Car washing task management and distribution method and system applied to intelligent car washing machine

Abstract

The invention discloses a car washing task management and distribution method and system applied to an intelligent car washer, and the method comprises the steps: obtaining input information which comprises a shop address, a machine code and the number of intelligent car washers; according to the shop address and the number of the intelligent car washing machines, allocating commission extraction proportion and account period; acquiring equipment states of all the intelligent car washers according to the machine codes, and determining a first number of the intelligent car washers of which the equipment states are online states; the car washing frequency of each intelligent car washing machine and the car washing record of each time of car washing are obtained; according to the first number, the number of times of car washing of each intelligent car washing machine and the car washing record, the turnover is determined; determining a net turnover according to the commission extraction proportion and the turnover; predicting the net turnover of the target shop in each quarter in the future; predicting to obtain the equipment utilization rate of all the intelligent car washing machines in each quarter in the future; and according to the net turnover and the equipment utilization rate of each quarter in the future, allocating the car washing task of the target shop in the latest quarter in the future.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of task allocation, and in particular to a method and system for managing and allocating car wash tasks applied to an intelligent car wash machine. Background Art

[0002] The direct-operated model is a common business model in the car wash industry, where the head office directly invests, operates and manages all its direct-operated stores. Under this model, the head office uniformly formulates business strategies, service standards and management systems, and directly allocates tasks and resources to each direct-operated store to ensure brand consistency, service quality and management efficiency.

[0003] For the convenience of management, the headquarters often uses a unified task allocation standard to allocate tasks to each directly-operated store. However, due to the differences in geographical locations among the directly-operated stores, stores in different geographical locations face different market environments. For example, stores in commercial areas may have large customer traffic but limited parking spaces, while stores in suburban areas may have less customer traffic but ample space. Therefore, the above-mentioned unified task allocation standard does not apply to all directly-operated stores, making the task allocation of some directly-operated stores inconsistent with the actual situation, resulting in some directly-operated stores having too heavy or too light tasks. Stores with too heavy tasks may cause equipment to overload, increase the risk of failure and maintenance costs, while stores with insufficient tasks will cause a waste of manpower and equipment resources, which in turn leads to a decrease in overall resource utilization and a decrease in overall operational efficiency.

[0004] There is currently no better solution to the above problems. Summary of the invention

[0005] The embodiments of the present application provide a method and system for managing and allocating car wash tasks applied to an intelligent car wash machine, which are used to effectively improve the overall resource utilization rate under the direct sales model, thereby improving the overall operational efficiency.

[0006] To achieve the above objectives, the embodiments of the present application adopt the following technical solutions: In a first aspect, a method for managing and allocating car wash tasks applied to an intelligent car wash machine is provided, which is applied to an intelligent car wash machine management platform, wherein the intelligent car wash machine management platform is used to manage at least one directly-operated store, and the method comprises: In response to an input instruction of a user terminal of a target store, input information is obtained, wherein the input information includes a store address, a machine code of an intelligent car washing machine, and the number of intelligent car washing machines, and each intelligent car washing machine has a unique machine code; Allocate the commission ratio and account period of the target store according to the store address and the number of smart car wash machines; Acquire the device status of all the smart car washing machines in the target store within a preset time period according to the machine code of the smart car washing machine, and determine a first number of the smart car washing machines whose device status is online in each unit time within the preset time period; and Obtaining the number of car washes by each of the intelligent car wash machines in each unit time within a preset time period and the car wash record of each car wash; Determine the average turnover per unit time of the target store within a preset time period according to the first number of the smart car washing machines, the number of car washings of each of the smart car washing machines, and the car washing record of each car washing; Determine the net turnover of the target store in each unit time within the preset time period according to the commission ratio of the target store and the turnover of the target store in each unit time within the preset time period; According to the net turnover of the target store in each unit time during a preset time period, the net turnover of the target store in each future quarter is predicted; According to the device status of all the smart car wash machines in a preset time period and the first number of the smart car wash machines whose device status is online in each unit time in the preset time period, predict the device utilization rate of all the smart car wash machines in the target store in each future quarter; The car washing tasks of the target store in the most recent quarter in the future are allocated according to the net turnover of the target store in each quarter in the future and the equipment utilization rate of all the smart car washing machines of the target store in each quarter in the future.

[0007] In a possible implementation of the first aspect, allocating the commission ratio and the account period of the target store according to the store address and the number of the smart car wash machines includes: Determine the urban area where the store address is located, and obtain historical population data and historical vehicle ownership of the urban area; Determine whether there is a target directly-operated store located in the same urban area as the target store among all the directly-operated stores; If the target direct-sale store exists, obtaining a set of car wash machine work logs of each target direct-sale store; Predicting future car wash demand data for the urban area based on the car wash machine work log set, the historical population data, and the historical vehicle ownership; Obtain the growth trend of the number of car wash machine stores and the growth trend of the total number of car wash machines in the urban area; According to the work log of the car washing machine, the average daily processing volume of each car washing machine is counted; Calculate the future competitiveness score of the target store based on the growth trend of the number of car wash machine stores, the average daily processing volume, the growth trend of the total number of car wash machines, the future growth trend of car wash demand and the number of car wash machines; Querying a preset database to obtain an annual income range corresponding to the future competitiveness score; Determine the corresponding commission rate according to the annual income range, and determine the account period according to the commission rate and the competitiveness score; Among them, in the first case, the commission ratio is greater than the first preset value and the future competitiveness score is greater than the second preset value, and the account period is determined to be 90 days. In the second case, the commission ratio is greater than the third preset value and the future competitiveness score is greater than the fourth preset value, and the account period is determined to be 60 days. The third preset value is less than the first preset value, and the fourth preset value is greater than the second preset value. In other cases, the account period is determined to be 30 days.

[0008] In another possible implementation of the first aspect, predicting future car wash demand data of the urban area according to the car wash machine work log set, the historical population data, and the historical vehicle ownership includes: Determine the number of car washes per day and the total number of car washes per quarter based on the car wash machine work log set; Calculate the quarterly population growth rate based on the historical population data; Calculate the quarterly growth rate of vehicle stock based on the historical vehicle stock; Performing a correlation analysis on the total number of car washes per quarter, the quarterly population growth rate, and the quarterly growth rate of the number of vehicles owned, to obtain a first correlation coefficient between the total number of car washes per quarter and the quarterly population growth rate, and a second correlation coefficient between the total number of car washes per quarter and the quarterly growth rate of the number of vehicles owned; Inputting the historical population data into a pre-trained time series model to obtain future population data; and Inputting the historical vehicle population into the time series model to obtain the future vehicle population; Inputting the future population data and the future vehicle ownership into a pre-trained machine learning model to obtain future car wash demand data to be corrected; taking the sum of the absolute value of the first correlation coefficient and the absolute value of the second correlation coefficient as the total correlation; Dividing the absolute value of the first correlation coefficient by the total correlation to obtain a population weight; and Dividing the absolute value of the second correlation coefficient by the total correlation to obtain a vehicle weight; Based on the future population data, calculate the population growth rate at each time point relative to the previous time point; and Calculate the vehicle growth rate at each time point relative to the previous time point based on the future vehicle ownership; The product of the population growth rate and the population weight is used as a first growth rate, the product of the vehicle growth rate and the vehicle weight is used as a second growth rate, and the sum of the first growth rate and the second growth rate is used as a comprehensive growth rate; Starting from the second time point in the future, the future car wash demand data to be corrected at each time point is multiplied by the correction factor to obtain the corrected future car wash demand data at each time point in the future, wherein the value of the correction factor is the comprehensive growth rate corresponding to the future car wash demand data plus 1.

[0009] In another possible implementation of the first aspect, the calculating the future competitiveness score of the target store according to the growth trend of the number of car wash machine stores, the average daily processing volume, the growth trend of the total number of car wash machines, the future growth trend of car wash demand, and the number of car wash machines includes: Calculate the demand satisfaction rate index at each future time point according to the number of car wash machines, the average daily processing volume of each car wash machine and the future growth trend of car wash demand; According to the growth trend of the total amount of car wash machines, calculate the relative growth rate at each future time point; According to the growth trend of the total number of car wash machines and the growth trend of the number of car wash machine shops, the average number of car wash machines in each car wash machine shop at each future time point is calculated, and according to the number of car wash machines and the average number of car wash machines in each car wash machine shop at each future time point, a competition intensity index is calculated; The demand satisfaction rate index, the relative growth rate and the competition intensity index are normalized, and a weighted sum is taken of the normalized demand satisfaction rate index, the relative growth rate and the competition intensity index to obtain a future competitiveness score of the target store at each future time point.

