CN110503477B - Zxfoom zxfoom Muli (Maoli) abnormality of a system(s) apparatus and storage medium - Google Patents
Zxfoom zxfoom Muli (Maoli) abnormality of a system(s) apparatus and storage medium Download PDFInfo
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Abstract
The invention discloses an order gross profit abnormity reason analysis method, system and device and a storage medium, and the method comprises the steps: presetting a plurality of abnormity reason types corresponding to different orders with gross profit abnormity; obtaining order data of the target order; judging whether the target order has gross profit abnormity or not according to the order data, if yes, judging whether the order data meets a first preset condition corresponding to the abnormity reason type or not, and if yes, determining that the abnormity reason type is a target abnormity reason type corresponding to the target order; and judging whether the order data meets a second preset condition corresponding to the abnormal reason in the target abnormal reason type or not, andif yes, determining that the abnormal reason is a target abnormal reason corresponding to the target order. According to the method, automatic analysis and rapid positioning are realized to obtain thetarget abnormal reason of the target order with negative gross profit, the analysis speed and the analysis accuracy are improved, the analysis efficiency is improved, the negative gross profit proportion is reduced, and the income is further improved.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to an analysis method, system, equipment and storage medium for a gross profit abnormality reason of an order.
Background
As the number of orders on internet platforms becomes larger, the number of gross profit anomalies (i.e., negative gross profit) that occur increases (and the corresponding negative gross profit totals increase). The reasons for generating negative gross profit of the order are many, such as abnormal adjustment of the bottom price, full position of the train ticket, selling price of the hotel bottom, and the like.
At present, the reasons of negative gross profit of the orders are mainly obtained through manual inquiry and analysis, so that the negative gross profit is numerous in abnormal reasons, complicated in positioning, and therefore, a great amount of labor cost is required to be consumed, the orders with abnormal gross profit cannot be analyzed in time, and in addition, the analysis speed is low, the analysis accuracy is low, the analysis efficiency is low and the like.
Disclosure of Invention
The invention aims to overcome the defects that the manual analysis of the cause of negative gross profit of an order in the prior art has high labor cost, can not realize timely analysis of the order with abnormal gross profit and has low analysis efficiency, and provides an analysis method, a system, equipment and a storage medium for the cause of abnormal gross profit of the order.
The invention solves the technical problems by the following technical scheme:
the invention provides an analysis method for a gross profit abnormality reason of an order, which comprises the following steps:
s1, presetting a plurality of abnormality cause types corresponding to different orders with gross profit abnormality;
wherein each abnormality cause type corresponds to at least one abnormality cause;
s2, acquiring order data of a target order;
s3, judging whether the target order has gross profit abnormality according to the order data, and if so, executing a step S4;
s4, judging whether the order data meets a first preset condition corresponding to the abnormal reason type, if so, determining that the abnormal reason type is a target abnormal reason type corresponding to the target order;
wherein each abnormality cause type corresponds to a different first preset condition;
s5, judging whether the order data meets a second preset condition corresponding to the abnormality reason in the target abnormality reason type, if yes, determining that the abnormality reason is a target abnormality reason corresponding to the target order;
wherein each of the abnormality causes corresponds to a different one of the second preset conditions.
Preferably, after step S1 and before step S2, the method further comprises:
s201, presetting priorities among different abnormality cause types;
s202, presetting priorities among the abnormality reasons corresponding to the same abnormality reason type;
the step S4 includes:
s41, sequentially judging whether the order data meets a first preset condition corresponding to the abnormal reason type according to the order of the priority of the abnormal reason type from high to low, and if so, determining that the abnormal reason type is a target abnormal reason type corresponding to the target order;
the step S5 comprises the following steps:
s51, judging whether the order data meets a second preset condition corresponding to the abnormality reason in the target abnormality reason type according to the order of the priority of the abnormality reason type from high to low, and if so, determining that the abnormality reason is the target abnormality reason corresponding to the target order.
Preferably, step S3 includes:
acquiring income data and cost data corresponding to the target order according to the order data;
when the revenue data is less than the cost data, determining that a gross profit anomaly exists for the target order; and/or the number of the groups of groups,
Acquiring profit data corresponding to the target order according to the order data;
judging whether the profit data is smaller than a first set threshold value, and if so, determining that the target order has gross profit abnormality.
Preferably, after step S2 and before step S3, the method further comprises:
when at least one of the base price data, the selling price data and the income data in the order data changes, determining that the order data changes, and generating a notification message of the change of the order data of the target order; and/or the number of the groups of groups,
periodically updating the order data of the target order; and/or the number of the groups of groups,
step S5 is followed by:
and generating a reminding message for processing the abnormal situation.
Preferably, when monitoring the order data of each target order in real time, step S3 further includes:
counting the number of first orders corresponding to the target orders with gross profit abnormality in a first set time period;
when the first order quantity is larger than or equal to a second set threshold value, generating first alarm information; and/or the number of the groups of groups,
when monitoring the order data of each of the target orders for N days of history, step S3 further comprises:
Counting the number of second orders corresponding to the target orders with gross profit abnormality in a second set time period; wherein N is more than or equal to 1 and N is an integer;
and when the second order quantity is larger than or equal to a third set threshold value, generating second alarm information.
The invention also provides an analysis system of the gross profit abnormal reasons of the orders, which comprises a preset module, an order data acquisition module, a first judgment module, a second judgment module, a target type determination module, a third judgment module and a target reason determination module;
the presetting module is used for presetting a plurality of abnormality cause types corresponding to different orders with gross profit abnormality;
wherein each abnormality cause type corresponds to at least one abnormality cause;
the order data acquisition module is used for acquiring order data of a target order;
the first judging module is used for judging whether the target order has a gross profit abnormality according to the order data, and if so, the second judging module is called;
the second judging module is used for judging whether the order data meets a first preset condition corresponding to the abnormal cause type, and if so, the target type determining module is called to determine that the abnormal cause type is a target abnormal cause type corresponding to the target order;
Wherein each abnormality cause type corresponds to a different first preset condition;
the third judging module is used for judging whether the order data meets a second preset condition corresponding to the abnormal reason in the target abnormal reason type, and if so, the target reason determining module is called to determine that the abnormal reason is a target abnormal reason corresponding to the target order;
wherein each of the abnormality causes corresponds to a different one of the second preset conditions.
