Monitoring system and method based on business operation data brain

Abstract

The invention discloses a monitoring system and method based on a business operation data brain, relates to the field of business, and solves the problem that an existing monitoring system based on the business operation data brain is poor in monitoring effect. Comprising a data acquisition module which is used for obtaining a business period sales index value, business period evaluation data and a period income index value of each target market business in a market business monitoring period to obtain business operation basic data, the data processing module is used for carrying out visual analysis on the favorable comment proportion of each target market business in the market business monitoring period according to the business operation basic data to obtain periodic favorable comment processing data, and the risk monitoring module is used for carrying out operation risk early warning on the target market business according to the business operation basic data and the periodic good reputation processing data. According to the invention, the accuracy and the real-time performance of a business monitoring result can be improved.

Description

Technical Field

[0001] The present invention belongs to the commercial field and relates to data analysis technology, specifically a monitoring system and method based on a business operation data brain. Background Art

[0002] The existing monitoring system based on the business operation data brain has the following defects when monitoring the duration business data:

[0003] 1. The existing monitoring system based on the business operation data brain uses business revenue data to conduct business operation risk warnings, which may lead to a lack of comprehensiveness in the warning results;

[0004] 2. The existing monitoring system based on the business operation data brain can only monitor the business operation data of a past historical period, and cannot dynamically update the monitoring period corresponding to the business operation data, which leads to the lack of real-time and continuity of the monitoring results.

[0005] To this end, we propose a monitoring system and method based on the business operation data brain. Summary of the invention

[0006] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a monitoring system and method based on a business operation data brain. The present invention obtains the business cycle sales index value, business cycle evaluation data and cycle revenue index value of each target market business in the market business monitoring cycle to obtain the basic business operation data, and performs a visual analysis on the proportion of positive reviews of each target market business in the market business monitoring cycle according to the basic business operation data, and obtains the coefficient of change of the proportion of periodic positive reviews of each target market business in the market business monitoring cycle to obtain periodic positive review processing data, and obtains the operating efficiency coefficient corresponding to each target market business according to the basic business operation data and the periodic positive review processing data, and obtains the operating efficiency coefficient threshold value and compares it with each operating efficiency coefficient respectively, and issues an operating risk warning for the target market business according to the numerical comparison result.

[0007] In order to achieve the above purpose, the present invention adopts the following technical solution: Based on the monitoring system of the business operation data brain, the specific working process of each module is as follows:

[0008] Data collection module: used to obtain the business cycle sales index value, business cycle evaluation data and cycle revenue index value of each target market business within the market business monitoring cycle, and obtain the basic data of business operation;

[0009] Data processing module: used to visualize the proportion of favorable comments of each target market business within the market business monitoring cycle according to the basic business operation data, and obtain the change coefficient of the periodic favorable comments proportion of each target market business within the market business monitoring cycle, and obtain the periodic favorable comments processing data;

[0010] Risk monitoring module: used to obtain the operating efficiency coefficient corresponding to each target market business based on the basic business operation data and periodic positive evaluation processing data, and obtain the operating efficiency coefficient threshold and compare it with each operating efficiency coefficient respectively, and issue operating risk warnings for the target market business based on the numerical comparison results.

[0011] Further, the data acquisition module includes a sales data unit, a customer feedback unit, and a revenue data unit;

[0012] Acquire multiple market businesses carried out by the company to obtain multiple target market businesses, and randomly select one target market business from the multiple target market businesses obtained as a characteristic market business;

[0013] In the process of business monitoring of multiple target market businesses, the time value corresponding to the current moment is marked as a first business monitoring time point, a characteristic business monitoring period before the first business monitoring time point is marked as a second business monitoring time point, and the period between the first business monitoring time point and the second business monitoring time point is marked as a market business monitoring cycle;

[0014] The sales data unit collects and analyzes the evaluation data of the characteristic market business in the market business monitoring cycle to obtain the business cycle evaluation data corresponding to the characteristic market business;

[0015] Acquire the business cycle sales index value of each target market business within the market business monitoring period to obtain multiple business cycle sales index values;

[0016] The customer feedback unit collects evaluation data of the characteristic market business in the market business monitoring cycle to obtain business cycle evaluation data corresponding to the target market business;

[0017] Acquire the business cycle evaluation data of each target market business within the market business monitoring cycle to obtain multiple business cycle evaluation data;

[0018] The revenue data unit collects and analyzes the revenue data of the characteristic market business in the market business monitoring period to obtain the periodic revenue index value corresponding to the characteristic market business;

[0019] The periodic revenue index value of each target market business within the market business monitoring period is obtained respectively to obtain multiple periodic revenue index values;

[0020] A plurality of business cycle sales index values, a plurality of business cycle evaluation data, and a plurality of cycle revenue index values ​​are defined as basic business operation data.

[0021] Furthermore, the sales data unit obtains the business cycle sales index value corresponding to the target market business;

[0022] The details are as follows:

[0023] The market business monitoring cycle is divided into a number of sales monitoring sub-periods of equal duration, and the divided sales monitoring sub-periods are named as the first sales monitoring sub-period to the ath sales monitoring sub-period in chronological order;

[0024] Obtain the number of business orders corresponding to the target market business in the first sales monitoring sub-period to the a-th sales monitoring sub-period, and obtain the order volume from the first period to the a-th period;

[0025] Obtain the business order volume corresponding to the same type of market business in the target market business from the first sales monitoring sub-period to the a-th sales monitoring sub-period through big data, and obtain the total volume of similar orders from the first period to the a-th period;

[0026] Obtain the corresponding ratios of the order volume from the first period to the order volume of the ath period to the total amount of similar orders from the first period to the total amount of similar orders from the ath period, and obtain the market share of orders from the first period to the market share of orders from the ath period;

[0027] Obtain the historical sales data corresponding to the target market business, obtain the business order volume of the target market business in each historical monitoring sub-period based on the historical sales data, obtain multiple historical business order volumes, compare the numerical values ​​of the multiple historical business order volumes, and mark the historical business order volume with the largest numerical value as the historical best order volume;

[0028] Calculate the ratios of the order volume from the first period to the ath period to the historical best order volume, and obtain the ratio of the historical order volume from the first period to the historical order volume from the ath period;

[0029] The business cycle sales index value corresponding to the target market business is obtained by calculating the order volume from the first period to the order volume from the ath period, the market share of orders from the first period to the market share of orders from the ath period, and the historical order ratio from the first period to the historical order ratio from the ath period;

[0030] Calculate the business cycle sales index value corresponding to the characteristic market business. The specific formula is as follows:

[0031]

[0032] Among them, Xzb is the business cycle sales indicator value corresponding to the characteristic market business, Dll1 to Dlla are the order volume from the first period to the order volume in the ath period, Dsb1 to Dsba are the market share of orders from the first period to the market share of orders in the ath period, and Ldb1 to Ldba are the historical order ratio from the first period to the historical order ratio from the ath period.

[0033] Furthermore, the customer feedback unit acquires the business cycle evaluation data corresponding to the target market business, specifically as follows:

[0034] The market business monitoring cycle is divided into a number of evaluation monitoring sub-periods of equal duration, and the divided evaluation monitoring sub-periods are named as the first evaluation monitoring sub-period to the bth evaluation monitoring sub-period in chronological order;

[0035] Respectively obtain the time period business evaluation content data corresponding to the first evaluation monitoring sub-period to the bth evaluation monitoring sub-period of the characteristic market business, and obtain the first time period business evaluation content data to the bth time period business evaluation content data;

[0036] Create a model to differentiate between evaluation types;

[0037] Obtain the percentage of favorable comments on the characteristic market business in the period corresponding to the first evaluation monitoring sub-period, and obtain the percentage of favorable comments on the business in the first period;

[0038] The business favorable comments ratios of the time periods corresponding to the second evaluation monitoring sub-period to the bth evaluation monitoring sub-period are obtained respectively, and the business favorable comments ratios of the second period to the bth period are obtained;

[0039] The proportion of favorable business comments in the first period to the proportion of favorable business comments in the bth period are defined as the business cycle evaluation data corresponding to the characteristic market business.

