the operating transaction system and the consulting service system, and then filter and analyze the transaction log data and operation log data through the data analysis module, and finally obtain the optimized consulting service with the best data based on the target transaction data and preset consulting service obtained after analysis and send it to the target service node of the consulting service system, so that the consulting service system has the ability of runtime adaptive optimization, thereby meeting the diversified consulting service needs of users. Therefore, the present embodiment can enable the consulting service system located at the target service node to dynamically adjust the consulting strategy according to the current user transaction data, thereby improving the efficiency of user contract consulting services.

[0069] Reference Figure 3 , Figure 3 This is a structural block diagram of the second embodiment of the consulting service optimization system of the present invention. Figure 1 The illustrated embodiment provides a second embodiment of the consulting service optimization system of the present invention.

[0070] like Figure 3 As shown, in this embodiment, the data analysis module includes: a transaction analysis submodule and a cost benchmark submodule;

[0071] The transaction analysis submodule is used to filter the transaction log data, obtain target transaction instruction data, and send the target transaction instruction data to the service optimization module;

[0072] The fee benchmark submodule is used to determine target commission data based on the operation log data and send the target commission data to the service optimization module.

[0073] It can be understood that in this embodiment, the target transaction data includes the target transaction instruction data and the target commission data. Accordingly, the collection agent deployed in the centralized trading system in the data collection module can collect the transaction log data Dn{C, SC, DM, JE, CB...} in real time when the centralized trading system is running. Among them, C can represent the user code, SC represents the trading market, DM represents the securities code, JE represents the transaction amount, CB represents the cost and other core elements, and regularly push the transaction log data to the transaction analysis submodule.

[0074] The collection agent deployed in the consulting service system can collect the user contract consulting service data (including the user's current commission model data) in real time when the consulting service system is running, and push the data to the fee benchmark sub-module at regular intervals.

[0075] Among them, the transaction analysis submodule can perform data analysis and processing on the transaction log data, and filter out transaction instructions, such as buy instructions and sell instructions. Therefore, in a feasible implementation, in this embodiment, the transaction analysis submodule includes: a filter and a behavior analyzer;

[0076] The filter is used to filter the transaction log data according to the preset tag data to obtain the processed transaction data;

[0077] The behavior analyzer is used to perform preset behavior marking on the processed transaction data, obtain target transaction instruction data, and send the target transaction instruction data to the service optimization module.

[0078] It should be understood that this embodiment filters out the user's real-time transaction data contained in the transaction log data based on the filter, that is, the above-mentioned processed transaction data. Then, each processed transaction data is analyzed, marked with a buy B or sell S mark, and the target transaction instruction data is obtained.

[0079] Specifically, the transaction log data can first enter the filter to filter out the data of the user identification, securities market, securities code, trading market, and transaction price elements contained therein, that is, the preset marked data, and remove the unmarked noise data to obtain the processed transaction data, and then send the processed transaction data to the behavior analyzer. The behavior analyzer then analyzes the transaction behavior corresponding to the processed transaction data, and marks the buying behavior with B and the selling behavior with S.

[0080] In a feasible implementation manner, in this embodiment, the fee benchmark submodule includes: a commission analysis unit and a price setting unit;

[0081] The commission analysis unit is used to perform pre-design fee analysis based on the operation log data, obtain a target commission model, and send the target commission model to the service optimization module;

[0082] The price setting unit is used to receive the expected transaction level input by the user and send the expected transaction level to the service optimization module.

[0083] It should be understood that in this embodiment, the target commission data includes the target commission model and the expected trading gear, and the commission analysis unit can obtain and analyze the runtime log of the consulting service system in real time, that is, the operation log data. The operation log data may include log data such as user identification, trading market, securities code, recommended price, product code, instruction number, commission model number, etc.

[0084] It is easy to understand that different consulting services can provide users with reference transaction information based on different buying and selling instructions. The purpose of this embodiment is to generate the user's most optimized buying and selling factors and distribute them to the consulting service system for collection to replace the current user's commission model. Therefore, in this embodiment, it is necessary to analyze the commission models corresponding to the buying and selling factors in various consulting services. The above-mentioned pre-design fee analysis process can be that the commission analysis unit filters out the buy instructions B and sell instructions S corresponding to different commission models from the operation log data, and then determines the commission method corresponding to each currently configurable consulting service, that is, the above-mentioned target commission model.