Preferably, the preset module is further configured to preset priorities among different types of the abnormality causes;
the preset module is further used for presetting priorities among the abnormal reasons corresponding to the same abnormal reason type;
the second judging module is used for judging whether the order data meets a first preset condition corresponding to the abnormal reason type according to the order of the priority of the abnormal reason type from high to low, and if so, the target type determining module is called to determine that the abnormal reason type is a target abnormal reason type corresponding to the target order;
the third judging module is configured to sequentially judge whether the order data meets a second preset condition corresponding to the abnormality reason in the target abnormality reason type according to the order of the abnormality reason type from high to low, and if yes, call the target reason determining module to determine that the abnormality reason is a target abnormality reason corresponding to the target order.
Preferably, the first judging module is used for acquiring income data and cost data corresponding to the target order according to the order data;
the first judging module is further used for determining that the target order has gross profit abnormality when the income data is smaller than the cost data; and/or the number of the groups of groups,
the first judging module is used for acquiring profit data corresponding to the target order according to the order data;
the first judging module is further used for judging whether the profit data is smaller than a first set threshold value, and if so, determining that the target order has gross profit abnormality.
Preferably, the analysis system further comprises a change determination module;
the change determining module is used for determining that the order data changes when at least one of the base price data, the selling price data and the income data in the order data changes, and generating a notification message of the change of the order data of the target order; and/or the number of the groups of groups,
the analysis system also comprises a timing update module;
the timing updating module is used for updating the order data of the target order at fixed time; and/or the analysis system further comprises a reminding message generation module;
The reminding message generation module is used for generating a reminding message for processing abnormal conditions.
Preferably, the analysis system further comprises a statistics module and an alarm information generation module;
when the order data of each target order is monitored in real time, the statistics module is used for counting the first order quantity corresponding to the target orders with gross profit abnormality in a first set time period;
the alarm information generation module is used for generating first alarm information when the first order number is larger than or equal to a second set threshold value; and/or the number of the groups of groups,
when monitoring the order data of each target order in the historical N days, the statistics module is used for counting the second order quantity corresponding to the target orders with gross profit abnormality in a second set time period; wherein N is more than or equal to 1 and N is an integer;
the alarm information generation module is used for generating second alarm information when the second order number is larger than or equal to a third set threshold value.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the analysis method of the cause of the gross profit abnormality of the order when executing the computer program.
The present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of analyzing causes of gross profit anomalies of orders as described above.
The invention has the positive progress effects that:
in the invention, when the target order has the gross profit abnormality, the type of the target abnormality reason corresponding to the target order is quickly positioned, and then the target abnormality reason corresponding to the target order is quickly positioned, so that the automatic analysis and quick positioning are realized to obtain the target abnormality reason of the target order with negative gross profit, the analysis speed is high, the analysis accuracy is improved, the analysis efficiency is improved, the negative gross profit proportion is reduced, and the benefit is further improved.
Drawings
Fig. 1 is a flowchart of a method for analyzing the cause of a gross profit abnormality of an order according to embodiment 1 of the present invention.
Fig. 2 is a flowchart of a method for analyzing the cause of the abnormality in the order of embodiment 2 of the present invention.
Fig. 3 is a diagram showing the decrease in the gross margin in the analysis method of the cause of gross margin abnormality of the order of example 2 of the present invention.
Fig. 4 is a diagram showing gross hair volume reduction in the analysis method of the cause of gross hair abnormality of the order of embodiment 2 of the present invention.
Fig. 5 is a diagram showing the distribution of the number of various abnormality causes in the analysis method of the abnormality cause of the order of embodiment 2 of the present invention.
Fig. 6 is a schematic diagram showing the distribution of the duty ratio of various abnormality causes in the analysis method of the abnormality cause of the order of example 2 of the present invention.
Fig. 7 is a schematic diagram of a first monitoring report in the analysis method of the cause of the abnormal gross profit of the order in embodiment 2 of the present invention.
Fig. 8 is a schematic diagram of a second monitoring report in the analysis method of the cause of the abnormal gross profit of the order in embodiment 2 of the present invention.
Fig. 9 is a schematic block diagram of an analysis system for the cause of a gross profit abnormality of an order according to embodiment 3 of the present invention.
Fig. 10 is a schematic block diagram of an analysis system for the cause of a gross profit abnormality of an order according to embodiment 4 of the present invention.
Fig. 11 is a schematic diagram of the structure of an electronic device for implementing the analysis method of the cause of the abnormality of the custom in embodiment 5 of the present invention.
Detailed Description
The invention is further illustrated by means of the following examples, which are not intended to limit the scope of the invention.
Example 1
As shown in fig. 1, the analysis method of the cause of the gross profit abnormality of the order of the present embodiment includes:
s101, presetting a plurality of abnormality cause types corresponding to different orders with gross profit abnormality;
Wherein, each abnormal cause type corresponds to at least one abnormal cause;
specifically, the types of abnormality reasons corresponding to different orders can be preset by presetting the types of abnormality reasons corresponding to orders with gross profit abnormality in the historical setting time and the types of other abnormality reasons newly added according to the actual demand.
For example, for the OTA (Online Travel Agency, online travel) industry, the types of abnormality causes include mainly 7 types, specifically: system abnormality cause, special order gross profit abnormality, business setting cause, OP operation (artificial operation) abnormality, system resource reverse hanging, channel yield, other negative gross profit and the like.
Among them, the causes of system abnormality include the following 9 types: the system hotel refund fee is not synchronous, the air ticket refund fee is synchronous (checked), the agent occupation list is other, the agent occupation list is totally refunded, the automatic totally refund is failed, the system operation base price is adjusted, the agent occupation list is adjusted to the OP price, and the like.
