Assessment and analysis method and device for logistics reserve franchise, medium and equipment

Abstract

The invention provides an evaluation analysis method and device for a logistics reserve alliance agent, a medium and equipment, and the method comprises the steps: obtaining the multi-dimensional data information of the alliance agent, and the multi-dimensional data information comprises operation data, market feedback information and financial data; constructing a prediction model, and training the prediction model by using the historical basic information of the franchise, the market environment factors and the characteristics of the franchise to obtain a trained prediction model; predicting a future operation risk score of the franchise by using the trained prediction model; and adjusting the priority of the reserve franchise according to the predicted future operation risk score of the franchise. According to the invention, decision-making efficiency is improved, and service interruption caused by abnormities of the franchise is reduced; the network stability is enhanced, and the service quality and customer satisfaction are ensured; and the alliance business structure is dynamically optimized, and the healthy development of the whole network is promoted.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to an evaluation and analysis method, device, medium and equipment for logistics reserve franchisees. Background Art

[0002] In the era of rapid development of e-commerce, the express delivery industry is a key bridge connecting online and offline consumption. Its operational efficiency and service quality are directly related to consumers' shopping experience and merchants' logistics cost control. With the rapid expansion of business scale, express delivery outlets, as the basic units in the logistics system, are facing unprecedented operational challenges, including but not limited to processing capacity bottlenecks caused by the surge in order volume, rising operating costs, and uneven quality of franchisees. Therefore, how to achieve efficient operation and sustainable management of express delivery outlets has become an important issue that express delivery companies need to solve urgently.

[0003] Traditional franchisee evaluation methods mostly rely on manual review, historical performance review or simple financial indicator analysis, which have many shortcomings. On the one hand, they are difficult to fully capture the multi-dimensional information of market dynamics, changes in consumer behavior and franchisee's own capabilities; on the other hand, due to the lack of real-time and predictive capabilities, they cannot effectively respond to emergencies or identify potential risks in advance, resulting in delayed and inaccurate management decisions. Summary of the invention

[0004] The main purpose of the present invention is to solve the problem that the franchisee evaluation method in the prior art is difficult to fully capture the multi-dimensional information of market dynamics, changes in consumer behavior and franchisee's own capabilities, and lacks real-time and predictive capabilities.

[0005] A first aspect of the present invention provides a method for evaluating and analyzing a logistics reserve franchisee, comprising: Acquire multi-dimensional data information of franchisees, wherein the multi-dimensional data information includes: operation data, market feedback information and financial data; Construct a prediction model, and train the prediction model using basic historical information of franchisees, market environment factors, and franchisee characteristics to obtain a trained prediction model; Use the trained prediction model to predict the franchisee's future business risk score; Adjust the priority of reserve franchisees based on the predicted future operating risk scores of franchisees.

[0006] Optionally, in a first implementation of the first aspect of the present invention, the multi-dimensional data information of the franchisee is obtained, and the multi-dimensional data information includes: operation data, market feedback information and financial data, including: Obtaining franchisee data information based on data interfaces and crawler technology; Wherein, the data information of the franchisee obtained based on the data interface includes: obtaining the franchisee's operation data, customer evaluation and complaint information, and internal information of financial statements based on the data interface designed based on the RESTful API; The method of obtaining franchisee data information based on crawler technology includes: crawling external public information including market feedback from social media, industry forums, and news reports based on the Scrapy framework combined with XPath or CSS selectors; Wherein, the acquiring of the franchisee's data information based on the crawler technology includes: acquiring the franchisee's data information based on the distributed crawler technology.

[0007] Optionally, in a second implementation of the first aspect of the present invention, the constructing the prediction model includes: Build a prediction model based on random forest; The random forest is constructed by integrating multiple decision trees; The prediction model includes: in, represents the number of trees in the forest; Indicates Tree pair samples The final predicted risk score is the average of all tree prediction results.

[0008] Optionally, in a third implementation of the first aspect of the present invention, constructing a prediction model includes: Build a prediction model based on a neural network model; The neural network model is constructed using a multi-layer perceptron; The prediction model includes: in, represents the predicted risk score; represents the activation function; represents weight; Represents input features; Represents the bias term.

