Intelligent management and data analysis system for business and customer managers

Abstract

The invention relates to the technical field of merchant management systems, in particular to an intelligent management and data analysis system for merchant and customer managers, which comprises a data integration and cleaning module, the data integration and cleaning module is coupled and connected with a usability optimization module, the usability optimization module is coupled and connected with a real-time data analysis module, and the real-time data analysis module is coupled and connected with a server. And the real-time data analysis module is coupled and connected with an intelligent decision support module. The intelligent management and data analysis system for the business and customer managers solves the problems that in the prior art, data sources are diversified, but the integration level is not high in the actual use process of an intelligent management and data analysis system for the business and customer managers, data redundancy and inconsistency are caused, data quality is uneven, and the situations of missing, errors and repetition exist; the problems of influence on the accuracy of an analysis result, limited system intelligence degree, lack of advanced functions such as intelligent recommendation and prediction, hidden danger of data security, and easy attack and leakage in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of merchant management systems, and in particular to an intelligent management and data analysis system for merchant managers. Background Art

[0002] The intelligent management and data analysis system for merchant managers is a tool that integrates advanced technology and data analysis capabilities. It is designed to help merchant managers manage customer relationships more effectively, optimize sales strategies, improve business efficiency and make smarter business decisions. It centrally stores and maintains basic customer information, transaction history, communication records, etc., and segments customers based on factors such as customer behavior, purchase history, preferences, etc. to implement personalized marketing strategies. It tracks and records all interactions with customers, including emails, phone calls, meetings, etc., and visually displays sales stages to help identify potential bottlenecks and accelerate sales processes. It uses historical sales data and market trends to predict future sales performance, set and track sales targets, and the performance of team members. It displays key business indicators such as sales, customer retention rate, customer satisfaction, etc. in the form of charts, dashboards, etc. It uses machine learning algorithms to conduct in-depth data mining to discover potential opportunities and improvement points, and generates customized reports based on needs. Automated reports are easy to report to superiors or share internally, and marketing activities such as email marketing and social media advertising are automated to improve efficiency and personalization. Seamless integration with ERP, financial systems, e-commerce platforms, etc. enables data sharing and process automation. Customer information and sales data can be accessed in real time through mobile phones or tablets. Basic customer management and record updates can be performed even without a network. Through real-time data analysis and visualization, merchant managers can more quickly identify market trends and customer needs, thereby making more timely decisions. Centralized customer information management helps to gain a deeper understanding of customers, implement more personalized marketing strategies, and improve customer satisfaction and loyalty. Automated sales and marketing processes reduce manual operations and improve work efficiency. At the same time, the sales forecasting function helps to formulate more reasonable sales strategies. The intelligent management and data analysis system for merchant managers is an important tool for modern enterprises to enhance their competitiveness, optimize customer relationships and sales management. It not only improves business efficiency, but also brings companies deeper market insights and smarter business decisions.

[0003] However, the existing intelligent management and data analysis system for merchant managers has the following disadvantages:

[0004] Data sources are diverse but not highly integrated, resulting in data redundancy and inconsistency;

[0005] The data quality varies, with omissions, errors, and duplications, which affect the accuracy of the analysis results;

[0006] The system has limited intelligence and lacks advanced features such as intelligent recommendation and prediction;

[0007] Data security is vulnerable to attack and leakage;

[0008] In response to the above problems, an intelligent management and data analysis system for merchant managers is provided. Summary of the invention

[0009] The purpose of the present invention is to provide an intelligent management and data analysis system for merchant managers to solve the problems raised in the above background technology. To achieve the above purpose, the present invention provides the following technical solutions: an intelligent management and data analysis system for merchant managers, including a data integration and cleaning module, the data integration and cleaning module is coupled to an ease of use optimization module, the ease of use optimization module is coupled to a real-time data analysis module, the real-time data analysis module is coupled to an intelligent decision support module, and the intelligent decision support module is coupled to a security and privacy protection module.

[0010] Preferably, the data integration and cleaning module includes a data source access module, the data source access module is coupled to a data mapping and conversion module, the data mapping and conversion module is coupled to a data integration module, the data integration module is coupled to a data verification and validation module, the data verification and validation module is coupled to a data audit module, the data audit module is coupled to a data cleaning workflow definition module, the data cleaning workflow definition module is coupled to a data cleaning execution module, the data cleaning execution module is coupled to a data cleaning result verification module, and the data cleaning result verification module is coupled to a data archiving and storage module.

