Cooperative machine-based reward commission auxiliary analysis method and system

Abstract

The invention discloses a cooperative machine-based reward commission auxiliary analysis method and system. According to the method, a multi-layer security architecture is adopted, a dynamic biometric authentication and distributed firewall is embedded in a login module, and spatial topology modeling of a cooperation point location is realized in combination with a map positioning engine; the customer distribution module fuses the geo-fencing and consumption characteristic data based on an improved K-means algorithm, and intelligently guides the customer flow to the optimal point; and the sales data module collects transaction information in real time through a time sequence database, generates a 15-dimensional dynamic analysis board through an aggregation calculation engine, and finally automatically generates a visual report through an encrypted link and synchronizes the visual report to a management terminal. According to the method, data fusion analysis of the short-term cooperation point location and the conventional channel is realized, manual configuration operation is reduced by 85% through automatic data flow closed loop, the resource configuration error rate of merging assessment is reduced from 1.7% to 0.03%, meanwhile, server load is reduced by 63% depending on edge computing node deployment, and the resource allocation efficiency is improved. And refined operation of an enterprise multi-channel sales network is effectively supported.

Description

Technical Field

[0001] The present invention relates to the field of distributed computing technology, and in particular to a reward commission auxiliary analysis method and system based on cooperative machines. Background Art

[0002] In today's business environment, companies often introduce short-term external partners to supplement their existing sales channels in order to expand sales channels and boost sales performance. However, existing incentive commission systems and performance evaluation methods have many shortcomings when dealing with complex sales networks involving external partners.

[0003] Existing technologies, such as the intelligent performance appraisal system disclosed in patent CN116128366A, can achieve intelligent and efficient management of employee performance appraisals, but its primary focus is on the performance appraisal and incentive mechanisms for internal employees, and does not address the potential impact of short-term sales points introduced by external partners on normal sales performance. Similarly, patent CN117593031A discloses a method for improving the marketing capabilities of large power customers. Although it involves internal appraisal management, it also does not provide a solution to the problem of fluctuations in internal sales performance caused by the introduction of external cooperation points.

[0004] While these existing assessment systems and methods optimize performance and risk management in their respective fields, they lack effective strategies for addressing the disruption of sales performance at regular locations caused by external partners. In particular, existing technologies offer no practical solutions for rationally evaluating and allocating sales data generated by the introduction of external partners without compromising the stability of the assessment system for regular locations. This can lead to unfair and volatile sales performance assessments at regular locations when external partners are introduced, impacting the enthusiasm and stability of channel personnel and hindering the refined operation of a company's multi-channel sales network and the stable development of its business. Summary of the Invention

[0005] Based on this, the embodiment of the present application provides a reward commission auxiliary analysis method and system based on cooperative machines, which solves the shortcomings of the existing technology when facing the layout of external cooperation points by optimizing the data processing mode and assessment mechanism.

[0006] In a first aspect, a method for assisting in analyzing reward commissions based on cooperative machines is provided, the method comprising:

[0007] Collect login data for user identity verification; verification is performed by combining dynamic biometric authentication with a distributed firewall;

[0008] After user authentication is passed, the geofencing engine and device fingerprinting technology are used to intelligently identify sales locations and establish a spatial topology model of the locations. The geofencing engine uses the Haversine formula to dynamically calculate the location radius, and the device fingerprinting technology combines the MAC address, SIM card IMSI, and device sensor data to generate a unique identifier.

[0009] Based on the established point space topology model, the improved K-means algorithm is used to allocate customers;

[0010] After customer assignment is completed, transaction information is aggregated in real time through the time series database, and a multi-dimensional dynamic analysis dashboard is generated using the data aggregation engine to conduct real-time tracking and multi-dimensional analysis of sales data.

[0011] Reward commission decisions are made based on the analysis results, visual reports are automatically generated through encrypted links, and the results are automatically distributed to the management terminal.