[0009] Optionally, in a fourth implementation of the first aspect of the present invention, the method of training the prediction model using the franchisee's historical basic information, market environment factors, and franchisee characteristics to obtain the trained prediction model includes: Using the acquired historical data information including franchisees, market environment factors and franchisee characteristics, the prediction model is trained through cross-validation and network search optimization to obtain a trained prediction model; The market environment factors include: economic index and competitor behavior; The franchisee characteristics include: geographical location and scale of operations.

[0010] Optionally, in a fifth implementation of the first aspect of the present invention, adjusting the priority of the reserve franchisee according to the predicted future business risk score of the franchisee includes: Based on the real-time changing business needs and the latest status of franchisees, the business matching degree is obtained through genetic algorithm or simulated annealing algorithm; Based on the franchisee's future operating risk score and business matching, the priority list is updated in real time to ensure that highly adaptable candidate franchisees are at the top.

[0011] Optionally, in a sixth implementation of the first aspect of the present invention, the method further includes: Build a real-time monitoring system to track the health scores of all franchisees. When the health score of a franchisee is less than the threshold, an early warning mechanism is triggered. The franchisee's health score includes: obtaining an operational efficiency index score, a service quality index score, and a customer satisfaction index score, and calculating the health score by weighted average; The early warning mechanism includes: replacing franchisees in the franchisee priority list whose health score is less than a threshold.

[0012] A second aspect of the present invention provides an evaluation and analysis device for logistics reserve franchisees, characterized in that it includes: A data information acquisition module is used to acquire multi-dimensional data information of franchisees, wherein the multi-dimensional data information includes: operation data, market feedback information and financial data; A prediction model building module is used to build a prediction model, and train the prediction model using basic historical information of franchisees, market environment factors, and franchisee characteristics to obtain a trained prediction model; The prediction module is used to predict the franchisee's future operating risk score using the trained prediction model; The real-time adjustment module is used to adjust the priority of reserve franchisees based on the predicted future operating risk scores of the franchisees.

[0013] Optionally, in a first implementation of the second aspect of the present invention, the data information acquisition module includes: Obtaining franchisee data information based on data interfaces and crawler technology; Wherein, the data information of the franchisee obtained based on the data interface includes: obtaining the franchisee's operation data, customer evaluation and complaint information, and internal information of financial statements based on the data interface designed based on the RESTful API; The method of obtaining franchisee data information based on crawler technology includes: crawling external public information including market feedback from social media, industry forums, and news reports based on the Scrapy framework combined with XPath or CSS selectors; Wherein, the acquiring of the franchisee's data information based on the crawler technology includes: acquiring the franchisee's data information based on the distributed crawler technology.

[0014] Optionally, in a second implementation of the second aspect of the present invention, the constructing the prediction model includes: Build a prediction model based on random forest; The random forest is constructed by integrating multiple decision trees; The prediction model includes: in, represents the number of trees in the forest; Indicates Tree pair samples The final predicted risk score is the average of all tree prediction results.

[0015] Optionally, in a third implementation of the second aspect of the present invention, the constructing the prediction model includes: Build a prediction model based on a neural network model; The neural network model is constructed using a multi-layer perceptron; The prediction model includes: in, represents the predicted risk score; represents the activation function; represents weight; Represents input features; Represents the bias term.

[0016] Optionally, in a fourth implementation of the first aspect of the present invention, the prediction model building module includes: Using the acquired historical data information including franchisees, market environment factors and franchisee characteristics, the prediction model is trained through cross-validation and network search optimization to obtain a trained prediction model; The market environment factors include: economic index and competitor behavior; The franchisee characteristics include: geographical location and scale of operations.

[0017] Optionally, in a fifth implementation of the first aspect of the present invention, the real-time adjustment module includes: The matching submodule is used to obtain the business matching degree through genetic algorithm or simulated annealing algorithm based on the real-time changing business needs and the latest status of franchisees; The update priority submodule is used to update the priority list in real time according to the franchisee's future operating risk score and business matching degree, ensuring that highly adaptable candidate franchisees are placed at the top.

[0018] Optionally, in a sixth implementation of the first aspect of the present invention, the system further comprises: an early warning module, configured to construct a real-time monitoring system, and to track the health scores of all franchisees using the real-time monitoring system, and to trigger an early warning mechanism when the health score of a franchisee is less than a threshold; The franchisee's health score includes: obtaining an operational efficiency index score, a service quality index score, and a customer satisfaction index score, and calculating the health score by weighted average; The early warning mechanism includes: replacing franchisees in the franchisee priority list whose health score is less than a threshold.