[0011] Preferably, the data mapping and conversion module adopts a data mapping algorithm and a data conversion algorithm.

[0012] Preferably, the data mapping algorithm operation further comprises the following steps:

[0013] S1. Use the spectral hashing algorithm to hash the high-dimensional data samples to obtain a k-dimensional binary hash code for each high-dimensional data item;

[0014] S2. Using the Z-curve method, the k-dimensional binary hash code of each high-dimensional data item is converted to obtain the Z-Value of each high-dimensional data item;

[0015] S3. Use the Chord method to construct a distributed node network, which is composed of a Chord ring and m node servers distributed on the Chord ring; map and store the Z-Value value of each high-dimensional data item obtained to the node server on the Chord ring, and update the node server routing table of the node that has obtained the Z-Value value of the stored high-dimensional data item;

[0016] S4. Perform a correlation query based on the high-dimensional data item to be queried, including: determining the Z-Value of the high-dimensional data item to be queried, and searching the node server routing table of each node clockwise to obtain the node server where the corresponding high-dimensional data item is located based on the determined Z-Value of the high-dimensional data item to be queried.

[0017] Preferably, the data conversion algorithm further includes:

[0018] S1. Perform data conversion through a data conversion module. The data conversion process includes two methods: converting disordered data into uniform format data and converting uniform format data into disordered data.

[0019] S2. Monitor and analyze the data conversion efficiency through the conversion monitoring module and obtain the rotation speed value and the rotation speed performance value, and determine whether the data conversion efficiency of the data conversion module is qualified according to the numerical values ​​of the rotation speed value and the rotation speed performance value;

[0020] S3, load and execute the process list through the process management module, and output the valid data generated during the process execution through the communication module;

[0021] S4. Monitor and analyze the process execution efficiency through the process recommendation module and obtain the process execution level. Mark the recommended method based on the monitoring and analysis results and send the recommended method to the management platform.

[0022] Preferably, the real-time data analysis module adopts a machine learning algorithm, and the machine learning algorithm includes: S1, using a parameterized quantum circuit PQC as a model structure;

[0023] S2, using quantum feature mapping QFM to transform data and tasks into a form suitable for PQC;

[0024] S3, using a quantum computer to execute and measure the data and tasks of the PQC transformed by the quantum feature map QFM, and obtain the output and loss function of the model, which is denoted as L;

[0025] S4, using the numerical method finite difference method FDM to calculate the gradient of the loss function through a classical computer, the gradient is denoted as g;

[0026] S5, using classical computers to perform iterative calculations and update model parameters according to the AGD method to avoid gradient disappearance or explosion;

[0027] S6, based on the AGD method, introduces an adaptive factor that dynamically adjusts the step size and direction of the gradient descent according to the noise level and complexity of PQC.

[0028] Preferably, the adaptive factor is a method for dynamically adjusting the step size and direction of gradient descent according to the noise level and complexity of PQC, and the adaptive factor is defined as:

[0029] λN(θ)f(θ)=e

[0030] Among them, λ is a hyperparameter used to control the influence of the adaptive factor, and its value is generally λ = 0.01. N(θ( is a comprehensive evaluation index of the noise level and complexity of PQC under given model parameters, and its calculation formula is:

[0031]

[0032] Where M is the total number of quantum gates of PQC, ni(θi is the noise level and complexity evaluation index of the ith quantum gate under given model parameters, and the calculation formula is:

[0033]

[0034] Where |ψi(θ)> is the output quantum state of the i-th quantum gate, and |ψi1(θ)> is the input quantum state of the i-th quantum gate.

[0035] Preferably, the role of the adaptive factor is to use an exponential function to correct the step size and gradient of the AGD method to obtain the following update formula:

[0036] θt+1=θtf(θt)ηtgt

[0037] Where f(θ(θ) is the adaptive factor at the tth iteration.

[0038] Preferably, the QVAGD algorithm is verified by theoretical analysis and numerical experiments. The numerical experiments mainly include selecting binary classification problems, multi-classification problems and clustering problems as test cases, using a variety of different data sets as output data, using a variety of different quantum devices as platforms for executing and measuring PQC, using accuracy and convergence time as evaluation indicators, performing multiple repeated experiments on each test case, and taking the average value as the final result.