[0012] Optionally, collect login data for user authentication, including:

[0013] Receiving biometric data input by a user, wherein the biometric data includes fingerprint and voiceprint data;

[0014] Comparing the received biometric data with a pre-stored user biometric template;

[0015] If the comparison results are consistent, the verification is successful and the user is allowed to log in to the system; if the comparison results are inconsistent, the verification fails and the user is denied login.

[0016] Optionally, an improved K-means algorithm is used for customer allocation, including:

[0017] Initialize the cluster center and select K cooperation points as the initial cluster center;

[0018] For each customer, a customer feature vector is constructed, and distance calculation is performed by integrating geographical distance and weighted Euclidean distance of consumption characteristics. The allocation weight is adjusted in real time according to the point saturation, and the customer flow is intelligently guided to the optimal point to achieve matching between customers and points. The customer feature vector includes at least geographical location, consumption capacity index, purchase frequency and preference category.

[0019] Optionally, the allocation weights are adjusted in real time according to the point saturation, specifically including:

[0020] The improved isolation forest anomaly scoring formula is used for anomaly detection, the formula is:

[0021]

[0022] Where s(x,n) is the anomaly score, E(h(x)) is the average path length of sample x, n is the number of samples, and c(n) is the correction function;

[0023] The correction function c(n) is:

[0024]

[0025] Here, H(n-1) is the harmonic number, which is used to replace the natural logarithm ln(n-1) in the traditional isolation forest formula to enhance stability in small sample scenarios.

[0026] Optionally, real-time collection of transaction information through a time series database includes:

[0027] Collect transaction data in real time from the transaction systems of various sales points; wherein the transaction data includes transaction time, transaction amount, product information, and customer information;

[0028] Store the collected transaction data in a time series database and organize and manage it according to time series;

[0029] The data aggregation engine is used to aggregate transaction data in the time series database to generate a multi-dimensional dynamic analysis dashboard.

[0030] Optionally, make reward commission decisions based on the analysis results, including:

[0031] Analyze sales data in the dynamic analysis dashboard and extract key indicators, including sales volume, sales growth rate, and customer satisfaction;

[0032] Calculate the reward commission amount for each sales point based on the preset reward commission rules and the extracted key indicators;

[0033] Generate visual reports containing reward commission amounts and related analytical data;

[0034] Visual reports are automatically distributed to management terminals via encrypted links.

[0035] In a second aspect, a reward commission auxiliary analysis system based on cooperative machines is provided, which includes:

[0036] The login verification module is used to collect login data for user identity verification; wherein, verification is performed by combining dynamic biometric authentication with a distributed firewall;

[0037] The point management module is used to intelligently identify sales points and establish a spatial topology model of the points after user identity verification using a geofencing engine and device fingerprinting technology. The geofencing engine uses the Haversine formula to dynamically calculate the point radius, and the device fingerprinting technology combines the MAC address, SIM card IMSI, and device sensor data to generate a unique identifier.

[0038] Intelligent allocation module, which is used to allocate customers based on the established point space topology model and adopts the improved K-means algorithm;

[0039] The sales tracking module is used to collect transaction information in real time through the time series database after customer allocation is completed, and use the data aggregation engine to generate a multi-dimensional dynamic analysis dashboard to conduct real-time tracking and multi-dimensional analysis of sales data;

[0040] The analysis and decision-making module is used to make reward commission decisions based on the analysis results and automatically generate visual reports through encrypted links;

[0041] The data push module is used to automatically distribute the results to the management terminal.

[0042] In a third aspect, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the reward commission auxiliary analysis method described in any one of the first aspects is implemented.

[0043] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the reward commission auxiliary analysis method described in any one of the above-mentioned first aspects is implemented.

[0044] In a fifth aspect, a computer program product is provided, on which a computer program is stored. When the computer program is executed by a processor, the reward commission auxiliary analysis method described in any one of the above-mentioned first aspects is implemented.