[0010] In another possible implementation of the first aspect, the car wash record includes a car wash price, and determining the average turnover per unit time of the target store within a preset time period according to the first number of the intelligent car wash machines, the number of car washes of each of the intelligent car wash machines, and the car wash record of each car wash, includes: Calculate the product of the number of car washes of each smart car washing machine per unit time within a preset time period and the corresponding car washing price to obtain the turnover of each smart car washing machine per unit time; Adding the turnover of each of the smart car washing machines in each unit time in the preset time period to obtain the turnover of each of the smart car washing machines in the preset time period; Add up the sales of all the first number of smart car wash machines in the preset time period to obtain the total sales of the target store in the preset time period, and divide the total sales by the number of unit times in the preset time period to obtain the average sales of the target store per unit time in the preset time period.

[0011] In another possible implementation of the first aspect, determining the net turnover of the target store in each unit time within the preset time period according to the commission ratio of the target store and the turnover of the target store in each unit time within the preset time period includes: The value obtained by subtracting the commission rate from 1 is used as the net sales rate of the target store; The product of the target store's turnover in each unit time within the preset time period and the net turnover ratio is taken as the target store's net turnover in each unit time within the preset time period.

[0012] In another possible implementation of the first aspect, allocating car washing tasks of the target store in the most recent quarter in the future according to the net turnover of the target store in each quarter in the future and the equipment utilization rate of all the smart car wash machines of the target store in each quarter in the future includes: According to the equipment utilization rate of all the smart car wash machines of the target store in each future quarter, determine the number of available equipment of all the smart car wash machines of the target store in the most recent future quarter, where the number of available equipment refers to the number of the smart car wash machines whose equipment status is online; Calculate the maximum car washing capacity of the target store in the next quarter according to the number of available devices of all the smart car washing machines of the target store in the next quarter; Determine the net turnover of the target store in the most recent quarter in the future based on the net turnover of the target store in each of the future quarters; The car washing tasks of the target store in the next most recent quarter are allocated according to the maximum car washing capacity of the target store in the next most recent quarter and the net turnover of the target store in the next most recent quarter.

[0013] In another possible implementation of the first aspect, the car wash record further includes a car wash type and a car wash duration, and the calculating, based on the number of available devices of all the smart car wash machines of the target store in the most recent quarter in the future, the maximum car wash capacity of the target store in the most recent quarter in the future includes: Determine the maximum number of car washes per hour for each of the intelligent car wash machines according to the car wash duration of each car wash; The product of the car washing time of each car washing and the number of car washing times of each intelligent car washing machine in each unit time is taken as the effective operating hours of each intelligent car washing machine in each unit time; The product of the effective operating hours of each intelligent car washing machine in each unit time and the maximum number of car washing times per hour of each intelligent car washing machine is taken as the car washing capacity per unit time of each intelligent car washing machine; The product of the car washing capacity per unit time of each of the intelligent car washing machines, the number of available equipment and the number per unit time in the next most recent quarter is taken as the maximum car washing capacity of the target store in the next most recent quarter.

[0014] In another possible implementation of the first aspect, allocating car wash tasks of the target store in the next most recent quarter according to the maximum car wash capacity of the target store in the next most recent quarter and the net turnover of the target store in the next most recent quarter includes: Determine whether the net sales of the target store in the most recent quarter in the future is greater than a preset sales threshold; When the net turnover of the target store in the most recent quarter in the future is greater than a preset turnover threshold, determining the most recent quarter in the future as a peak period, and obtaining preset peak period parameters; The product of the maximum car washing capacity and the preset peak period parameter is used as the car washing task of the target store in the next quarter; When the net turnover of the target store in the most recent quarter in the future is less than or equal to a preset turnover threshold, determining that the most recent quarter in the future is a flat peak period, and obtaining preset flat peak period parameters; The product of the maximum car washing capacity and the preset off-peak period parameter is used as the car washing task of the target store in the next quarter, wherein the preset peak period parameter is greater than 1 and the preset off-peak period parameter is less than 1.

[0015] In a second aspect, the present application also provides a car wash task management and allocation system applied to an intelligent car wash machine, comprising: a memory configured to store instructions; and The processor is configured to call the instructions from the memory and implement the above-mentioned car washing task management and allocation method applied to the intelligent car washing machine when executing the instructions.

[0016] Through the above technical solution, the input information of the target store is first obtained, and the commission ratio and account period of the target store are allocated according to the store address and the number of smart car wash machines in the input information, so that the intelligent allocation of the commission ratio and account period of the target store can be realized, and the flexibility and fairness of financial management are improved. In addition, by obtaining the equipment status of all smart car wash machines in the target store within the preset time period, determining the first number of smart car wash machines with the equipment status of online in each unit time in the preset time period, and obtaining the number of car washes of each smart car wash machine in each unit time in the preset time period and the car wash record of each car wash, the accurate record of the use of the smart car wash equipment in the target store can be realized. Then, by determining the turnover of the target store in each unit time in the preset time period and determining the net turnover of the target store in each unit time in the preset time period, the actual operating conditions of the target store are analyzed more comprehensively, and the influence of equipment utilization rate and number of car washes on turnover is considered. Finally, the predicted net turnover and equipment utilization rate are comprehensively considered to realize personalized task allocation for each store, and the problem of unbalanced task allocation of direct-operated stores is solved. In addition, by accurately tracking equipment usage, we can optimize resource allocation and avoid problems such as idle or overused equipment. We also take into account the geographical differences and market environment of each store to rationalize task allocation. Finally, based on historical data and predictive analysis, we can achieve more accurate task allocation and improve overall operational efficiency.

[0017] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flow chart of a method for managing and allocating car wash tasks applied to an intelligent car wash machine provided in an embodiment of the present application; Figure 2 A schematic diagram of a front-end interface provided in an embodiment of the present application; Figure 3 Another front-end interface schematic diagram provided for an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0020] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back...), such directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0021] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0022] Figure 1 The following schematically shows a flow chart of a method for managing and allocating car wash tasks applied to a smart car wash machine according to an embodiment of the present application. Figure 1 As shown, an embodiment of the present application provides a car wash task management and allocation method applied to a smart car wash machine, which is applied to a smart car wash machine management platform. The smart car wash machine management platform is used to manage at least one directly-operated store. The method may include the following steps.

[0023] S110, in response to an input instruction of a user terminal of a target store, obtaining input information, wherein the input information includes a store address, a machine code of an intelligent car washing machine, and the number of intelligent car washing machines, and each intelligent car washing machine has a unique machine code; S120, allocating the commission ratio and payment period of the target store according to the store address and the number of smart car wash machines; S130, obtaining the device status of all smart car wash machines in the target store within a preset time period according to the machine code of the smart car wash machine, and determining a first number of smart car wash machines whose device status is online in each unit time within the preset time period; and S140, obtaining the number of car washes of each intelligent car washing machine in each unit time within a preset time period and the car washing record of each car wash; S150, determining an average turnover per unit time of the target store within a preset time period according to the first number of smart car washing machines, the number of car washings of each smart car washing machine, and the car washing record of each car washing; S160, determining the net turnover of the target store in each unit time within the preset time period according to the commission ratio of the target store and the turnover of the target store in each unit time within the preset time period; S170, predicting the net sales of the target store in each future quarter based on the net sales of the target store in each unit time within a preset time period; S180, predicting the equipment utilization rate of all the smart car wash machines in the target store in each future quarter according to the equipment status of all the smart car wash machines in the preset time period and the first number of the smart car wash machines whose equipment status is online in each unit time in the preset time period; S190. Allocate the car washing tasks of the target store in the most recent quarter in the future according to the net sales of the target store in each quarter in the future and the equipment utilization rate of all the smart car washing machines of the target store in each quarter in the future.

[0024] First, the smart car wash management platform will receive input instructions from the user terminal of the target store, which may be sent through a specific user interface or API. After receiving the instruction, the smart car wash management platform will obtain input containing three key information: store address, store name, smart car wash machine machine code and number of smart car wash machines.

[0025] The store address is used to locate the store. The machine code of the smart car wash machine is the unique identifier of each device. The machine code is a string of letters and numbers, such as "WM2024A001". The uniqueness of the machine code ensures that each device can be accurately identified and tracked. The number of smart car wash machines directly reflects the size and potential service capabilities of the store. In actual operation, users can enter information through a form interface.

[0026] After obtaining the basic information of the target store, the commission rate and payment period of the target store are allocated according to the store address and the number of smart car wash machines. Since stores in different geographical locations face different market environments, competition conditions and operating costs, the number of smart car wash machines reflects the scale and potential service capabilities of the store. Therefore, the commission rate and payment period of the target store can be allocated according to the store address and the number of smart car wash machines.