[0019] A third aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for evaluating and analyzing logistics reserve franchisees.

[0020] A fourth aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the above-mentioned method for evaluating and analyzing logistics reserve franchisees.

[0021] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention improves decision-making efficiency and reduces service interruptions caused by franchisee anomalies; 2. The present invention enhances network stability and ensures service quality and customer satisfaction; 3. The present invention dynamically optimizes the franchisee structure and promotes the healthy development of the overall network. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings: Figure 1 A first flow chart of the method for evaluating and analyzing logistics reserve franchisees provided in an embodiment of the present invention.

[0023] Figure 2 A second flow chart of the method for evaluating and analyzing logistics reserve franchisees provided in an embodiment of the present invention.

[0024] Figure 3 A third flow chart of the method for evaluating and analyzing logistics reserve franchisees provided in an embodiment of the present invention.

[0025] Figure 4 A schematic diagram of the structure of a logistics reserve franchisee evaluation and analysis device provided in an embodiment of the present invention.

[0026] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The embodiment of the present invention provides an evaluation and analysis method, device, medium and equipment for logistics reserve franchisees, which obtains multi-dimensional data information of franchisees, and the multi-dimensional data information includes: operation data, market feedback information and financial data; constructs a prediction model, and uses the historical basic information of franchisees, market environment factors and franchisee characteristics to train the prediction model to obtain a trained prediction model; uses the trained prediction model to predict the future operating risk score of the franchisee; adjusts the priority of the reserve franchisee according to the predicted future operating risk score of the franchisee; constructs a real-time monitoring system, and uses the real-time monitoring system to track the health scores of all franchisees. When the health score of the franchisee is less than a threshold, an early warning mechanism is triggered; wherein the early warning mechanism includes replacing the franchisee in the franchisee priority list whose health score is less than the threshold. The present invention solves the problem of difficulty in fully capturing multi-dimensional information of market dynamics, changes in consumer behavior and franchisee's own capabilities, and lacks real-time and predictive properties.

[0028] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1The first embodiment of the evaluation and analysis method of the logistics reserve franchisee in the embodiment of the present invention includes: 101. Obtain multi-dimensional data information of the franchisee, wherein the multi-dimensional data information includes: operation data, market feedback information and financial data; In this embodiment, the data information of the franchisee is obtained based on the data interface, and the data information of the franchisee is obtained based on the crawler technology; The data information of the franchisee obtained based on the data interface includes: the data interface obtained based on the RESTful API design includes the franchisee's operating data, customer evaluation and complaint information, and internal information of financial statements; wherein the franchisee's operating data includes the parcel processing volume and delay rate; The method of obtaining franchisee data information based on crawler technology includes: crawling external public information including market feedback from social media, industry forums, and news reports based on the Scrapy framework combined with XPath or CSS selectors; Wherein, the acquiring of franchisee data information based on crawler technology includes: acquiring franchisee data information based on distributed crawler technology to improve data collection efficiency.

[0030] This embodiment adopts the RESTful API design principle to ensure the stability and usability of the data interface; the internal database interface must follow the best practices for database access, such as using parameterized queries to prevent security threats such as SQL injection.

[0031] In this embodiment, the crawler will run regularly and follow the Robots protocol to ensure that data is obtained legally and compliantly.

[0032] 102. Construct a prediction model, and train the prediction model using basic historical information of franchisees, market environment factors, and franchisee characteristics to obtain a trained prediction model; In this embodiment, random forest is selected as the main prediction model. Random forest improves prediction accuracy and robustness by integrating multiple decision trees. Its basic idea is "collective wisdom" - multiple weak learners are integrated to generate a strong learner.

[0033] The prediction model includes: in, represents the number of trees in the forest; Indicates Tree pair samples The final predicted risk score is the average of all tree prediction results.

[0034] Using the acquired historical data information including franchisees, market environment factors and franchisee characteristics, the prediction model is trained through cross-validation and network search optimization to obtain a trained prediction model; The market environment factors include: economic index and competitor behavior; The franchisee characteristics include: geographical location and scale of operations.