Specific order gross profit anomalies include 8 types as follows: the self-management and place-occupying single negative gross profit, local guide negative gross profit, lead negative gross profit, ticket spelling negative gross profit, cancel negative gross profit, local pass negative gross profit, tourist negative gross profit, custom game negative gross profit and the like.
The service setting reasons include the following 17 kinds: abnormal bottom price adjustment, train ticket filling, hotel filling, air ticket filling, flight cancellation, flight variation, predetermined total bottom price greater than order amount, business yield, loss, coupon (profit offset), eating price/saving price, selectable bottom selling price hanging, single option airliner bottom selling price hanging, order real collection unequal to receivable, hundred-way trade price yield, order gross profit abnormality caused by price change, and the like.
OP operational anomalies included the following 5: manual total refund, option operations, partial refund, total refund resources not cancelled, total refund fare refund, etc.
The system resource reverse hanging comprises the following 7 types: system air ticket resource hanging, hotel bottom selling price hanging, playing resource hanging, insurance resource hanging, entrance ticket resource hanging, train ticket resource hanging, and the like.
Channel offers include the following 2 types: trade price giving order negative gross profit, hotel trade price bottom selling price hanging upside down, etc.
Other negative gross edges include 8 as follows: abnormal hundred-pass standard single gross profit, abnormal guide order gross profit, negative gross profit amount less than 10 yuan, order negative gross profit of other reasons, hundred-pass standard single unsubscribe, hundred-pass standard single-OP price adjustment, ultrahigh gross profit, normal gross profit and the like.
S102, acquiring order data of a target order;
the order data of each target order is stored in a data table, and specifically, the data table comprises a unique key (a table main key), an order number, a service type, a service name, a settlement running number, a status code, an information code, a table insertion time, a negative gross code, negative gross information, order income, order cost, order profit, order type, preset time, departure time, return time, last update time, product id, product manager and the like.
The data table is stored according to each data in the tables of fields, field names, types, original fields, indexes and the like. For example, for table insertion time, field: datacreate_lasttime, field name: table insertion time, type: datetime, original field: datacreatesttime; other data in the table is also stored in the table in a similar manner as described above, and therefore will not be described in detail here.
S103, judging whether the target order has gross profit abnormality according to the order data, and if so, executing a step S104;
s104, judging whether the order data meets a first preset condition corresponding to the abnormal reason type, if so, determining that the abnormal reason type is a target abnormal reason type corresponding to the target order;
Wherein, each abnormal reason type corresponds to a different first preset condition;
s105, judging whether the order data meets a second preset condition corresponding to the abnormal reason in the target abnormal reason type, if so, determining that the abnormal reason is a target abnormal reason corresponding to the target order;
wherein each abnormality cause corresponds to a different second preset condition.
For example, taking an order for negative gross profit as an example, a hotel full:
(1) If the income of the order is less than the cost, determining that the order has negative gross profit;
(2) If the order has the event that the hotel is full, and the total selling price of the hotel of the order is smaller than the total base price of the hotel of the order, and the difference and the negative gross profit of the order meet certain conditions, the reason that the order is negative is indicated to be that the hotel is full.
The determination of whether the order is of other abnormality causes is similar to the above determination, and will not be described here.
In this embodiment, when there is a gross profit abnormality in the target order, the type of the target abnormality cause corresponding to the target order is quickly located first, and then the target abnormality cause corresponding to the target order is quickly located, so as to automatically analyze and quickly locate the target abnormality cause of the target order, thereby improving the analysis speed, the analysis accuracy, and the analysis efficiency, reducing the negative gross profit proportion, and further improving the income.
Example 2
As shown in fig. 2, the analysis method of the cause of the gross profit abnormality of the order of the present embodiment includes:
after step S101, before step S102, the method further comprises:
s10201, presetting priorities among different abnormality cause types;
s10202, presetting priorities among abnormality reasons corresponding to the same abnormality reason type;
step S104 includes:
s1041, judging whether order data meet a first preset condition corresponding to the abnormal reason type according to the order of the priority of the abnormal reason types from high to low, if so, determining that the abnormal reason type is a target abnormal reason type corresponding to the target order;
step S105 includes:
s1051, judging whether order data meet a second preset condition corresponding to the abnormality reason in the target abnormality reason type according to the order of the priority of the abnormality reason types from high to low, and if so, determining that the abnormality reason is the target abnormality reason corresponding to the target order.
Specifically, for negative gross profit reasons in the same abnormal cause type, judging according to the priority, and taking one cause with the highest priority; if there are two or more negative hair reasons and the negative hair reasons belong to different negative hair reasons, the negative hair reason with the highest priority is selected according to the priority of the negative hair reasons.
Specifically, according to actual conditions, automatic total unsubscribing among system abnormality causes is set to 46 th level, the unsynchronized system hotel fare is set to 46 th level, and the others are set to 36 th level. Setting each abnormality cause in the special order gross profit abnormality as 12 th level; setting each abnormal reason in the system resource reverse hanging as a 22 th level; setting the base price adjustment anomaly in the service setting reason as 26 th level, setting the preset total base price to be larger than the order amount as 29 th level, setting the order real receipt to be unequal to the receivable as 97 th level, setting the common price yield as 28 th level, setting the order gross profit anomaly as 33 level due to the price change and the like; other reasons for the abnormality are similarly set in priority, and thus will not be described in detail here.
The higher the priority level (the smaller the level value), the earlier the corresponding abnormality cause participates in the judgment analysis.
Step S103 further includes, but is not limited to:
acquiring income data and cost data corresponding to a target order according to the order data;
when the income data is smaller than the cost data, determining that the target order has gross profit abnormality;
acquiring profit data (such as profit margin) corresponding to the target order according to the order data;
judging whether the profit data is smaller than a first set threshold value, if so, determining that the target order has gross profit abnormality, namely, when the profit margin is smaller than a set proportion, indicating that the target order has gross profit abnormality.