[0085] Specifically, this embodiment may include a fixed commission model, a user profit model, a product profit model, a periodic commission model or a single commission model, and the above-mentioned target commission model may include at least one of a fixed commission model, a user profit model, a product profit model, a periodic commission model or a single commission model, and the commission model number corresponding to each commission model is represented as: fixed commission model (model1 identifier), user profit model (model2 identifier), product profit model (model3 identifier), periodic commission model (model4 identifier), single commission model (model5 identifier). That is, this embodiment can perform pre-design fee analysis based on different commission models to obtain the commission methods corresponding to the consulting service model signed by the user and the consulting service model not signed in the current transaction process.

[0086] Therefore, in this embodiment, the above-mentioned target commission model can correspond to different commission models, and each commission model can be combined into different consulting services, so that the subsequent optimized consulting services can include multiple commission models, so that users can sign multiple consulting services at the same time.

[0087] Furthermore, in this embodiment, the target commission model may also include buy and sell instruction data, that is, the commission analysis unit in this embodiment also needs to process and analyze the operation log data, and filter out the buy and sell instruction sets corresponding to each commission model, so as to obtain the final service billing model, that is, the above-mentioned target commission model. In a feasible implementation, in this embodiment, the commission analysis unit includes: an instruction identification subunit and a model analyzer;

[0088] The instruction recognition subunit is used to perform instruction recognition on the operation log data to obtain a target instruction set;

[0089] The model analyzer is used to match the target instruction set according to a preset commission model, obtain the target commission model, and send the target commission model to the service optimization module.

[0090] It is easy to understand that the target instruction set obtained by the instruction recognition subunit is mainly based on the consulting service information signed by the current user in the operation log data, and obtains the user's real-time buying and selling instructions, which may include a buy instruction B{C, modeln} and a sell instruction S{C, modeln}. C represents the user identifier, and modeln takes the value of {model1, model2, model3, model4, model5}.

[0091] It should be understood that the model analyzer can obtain the target commission model by matching the preset commission model according to the target instruction set received about the user's real-time buying and selling information. For example, if it is assumed that except for the second commission model and the third commission model, there are only selling instructions, and the first, fourth and fifth commission models all have buying and selling instructions, then the target commission model can be as shown in Table 1, where T1 / T2 / T3 / T4 / T5 represent different billing models, N represents that the user has not signed the commission model, and Y represents that the user has signed the commission model.

[0092] Table 1 Target commission model

[0093]

[0094] It is understandable that the actual calculated values ​​of T1 / T2 / T3 / T4 / T5 of the above different billing models can be the service price levels supported by the current consulting service system, and their specific values ​​can be determined by the expected transaction level input by the user received by the price setting unit. Therefore, the subsequent service optimization module can obtain the service fee corresponding to each consulting service after receiving the target commission model sent by the commission analysis unit and the expected transaction level sent by the price setting unit. The expected transaction level is sent to the service optimization module.

[0095] It should be understood that the above-mentioned preset transaction levels may include different billing methods Tn{T1, T2, T3, T4}, where the value of T is: {R}, R includes {R1, R2, R3, ...Rn}, Rn represents the fee rate, for example R1 = 0.03% (i.e. 3 / 10,000).

[0096] In a feasible implementation, in this embodiment, the service optimization module includes: an income and expenditure analysis submodule and a service distribution submodule;

[0097] The income and expenditure analysis submodule is used to determine target consulting data according to the target commission model, the expected transaction gear and the target transaction instruction data;

[0098] The service distribution submodule is used to generate an optimized consulting service according to the preset consulting service and the target consulting data, and send the optimized consulting service to the target service node.

[0099] It is easy to understand that this embodiment can calculate and analyze the profit value of users under each commission model included in each consulting service based on the target transaction instruction data involving real-time transactions of users, the target commission model involving the commission model, and the expected transaction level involving the price template, and select the buying and selling instructions with the largest profit value for each user, group them together to form a target buying and selling factor, and finally determine the optimized consulting service based on the target buying and selling factor, and send the optimized consulting service to the target service node corresponding to the consulting service system.

[0100] In a feasible implementation manner, in this embodiment, the income and expenditure analysis submodule includes: an expense accrual unit and a revenue planning unit;

[0101] The fee charging unit is used to determine the target service fee according to the target commission model and the expected transaction level;

[0102] The revenue planning unit is used to perform instruction matching on the target service fee and the target transaction instruction data to obtain the target transaction revenue.

