CN116402457A - Operation management system and management method thereof - Google Patents
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Abstract
The invention discloses an operation management system and a management method thereof, belonging to the technical field of data management. The operation management system comprises an acquisition module, a server, a statistics module and an output module; the acquisition module acquires order sales price, order sales quantity, order refund quantity and system complementary payment, and the actual refund amount is uploaded to the server; the statistics module is used for acquiring data from the server and counting GMV achievement; the output module is used for outputting the statistical result of the statistical module. The management method of the operation management system comprises the following steps: s1, acquiring order sales price, order sales quantity, order refund quantity, system complementary payment and actual refund amount, and uploading the obtained amounts to a server; s2, acquiring data from the server and counting GMV to achieve; s3, outputting the statistical result of the step S2. The system provided by the invention can efficiently realize GMV data collection and arrangement.
Description
Technical Field
The invention relates to the technical field of data management, in particular to an operation management system and a management method thereof.
Background
With the continuous development of the internet of things, a more convenient and intelligent new retail business model appears in recent years and is developed rapidly. Unmanned retailing has revolutionized traditional retail business models and is favored by consumers, especially the development and layout of unmanned retailing that facilitates the advent of the contactless shopping concept under epidemic conditions. The unmanned retail shelf has unique consumption experience, continuously iterated artificial intelligent technologies such as face recognition and mobile payment, and the like, provides technical support for rapid development of unmanned retail, and expands the terminal use scene from the initial subway, airport, hotel and the like to communities, parks, office buildings, markets and the like.
The urban and point positions of unmanned intelligent container layout are rapidly increased, the complexity of the faced business and scene can be correspondingly increased, the rapidly-increased business demands cannot be met by means of the traditional operation management mode and experience, and the traditional operation system has more limitations in realizing business functions, simply relying on manual collection and arrangement of platform system data, cannot efficiently, timely and intelligently solve the problems, and cannot give professional technical guidance to future decisions.
In daily operations and management, a large amount of revenue data of different businesses and different dimensions are required, for example, it is required for a company to manage this, to know whether the revenue data of each regional city every day reaches a target, what risk is present, and so on. However, in the past, GMV revenue data of each service system is manually queried, because each system is relatively independent and scattered, a large amount of time is required for collection and arrangement, the service data volume is relatively large, statistics according to different dimensions and services are very tedious, different service departments are easy to make statistics possibly have different rule differences, and if the data differences are generated, the verification is difficult. And reporting or summarizing the time of week or month, and finally determining policy.
Along with the rapid development and layout of the company scale, company decision makers have various problems in daily operation and operation, such as incapacity of guaranteeing data failure, increased labor cost, unified index caliber, no rapid replication experience precipitation and the like. Particularly, for each decision maker of a company, no effective real-time GMV index data support exists, analysis and decision can not be made on company operation from the revenue layer, intelligent monitoring and early warning can not be performed on the existing business campaigns, health degree evaluation can not be performed on each business campaigns of the company, traditional GMV data collection and arrangement efficiency is low, and each business department is easy to diverge, so that how to efficiently realize GMV data collection and arrangement is a problem to be solved in the prior art.
Disclosure of Invention
The invention aims to overcome the technical defects, provides an operation management system and a management method thereof, and solves the technical problem of how to efficiently realize GMV data collection and arrangement in the prior art.
In order to achieve the technical purpose, the technical scheme of the invention provides an operation management system, which comprises an acquisition module, a server, a statistics module and an output module; the acquisition module acquires order sales price, order sales quantity, order refund quantity and system complementary payment, and the actual refund amount is uploaded to the server;
the statistics module is used for acquiring data from the server and counting GMV achievement;
the output module is used for outputting the statistical result of the statistical module.
Further, the calculation method achieved according to the data statistics GMV collected by the collection module includes:
GMV = Σ order sales price × order sales quantity-order sales price × order refund quantity + system payback;
GMV actual receipt = actual payment amount of all online orders in the period + system payback-actual refund amount;
GMV loop ratio/homoratio= (current GMV-upper phase/contemporaneous GMV)/|upper phase/contemporaneous gmv| 100%.
Further, the data collected by the collection module further includes the GMV generated by the current whole point from the present day to the present day in the morning of the selected region, and the GMV target value of the present day of the selected region.