In order to analyze the abnormal condition of each target order in real time, a QMQ (message queue) mechanism is adopted to realize asynchronous processing. The object for sending QMQ notification message includes a payment subsystem, a transaction subsystem, a resource subsystem, a tax printing subsystem, a return platform, etc.
Specifically, whether the base price data, the selling price data and the income data corresponding to the order data in the data table are changed or not is judged in real time, when at least one of the base price data, the selling price data and the income data in the order data is changed, the change of the order data is determined, and a notification message of the change of the order data of the target order is generated, namely, the change of the order data of the target order can be monitored in real time, notification is timely carried out, and meanwhile, the condition of judging whether the target order after the change of the order data has gross profit abnormality or not is timely triggered, namely, real-time analysis of the target order is realized.
If the target order has gross profit abnormality, the latest order data of the target order is subjected to table falling, and the occurrence of an event is processed for corresponding business personnel, so that the target order gross profit abnormality is solved in time, and further negative gross profit is avoided.
In order to avoid the situation that order gross profit is not updated in time due to the loss of notification messages or other reasons, a Qschedule (timing mechanism) is adopted to update order data of a target order in a timing way, and the gross profit situation of the list-falling order is refreshed.
If the order is changed to normal, the data table is updated.
Step S1051 is followed by:
s106, generating a reminding message for processing the abnormal situation.
The floor data of the gross profit abnormality are synchronously generated to service personnel, the service personnel confirms corresponding orders after receiving the reminding message, and meanwhile, developers intervene in the problems existing in the analysis system, so that the orders of the gross profit abnormality can be effectively processed in time.
The following description is made in connection with specific examples:
details of negative bristled feedback at 2019/5/24-2019/5/30, see tables 1 and 2 (tables 1 and 2 each correspond to feedback rate during this period):
TABLE 1
| Push as expected | Is that | Whether or not | Totals to | Feedback rate |
| 2019/5/24 | 13 | 2 | 15 | 87％ |
| 2019/5/25 | 16 | 3 | 19 | 84％ |
| 2019/5/26 | 8 | 6 | 14 | 57％ |
| 2019/5/27 | 22 | 5 | 27 | 81％ |
| 2019/5/28 | 14 | 3 | 17 | 82％ |
| 2019/5/29 | 6 | 5 | 11 | 55％ |
| 2019/5/30 | 4 | 8 | 12 | 33％ |
| Totals to | 83 | 32 | 115 | 72％ |
TABLE 2
From the contents of the two tables, it can be known that the quantity of correction orders (orders with gross profit abnormality) is counted up to 166, and the corresponding negative gross profit total is 53.1 ten thousand, namely, the analysis accuracy of gross profit abnormality is improved, and the negative gross profit total is effectively reduced.
In addition, as shown in fig. 3, after the bristled anomaly analysis scheme of the present embodiment is adopted, the negative bristled ratio of a certain internet platform is reduced within half a year of 2018/12/18-2019/5/19. The horizontal axis represents time and the vertical axis represents specific gravity of a negative gross order. As shown in fig. 4, the horizontal axis represents time and the vertical axis represents the negative gross profit margin of the service. Practice proves that the negative gross profit ratio is reduced by 34% in the period, the corresponding annual income exceeds 200 ten thousand, the business negative gross profit is reduced by 13.78%, and the accuracy of the automatic analysis of gross profit abnormality reaches 90%, namely the analysis speed and accuracy of gross profit abnormality are effectively improved.
In addition, when order data of each target order is monitored in real time, step S103 further includes:
counting the number of first orders corresponding to the target orders with gross profit abnormality in a first set time period;
and when the first order quantity is greater than or equal to the second set threshold value, generating first alarm information.
Specifically, as shown in fig. 5, the analysis system monitors the number of the abnormality reasons corresponding to each time point in the day in real time, the horizontal axis represents each time point in the day, the vertical axis represents the number of the abnormality reasons, and the different abnormality reasons correspond to one curve.
As shown in fig. 6, the ratio distribution of the number of each abnormality cause to the total number of orders in the first set time is shown. When a certain duty ratio exceeds a set value, the existence of abnormality is indicated to require further confirmation by service personnel.
When monitoring the order data of each target order in the history N days, the step S3 further includes:
counting the number of second orders corresponding to the target orders with gross profit abnormality in a second set time period; wherein N is more than or equal to 1 and N is an integer;
and when the second order quantity is greater than or equal to a third set threshold value, generating second alarm information.
The condition of occurrence of the gross profit abnormality can be more comprehensively found through three-level monitoring by monitoring order data of each day, the previous week and the past N days, and meanwhile, the condition is matched with business personnel to check and confirm whether the abnormality actually occurs.
Specifically, as shown in fig. 7, the transaction subsystem negative gross profit monitoring table (order negative gross profit for the past week in 2019, 6, 10 days).
Specifically, as shown in fig. 8, an order situation in which negative gross profit occurs in the past N days is shown.
In addition, the alarm can be specifically performed by means of mail, for example, the alarm mail includes the following contents: time 2019-06-06 11:01:00 to 2019-06-06 11:02:00, and table 3 below:
TABLE 3 Table 3
| Monitoring item names | Polymerization mode | Environment (environment) | Actual response value | Alarm threshold |
| Order gross profit abnormality | Sum | prod | 11.0 | 10.0 |
In the monitoring system, the situation that negative gross profit is caused is reflected through business events, such as train ticket full position, hotel full room, air ticket full position, flight cancellation, flight change and the like; acquiring the backward proportion of resources or products through VBK (provider subsystem), the unsubscribing proportion of providers and the like; and finally submitting the order with the questionable amount to a second line department by staff, submitting the event to technical processing by the second line department, and repairing the system in time when the technical finding that the order is abnormal is a system problem, so that the problem is fed back, analyzed and processed in time, and the overall working efficiency is improved.