[0040] Furthermore, the customer feedback unit creates an evaluation type differentiation model, which is as follows:

[0041] Obtain historical evaluation data of characteristic market businesses through big data, and obtain multiple text evaluation contents based on the historical evaluation data of characteristic market businesses;

[0042] manually annotating the plurality of text evaluation contents into first-type evaluation contents and second-type evaluation contents to obtain evaluation content annotated data;

[0043] The evaluation content annotation data is divided into an evaluation test set and an evaluation training set according to a preset training test ratio;

[0044] Create a text recognition model through the existing AI model platform, and train the text recognition model using the evaluation test set and the evaluation training set until each text evaluation content in the evaluation training set is trained at least once;

[0045] Use the evaluation test set to test the text recognition model, and obtain the accuracy of the test results to obtain the model recognition accuracy, obtain the target model recognition accuracy, and numerically compare the model recognition accuracy with the target model recognition accuracy. When the model recognition accuracy is greater than or equal to the target model recognition accuracy, the text recognition model training is completed and the evaluation type differentiation model is obtained. When the model recognition accuracy is less than the target model recognition accuracy, continue to use the evaluation training set to train the text recognition model until the model recognition accuracy is greater than or equal to the target model recognition accuracy.

[0046] Furthermore, the customer feedback unit obtains the proportion of favorable reviews of the services in the first period, as follows:

[0047] Using the evaluation type differentiation model to differentiate the evaluation contents of the business evaluation content data of the first period, respectively, obtaining the number of comments corresponding to the first type of evaluation content in the business evaluation content data of the first period, and obtaining the first characteristic number of comments;

[0048] The total number of comments in the business evaluation content data in the first period is numerically obtained to obtain a second characteristic comment quantity value;

[0049] Calculate the ratio of the number of comments with the first feature to the number of comments with the second feature to obtain the percentage of favorable comments on the business in the first period corresponding to the characteristic market business.

[0050] Furthermore, the revenue data unit obtains the periodic revenue index value corresponding to the characteristic market business, as follows:

[0051] Obtain the cumulative investment amount of the characteristic market business during the market business monitoring period, and obtain the cumulative income amount of the characteristic market business during the market business monitoring period;

[0052] Calculate the difference between the cumulative income amount and the cumulative investment amount. If the cumulative income amount is greater than the cumulative investment amount, use the difference between the cumulative income amount and the cumulative investment amount as the periodic income indicator value corresponding to the characteristic market business. If the cumulative income amount is less than or equal to the cumulative investment amount, use the value 0 to assign parameters to the periodic income indicator value corresponding to the characteristic market business.

[0053] Furthermore, the data processing module acquires periodic positive review processing data, specifically as follows:

[0054] Obtain basic business operation data, and obtain business cycle evaluation data corresponding to each target market business based on the basic business operation data;

[0055] Randomly select a target market business from multiple target market businesses as a sample market business;

[0056] Visualize the business cycle evaluation data corresponding to the sample market business to obtain the change coefficient of the proportion of periodic positive reviews corresponding to the sample market business;

[0057] The details are as follows:

[0058] Obtain the first evaluation monitoring sub-period to the bth evaluation monitoring sub-period;

[0059] According to the business cycle evaluation data corresponding to the sample market business, obtain the percentage of business favorable comments in the first period to the percentage of business favorable comments in the bth period;

[0060] In the existing plane rectangular coordinate system model, the evaluation monitoring sub-period is recorded as the coordinate x-axis, and the proportion of good reviews of the period business is marked as the coordinate Y-axis, and the plane rectangular coordinate system of the change of the good reviews period is obtained;

[0061] In the rectangular coordinate system of the plane of the change of the favorable comments period, the first evaluation monitoring sub-period is set as the horizontal coordinate point, and the proportion of favorable comments of the business in the first period is set as the vertical coordinate point to obtain the first favorable comments coordinate point, the second evaluation monitoring sub-period is set as the horizontal coordinate point, and the proportion of favorable comments of the business in the second period is set as the vertical coordinate point to obtain the second favorable comments coordinate point, and so on, the bth evaluation monitoring sub-period is set as the horizontal coordinate point, and the proportion of favorable comments of the business in the bth period is set as the vertical coordinate point to obtain the bth favorable comments coordinate point;

[0062] Mark the first to b-th favorable comment coordinate points in the rectangular coordinate system of the favorable comment period change plane respectively, and connect the first to b-th favorable comment coordinate points in sequence;

[0063] The line between the first favorable comment coordinate point and the second favorable comment coordinate point is marked as the first favorable comment line, the line between the second favorable comment coordinate point and the third favorable comment coordinate point is marked as the second favorable comment line, and so on, the line between the b-1 favorable comment coordinate point and the b-th favorable comment coordinate point is marked as the b-1-th favorable comment line;

[0064] Obtain the line slopes from the first favorable comment line to the b-1th favorable comment line respectively, obtain b-1 line slope values, and calculate the average of the b-1 line slope values ​​to obtain the time change rate of favorable comments corresponding to the market business monitoring period;

[0065] Calculate the average of the percentage of favorable reviews from the first period to the percentage of favorable reviews from the bth period to obtain the average percentage of favorable reviews for each period;

[0066] The average percentage of favorable comments in each period and the rate of change of favorable comments over time are calculated to obtain the coefficient of change of the percentage of favorable comments in each period corresponding to the sample market business;

[0067] The coefficient of change of the proportion of positive reviews in the sample market business is calculated. The specific formula is as follows:

[0068] Hpz=Shp×(1+Zbx)+Shp;

[0069] Among them, Hpz is the coefficient of change of the proportion of positive reviews in the sample market business, Shp is the average proportion of positive reviews in the time period, and Zbx is the time change rate of positive reviews;

[0070] Obtain the change coefficient of the proportion of favorable comments during the market business monitoring period for each target market business, and obtain the change coefficient of the proportion of favorable comments during multiple periods;

[0071] The coefficient of change of the proportion of positive reviews over multiple periods is defined as the periodic positive review processing data.