[0027] Specifically, the future demand for car washes in the city can be predicted based on the population data and vehicle ownership data of the city where the store address is located, as well as the car wash machine data of the directly-operated stores in the same city, and the average daily processing volume of each car wash machine can be determined based on the car wash machine data. Since the number of car wash machine stores and the total number of car wash machines in the city area change in real time, the future competitiveness of the target store can be determined by the growth trend of the number of car wash machine stores and the total number of car wash machines, combined with the average daily processing volume of each car wash machine and the future demand for car washes.

[0028] After determining the future competitiveness of the target store, the corresponding annual income range can be estimated, and the commission rate can be determined based on the annual income range. The payment period can be determined based on the commission rate and future competitiveness.

[0029] Afterwards, the device status of all smart car wash machines in the target store within a preset time period is obtained according to the machine code of the smart car wash machine, and the first number of smart car wash machines whose device status is online in each unit time within the preset time period is determined.

[0030] First, based on the machine code of the smart car wash machine, the smart car wash machine management platform (hereinafter referred to as the platform) will query the device status of each device within a preset time period. The preset time period can be the past week, month, or quarter. The device status includes the main states such as online, offline, and idle. The device status data is recorded once at a fixed time interval (such as every 5 minutes or every hour).

[0031] Figure 3 Another front-end interface schematic diagram provided in the embodiment of the present application is shown in FIG. Figure 3 As shown, the real-time device status of the smart car washing machine and the preset parameters of the smart car washing machine can be obtained through the machine code. The preset parameters can be as follows Figure 3 The standard price for each car wash, the time for each car wash, and the price per minute of car wash are shown. In addition, the preset parameters can also be obtained through the car wash records. Figure 3 In the example, since the smart car wash machine is in an idle state, the car wash category displayed at this time is the car wash category of the last car wash by the smart car wash machine. In this embodiment, each smart car wash machine is equipped with a sensor and a communication module that can transmit status information in real time. The smart car wash machine will periodically send status updates to the central server, including whether the device is online, whether a car wash operation is in progress, whether it is in an idle state, etc. It should be noted that if a smart car wash machine device does not send device status data at a certain point in time, the platform will use the last known status to fill in.

[0032] Next, the number of smart car wash machines in the online state in each unit time is determined, and the unit time may be per hour or per day. For example, if the unit time is per day, and the number of smart car wash machines in the online state in one day is 3, then the first number is 3.

[0033] After that, the number of car washes and the car wash records of each smart car wash machine in each unit time within the preset time period are obtained. Specifically, the number of car washes and car wash records are first obtained from each smart car wash machine. The car wash records include information such as car wash time, car wash type, amount of water and detergent used, car wash fee and customer satisfaction score. Figure 3 The car wash categories shown) include simple washing, fine washing, standard washing, etc.

[0034] According to the first number of smart car wash machines, the number of car washes of each smart car wash machine and the car wash records of each car wash, the turnover of the target store per unit time in the preset time period can be calculated. Specifically, suppose that in a preset time period (such as a week), there are 10 smart car wash machines, and each completes 20 car washes per day on average. If the price of ordinary car wash is 30 yuan, and the price of fine car wash is 50 yuan, and assuming that the ratio of ordinary car wash to fine car wash is 7:3, then the daily turnover can be calculated using the following formula: Turnover per unit time = number of car washing machines × average daily car wash times per machine × (ordinary car wash price × ordinary car wash ratio + fine car wash price × fine car wash ratio) = 10 × 20 × (30 × 0.7 + 50 × 0.3) = 10 × 20 × 36 = 7,200 yuan.

[0035] The daily turnover can be obtained according to the above calculation. Multiply the daily turnover by the commission rate to get the commission of the target store. The net turnover is the daily turnover of the target store minus the commission. The specific calculation formula is as follows: Net turnover = turnover × (1 commission rate).

[0036] After obtaining the net sales of the target store for each unit time in the preset time period, the net sales of each unit time in the preset time period can be input into the pre-trained prediction model to obtain the net sales of the target store in each future quarter.

[0037] It should be noted that the net turnover data is data from at least the past 4 to 8 quarters, so that the prediction model can better predict the net turnover of the target store. The prediction model in this embodiment is a time series model, so the net turnover data is a time series arranged by time. Finally, the prediction result is output in the form of an interval, for example: "The net turnover of the next quarter is expected to be between 500,000 yuan and 550,000 yuan, with a confidence level of 90%."

[0038] Afterwards, based on the equipment status of all smart car wash machines in the preset time period and the first number of smart car wash machines whose equipment status is online in each unit time in the preset time period, the equipment utilization rate of all smart car wash machines in the target store in each future quarter is predicted.

[0039] In one embodiment, the equipment utilization rate indicates the efficiency of the car washing machine. First, the equipment status of all smart car washing machines within a preset time period is obtained. The equipment status can be obtained through real-time reporting by the smart car washing machine. For example, assuming that the target store has 10 car washing machines, within a working day (12 hours of business hours), 8 of them remain online, 1 is offline for 4 hours due to a fault, and 1 undergoes 2 hours of routine maintenance. The average online rate for the day can be calculated as: Average online rate = (8×12+1×8+1×10) / (10×12)≈95%.

[0040] Next, calculate the actual utilization rate of the equipment. Assume that 10 smart car washers complete a total of 150 car washes in one day, and each car wash takes an average of 20 minutes. Then the actual utilization rate can be calculated as: Actual utilization rate = (150 × 20 minutes) / (10 machines × 12 hours × 60 minutes) ≈ 41.7% In another embodiment, the equipment utilization rate indicates the proportion of car wash machines that are online to all car wash machines.

[0041] Based on the above historical data of equipment utilization, the future equipment utilization can be predicted by a prediction model, which can be a time series model.

[0042] Finally, the car washing tasks of the target store in the most recent quarter in the future are allocated according to the net turnover of the target store in each future quarter and the equipment utilization rate of all the smart car wash machines in the target store in each future quarter. Specifically, the maximum car washing capacity of the target store in the most recent quarter can be determined based on the equipment utilization rate of all the smart car wash machines in the target store in each future quarter. Then, it can be determined whether the most recent quarter is a peak period or an off-peak period based on the net turnover, and the car washing tasks can be allocated according to the peak period and the off-peak period. Since the peak period indicates that the urban area where the target store is located has a large demand for car washing, the allocation goal of the car washing tasks can be to allocate more car washing tasks during the peak period than during the off-peak period.

[0043] This embodiment first obtains the input information of the target store, and allocates the commission ratio and account period of the target store according to the store address and the number of smart car wash machines in the input information, so as to realize the intelligent allocation of the commission ratio and account period for the target store, and improve the flexibility and fairness of financial management. In addition, by obtaining the device status of all smart car wash machines in the target store within the preset time period, determining the first number of smart car wash machines whose device status is online in each unit time in the preset time period, and obtaining the number of car washes of each smart car wash machine in each unit time in the preset time period and the car wash record of each car wash, it is possible to accurately record the use of the smart car wash equipment in the target store. Then, by determining the turnover of the target store in each unit time in the preset time period and determining the net turnover of the target store in each unit time in the preset time period, the actual operating conditions of the target store are analyzed more comprehensively, and the influence of the equipment utilization rate and the number of car washes on the turnover is considered. Finally, the predicted net turnover and equipment utilization rate are comprehensively considered, and personalized task allocation for each store is realized, solving the problem of unbalanced task allocation of direct-operated stores. In addition, by accurately tracking equipment usage, we can optimize resource allocation and avoid problems such as idle or overused equipment. We also take into account the geographical differences and market environment of each store to rationalize task allocation. Finally, based on historical data and predictive analysis, we can achieve more accurate task allocation and improve overall operational efficiency.

[0044] In one implementation of this embodiment, the commission ratio and payment period of the target store are allocated according to the store address and the number of smart car wash machines, including the following steps: S210, determining the urban area where the store address is located, and obtaining historical population data and historical vehicle ownership data of the urban area; S220, determining whether there is a target direct-sale store located in the same city area as the target store among all the direct-sale stores; S230: if there are target direct-sale stores, obtain a car wash machine work log set of each target direct-sale store; S240, predicting future car wash demand data in the urban area based on the car wash machine work log set, historical population data, and historical vehicle ownership; S250, obtaining the growth trend of the number of car wash machine stores and the growth trend of the total number of car wash machines in the urban area; S260. Count the average daily processing volume of each car washing machine according to the car washing machine work log; S270, calculating the future competitiveness score of the target store based on the growth trend of the number of car wash machine stores, the average daily processing volume, the growth trend of the total number of car wash machines, the future growth trend of car wash demand and the number of car wash machines; S280, querying a preset database to obtain an annual income range corresponding to the future competitiveness score; S290. Determine the corresponding commission rate based on the annual income range, and determine the payment period based on the commission rate and competitiveness score; Among them, in the first case, the commission ratio is greater than the first preset value and the future competitiveness score is greater than the second preset value, and the account period is determined to be 90 days. In the second case, the commission ratio is greater than the third preset value and the future competitiveness score is greater than the fourth preset value, and the account period is determined to be 60 days. The third preset value is less than the first preset value, and the fourth preset value is greater than the second preset value. In other cases, the account period is determined to be 30 days.