In the embodiment, when the target order has the gross profit abnormality, the type of the target abnormality reason corresponding to the target order is quickly positioned, and then the target abnormality reason corresponding to the target order is quickly positioned, so that automatic analysis and quick positioning are realized to obtain the target abnormality reason of the target order with negative gross profit, the analysis speed is high, the analysis accuracy is improved, the analysis efficiency is improved, the negative gross profit proportion is reduced, and the income is further improved; at the same time, daily and past N days of order data are monitored to more fully discover the occurrence of gross profit anomalies.
Example 3
As shown in fig. 9, the analysis system of the cause of the gross profit abnormality of the order of the present embodiment includes a preset module 1, an order data acquisition module 2, a first judgment module 3, a second judgment module 4, a target type determination module 5, a third judgment module 6, and a target cause determination module 7.
The presetting module 1 is used for presetting various abnormality cause types corresponding to different orders with gross profit abnormality;
wherein each abnormality cause type corresponds to at least one abnormality cause.
Specifically, the types of abnormality reasons corresponding to different orders can be preset by presetting the types of abnormality reasons corresponding to orders with gross profit abnormality in the historical setting time and the types of other abnormality reasons newly added according to the actual demand.
For example, for the OTA industry, the types of causes of anomaly include mainly 7 types, specifically: system abnormality reasons, special order gross profit abnormality, business setting reasons, OP operation abnormality, system resource hanging, channel yield, other negative gross profit and the like.
Among them, the causes of system abnormality include the following 9 types: the system hotel refund fee is not synchronous, the air ticket refund fee is synchronous (checked), the agent occupation list is other, the agent occupation list is totally refunded, the automatic totally refund is failed, the system operation base price is adjusted, the agent occupation list is adjusted to the OP price, and the like.
Specific order gross profit anomalies include 8 types as follows: the self-management and place-occupying single negative gross profit, local guide negative gross profit, lead negative gross profit, ticket spelling negative gross profit, cancel negative gross profit, local pass negative gross profit, tourist negative gross profit, custom game negative gross profit and the like.
The service setting reasons include the following 17 kinds: abnormal bottom price adjustment, train ticket filling, hotel filling, air ticket filling, flight cancellation, flight variation, predetermined total bottom price greater than order amount, business yield, loss, coupon (profit offset), eating price/saving price, selectable bottom selling price hanging, single option airliner bottom selling price hanging, order real collection unequal to receivable, hundred-way trade price yield, order gross profit abnormality caused by price change, and the like.
OP operational anomalies included the following 5: manual total refund, option operations, partial refund, total refund resources not cancelled, total refund fare refund, etc.
The system resource reverse hanging comprises the following 7 types: system air ticket resource hanging, hotel bottom selling price hanging, playing resource hanging, insurance resource hanging, entrance ticket resource hanging, train ticket resource hanging, and the like.
Channel offers include the following 2 types: trade price giving order negative gross profit, hotel trade price bottom selling price hanging upside down, etc.
Other negative gross edges include 8 as follows: abnormal hundred-pass standard single gross profit, abnormal guide order gross profit, negative gross profit amount less than 10 yuan, order negative gross profit of other reasons, hundred-pass standard single unsubscribe, hundred-pass standard single-OP price adjustment, ultrahigh gross profit, normal gross profit and the like.
S102, acquiring order data of a target order;
the order data of each target order is stored in a data table, and specifically, the data table comprises a unique key, an order number, a service type, a service name, a settlement running number, a status code, an information code, a table insertion time, a negative gross code, negative gross information, order income, order cost, order profit, order type, preset time, departure time, return time, final update time, product id, product manager and the like.
The data table is stored according to each data in the tables of fields, field names, types, original fields, indexes and the like. For example, for table insertion time, field: datacreate_lasttime, field name: table insertion time, type: datetime, original field: datacreatesttime; other data in the table is also stored in the table in a similar manner as described above, and therefore will not be described in detail here.
The order data acquisition module 2 is used for acquiring order data of a target order;
the first judging module 3 is used for judging whether the target order has gross profit abnormality according to the order data, and if so, the second judging module is called;
the second judging module 4 is configured to judge whether the order data meets a first preset condition corresponding to the abnormal cause type, and if yes, call the target type determining module 5 to determine that the abnormal cause type is a target abnormal cause type corresponding to the target order;
wherein, each abnormal reason type corresponds to a different first preset condition;
the third judging module 6 is configured to judge whether the order data meets a second preset condition corresponding to an abnormal cause in the target abnormal cause types, and if yes, call the target cause determining module 7 to determine that the abnormal cause is a target abnormal cause corresponding to the target order;
Wherein each abnormality cause corresponds to a different second preset condition.
For example, taking an order for negative gross profit as an example, a hotel full:
(1) If the income of the order is less than the cost, determining that the order has negative gross profit;
(2) If the order has the event that the hotel is full, and the total selling price of the hotel of the order is smaller than the total base price of the hotel of the order, and the difference and the negative gross profit of the order meet certain conditions, the reason that the order is negative is indicated to be that the hotel is full.
The determination of whether the order is of other abnormality causes is similar to the above determination, and will not be described here.
In this embodiment, when there is a gross profit abnormality in the target order, the target abnormality cause type corresponding to the target order is obtained first, and when there is a gross profit abnormality in the target order, the target abnormality cause type corresponding to the target order is located first and then the target abnormality cause corresponding to the target order is located quickly, so as to realize automatic analysis and quick location to obtain the target abnormality cause of the target order with negative gross profit, thereby improving the analysis speed and the analysis accuracy, and improving the analysis efficiency, thereby reducing the negative gross profit proportion and further improving the income.
Example 4
As shown in fig. 10, the analysis system of the cause of the gross profit abnormality of the order of the present embodiment is a further improvement of embodiment 3, specifically:
the presetting module 1 is also used for presetting priorities among different abnormality cause types;
the presetting module 1 is further used for presetting priorities among the abnormality reasons corresponding to the same abnormality reason type;
the second judging module 4 is configured to sequentially judge whether the order data meets a first preset condition corresponding to the abnormal cause type according to the order of the priority of the abnormal cause types from high to low, and if so, call the target type determining module 5 to determine that the abnormal cause type is a target abnormal cause type corresponding to the target order;
the third determining module 6 is configured to sequentially determine, according to the order of the priority of the abnormality cause types from high to low, whether the order data meets a second preset condition corresponding to the abnormality cause in the target abnormality cause type, and if yes, call the target cause determining module 7 to determine that the abnormality cause is a target abnormality cause corresponding to the target order.