[0072] Furthermore, the risk monitoring module provides early warning of operating risks for the target market business, as follows:

[0073] Obtain basic business operation data, and obtain the business cycle sales index value and cycle revenue index value corresponding to each target market business based on the basic business operation data;

[0074] Obtain periodic positive review processing data, and obtain the periodic positive review ratio change coefficient corresponding to each target market business based on the periodic positive review processing data;

[0075] The operating efficiency coefficient is obtained by calculating the change coefficient of the proportion of periodic positive reviews, the business periodic sales index value and the periodic revenue index value corresponding to the same target market business;

[0076] The specific formula for calculating the operating efficiency coefficient is as follows:

[0077] Jyx=Syz 2 +Hpz+Xzb

[0078] Among them, Jyx is the operating efficiency coefficient, Hpz is the change coefficient of the proportion of periodic positive reviews, Xzb is the sales index value of the business cycle, and Syz is the value of the periodic income index;

[0079] Obtaining the threshold of the operating efficiency coefficient, comparing the threshold of the operating efficiency coefficient with each operating efficiency coefficient respectively, and conducting an operating risk warning for the target market business based on the numerical comparison results;

[0080] The details are as follows:

[0081] Obtain the change coefficient threshold of the proportion of positive reviews during a period, the sales index threshold of the business period, and the periodic revenue index threshold respectively;

[0082] The threshold of the change coefficient of the proportion of periodic favorable comments, the threshold of the sales index of the business period, and the threshold of the periodic revenue index are calculated to obtain the threshold of the operating efficiency coefficient;

[0083] The operating efficiency coefficient threshold is calculated using the following formula:

[0084] Jyxy=Syzy 2 +Hpzy+Xzby;

[0085] Among them, Jyxy is the threshold of the operating efficiency coefficient, Hpzy is the threshold of the change coefficient of the proportion of periodic positive reviews, Xzby is the threshold of the sales index of the business cycle, and Syzy is the threshold of the periodic profit index;

[0086] When the operating efficiency coefficient is greater than or equal to the operating efficiency coefficient threshold, no operating risk warning will be issued for the corresponding target market business;

[0087] When the operating efficiency coefficient is less than the operating efficiency coefficient threshold, an operating risk warning will be issued for the corresponding target market business.

[0088] The monitoring method based on the business operation data brain includes the following specific steps:

[0089] Step S1: respectively obtain the business cycle sales index value, business cycle evaluation data and cycle revenue index value of each target market business within the market business monitoring cycle to obtain business operation basic data;

[0090] Step S2: Perform a visual analysis of the proportion of favorable comments of each target market business within the market business monitoring cycle according to the basic business operation data, and obtain the change coefficient of the periodic favorable comment proportion of each target market business within the market business monitoring cycle to obtain periodic favorable comment processing data;

[0091] Step S3: Obtain the operating efficiency coefficient corresponding to each target market business based on the business operation basic data and periodic positive review processing data, and obtain the operating efficiency coefficient threshold to compare with each operating efficiency coefficient respectively, and issue an operating risk warning for the target market business based on the numerical comparison results.

[0092] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0093] 1. The present invention monitors the business of the target market through sales data, customer feedback data and project revenue data, which can effectively ensure the comprehensiveness of the monitoring results;

[0094] 2. The present invention can improve the real-time and continuity of monitoring results by dynamically updating the monitoring period corresponding to the business operation data. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0096] Figure 1 is the overall system block diagram of the present invention;

[0097] Figure 2 It is a diagram of the implementation steps of the present invention;

[0098] Figure 3 It is the rectangular coordinate system of the plane of the favorable review period change of the present invention. DETAILED DESCRIPTION

[0099] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0100] Embodiment 1

[0101] See also Figure 1 , the present invention provides a technical solution: a monitoring system based on a business operation data brain, comprising a data acquisition module, a data processing module, a risk monitoring module and a server, wherein the data acquisition module, the data processing module and the risk monitoring module are respectively connected to the server, and the server controls the data acquisition module, the data processing module and the risk monitoring module respectively;

[0102] The data collection module obtains the business cycle sales index value, business cycle evaluation data and cycle revenue index value of each target market business within the market business monitoring cycle to obtain the basic data of business operation;

[0103] The data acquisition module includes a sales data unit, a customer feedback unit, and a revenue data unit;

[0104] Acquire multiple market businesses carried out by the company to obtain multiple target market businesses, and randomly select one target market business from the multiple target market businesses obtained as a characteristic market business;

[0105] In the process of business monitoring of multiple target market businesses, the time value corresponding to the current moment is marked as a first business monitoring time point, a characteristic business monitoring period before the first business monitoring time point is marked as a second business monitoring time point, and the period between the first business monitoring time point and the second business monitoring time point is marked as a market business monitoring cycle;

[0106] It should be noted here that:

[0107] In this application, the time length corresponding to the characteristic service monitoring period is specifically one month;

[0108] In the present application, as the time value corresponding to the current moment changes, the first business monitoring time point and the second business monitoring time point also change accordingly, thereby realizing the dynamic update of the market business monitoring cycle;

[0109] The sales data unit collects sales data of the characteristic market business in the market business monitoring period to obtain the business cycle sales index value corresponding to the target market business;

[0110] The details are as follows:

[0111] The market business monitoring cycle is divided into a number of sales monitoring sub-periods of equal duration, and the divided sales monitoring sub-periods are named as the first sales monitoring sub-period to the ath sales monitoring sub-period in chronological order;

[0112] It should be noted here that:

[0113] In this application, a is the quantity value corresponding to the sales monitoring sub-period, and a is an integer greater than 0;

[0114] Obtain the number of business orders corresponding to the target market business in the first sales monitoring sub-period to the a-th sales monitoring sub-period, and obtain the order volume from the first period to the a-th period;

[0115] Obtain the business order volume corresponding to the same type of market business in the target market business from the first sales monitoring sub-period to the a-th sales monitoring sub-period through big data, and obtain the total volume of similar orders from the first period to the a-th period;

[0116] Obtain the corresponding ratios of the order volume from the first period to the order volume of the ath period to the total amount of similar orders from the first period to the total amount of similar orders from the ath period, and obtain the market share of orders from the first period to the market share of orders from the ath period;

[0117] Obtain the historical sales data corresponding to the target market business, obtain the business order volume of the target market business in each historical monitoring sub-period based on the historical sales data, obtain multiple historical business order volumes, compare the numerical values ​​of the multiple historical business order volumes, and mark the historical business order volume with the largest numerical value as the historical best order volume;

[0118] It should be noted here that:

[0119] In this application, the historical best order volume involved here allows for the existence of numerical ties;

[0120] Calculate the ratios of the order volume from the first period to the ath period to the historical best order volume, and obtain the ratio of the historical order volume from the first period to the historical order volume from the ath period;

[0121] The business cycle sales index value corresponding to the target market business is obtained by calculating the order volume from the first period to the order volume from the ath period, the market share of orders from the first period to the market share of orders from the ath period, and the historical order ratio from the first period to the historical order ratio from the ath period;

[0122] Calculate the business cycle sales index value corresponding to the characteristic market business. The specific formula is as follows:

[0123]

[0124] Among them, Xzb is the business cycle sales indicator value corresponding to the characteristic market business, Dll1 to Dlla are respectively the order volume from the first period to the order volume of the ath period, Dsb1 to Dsba are respectively the market share of orders from the first period to the market share of orders from the ath period, and Ldb1 to Ldba are respectively the historical order ratio from the first period to the historical order ratio from the ath period;

[0125] Acquire the business cycle sales index value of each target market business within the market business monitoring period to obtain multiple business cycle sales index values;

[0126] The customer feedback unit collects evaluation data of the characteristic market business in the market business monitoring cycle to obtain business cycle evaluation data corresponding to the target market business;

[0127] The details are as follows:

[0128] The market business monitoring cycle is divided into a number of evaluation monitoring sub-periods of equal duration, and the divided evaluation monitoring sub-periods are named as the first evaluation monitoring sub-period to the bth evaluation monitoring sub-period in chronological order;

[0129] It should be noted here that:

[0130] In this application, b is the quantity value corresponding to the evaluation monitoring sub-period, and b is an integer greater than 0;

[0131] Respectively obtain the time period business evaluation content data corresponding to the first evaluation monitoring sub-period to the bth evaluation monitoring sub-period of the characteristic market business, and obtain the first time period business evaluation content data to the bth time period business evaluation content data;

[0132] Create a model to differentiate between evaluation types;

[0133] The details are as follows:

