CN117670419A - ERP system-based passenger flow analysis method, device, equipment and medium - Google Patents
ERP system-based passenger flow analysis method, device, equipment and medium Download PDFInfo
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Abstract
The embodiment of the application discloses a passenger flow analysis method, a device, equipment and a medium based on an ERP system, wherein the method comprises the following steps: acquiring video data of an area where a target store is located; processing the video data through an ERP system to obtain passenger flow characteristics of the target store; the passenger flow characteristics comprise the number of passenger flows in a target period; calculating the passenger flow stay degree of the target store according to the passenger flow characteristics; calculating the passenger flow conversion rate of the target store according to the passenger flow stay degree; and determining the passenger flow optimization strategy of the target store according to the passenger flow conversion rate. The method and the system can improve the efficiency and accuracy of store passenger flow data analysis and store passenger flow optimization decision making.
Description
Technical Field
The application relates to the technical field of data identification, in particular to a passenger flow analysis method, device, equipment and medium based on an ERP system.
Background
The ERP system refers to an enterprise resource planning (Enterprise Resource Planning) system, which is a comprehensive information management system used for coordinating and integrating different functions and flows of various departments inside and outside an enterprise. The ERP system can be used for counting and analyzing the passenger flow of the enterprise store, so that a strategy for optimizing the passenger flow of the store is formulated according to the passenger flow characteristics of the store.
However, most of the traditional ERP systems only rely on cameras near shops to count passenger flows, only analyze the number of the passenger flows in the passenger flow data, not dig out available and valuable features in the passenger flow data, and not analyze the relevance between the passenger flow data and sales data of the shops, so that the result of analyzing the passenger flow data is inaccurate, and the efficiency and accuracy of formulating an optimization strategy for the shops in the follow-up process are poor.
Therefore, in order to effectively solve the above problems, it is necessary to provide an intelligent passenger flow analysis method based on an ERP system to solve the above problems.
Disclosure of Invention
The embodiment of the application provides a passenger flow analysis method, device, equipment and medium based on an ERP system, which can improve the efficiency and accuracy of analysis of store passenger flow data and decision making of store passenger flow optimization.
A first aspect of an embodiment of the present application provides a passenger flow analysis method based on an ERP system, where the method includes:
acquiring video data of an area where a target store is located;
identifying the video data through an ERP system to obtain passenger flow characteristics of the target store; the passenger flow characteristics comprise the number of passenger flows in a target period;
Calculating the passenger flow stay degree of the target store according to the passenger flow characteristics;
calculating the passenger flow conversion rate of the target store according to the passenger flow stay degree;
and determining the passenger flow optimization strategy of the target store according to the passenger flow conversion rate.
Optionally, the calculating the passenger flow stay degree of the target store according to the passenger flow characteristics includes:
determining the actual passenger flow of the target store according to the passenger flow characteristics;
determining the continuous stay time of each guest of the target store according to the actual passenger flow;
acquiring the commodity category number and the area of the target store through the ERP system;
and calculating the passenger flow stay degree of the target store according to the actual passenger flow volume, the continuous stay time, the commodity type number and the area aiming at the target store.
Optionally, the calculating the passenger flow stay degree of the target store according to the actual passenger flow volume, the continuous stay time, the commodity category number and the area includes:
calculating the average residence time of the passenger flow of the target store according to the continuous residence time of each guest;
Calculating the commodity market ratio of the commodity category number of the target store in a target area according to the commodity category number;
calculating the area occupation ratio of the target store in the target area according to the area;
and calculating the passenger flow stay degree of the target store according to the average stay time, the commodity market ratio and the area ratio.
Optionally, the calculating the passenger flow conversion rate of the target store according to the passenger flow stay degree includes:
obtaining the sales sum of the target store through the ERP system;
calculating the sales sum duty ratio of the target store in the target area according to the sales sum;
and calculating the passenger flow conversion rate of the target store according to the passenger flow stay degree and the sales sum proportion.
Optionally, the calculating the passenger flow conversion rate of the target store according to the passenger flow stay degree and the sales sum ratio comprises the following steps:
calculating a weighted average ratio of the target store to a first customer flow rate residence of the store in the target area according to the customer flow residence;
and calculating the product of the weighted average proportion and the sales sum duty ratio of the target store to obtain the passenger flow conversion rate of the target store.