[0045] The beneficial effects of the technical solutions provided in the embodiments of the present application include at least:

[0046] (1) Through the geo-fencing engine and device fingerprint technology, sales points can be accurately identified and managed, and a detailed point-of-sale spatial topology model can be established. This enables enterprises to more efficiently manage and optimize sales networks, reduce manual configuration operations, and improve the accuracy of resource allocation.

[0047] (2) By using an improved K-means algorithm combined with geo-fencing and consumer profile data, the system can intelligently assign customers to the optimal location. This precise customer allocation not only improves customer satisfaction but also optimizes the operational efficiency of sales locations and improves overall sales performance.

[0048] (3) Dynamic biometric authentication and distributed firewalls are used to ensure the security of user identity authentication. At the same time, through blockchain evidence storage technology, key operation data is securely recorded on the tamper-proof blockchain, enhancing the integrity and credibility of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other implementation drawings based on the provided drawings without inventive effort.

[0050] Figure 1 A flowchart of a method for assisting in analyzing reward commissions based on cooperative machines provided in an embodiment of the present application;

[0051] Figure 2 A technical architecture diagram provided for an optional embodiment of the present application;

[0052] Figure 3 This is a diagram of the architecture of the reward commission auxiliary analysis system provided in the embodiment of the present application;

[0053] Figure 4 A schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0055] In the description of the present invention, the terms "comprise", "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may also include other steps or units that are not explicitly listed but are inherent to these processes, methods, products or apparatuses, or steps or units that are added based on further optimization schemes conceived by the present invention.

[0056] In the existing technology, such as the intelligent performance appraisal system disclosed in reference patent CN116128366A, although it can realize the intelligent and efficient management of employee performance appraisal, its main focus is on the performance appraisal and incentive mechanism of internal employees, and does not involve the potential impact of short-term points introduced by external partners on normal sales performance.

[0057] Similarly, reference patent CN117593031A discloses a method for improving the marketing capabilities of large power customers. Although it involves internal assessment management, it also does not provide a solution to the problem of fluctuations in sales performance of normal internal points caused by the introduction of external cooperation points.

[0058] Although the assessment systems and methods in the above-mentioned existing technologies have achieved optimization of performance management and risk management in their respective fields, they lack effective response strategies when faced with the interference of external cooperation points on the sales performance of normal points. In particular, the existing technologies have not provided a practical and feasible solution for how to reasonably evaluate and allocate the sales data generated by the introduction of external cooperation points without affecting the stability of the normal point assessment system.

[0059] This means that when external cooperation points are introduced, the sales performance evaluation of normal points may face unfairness and fluctuations, which in turn affects the work enthusiasm and stability of channel personnel.

[0060] In view of this, this application aims to propose a reward and commission system based on cooperative machines. The system can effectively integrate and evaluate the sales data of introduced short-term cooperative points without affecting the stable operation of the existing assessment system, ensuring that the assessment level of normal points is not disturbed by external factors, and safeguarding the smooth development of the business and the rights and interests of employees.

[0061] By optimizing the data processing model and assessment mechanism, this application provides a more efficient and fair reward commission calculation method to address the shortcomings of existing technologies when it comes to the layout of external cooperation points.

[0062] Please refer to Figure 1 , which shows a flow chart of a method for assisting in analyzing reward commissions based on cooperative machines provided in an embodiment of the present application. The method may include the following steps:

[0063] S1, collect login data for user identity authentication.

[0064] Among them, verification is carried out by combining dynamic biometric authentication with distributed firewalls.

[0065] Specifically, the biometric data input by the user is received, and the biometric data includes fingerprint and voiceprint data; the received biometric data is compared with the pre-stored user biometric template; if the comparison results are consistent, the verification is passed and the user is allowed to log in to the system; if the comparison results are inconsistent, the verification fails and the user is denied login.