Further, the statistics module further includes statistics on a GMV achievement rate, and a calculation formula of the GMV achievement rate is: the selected region is today in the morning to the current nearest whole-spot generated GMV/the selected region is the current GMV target value.
Further, the statistics module is used for acquiring data from the server and counting one or two of the GMV comprehensive loss and the GMV comprehensive loss ratio.
Further, the data collected by the collection module further comprises frame withdrawal loss, competing loss, backorder loss and network disconnection loss.
Further, the GMV aggregate loss is the sum of a withdrawal loss, a maximum backorder loss, a maximum outage loss, and a bid loss.
Further, the calculation method of the network disconnection loss comprises the following steps: Σ (the hour off-grid time duty cycle for each hour region scene is about 30 days) per smart device.
Further, the statistics module also includes statistics of 0 sales counter number, low sales counter duty ratio, new low sales duty ratio, and low sales loop ratio.
In addition, the invention also provides a management method of the operation management system, which comprises the following steps:
s1, acquiring order sales price, order sales quantity, order refund quantity, system complementary payment and actual refund amount, and uploading the obtained amounts to a server;
s2, acquiring data from the server and counting GMV to achieve;
s3, outputting the statistical result of the step S2.
Compared with the prior art, the invention has the beneficial effects that: the operation management system provided by the invention comprises an acquisition module, a server, a statistics module and an output module; the acquisition module acquires order sales price, order sales quantity, order refund quantity and system complementary payment, and the actual refund amount is uploaded to the server; the statistics module is used for acquiring data from the server and counting GMV achievement; the output module is used for outputting the statistical result of the statistical module, and a manager can check the statistical result of the GMV at any time, so that the collection and arrangement of the GMV data are efficiently realized.
Drawings
Fig. 1 is a block diagram of an operation management system according to an embodiment of the present invention;
fig. 2 is a flow chart of a management method of an operation management system according to an embodiment of the present invention.
Detailed Description
With reference to fig. 1, this embodiment provides an operation management system, which includes an acquisition module, a server, a statistics module and an output module; the acquisition module acquires order sales price, order sales quantity, order refund quantity and system complementary payment, and the actual refund amount is uploaded to the server;
the statistics module is used for acquiring data from the server and counting GMV achievement;
the output module is used for outputting the statistical result of the statistical module.
In some embodiments, the method for calculating the GMV according to the data statistics collected by the collection module comprises the following steps:
GMV = Σ order sales price × order sales quantity-order sales price × order refund quantity + system payback;
GMV actual receipt = actual payment amount of all online orders in the period + system payback-actual refund amount;
GMV loop ratio/homoratio= (current GMV-upper phase/contemporaneous GMV)/|upper phase/contemporaneous gmv| 100%.
In some embodiments, the data collected by the collection module further includes a GMV generated by the current nearest whole point from the present morning to the present day in the selected region, and the GMV target value of the present day in the selected region.
In some embodiments, the statistics module further comprises a statistics module for calculating a GMV achievement rate, the calculation formula of the GMV achievement rate being: the selected region is today in the morning to the current nearest whole-spot generated GMV/the selected region is the current GMV target value.
In some embodiments, the statistics module is configured to obtain data from the server and to count one or both of GMV integrated loss and GMV integrated loss duty cycle; the data collected by the collection module further comprises frame withdrawal loss, competing loss, backorder loss and network disconnection loss; the GMV comprehensive loss is the sum of frame withdrawal loss, maximum backdrop loss, maximum grid disconnection loss and competing loss; the calculation method of the broken network loss comprises the following steps: Σ (the hour off-grid time duty cycle for each hour region scene is about 30 days) per smart device.
In certain embodiments, the statistics module further comprises counting 0 sales counter counts, low sales counter duty cycles, new low sales duty cycles, and low sales loop ratios.
With reference to fig. 2, this embodiment further provides a management method of an operation management system, including the following steps:
s1, acquiring order sales price, order sales quantity, order refund quantity, system complementary payment and actual refund amount, and uploading the obtained amounts to a server;
s2, acquiring data from the server and counting GMV to achieve;
s3, outputting the statistical result of the step S2.