[0039] Preferably, the theoretical analysis mainly includes constructing a potential energy function about the loss function and the model parameters, which is denoted as V, and proving that the potential energy function is a convex function. The update formula of the potential function and the QVAGD algorithm is as follows:

[0040] θt+1=θtf(θt)ηtgt

[0041] A Lyapunov function about the loss function and model parameters is also constructed. The Lyapunov function is denoted as S, and it is proved that the Lyapunov function is a monotonically decreasing function. The update formula of the Lyapunov function and the QVAGD algorithm is as follows:

[0042]

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] In this case, through the cooperation of data integration and cleaning module, usability optimization module, real-time data analysis module, intelligent decision support module, security and privacy protection module, the problem of diverse data sources but low integration in the actual use of the intelligent management and data analysis system for merchant managers in the existing technology is solved, which leads to data redundancy, inconsistency, uneven data quality, missing, wrong and duplication, affecting the accuracy of analysis results, limited system intelligence, lack of advanced functions such as intelligent recommendation and prediction, hidden dangers in data security, and vulnerability to attacks and leaks. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flowchart of an intelligent management and data analysis system for merchant managers of the present invention;

[0046] Figure 2 The present invention is a system block diagram of a data integration and cleaning module of an intelligent management and data analysis system for merchant managers. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technical personnel in this field without creative work are within the scope of protection of the present invention.

[0048] See also Figure 1 to Figure 2The present invention provides a technical solution: an intelligent management and data analysis system for merchant managers, including a data integration and cleaning module, the data integration and cleaning module is coupled with an ease of use optimization module, the ease of use optimization module is coupled with a real-time data analysis module, the real-time data analysis module is coupled with an intelligent decision support module, and the intelligent decision support module is coupled with a security and privacy protection module.

[0049] In this embodiment, the data integration and cleaning module includes a data source access module, the data source access module is coupled to the data mapping and conversion module, the data mapping and conversion module is coupled to the data integration module, the data integration module is coupled to the data verification and validation module, the data verification and validation module is coupled to the data audit module, the data audit module is coupled to the data cleaning workflow definition module, the data cleaning workflow definition module is coupled to the data cleaning execution module, the data cleaning execution module is coupled to the data cleaning result verification module, and the data cleaning result verification module is coupled to the data archiving and storage module.

[0050] In this embodiment, the data mapping and conversion module adopts a data mapping algorithm and a data conversion algorithm.

[0051] In this embodiment, the data mapping algorithm operation also includes the following steps:

[0052] S1. Use the spectral hashing algorithm to hash the high-dimensional data samples to obtain a k-dimensional binary hash code for each high-dimensional data item;

[0053] S2. Using the Z-curve method, the k-dimensional binary hash code of each high-dimensional data item is converted to obtain the Z-Value of each high-dimensional data item;

[0054] S3. Use the Chord method to construct a distributed node network, which consists of a Chord ring and m node servers distributed on the Chord ring; map and store the Z-Value value of each high-dimensional data item to the node server on the Chord ring, and update the node server routing table of the node that has obtained the Z-Value value of the stored high-dimensional data item;

[0055] S4. Perform a correlation query based on the high-dimensional data item to be queried, including: determining the Z-Value of the high-dimensional data item to be queried, and searching the node server routing table of each node clockwise to obtain the node server where the corresponding high-dimensional data item is located based on the determined Z-Value of the high-dimensional data item to be queried.

[0056] In this embodiment, the data conversion algorithm also includes:

[0057] S1. Perform data conversion through a data conversion module. The data conversion process includes two methods: converting disordered data into uniform format data and converting uniform format data into disordered data.

[0058] S2. Monitor and analyze the data conversion efficiency through the conversion monitoring module and obtain the rotation speed value and the rotation speed performance value, and determine whether the data conversion efficiency of the data conversion module is qualified according to the numerical values ​​of the rotation speed value and the rotation speed performance value;

[0059] S3, load and execute the process list through the process management module, and output the valid data generated during the process execution through the communication module;

[0060] S4. Monitor and analyze the process execution efficiency through the process recommendation module and obtain the process execution level. Mark the recommended method based on the monitoring and analysis results and send the recommended method to the management platform.