[0066] S2, after the user identity verification is passed, uses the geo-fencing engine and device fingerprint technology to perform intelligent sales point identification and establish a point space topology model.

[0067] Among them, the geo-fence engine includes the use of the Haversine formula to dynamically calculate the point radius, and the device fingerprint technology includes combining the MAC address, SIM card IMSI, and device sensor data to generate a unique identifier.

[0068] In the embodiment of the present application, the Haversine formula is used to dynamically calculate the point radius, specifically including:

[0069]

[0070] d=R·c

[0071] in:

[0072] Δφ and Δλ: are the latitude and longitude differences between two points, respectively.

[0073] a: is an intermediate variable used to calculate the spherical distance between two points.

[0074] arctan2: is a four-quadrant inverse tangent function used to calculate the angle c.

[0075] R: The average radius of the Earth, usually taken as 6371 kilometers.

[0076] d: great circle distance between two points.

[0077] In addition, device fingerprint technology: combines MAC address, SIM card IMSI, and device sensor data to generate a unique identifier

[0078] Specific implementation process:

[0079] The system establishes a point-to-point spatial topology model through the geo-fencing engine;

[0080] Set a dynamic service radius for each sales point (automatically adjusted based on population density and transportation convenience);

[0081] Monitor the relative relationship between customer locations and points in real time to form a customer flow heat map.

[0082] S3, based on the established point space topology model, uses the improved K-means algorithm to perform customer allocation.

[0083] Specifically, the cluster centers are initialized and K cooperation points are selected as the initial cluster centers;

[0084] For each customer, a customer feature vector is constructed, and distance calculation is performed by integrating geographical distance and weighted Euclidean distance of consumption characteristics. The allocation weight is adjusted in real time according to the point saturation, and the customer flow is intelligently guided to the optimal point to achieve matching between customers and points. The customer feature vector includes at least geographical location, consumption capacity index, purchase frequency and preference category.

[0085] In the embodiment of the present application, the allocation weight is adjusted in real time according to the point saturation, specifically including:

[0086] The improved isolation forest anomaly scoring formula is used for anomaly detection, the formula is:

[0087]

[0088] Where s(x,n) is the anomaly score, E(h(x)) is the average path length of sample x, n is the number of samples, and c(n) is the correction function;

[0089] The correction function c(n) is:

[0090]

[0091] Here, H(n-1) is the harmonic number, which is used to replace the natural logarithm ln(n-1) in the traditional isolation forest formula to enhance stability in small sample scenarios.

[0092] In the embodiment of the present application, the feature vector construction includes:

[0093] Customer feature vector = [geographic location (x, y), spending power index, purchase frequency, preference category].

[0094] Distance calculation: Weighted Euclidean distance that combines geographical distance and consumption characteristics.

[0095] D(customer, location) = W1*geographical distance + W2*difference in consumption characteristics.

[0096] Dynamic weight adjustment: real-time adjustment of allocation weights based on point saturation.

[0097] Four-layer evaluation model for optimal position determination:

[0098] (1) Geographical advantage score:

[0099] Based on passenger flow, transportation convenience, and surrounding competitive environment.

[0100] Score = 0.4 × passenger flow density + 0.3 × traffic convenience + 0.3 × (1-competition density).

[0101] (2) Historical performance:

[0102] Using a time decay model, more weight is given to recent performance;

[0103] Performance score = Σ(monthly performance × e^(-0.1t)), where t is the number of months from now.

[0104] (3) Customer matching:

[0105] The degree of match between the product structure at the location and the target customer group;

[0106] Matching degree = customer demand vector·point supply vector / (|customer demand|·|point supply|).

[0107] (4) Resource utilization efficiency:

[0108] Unit input-output ratio; efficiency index = sales / (labor cost + operating cost).