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
Related terms are explained as follows:
RM: relationship manager;
CM: management layer CM (custommanager), customer manager;
GMV: commodity transaction total (Gross Merchandise Volume, GMV for short) is the total of deals (within a certain period of time);
and (3) new packaging: activating the goods shelf within the statistical time range;
the stock is: activating a goods shelf before counting a time range;
the same ratio is as follows: current date/last year contemporaneous data;
the ratio is fixed: the present period data/fixed period data;
ring ratio: the current date/the last date;
smooth explosion: sales level, burst, free sales, flat sales;
and (3) frame-wise: sigma (single shelf GMV/days of day of conversion)/shelf quantity, rejecting the withdrawn shelf (days of day 1, day of week 0.5, day of week and holiday 0.4, newly loaded shelf is converted by the activation time to the statistics day);
target user: headquarter core management layer/regional responsible person/department responsible person/RM/CM
Basic dimensions:
1) Real-time today, yesterday, weekly, monthly, quarterly, yearly, custom time;
2) Urban, regional, large, all;
3) The intelligent cabinet, the self-vending machine, the unmanned goods shelf, the virtual goods shelf, other goods shelves and all;
4) New package, stock, all;
the GMV achievement business content comprises GMV achievement, time period trend, GMV achievement data, actual receipt achievement data, achievement rate ranking and achievement rate detail, and the statistical rules are as follows:
GMV = Σ order sales price × order sales quantity-order sales price × order refund quantity + system payback;
GMV actual receipt = actual payment amount of all online orders in the statistics period + system payback-actual refund amount;
GMV ring ratio/homoratio= (current-upper-period/same-period GMV)/|upper-period/same-period gmv| 100%;
GMV objective = statistics by day, workday coefficient = 1, legal holiday coefficient = 0.4, holiday sunday coefficient = 0.5, holiday sunday coefficient = 0.4, regional intelligent cabinet retention day objective: area gmv storing daily objective (Intelligent cabinet gmv/(Intelligent cabinet gmv +self vending machine gmv +unmanned goods shelf gmv))
Point location: namely, a container, including various container types;
taking the current real-time statistics of GMV amount as an example:
actual GMV = GMV generated by the current nearest whole point in the selected region today in the morning to today;
target GMV = GMV target value today for the selected region;
GMV achievement rate = GMV produced by the selected region today in the morning to the current nearest whole point today/GMV target value of the selected region today;
the GMV amount data source is order flow generated by the consumption actions such as purchasing by a user at equipment terminals such as an intelligent cabinet, a self-vending machine, an unmanned container and the like, the order flow can be synchronized to business application systems such as OMS orders, WMS logistics, ERP and the like, if refund and preferential activity compensation are carried out, the consumption flow is also generated, and the order flow is associated to form a business closed loop.
The GMV big data platform performs real-time data interaction with each business system, gathers and counts various dimensions of goods shelves, time, cabinet age and regions to produce middle table data, and the decision and report system performs secondary processing and algorithm processing according to different dimensions to display different business data graphs according to the GMV target amount and the GMV amount of each region.
The current GMV achieved health condition is known through ring ratio, comparison and comparison of the GMV amounts in the same period and the same region, whether the target GMV amount can be achieved in the period or not can be prejudged through the data such as the GMV achievement rate, trend, gap and the like, the situation of the historical contemporaneous GMV amounts is prejudged, the GMV amount trend of an operation system is referred to for prejudgment, the situation of achievement of indexes is combined to comprehensively judge, for example, the achievement rate of the GMV in the month is 23.1%, the time progress is 32.3%, the achievement rate can be known to be lower than the time progress, if no new business adjustment is carried out, the influence of factors such as the introduction of new products, the reduction of the shortage rate or seasons possibly causes that the region cannot achieve the target, so that the operation department takes corresponding measures in advance according to prejudgment information, for example, the GMV achievement rate-time progress is more than or equal to-5% in the normal range; if the GMV achievement rate-time progress is more than or equal to-15% and less than-5%, checking whether the index is abnormal or not is needed, such as reducing the stock shortage rate, reducing the low-sales shelf, improving the stock-in efficiency and the like; if the GMV achievement rate-time progress is less than-15%, the GMV achievement rate-time progress belongs to the high risk of GMV standard, and the GMV standard condition needs to be reported and a scheme is formulated, such as adjusting the commodity proportion of the area, introducing new smooth selling products, adding promotion preferential activities and the like.