Optionally, the identifying the video data through the ERP system to obtain the passenger flow characteristics of the target store includes:
performing pose recognition on the object in the video data through the ERP system to obtain pose information and label information of the object;
and carrying out fusion analysis on the pose information and the tag information to obtain the passenger flow characteristics.
Optionally, the determining a passenger flow optimization strategy of the target store according to the passenger flow conversion rate includes:
determining a section to be optimized of the passenger flow conversion rate;
and determining the passenger flow optimization strategy of the target store according to the interval to be optimized.
Accordingly, a second aspect of the embodiments of the present application provides a passenger flow analysis device based on an ERP system, where the device includes:
the video acquisition module is used for acquiring video data of the area where the target store is located;
the passenger flow characteristic module is used for processing the video data through an ERP system to obtain passenger flow characteristics of the target store; the passenger flow characteristics comprise the number of passenger flows in a target period;
the stay degree module is used for calculating the stay degree of the passenger flow of the target store according to the passenger flow characteristics;
The conversion rate module is used for calculating the passenger flow conversion rate of the target store according to the passenger flow stay degree;
and the optimizing strategy module is used for determining the passenger flow optimizing strategy of the target store according to the passenger flow conversion rate.
An apparatus provided in a third aspect of an embodiment of the present application includes: a processor, a storage medium, and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating over the bus when the device is operating, the processor executing the machine-readable instructions to perform the steps of the method as provided in the first aspect when executed.
A fourth aspect of the embodiments of the present application also provides a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method as provided in the first aspect.
From this, the method according to the embodiment of the present application can bring the following beneficial effects:
(1) According to the scheme, through passenger flow data collection and pretreatment based on image recognition, objects which do not meet passenger flow statistics requirements, such as pets and baby carriages, can be effectively filtered, and therefore the quality of passenger flow data is improved. In addition, filtering and cleaning of ERP system data also helps to ensure accuracy and integrity of sales data.
(2) According to the scheme, passenger flow data and sales data are combined, and the conversion rate of passenger flow and sales is calculated through an intelligent algorithm. The method helps enterprises to more comprehensively understand the relationship between the client behaviors and sales, and provides more powerful basis for decision making.
(3) By calculating the passenger flow retention degree value and the passenger flow conversion rate, enterprises can evaluate the performance of different stores. Helps identify which stores are excellent in customer flow attraction and sales conversion and which need improvement to optimize inventory management and promotion strategies.
(4) The scheme allows real-time monitoring of passenger flow direction, analysis of the position and behavior of clients in enterprises, and analysis and prediction by combining with AI. The method is beneficial to predicting market trend of enterprises, timely making adjustment and dealing with market change in advance.
(5) And displaying the passenger flow analysis, sales sum and passenger flow conversion rate results to enterprise management staff in a line graph and the like, and making decisions and optimizing schemes according to data of different time periods, such as passenger flow peak values and passenger flow conversion rates, wherein the decision making and optimizing schemes comprise adjustment of promotion activities, improvement of customer experience, optimization of staff scheduling and the like.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is an application scenario schematic diagram of a data classification system provided in an embodiment of the present application;
fig. 2 is a flow chart of a passenger flow analysis method based on an ERP system provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a passenger flow analysis device based on an ERP system according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an apparatus according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
The embodiment of the application provides a passenger flow analysis method, device, equipment and medium based on an ERP system, wherein the equipment can be Internet of things equipment, and the storage medium can be a computer storage medium. The passenger flow analysis device based on the ERP system can be integrated in equipment, and the equipment can be a server, a terminal and other equipment.
The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, network acceleration services (Content Delivery Network, CDN), basic cloud computing services such as big data and an artificial intelligent platform.
The terminal may be, but not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, and the like. The terminal and the server may be directly or indirectly connected through wired or wireless communication, which is not limited herein.
For example, as shown in fig. 1, the server may acquire video data of an area where the target store is located; identifying the video data through an ERP system to obtain passenger flow characteristics of the target store; the passenger flow characteristics comprise the number of passenger flows in a target period; calculating the passenger flow stay degree of the target store according to the passenger flow characteristics; calculating the passenger flow conversion rate of the target store according to the passenger flow stay degree; and determining the passenger flow optimization strategy of the target store according to the passenger flow conversion rate.
The steps performed in the server may be performed in the terminal, and the present embodiment is not limited to this.