[0035] 103. Use the trained prediction model to predict the franchisee’s future business risk score; 104. Adjust the priority of reserve franchisees based on the predicted future operating risk scores of franchisees.

[0036] In this embodiment, based on the real-time changing business needs and the latest status of the franchisee, the business matching degree is obtained through a genetic algorithm or a simulated annealing algorithm; Based on the franchisee's future operating risk score and business matching, the priority list is updated in real time to ensure that highly adaptable candidate franchisees are at the top.

[0037] This embodiment can improve decision-making efficiency and reduce service interruptions caused by franchisee anomalies; at the same time, it can enhance network stability, ensure service quality and customer satisfaction; dynamically optimize the franchisee structure and promote the healthy development of the overall network.

[0038] See also Figure 2 The second embodiment of the method for evaluating and analyzing logistics reserve franchisees in the embodiment of the present invention includes: 201. Acquire multi-dimensional data information of the franchisee, wherein the multi-dimensional data information includes: operation data, market feedback information and financial data; In this embodiment, a data interface and crawler system are designed to collect multi-dimensional information such as franchisee operation data, market feedback, and financial status from internal operation databases and external public information sources; The design phase first clarifies the scope and structure of the required data, including but not limited to internal information such as franchisee daily operation data, customer reviews and complaints, financial statements, and external public information such as market feedback in social media, industry forums, and news reports. To achieve efficient data collection, we will adopt the RESTful API design principle to ensure the stability and ease of use of the data interface. The internal database interface must follow the best practices for database access, such as using parameterized queries to prevent security threats such as SQL injection; the daily operation data includes: package processing volume, delay rate; For the crawling of external information, we will build a highly customized crawler system, using the Scrapy framework or similar technologies, combined with XPath, CSS selectors and other technologies to accurately extract relevant information from web pages. The crawler will run regularly, following the Robots protocol to ensure that data is obtained legally and compliantly. At the same time, the distributed crawler technology is introduced to cope with large-scale data crawling needs and improve data collection efficiency.

[0039] This embodiment can more comprehensively and accurately evaluate the overall health status and operating capacity of franchisees by acquiring multi-dimensional data information, including operational data, market feedback information, and financial data.

[0040] 202. Construct a prediction model, and train the prediction model using basic historical information of the franchisee, market environment factors, and franchisee characteristics to obtain a trained prediction model; In this embodiment, a neural network is selected as the main prediction model. For the neural network model, a multi-layer perceptron (MLP) is used, which can process complex input-output relationships through nonlinear transformations. The model architecture may include an input layer, several hidden layers, and an output layer. Each layer consists of multiple neurons, and the neurons are connected by weights, which are trained by the back propagation algorithm.

[0041] The prediction model includes: in, Score the risk of the output, is the activation function (such as Sigmoid or ReLU), is the weight, is the input feature, and b is the bias term. When training the model, historical performance data, market environment factors (such as economic index, competitor behavior), franchisee characteristics (such as geographical location, business scale), etc. are used as input features and sent to the model after feature engineering. Hyperparameters are optimized through cross-validation and grid search to ensure the generalization ability of the model.

[0042] After the prediction model is constructed and trained in this embodiment, a quantitative score can be made for the franchisee's future operating risks; this quantitative risk score can help management identify potential risks in advance and make more timely decisions.

[0043] 203. Use the trained prediction model to predict the franchisee's future business risk score; 204. Adjust the priority of reserve franchisees according to the predicted future operating risk scores of franchisees; In this embodiment, the dynamic sorting and matching algorithm is based on the future business risk score output by the prediction model, combined with the real-time changing business needs and the latest status of the franchisee, to design a real-time updated priority list. The algorithm can adopt an online learning mechanism, such as online gradient descent, so that the newly acquired information can be reflected in the sorting immediately. In specific implementation, a priority queue (such as a minimum heap) can be used to maintain a list of reserve franchisees, and the queue order can be dynamically adjusted according to the risk score and business matching degree to ensure that highly adaptable candidates are in the forefront. In addition, optimization methods such as genetic algorithms or simulated annealing algorithms are introduced to explore better matching solutions and improve the efficiency and accuracy of replacement decisions.