[0103] It should be understood that in this embodiment, the target consulting data may include target service fees and target transaction income. At this time, the target commission model can be expressed as: the value of model1 {T1, B|S}, the value of model2 {T2, S}, the value of model3 {T3, S}, the value of model4 {T4, B|S}, and the value of model5 {T5, B|S}, where T1 / T2 / T3 / T4 / T5 represent different billing models, B represents buy instruction billing, and S represents sell instruction billing. Therefore, the fee accounting unit can be responsible for calculating the real-time transaction data of each user according to the data of the target commission model and the expected transaction level, and derive the fee Fn=JE*R that should be charged for each transaction corresponding to each commission model, where Fn={F1, F2, F3, F4, F5}, JE represents the transaction amount in the transaction data Dn, and R represents the R rate value in the price model Tn, so as to obtain the service fee corresponding to each consulting service, that is, the target service fee.

[0104] The profit calculation unit may be responsible for matching the user's real-time transaction data, that is, the buy and sell instructions contained in the target transaction instruction data with each target commission model, and determining the final user profit corresponding to each consulting service, that is, the target transaction profit, based on the service fees corresponding to the matched target commission model.

[0105] Specifically, in this embodiment, the commission model that matches the target transaction instruction data with the target commission model can be marked with a Y label, so that the revenue V can be calculated by calculating all the commission model data marked with the Y label. The revenue value V = transaction amount JR-cost price CB-commission amount F, where JR represents the transaction price when the user sells, CB represents the transaction cost price when the user buys, and F represents different

[0106] The amount of fees charged under the commission model.

[0107] It is easy to understand that the above-mentioned target consulting data can be buying and selling factor data, so it can also include user code and commission model code, so the target consulting data can be expressed as {C, M, V, F}, C is the user code, M is the commission model code, V is the user income, and F is the amount of commission. Therefore, the service distribution sub-module mainly selects the buying and selling factor data with the larger V and the larger F according to the received target consulting data {C, M, V, F} for various consulting services that can be configured under the name of the same customer, and pushes the best consulting service to the consulting service system.

[0108] In this embodiment, the income and expenditure analysis submodule can receive the billing model data, i.e., the target commission model and expected transaction unit, and the current user's real-time transaction data, i.e., the target transaction instruction data, and calculate the target service fee corresponding to each consulting service based on the target commission model, expected transaction unit, and target transaction instruction data, thereby obtaining the target transaction income corresponding to each consulting service, so that the service distribution submodule can determine the target buying and selling factor with the best income, and then determine the final optimized consulting service based on the target buying and selling factor, i.e., the optimized consulting service. Therefore, this embodiment can push the buying and selling factor with the best user income to the consulting service system through the commission analysis unit, the price setting unit, the income and expenditure analysis submodule, and the service distribution submodule based on the log data of large-scale user transactions and the diversified consulting service model, so that the consulting service has the ability to intelligently and dynamically recommend buying and selling instructions to users, thereby improving the user's signing efficiency.

[0109] The present embodiment discloses that the data analysis module includes: a filter, a behavior analyzer, an instruction recognition subunit, a model analyzer and a price setting unit; the filter is used to filter the transaction log data according to the preset tag data to obtain the processed transaction data; the behavior analyzer is used to preset the behavior tag for the processed transaction data, obtain the target transaction instruction data, and send the target transaction instruction data to the service optimization module. The instruction recognition subunit is used to perform instruction recognition on the operation log data to obtain the target instruction set; the model analyzer is used to match the target instruction set according to the preset commission model, obtain the target commission model, and send the target commission model to the service optimization module; the price setting unit is used to receive the expected transaction gear input by the user, and send the expected transaction gear to the service optimization module. In addition, in the present embodiment, the service optimization module includes: a fee accrual unit, a revenue planning unit and a service distribution submodule; the fee accrual unit is used to determine the target service fee according to the target commission model and the expected transaction gear; the revenue planning unit is used to perform instruction matching on the target service fee and the target transaction instruction data to obtain the target transaction income; the service distribution submodule is used to generate an optimized consulting service according to the preset consulting service and the target consulting data, and send the optimized consulting service to the target service node. This embodiment can push the best buying and selling factors for users to the consulting service system based on the log data of large-scale user transactions and the diversified consulting service model through the commission analysis unit, price setting unit, income and expenditure analysis sub-module and service distribution sub-module, so that the consulting service has the ability to intelligently and dynamically recommend buying and selling instructions to users, thereby improving the user's signing efficiency.