[0134] Obtain historical evaluation data of characteristic market businesses through big data, and obtain multiple text evaluation contents based on the historical evaluation data of characteristic market businesses;

[0135] manually annotating the plurality of text evaluation contents into first-type evaluation contents and second-type evaluation contents to obtain evaluation content annotated data;

[0136] It should be noted here that:

[0137] The first type of evaluation content involved here is a favorable comment on the market business, and the second type of evaluation content is a negative comment on the market business;

[0138] The evaluation content annotation data is divided into an evaluation test set and an evaluation training set according to a preset training test ratio;

[0139] It should be noted here that:

[0140] In this application, the preset training-test ratio involved here is specifically 2:8;

[0141] Create a text recognition model through the existing AI model platform, and train the text recognition model using the evaluation test set and the evaluation training set until each text evaluation content in the evaluation training set is trained at least once;

[0142] Use the evaluation test set to test the text recognition model, obtain the accuracy of the test results, obtain the model recognition accuracy, obtain the target model recognition accuracy, and compare the model recognition accuracy with the target model recognition accuracy. When the model recognition accuracy is greater than or equal to the target model recognition accuracy, the text recognition model training is completed and the evaluation type differentiation model is obtained. When the model recognition accuracy is less than the target model recognition accuracy, continue to use the evaluation training set to train the text recognition model until the model recognition accuracy is greater than or equal to the target model recognition accuracy.

[0143] Obtain the percentage of favorable comments on the characteristic market business in the period corresponding to the first evaluation monitoring sub-period, and obtain the percentage of favorable comments on the business in the first period;

[0144] The details are as follows:

[0145] Using the evaluation type differentiation model to differentiate the evaluation contents of the business evaluation content data of the first period, respectively, obtaining the number of comments corresponding to the first type of evaluation content in the business evaluation content data of the first period, and obtaining the first characteristic number of comments;

[0146] The total number of comments in the business evaluation content data in the first period is numerically obtained to obtain a second characteristic comment quantity value;

[0147] Calculate the ratio of the number of reviews of the first feature to the number of reviews of the second feature to obtain the percentage of favorable comments of the business in the first period corresponding to the feature market business;

[0148] The business favorable comments ratios of the time periods corresponding to the second evaluation monitoring sub-period to the bth evaluation monitoring sub-period are obtained respectively, and the business favorable comments ratios of the second period to the bth period are obtained;

[0149] The percentage of favorable business reviews in the first period to the percentage of favorable business reviews in the bth period are defined as the business cycle evaluation data corresponding to the characteristic market business;

[0150] Acquire the business cycle evaluation data of each target market business within the market business monitoring cycle to obtain multiple business cycle evaluation data;

[0151] The revenue data unit collects and analyzes the revenue data of the characteristic market business in the market business monitoring period to obtain the periodic revenue index value corresponding to the characteristic market business;

[0152] Obtain the cumulative investment amount of the characteristic market business during the market business monitoring period, and obtain the cumulative income amount of the characteristic market business during the market business monitoring period;

[0153] Calculate the difference between the cumulative income amount and the cumulative investment amount. If the cumulative income amount is greater than the cumulative investment amount, use the difference between the cumulative income amount and the cumulative investment amount as the periodic income indicator value corresponding to the characteristic market business. If the cumulative income amount is less than or equal to the cumulative investment amount, use the value 0 to assign a parameter to the periodic income indicator value corresponding to the characteristic market business.

[0154] The periodic revenue index value of each target market business within the market business monitoring period is obtained respectively to obtain multiple periodic revenue index values;

[0155] Define multiple business cycle sales index values, multiple business cycle evaluation data, and multiple cycle revenue index values ​​as business operation basic data;

[0156] The data collection module acquires basic business operation data and transmits it to the data processing module;

[0157] The data processing module conducts a visual analysis of the proportion of favorable comments of each target market business within the market business monitoring cycle based on the basic business operation data, and obtains the change coefficient of the periodic favorable comments proportion of each target market business within the market business monitoring cycle to obtain the periodic favorable comments processing data;

[0158] Obtain basic business operation data, and obtain business cycle evaluation data corresponding to each target market business based on the basic business operation data;

[0159] Randomly select a target market business from multiple target market businesses as a sample market business;

[0160] Visualize the business cycle evaluation data corresponding to the sample market business to obtain the change coefficient of the proportion of periodic positive reviews corresponding to the sample market business;

[0161] The details are as follows:

[0162] Obtain the first evaluation monitoring sub-period to the bth evaluation monitoring sub-period;

[0163] According to the business cycle evaluation data corresponding to the sample market business, obtain the percentage of business favorable comments in the first period to the percentage of business favorable comments in the bth period;

[0164] See also Figure 3 In the existing rectangular coordinate system model, the evaluation monitoring sub-period is recorded as the coordinate x-axis, and the proportion of good reviews of the period business is marked as the coordinate Y-axis, and the rectangular coordinate system of the change of the good reviews period is obtained;

[0165] In the rectangular coordinate system of the plane of the change of the favorable comments period, the first evaluation monitoring sub-period is set as the horizontal coordinate point, and the proportion of favorable comments of the business in the first period is set as the vertical coordinate point to obtain the first favorable comments coordinate point, the second evaluation monitoring sub-period is set as the horizontal coordinate point, and the proportion of favorable comments of the business in the second period is set as the vertical coordinate point to obtain the second favorable comments coordinate point, and so on, the bth evaluation monitoring sub-period is set as the horizontal coordinate point, and the proportion of favorable comments of the business in the bth period is set as the vertical coordinate point to obtain the bth favorable comments coordinate point;

[0166] Mark the first to b-th favorable comment coordinate points in the rectangular coordinate system of the favorable comment period change plane respectively, and connect the first to b-th favorable comment coordinate points in sequence;

[0167] The line between the first favorable comment coordinate point and the second favorable comment coordinate point is marked as the first favorable comment line, the line between the second favorable comment coordinate point and the third favorable comment coordinate point is marked as the second favorable comment line, and so on, the line between the b-1 favorable comment coordinate point and the b-th favorable comment coordinate point is marked as the b-1-th favorable comment line;

[0168] Obtain the line slopes from the first favorable comment line to the b-1th favorable comment line respectively, obtain b-1 line slope values, and calculate the average of the b-1 line slope values ​​to obtain the time change rate of favorable comments corresponding to the market business monitoring period;

[0169] Calculate the average of the percentage of favorable reviews from the first period to the percentage of favorable reviews from the bth period to obtain the average percentage of favorable reviews for each period;

[0170] The average percentage of favorable comments in each period and the rate of change of favorable comments over time are calculated to obtain the coefficient of change of the percentage of favorable comments in each period corresponding to the sample market business;

[0171] The coefficient of change of the proportion of positive reviews in the sample market business is calculated. The specific formula is as follows:

[0172] Hpz=Shp×(1+Zbx)+Shp;

[0173] Among them, Hpz is the coefficient of change of the proportion of positive reviews in the sample market business, Shp is the average proportion of positive reviews in the time period, and Zbx is the time change rate of positive reviews;

[0174] Obtain the change coefficient of the proportion of favorable comments during the market business monitoring period for each target market business, and obtain the change coefficient of the proportion of favorable comments during multiple periods;

[0175] The coefficient of variation of the proportion of favorable comments in multiple periods is defined as the period favorable comment processing data;

[0176] The data processing module acquires the periodic positive review processing data and transmits it to the risk monitoring module;