The term "plurality" in the embodiments of the present application refers to two or more. "first" and "second" and the like in the embodiments of the present application are used for distinguishing descriptions and are not to be construed as implying relative importance.
The following will describe in detail. The following description of the embodiments is not intended to limit the preferred embodiments.
Referring to fig. 2, in this embodiment, a passenger flow analysis method based on an ERP system is provided and applied to a data processing system, where the data processing system includes a sensing layer, a network layer, a service layer and an application layer, as shown in fig. 2, a specific flow of the passenger flow analysis method based on the ERP system may be as follows:
and 101, acquiring video data of an area where a target store is located.
In some embodiments, a camera device, such as a sea's camera, may be used to mount in the area of the target store to capture events and activities occurring within the area. These cameras can cover key locations such as store entrances, exits, near shelves, etc. to obtain visual information of the customer.
It will be appreciated that the above cameras will periodically take pictures of the area, capturing customers and behaviors entering the store. These pictures will be used as sources of passenger flow data for analyzing information such as the number of customers, action trajectories, residence time, etc.
Specifically, when the acquired video data is pre-stored on a suitable device or server for subsequent analysis and processing. This may include saving the video file in a local store or cloud storage for long-term archiving and retrospective analysis.
In particular, once the video data is collected and pre-stored, the videos may be analyzed using image recognition techniques. May include identifying the customer, distinguishing the customer from other objects (e.g., pets, strollers, etc.), monitoring the residence time of the customer, identifying the sex and behavior of the customer, etc.
By acquiring video data of the region where the target store is located, enterprises can better know the behaviors and habits of clients, and provide important visual information support for passenger flow analysis, sales data statistics and real-time monitoring and prediction, so that decision making and optimizing operation strategies are supported. These video data help to improve the quality of the data and to deeply analyze customer behavior to better meet customer needs and improve business operations.
And 102, identifying the video data through an ERP system to obtain the passenger flow characteristics of the target store.
In some embodiments, the ERP system may receive video data acquired from cameras and video storage devices that includes visual information of the area where the target store is located, such as the customer's behavior, location, and activity.
Further, the ERP system may analyze and process the video data using image recognition techniques. Including identifying the customer, identifying the sex, age group, behavior (e.g., waiting, shopping, playing a cell phone, etc.) of the customer, interactions between the customers, etc.
Further, the ERP system extracts specific features from the video data regarding the passenger flow. Wherein the traffic characteristics include a number of traffic within the target time period. In some embodiments, when the video data can be identified through the ERP system, specific information about the passenger flow of the store can be obtained, and the number of customers in the target period is covered in the specific information, so that analysis of passenger flow characteristics is formed. Through the processing of the video data, the ERP system can accurately identify and record the number of customers occurring in the store within a specific time period, thereby providing a detailed insight for the business regarding the volume of customers. The passenger flow characteristic analysis is helpful for enterprises to better understand and plan business operation so as to more effectively meet customer demands.
In some embodiments, the customer flow characteristics may also include the customer's residence time at the store, the distribution of customer attribute characteristics (e.g., age, gender, etc.), the time distribution of customer behavior, and so forth.
Finally, the ERP system combines the extracted passenger flow characteristics with sales data to calculate the conversion rate between passenger flow and sales. In addition, it can be used to evaluate store performance, such as traffic retention, to learn traffic behavior and performance between different stores.
By using the ERP system to identify and analyze video data, businesses can more fully understand the traffic situation and customer behavior of their stores, which helps optimize inventory management, improve sales strategies, enhance customer experience, and make more targeted decisions. This integration approach may provide a deeper insight to the enterprise and help to better understand customer needs and market trends.
Alternatively, step 102 may comprise the steps of:
performing pose recognition on the object in the video data through the ERP system to obtain pose information and label information of the object;
and carrying out fusion analysis on the pose information and the tag information to obtain the passenger flow characteristics.
In some embodiments, the ERP system may receive video data that includes visual information of objects within the area of the target store, such as customers, pets, strollers, etc.
Further, ERP systems use image processing and computer vision techniques to identify the pose of objects in video data. This includes detecting and tracking information of the position, orientation, motion, etc. of the object to determine pose information.
Further, the ERP system may also extract tag information of the object from the video data. This may include identifying a particular identification of the object, such as a customer's facial recognition, a tag on a shopping cart, a bar code on an item, and so forth.