[0110] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0111] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0112] It should be noted that the execution subject of this embodiment can be a consulting service optimization system, or a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, etc., or a consulting service optimization device capable of realizing the above functions, etc., and this embodiment does not specifically limit this. The following takes the consulting service optimization system as an example of the execution subject to explain this embodiment and the following embodiments.

[0113] The embodiment of the present invention provides a consulting service optimization method based on the above consulting service optimization system, referring to Figure 4 , Figure 4 This is a flow chart of an embodiment of the application consulting service optimization method.

[0114] In this embodiment, the consulting service optimization method includes steps S10 to S30:

[0115] Step S10, the data collection module collects operation log data and transaction log data, and sends the operation log data and the transaction log data to the data analysis module;

[0116] Step S20, the data analysis module performs filtering analysis on the operation log data and the transaction log data to obtain target transaction data;

[0117] Step S30: the service optimization module generates an optimized consulting service according to the preset consulting service and the target transaction data, and sends the optimized consulting service to the target service node.

[0118] In a feasible implementation, the data analysis module includes: a filter, a behavior analyzer, an instruction recognition subunit, a model analyzer, and a price setting unit. In this embodiment, step S20 may include steps A1 to A5:

[0119] Step A1, the filter filters the transaction log data according to preset tag data to obtain processed transaction data;

[0120] Step A2, the behavior analyzer performs preset behavior marking on the processed transaction data, obtains target transaction instruction data, and sends the target transaction instruction data to the service optimization module;

[0121] Step A3, the instruction recognition subunit performs instruction recognition on the operation log data to obtain a target instruction set;

[0122] Step A4, the model analyzer matches the target instruction set according to the preset commission model, obtains the target commission model, and sends the target commission model to the service optimization module;

[0123] Step A5: the price setting unit receives the expected transaction level input by the user, and sends the expected transaction level to the service optimization module.

[0124] In a feasible implementation manner, the service optimization module includes: a fee accrual unit, a revenue planning unit, and a service distribution submodule. In this embodiment, step S30 may include steps B1 to B4:

[0125] Step B1, the fee charging unit determines a target service fee according to the target commission model and the expected transaction level;

[0126] Step B2, the revenue planning unit performs instruction matching on the target service fee and the target transaction instruction data to obtain the target transaction revenue;

[0127] Step B3: the service distribution submodule generates an optimized consulting service according to the preset consulting service and the target consulting data, and sends the optimized consulting service to the target service node.

[0128] For ease of understanding, based on Figure 5 The dynamic optimization process of consulting services in this embodiment is briefly described. Figure 5 Schematic diagram of the process of optimizing the consulting service for this application.

[0129] like Figure 5 As shown, in this embodiment, the consulting service optimization system can first obtain the operation log data and the transaction log data through the data collection module, specifically, the collection agent deployed in the consulting service system and the transaction system.

[0130] Then in the fee benchmark submodule, the commission analysis unit analyzes the operation log data to obtain the target commission model and sends it to the service optimization module; the price setting unit also sends the expected transaction level to the service optimization module after receiving the expected transaction level input by the user. The transaction analysis submodule filters the transaction log data, obtains the target transaction instruction data, and sends it to the service optimization module.

[0131] Finally, in the service optimization module, the income and expenditure analysis submodule determines the buying and selling factor data based on the target commission data, expected transaction gear) and target transaction instruction data, and then sends it to the service distribution submodule. The service distribution submodule selects the buying and selling factor data with the best user benefits to form the final optimized consulting service, and sends the optimized consulting service back to the consulting service system, thereby realizing the adaptive optimization of the consulting service system.

[0132] The consulting service optimization method provided in this application is a system operation method proposed based on the consulting service optimization system in the above embodiment, and can also solve the technical problem of meeting the diversified consulting service needs of users. Compared with the prior art, the beneficial effects of the consulting service optimization method provided in this application are the same as the beneficial effects of the consulting service optimization system provided in the above embodiment, and the other technical features in the consulting service optimization method are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0133] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0134] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0135] The above are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A consulting service optimization system, characterized in that: The system includes: a data collection module, a data analysis module and a service optimization module; The data collection module is used to collect operation log data and transaction log data, and send the operation log data and transaction log data to the data analysis module; The data analysis module is used to filter and analyze the operation log data and the transaction log data to obtain target transaction data; The service optimization module is used to generate an optimized consulting service according to the preset consulting service and the target transaction data, and send the optimized consulting service to the target service node.