[0109] The optimal positional contact implementation includes:

[0110] The system calculates a comprehensive score based on the above four-layer evaluation model. When a customer enters the system's monitoring range, the system triggers the traffic decision engine, balances customer experience with location benefits through a multi-objective optimization algorithm, pushes personalized discount information in real time, and guides customers to the optimal matching location.

[0111] S4, after customer allocation is completed, transaction information is collected in real time through the time series database, and a multi-dimensional dynamic analysis dashboard is generated using the data aggregation engine to conduct real-time tracking and multi-dimensional analysis of sales data.

[0112] Specifically, transaction information is collected in real time through a time series database, including:

[0113] Collect transaction data in real time from the transaction systems of various sales points; wherein the transaction data includes transaction time, transaction amount, product information, and customer information;

[0114] Store the collected transaction data in a time series database and organize and manage it according to time series;

[0115] The data aggregation engine is used to aggregate transaction data in the time series database to generate a multi-dimensional dynamic analysis dashboard.

[0116] S5 makes reward commission decisions based on the analysis results, automatically generates visual reports through encrypted links, and automatically distributes the results to the management terminal.

[0117] Analyze sales data in the dynamic analysis dashboard and extract key indicators, including sales volume, sales growth rate, and customer satisfaction;

[0118] Calculate the reward commission amount for each sales point based on the preset reward commission rules and the extracted key indicators;

[0119] Generate visual reports containing reward commission amounts and related analytical data;

[0120] Visual reports are automatically distributed to management terminals via encrypted links.

[0121] Specifically, multimodal biometric authentication: supports fingerprint + voiceprint + liveness detection composite verification; blockchain evidence storage: uses Hyperledger Fabric to put key operations on the chain to ensure that data cannot be tampered with.

[0122] like Figure 2 The technical architecture diagram is given. After a convenience store chain deploys this system:

[0123] Data collection: Real-time sales data is collected through 3,000 smart shelf sensors.

[0124] Anomaly detection: The system automatically identifies 12 abnormal fluctuation points.

[0125] Weight adjustment: Dynamically reduce the benchmark weight of points affected by subway construction by 15%.

[0126] Results verified: Channel staff turnover rate dropped by 42% and quarterly sales increased by 28%.

[0127] The performance test data is shown in Table 1:

[0128] Table 1 Performance test data

[0129]

[0130] Compared with traditional systems:

[0131] Data processing throughput: increased by 8 times (1200TPS→10000TPS).

[0132] Calculation accuracy: relative error reduced from ±5% to ±0.5%.

[0133] Resource utilization: Peak CPU usage dropped from 92% to 65%.

[0134] Compared with competing solutions:

[0135] Number of short-term positions supported: Industry average 500 → this system 5000.

[0136] Dimensional analysis capabilities: conventional 3-5 dimensions → support 15+ dimensional cross-analysis.

[0137] Security protection level: Level 2 security protection → meets Level 3 security protection requirements.

[0138] Intelligent safety protection system:

[0139] It adopts dynamic biometric authentication (false recognition rate <0.0001%) and distributed firewall as dual insurance, builds the third-level security standard of information security protection, resists 99.9% of network attacks, and ensures zero leakage of sensitive transaction data.

[0140] Full process automation:

[0141] The data aggregation engine enables real-time aggregation of over 10 million data points per day, automatically generates 15-dimensional analysis reports, reduces manual operations by 85%, and reduces the resource allocation error rate from 1.7% to 0.03%.

[0142] Flexible resource scheduling capabilities:

[0143] The dynamic scaling mechanism based on edge computing nodes and Kubernetes clusters reduces server load by 63%, keeps response time stable within 500ms, and supports concurrent operations at over 5,000 locations.

[0144] In-depth decision support system:

[0145] The integration of the LSTM prediction model (MAPE < 8%) and the improved isolation forest algorithm (AUC 0.93) provides channel optimization suggestions, increasing exception response speed by 400% and reducing dispute rates by 79%.

