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CN113326879B - Service data monitoring method and device - Google Patents

Service data monitoring method and device Download PDF

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CN113326879B
CN113326879B CN202110597265.4A CN202110597265A CN113326879B CN 113326879 B CN113326879 B CN 113326879B CN 202110597265 A CN202110597265 A CN 202110597265A CN 113326879 B CN113326879 B CN 113326879B
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identified
image
pixel point
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CN113326879A (en
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伍日杰
王志远
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WeBank Co Ltd
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Abstract

The invention discloses a method and a device for monitoring service data, comprising the following steps: the method comprises the steps of obtaining first service data in a preset period and second service data of a historical period associated with the preset period, generating images to be identified representing the first service data and the second service data aiming at the same service index, inputting the images to be identified into a convolutional neural network model, determining a monitoring result of the service data in the preset period under the service index, wherein the convolutional neural network model is obtained through training according to a historical identification image with a historical monitoring result label, and the historical monitoring result is determined according to the relation between the first service data and the second service data in the historical identification image. The convolution neural network model integrates the service data of each history period, so that the convolution neural network model can be suitable for monitoring the service data in all periods, the service data can be dynamically monitored, and the accuracy of service data monitoring is improved.

Description

Service data monitoring method and device
Technical Field
The invention relates to the field of financial science and technology (Fintech), in particular to a method and a device for monitoring business data.
Background
With the development of computer technology, more and more technologies (such as blockchain, cloud computing or big data) are applied in the financial field, and the traditional financial industry is gradually changed to the financial technology, so that the big data technology is not exceptional, but the big data technology is also required to be higher due to the requirements of safety and real-time performance of the financial and payment industries.
Currently, a method for monitoring service data generally compares service data in a current period with service data in a plurality of historical periods, for example, in a period unit of a day, if monitoring whether service data in the current period (for example, 5 months and 8 days) is abnormal, the service data in the current period needs to be compared with service data in a plurality of historical periods (for example, 5 months and 5 days, 5 months and 6 days and 5 months and 7 days) before the current period, and if fluctuation (increase or decrease) of service data in the current period is within a threshold range, the service data in the current period is determined to be normal data.
However, the above threshold range needs to be manually set and adjusted, and the threshold range for one period cannot be adapted to the full period of the period, so that the service data cannot be dynamically monitored, and the accuracy is not high. For example, in a period unit of a day, more traffic data is in 8:00-17:00 in a certain scene, less traffic data is in 17:00-8:00 in a period of only 17:00-18:00, the traffic data drops sharply, and exceeds a threshold range, and at this time, the generated abnormality is wrong, and is actually normal traffic data.
Therefore, a method for monitoring service data is needed to realize dynamic monitoring of service data and improve accuracy of service data monitoring.
Disclosure of Invention
The embodiment of the invention provides a method and a device for monitoring service data, which are used for dynamically monitoring the service data and improving the accuracy of service data monitoring.
In a first aspect, an embodiment of the present invention provides a method for monitoring service data, including:
acquiring first service data in a preset time period and second service data of a history time period associated with the preset time period; the first service data and the second service data are aimed at the same service index;
generating images to be identified which characterize the first service data and the second service data;
Inputting the image to be identified into a convolutional neural network model, and determining a monitoring result of the business data in the preset period under the business index; the convolutional neural network model is obtained by training according to a historical identification image with a historical monitoring result label; the history monitoring result is determined according to the relation between the first service data and the second service data in the history identification image.
The monitoring threshold in the prior art is set manually according to experience; the convolutional neural network model is obtained by training according to the historical identification image; each history identification image comprises first service data (current service data) and second service data (history service data) and is provided with a label for representing a history monitoring result; the first convolutional neural network model is trained through the historical identification image, so that the first convolutional neural network model can identify the relation between the first service data and the second service data and determine whether the first service data is abnormal according to the relation between the first service data and the second service data. Each history identification image in the application represents various history time periods respectively, so that the convolutional neural network model is actually the condition of integrating the service data of each history time period, and can be suitable for monitoring the whole time period of the service data. Therefore, the service data can be dynamically monitored, and the accuracy of the service data monitoring is improved.
Optionally, the service index is at least one of the following: business transaction amount, business average time consumption and business success rate;
The history period comprises a cycle period of the preset period and/or a same cycle period of the preset period.