[0177] The risk monitoring module obtains the operating efficiency coefficient corresponding to each target market business based on the basic business operation data and periodic positive evaluation processing data, and obtains the operating efficiency coefficient threshold and compares it with each operating efficiency coefficient respectively, and issues an operating risk warning for the target market business based on the numerical comparison results;

[0178] Obtain basic business operation data, and obtain the business cycle sales index value and cycle revenue index value corresponding to each target market business based on the basic business operation data;

[0179] Obtain periodic positive review processing data, and obtain the periodic positive review ratio change coefficient corresponding to each target market business based on the periodic positive review processing data;

[0180] The operating efficiency coefficient is obtained by calculating the change coefficient of the proportion of periodic positive reviews, the business periodic sales index value and the periodic revenue index value corresponding to the same target market business;

[0181] The specific formula for calculating the operating efficiency coefficient is as follows:

[0182] Jyx=Syz 2 +Hpz+Xzb

[0183] Among them, Jyx is the operating efficiency coefficient, Hpz is the change coefficient of the proportion of periodic positive reviews, Xzb is the sales index value of the business cycle, and Syz is the value of the periodic income index;

[0184] Obtaining the threshold of the operating efficiency coefficient, comparing the threshold of the operating efficiency coefficient with each operating efficiency coefficient respectively, and conducting an operating risk warning for the target market business based on the numerical comparison results;

[0185] The details are as follows:

[0186] Obtain the change coefficient threshold of the proportion of positive reviews during a period, the sales index threshold of the business period, and the periodic revenue index threshold respectively;

[0187] It should be noted here that:

[0188] In this application, the cycle favorable comment ratio change coefficient threshold, business cycle sales index threshold and cycle revenue index threshold involved here are respectively the maximum cycle favorable comment ratio change coefficient, the maximum business cycle sales index value and the maximum cycle revenue index value corresponding to the target market business that needs to be warned of business risks;

[0189] The threshold of the change coefficient of the proportion of periodic favorable comments, the threshold of the sales index of the business period, and the threshold of the periodic revenue index are calculated to obtain the threshold of the operating efficiency coefficient;

[0190] The operating efficiency coefficient threshold is calculated using the following formula:

[0191] Jyxy=Syzy 2 +Hpzy+Xzby;

[0192] Among them, Jyxy is the threshold of the operating efficiency coefficient, Hpzy is the threshold of the change coefficient of the proportion of periodic positive reviews, Xzby is the threshold of the sales index of the business cycle, and Syzy is the threshold of the periodic profit index;

[0193] When the operating efficiency coefficient is greater than or equal to the operating efficiency coefficient threshold, no operating risk warning will be issued for the corresponding target market business;

[0194] When the operating efficiency coefficient is less than the operating efficiency coefficient threshold, an operating risk warning will be issued for the corresponding target market business.

[0195] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0196] Embodiment 2

[0197] See also Figure 2 Based on another concept of the same invention, a monitoring method based on the business operation data brain is proposed, comprising the following steps:

[0198] Step S1: respectively obtain the business cycle sales index value, business cycle evaluation data and cycle revenue index value of each target market business within the market business monitoring cycle to obtain business operation basic data;

[0199] Step S11: acquiring multiple market businesses carried out by the company to obtain multiple target market businesses, and randomly selecting a target market business from the multiple target market businesses obtained as a characteristic market business;

[0200] Step S12: in the process of business monitoring of multiple target market businesses, the time value corresponding to the current moment is marked as a first business monitoring time point, a characteristic business monitoring period before the first business monitoring time point is marked as a second business monitoring time point, and the period between the first business monitoring time point and the second business monitoring time point is marked as a market business monitoring cycle;

[0201] Step S13: collecting sales data of the characteristic market business in the market business monitoring period to obtain the business cycle sales index value corresponding to the target market business;

[0202] The details are as follows:

[0203] Step S131: dividing the market business monitoring cycle into a number of sales monitoring sub-periods of equal duration, and naming the divided sales monitoring sub-periods as the first sales monitoring sub-period to the ath sales monitoring sub-period in chronological order;

[0204] Step S132: Obtain the number of business orders corresponding to the target market business in the first sales monitoring sub-period to the ath sales monitoring sub-period, and obtain the order volume from the first period to the ath period;

[0205] Step S133: Obtain the business order volumes corresponding to the same type of market business as the target market business in the first sales monitoring sub-period to the ath sales monitoring sub-period through big data, and obtain the total volume of the same type of orders from the first period to the ath period;

[0206] Step S134: Obtain the corresponding ratios of the order volume from the first period to the ath period to the total amount of similar orders from the first period to the ath period, and obtain the market share of orders from the first period to the ath period;

[0207] Step S135: Obtain historical sales data corresponding to the target market business, obtain the business order volume of the target market business in each historical monitoring sub-period according to the historical sales data, obtain multiple historical business order volumes, compare the numerical values ​​of the obtained multiple historical business order volumes, and mark the historical business order volume with the largest numerical value as the historical best order volume;

[0208] Step S136: Calculate the ratios of the order volume from the first period to the ath period to the historical best order volume, and obtain the ratio of the historical order volume from the first period to the historical order volume from the ath period;

[0209] Step S137: Calculate the business cycle sales index value corresponding to the target market business by combining the order volume from the first period to the order volume from the ath period, the market share of orders from the first period to the market share of orders from the ath period, and the historical order ratio from the first period to the historical order ratio from the ath period;

[0210] Calculate the business cycle sales index value corresponding to the characteristic market business. The specific formula is as follows:

[0211]

[0212] Among them, Xzb is the business cycle sales indicator value corresponding to the characteristic market business, Dll1 to Dlla are respectively the order volume from the first period to the order volume of the ath period, Dsb1 to Dsba are respectively the market share of orders from the first period to the market share of orders from the ath period, and Ldb1 to Ldba are respectively the historical order ratio from the first period to the historical order ratio from the ath period;

[0213] Step S14: acquiring a business cycle sales index value for each target market business within a market business monitoring period to obtain a plurality of business cycle sales index values;

[0214] Step S15: collecting evaluation data of the characteristic market business in the market business monitoring cycle to obtain business cycle evaluation data corresponding to the target market business;

[0215] The details are as follows:

[0216] Step S151: dividing the market business monitoring cycle into a number of evaluation monitoring sub-periods of equal duration, and naming the divided evaluation monitoring sub-periods as the first evaluation monitoring sub-period to the bth evaluation monitoring sub-period in chronological order;

[0217] Step S152: respectively obtaining the time period business evaluation content data corresponding to the first evaluation monitoring sub-period to the bth evaluation monitoring sub-period of the characteristic market business, and obtaining the first time period business evaluation content data to the bth time period business evaluation content data;

[0218] Step S153: creating an evaluation type differentiation model;

[0219] The details are as follows:

[0220] Step S1531: Acquire characteristic market business historical evaluation data through big data, and acquire multiple text evaluation contents according to the characteristic market business historical evaluation data;

[0221] Step S1532: manually annotating the plurality of text evaluation contents into first type evaluation contents and second type evaluation contents to obtain evaluation content annotation data;

[0222] Step S1533: dividing the evaluation content annotation data into an evaluation test set and an evaluation training set according to a preset training test ratio;

[0223] Step S1534: Create a text recognition model through the existing AI model platform, and train the text recognition model using the evaluation test set and the evaluation training set until each text evaluation content in the evaluation training set is trained at least once;

[0224] Step S1535: Use the evaluation test set to test the text recognition model, and obtain the accuracy of the test results to obtain the model recognition accuracy, obtain the target model recognition accuracy, and compare the model recognition accuracy with the target model recognition accuracy. When the model recognition accuracy is greater than or equal to the target model recognition accuracy, the text recognition model training is completed and the evaluation type differentiation model is obtained. When the model recognition accuracy is less than the target model recognition accuracy, continue to use the evaluation training set to train the text recognition model until the model recognition accuracy is greater than or equal to the target model recognition accuracy.