[0045] The city area where the target store is located can be determined through geocoding or administrative division databases. After the city area is determined, the historical population data and historical vehicle ownership of the city area in the past 5-10 years can be obtained from the demographic database and the database of the vehicle management department.

[0046] In order to more accurately evaluate the market environment of the target store, the operating data of existing direct-sale stores in the same city area can be used for evaluation. First, the direct-sale store database is queried to determine whether there are other direct-sale stores in the same city area as the target store. When implementing it, SQL statements can be used to query the database. If the query result is not empty, it means that the target direct-sale store exists.

[0047] When the target direct-sale store exists, obtain the car wash machine work log set of each target direct-sale store. The car wash machine work log set includes detailed work records of the car wash machine, such as the number of car washes per day, the time of each car wash, the car wash program used, the water and electricity consumed, etc. Through the car wash machine work log set, key indicators such as the average number of car washes per day, peak hours, and energy consumption of each car wash machine can be obtained.

[0048] Through the collected car wash machine work log set, historical population data and historical vehicle ownership, the future car wash demand data of the urban area can be predicted. Specifically, first, the car wash machine work log data is summarized by time to obtain the total number of car washes per day or month. Then, the car wash machine work log data is combined with the historical population data and historical vehicle ownership data of the same period to establish a regression model. The regression model can be a multivariate linear regression model. Then, the predicted future population data and future vehicle ownership are substituted into the equation to obtain the future car wash demand forecast.

[0049] Next, you can obtain the registration data of car wash-related companies in the city area in recent years through the industrial and commercial administration department or industry association, count the number of new car wash shops each year, and count the total number of new car wash machines each year, so as to obtain the growth trend of the number of car wash machine shops and the total number of car wash machines in the city area. If the growth rate of the total number of car wash machines is significantly higher than the growth rate of the number of shops, it indicates that the existing shops are expanding in scale and the market competition may be more intense. These trend data provide an important basis for the subsequent competitiveness score, which helps to more accurately predict the competitive position of new shops in the future market.

[0050] According to the work log of the car washing machine, the average daily processing volume of each car washing machine is counted. In the specific implementation, the number of car washings per day of each machine is first extracted from the work log of the car washing machine, and then the average value within a preset time period (such as a month or a quarter) is calculated. For example, the average daily number of car washings of a car washing machine in the past 30 days may be: (50+48+52+…+45) / 30=47 times.

[0051] Next, the future competitiveness score of the target store can be calculated based on the growth trend of the number of car wash machine shops, the average daily processing volume, the growth trend of the total number of car wash machines, the future growth trend of car wash demand and the number of car wash machines. Specifically, the competitiveness score can be calculated by the following steps: first, according to the number of car wash machines, the average daily processing volume of each car wash machine and the future growth trend of car wash demand, the demand satisfaction rate index at each future time point is calculated; then, according to the growth trend of the total number of car wash machines, the relative growth rate at each future time point is calculated; and according to the growth trend of the total number of car wash machines and the growth trend of the number of car wash machine shops, the average number of car wash machines in each car wash machine shop at each future time point is calculated, and the competition intensity index is calculated according to the number of car wash machines and the average number of car wash machines in each car wash machine shop at each future time point; finally, the demand satisfaction rate index, relative growth rate and competition intensity index are normalized, and the normalized demand satisfaction rate index, relative growth rate and competition intensity index are weighted and summed to obtain the future competitiveness score of the target store at each future time point. The higher the competitiveness score, the stronger the competitiveness.

[0052] After calculating the competitiveness score, the annual income range corresponding to the future competitiveness score is queried in the preset database. The database pre-stores the corresponding relationship between the competitiveness score and the annual income range. Assuming that the competitiveness score calculated in step S270 is 3.62, the corresponding annual income range obtained after querying the database is "810,000-1.2 million". This income range reflects the expectation of the store's future business conditions based on the competitiveness score.

[0053] Figure 2 A schematic diagram of a front-end interface provided by an embodiment of the present application is shown. Figure 2As shown, the store ID is a unique identification code representing the target store. The commission rate and payment period are determined as follows: After determining the annual income range and competitiveness score, determine the commission rate based on the annual income range, and determine the account period based on the competitiveness score. First, query the preset commission rate table based on the annual income range, for example: {"300,000-500,000": 5%, "510,000-800,000": 6%, "810,000-1.2 million": 7%, "1.21 million-2 million": 8%, ">2 million": 9%}, then for the annual income range of "810,000-1.2 million", the commission rate is determined to be 7%.

[0054] Then, based on the commission ratio and competitiveness score, the payment period is determined according to the preset conditions and is divided into the following situations: Case 1: Commission rate > first preset value (assuming 6%) and competitiveness score > second preset value (assuming 3.5), account period is 90 days; Case 2: Commission rate > third preset value (assumed to be 5%) and competitiveness score > fourth preset value (assumed to be 3.0), and the account period is 60 days; Other cases: the payment period is 30 days.

[0055] In this example, the commission rate is 7%>6%, and the competitiveness score is 3.62>3.5, which meets the first situation, so the account period is determined to be 90 days.

[0056] This implementation method realizes the intelligent and personalized allocation of commission rates and payment periods for direct-operated stores. When allocating commission rates and payment periods, multiple factors such as geographical location, market demand, and competitiveness are taken into consideration, and a flexible and fair commission and payment period system is established to adapt to the operating conditions of different direct-operated stores.

[0057] In one implementation of this embodiment, predicting future car wash demand data in an urban area based on a car wash machine work log set, historical population data, and historical vehicle ownership includes the following steps: S301, determining the number of car washes per day and the total number of car washes per quarter according to the car wash machine work log set; S302. Calculate the population growth rate every quarter based on historical population data; S303, calculating the quarterly growth rate of vehicle ownership based on the historical vehicle ownership; S304, performing a correlation analysis on the total number of car washes per quarter, the quarterly population growth rate, and the quarterly growth rate of vehicle ownership to obtain a first correlation coefficient between the total number of car washes per quarter and the quarterly population growth rate, and a second correlation coefficient between the total number of car washes per quarter and the quarterly growth rate of vehicle ownership; S305, inputting historical population data into a pre-trained time series model to obtain future population data; and S306, inputting the historical vehicle ownership into the time series model to obtain the future vehicle ownership; S307, inputting the future population data and the future vehicle ownership into the pre-trained machine learning model to obtain the future car wash demand data to be corrected; S308, taking the sum of the absolute value of the first correlation coefficient and the absolute value of the second correlation coefficient as the total correlation; S309, dividing the absolute value of the first correlation coefficient by the total correlation to obtain a population weight; and S310, dividing the absolute value of the second correlation coefficient by the total correlation to obtain a vehicle weight; S311. Calculate the population growth rate at each time point relative to the previous time point based on future population data; and S312, calculating the vehicle growth rate at each time point relative to the previous time point based on the future vehicle ownership; S313, taking the product of the population growth rate and the population weight as the first growth rate, taking the product of the vehicle growth rate and the vehicle weight as the second growth rate, and taking the sum of the first growth rate and the second growth rate as the comprehensive growth rate; S314. Starting from the second time point in the future, multiply the future car wash demand data to be corrected at each time point by the correction factor to obtain the corrected future car wash demand data at each time point in the future, wherein the value of the correction factor is the comprehensive growth rate corresponding to the future car wash demand data plus 1.

[0058] This embodiment firstly extracts the number of car washes per day by analyzing the detailed work records of the car wash machine, and summarizes the number of car washes per day by quarter. In the specific implementation, firstly, the work logs of the car wash machine are sorted by date, and the number of car washes per day is counted. Then, the daily data is grouped by quarter and summed to obtain the total number of car washes per quarter.

[0059] Next, use the population data for consecutive time periods to calculate the relative population growth for each quarter. In the specific implementation, first, collect the population data for the past few years by quarter. Then, use the following growth rate calculation formula to calculate the population growth rate for each quarter: Growth rate = (current period population - previous period population) / previous period population × 100%.

