Cosmetic and hairdressing customer consumption tracking and collection method based on multi-channel data fusion

Abstract

The invention discloses a beauty and hairdressing customer consumption tracking and collection method based on multi-channel data fusion, and relates to the technical field of information technology and commercial service. The method comprises the following steps that S1, a distributed data acquisition framework is adopted, terminal equipment and a cloud server cooperatively work, and the terminal equipment is responsible for acquiring multi-channel data of online reservation, offline payment and member point exchange of clients and uploading the data to the cloud server for unified storage; and S2, preprocessing the received multi-channel data by using a timestamp synchronization module. According to the invention, through reasonable data acquisition and integration strategies and intelligent analysis means, the recording capability of the system for customer consumption behaviors is improved, the transaction process is optimized in a scene of multiple payment modes, and meanwhile, the service quality and the operation efficiency are improved through deep data mining, so that the technical scheme has a wide application prospect.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of information technology and business service technology, and specifically relates to a multi-channel data fusion-based beauty and hairdressing customer consumption tracking and collection method. BACKGROUND

[0002] In the beauty and hairdressing industry, customer consumption tracking and collection management is of great significance to improving service quality and operational efficiency. Traditional consumption tracking and collection methods usually rely on single-channel data recording, such as manual accounting or independent cash register systems. However, this approach has certain limitations in data integration and analysis, especially when facing multi-channel payment methods and diversified customer needs, it is difficult to fully reflect the customer's consumption behavior and preferences.

[0003] In recent years, with the development of mobile payment, membership management systems and online and offline integration technologies, the beauty and hairdressing industry has gradually introduced various digital tools to optimize customer management processes. These tools record customer consumption history, payment methods, service evaluations and other information, providing businesses with richer data support. However, existing digital tools still have some problems in actual application, such as lack of effective integration of data between different channels, leading to the phenomenon of information silos, affecting the overall utilization efficiency of data.

[0004] Currently, the beauty and hairdressing industry's customer consumption tracking and collection method mainly faces the following challenges: Insufficient multi-channel data integration: Customer consumption behavior often involves online booking, offline payment, member points exchange and other multiple links, while existing systems usually fail to seamlessly integrate cross-platform data, limiting the comprehensive tracking of customer consumption tracks.

[0005] Complexity brought by payment method diversity: With the popularity of electronic payment methods, customers may choose WeChat, Alipay, bank cards and other payment methods, and existing collection systems may have compatibility problems when handling multiple payment channels, increasing the complexity of operations.

[0006] Limited data analysis capabilities: Although some systems can record customer consumption data, the depth of data mining and analysis capabilities still needs to be improved, especially in personalized service recommendation and precision marketing, the value of data is not fully realized.

[0007] Therefore, the present application is proposed. SUMMARY

[0008] The technical problem to be solved by the present application is to overcome the shortcomings of the prior art and provide a multi-channel data fusion-based beauty and hairdressing customer consumption tracking and collection method.

[0009] To solve the above technical problems, the basic idea of the technical scheme adopted by the present application is: A multi-channel data fusion beauty and hairdressing customer consumption tracking and collection method, comprising the following steps: Step S1, using a distributed data collection architecture, through the cooperation of terminal equipment and cloud servers, the terminal equipment is responsible for collecting multi-channel data such as customer online appointment, offline payment, member points exchange, etc., and uploading the data to the cloud server for unified storage; Step S2, using a timestamp synchronization mechanism to preprocess the received multi-channel data, eliminating the time deviation caused by different channel data transmission delays, and simultaneously cleaning and repairing abnormal data; Step S3, based on the preprocessed data, a consumption behavior model is constructed, the customer's consumption preferences and payment habits are analyzed through a multi-dimensional feature extraction algorithm, and a personalized service recommendation list is generated; Step S4, based on the consumption behavior model, a payment compatibility optimization strategy is designed, the optimal payment channel is selected through a dynamic routing algorithm to complete the transaction, and seamless switching of multiple payment methods is supported; Step S5, through deep mining and analysis of consumption data, an operation efficiency evaluation report is generated, the service strategy is adjusted combined with customer feedback information, and the continuous optimization of service quality is realized.