[0225] Step S154: Obtain the percentage of favorable comments of the characteristic market business in the period corresponding to the first evaluation monitoring sub-period, and obtain the percentage of favorable comments of the business in the first period;

[0226] The details are as follows:

[0227] Step S1541: using the evaluation type differentiation model to differentiate the evaluation contents of the business evaluation content data of the first period, obtaining the number of comments corresponding to the first type of evaluation content in the business evaluation content data of the first period, and obtaining the first characteristic number of comments;

[0228] Step S1542: obtaining a numerical value of the total number of comments in the business evaluation content data of the first period, and obtaining a second characteristic comment quantity value;

[0229] Step S1543: Calculate the ratio of the number of first characteristic reviews to the number of second characteristic reviews to obtain the percentage of favorable comments of the first period of time corresponding to the characteristic market business;

[0230] Step S155: respectively obtaining the business favorable comments ratios of the second evaluation monitoring sub-period to the bth evaluation monitoring sub-period, and obtaining the business favorable comments ratios of the second period to the bth period;

[0231] Step S156: defining the percentage of favorable business reviews in the first period to the percentage of favorable business reviews in the bth period as business cycle evaluation data corresponding to the characteristic market business;

[0232] Step S16: acquiring the business cycle evaluation data of each target market business within the market business monitoring period, to obtain a plurality of business cycle evaluation data;

[0233] Step S17: collecting and analyzing the revenue data of the characteristic market business in the market business monitoring period to obtain the period revenue index value corresponding to the characteristic market business;

[0234] The details are as follows:

[0235] Step S171: Obtain the cumulative investment amount of the characteristic market business within the market business monitoring period, and obtain the cumulative income amount of the characteristic market business within the market business monitoring period;

[0236] Step S172: Calculate the difference between the accumulated income amount and the accumulated investment amount. If the accumulated income amount is greater than the accumulated investment amount, use the difference between the accumulated income amount and the accumulated investment amount as the periodic income indicator value corresponding to the characteristic market business. If the accumulated income amount is less than or equal to the accumulated investment amount, use the value 0 to assign a parameter to the periodic income indicator value corresponding to the characteristic market business.

[0237] Step S18: acquiring the periodic revenue index value of each target market business within the market business monitoring period, and obtaining a plurality of periodic revenue index values;

[0238] Step S19: defining a plurality of business cycle sales index values, a plurality of business cycle evaluation data, and a plurality of cycle revenue index values ​​as business operation basic data;

[0239] Step S2: Perform a visual analysis of the proportion of favorable comments of each target market business within the market business monitoring cycle according to the basic business operation data, and obtain the change coefficient of the periodic favorable comment proportion of each target market business within the market business monitoring cycle to obtain periodic favorable comment processing data;

[0240] Step S21: Acquire basic business operation data, and acquire business cycle evaluation data corresponding to each target market business according to the basic business operation data;

[0241] Step S22: randomly selecting a target market business from multiple target market businesses as a sample market business;

[0242] Step S23: Performing visualization processing on the business cycle evaluation data corresponding to the sample market business to obtain a change coefficient of the periodic favorable rating ratio corresponding to the sample market business;

[0243] The details are as follows:

[0244] Step S231: obtaining the first evaluation monitoring sub-period to the bth evaluation monitoring sub-period;

[0245] Step S232: obtaining the percentage of favorable comments on the business in the first period to the percentage of favorable comments on the business in the bth period according to the business cycle evaluation data corresponding to the sample market business;

[0246] Step S233: In the existing plane rectangular coordinate system model, the evaluation monitoring sub-period is recorded as the coordinate x-axis, and the proportion of good reviews of the period business is marked as the coordinate Y-axis, so as to obtain the plane rectangular coordinate system of the change of the good reviews period;

[0247] Step S234: In the rectangular coordinate system of the change plane of the favorable comment period, the first evaluation monitoring sub-period is set as the horizontal coordinate point, and the proportion of favorable comments of the business in the first period is set as the vertical coordinate point to obtain the first favorable comment coordinate point, the second evaluation monitoring sub-period is set as the horizontal coordinate point, and the proportion of favorable comments of the business in the second period is set as the vertical coordinate point to obtain the second favorable comment coordinate point, and so on, the bth evaluation monitoring sub-period is set as the horizontal coordinate point, and the proportion of favorable comments of the business in the bth period is set as the vertical coordinate point to obtain the bth favorable comment coordinate point;

[0248] Step S235: Mark the first to b-th favorable comment coordinate points in the rectangular coordinate system of the favorable comment period change plane respectively, and connect the first to b-th favorable comment coordinate points in sequence;

[0249] Step S236: Mark the line between the first favorable comment coordinate point and the second favorable comment coordinate point as the first favorable comment line, mark the line between the second favorable comment coordinate point and the third favorable comment coordinate point as the second favorable comment line, and so on, mark the line between the b-1th favorable comment coordinate point and the bth favorable comment coordinate point as the b-1th favorable comment line;

[0250] Step S237: Obtain the line slopes from the first favorable comment line to the b-1th favorable comment line respectively, obtain b-1 line slope values, and average the obtained b-1 line slope values ​​to obtain the time change rate of favorable comments corresponding to the market business monitoring period;

[0251] Step S238: Calculate the average of the percentage of favorable comments from the first period to the percentage of favorable comments from the bth period to obtain the average percentage of favorable comments from each period;

[0252] Step S239: Calculate the average percentage of favorable comments in each time period and the time change rate of favorable comments to obtain a period favorable comment percentage change coefficient corresponding to the sample market business;

[0253] The coefficient of change of the proportion of positive reviews in the sample market business is calculated. The specific formula is as follows:

[0254] Hpz=Shp×(1+Zbx)+Shp;

[0255] Among them, Hpz is the coefficient of change of the proportion of positive reviews in the sample market business, Shp is the average proportion of positive reviews in the time period, and Zbx is the time change rate of positive reviews;

[0256] Step S24: acquiring the periodic favorable comment ratio variation coefficient of each target market business in the market business monitoring period, and obtaining multiple periodic favorable comment ratio variation coefficients;

[0257] Step S25: defining the coefficient of variation of the percentage of favorable comments in multiple periods as period favorable comment processing data;

[0258] Step S3: Obtain the operating efficiency coefficient corresponding to each target market business based on the business operation basic data and the periodic positive evaluation processing data, obtain the operating efficiency coefficient threshold and compare it with each operating efficiency coefficient respectively, and issue an operating risk warning for the target market business based on the numerical comparison results;

[0259] Step S31: Acquire basic business operation data, and acquire the business cycle sales index value and cycle revenue index value corresponding to each target market business according to the basic business operation data;

[0260] Step S32: Obtain periodic positive review processing data, and obtain the periodic positive review ratio change coefficient corresponding to each target market business according to the periodic positive review processing data;

[0261] Step S33: Calculate the change coefficient of the periodic favorable comments ratio, the business periodic sales index value, and the periodic revenue index value corresponding to the same target market business to obtain the operating efficiency coefficient;

[0262] The specific formula for calculating the operating efficiency coefficient is as follows:

[0263] Jyx=Syz 2 +Hpz+Xzb

[0264] Among them, Jyx is the operating efficiency coefficient, Hpz is the change coefficient of the proportion of periodic positive reviews, Xzb is the sales index value of the business cycle, and Syz is the value of the periodic income index;

[0265] Step S34: obtaining an operating efficiency coefficient threshold, comparing the operating efficiency coefficient threshold with each operating efficiency coefficient, and issuing an operating risk warning for the target market business according to the numerical comparison result;

[0266] The details are as follows:

[0267] Step S341: respectively obtaining a periodic favorable comment ratio change coefficient threshold, a business periodic sales index threshold, and a periodic revenue index threshold;

[0268] Step S342: Calculate the operating efficiency coefficient threshold by using the period positive reviews ratio change coefficient threshold, the business period sales index threshold, and the period revenue index threshold;

[0269] Step S343: when the operating efficiency coefficient is greater than or equal to the operating efficiency coefficient threshold, no operating risk warning is issued for the corresponding target market business;

[0270] Step S344: When the operating efficiency coefficient is less than the operating efficiency coefficient threshold, an operating risk warning is issued for the corresponding target market business.