[0061] In this embodiment, the real-time data analysis module adopts a machine learning algorithm, and the machine learning algorithm includes: S1, using a parameterized quantum circuit PQC as a model structure;

[0062] S2, using quantum feature mapping QFM to transform data and tasks into a form suitable for PQC;

[0063] S3, using a quantum computer to execute and measure the data and tasks of the PQC transformed by the quantum feature map QFM, and obtain the output and loss function of the model, which is denoted as L;

[0064] S4, using the numerical method finite difference method FDM to calculate the gradient of the loss function through a classical computer, the gradient is denoted as g;

[0065] S5, using classical computers to perform iterative calculations and update model parameters according to the AGD method to avoid gradient disappearance or explosion;

[0066] S6, based on the AGD method, introduces an adaptive factor that dynamically adjusts the step size and direction of the gradient descent according to the noise level and complexity of PQC.

[0067] In this embodiment, the adaptive factor is a method for dynamically adjusting the step size and direction of the gradient descent according to the noise level and complexity of the PQC. The adaptive factor is defined as:

[0068] λN(θ)f(θ)=e

[0069] Among them, λ is a hyperparameter used to control the influence of the adaptive factor, and its value is generally λ = 0.01. N(θ( is a comprehensive evaluation index of the noise level and complexity of PQC under given model parameters, and its calculation formula is:

[0070]

[0071] Where M is the total number of quantum gates of PQC, ni(θi is the noise level and complexity evaluation index of the ith quantum gate under given model parameters, and the calculation formula is:

[0072]

[0073] Where |ψi(θ)> is the output quantum state of the i-th quantum gate, and |ψi1(θ)> is the input quantum state of the i-th quantum gate.

[0074] In this embodiment, the role of the adaptive factor is to use the exponential function to correct the step size and gradient of the AGD method, and obtain the following update formula:

[0075] θt+1=θtf(θt)ηtgt

[0076] Where f(θ(θ) is the adaptive factor at the tth iteration.

[0077] In this embodiment, the QVAGD algorithm is verified through theoretical analysis and numerical experiments. The numerical experiments mainly include selecting binary classification problems, multi-classification problems and clustering problems as test cases, using a variety of different data sets as output data, using a variety of different quantum devices as platforms for executing and measuring PQC, using accuracy and convergence time as evaluation indicators, and performing multiple repeated experiments on each test case, taking the average value as the final result.

[0078] In this embodiment, the theoretical analysis mainly includes constructing a potential energy function about the loss function and the model parameters, the potential energy function is denoted as V, and it is proved that the potential energy function is a convex function. The update formula of the potential function and the QVAGD algorithm is as follows:

[0079] θt+1=θtf(θt)ηtgt

[0080] A Lyapunov function about the loss function and model parameters is also constructed. The Lyapunov function is denoted as S, and it is proved that the Lyapunov function is a monotonically decreasing function. The update formula of the Lyapunov function and the QVAGD algorithm is as follows:

[0081]

[0082] The above shows and describes the basic principles, main features and advantages of the present invention. Technical personnel in this industry should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An intelligent management and data analysis system for merchant managers, characterized in that: It includes a data integration and cleaning module, which is coupled to a usability optimization module, which is coupled to a real-time data analysis module, which is coupled to an intelligent decision support module, and which is coupled to a security and privacy protection module.

2. The intelligent management and data analysis system for merchant managers according to claim 1, characterized in that: The data integration and cleaning module includes a data source access module, the data source access module is coupled to a data mapping and conversion module, the data mapping and conversion module is coupled to a data integration module, the data integration module is coupled to a data check and verification module, the data check and verification module is coupled to a data audit module, the data audit module is coupled to a data cleaning workflow definition module, the data cleaning workflow definition module is coupled to a data cleaning execution module, the data cleaning execution module is coupled to a data cleaning result verification module, and the data cleaning result verification module is coupled to a data archiving and storage module.

3. The intelligent management and data analysis system for merchant managers according to claim 1 is characterized by: The data mapping and conversion module adopts a data mapping algorithm and a data conversion algorithm.