In some embodiments, the ERP system may combine pose information and tag information together for fusion analysis. This may include correlating pose information with tag information to more accurately identify and track objects and analyze actions, dwell times, and interactions. Finally, the ERP system generates passenger flow characteristics by analyzing the fused pose information and label information. These flow characteristics include the location, behavior, specific identification (e.g., facial recognition or merchandise tags) of objects (e.g., customers), and relationships and interactions between them.
In some embodiments, the initial traffic statistics may be purged based on analysis of traffic characteristics to ensure quality of the traffic data. For example, objects that do not fit the traffic statistics, such as pets, strollers (because infants cannot be listed as consumer-capable customers), may be first identified based on image recognition; then, gesture recognition is performed on the object in the video data, and passenger flows which do not meet the preset requirements, such as too short stay time or actual products standing in store a and store b, are filtered. In the identification of the re-tag information, for example, invalid passenger flow data can be removed by identifying a man customer waiting for or playing a mobile phone at a female commodity store.
Through this process, businesses can more fully understand the traffic conditions and customer behavior within the store, including customer location, residence time, shopping behavior, etc., to better understand customer needs, improve sales strategies, optimize product layout, and make more targeted decisions and optimizations. Such passenger flow profile analysis helps to improve store operating efficiency and customer experience.
And step 103, calculating the passenger flow stay degree of the target store according to the passenger flow characteristics.
In some embodiments, the formula for calculating the target store's occupancy level may be expressed as:
wherein,a passenger flow volume stay value representing an a-th store of the target market at present; />Representing a continuous stay time up to an i-th guest on the a-th store; i=1, 2, …, +.>；/>The passenger flow of the current a-th store identified by the image; a=1, 2, …, m; m is the number of stores in the target market.
The letter part, representing the average stay time of the customers at the a-th store, takes into account the combination of the different stores, wherein +.>Representing the proportion of the commodity category number of store a in the market, < >>Indicating the proportion of the area of store a in the market. Thus, the passenger flow retention degree is comprehensively evaluated, and the diversity and the size of different stores are considered.
The number of commodity types in the a-th store is shown. In particular, it reflects the number of different types of merchandise offered at the store. For example, if food, clothing and household items are sold in a store, then +.>Namely 3, representing three different commodity categories. />Indicating the area of store a.
Alternatively, step 103 may comprise the steps of:
Determining the actual passenger flow of the target store according to the passenger flow characteristics;
determining the continuous stay time of each guest of the target store according to the actual passenger flow;
acquiring the commodity category number and the area of the target store through the ERP system;
and calculating the passenger flow stay degree of the target store according to the actual passenger flow volume, the continuous stay time, the commodity type number and the area aiming at the target store. In some embodiments, the length of stay for each customer in the target store may be determined based on actual customer flow data, typically involving dividing the total stay time by the actual customer flow to calculate an average length of stay.
In some embodiments, the length of stay for each customer in the target store may be determined based on actual passenger flow data. The average residence time is typically calculated by dividing the total residence time by the actual passenger flow volume.
In some embodiments, the ERP system may be used to extract relevant information from the business's inventory data, including the number of merchandise categories for the target store and the area of the store. Such information may aid in further passenger flow analysis and assessment of store performance.
In some embodiments, the target store's volume of stay may be calculated from the data described above, including a combination of factors such as actual volume of stay, average length of stay, number of categories of merchandise, and store area. This calculation can help businesses evaluate store attractions and customer flow experiences to better understand the differences between different stores and take corresponding action to improve.
Optionally, the step of calculating the passenger flow stay degree of the target store according to the actual passenger flow volume, the continuous stay time length, the commodity category number and the area includes:
calculating the average residence time of the passenger flow of the target store according to the continuous residence time of each guest;
calculating the commodity market ratio of the commodity category number of the target store in a target area according to the commodity category number;
calculating the area occupation ratio of the target store in the target area according to the area;
and calculating the passenger flow stay degree of the target store according to the average stay time, the commodity market ratio and the area ratio.
In some embodiments, the average length of stay for the target store's passenger flow may be calculated by averaging the continuous length of stay for each customer. This index reflects the average residence time of the customer in the store and can be used to evaluate the store's appeal and customer experience.
The target area is an area within a certain range from the target store. In some embodiments, the duty cycle may be calculated by comparing the number of merchandise categories provided by the target store with the total number of merchandise categories in the merchandise market for the target area. This ratio reflects the relative competitiveness of the kind of merchandise offered by the store in the nearby market.