[0271] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A monitoring system based on business operation data brain, characterized in that: include: Data collection module: used to obtain the business cycle sales index value, business cycle evaluation data and cycle revenue index value of each target market business within the market business monitoring cycle, and obtain the basic data of business operation; Data processing module: used to visualize the proportion of favorable comments of each target market business within the market business monitoring cycle according to the basic business operation data, and obtain the change coefficient of the periodic favorable comments proportion of each target market business within the market business monitoring cycle, and obtain the periodic favorable comments processing data; Risk monitoring module: used to obtain the operating efficiency coefficient corresponding to each target market business based on the basic business operation data and periodic positive evaluation processing data, and obtain the operating efficiency coefficient threshold and compare it with each operating efficiency coefficient respectively, and issue operating risk warnings for the target market business based on the numerical comparison results.

2. The monitoring system based on the business operation data brain according to claim 1 is characterized in that: The data acquisition module includes a sales data unit, a customer feedback unit, and a revenue data unit; Acquire multiple market businesses carried out by the company to obtain multiple target market businesses, and randomly select one target market business from the multiple target market businesses obtained as a characteristic market business; In the process of business monitoring of multiple target market businesses, the time value corresponding to the current moment is marked as a first business monitoring time point, a characteristic business monitoring period before the first business monitoring time point is marked as a second business monitoring time point, and the period between the first business monitoring time point and the second business monitoring time point is marked as a market business monitoring cycle; The sales data unit collects and analyzes the evaluation data of the characteristic market business in the market business monitoring cycle to obtain the business cycle evaluation data corresponding to the characteristic market business; Acquire the business cycle sales index value of each target market business within the market business monitoring period to obtain multiple business cycle sales index values; The customer feedback unit collects evaluation data of the characteristic market business in the market business monitoring cycle to obtain business cycle evaluation data corresponding to the target market business; Acquire the business cycle evaluation data of each target market business within the market business monitoring cycle to obtain multiple business cycle evaluation data; The revenue data unit collects and analyzes the revenue data of the characteristic market business in the market business monitoring period to obtain the periodic revenue index value corresponding to the characteristic market business; The periodic revenue index value of each target market business within the market business monitoring period is obtained respectively to obtain multiple periodic revenue index values; A plurality of business cycle sales index values, a plurality of business cycle evaluation data, and a plurality of cycle revenue index values ​​are defined as basic business operation data.

3. The monitoring system based on the business operation data brain according to claim 2 is characterized in that: The sales data unit acquires the business cycle sales index value corresponding to the target market business; The details are as follows: The market business monitoring cycle is divided into a number of sales monitoring sub-periods of equal duration, and the divided sales monitoring sub-periods are named as the first sales monitoring sub-period to the ath sales monitoring sub-period in chronological order; Obtain the number of business orders corresponding to the target market business in the first sales monitoring sub-period to the a-th sales monitoring sub-period, and obtain the order volume from the first period to the a-th period; Obtain the business order volume corresponding to the same type of market business in the target market business from the first sales monitoring sub-period to the a-th sales monitoring sub-period through big data, and obtain the total volume of similar orders from the first period to the a-th period; Obtain the corresponding ratios of the order volume from the first period to the order volume of the ath period to the total amount of similar orders from the first period to the total amount of similar orders from the ath period, and obtain the market share of orders from the first period to the market share of orders from the ath period; Obtain the historical sales data corresponding to the target market business, obtain the business order volume of the target market business in each historical monitoring sub-period based on the historical sales data, obtain multiple historical business order volumes, compare the numerical values ​​of the multiple historical business order volumes, and mark the historical business order volume with the largest numerical value as the historical best order volume; Calculate the ratios of the order volume from the first period to the ath period to the historical best order volume, and obtain the ratio of the historical order volume from the first period to the historical order volume from the ath period; The business cycle sales index value corresponding to the target market business is obtained by calculating the order volume from the first period to the order volume from the ath period, the market share of orders from the first period to the market share of orders from the ath period, and the historical order ratio from the first period to the historical order ratio from the ath period; Calculate the business cycle sales index value corresponding to the characteristic market business. The specific formula is as follows: Among them, Xzb is the business cycle sales indicator value corresponding to the characteristic market business, Dll1 to Dlla are the order volume from the first period to the order volume in the ath period, Dsb1 to Dsba are the market share of orders from the first period to the market share of orders in the ath period, and Ldb1 to Ldba are the historical order ratio from the first period to the historical order ratio from the ath period.

4. The monitoring system based on the business operation data brain according to claim 2 is characterized in that: The customer feedback unit acquires the business cycle evaluation data corresponding to the target market business, specifically as follows: The market business monitoring cycle is divided into a number of evaluation monitoring sub-periods of equal duration, and the divided evaluation monitoring sub-periods are named as the first evaluation monitoring sub-period to the bth evaluation monitoring sub-period in chronological order; Respectively obtain the time period business evaluation content data corresponding to the first evaluation monitoring sub-period to the bth evaluation monitoring sub-period of the characteristic market business, and obtain the first time period business evaluation content data to the bth time period business evaluation content data; Create a model to differentiate between evaluation types; Obtain the percentage of favorable comments on the characteristic market business in the period corresponding to the first evaluation monitoring sub-period, and obtain the percentage of favorable comments on the business in the first period; The business favorable comments ratios of the time periods corresponding to the second evaluation monitoring sub-period to the bth evaluation monitoring sub-period are obtained respectively, and the business favorable comments ratios of the second period to the bth period are obtained; The proportion of favorable business comments in the first period to the proportion of favorable business comments in the bth period are defined as the business cycle evaluation data corresponding to the characteristic market business.

5. The monitoring system based on the business operation data brain according to claim 4 is characterized in that: The customer feedback unit creates a model for distinguishing evaluation types, as follows: Obtain historical evaluation data of characteristic market businesses through big data, and obtain multiple text evaluation contents based on the historical evaluation data of characteristic market businesses; manually annotating the plurality of text evaluation contents into first-type evaluation contents and second-type evaluation contents to obtain evaluation content annotated data; The evaluation content annotation data is divided into an evaluation test set and an evaluation training set according to a preset training test ratio; Create a text recognition model through the existing AI model platform, and train the text recognition model using the evaluation test set and the evaluation training set until each text evaluation content in the evaluation training set is trained at least once; Use the evaluation test set to test the text recognition model, and obtain the accuracy of the test results to obtain the model recognition accuracy, obtain the target model recognition accuracy, and numerically compare the model recognition accuracy with the target model recognition accuracy. When the model recognition accuracy is greater than or equal to the target model recognition accuracy, the text recognition model training is completed and the evaluation type differentiation model is obtained. When the model recognition accuracy is less than the target model recognition accuracy, continue to use the evaluation training set to train the text recognition model until the model recognition accuracy is greater than or equal to the target model recognition accuracy.