[0044] In this embodiment, the manager can reasonably adjust the priority of reserve franchisees based on the predicted business risk scores. This flexibility helps to optimize resource allocation and ensure the smooth operation of the business.

[0045] See also Figure 3 The third embodiment of the method for evaluating and analyzing logistics reserve franchisees in the embodiment of the present invention includes: 301. Design data interface and crawler system to collect multi-dimensional information such as franchisee operation data, market feedback, financial status, etc. from internal operation database and external public information sources; 302. Use machine learning algorithms (such as random forests and neural networks) to train prediction models. The model inputs include historical performance data, market environment factors, franchisee characteristics, etc., and the output is the future business risk score; 303. Develop dynamic sorting and matching algorithms to automatically adjust the priority of reserve franchisees based on model prediction results, ensuring that the most suitable candidates can be quickly identified and activated when needed; 304. Build a real-time monitoring system to track the health scores of all franchisees. When the health score of a franchisee is less than the threshold, the early warning mechanism is triggered; In this embodiment, a real-time monitoring system is established to track the health scores of all outlets. The health score combines multiple dimensions such as operational efficiency, service quality, and customer satisfaction, and is obtained through weighted average or other comprehensive evaluation methods. The system presets a series of thresholds that characterize the sub-health or crisis status of outlets. Once the score of a certain outlet falls below these thresholds, the system automatically triggers an early warning mechanism.

[0046] The early warning mechanism not only includes notifying relevant personnel, but also executing subsequent operations through preset automated workflows. For example, the decision-making process for franchisee replacement is initiated. Based on the results of the above dynamic sorting and matching algorithm, the process automatically screens and recommends the most suitable candidate franchisees, and notifies relevant departments for review and confirmation to ensure that the necessary change procedures are completed in the shortest time possible to maintain service continuity and stability. The degree of automation of the entire process can be flexibly adjusted according to the actual needs and risk preferences of the enterprise.

[0047] This embodiment can continuously track the health scores of franchisees by building a real-time monitoring system. This real-time feedback mechanism enables the company to detect problems in a timely manner and respond to changes quickly.

[0048] The above describes the evaluation and analysis method for logistics reserve franchisees in the embodiment of the present invention. The following describes the evaluation and analysis device for logistics reserve franchisees in the embodiment of the present invention. Figure 4 In one embodiment of the present invention, an evaluation and analysis device for logistics reserve franchisees includes: The data information acquisition module 401 is used to acquire the multi-dimensional data information of the franchisee, wherein the multi-dimensional data information includes: operation data, market feedback information and financial data; The data information acquisition module 401 includes: Design data interfaces and crawler systems to collect multi-dimensional information such as franchisee operation data, market feedback, financial status, etc. from internal operation databases and external public information sources; The design phase first clarifies the scope and structure of the required data, including but not limited to internal information such as franchisee daily operation data, customer reviews and complaints, financial statements, and external public information such as market feedback from social media, industry forums, and news reports. To achieve efficient data collection, we will adopt the RESTful API design principle to ensure the stability and ease of use of the data interface. The internal database interface must follow the best practices for database access, such as using parameterized queries to prevent security threats such as SQL injection; the daily operation data includes package processing volume and delay rate; For the crawling of external information, we will build a highly customized crawler system, using the Scrapy framework or similar technologies, combined with XPath, CSS selectors and other technologies to accurately extract relevant information from web pages. The crawler will run regularly, following the Robots protocol to ensure that data is obtained legally and compliantly. At the same time, the distributed crawler technology is introduced to cope with large-scale data crawling needs and improve data collection efficiency.

[0049] A prediction model building module 402 is used to build a prediction model, and train the prediction model using basic historical information of the franchisee, market environment factors, and franchisee characteristics to obtain a trained prediction model; The prediction model building module 402 includes: Use machine learning algorithms to train prediction models. The model input includes historical performance data, market environment factors, franchisee characteristics, etc., and the output is the future business risk score. Among them, machine learning algorithms include: random forest, neural network; Random forest and neural network are selected as the main prediction models. Random forest improves prediction accuracy and robustness by integrating multiple decision trees. Its basic idea is "collective wisdom" - multiple weak learners are integrated to produce strong learners. The model formula can be summarized as: in, represents the number of trees in the forest; Indicates Tree pair samples The final predicted risk score is the average of all tree prediction results.