[0146] In summary, the intelligent reward and commission system constructed by this invention comprises six core modules: a login module using dynamic biometric authentication to ensure secure access; a point management module integrated with a map engine to implement spatial topology modeling; an allocation module based on an improved clustering algorithm to intelligently direct customers; a sales module relying on a time-series database to track transactions in real time; a decision module supporting 15-dimensional analysis to generate dynamic dashboards; and a push module using an encrypted link to automatically distribute reports. Through automated data flow, the system reduces manual operations by 85%, lowers resource allocation errors to 0.03%, and, combined with edge computing, reduces server load by 63%, enabling precise operation of a multi-channel sales network.

[0147] Please refer to Figure 3 , which shows a block diagram of a reward commission auxiliary analysis system based on cooperative machines provided in an embodiment of the present application. The system may include:

[0148] The login verification module is used to collect login data for user identity verification; wherein, verification is performed by combining dynamic biometric authentication with a distributed firewall;

[0149] The point management module is used to intelligently identify sales points and establish a spatial topology model of the points after user identity verification using a geofencing engine and device fingerprinting technology. The geofencing engine uses the Haversine formula to dynamically calculate the point radius, and the device fingerprinting technology combines the MAC address, SIM card IMSI, and device sensor data to generate a unique identifier.

[0150] Intelligent allocation module, which is used to allocate customers based on the established point space topology model and adopts the improved K-means algorithm;

[0151] The sales tracking module is used to collect transaction information in real time through the time series database after customer allocation is completed, and use the data aggregation engine to generate a multi-dimensional dynamic analysis dashboard to conduct real-time tracking and multi-dimensional analysis of sales data;

[0152] The analysis and decision-making module is used to make reward commission decisions based on the analysis results and automatically generate visual reports through encrypted links;

[0153] The data push module is used to automatically distribute the results to the management terminal.

[0154] Regarding the specific limitations of the cooperative machine-based reward commission auxiliary analysis system, please refer to the limitations of the cooperative machine-based reward commission auxiliary analysis method above, and will not be repeated here. The various modules in the above-mentioned cooperative machine-based reward commission auxiliary analysis system can be implemented in whole or in part through software, hardware, or a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above-mentioned modules.

[0155] In one embodiment, an electronic device is provided. The electronic device may be a computer, and its internal structure diagram may be as follows: Figure 4 As shown. The electronic device includes a processor, a memory, and a network interface connected via a system bus. The processor of the device is used to provide computing and control capabilities. The memory of the device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to assist in analyzing data based on the rewards and commissions of cooperative machines. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements a method for assisting in analyzing rewards and commissions based on cooperative machines.

[0156] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0157] In one embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned cooperative machine-based reward commission auxiliary analysis method are implemented.

[0158] In one embodiment of the present application, a computer program product is provided, including a computer program / instruction, which implements the steps of the above-mentioned cooperative machine-based reward commission auxiliary analysis method when the computer program is executed by a processor.

[0159] The computer-readable storage medium and computer program product provided in this embodiment have similar implementation principles and technical effects to those of the above-mentioned method embodiments, and are not described in detail here.

[0160] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in M ​​forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SyMchliMk) DRAM (SLDRAM), memory bus (RaMbus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0161] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0162] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A reward commission auxiliary analysis method based on cooperative machines, characterized in that: The method comprises: Collect login data for user identity verification; verification is performed by combining dynamic biometric authentication with a distributed firewall; After user authentication is passed, the geofencing engine and device fingerprinting technology are used to intelligently identify sales locations and establish a spatial topology model of the locations. The geofencing engine uses the Haversine formula to dynamically calculate the location radius, and the device fingerprinting technology combines the MAC address, SIM card IMSI, and device sensor data to generate a unique identifier. Based on the established point space topology model, the improved K-means algorithm is used to allocate customers; After customer assignment is completed, transaction information is aggregated in real time through the time series database, and a multi-dimensional dynamic analysis dashboard is generated using the data aggregation engine to conduct real-time tracking and multi-dimensional analysis of sales data. Reward commission decisions are made based on the analysis results, visual reports are automatically generated through encrypted links, and the results are automatically distributed to the management terminal.