6. The monitoring system based on the business operation data brain according to claim 4 is characterized in that: The customer feedback unit obtains the proportion of favorable reviews of the services in the first period, as follows: Using the evaluation type differentiation model to differentiate the evaluation contents of the business evaluation content data of the first period, respectively, obtaining the number of comments corresponding to the first type of evaluation content in the business evaluation content data of the first period, and obtaining the first characteristic number of comments; The total number of comments in the business evaluation content data in the first period is numerically obtained to obtain a second characteristic comment quantity value; Calculate the ratio of the number of comments with the first feature to the number of comments with the second feature to obtain the percentage of favorable comments on the business in the first period corresponding to the characteristic market business.

7. The monitoring system based on the business operation data brain according to claim 2 is characterized in that: The revenue data unit obtains the periodic revenue index value corresponding to the characteristic market business, as follows: Obtain the cumulative investment amount of the characteristic market business during the market business monitoring period, and obtain the cumulative income amount of the characteristic market business during the market business monitoring period; Calculate the difference between the cumulative income amount and the cumulative investment amount. If the cumulative income amount is greater than the cumulative investment amount, use the difference between the cumulative income amount and the cumulative investment amount as the periodic income indicator value corresponding to the characteristic market business. If the cumulative income amount is less than or equal to the cumulative investment amount, use the value 0 to assign parameters to the periodic income indicator value corresponding to the characteristic market business.

8. The monitoring system based on the business operation data brain according to claim 1 is characterized in that: The data processing module acquires periodic positive review processing data, specifically as follows: Obtain basic business operation data, and obtain business cycle evaluation data corresponding to each target market business based on the basic business operation data; Randomly select a target market business from multiple target market businesses as a sample market business; Visualize the business cycle evaluation data corresponding to the sample market business to obtain the change coefficient of the proportion of periodic positive reviews corresponding to the sample market business; The details are as follows: Obtain the first evaluation monitoring sub-period to the bth evaluation monitoring sub-period; According to the business cycle evaluation data corresponding to the sample market business, obtain the percentage of business favorable comments in the first period to the percentage of business favorable comments in the bth period; In the existing plane rectangular coordinate system model, the evaluation monitoring sub-period is recorded as the coordinate x-axis, and the proportion of good reviews of the period business is marked as the coordinate Y-axis, and the plane rectangular coordinate system of the change of the good reviews period is obtained; In the rectangular coordinate system of the plane of the change of the favorable comments period, the first evaluation monitoring sub-period is set as the horizontal coordinate point, and the proportion of favorable comments of the business in the first period is set as the vertical coordinate point to obtain the first favorable comments coordinate point, the second evaluation monitoring sub-period is set as the horizontal coordinate point, and the proportion of favorable comments of the business in the second period is set as the vertical coordinate point to obtain the second favorable comments coordinate point, and so on, the bth evaluation monitoring sub-period is set as the horizontal coordinate point, and the proportion of favorable comments of the business in the bth period is set as the vertical coordinate point to obtain the bth favorable comments coordinate point; Mark the first to b-th favorable comment coordinate points in the rectangular coordinate system of the favorable comment period change plane respectively, and connect the first to b-th favorable comment coordinate points in sequence; The line between the first favorable comment coordinate point and the second favorable comment coordinate point is marked as the first favorable comment line, the line between the second favorable comment coordinate point and the third favorable comment coordinate point is marked as the second favorable comment line, and so on, the line between the b-1 favorable comment coordinate point and the b-th favorable comment coordinate point is marked as the b-1-th favorable comment line; Obtain the line slopes from the first favorable comment line to the b-1th favorable comment line respectively, obtain b-1 line slope values, and calculate the average of the b-1 line slope values ​​to obtain the time change rate of favorable comments corresponding to the market business monitoring period; Calculate the average of the percentage of favorable reviews from the first period to the percentage of favorable reviews from the bth period to obtain the average percentage of favorable reviews for each period; The average percentage of favorable comments in each period and the rate of change of favorable comments over time are calculated to obtain the coefficient of change of the percentage of favorable comments in each period corresponding to the sample market business; The coefficient of change of the proportion of positive reviews in the sample market business is calculated. The specific formula is as follows: Hpz=Shp×(1+Zbx)+Shp; Among them, Hpz is the coefficient of change of the proportion of positive reviews in the sample market business, Shp is the average proportion of positive reviews in the time period, and Zbx is the time change rate of positive reviews; Obtain the change coefficient of the proportion of favorable comments during the market business monitoring period for each target market business, and obtain the change coefficient of the proportion of favorable comments during multiple periods; The coefficient of change of the proportion of positive reviews over multiple periods is defined as the periodic positive review processing data.

9. The monitoring system based on the business operation data brain according to claim 1 is characterized in that: The risk monitoring module provides early warning of business risks for target market businesses, as follows: Obtain basic business operation data, and obtain the business cycle sales index value and cycle revenue index value corresponding to each target market business based on the basic business operation data; Obtain periodic positive review processing data, and obtain the periodic positive review ratio change coefficient corresponding to each target market business based on the periodic positive review processing data; The operating efficiency coefficient is obtained by calculating the change coefficient of the proportion of periodic positive reviews, the business periodic sales index value and the periodic revenue index value corresponding to the same target market business; The specific formula for calculating the operating efficiency coefficient is as follows: Jyx=Syz 2 +Hpz+Xzb Among them, Jyx is the operating efficiency coefficient, Hpz is the change coefficient of the proportion of positive reviews in the period, Xzb is the sales index value of the business period, and Syz is the value of the period profit index; Obtaining the threshold of the operating efficiency coefficient, comparing the threshold of the operating efficiency coefficient with each operating efficiency coefficient respectively, and conducting an operating risk warning for the target market business based on the numerical comparison results; The details are as follows: Obtain the change coefficient threshold of the proportion of positive reviews during a period, the sales index threshold of the business period, and the periodic revenue index threshold respectively; The threshold of the change coefficient of the proportion of periodic favorable comments, the threshold of the sales index of the business period, and the threshold of the periodic revenue index are calculated to obtain the threshold of the operating efficiency coefficient; When the operating efficiency coefficient is greater than or equal to the operating efficiency coefficient threshold, no operating risk warning will be issued for the corresponding target market business; When the operating efficiency coefficient is less than the operating efficiency coefficient threshold, an operating risk warning will be issued for the corresponding target market business.

10. A monitoring method based on a business operation data brain, applicable to the monitoring system based on a business operation data brain according to any one of claims 1 to 9, characterized in that: The monitoring method comprises the following specific steps: Step S1: respectively obtain the business cycle sales index value, business cycle evaluation data and cycle revenue index value of each target market business within the market business monitoring cycle to obtain business operation basic data; Step S2: Perform a visual analysis of the proportion of favorable comments of each target market business within the market business monitoring cycle according to the basic business operation data, and obtain the change coefficient of the periodic favorable comment proportion of each target market business within the market business monitoring cycle to obtain periodic favorable comment processing data; Step S3: Obtain the operating efficiency coefficient corresponding to each target market business based on the business operation basic data and periodic positive review processing data, and obtain the operating efficiency coefficient threshold to compare with each operating efficiency coefficient respectively, and issue an operating risk warning for the target market business based on the numerical comparison results.

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