Specifically, for negative gross profit reasons in the same abnormal cause type, judging according to the priority, and taking one cause with the highest priority; if there are two or more negative hair reasons and the negative hair reasons belong to different negative hair reasons, the negative hair reason with the highest priority is selected according to the priority of the negative hair reasons.
Specifically, according to actual conditions, automatic total unsubscribing among system abnormality causes is set to 46 th level, the unsynchronized system hotel fare is set to 46 th level, and the others are set to 36 th level. Setting each abnormality cause in the special order gross profit abnormality as 12 th level; setting each abnormal reason in the system resource reverse hanging as a 22 th level; setting the base price adjustment anomaly in the service setting reason as 26 th level, setting the preset total base price to be larger than the order amount as 29 th level, setting the order real receipt to be unequal to the receivable as 97 th level, setting the common price yield as 28 th level, setting the order gross profit anomaly as 33 level due to the price change and the like; other reasons for the abnormality are similarly set in priority, and thus will not be described in detail here.
The higher the priority level (the smaller the level value), the earlier the corresponding abnormality cause participates in the judgment analysis.
In addition, the first judging module 3 is used for acquiring income data and cost data corresponding to the target order according to the order data;
the first judging module 3 is further used for determining that the target order has gross profit abnormality when the income data is smaller than the cost data; and/or the number of the groups of groups,
the first judging module 3 is used for acquiring profit data (such as profit margin) corresponding to the target order according to the order data;
The first determining module 3 is further configured to determine whether the profit data is smaller than a first set threshold, and if so, determine that the target order has a gross profit abnormality, that is, if the profit margin is smaller than a set proportion, then indicate that the target order has a gross profit abnormality.
In order to analyze the abnormal condition of each target order in real time, a QMQ mechanism is adopted to realize asynchronous processing. The object for sending QMQ notification message includes a payment subsystem, a transaction subsystem, a resource subsystem, a tax printing subsystem, a return platform, etc. Specifically, the analysis system further includes a change determination module and a timing update module.
The change determining module determines whether the base price data, the selling price data and the income data corresponding to the order data in the data table are changed in real time, when at least one of the base price data, the selling price data and the income data in the order data is changed, the change of the order data is determined, and a notification message of the change of the order data of the target order is generated, namely, the change of the order data of the target order can be monitored in real time, notification is timely carried out, and meanwhile, the condition of judging whether the target order after the change of the order data has gross profit abnormality is timely triggered, namely, real-time analysis of the target order is realized.
If the target order has gross profit abnormality, the latest order data of the target order is subjected to table falling, and the occurrence of an event is processed for corresponding business personnel, so that the target order gross profit abnormality is solved in time, and further negative gross profit is avoided.
In order to avoid the situation that order gross profit is not updated in time due to the loss of notification messages or other reasons, a Qschedule is adopted, order data of a target order is updated in a timing mode by adopting a timing updating module, and the gross profit situation of the order which is already in a list is refreshed.
If the order is changed to normal, the data table is updated.
The analysis system further comprises a reminder message generation module 8.
The reminding message generating module 8 is used for generating a reminding message for processing abnormal conditions.
The floor data of the gross profit abnormality are synchronously generated to service personnel, the service personnel confirms corresponding orders after receiving the reminding message, and meanwhile, developers intervene in the problems existing in the analysis system, so that the orders of the gross profit abnormality can be effectively processed in time.
The following description is made in connection with specific examples:
details of negative bristled feedback at 2019/5/24-2019/5/30, see tables 1 and 2 (tables 1 and 2 each correspond to feedback rate during this period):
TABLE 1
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TABLE 2
| Service type | Is that | Whether or not | Totals to | Feedback rate |
| Domestic agency | 2 | 2 | 0％ | |
| Domestic self-nutrient | 13 | 13 | 0％ | |
| Overseas self-nutrient | 38 | 1 | 39 | 97％ |
| Jing Jiu housekeeper | 1 | 3 | 4 | 25％ |
| Free travel | 44 | 13 | 57 | 77％ |
| Totals to | 83 | 32 | 115 | 72％ |
From the contents of the two tables, it can be known that the quantity of correction orders (orders with gross profit abnormality) is counted up to 166, and the corresponding negative gross profit total is 53.1 ten thousand, namely, the analysis accuracy of gross profit abnormality is improved, and the negative gross profit total is effectively reduced.
In addition, as shown in fig. 3, after the bristled anomaly analysis scheme of the present embodiment is adopted, the negative bristled ratio of a certain internet platform is reduced within half a year of 2018/12/18-2019/5/19. The horizontal axis represents time and the vertical axis represents specific gravity of a negative gross order. As shown in fig. 4, the horizontal axis represents time and the vertical axis represents the negative gross profit margin of the service. Practice proves that the negative gross profit ratio is reduced by 34% in the period, the corresponding annual income exceeds 200 ten thousand, the business negative gross profit is reduced by 13.78%, and the accuracy of the automatic analysis of gross profit abnormality reaches 90%, namely the analysis speed and accuracy of gross profit abnormality are effectively improved.
In addition, the analysis system also comprises a statistics module 9 and an alarm information generation module 10.
When order data of each target order is monitored in real time, the statistics module 9 is used for counting first order quantity corresponding to the target orders with gross profit abnormality in a first set time period;
The alarm information generating module 10 is configured to generate first alarm information when the first order number is greater than or equal to a second set threshold.
Specifically, as shown in fig. 5, the analysis system monitors the number of the abnormality reasons corresponding to each time point in the day in real time, the horizontal axis represents each time point in the day, the vertical axis represents the number of the abnormality reasons, and the different abnormality reasons correspond to one curve.