[0010] Preferably, the step S1 specifically comprises: Consider a distributed data collection system, which includes multiple terminal devices (such as appointment terminals, cash registers, mobile payment terminals) and a cloud server, the terminal devices are connected with the cloud server through wireless network or wired network. Taking the appointment terminal as an example, the data collected by it includes customer name, contact information, appointment time and service item, which is represented as: Wherein, Ci is the customer number, T i is the appointment timestamp, S i is the service item number, E i is the terminal device number; Assuming that multiple terminal devices are distributed in different physical locations, the communication delay between the ith terminal device and the cloud server is set as Δti, then the influence of the delay on data synchronization is represented as: Wherein, R is the data transmission rate, and P is the data packet size; Assuming that the data type collected by the ith terminal device is W j , then the priority of different types of data is represented as: Wherein, αj a weight coefficient of a data type; Under the action of multi-channel data, the data packet uploaded by the i-th terminal device is expressed as: wherein, I k is an index of the data packet, T k is a timestamp of the data packet, C k is content of the data packet, W k is a priority of the data packet; For the terminal device, the collected data is compressed and encoded and then uploaded to the cloud server. In addition to the cloud server, the data packet uploaded by the i-th terminal device is expressed as: wherein, F c is a compression and encoding function, P o and P c respectively represent the original size and the compressed size of the data packet; For the cloud server, the received data packet is decoded and restored for storage. Assuming that the first terminal device in the distributed data collection system is a center node, the data packet of the center node is expressed as: wherein, I s is a number of the first terminal device, T s is a timestamp of the data packet uploaded by the first terminal device, C s is content of the data packet of the first terminal device.

[0011] Preferably, the step S2 specifically comprises: After using the obtained multi-channel data and the timestamp synchronization mechanism to calibrate the data, the data calibration result of the i-th terminal device is expressed as: wherein, F is a calibration function, T k is an original timestamp, T k ' is a calibrated timestamp, I k is an index of the data packet, I s is a number of the terminal device; In the data calibration process, the operation of cleaning and repairing the abnormal data is expressed as: wherein, H is an abnormality detection function, G is a data repair function, D m is an index of the abnormal data, D m ' is a repaired data value.

[0012] Preferably, the step S3 specifically comprises: The consumption behavior model improves the accuracy of personalized service recommendation by multi-dimensional feature extraction on multi-channel data, and constructs customer portrait by using customer's historical consumption records, payment method preferences and service evaluation information, etc. Wherein, v1 is the index of feature dimension, v n is the nth feature value; The analysis of payment habits extracts the payment frequency and payment method distribution of customers through statistical analysis algorithm, and the probability density function of payment method distribution is expressed as: Wherein, p1 is the index of payment method, p x is the probability value of the xth payment method; The results of consumption preference and payment habit are finally expressed in the form of feature vector.

[0013] Preferably, the step S4 specifically comprises the following steps: First, define the search range and resolution of payment compatibility optimization strategy, and establish the dynamic routing matrix corresponding to the payment method in the traversal process, and then quickly select the optimal payment channel to complete the transaction; The search range S1 and S n of payment compatibility optimization strategy are calculated by the maximum value S max of payment method theory of the system, S max is calculated as follows: The preference distribution of customers in different payment methods is determined by formula G ij ; The different payment channels g i1 and g i2 on the payment method plane are divided by equidistant discrete points, and the number of points is determined by the payment method type g ij , and the division formula is as follows: Wherein, g ij is the discrete point of the ith payment channel in the payment method plane, and through the above formula, the payment method plane is divided into a g ij two-dimensional discrete search grid, and each grid point g ij represents a payment method; For each payment method, the transaction cost and success rate of the payment method are calculated according to the payment history of the customer and the current transaction amount, and the calculation formula is: Wherein, R and r1 are the coefficients related to the r n th payment method, which are determined by the rate and stability of the payment channel, and are expressed as: Wherein, M is the transaction amount, and a is the rate of the payment channel; After obtaining the cost and success rate of each payment method, the optimal payment channel index is found by matching the calculated values with the discrete points of the payment method plane: Wherein, argmin is the independent variable value that minimizes the function value, and C is the discrete point value of the payment method plane; By traversing each point of the payment method plane, the optimal payment channel is quickly selected using the pre-stored dynamic routing matrix, and the optimal channel value of the payment method plane is obtained: Wherein, P opt is the optimal channel value of the P n th payment method; The optimal payment scheme is obtained by integrating the channel values of all payment methods through the dynamic routing algorithm: Preferably, the step S5 specifically comprises: According to the optimal payment scheme P opt , the maximum and minimum values of the payment success rate and transaction cost are extracted to generate an operation efficiency evaluation report, and the expression of the evaluation report is: Based on the evaluation report, the improvement space of service quality is identified, wherein r1 is the low-efficiency service hypothesis, and r n is the high-efficiency service hypothesis, and the standard service quality evaluation problem is described as a binary hypothesis test: For the evaluation index Q, a threshold T is used to determine whether the service quality belongs to H1 or H0: Wherein, the threshold T is adjusted according to the customer satisfaction S and the complaint rate C to meet the service quality requirements; The performance of the service quality is further evaluated through H0 and H1, wherein: In the formula, P is a probability, H0 is a low-efficiency service hypothesis, H1 is a high-efficiency service hypothesis, and T is a threshold value; Finally, by evaluating the index Q and the performance index Q < T and The effective evaluation of the service quality is realized through the analysis of the index Q and the performance index Q < T and