4. The intelligent management and data analysis system for merchant managers according to claim 3 is characterized by: The data mapping algorithm operation also includes the following steps: S1. Use the spectral hashing algorithm to hash the high-dimensional data samples to obtain a k-dimensional binary hash code for each high-dimensional data item; S2. Using the Z-curve method, the k-dimensional binary hash code of each high-dimensional data item is converted to obtain the Z-Value of each high-dimensional data item; S3. Use the Chord method to construct a distributed node network, which is composed of a Chord ring and m node servers distributed on the Chord ring; map and store the Z-Value value of each high-dimensional data item obtained to the node server on the Chord ring, and update the node server routing table of the node that has obtained the Z-Value value of the stored high-dimensional data item; S4. Perform a correlation query based on the high-dimensional data item to be queried, including: determining the Z-Value of the high-dimensional data item to be queried, and searching the node server routing table of each node clockwise to obtain the node server where the corresponding high-dimensional data item is located based on the determined Z-Value of the high-dimensional data item to be queried.

5. The intelligent management and data analysis system for merchant managers according to claim 3 is characterized by: The data conversion algorithm also includes: S1. Perform data conversion through a data conversion module. The data conversion process includes two methods: converting disordered data into uniform format data and converting uniform format data into disordered data. S2. Monitor and analyze the data conversion efficiency through the conversion monitoring module and obtain the rotation speed value and the rotation speed performance value, and determine whether the data conversion efficiency of the data conversion module is qualified according to the numerical values ​​of the rotation speed value and the rotation speed performance value; S3, load and execute the process list through the process management module, and output the valid data generated during the process execution through the communication module; S4. Monitor and analyze the process execution efficiency through the process recommendation module and obtain the process execution level. Mark the recommended method based on the monitoring and analysis results and send the recommended method to the management platform.

6. The intelligent management and data analysis system for merchant managers according to claim 1, characterized in that: The real-time data analysis module adopts a machine learning algorithm, and the machine learning algorithm includes: S1, using a parameterized quantum circuit PQC as a model structure; S2, using quantum feature mapping QFM to transform data and tasks into a form suitable for PQC; S3, using a quantum computer to execute and measure the data and tasks of the PQC transformed by the quantum feature map QFM, and obtain the output and loss function of the model, which is denoted as L; S4, using the numerical method finite difference method FDM to calculate the gradient of the loss function through a classical computer, the gradient is denoted as g; S5, using classical computers to perform iterative calculations and update model parameters according to the AGD method to avoid gradient disappearance or explosion; S6, based on the AGD method, introduces an adaptive factor that dynamically adjusts the step size and direction of the gradient descent according to the noise level and complexity of PQC.

7. The intelligent management and data analysis system for merchant managers according to claim 1 is characterized by: The adaptive factor is a method for dynamically adjusting the step size and direction of gradient descent according to the noise level and complexity of PQC. The adaptive factor is defined as: λN(θ)f(θ)=e Among them, λ is a hyperparameter used to control the influence of the adaptive factor, and its value is generally λ = 0.

01. N(θ( is a comprehensive evaluation index of the noise level and complexity of PQC under given model parameters, and its calculation formula is: Where M is the total number of quantum gates of PQC, ni(θi is the noise level and complexity evaluation index of the ith quantum gate under given model parameters, and the calculation formula is: Where |ψi(θ)> is the output quantum state of the i-th quantum gate, and |ψi1(θ)> is the input quantum state of the i-th quantum gate.

8. The intelligent management and data analysis system for merchant managers according to claim 7 is characterized by: The role of the adaptive factor is to use the exponential function to correct the step size and gradient of the AGD method, and obtain the following update formula: θt+1=θtf(θt)ηtgt Where f(θ(θ) is the adaptive factor at the tth iteration.

9. The intelligent management and data analysis system for merchant managers according to claim 7 is characterized by: The QVAGD algorithm is verified through theoretical analysis and numerical experiments. The numerical experiments mainly include selecting binary classification problems, multi-classification problems and clustering problems as test cases, using a variety of different data sets as output data, using a variety of different quantum devices as platforms for executing and measuring PQC, using accuracy and convergence time as evaluation indicators, and performing multiple repeated experiments on each test case, taking the average value as the final result.

10. The intelligent management and data analysis system for merchant managers according to claim 9, characterized in that: The theoretical analysis mainly includes constructing a potential energy function about the loss function and model parameters, which is denoted as V, and proving that the potential energy function is a convex function. The update formula of the potential function and the QVAGD algorithm is as follows: θt+1=θtf(θt)ηtgt A Lyapunov function about the loss function and model parameters is also constructed. The Lyapunov function is denoted as S, and it is proved that the Lyapunov function is a monotonically decreasing function. The update formula of the Lyapunov function and the QVAGD algorithm is as follows:

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