As shown in fig. 6, the ratio distribution of the number of each abnormality cause to the total number of orders in the first set time is shown. When a certain duty ratio exceeds a set value, the existence of abnormality is indicated to require further confirmation by service personnel.
When monitoring the order data of each target order in the history N days, the statistics module 9 is used for counting the second order quantity corresponding to the target orders with gross profit abnormality in a second set time period; wherein N is more than or equal to 1 and N is an integer;
the alarm information generating module 10 is configured to generate second alarm information when the second order number is greater than or equal to a third set threshold.
The condition of occurrence of the gross profit abnormality can be more comprehensively found through three-level monitoring by monitoring order data of each day, the previous week and the past N days, and meanwhile, the condition is matched with business personnel to check and confirm whether the abnormality actually occurs.
Specifically, as shown in fig. 7, the transaction subsystem negative gross profit monitoring table (order negative gross profit for the past week in 2019, 6, 10 days).
Specifically, as shown in fig. 8, an order situation in which negative gross profit occurs in the past N days is shown.
In addition, the alarm can be specifically performed by means of mail, for example, the alarm mail includes the following contents: time 2019-06-06 11:01:00 to 2019-06-06 11:02:00, and table 3 below:
TABLE 3 Table 3
| Monitoring item names | Polymerization mode | Environment (environment) | Actual response value | Alarm threshold |
| Order gross profit abnormality | Sum | prod | 11.0 | 10.0 |
In the monitoring system, the situation that negative gross profit is caused is reflected through business events, such as train ticket full position, hotel full room, air ticket full position, flight cancellation, flight change and the like; acquiring the backward proportion of resources or products through VBK, and unsubscribing the proportion of suppliers and the like; and finally submitting the order with the questionable amount to a second line department by staff, submitting the event to technical processing by the second line department, and repairing the system in time when the technical finding that the order is abnormal is a system problem, so that the problem is fed back, analyzed and processed in time, and the overall working efficiency is improved.
In the embodiment, when the target order has the gross profit abnormality, the type of the target abnormality reason corresponding to the target order is quickly positioned, and then the target abnormality reason corresponding to the target order is quickly positioned, so that automatic analysis and quick positioning are realized to obtain the target abnormality reason of the target order with negative gross profit, the analysis speed is high, the analysis accuracy is improved, the analysis efficiency is improved, the negative gross profit proportion is reduced, and the income is further improved; at the same time, daily and past N days of order data are monitored to more fully discover the occurrence of gross profit anomalies.
Example 5
Fig. 11 is a schematic structural diagram of an electronic device according to embodiment 5 of the present invention. The electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the method of analyzing the cause of the gross profit abnormality of the order in any one of embodiments 1 or 2 when executing the program. The electronic device 30 shown in fig. 11 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 11, the electronic device 30 may be embodied in the form of a general purpose computing device, which may be a server device, for example. Components of electronic device 30 may include, but are not limited to: the at least one processor 31, the at least one memory 32, a bus 33 connecting the different system components, including the memory 32 and the processor 31.
The bus 33 includes a data bus, an address bus, and a control bus.
Memory 32 may include volatile memory such as Random Access Memory (RAM) 321 and/or cache memory 322, and may further include Read Only Memory (ROM) 323.
Memory 32 may also include a program/utility 325 having a set (at least one) of program modules 324, such program modules 324 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The processor 31 executes various functional applications and data processing, such as a method of analyzing the cause of a gross profit abnormality of an order in any of embodiments 1 or 2 of the present invention, by running a computer program stored in the memory 32.
The electronic device 30 may also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). Such communication may be through an input/output (I/O) interface 35. Also, model-generating device 30 may also communicate with one or more networks, such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet, via network adapter 36. As shown in fig. 11, network adapter 36 communicates with the other modules of model-generating device 30 via bus 33. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in connection with the model-generating device 30, including, but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, data backup storage systems, and the like.
It should be noted that although several units/modules or sub-units/modules of an electronic device are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module in accordance with embodiments of the present invention. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.
Example 12
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps in the method of analyzing the cause of a gross profit abnormality of an order in either of embodiments 1 or 2.
More specifically, among others, readable storage media may be employed including, but not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps of the analysis method of the cause of a gross profit anomaly of an order in any one of embodiments 1 or 2 when the program product is run on the terminal device.
Wherein the program code for carrying out the invention may be written in any combination of one or more programming languages, the program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device, partly on a remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the principles and spirit of the invention, but such changes and modifications fall within the scope of the invention.
Claims (12)
1. A method for analyzing causes of gross profit anomalies in an order, the method comprising:
s1, presetting a plurality of abnormality cause types corresponding to different orders with gross profit abnormality;
wherein each abnormality cause type corresponds to at least one abnormality cause;
s2, acquiring order data of a target order;
s3, judging whether the target order has gross profit abnormality according to the order data, and if so, executing a step S4;
s4, judging whether the order data meets a first preset condition corresponding to the abnormal reason type, if so, determining that the abnormal reason type is a target abnormal reason type corresponding to the target order;
wherein each abnormality cause type corresponds to a different first preset condition;
S5, judging whether the order data meets a second preset condition corresponding to the abnormality reason in the target abnormality reason type, if yes, determining that the abnormality reason is a target abnormality reason corresponding to the target order;
wherein each abnormality cause corresponds to a different second preset condition;
the step S4 includes:
s41, sequentially judging whether the order data meets a first preset condition corresponding to the abnormal reason type according to the order of the priority of the abnormal reason type from high to low, and if so, determining that the abnormal reason type is a target abnormal reason type corresponding to the target order;
the step S5 comprises the following steps:
s51, judging whether the order data meets a second preset condition corresponding to the abnormality reason in the target abnormality reason type according to the order of the priority of the abnormality reason type from high to low, and if so, determining that the abnormality reason is the target abnormality reason corresponding to the target order.
2. The method for analyzing the cause of the abnormality of the order according to claim 1, further comprising, after step S1 and before step S2:
s201, presetting priorities among different abnormality cause types;
S202, presetting priorities among the abnormality reasons corresponding to the same abnormality reason type.