The embodiment provides an operation management system, which comprises an acquisition module, a server, a statistics module and an output module; the acquisition module acquires order sales price, order sales quantity, order refund quantity and system complementary payment, and the actual refund amount is uploaded to the server;
the statistics module is used for acquiring data from the server and counting GMV achievement;
the output module is used for outputting the statistical result of the statistical module.
On the basis of the foregoing embodiment, the achieving, according to the data statistics GMV collected by the collecting module, the GMV in this embodiment includes:
GMV = Σ order sales price × order sales quantity-order sales price × order refund quantity + system payback;
GMV actual receipt = actual payment amount of all online orders in the period + system payback-actual refund amount;
GMV loop ratio/homoratio= (current GMV-upper phase/contemporaneous GMV)/|upper phase/contemporaneous gmv| 100%.
Based on the above embodiment, the data collected by the collecting module in this embodiment further includes GMV generated by the current whole point from the present morning to the present day in the selected region, and the present GMV target value in the selected region.
Taking the current real-time statistics of GMV amount as an example:
actual GMV = GMV generated by the current nearest whole point in the selected region today in the morning to today;
target GMV = GMV target value today for the selected region;
GMV achievement rate = GMV produced by the selected region today in the morning to the current nearest whole point today/GMV target value of the selected region today;
the GMV amount data source is order flow generated by the consumption actions such as purchasing by a user at equipment terminals such as an intelligent cabinet, a self-vending machine, an unmanned container and the like, the order flow can be synchronized to business application systems such as OMS orders, WMS logistics, ERP and the like, if refund and preferential activity compensation are carried out, the consumption flow is also generated, and the order flow is associated to form a business closed loop.
The GMV big data platform performs real-time data interaction with each business system, gathers and counts various dimensions of goods shelves, time, cabinet age and regions to produce middle table data, and the decision and report system performs secondary processing and algorithm processing according to different dimensions to display different business data graphs according to the GMV target amount and the GMV amount of each region.
Based on the foregoing embodiment, the statistics module in this embodiment further includes a statistics module for calculating a GMV achievement rate, where a calculation formula of the GMV achievement rate is: the selected region is today in the morning to the current nearest whole-spot generated GMV/the selected region is the current GMV target value.
On the basis of the above embodiment, the statistics module in this embodiment is configured to obtain data from the server and count one or both of GMV total loss and GMV total loss ratio.
Based on the above embodiment, the data collected by the collection module in this embodiment further includes a frame withdrawal loss, a race loss, a stock loss, and a network disconnection loss.
On the basis of the foregoing embodiment, the statistics module in this embodiment further includes comparing whole network averages: the ratio of the total loss of a certain region in a certain period of time is compared with the ratio of the total loss of the whole network, and the comparison of the total loss of the whole network shows that the ratio of the total loss of the whole region is plus, minus and equal to 0, for example, the ratio of the total loss of the whole region yesterday is 10%, the ratio of the total loss of the whole region of Hubei yesterday is 15%, and the average ratio of the total loss of the whole network of the whole region of Hubei is +5%.
Based on the above embodiment, the GMV total loss in this embodiment is the sum of the withdrawal loss, the maximum out-of-stock loss, the maximum off-grid loss, and the bid-to-bid loss.
Based on the above embodiment, the calculation formula of the outage loss in this embodiment is: Σ (the hour off-grid time duty cycle for each hour region scene is about 30 days) per smart device.
Loss of frame removal: for a certain period of time, all the shelf units are withdrawn, Σ (average daily shelf units of each shelf unit before 6 months before the shelf unit is withdrawn) x the number of days of withdrawal in a certain period of time + (loss of shelf units withdrawn in the last month in the current month).
Loss of stock: counting the backloss of the selected area in the selected time according to the current existing caliber total backloss: and (3) calculating the total loss (including new products and diapause) of the intelligent equipment, and counting the data of the loss of the smooth goods caused by the lack of the goods in a certain period of time by a certain shelf.
Competing for loss: coexistence daily loss=average daily pin-current day coexistence point GMV of six months before coexistence, taking the maximum value (if coexistence daily loss > current day coexistence point GMV, taking the calculated value of coexistence daily loss; if coexistence daily loss < current day coexistence point GMV, taking the value of 0).