[0050] For the neural network model, we use a multi-layer perceptron (MLP), which can process complex input-output relationships through nonlinear transformations. The model architecture may include an input layer, several hidden layers, and an output layer. Each layer consists of multiple neurons, and the neurons are connected by weights, which are trained by the back-propagation algorithm. The simplified feedforward neural network output formula is as follows: in, Score the risk of the output, is the activation function (such as Sigmoid or ReLU), is the weight, is the input feature, and b is the bias term. When training the model, historical performance data, market environment factors (such as economic index, competitor behavior), franchisee characteristics (such as geographical location, business scale), etc. are used as input features and sent to the model after feature engineering. Hyperparameters are optimized through cross-validation and grid search to ensure the generalization ability of the model.

[0051] Prediction module 403, used to predict the future business risk score of the franchisee using the trained prediction model; The real-time adjustment module 404 is used to adjust the priority of the reserve franchisees according to the predicted future business risk scores of the franchisees.

[0052] The real-time adjustment module 404 includes: a dynamic sorting and matching algorithm based on the future business risk score output by the prediction model, combined with the real-time changing business needs and the latest status of the franchisee, to design a real-time updated priority list. The algorithm can adopt an online learning mechanism, such as online gradient descent, so that the newly acquired information can be reflected in the sorting immediately. In specific implementation, a priority queue (such as a minimum heap) can be used to maintain a list of reserve franchisees, and the queue order can be dynamically adjusted according to the risk score and business matching degree to ensure that highly adaptable candidates are in the forefront. In addition, optimization methods such as genetic algorithms or simulated annealing algorithms are introduced to explore better matching solutions and improve the efficiency and accuracy of replacement decisions.

[0053] The early warning module 405 is used to build a real-time monitoring system, and use the real-time monitoring system to track the health scores of all franchisees. When the health score of a franchisee is less than a threshold, the early warning mechanism is triggered; The early warning module 405 includes: establishing a real-time monitoring system to track the health scores of all outlets. The health score combines multiple dimensions such as operational efficiency, service quality, and customer satisfaction, and is obtained through weighted average or other comprehensive evaluation methods. The system presets a series of thresholds that characterize the sub-health or crisis status of outlets. Once the score of a certain outlet is lower than these thresholds, the system automatically triggers the early warning mechanism.

[0054] The early warning mechanism not only includes notifying relevant personnel, but also executing subsequent operations through preset automated workflows. For example, the decision-making process for franchisee replacement is initiated. Based on the results of the above dynamic sorting and matching algorithm, the process automatically screens and recommends the most suitable candidate franchisees, and notifies relevant departments for review and confirmation to ensure that the necessary change procedures are completed in the shortest time possible to maintain service continuity and stability. The degree of automation of the entire process can be flexibly adjusted according to the actual needs and risk preferences of the enterprise.

[0055] This embodiment can improve decision-making efficiency, reduce service interruptions caused by abnormal outlets, enhance network stability, ensure service quality and customer satisfaction, dynamically optimize the franchisee structure, and promote the healthy development of the overall network.

[0056] This embodiment can improve the efficiency of evaluation and management and reduce the deviation and error of manual judgment through the data-driven decision-making process. Through scientific evaluation and timely adjustment, the enterprise can maintain a good franchise network, thereby enhancing the overall market competitiveness and improving customer satisfaction.

[0057] above Figure 4 The evaluation and analysis device for logistics reserve franchisees in the embodiment of the present invention is described in detail from the perspective of modular functional entities, and the electronic device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0058] Figure 5 7 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device 700 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 710 (for example, one or more processors) and a memory 720, and one or more storage media 730 (for example, one or more mass storage devices) storing application programs 733 or data 732. Among them, the memory 720 and the storage medium 730 can be temporary storage or permanent storage. The program stored in the storage medium 730 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the electronic device 700. Furthermore, the processor 710 may be configured to communicate with the storage medium 730 to execute a series of instruction operations in the storage medium 730 on the electronic device 700.

[0059] The electronic device 700 may also include one or more power supplies 740, one or more wired or wireless network interfaces 750, one or more input and output interfaces 750, and / or one or more operating systems 731, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 5 The structure of the electronic device shown does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine some components, or arrange the components differently.

[0060] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the evaluation and analysis method for logistics reserve franchisees.