[0060] Similarly, the vehicle ownership calculation formula is used to calculate the quarterly growth rate of vehicle ownership based on the historical vehicle ownership. The formula is as follows: Growth rate = (number of vehicles in this period - number of vehicles in the previous period) / number of vehicles in the previous period × 100%.

[0061] Next, a correlation analysis is performed on the total number of car washes per quarter, the quarterly population growth rate, and the quarterly growth rate of vehicle ownership to obtain the first correlation coefficient between the total number of car washes per quarter and the quarterly population growth rate, and the second correlation coefficient between the total number of car washes per quarter and the quarterly growth rate of vehicle ownership. Specifically, the Pearson correlation coefficient can be used for calculation. First, the total number of car washes per quarter is paired with the population growth rate and their correlation coefficient is calculated; then, the total number of car washes per quarter is paired with the vehicle ownership growth rate and their correlation coefficient is calculated.

[0062] Input historical population data into a pre-trained time series model to obtain future population data. The implementation principle is to use the time series prediction technology in machine learning to predict future trends based on historical data. The specific implementation method can choose the ARIMA model or the LSTM model. First, the collected historical population data is arranged in chronological order and necessary preprocessing is performed, such as processing missing values, outliers, etc. Then, the processed data is input into the pre-trained time series model. The model will learn the time dependency and seasonal patterns in the data and predict future population data based on these patterns.

[0063] Input the historical vehicle ownership into the time series model to obtain the future vehicle ownership. Specific implementation methods can use time series prediction models such as ARIMA model or LSTM model. First, arrange the historical vehicle ownership data in chronological order, perform data cleaning and preprocessing. Then, input the processed data into the pre-trained model. The model will analyze the historical change pattern of vehicle ownership, consider possible seasonal factors and long-term trends, and predict the vehicle ownership in the future.

[0064] Input the future population data and future vehicle ownership into the pre-trained machine learning model to obtain the future car wash demand data to be corrected. In the specific implementation, the supervised learning method can be used to establish a relationship model between population, vehicle ownership and car wash demand. First, use historical data (including historical population, vehicle ownership and corresponding car wash times) to train the model. Then, input the predicted future population data and future vehicle ownership data into the trained model to obtain a preliminary future car wash demand forecast.

[0065] The sum of the absolute value of the first correlation coefficient and the absolute value of the second correlation coefficient is taken as the total correlation to quantify the influence of population factors and vehicle factors on car wash demand. The specific implementation method is to take the absolute values ​​of the two correlation coefficients calculated in step S304 and then add them. For example, if the absolute value of the first correlation coefficient (the correlation coefficient between the number of car washes and the population growth rate) is 0.7, and the absolute value of the second correlation coefficient (the correlation coefficient between the number of car washes and the vehicle ownership growth rate) is 0.9, then the total correlation is 0.7+0.9=1.6. By calculating the total correlation, it can provide a basis for the subsequent weight calculation, which helps to more accurately evaluate the comprehensive impact of different factors on car wash demand.

[0066] Divide the absolute value of the first correlation coefficient by the total correlation to get the population weight. For example, if the absolute value of the first correlation coefficient is 0.7 and the total correlation is 1.6, then the population weight is 0.7 / 1.6≈0.4375. Divide the absolute value of the second correlation coefficient by the total correlation to get the vehicle weight. For example, if the absolute value of the second correlation coefficient is 0.9 and the total correlation is 1.6, then the vehicle weight is 0.9 / 1.6≈0.5625.

[0067] After that, based on future population data, calculate the population growth rate at each time point relative to the previous time point. By comparing the population data at adjacent time points, calculate the short-term population change rate. The formula is as follows: Growth rate = (population at current time point - population at previous time point) / population at previous time point × 100%.

[0068] For example, if the predicted population in the first quarter of 2025 is 1.05 million and in the second quarter is 1.06 million, then the population growth rate in the second quarter relative to the first quarter is (106-105) / 105×100%≈0.95%.

[0069] Similarly, the vehicle growth rate at each time point relative to the previous time point can be calculated based on the future vehicle ownership.

[0070] Starting from the second time point in the future, the future car wash demand data at each time point is corrected. Specifically, the future car wash demand data at each time point can be multiplied by the corresponding correction factor to obtain the corrected future car wash demand data. The correction factor here is obtained by adding 1 to the comprehensive growth rate at the corresponding time point. Among them, the product of the population growth rate and the population weight is taken as the first growth rate, the product of the vehicle growth rate and the vehicle weight is taken as the second growth rate, and the sum of the first growth rate and the second growth rate is taken as the comprehensive growth rate.

[0071] This implementation method can more accurately predict future car wash demand by comprehensively considering historical car wash data, population changes, and changes in vehicle ownership. It first uses a time series model to predict future population and vehicle ownership, and then uses a machine learning model to preliminarily predict car wash demand. Next, by analyzing the correlation between the number of car washes and the population and vehicle growth rates, the weights of population and vehicle factors are calculated. Finally, these weights are combined with the predicted population and vehicle growth rates to revise the preliminary predicted car wash demand. Not only does it take into account historical trends, but it can also adapt to the impact of changes in population and vehicle ownership, thereby providing a more accurate and reliable prediction of car wash demand.

[0072] In one implementation of this embodiment, the future competitiveness score of the target store is calculated according to the growth trend of the number of car wash machine stores, the average daily processing volume, the growth trend of the total number of car wash machines, the future growth trend of car wash demand and the number of car wash machines, including the following steps: S410, calculating the demand satisfaction rate index at each future time point according to the number of car wash machines, the average daily processing volume of each car wash machine, and the future growth trend of car wash demand; S420, calculating the relative growth rate at each future time point according to the growth trend of the total number of car washing machines; S430, calculating the average number of car wash machines in each car wash shop at each future time point according to the growth trend of the total number of car wash machines and the growth trend of the number of car wash shops, and calculating the competition intensity index according to the number of car wash machines and the average number of car wash machines in each car wash shop at each future time point; S440, normalizing the demand satisfaction rate index, relative growth rate and competition intensity index, and performing weighted summation on the normalized demand satisfaction rate index, relative growth rate and competition intensity index to obtain the future competitiveness score of the target store at each future time point.

[0073] In this embodiment, according to the number of car wash machines, the average daily processing volume of each car wash machine and the future growth trend of car wash demand, the demand satisfaction rate index at each future time point is calculated, which can be calculated by the following formula: Demand satisfaction rate index = (number of car wash machines × average daily processing volume of each car wash machine) / predicted average daily demand at a future time point × 100%.

[0074] The higher the demand satisfaction rate index, the better the store can meet market demand; if the index is lower than 100%, it means that supply is insufficient to meet demand, and it may be necessary to increase the number of car wash machines or improve processing efficiency. The demand satisfaction rate index can help you understand the degree of match between future market demand and your own capabilities in a timely manner.

[0075] Next, we can calculate the relative growth rate at each future time point based on the growth trend of the total number of car wash machines. The calculation formula for the relative growth rate can be expressed as: Relative growth rate = (target store growth rate x overall market growth rate) / overall market growth rate × 100%; Relative growth rate is used to reflect the target store's growth rate relative to the overall market. A positive value means the store is growing faster than the market average, while a negative value means the store is growing slower than the market average.

[0076] After that, the competition intensity index is calculated, which is used to reflect the relative competitive position of the target store in the market. First, based on the growth trend of the total number of car wash machines and the growth trend of the number of car wash machine stores, the average number of car wash machines per car wash machine store at each future time point is calculated. Then, based on the number of car wash machines and the average number of car wash machines per car wash machine store at each future time point, the competition intensity index is calculated.

[0077] In specific implementation, the calculation formula of the competition intensity index can be expressed as: Competition intensity index = number of car wash machines in the target store / average number of car wash machines in each car wash store × 100%.

[0078] The competitive intensity index is used to reflect the scale advantage of the target store relative to the industry average. An index greater than 100% indicates that the store size is higher than the industry average, while a value less than 100% indicates that the store size is lower than the industry average.

[0079] Finally, the future competitiveness score of the target store at each future time point is obtained by comprehensively considering the demand satisfaction rate index, relative growth rate and competition intensity index. First, the three indicators calculated above need to be normalized, and then different weights are assigned according to the importance of each indicator, and finally the weighted sum is performed to obtain the final competitiveness score.

[0080] In specific implementation, different weights can be assigned according to the degree of influence of each indicator on future competitiveness. For example, the weight of demand satisfaction index is 0.4, the weight of relative growth rate is 0.3, and the weight of competition intensity index is 0.3. The future competitiveness score at each future time point is calculated using the following formula: Future competitiveness score = normalized demand satisfaction rate index × 0.4 + normalized relative growth rate × 0.3 + normalized competition intensity index × 0.3.