[0014] Compared with the prior art, the technical scheme has the following beneficial effects, of course, any product implementing the present application does not necessarily need to achieve all the advantages described below: The multi-channel data fusion beauty and hair customer consumption tracking and payment method can improve the recording ability of the system for customer consumption behavior through reasonable data collection and integration strategies and intelligent analysis means, optimize the transaction process in a multi-payment mode scenario, and improve the service quality and operation efficiency through deep data mining, and is a technical scheme with wide application prospects.

[0015] The specific embodiments of the present application will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0016] The drawings in the following description are only some embodiments, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the drawings. In the drawings: Figure 1 The step flowchart of an embodiment of the present application is shown in the drawing; It should be noted that these drawings and written descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0017] The present application will now be further described in detail with reference to the accompanying drawings.

[0018] Please refer to Figure 1 In the present embodiment, a multi-channel data fusion beauty and hair customer consumption tracking and payment method is provided, which realizes full-process recording and efficient management of customer consumption behavior through the cooperative work of terminal equipment, a cloud server, a time stamp synchronization module, a consumption behavior model, a payment compatibility optimization module, and an operation efficiency evaluation module. The specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0019] In practical application scenarios, terminal devices include appointment terminals, cash registers, and mobile payment terminals, etc. These devices establish connections with cloud servers through wireless networks or wired networks. Terminal devices are responsible for collecting multi-channel data such as customer online appointments, offline payments, and member point exchanges, and uploading the data to cloud servers for unified storage. Taking an appointment terminal as an example, the data collected by the terminal includes customer name, contact information, appointment time, and service item, which is represented as formula where C i is the customer number, T i is the appointment timestamp, S i is the service item number, and E i is the terminal device number. The communication delay between the ith terminal device and the cloud server is set as Δt i , and the influence of the communication delay on data synchronization is represented as: where R is the data transmission rate and P is the data packet size. The priority of different types of data is represented as formula where α j is the weight coefficient of the data type. The data packet uploaded by the ith terminal device is represented as formula where I k is the index of the data packet, T k is the timestamp of the data packet, C k is the content of the data packet, and W k is the priority of the data packet. For terminal devices, the collected data is compressed and encoded before being uploaded to the cloud server. In addition to the cloud server, the data packet uploaded by the ith terminal device is represented as formula where F c is the compression and encoding function, P o is the original size of the data packet, and P c is the compressed size. For the cloud server, the received data packet is decoded and restored for storage. Assuming that the first terminal device is the center node in the distributed data collection system, the data packet of the center node is represented as formula where is the number of the first terminal device, is the timestamp of the data packet uploaded by the first terminal device, and is the content of the data packet of the first terminal device.

[0020] After the data is uploaded to the cloud server, the timestamp synchronization module preprocesses the received multi-channel data. After using the obtained multi-channel data and the timestamp synchronization mechanism to calibrate the data, the data calibration result of the ith terminal device is represented as formula where F is the calibration function, T k is the original timestamp, and T k′ is the timestamp after calibration, and is the terminal device number. During the data calibration process, the anomaly detection function is used to identify abnormal data, and the data repair function is used to clean and repair abnormal data. The operation is expressed as the formula , where H is the index of the abnormal data and G is the repaired data value. Through the above steps, the timestamp synchronization module eliminates the time deviation caused by the delay of data transmission from different channels and completes the cleaning and repair of abnormal data.