3. The method for analyzing the cause of the abnormality of the gross profit of the order as set forth in claim 1, wherein the step S3 includes:
acquiring income data and cost data corresponding to the target order according to the order data;
when the revenue data is less than the cost data, determining that a gross profit anomaly exists for the target order; and/or the number of the groups of groups,
acquiring profit data corresponding to the target order according to the order data;
judging whether the profit data is smaller than a first set threshold value, and if so, determining that the target order has gross profit abnormality.
4. The method for analyzing the cause of the abnormality of the order according to claim 1, further comprising, after step S2 and before step S3:
when at least one of the base price data, the selling price data and the income data in the order data changes, determining that the order data changes, and generating a notification message of the change of the order data of the target order; and/or the number of the groups of groups,
periodically updating the order data of the target order; and/or the number of the groups of groups,
Step S5 is followed by:
and generating a reminding message for processing the abnormal situation.
5. The method of analyzing causes of gross profit anomalies in orders according to claim 1, further comprising, after step S3, when monitoring the order data of each of the target orders in real time:
counting the number of first orders corresponding to the target orders with gross profit abnormality in a first set time period;
when the first order quantity is larger than or equal to a second set threshold value, generating first alarm information; and/or the number of the groups of groups,
when monitoring the order data of each of the target orders for N days of history, step S3 further comprises:
counting the number of second orders corresponding to the target orders with gross profit abnormality in a second set time period; wherein N is more than or equal to 1 and N is an integer;
and when the second order quantity is larger than or equal to a third set threshold value, generating second alarm information.
6. The analysis system for the cause of the gross profit abnormality of the order is characterized by comprising a preset module, an order data acquisition module, a first judgment module, a second judgment module, a target type determination module, a third judgment module and a target cause determination module;
The presetting module is used for presetting a plurality of abnormality cause types corresponding to different orders with gross profit abnormality;
wherein each abnormality cause type corresponds to at least one abnormality cause;
the order data acquisition module is used for acquiring order data of a target order;
the first judging module is used for judging whether the target order has a gross profit abnormality according to the order data, and if so, the second judging module is called;
the second judging module is used for judging whether the order data meets a first preset condition corresponding to the abnormal cause type, and if so, the target type determining module is called to determine that the abnormal cause type is a target abnormal cause type corresponding to the target order;
wherein each abnormality cause type corresponds to a different first preset condition;
the third judging module is used for judging whether the order data meets a second preset condition corresponding to the abnormal reason in the target abnormal reason type, and if so, the target reason determining module is called to determine that the abnormal reason is a target abnormal reason corresponding to the target order;
wherein each abnormality cause corresponds to a different second preset condition;
The second judging module is used for judging whether the order data meets a first preset condition corresponding to the abnormal reason type according to the order of the priority of the abnormal reason type from high to low, and if so, the target type determining module is called to determine that the abnormal reason type is a target abnormal reason type corresponding to the target order;
the third judging module is configured to sequentially judge whether the order data meets a second preset condition corresponding to the abnormality reason in the target abnormality reason type according to the order of the abnormality reason type from high to low, and if yes, call the target reason determining module to determine that the abnormality reason is a target abnormality reason corresponding to the target order.
7. The system for analyzing the causes of gross profit anomalies in an order according to claim 6, wherein the presetting module is further used for presetting priorities among different types of anomalies;
the preset module is further configured to preset a priority between the abnormality reasons corresponding to the same abnormality reason type.
8. The system for analyzing the cause of a gross profit abnormality of an order according to claim 6, wherein the first judging module is configured to obtain revenue data and cost data corresponding to the target order according to the order data;
The first judging module is further used for determining that the target order has gross profit abnormality when the income data is smaller than the cost data; and/or the number of the groups of groups,
the first judging module is used for acquiring profit data corresponding to the target order according to the order data;
the first judging module is further used for judging whether the profit data is smaller than a first set threshold value, and if so, determining that the target order has gross profit abnormality.
9. The system for analyzing causes of gross profit anomalies in an order according to claim 6, further comprising a change determination module;
the change determining module is used for determining that the order data changes when at least one of the base price data, the selling price data and the income data in the order data changes, and generating a notification message of the change of the order data of the target order; and/or the number of the groups of groups,
the analysis system also comprises a timing update module;
the timing updating module is used for updating the order data of the target order at fixed time; and/or the number of the groups of groups,
the analysis system also comprises a reminding message generation module;
the reminding message generation module is used for generating a reminding message for processing abnormal conditions.
10. The system for analyzing causes of gross profit anomalies of orders according to claim 6, further comprising a statistics module and an alert information generation module;
when the order data of each target order is monitored in real time, the statistics module is used for counting the first order quantity corresponding to the target orders with gross profit abnormality in a first set time period;
the alarm information generation module is used for generating first alarm information when the first order number is larger than or equal to a second set threshold value; and/or the number of the groups of groups,
when monitoring the order data of each target order in the historical N days, the statistics module is used for counting the second order quantity corresponding to the target orders with gross profit abnormality in a second set time period; wherein N is more than or equal to 1 and N is an integer;
the alarm information generation module is used for generating second alarm information when the second order number is larger than or equal to a third set threshold value.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method of analyzing the cause of a gross profit anomaly of an order as claimed in any one of claims 1 to 5.
12. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method for analyzing the cause of a gross profit anomaly of an order according to any one of claims 1 to 5.
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| CN107067178A (en) * | 2017-04-18 | 2017-08-18 | 携程计算机技术（上海）有限公司 | Order method for evaluating quality and system |
| CN107707376A (en) * | 2017-06-09 | 2018-02-16 | 贵州白山云科技有限公司 | A kind of method and system for monitoring and alerting |
| CN108230120A (en) * | 2018-02-07 | 2018-06-29 | 上海携程商务有限公司 | Method, system, equipment and the storage medium of order price abnormal monitoring |
| CN109285056A (en) * | 2018-09-28 | 2019-01-29 | 江苏满运软件科技有限公司 | A kind of processing method and system of exception order |
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