2. The consulting service optimization system according to claim 1, characterized in that: The data analysis module includes: a transaction analysis submodule and a fee benchmark submodule; The transaction analysis submodule is used to filter the transaction log data, obtain target transaction instruction data, and send the target transaction instruction data to the service optimization module; The fee benchmark submodule is used to determine target commission data based on the operation log data and send the target commission data to the service optimization module.

3. The consulting service optimization system according to claim 2, characterized in that: The transaction analysis submodule includes: a filter and a behavior analyzer; The filter is used to filter the transaction log data according to the preset tag data to obtain the processed transaction data; The behavior analyzer is used to perform preset behavior marking on the processed transaction data, obtain target transaction instruction data, and send the target transaction instruction data to the service optimization module.

4. The consulting service optimization system according to claim 2, characterized in that: The fee benchmark submodule includes: a commission analysis unit and a price setting unit; The commission analysis unit is used to perform pre-design fee analysis based on the operation log data, obtain a target commission model, and send the target commission model to the service optimization module; The price setting unit is used to receive the expected transaction level input by the user and send the expected transaction level to the service optimization module.

5. The consulting service optimization system according to claim 4, characterized in that: The commission analysis unit includes: an instruction recognition subunit and a model analyzer; The instruction recognition subunit is used to perform instruction recognition on the operation log data to obtain a target instruction set; The model analyzer is used to match the target instruction set according to a preset commission model, obtain the target commission model, and send the target commission model to the service optimization module.

6. The consulting service optimization system according to claim 4, characterized in that: The service optimization module includes: an income and expenditure analysis submodule and a service distribution submodule; The income and expenditure analysis submodule is used to determine target consulting data according to the target commission model, the expected transaction gear and the target transaction instruction data; The service distribution submodule is used to generate an optimized consulting service according to the preset consulting service and the target consulting data, and send the optimized consulting service to the target service node.

7. The consulting service optimization system according to claim 6, characterized in that: The income and expenditure analysis submodule includes: an expense accrual unit and an income planning unit; The fee charging unit is used to determine the target service fee according to the target commission model and the expected transaction level; The revenue planning unit is used to perform instruction matching on the target service fee and the target transaction instruction data to obtain the target transaction revenue.

8. A consulting service optimization method based on the consulting service optimization system according to any one of claims 1 to 7, characterized in that: The method comprises: The data collection module collects operation log data and transaction log data, and sends the operation log data and the transaction log data to the data analysis module; The data analysis module filters and analyzes the operation log data and the transaction log data to obtain target transaction data; The service optimization module generates an optimized consulting service according to the preset consulting service and the target transaction data, and sends the optimized consulting service to a target service node.

9. The consulting service optimization method according to claim 8, characterized in that: The data analysis module includes: a filter, a behavior analyzer, an instruction recognition subunit, a model analyzer and a price setting unit; The filter filters the transaction log data according to the preset tag data to obtain the processed transaction data; The behavior analyzer performs preset behavior marking on the processed transaction data, obtains target transaction instruction data, and sends the target transaction instruction data to the service optimization module; The instruction recognition subunit performs instruction recognition on the operation log data to obtain a target instruction set; The model analyzer matches the target instruction set according to the preset commission model, obtains the target commission model, and sends the target commission model to the service optimization module; The price setting unit is used to receive the expected transaction level input by the user and send the expected transaction level to the service optimization module.

10. The consulting service optimization method according to claim 9, characterized in that: The service optimization module includes: a fee accrual unit, a revenue planning unit, and a service distribution submodule; The step of the service optimization module generating an optimized consulting service according to the preset consulting service and the target transaction data, and sending the optimized consulting service to the target service node includes: The fee calculation unit determines a target service fee according to the target commission model and the expected transaction level; The revenue planning unit performs instruction matching on the target service fee and the target transaction instruction data to obtain the target transaction revenue; The service distribution submodule generates an optimized consulting service according to the preset consulting service and the target consulting data, and sends the optimized consulting service to the target service node.

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