In some embodiments, the duty cycle may be calculated by comparing the actual area of the target store with the total area of the target area. This ratio reflects the size of the floor space of the store in the target area relative to the entire area.
In some embodiments, the above calculation results may be taken into consideration in combination, and the target store passenger flow stay level may be calculated using a given formula. This level value can be used to compare performance between different stores, evaluate store appeal and efficiency, and support decision-making and optimization.
Through the calculation steps, enterprises can more comprehensively know the passenger flow condition and the competition status of the target store, help to better manage the store, formulate marketing strategies, promote customer experience and make more intelligent decisions so as to improve operation efficiency.
And 104, calculating the passenger flow conversion rate of the target store according to the passenger flow stay degree.
In some embodiments, the passenger flow conversion of the target store is calculatedThe formula of (c) can be expressed as:
wherein,for the sales amount of store a, the first term considers the comprehensive influence of the passenger flow amount retention degree value, the commodity type number and the stall area, so that the passenger flow amount retention ratio relative to other stalls is obtained, and the second term of the relative proportion of the sales amount in the whole market is obtained, so that the sales amount factor is included in the calculation of the passenger flow conversion rate. For the meaning of the other parameters in this formula, please refer to the formula for calculating the value of the stay of the passenger flow volume.
Optionally, step 104 may include the steps of:
obtaining the sales sum of the target store through the ERP system;
calculating the sales sum duty ratio of the target store in the target area according to the sales sum;
and calculating the passenger flow conversion rate of the target store according to the passenger flow stay degree and the sales sum proportion.
In some embodiments, the target store's sales volume may be extracted from the sales data using the ERP system of the enterprise. The ERP system records the business listing data of the business, including sales amount, and thus can be used to obtain the sales volume for the target store.
In some embodiments, the target store's total sales may be compared to the target area's total sales to calculate a ratio that reflects the relative share of the target store in the target area's total sales.
In some embodiments, the passenger flow retention level value (previously calculated passenger flow retention level) may be integrated with the sales aggregate ratio, and the passenger flow conversion for the target store may be calculated using a given formula. Passenger flow conversion rate is a key index which reflects the relationship between customers attracted to a store and actual sales and helps enterprises to know the sales efficiency and customer conversion capability of the store.
Through the calculation steps, enterprises can better understand sales conditions of target stores, evaluate market share of the stores in a target area and effect of passenger flow conversion. Such data helps the enterprise optimize sales strategies, improve marketing campaigns, and support more intelligent business decisions.
Optionally, the step of calculating the passenger flow conversion rate of the target store according to the passenger flow stay degree and the sales sum ratio includes:
calculating a weighted average ratio of the target store to a first customer flow rate residence of the store in the target area according to the customer flow residence;
And calculating the product of the weighted average proportion and the sales sum duty ratio of the target store to obtain the passenger flow conversion rate of the target store.
In some embodiments, the ratio of the amount of traffic to stay between the target store and other stores in the target area may be calculated based on the previously calculated amount of traffic to stay values. This ratio represents the target store's performance in terms of the amount of traffic stay relative to other stores.
In some embodiments, the weighted average ratio calculated above may be multiplied by the target store sales total duty cycle to obtain the target store passenger flow conversion. Passenger flow conversion rate is a key index which reflects the relationship between customers attracted to a store and actual sales and helps enterprises to know the sales efficiency and customer conversion capability of the store.
Through these calculation steps, the business can better understand the performance of the target store in terms of the passenger flow stay and the sales volume ratio to determine the passenger flow conversion rate. This helps the enterprise evaluate sales effects at the store, optimize operating strategies, and support decision making.
And 105, determining a passenger flow optimization strategy of the target store according to the passenger flow conversion rate.
In some embodiments, passenger flow conversion is a key performance indicator that reflects the efficiency of a store in attracting and servicing customers. According to different passenger flow conversion rates, different passenger flow optimization strategies can be adopted to improve performance and benefit of stores.
In some embodiments, if the target store has a higher customer flow conversion rate, this means that the store successfully attracts the customer and successfully converts to an actual sale. In this case, further increases in store appeal, such as increasing advertising, improving product display, and improving customer experience, may be considered to attract more potential customers.
In some embodiments, if the passenger flow conversion is low, it is necessary to examine various aspects of store operation to find potential problems. This includes consideration of improving sales strategies, training staff to improve sales skills, improving product quality, or improving customer service. In addition, the offering of promotional campaigns or discounts may be considered to encourage more purchase.