GMV integrated loss is shown in table 1, for example:
in combination with Table 1, the GMV loss amount of the container A, B, C, D, E is counted from 1 day to 2 days of 8 months, the total of all the goods shelf loss amounts from 1 day to 2 days is counted in the loss of stock from 1 day to 2 days, and the total of the maximum loss amounts of the daily goods shelf loss and the loss of stock from stock is counted in the comprehensive loss.
The intelligent container is required to be removed for some reasons, and after the OMS order system, the ERP financial system, the WMS supply chain system and other related circulation orders are completed, each business department completes approval and adjusts the container state to be removed.
When the activated state container is operated, the equipment system of the activated state container can be in communication connection with the server, if the server cannot establish connection with the equipment system, the server system records the networking state of the intelligent cabinet equipment, and when the network disconnection time exceeds half an hour, network disconnection records are newly added.
The shortage is defined as a single goods shelf or intelligent container, the single goods stock is 0, and the following three conditions are satisfied, (1) the goods dimension replenishment identification of the goods shelf is normal; (2) the sales mark is smooth; (3) and (3) a goods shelf: the status is activated (excluding the rack-removing status is "to inventory" and "rack-removing").
The system can acquire basic information such as network interruption, stock shortage and the like from application systems such as an OMS order system, an intelligent container system and the like, an index algorithm system processes and checks data to form task information, an abnormal early warning message is pushed to relevant regional responsibility people through a message management center, the main content of the early warning message is information of continuous or serious data abnormality, personalized configuration can be carried out according to regional actual condition standards, an operation responsible person forwards relevant personnel processing in time according to the abnormal early warning information, for example, the regional network interruption rate is high continuously, reasons (equipment, environment, management and the like) of the condition of the shelf network interruption need to be checked, a solution is provided for reporting explanation reasons, working progress and completion conditions are continuously reported, and a service closed loop is formed from the generation of the abnormal early warning information to the processing and the solution of the problem.
Based on the above embodiment, the statistics module in this embodiment further includes statistics of 0 sales counter number, low sales counter duty ratio, newly installed low sales duty ratio, and low sales loop ratio, and these data are point quality.
The specific statistical rules are as follows:
1. the GMV is less than the low pin standard in the counting period and is considered as low pin, and 0 pin is contained;
2. 0 sales counter number: the number of shelves with GMV less than or equal to 0 under the corresponding statistical condition;
3. low pin container ratio: counting the number of low sales counter under the corresponding condition in the period/counting the total number of counter under the corresponding condition in the period;
4. new low pin duty cycle: counting the number of newly-installed low-sales containers in the period/counting the total number of newly-installed containers in the period, and identifying the newly-installed goods shelves uniformly according to whether the activation date falls in the counting period or not, so that the goods shelves activated by replacement are removed;
5. the low-sales and the 0-sales only count the intelligent cabinet and the self-vending machine, and no unmanned goods shelf is contained;
6. low pin-to-ring ratio calculation formula: (number of low sales counter in this period-number of low sales counter in the upper period)/number of low sales counter in the upper period, and 0 sales are the same.
The low sales counter standard defines the minimum sales counter per day according to counter type, the sum of counter GMV sum and minimum sales counter sum in the counting period is compared, the lowest sales counter sum is smaller than or equal to the minimum sales counter sum, the low sales counter is judged to be a low sales counter, the goods counter is larger than the minimum sales counter sum is judged to be a normal sales counter, and if the sum of goods counter sales counter sum in the counting period is 0, the goods counter sum is judged to be 0 sales counter.
The intra-period shelf GMV statistical time range is related to shelf activation state time, and not all statistical periods start to expiration time, e.g., the current shelf state is active, the GMV time range rule is as follows:
and if the activation time is smaller than or equal to the statistical period starting time, the GMV time range is from the statistical period starting time to the statistical period ending time, if the activation time is smaller than the statistical period ending time, the GMV time range is from the shelf activation time to the statistical period ending time, and if the activation time is larger than the statistical period ending time, the GMV time range does not participate in statistics.