[0081] This implementation method first calculates the demand satisfaction rate index, relative growth rate and competition intensity index, and obtains the future competitiveness score through normalization and weighted summation, which can fully reflect the comprehensive competitive strength of the target store.

[0082] In one implementation of this embodiment, the car wash record includes a car wash price, and according to the first number of smart car wash machines, the number of car washes of each smart car wash machine, and the car wash record of each car wash, determining the average turnover per unit time of the target store within a preset time period includes the following steps: S510, calculating the product of the number of car washes of each smart car wash machine per unit time in a preset time period and the corresponding car wash price, to obtain the turnover of each smart car wash machine per unit time; S520, adding up the turnover of each smart car washing machine in each unit time in the preset time period to obtain the turnover of each smart car washing machine in the preset time period; S530. Add up the sales of all the first number of smart car wash machines in the preset time period to obtain the total sales of the target store in the preset time period, and divide the total sales by the number of unit times in the preset time period to obtain the average sales of the target store per unit time in the preset time period.

[0083] In this embodiment, the preset time period is one week, and the unit time is one day. For each smart car washing machine, the daily car washing records are counted, including the number of different types of car washing and the corresponding prices. The calculation formula can be expressed as: Turnover per unit time = ∑(number of car washes of each type × corresponding car wash price).

[0084] After that, the turnover of each smart car washing machine in each unit time in the preset time period is added together to obtain the turnover of each smart car washing machine in the preset time period. For example, assuming that the preset time period is one week and the unit time is one day. For each smart car washing machine, after obtaining the turnover data of each day in a week, the turnover of seven days is added together to obtain the total turnover of the machine in a week.

[0085] Finally, the total number of smart car wash machines owned by the target store (i.e., the first number) is counted. Then, the turnover of each smart car wash machine calculated in S520 within the preset time period is added together to obtain the total turnover of the entire store within the preset time period. Finally, the total turnover is divided by the number of unit times included in the preset time period to obtain the average turnover per unit time.

[0086] The calculation formula can be expressed as: the total sales of the store in the preset time period = ∑ (the sales of each smart car washing machine in the preset time period), the average sales per unit time = the total sales of the store in the preset time period / the number of unit times included in the preset time period.

[0087] This implementation method realizes a comprehensive analysis from the daily operation data of a smart car washing machine to the entire store operation status. It can not only accurately track the performance of each car washing machine equipment, but also realize the quantification of the overall store operation trend.

[0088] In one implementation of this embodiment, determining the net turnover of the target store in each unit time within the preset time period according to the commission ratio of the target store and the turnover of the target store in each unit time within the preset time period includes the following steps: S610: Subtract the commission rate from 1 as the net sales rate of the target store; S620: The product of the target store's turnover in each unit time within the preset time period and the net turnover ratio is taken as the target store's net turnover in each unit time within the preset time period.

[0089] The actual income of the target store is the remaining part after deducting the commission from the total turnover. The commission rate is usually expressed as a percentage, which indicates the proportion of the total turnover of the store taken by the platform or the superior organization. By subtracting this commission rate from 1 (representing 100%), you can get the proportion of income that the store can actually keep, that is, the net turnover ratio.

[0090] The product of the target store's turnover in each unit time during the preset time period and the net turnover ratio is taken as the target store's net turnover in each unit time during the preset time period.

[0091] This implementation method first calculates the net turnover ratio to clarify the portion of each unit of revenue that the store can actually obtain. Then, the net turnover ratio is multiplied by the total turnover of each time unit to obtain the net turnover, which can evaluate the actual operating status of the target store and more clearly understand the profitability of the target store, thereby facilitating the formulation of targeted task allocation strategies.

[0092] In one implementation of this embodiment, according to the net turnover of the target store in each future quarter and the equipment utilization rate of all intelligent car wash machines in the target store in each future quarter, the car wash task of the target store in the most recent future quarter is allocated, including the following steps: S710. Determine the number of available devices of all smart car wash machines in the target store in the most recent quarter in the future according to the device utilization rate of all smart car wash machines in the target store in each quarter in the future. The number of available devices refers to the number of smart car wash machines in the online state. S720, calculating the maximum car washing capacity of the target store in the next quarter according to the number of available devices of all intelligent car washing machines of the target store in the next quarter; S730, determining the net turnover of the target store in the most recent quarter in the future based on the net turnover of the target store in each of the future quarters; S740. Allocate the car washing tasks of the target store in the next most recent quarter according to the maximum car washing capacity of the target store in the next most recent quarter and the net turnover of the target store in the next most recent quarter.

[0093] According to the equipment utilization rate of all smart car washers in the target store in each future quarter, the number of available equipment of all smart car washers in the target store in the most recent future quarter is determined. The number of available equipment refers to the number of smart car washers whose equipment status is online. For example, assuming that the target store has 5 smart car washers, according to historical data and prediction models, the expected utilization rates in the most recent future quarter can be obtained as 85%, 92%, 78%, 88% and 90% respectively. Then, through a utilization threshold, the equipment below this threshold indicates that it may be offline. Assuming that the threshold is set to 80%, the equipment with an expected utilization rate below 80% may require more maintenance or have other problems and may not be able to remain online. In this embodiment, there are 4 devices with an expected utilization rate higher than 80%, so it can be determined that the number of available equipment in the most recent future quarter is 4.

[0094] According to the number of available devices of all smart car wash machines in the target store in the next quarter, calculate the maximum car wash capacity of the target store in the next quarter. The implementation principle of this step is to estimate the maximum car wash capacity of the entire store based on the number of available devices and the maximum car wash efficiency of each device. First, it is necessary to determine the maximum car wash efficiency of each smart car wash machine under ideal conditions, which can be determined by equipment specifications and historical maximum efficiency data. Assume that each smart car wash machine can complete 6 car washes per hour under optimal conditions. Then, considering the business hours of the store, assuming that it is open for 12 hours a day and a quarter is calculated as 90 days. Therefore, the maximum car wash capacity can be calculated using the following formula: Maximum car wash capacity = number of available devices × maximum number of car washes per hour × number of business hours per day × number of days in the quarter. Substituting the number of 4 available devices obtained in the previous step, the calculation process is as follows: 4×6×12×90=25920 times. This means that under ideal conditions, the maximum car wash capacity of the target store in the next quarter is 25920 times.

[0095] Since the net sales of the target store in each future quarter have been obtained, the net sales of the target store in the most recent future quarter can be determined.

[0096] The car washing tasks of the target store in the next most recent quarter are allocated according to the target store's maximum car washing capacity in the next most recent quarter and the target store's net turnover in the next most recent quarter.

[0097] Specifically, we can first determine whether the target store's net sales in the most recent quarter in the future is greater than the preset sales threshold; when the target store's net sales in the most recent quarter in the future is greater than the preset sales threshold, determine that the most recent quarter in the future is a peak period, and obtain the preset peak period parameters; then use the product of the maximum car wash capacity and the preset peak period parameters as the target store's car wash task in the most recent quarter in the future; when the target store's net sales in the most recent quarter in the future is less than or equal to the preset sales threshold, determine that the most recent quarter in the future is a non-peak period, and obtain the preset non-peak period parameters; finally, use the product of the maximum car wash capacity and the preset non-peak period parameters as the target store's car wash task in the most recent quarter in the future, where the preset peak period parameter is greater than 1 and the preset non-peak period parameter is less than 1.

[0098] This implementation method can obtain a fair number of car washing tasks by comprehensively considering equipment utilization, maximum car washing capacity and expected net turnover. This is different from the unified task allocation standard, effectively improving the overall resource utilization under the direct sales model, and thus improving the overall operational efficiency.

[0099] In one implementation of this embodiment, the car wash record also includes the car wash type and the car wash duration. According to the number of available devices of all smart car wash machines of the target store in the most recent quarter in the future, the maximum car wash capacity of the target store in the most recent quarter in the future is calculated, including the following steps: S810, determining the maximum number of car washes per hour for each intelligent car washing machine according to the car washing time of each car washing; S820, taking the product of the car washing time of each car washing and the number of car washing times of each intelligent car washing machine in each unit time as the effective operating hours of each intelligent car washing machine in each unit time; S830: The product of the effective operating hours of each intelligent car washing machine in each unit time and the maximum number of car washing times per hour of each intelligent car washing machine is taken as the car washing capacity per unit time of each intelligent car washing machine; S840: The product of the car washing capacity per unit time of each intelligent car washing machine, the number of available devices, and the number per unit time in the most recent quarter in the future is taken as the maximum car washing capacity of the target store in the most recent quarter in the future.