In some embodiments, if passenger flow conversion is at a medium level, comprehensive strategies may be employed to further improve store performance, including improving operational efficiency, refining sales and inventory management, increasing market share, and the like.
In combination, based on the passenger flow conversion rate, enterprises can formulate specific passenger flow optimization strategies to meet the demands of different stores and ensure that stores achieve optimal effects in attracting and converting customers. This helps to improve sales and performance and customer satisfaction, supporting long-term success of the business.
Alternatively, step 105 may include the steps of:
determining a section to be optimized of the passenger flow conversion rate;
and determining the passenger flow optimization strategy of the target store according to the interval to be optimized.
In some embodiments, after analyzing the passenger flow data and sales volume, the enterprise may set the interval to be optimized for passenger flow conversion. This interval is determined by factors such as industry standards, historical data, competitors' performance, etc. The interval to be optimized represents a target range which is reached by the expected passenger flow conversion rate of the enterprise.
In some embodiments, once the interval to be optimized is determined, the enterprise may take a corresponding traffic optimization strategy based on the behavior of traffic conversion. The following are some strategies:
in some embodiments, if the passenger flow conversion is below the lower limit of the interval to be optimized, the business may take steps to promote attractiveness of the store, such as improving advertising marketing, improving product quality, providing better customer service, etc.
In some embodiments, if the passenger flow conversion is in the middle of the interval to be optimized, but the sales aggregate is low, a sales promotion policy may be adopted, such as pushing out a promotional campaign, offering a discount, or increasing the sales channel.
In some embodiments, if both the passenger flow conversion and sales volume are within the interval to be optimized, but there is still room for improvement, the enterprise may take comprehensive strategies including improving operational efficiency, reducing costs, improving employee training, and continuously monitoring market trends and competitor performance.
By setting the interval to be optimized and the corresponding passenger flow optimization strategy, enterprises can manage the operation of stores more pertinently, so that the passenger flow conversion rate is improved, and the sales and profitability are improved. This helps the enterprise achieve better business performance and competitive advantage.
From this, the method according to the embodiment of the present application can bring the following beneficial effects:
(1) According to the scheme, through passenger flow data collection and pretreatment based on image recognition, objects which do not meet passenger flow statistics requirements, such as pets and baby carriages, can be effectively filtered, and therefore the quality of passenger flow data is improved. In addition, filtering and cleaning of ERP system data also helps to ensure accuracy and integrity of sales data.
(2) According to the scheme, passenger flow data and sales data are combined, and the conversion rate of passenger flow and sales is calculated through an intelligent algorithm. The method helps enterprises to more comprehensively understand the relationship between the client behaviors and sales, and provides more powerful basis for decision making.
(3) By calculating the passenger flow retention degree value and the passenger flow conversion rate, enterprises can evaluate the performance of different stores. Helps identify which stores are excellent in customer flow attraction and sales conversion and which need improvement to optimize inventory management and promotion strategies.
(4) The scheme allows real-time monitoring of passenger flow direction, analysis of the position and behavior of clients in enterprises, and analysis and prediction by combining with AI. The method is beneficial to predicting market trend of enterprises, timely making adjustment and dealing with market change in advance.
(5) The results of passenger flow analysis, sales sum and passenger flow conversion rate are displayed to enterprise management personnel in the form of line diagrams and the like, so that decision making and optimization schemes including promotion adjustment, customer experience improvement, personnel scheduling optimization and the like can be facilitated according to data of different time periods, such as passenger flow peak value and passenger flow conversion rate
The method described in the above embodiments will be described in further detail below.
As shown in fig. 3, a schematic structural diagram of a passenger flow analysis device based on an ERP system according to an embodiment of the present application is provided, where the device includes:
a video acquisition module 201, configured to acquire video data of an area where a target store is located;
the passenger flow feature module 202 is configured to process the video data through an ERP system to obtain passenger flow features of the target store; the passenger flow characteristics comprise the number of passenger flows in a target period;
the stay degree module 203 is configured to calculate a stay degree of the passenger flow of the target store according to the passenger flow feature;
a conversion rate module 204, configured to calculate a passenger flow rate conversion rate of the target store according to the passenger flow stay degree;
and the optimizing strategy module 205 is configured to determine a passenger flow optimizing strategy of the target store according to the passenger flow conversion rate.