The point location quality is mainly used for evaluating whether the GMV amount of container sales in the evaluation period meets the minimum GMV sales standard or not, the large data platform counts the GMV amount of container sales in the day before synchronizing from the application system every day, and comprehensive order refund, preferential sales, sales promotion and other information are counted according to the dimension of the goods shelf, the minimum sales amount is mainly defined by the low sales standard of the goods shelf according to the container type, the regional container cost, the sales time and the like, the index algorithm system counts the daily standard condition of the intelligent container according to the low sales judging flow, and the regional operation responsible person can check the information such as the low sales cabinet occupation ratio, the low sales ranking, the low sales ring ratio and the like. If the number of the low-sales shelves is less than 10% and belongs to a normal range, the low-sales shelves are between 10% and 25% and need to be checked and solve the problem, more than 25% need to report the problem and formulate a solution and continue reporting progress, and as the low-sales cycle is longer and has a certain delay, vigilance should be improved and the low-sales shelf is prevented from being damaged when the low-sales proportion is continuously increased, and measures such as improving the proportion of goods in a container, severely stagnating goods in the lower shelf, removing the shelf for a long time and the like can be taken to solve the low-sales problem.
The embodiment also provides a management method of the operation management system, which comprises the following steps:
s1, acquiring order sales price, order sales quantity, order refund quantity, system complementary payment and actual refund amount, and uploading the obtained amounts to a server;
s2, acquiring data from the server and counting GMV to achieve;
s3, outputting the statistical result of the step S2.
The operation management system provided by the invention is not only a statistical analysis report system, but also a management system with early warning and task functions, can perform data analysis and prediction through the index rule system, can carry out operation planning and decision making on decision makers of daily operation auxiliary companies, and can form a closed loop for data-driven business through the timely treatment of task system propulsion problems.
In short, the operation management system is widely applied in company management operation, helps operators to process daily and complicated data problems through data viewing and index analysis at any time and any place, enables managers to be liberated from ocean with big data, is more focused on decision operation of the company, becomes an integral part of the operation due to the professional and convenience of the operation management system, and is continuously optimized and iterated along with the rapid development of business and scale.
The above-described embodiments of the present invention do not limit the scope of the present invention. Any other corresponding changes and modifications made in accordance with the technical idea of the present invention shall be included in the scope of the claims of the present invention.
Claims (10)
1. An operation management system is characterized by comprising an acquisition module, a server, a statistics module and an output module; the acquisition module acquires order sales price, order sales quantity, order refund quantity and system complementary payment, and the actual refund amount is uploaded to the server;
the statistics module is used for acquiring data from the server and counting GMV achievement;
the output module is used for outputting the statistical result of the statistical module.
2. The operation management system according to claim 1, wherein the calculation method achieved according to the data statistics GMV collected by the collection module comprises:
GMV = Σ order sales price × order sales quantity-order sales price × order refund quantity + system payback;
GMV actual receipt = actual payment amount of all online orders in the period + system payback-actual refund amount;
GMV loop ratio/homoratio= (current GMV-upper phase/contemporaneous GMV)/|upper phase/contemporaneous gmv| 100%.
3. The operations management system of claim 1, wherein the data collected by the collection module further includes a GMV generated by a current nearest whole point in the selected region today in the morning to today, and a GMV target value for the selected region today.
4. The operation management system according to claim 3, wherein the statistics module further comprises a statistics of GMV achievement rate, and the calculation method of the GMV achievement rate is as follows: the selected region is today in the morning to the current nearest whole-spot generated GMV/the selected region is the current GMV target value.
5. The operations management system of claim 1, wherein the statistics module is configured to obtain data from the server and to count one or both of GMV integrated loss and or GMV integrated loss duty cycle.
6. The operations management system of claim 5, wherein the data collected by the collection module further comprises a withdrawal loss, a bid loss, a backout loss, and a grid outage loss.
7. The operations management system of claim 5, wherein the GMV aggregate loss is a sum of a withdrawal loss, a maximum backorder loss, a maximum outage loss, and a bid loss.
8. The operation management system according to claim 7, wherein the calculation formula of the outage loss is: Σ (the hour off-grid time duty cycle for each hour region scene is about 30 days) per smart device.
9. The operations management system of claim 1, wherein the statistics module further comprises counting 0 sales counter counts, low sales counter duty cycles, new low sales duty cycles, and low sales loop ratios.
10. A method of managing an operation management system according to any one of claims 1 to 9, characterized by comprising the steps of:
s1, acquiring order sales price, order sales quantity, order refund quantity, system complementary payment and actual refund amount, and uploading the obtained amounts to a server;
s2, acquiring data from the server and counting GMV to achieve;
s3, outputting the statistical result of the step S2.
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