[0021] The consumption behavior model is constructed based on pre-processed data. By extracting multi-dimensional features from multi-channel data, the customer's historical consumption records, payment method preferences, and service evaluation information are used to build a customer profile, thereby improving the accuracy of personalized service recommendations. The feature vector of the customer profile is expressed as the formula , where v1 is the index of the feature dimension, v n The analysis of payment habits uses statistical analysis algorithms to extract the customer's payment frequency and payment method distribution. The probability density function of the payment method distribution is expressed as the formula , where p1 is the index of the payment method, p x is the probability value of the xth payment method. The results of consumer preferences and payment habits are ultimately represented as a feature vector. The consumer behavior model generates a personalized service recommendation list to provide customers with services that better meet their needs.

[0022] The payment compatibility optimization module designs a payment compatibility optimization strategy based on the consumer behavior model. It selects the optimal payment channel to complete the transaction through a dynamic routing algorithm, while supporting seamless switching between multiple payment methods. First, the search range and resolution of the payment compatibility optimization strategy are defined, and a dynamic routing matrix corresponding to the payment method is established during the traversal process, thereby quickly selecting the optimal payment channel to complete the transaction. The search range and resolution of the payment compatibility optimization strategy are calculated based on the theoretical maximum value of the system's payment method, defined as the formula By formula The distribution of customer preferences for different payment methods is expressed as follows. Different payment channels on the payment method plane are divided by equally spaced discrete points. The number of points is determined by the type of payment method. The division formula is as follows: Formula , where is the discrete point of the i-th payment channel in the payment method plane, representing g ij is the discrete point of the i-th payment channel in the payment method plane. According to the above formula, the payment method plane is divided into g ij A two-dimensional discrete search grid, where each grid point g ij Represents a payment method. For each payment method, based on the customer's payment history and current transaction amount, calculate the transaction cost and success rate of the payment method. The calculation formula is , where R and r1 are related to the rth n The coefficient related to the payment method is determined by the rate and stability of the payment channel and is expressed as the formula , where M is the transaction amount and a is the payment channel rate. After obtaining the cost and success rate of each payment method, the optimal payment channel index is found by matching the calculated value with the discrete points of the payment method plane as follows: , where argmin is the independent variable value that minimizes the function value, and C is the discrete point value of the payment method plane. By traversing each point of the payment method plane and using the pre-stored dynamic routing matrix to quickly select the optimal payment channel, the optimal channel value of the payment method plane is obtained as follows: , where P opt For P n The optimal channel value of the payment method. By integrating the channel values ​​of all payment methods through the dynamic routing algorithm, the optimal payment solution is obtained as the formula .

[0023] The operational efficiency evaluation module generates an operational efficiency evaluation report through in-depth mining and analysis of consumption data, and adjusts service strategies based on customer feedback to achieve continuous optimization of service quality. Based on the optimal payment solution, the maximum values ​​of payment success rate and transaction cost are extracted to generate an operational efficiency evaluation report. The expression of the evaluation report is formula Based on the evaluation report, we can identify the improvement space of service quality, where M is the inefficient service hypothesis and b is the efficient service hypothesis. The standard service quality evaluation problem is described as a binary hypothesis test, which is expressed as formula For the evaluation index, a threshold T is used to determine whether the service quality belongs to H1 or H0, which is expressed as the formula , where the threshold T is adjusted according to customer satisfaction and complaint rate to meet the service quality requirements. The performance of service quality is further evaluated by, which is expressed as formula , where is the probability, C is the inefficient service hypothesis, S is the efficient service hypothesis, and T is the threshold. Finally, through the analysis of evaluation indicators and performance indicators, an effective evaluation of service quality is achieved, thus completing the multi-channel data fusion beauty salon customer consumption tracking and payment collection method.

[0024] In actual operation, the data collected by the terminal device is uploaded to the cloud server through wireless network or wired network. After receiving and storing the data, the cloud server, the timestamp synchronization module calibrates and cleans the data to ensure the consistency and accuracy of the data. The consumption behavior model generates customer portraits and service recommendation lists based on the processed data to provide personalized services for customers. The payment compatibility optimization module selects the optimal payment channel through a dynamic routing algorithm to ensure the efficiency and compatibility of the payment process. The operation efficiency evaluation module generates evaluation reports through deep analysis of consumption data and adjusts service strategies combined with customer feedback information to improve service quality and operation efficiency. The above modules cooperate closely through data flow and control flow to achieve the purpose of the invention.

[0025] In order to better enable those skilled in the art to fully understand and implement the present invention, the specific implementation principles of the present invention are supplemented below in conjunction with a specific application scenario.