[0100] The maximum number of car washes per hour for each smart car wash machine is determined based on the length of each car wash. The length of the car wash may vary depending on the type of car wash (such as quick car wash, standard car wash, fine car wash, etc.). In order to obtain more accurate results, the average length of each car wash type can be calculated, and then the weighted average car wash length can be calculated based on the proportion of each type. For example, suppose there are three types of car washes: quick car wash (10 minutes, accounting for 30%), standard car wash (20 minutes, accounting for 50%), and fine car wash (30 minutes, accounting for 20%). Then the weighted average car wash length is: 10×0.3+20×0.5+30×0.2=19 minutes. Divide 60 minutes by the average length of time to get the maximum number of car washes per hour. It should be noted that the theoretical maximum value needs to be appropriately adjusted according to the actual situation. Considering factors such as equipment maintenance, cleaning, and employee rest, an efficiency coefficient (such as 0.9) needs to be introduced to obtain a more feasible maximum number of car washes per hour. Finally, the maximum number of car washes per hour may be the maximum number of car washes per hour obtained above×0.9.

[0101] The product of the washing time of each car wash and the number of car washes of each smart car wash machine in each unit time is taken as the effective operating hours of each smart car wash machine in each unit time, in order to quantify the actual working time of the smart car wash machine. If the unit time is one day, and it is estimated that a smart car wash machine can complete 20 car washes in one day, then the number of car washes 20 multiplied by the average washing time is the effective operating hours of each smart car wash machine in each unit time.

[0102] The product of the effective operating hours of each smart car washing machine in each unit time and the maximum number of car washes per hour of each smart car washing machine is taken as the unit time car washing capacity of each smart car washing machine, which is used to accurately calculate the actual car washing capacity of each smart car washing machine in a given time period. Assume that the effective operating hours of each smart car washing machine in each unit time (day) are 6.33 hours, and the maximum number of car washes per hour is 2.84 times. Then, the unit time car washing capacity of each smart car washing machine is: 6.33 hours × 2.84 times / hour ≈ 17.98 times. It shows that under ideal conditions, each smart car washing machine can complete about 18 car washes per day.

[0103] Finally, the product of the car washing capacity per unit time of each smart car washing machine, the number of available equipment and the number per unit time in the next quarter is taken as the maximum car washing capacity of the target store in the next quarter.

[0104] This implementation method first determines the maximum number of car washes per hour for each smart car wash machine by analyzing the duration of each car wash, taking into account the impact of the car wash duration on the actual operating time, and calculates the effective operating hours of each car wash machine per unit time, taking into account the actual time consumption during the car wash process. In addition, the effective operating hours are multiplied by the maximum number of car washes per hour to obtain the actual car wash capacity of each car wash machine per unit time, taking into account equipment efficiency and time utilization. Finally, by multiplying the actual car wash capacity with the number of available equipment and the quarterly time span, the maximum car wash capacity of the target store in the next quarter is obtained. In summary, a maximum capacity estimate based on actual operating data and equipment performance can be provided. It can also adapt to different types of car wash services (by considering different car wash durations), as well as different time scales (which can be expanded from quarters to months or years), and can be widely used in various scenarios in the car wash industry, from the daily operation of a single store to the overall planning of chain enterprises, effectively improving operational efficiency.

[0105] In one implementation of this embodiment, allocating car wash tasks of a target store in the next most recent quarter according to the maximum car wash capacity of the target store in the next most recent quarter and the net turnover of the target store in the next most recent quarter includes the following steps: S910, determining whether the net sales of the target store in the most recent quarter in the future is greater than a preset sales threshold; S920: When the net sales of the target store in the most recent quarter in the future is greater than a preset sales threshold, determine that the most recent quarter in the future is a peak period, and obtain preset peak period parameters; S930, taking the product of the maximum car washing capacity and the preset peak period parameter as the car washing task of the target store in the next recent quarter; S940: When the net sales of the target store in the most recent quarter in the future is less than or equal to the preset sales threshold, determine that the most recent quarter in the future is a flat peak period, and obtain preset flat peak period parameters; S950. The product of the maximum car wash capacity and the preset off-peak period parameter is used as the car wash task of the target store in the next quarter, wherein the preset peak period parameter is greater than 1 and the preset off-peak period parameter is less than 1.

[0106] First, it can be determined whether the next quarter is a peak period based on whether the net turnover of the target store in the next quarter is greater than the preset turnover threshold. If the net turnover of the target store in the next quarter is greater than the preset turnover threshold, the next quarter is determined to be a peak period, and the preset peak period parameters are obtained. Among them, the peak period parameter is a value greater than 1, which is used to adjust the maximum car wash capacity to reflect the special situation of the peak period. It should be noted that the peak period parameter can be adjusted regularly according to actual conditions. For example, if it is found that the peak demand cannot be met when using 1.2 as a parameter, it may be necessary to consider increasing this parameter; conversely, if it is found that the actual demand growth is not as expected, the parameter can also be appropriately reduced. In addition, it is also possible to consider setting different parameters for different types of peak periods (such as holidays, promotional activities, etc.) to more accurately reflect the changes in demand under different circumstances. The product of the maximum car wash capacity and the preset peak period parameter is used as the car wash task of the target store in the next quarter.

[0107] When the net turnover of the target store in the most recent quarter in the future is less than or equal to the preset turnover threshold, the most recent quarter in the future is determined to be an off-peak period, and the preset off-peak period parameters are obtained. First, when it is determined that the net turnover in the most recent quarter in the future does not reach the peak period standard, it is defined as an off-peak period. The off-peak period usually means a normal business level. It should be noted that the off-peak period parameters are also not fixed and should be adjusted regularly according to actual conditions. For example, if it is found that the actual operating efficiency is too low when using 0.85 as a parameter, you may need to consider increasing this parameter to improve resource utilization; conversely, if it is found that there is often a shortage of supply even in the off-peak period, you may need to lower the parameter or re-evaluate the calculation of the maximum car wash capacity. The product of the maximum car wash capacity and the preset off-peak period parameters is used as the car wash task of the target store in the most recent quarter in the future. By comparing the predicted net turnover with the preset threshold, this implementation can accurately determine whether the next quarter is a peak period or a flat period, and then use the corresponding parameters to adjust the maximum car wash capacity to obtain a car wash task target that is more in line with the actual situation. Different from the unified task allocation standard, it can effectively improve the overall resource utilization rate under the direct sales model, thereby improving the overall operational efficiency. In addition, it can also help achieve a balance between maximizing revenue and minimizing costs, thereby improving overall profitability.

[0108] The embodiment of the present application also provides a car wash task management and allocation system applied to an intelligent car wash machine, including: a memory configured to store instructions; and The processor is configured to call instructions from the memory and implement the above-mentioned car washing task management and allocation method applied to the intelligent car washing machine when executing the instructions.

[0109] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0110] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0113] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0114] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0115] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0116] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0117] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A car washing task management and allocation method applied to an intelligent car washing machine, characterized in that: Applied to an intelligent car wash machine management platform, the intelligent car wash machine management platform is used to manage at least one directly-operated store, the method includes: In response to an input instruction of a user terminal of a target store, input information is obtained, wherein the input information includes a store address, a machine code of an intelligent car washing machine, and the number of intelligent car washing machines, and each intelligent car washing machine has a unique machine code; Allocate the commission ratio and account period of the target store according to the store address and the number of smart car wash machines; Acquire the device status of all the smart car washing machines in the target store within a preset time period according to the machine code of the smart car washing machine, and determine a first number of the smart car washing machines whose device status is online in each unit time within the preset time period; and Obtain the number of car washes by each of the intelligent car wash machines in each unit time within a preset time period and the car wash record of each car wash; Determine the average turnover per unit time of the target store within a preset time period according to the first number of the smart car washing machines, the number of car washings of each of the smart car washing machines, and the car washing record of each car washing; Determine the net turnover of the target store in each unit time within the preset time period according to the commission ratio of the target store and the turnover of the target store in each unit time within the preset time period; According to the net turnover of the target store in each unit time during a preset time period, the net turnover of the target store in each future quarter is predicted; According to the device status of all the smart car wash machines in a preset time period and the first number of the smart car wash machines whose device status is online in each unit time in the preset time period, predict the device utilization rate of all the smart car wash machines in the target store in each future quarter; The car washing tasks of the target store in the most recent quarter in the future are allocated according to the net turnover of the target store in each quarter in the future and the equipment utilization rate of all the smart car washing machines of the target store in each quarter in the future.