In the application, the video acquisition module 201 may acquire video data of an area where a target store is located, and then the passenger flow feature module 202 processes the video data through an ERP system to obtain passenger flow features of the target store, where the passenger flow features include passenger flow number in a target period; then, the stay level module 203 calculates the stay level of the passenger flow of the target store according to the passenger flow characteristics. The conversion module 204 then calculates the passenger flow conversion for the target store based on the passenger flow retention level. Finally, the optimization strategy module 205 determines the passenger flow optimization strategy of the target store according to the passenger flow conversion rate.
From this, the method according to the embodiment of the present application can bring the following beneficial effects:
(1) According to the scheme, through passenger flow data collection and pretreatment based on image recognition, objects which do not meet passenger flow statistics requirements, such as pets and baby carriages, can be effectively filtered, and therefore the quality of passenger flow data is improved. In addition, filtering and cleaning of ERP system data also helps to ensure accuracy and integrity of sales data.
(2) According to the scheme, passenger flow data and sales data are combined, and the conversion rate of passenger flow and sales is calculated through an intelligent algorithm. The method helps enterprises to more comprehensively understand the relationship between the client behaviors and sales, and provides more powerful basis for decision making.
(3) By calculating the passenger flow retention degree value and the passenger flow conversion rate, enterprises can evaluate the performance of different stores. Helps identify which stores are excellent in customer flow attraction and sales conversion and which need improvement to optimize inventory management and promotion strategies.
(4) The scheme allows real-time monitoring of passenger flow direction, analysis of the position and behavior of clients in enterprises, and analysis and prediction by combining with AI. The method is beneficial to predicting market trend of enterprises, timely making adjustment and dealing with market change in advance.
(5) And displaying the passenger flow analysis, sales sum and passenger flow conversion rate results to enterprise management staff in a line graph and the like, and making decisions and optimizing schemes according to data of different time periods, such as passenger flow peak values and passenger flow conversion rates, wherein the decision making and optimizing schemes comprise adjustment of promotion activities, improvement of customer experience, optimization of staff scheduling and the like.
Corresponding to the above method embodiment, the embodiment of the present invention further provides a computer device, where a computer device described below and a passenger flow analysis method based on an ERP system described above may be referred to correspondingly.
The computer device includes:
a memory for storing a computer program;
the processor is used for realizing the steps of the passenger flow analysis method based on the ERP system in the method embodiment when executing the computer program:
acquiring video data of an area where a target store is located;
identifying the video data through an ERP system to obtain passenger flow characteristics of the target store; the passenger flow characteristics comprise the number of passenger flows in a target period;
calculating the passenger flow stay degree of the target store according to the passenger flow characteristics;
calculating the passenger flow conversion rate of the target store according to the passenger flow stay degree;
And determining the passenger flow optimization strategy of the target store according to the passenger flow conversion rate.
Fig. 4 is a schematic structural diagram of a device provided in an embodiment of the present application, where the device may be a computing device with a data processing function.
The apparatus may include: a processor 301, and a memory 302.
The memory 302 is used for storing a program, and the processor 301 calls the program stored in the memory 302 to execute the above-described method embodiment. The specific implementation manner and the technical effect are similar, and are not repeated here.
Therein, the memory 302 stores program code that, when executed by the processor 301, causes the processor 301 to perform various steps in the methods according to various exemplary embodiments of the present application described in the above-described "exemplary methods" section of this specification.
The processor 301 may be a general purpose processor such as a Central Processing Unit (CPU), digital signal processor (DigitalSignal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, and may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present application. The general purpose processor may be a microprocessor or any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in the processor for execution.
The memory 302 is used as a nonvolatile storage medium for storing nonvolatile software programs, nonvolatile computer-executable programs, and modules. The Memory may include at least one type of storage medium, which may include, for example, flash Memory, hard disk, multimedia card, card Memory, random access Memory (Random Access Memory, RAM), static random access Memory (Static Random Access Memory, SRAM), programmable Read-Only Memory (Programmable Read Only Memory, PROM), read-Only Memory (ROM), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, eerom), magnetic Memory, magnetic disk, optical disk, and the like. The memory is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to such. The memory 302 in the present embodiment may also be circuitry or any other device capable of implementing a memory function for storing program instructions and/or data.