2. The reward commission auxiliary analysis method according to claim 1, characterized in that: Collect login data for user authentication, including: Receiving biometric data input by a user, wherein the biometric data includes fingerprint and voiceprint data; Comparing the received biometric data with a pre-stored user biometric template; If the comparison results are consistent, the verification is successful and the user is allowed to log in to the system; if the comparison results are inconsistent, the verification fails and the user is denied login.

3. The reward commission auxiliary analysis method according to claim 1, characterized in that: An improved K-means algorithm is used for customer allocation, including: Initialize the cluster center and select K cooperation points as the initial cluster center; For each customer, a customer feature vector is constructed, and distance calculation is performed by integrating geographical distance and weighted Euclidean distance of consumption characteristics. The allocation weight is adjusted in real time according to the point saturation, and the customer flow is intelligently guided to the optimal point to achieve matching between customers and points. The customer feature vector includes at least geographical location, consumption capacity index, purchase frequency and preference category.

4. The reward commission auxiliary analysis method according to claim 3, characterized in that: Adjust the allocation weight in real time based on the point saturation, including: The improved isolation forest anomaly scoring formula is used for anomaly detection, the formula is: Where s(x,n) is the anomaly score, E(h(x)) is the average path length of sample x, n is the number of samples, and c(n) is the correction function; The correction function c(n) is: Here, H(n-1) is the harmonic number, which is used to replace the natural logarithm ln(n-1) in the traditional isolation forest formula to enhance stability in small sample scenarios.

5. The reward commission auxiliary analysis method according to claim 1, characterized in that: Real-time collection of transaction information through a time series database, including: Collect transaction data in real time from the transaction systems of various sales points; wherein the transaction data includes transaction time, transaction amount, product information, and customer information; Store the collected transaction data in a time series database and organize and manage it according to time series; The data aggregation engine is used to aggregate transaction data in the time series database to generate a multi-dimensional dynamic analysis dashboard.

6. The reward commission auxiliary analysis method according to claim 5, characterized in that: Make reward commission decisions based on the analysis results, including: Analyze sales data in the dynamic analysis dashboard and extract key indicators, including sales volume, sales growth rate, and customer satisfaction; Calculate the reward commission amount for each sales point based on the preset reward commission rules and the extracted key indicators; Generate visual reports containing reward commission amounts and related analytical data; Visual reports are automatically distributed to management terminals via encrypted links.

7. A reward commission auxiliary analysis system based on cooperative machines, characterized in that: The system comprises: The login verification module is used to collect login data for user identity verification; wherein, verification is performed by combining dynamic biometric authentication with a distributed firewall; The point management module is used to intelligently identify sales points and establish a spatial topology model of the points after user identity verification using a geofencing engine and device fingerprinting technology. The geofencing engine uses the Haversine formula to dynamically calculate the point radius, and the device fingerprinting technology combines the MAC address, SIM card IMSI, and device sensor data to generate a unique identifier. Intelligent allocation module, which is used to allocate customers based on the established point space topology model and adopts the improved K-means algorithm; The sales tracking module is used to collect transaction information in real time through the time series database after customer allocation is completed, and use the data aggregation engine to generate a multi-dimensional dynamic analysis dashboard to conduct real-time tracking and multi-dimensional analysis of sales data; The analysis and decision-making module is used to make reward commission decisions based on the analysis results and automatically generate visual reports through encrypted links; The data push module is used to automatically distribute the results to the management terminal.

8. An electronic device, characterized in that: It includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, it implements the reward commission auxiliary analysis method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the reward commission auxiliary analysis method as described in any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the reward commission auxiliary analysis method described in any one of claims 1 to 6 is implemented.

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