[0026] First, in a beauty salon, customers make service reservations through a reservation terminal device 1. The reservation terminal device 1 collects the customer's name, contact information, reservation time, and service items, and uploads these data in the form of formula to the cloud server. At the same time, the cash register and mobile payment terminal device record the customer's offline payment behavior and member points exchange, respectively. These terminal devices are distributed in different physical locations, and the communication delay between them and the cloud server is described by formula . To ensure the efficiency of data transmission, the priority of different types of data is determined after calculation by formula , so that critical data can be uploaded first. Terminal device 1 uses a compression encoding function to compress the data packet before uploading, as shown in formula , to reduce network load and improve transmission efficiency.

[0027] Subsequently, after the cloud server receives the data uploaded by each terminal device, the timestamp synchronization module begins to preprocess the multi-channel data. Through the calibration function in formula , the timestamp synchronization module uniformly calibrates the timestamps uploaded by different terminal devices, eliminating the time deviation caused by network delay. In this process, the anomaly detection function identifies potential abnormal data and cleans and repairs them through the data repair function, as shown in formula . This operation ensures the consistency and accuracy of all data in the time dimension, laying a foundation for subsequent analysis.

[0028] Next, the consumption behavior model constructs customer portraits based on the preprocessed data. Through multi-dimensional feature extraction of multi-channel data, the consumption behavior model uses formula A feature vector is generated containing customer historical consumption records, payment preferences, and service evaluations. At the same time, the formula is used to analyze the distribution of the customer's payment methods, thereby revealing their payment habits. These feature vectors and payment habit information are further integrated to form a personalized service recommendation list, providing customers with service solutions that better meet their needs.

[0029] In the payment link, the payment compatibility optimization module selects the optimal payment channel through a dynamic routing algorithm. First, according to the formula the search range of the payment compatibility optimization strategy is defined, and the formula is used to divide the payment method plane into discrete grids. The cost and success rate of each payment method are calculated by the formula and the formula , where the transaction amount and payment channel rate are key parameters. By traversing each point on the payment method plane, the dynamic routing matrix quickly matches the optimal payment channel index. Finally, the payment compatibility optimization module integrates the channel values of all payment methods to generate the optimal payment scheme, as shown in the formula , thereby ensuring the efficiency and compatibility of the payment process.

[0030] Finally, the operation efficiency evaluation module generates an operation efficiency evaluation report through deep mining and analysis of consumption data. The expression of the evaluation report is given by the formula , where the maximum and minimum values of the payment success rate and transaction cost are extracted for evaluating service quality. By setting the threshold value in the formula , it is determined whether the service quality meets the requirements, and the formula is used to further analyze the probability distribution of low-efficiency service hypotheses and high-efficiency service hypotheses. Based on the evaluation results, the operation efficiency evaluation module adjusts the service strategy to continuously optimize service quality and operation efficiency.

[0031] In summary, the present application realizes the full-process recording and efficient management of customer consumption behavior through the collaborative work of the terminal device, cloud server, timestamp synchronization module, consumption behavior model, payment compatibility optimization module, and operation efficiency evaluation module.

[0032] The above steps are closely related to the flowchart in Figure 1 , which shows the overall operation principle from data collection to payment optimization and service quality evaluation, ensuring the efficiency and practicality of the system.

[0033] The present application is not limited to the above embodiments, and any structural changes made under the inspiration of the present application are within the scope of the present application. Any technical solution with the same or similar technical solutions as the present application falls within the scope of the present application. The technical, shape, and structure parts not described in detail in the present application are known technologies.

Claims

1. A method for tracking and collecting payments from beauty salon customers based on multi-channel data integration, characterized in that: The following steps are involved: Step S1: Using a distributed data collection architecture, the terminal device and the cloud server work together. The terminal device is responsible for collecting multi-channel data such as customers' online reservations, offline payments, and member points redemption, and uploading the data to the cloud server for unified storage; Step S2: Use the timestamp synchronization module to pre-process the received multi-channel data to eliminate the time deviation caused by the data transmission delay of different channels, and clean and repair the abnormal data; Step S3: Build a consumer behavior model based on the preprocessed data, analyze the customer's consumption preferences and payment habits through a multi-dimensional feature extraction algorithm, and generate a personalized service recommendation list; Step S4: Based on the consumer behavior model, a payment compatibility optimization strategy is designed to select the optimal payment channel to complete the transaction through a dynamic routing algorithm, while supporting seamless switching between multiple payment methods. Step S5: Generate an operational efficiency evaluation report through in-depth mining and analysis of consumption data, and adjust service strategies based on customer feedback.