Optionally, the present application also provides a program product, e.g. a storage medium, comprising a program for performing the above-mentioned method embodiments when being executed by a processor.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in hardware plus software functional units.
The integrated units implemented in the form of software functional units described above may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (english: processor) to perform part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: u disk, mobile hard disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
Claims (10)
1. A passenger flow analysis method based on an ERP system, the method comprising:
acquiring video data of an area where a target store is located;
identifying the video data through an ERP system to obtain passenger flow characteristics of the target store; the passenger flow characteristics comprise the number of passenger flows in a target period;
calculating the passenger flow stay degree of the target store according to the passenger flow characteristics;
calculating the passenger flow conversion rate of the target store according to the passenger flow stay degree;
And determining the passenger flow optimization strategy of the target store according to the passenger flow conversion rate.
2. The ERP system-based passenger flow analysis method of claim 1, wherein the calculating the passenger flow stay degree of the target store according to the passenger flow characteristics comprises:
determining the actual passenger flow of the target store according to the passenger flow characteristics;
determining the continuous stay time of each guest of the target store according to the actual passenger flow;
acquiring the commodity category number and the area of the target store through the ERP system;
and calculating the passenger flow stay degree of the target store according to the actual passenger flow volume, the continuous stay time, the commodity type number and the area aiming at the target store.
3. The ERP system-based passenger flow analysis method of claim 2, wherein the calculating the passenger flow stay degree of the target store according to the actual passenger flow volume, the continuous stay time period, the commodity kind number and the area comprises:
calculating the average residence time of the passenger flow of the target store according to the continuous residence time of each guest;
Calculating the commodity market ratio of the commodity category number of the target store in a target area according to the commodity category number;
calculating the area occupation ratio of the target store in the target area according to the area;
and calculating the passenger flow stay degree of the target store according to the average stay time, the commodity market ratio and the area ratio.
4. The ERP system-based passenger flow analysis method of claim 1, wherein the calculating the passenger flow conversion rate of the target store according to the passenger flow stay degree comprises:
obtaining the sales sum of the target store through the ERP system;
calculating the sales sum ratio of the target store in the target area according to the sales sum;
and calculating the passenger flow conversion rate of the target store according to the passenger flow stay degree and the sales sum proportion.
5. The ERP system-based passenger flow analysis method of claim 4, wherein the calculating the passenger flow conversion rate of the target store according to the passenger flow stay degree and the sales sum ratio comprises:
calculating a weighted average ratio of the target store to a first customer flow rate residence of the store in the target area according to the customer flow residence;
And calculating the product of the weighted average proportion and the sales sum duty ratio of the target store to obtain the passenger flow conversion rate of the target store.
6. The ERP system-based passenger flow analysis method of claim 1, wherein the identifying the video data by the ERP system to obtain the passenger flow characteristics of the target store comprises:
performing pose recognition on the object in the video data through the ERP system to obtain pose information and label information of the object;
and carrying out fusion analysis on the pose information and the tag information to obtain the passenger flow characteristics.
7. The ERP system-based passenger flow analysis method of claim 1, wherein the determining the passenger flow optimization strategy of the target store according to the passenger flow conversion rate comprises:
determining a section to be optimized of the passenger flow conversion rate;
and determining the passenger flow optimization strategy of the target store according to the interval to be optimized.
8. A passenger flow analysis device based on an ERP system, the device comprising:
the video acquisition module is used for acquiring video data of the area where the target store is located;
the passenger flow characteristic module is used for processing the video data through an ERP system to obtain passenger flow characteristics of the target store; the passenger flow characteristics comprise the number of passenger flows in a target period;
The stay degree module is used for calculating the stay degree of the passenger flow of the target store according to the passenger flow characteristics;
the conversion rate module is used for calculating the passenger flow conversion rate of the target store according to the passenger flow stay degree;
and the optimizing strategy module is used for determining the passenger flow optimizing strategy of the target store according to the passenger flow conversion rate.
9. An apparatus, comprising: a processor, a storage medium and a bus, the storage medium storing program instructions executable by the processor, the processor and the storage medium communicating over the bus when the device is running, the processor executing the program instructions to perform the steps of the method according to any one of claims 1 to 7 when executed.
10. A storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method according to any of claims 1 to 7.
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| CN202311714084.0A CN117670419A (en) | 2023-12-14 | 2023-12-14 | ERP system-based passenger flow analysis method, device, equipment and medium |
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