2. The method according to claim 1, characterized in that The allocating the commission ratio and the account period of the target store according to the store address and the number of the smart car washing machines includes: Determine the urban area where the store address is located, and obtain historical population data and historical vehicle ownership of the urban area; Determine whether there is a target directly-operated store located in the same urban area as the target store among all the directly-operated stores; If the target direct-sale store exists, obtaining a set of car wash machine work logs of each target direct-sale store; Predicting future car wash demand data for the urban area based on the car wash machine work log set, the historical population data, and the historical vehicle ownership; Obtain the growth trend of the number of car wash machine stores and the growth trend of the total number of car wash machines in the urban area; According to the work log of the car washing machine, the average daily processing volume of each car washing machine is counted; Calculate the future competitiveness score of the target store based on the growth trend of the number of car wash machine stores, the average daily processing volume, the growth trend of the total number of car wash machines, the future growth trend of car wash demand and the number of car wash machines; Querying a preset database to obtain an annual income range corresponding to the future competitiveness score; Determine the corresponding commission rate according to the annual income range, and determine the account period according to the commission rate and the competitiveness score; Among them, in the first case, the commission ratio is greater than the first preset value and the future competitiveness score is greater than the second preset value, and the account period is determined to be 90 days. In the second case, the commission ratio is greater than the third preset value and the future competitiveness score is greater than the fourth preset value, and the account period is determined to be 60 days. The third preset value is less than the first preset value, and the fourth preset value is greater than the second preset value. In other cases, the account period is determined to be 30 days.

3. The method according to claim 2, characterized in that The predicting of future car wash demand data of the urban area according to the car wash machine work log set, the historical population data and the historical vehicle ownership includes: Determine the number of car washes per day and the total number of car washes per quarter based on the car wash machine work log set; Calculate the quarterly population growth rate based on the historical population data; Calculate the quarterly growth rate of vehicle stock based on the historical vehicle stock; Performing a correlation analysis on the total number of car washes per quarter, the quarterly population growth rate, and the quarterly growth rate of the number of vehicles owned, to obtain a first correlation coefficient between the total number of car washes per quarter and the quarterly population growth rate, and a second correlation coefficient between the total number of car washes per quarter and the quarterly growth rate of the number of vehicles owned; Inputting the historical population data into a pre-trained time series model to obtain future population data; and Inputting the historical vehicle population into the time series model to obtain the future vehicle population; Inputting the future population data and the future vehicle ownership into a pre-trained machine learning model to obtain future car wash demand data to be corrected; taking the sum of the absolute value of the first correlation coefficient and the absolute value of the second correlation coefficient as the total correlation; Dividing the absolute value of the first correlation coefficient by the total correlation to obtain a population weight; and Dividing the absolute value of the second correlation coefficient by the total correlation to obtain a vehicle weight; Based on the future population data, calculate the population growth rate at each time point relative to the previous time point; and Calculate the vehicle growth rate at each time point relative to the previous time point based on the future vehicle ownership; The product of the population growth rate and the population weight is used as a first growth rate, the product of the vehicle growth rate and the vehicle weight is used as a second growth rate, and the sum of the first growth rate and the second growth rate is used as a comprehensive growth rate; Starting from the second time point in the future, the future car wash demand data to be corrected at each time point is multiplied by the correction factor to obtain the corrected future car wash demand data at each time point in the future, wherein the value of the correction factor is the comprehensive growth rate corresponding to the future car wash demand data plus 1.

4. The method according to claim 2, characterized in that: The calculating of the future competitiveness score of the target store according to the growth trend of the number of car wash machine stores, the average daily processing volume, the growth trend of the total number of car wash machines, the future growth trend of car wash demand and the number of car wash machines includes: Calculate the demand satisfaction rate index at each future time point according to the number of car wash machines, the average daily processing volume of each car wash machine and the future growth trend of car wash demand; According to the growth trend of the total amount of car wash machines, calculate the relative growth rate at each future time point; According to the growth trend of the total number of car wash machines and the growth trend of the number of car wash machine shops, the average number of car wash machines in each car wash machine shop at each future time point is calculated, and according to the number of car wash machines and the average number of car wash machines in each car wash machine shop at each future time point, a competition intensity index is calculated; The demand satisfaction rate index, the relative growth rate and the competition intensity index are normalized, and a weighted sum is taken of the normalized demand satisfaction rate index, the relative growth rate and the competition intensity index to obtain a future competitiveness score of the target store at each future time point.

5. The method according to claim 1, characterized in that: The car wash record includes a car wash price, and determining the average turnover per unit time of the target store within a preset time period according to the first number of the smart car wash machines, the number of car washes of each smart car wash machine, and the car wash record of each car wash, includes: Calculate the product of the number of car washes of each smart car washing machine per unit time within a preset time period and the corresponding car washing price to obtain the turnover of each smart car washing machine per unit time; Adding the turnover of each of the smart car washing machines in each unit time in the preset time period to obtain the turnover of each of the smart car washing machines in the preset time period; Add up the sales of all the first number of smart car wash machines in the preset time period to obtain the total sales of the target store in the preset time period, and divide the total sales by the number of unit times in the preset time period to obtain the average sales of the target store per unit time in the preset time period.

6. The method according to claim 1, characterized in that The determining of the net turnover of the target store in each unit time within the preset time period according to the commission ratio of the target store and the turnover of the target store in each unit time within the preset time period includes: The value obtained by subtracting the commission rate from 1 is used as the net sales rate of the target store; The product of the target store's turnover in each unit time within the preset time period and the net turnover ratio is taken as the target store's net turnover in each unit time within the preset time period.

7. The method according to claim 1, characterized in that The method of allocating car washing tasks of the target store in the most recent quarter in the future according to the net turnover of the target store in each quarter in the future and the equipment utilization rate of all the smart car washing machines of the target store in each quarter in the future includes: According to the equipment utilization rate of all the smart car wash machines of the target store in each future quarter, determine the number of available equipment of all the smart car wash machines of the target store in the most recent future quarter, where the number of available equipment refers to the number of the smart car wash machines whose equipment status is online; Calculate the maximum car washing capacity of the target store in the next quarter according to the number of available devices of all the smart car washing machines of the target store in the next quarter; Determine the net turnover of the target store in the most recent quarter in the future based on the net turnover of the target store in each of the future quarters; The car washing tasks of the target store in the next most recent quarter are allocated according to the maximum car washing capacity of the target store in the next most recent quarter and the net turnover of the target store in the next most recent quarter.

8. The method according to claim 7, characterized in that The car wash record also includes the car wash duration. The maximum car wash capacity of the target store in the next quarter is calculated based on the number of available devices of all the smart car wash machines of the target store in the next quarter, including: Determine the maximum number of car washes per hour for each of the intelligent car wash machines according to the car wash duration of each car wash; The product of the car washing time of each car washing and the number of car washing times of each intelligent car washing machine in each unit time is taken as the effective operating hours of each intelligent car washing machine in each unit time; The product of the effective operating hours of each intelligent car washing machine in each unit time and the maximum number of car washing times per hour of each intelligent car washing machine is taken as the car washing capacity per unit time of each intelligent car washing machine; The product of the car washing capacity per unit time of each of the intelligent car washing machines, the number of available equipment and the number per unit time in the next most recent quarter is taken as the maximum car washing capacity of the target store in the next most recent quarter.

9. The method according to claim 8, characterized in that The method of allocating car washing tasks of the target store in the next most recent quarter according to the maximum car washing capacity of the target store in the next most recent quarter and the net turnover of the target store in the next most recent quarter includes: Determine whether the net sales of the target store in the most recent quarter in the future is greater than a preset sales threshold; When the net turnover of the target store in the most recent quarter in the future is greater than a preset turnover threshold, determining the most recent quarter in the future as a peak period, and obtaining preset peak period parameters; The product of the maximum car washing capacity and the preset peak period parameter is used as the car washing task of the target store in the next quarter; When the net turnover of the target store in the most recent quarter in the future is less than or equal to the preset turnover threshold, determine that the most recent quarter in the future is an off-peak period, and obtain the preset off-peak period parameters; The product of the maximum car washing capacity and the preset off-peak period parameter is used as the car washing task of the target store in the next quarter, wherein the preset peak period parameter is greater than 1 and the preset off-peak period parameter is less than 1.

10. A car washing task management and allocation system applied to an intelligent car washing machine, characterized in that: include: a memory configured to store instructions; as well as A processor is configured to call the instruction from the memory and to implement the car wash task management and allocation method applied to an intelligent car washing machine according to any one of claims 1 to 9 when executing the instruction.

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