[0061] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0062] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0063] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating and analyzing logistics reserve franchisees, characterized in that: include: Acquire multi-dimensional data information of franchisees, wherein the multi-dimensional data information includes: operation data, market feedback information and financial data; Construct a prediction model, and train the prediction model using basic historical information of franchisees, market environment factors, and franchisee characteristics to obtain a trained prediction model; Use the trained prediction model to predict the franchisee's future business risk score; Adjust the priority of reserve franchisees based on the predicted future operating risk scores of franchisees.

2. The evaluation and analysis method for logistics reserve franchisees according to claim 1 is characterized in that: The multi-dimensional data information of the franchisee is obtained, and the multi-dimensional data information includes: operation data, market feedback information and financial data, including: Obtaining franchisee data information based on data interfaces and crawler technology; Wherein, the data information of the franchisee obtained based on the data interface includes: obtaining the franchisee's operation data, customer evaluation and complaint information, and internal information of financial statements based on the data interface designed based on the RESTful API; The method of obtaining franchisee data information based on crawler technology includes: crawling external public information including market feedback from social media, industry forums, and news reports based on the Scrapy framework combined with XPath or CSS selectors; Wherein, the acquiring of the franchisee's data information based on the crawler technology includes: acquiring the franchisee's data information based on the distributed crawler technology.

3. The evaluation and analysis method for logistics reserve franchisees according to claim 1 is characterized in that: The constructing of the prediction model comprises: Build a prediction model based on random forest; The random forest is constructed by integrating multiple decision trees; The prediction model includes: in, represents the number of trees in the forest; Indicates Tree pair samples The final predicted risk score is the average of all tree prediction results.

4. The method for evaluating and analyzing logistics reserve franchisees according to claim 1, characterized in that: The constructing of the prediction model comprises: Build a prediction model based on a neural network model; The neural network model is constructed using a multi-layer perceptron; The prediction model includes: in, represents the predicted risk score; represents the activation function; represents weight; Represents input features; Represents the bias term.

5. The evaluation and analysis method for logistics reserve franchisees according to claim 1 is characterized in that: The prediction model is trained by using the basic historical information of the franchisee, market environment factors and franchisee characteristics to obtain a trained prediction model, including: Using the acquired historical data information including franchisees, market environment factors and franchisee characteristics, the prediction model is trained through cross-validation and network search optimization to obtain a trained prediction model; The market environment factors include: economic index and competitor behavior; The franchisee characteristics include: geographical location and scale of operations.

6. The method for evaluating and analyzing logistics reserve franchisees according to claim 1, characterized in that: The step of adjusting the priority of the reserve franchisees according to the predicted future operating risk scores of the franchisees includes: Based on the real-time changing business needs and the latest status of franchisees, the business matching degree is obtained through genetic algorithm or simulated annealing algorithm; Based on the franchisee's future operating risk score and business matching, the priority list is updated in real time through dynamic sorting and matching algorithms to ensure that highly adaptable candidate franchisees are placed at the top.

7. The method for evaluating and analyzing logistics reserve franchisees according to claim 1, characterized in that: The method further comprises: Build a real-time monitoring system to track the health scores of all franchisees. When the health score of a franchisee is less than the threshold, an early warning mechanism is triggered. The franchisee's health score includes: obtaining an operational efficiency index score, a service quality index score, and a customer satisfaction index score, and calculating the health score by weighted average; The early warning mechanism includes: replacing franchisees in the franchisee priority list whose health score is less than a threshold.

8. An evaluation and analysis device for logistics reserve franchisees, characterized in that: include: A data information acquisition module is used to acquire multi-dimensional data information of franchisees, wherein the multi-dimensional data information includes: operation data, market feedback information and financial data; A prediction model building module is used to build a prediction model, and train the prediction model using basic historical information of franchisees, market environment factors, and franchisee characteristics to obtain a trained prediction model; The prediction module is used to predict the franchisee's future operating risk score using the trained prediction model; The real-time adjustment module is used to adjust the priority of reserve franchisees based on the predicted future operating risk scores of the franchisees.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for evaluating and analyzing logistics reserve franchisees according to any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the computer program is executed by a processor, the steps of the method for evaluating and analyzing logistics reserve franchisees according to any one of claims 1 to 7 are implemented.

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