2. The method for tracking and collecting payments for beauty salon customers based on multi-channel data fusion according to claim 1, characterized in that: The step S1 specifically includes: The terminal device establishes a connection with the cloud server via a wireless or wired network. The data collected by the terminal device includes the customer's name, contact information, appointment time, and service items, which are expressed as: Among them, C i For customer number, T i is the reservation timestamp, S i E is the service item number. i Number the terminal device; The communication delay between the i-th terminal device and the cloud server is set to Δt i ,The impact of communication delay on data synchronization is expressed as: Where R is the data transmission rate and P is the packet size; The priorities of different types of data are expressed as: Among them, α j is the weight coefficient of the data type; The data packet uploaded by the i-th terminal device is expressed as: Among them, I k is the index of the data packet, T k is the timestamp of the data packet, C k is the content of the data packet, W k The priority of the data packet.

3. The method for tracking and collecting payments for beauty salon customers based on multi-channel data fusion according to claim 1, characterized in that: The step S2 specifically includes: The timestamp synchronization module is used to calibrate the data of the i-th terminal device. The calibration result is expressed as: Where F is the calibration function, T k is the original timestamp, T k ′ is the timestamp after calibration; The operation of cleaning and repairing abnormal data is expressed as: Among them, H is the anomaly detection function, G is the data repair function, and D m is the index of abnormal data, D m ′ is the data value after repair.

4. The method for tracking and collecting payments from beauty salon customers based on multi-channel data fusion according to claim 1, characterized in that: The step S3 specifically includes: The consumer behavior model extracts multidimensional features from multi-channel data and uses the customer's historical consumption records, payment method preferences, and service evaluation information to build a customer profile. The feature vector of the customer profile is expressed as: Among them, v n is the nth eigenvalue; The analysis of payment habits uses statistical analysis algorithms to extract customers' payment frequency and payment method distribution. The probability density function of the payment method distribution is expressed as: Among them, p x is the probability value of the xth payment method.

5. The method for tracking and collecting payments from beauty salon customers based on multi-channel data fusion according to claim 1, characterized in that: The step S4 specifically includes: Defines the search scope and resolution of the payment compatibility optimization strategy. The search scope is expressed as: The formula for dividing different payment channels on the payment method plane is: Among them, g ij is the discrete point of the i-th payment channel in the payment method plane; For each payment method, the formula for calculating transaction cost and success rate is: Where a is the payment channel rate, M is the transaction amount, and b is the stability correlation coefficient; By traversing each point on the payment method plane, we find the optimal payment channel index: 。 6. The method for tracking and collecting payments for beauty salon customers based on multi-channel data fusion according to claim 1, characterized in that: In step S4, the channel values ​​of all payment methods are integrated through a dynamic routing algorithm to obtain the optimal payment solution: 。 7. The method for tracking and collecting payments from beauty salon customers based on multi-channel data fusion according to claim 1, characterized in that: The step S5 specifically includes: Based on the optimal payment solution, the maximum values ​​of payment success rate and transaction cost are extracted to generate an operational efficiency evaluation report. The expression of the evaluation report is: Among them, r n is the evaluation index value; The service quality evaluation problem is described as a binary hypothesis test: Among them, Q is the evaluation index and T is the threshold; The performance of the quality of service is evaluated by the probabilistic formula: 。 8. The method for tracking and collecting payments from beauty salon customers based on multi-channel data fusion according to claim 1, characterized in that: The data collected by the terminal device is compressed and uploaded to the cloud server. The data packet is represented as follows: Among them, F c is the compression coding function, P o is the original size of the data packet, P c The compressed size.

9. The method for tracking and collecting payments from beauty salon customers based on multi-channel data fusion according to claim 1, characterized in that: The data packets received by the cloud server are decoded and restored before storage. The data packets of the central node are represented as follows: Among them, I s is the number of the central node, T s is the timestamp, C s For data content.

10. The method for tracking and collecting payments from beauty salon customers based on multi-channel data fusion according to claim 1, characterized in that: The threshold T is adjusted according to customer satisfaction and complaint rate. The adjustment formula is: Among them, S is customer satisfaction and C is complaint rate.

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