According to the technical scheme, the service indexes are classified into different types, so that the comprehensiveness of service data monitoring is improved, and the service data can be better compared because the annular ratio time period and the same ratio time period of the preset time period are more relevant to the preset time period, so that the accuracy of service data monitoring is improved.
Optionally, generating the image to be identified for characterizing the first service data and the second service data includes:
generating a coordinate system by taking time as an abscissa and a business index as an ordinate;
Determining a first curve of the first service data under the coordinate system and a second curve of the second service data under the coordinate system to obtain a graph;
Preprocessing the curve graph to determine the image to be identified.
In the prior art, whether the service data is abnormal or not is determined according to the difference between the current service data and the historical data, and whether the difference is abnormal or not is not verified; in the application, the first distinction between the first service data and the second service data is visually represented in the form of the image, whether the first distinction is abnormal or not is further determined according to the second distinction of the history identification image in the convolutional neural network model, whether the first service data is abnormal or not is determined according to whether the first distinction is abnormal or not, which is equivalent to realizing twice monitoring, so that the accuracy of service data monitoring is increased.
Optionally, preprocessing the graph to determine the image to be identified, including:
generating a picture from the graph;
Scaling the resolution of the picture to a preset resolution, and determining the pixel value of any pixel point in the image to be identified according to the following formula (1) to obtain the image to be identified;
Wherein f (P) is a pixel value of any pixel point P in the image to be identified, (x, y) is a coordinate value of the pixel point P, and (x 1, y 1) is a coordinate value of an adjacent pixel point Q11 located at the lower left corner of the pixel point P in the image; (x 1, y 2) is the coordinate value of the adjacent pixel point Q12 located at the upper left corner of the pixel point P in the picture; (x 2, y 1) is the coordinate value of the adjacent pixel point positioned at the lower right corner Q21 of the pixel point P in the picture; (x 2, y 2) is the coordinate value of the adjacent pixel point Q22 located at the upper right corner of the pixel point P in the picture; f (Q11) is the pixel value of the pixel point Q11, f (Q12) is the pixel value of the pixel point Q12, f (Q21) is the pixel value of the pixel point Q21, and f (Q22) is the pixel value of the pixel point Q22.
In the above technical scheme, the values of the service data are different, so that the peak values in the coordinate system are different, the resolution of the generated picture is not uniform, and the abnormal condition of the convolutional neural network model monitoring exists, so that the uniform resolution of the image to be identified is realized through preprocessing, and the accuracy of the service data monitoring is improved.
Optionally, after the image to be identified is input into the convolutional neural network model and the monitoring result of the service data in the preset period under the service index is determined, the method further includes:
and determining the comprehensive monitoring result of the business data in the preset time period according to the preset weight of each business index and the monitoring result of the business data in the preset time period under each business index.
According to the technical scheme, the detection results obtained by the service indexes are aggregated according to the preset weights to obtain the comprehensive monitoring result, so that abnormal service data of one service index is prevented, and the first service data is judged to be abnormal by errors under the normal condition of the service data of the other service indexes, so that the accuracy of service data monitoring is improved.
Optionally, the convolutional neural network model is used for N classification;
Inputting the image to be identified into a convolutional neural network model, and determining a monitoring result of the service data in the preset period of time comprises the following steps:
inputting the image to be identified into a convolutional neural network model to obtain a classification result;
and determining a monitoring result corresponding to the classification result according to the comparison relation between N classification and M monitoring results, wherein N is greater than M.
In the above technical solution, the comparison relation between the N-class and the M-class monitoring results determines the classification result of the image to be identified, so as to determine whether the image to be identified is abnormal, where the comparison relation between the N-class and the M-class monitoring results may be preset by a user according to experience, so as to increase flexibility of monitoring service data.
Optionally, when the number of monitoring results of the error anomalies is determined to be greater than the number threshold, training the convolutional neural network model according to the images to be identified and the correction labels corresponding to the error anomalies, so as to obtain an updated convolutional neural network model.
In the above technical solution, for the abnormal service data determined by the convolutional neural network model, if the user determines that the abnormal service data is normal, that is, if the convolutional neural network model has error judgment, the abnormal service data is recorded, and when the number of the monitoring results of the error abnormality is greater than the number threshold, the convolutional neural network model is optimally trained according to the service data of the error abnormality, so that the identification accuracy of the convolutional neural network model is increased, and the accuracy of monitoring the service data is increased.
In a second aspect, an embodiment of the present invention provides a device for monitoring service data, including:
the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring first service data in a preset period and second service data of a history period associated with the preset period; the first service data and the second service data are aimed at the same service index;
The processing module is used for generating images to be identified, which characterize the first service data and the second service data;
Inputting the image to be identified into a convolutional neural network model, and determining a monitoring result of the business data in the preset period under the business index; the convolutional neural network model is obtained by training according to a historical identification image with a historical monitoring result label; the history monitoring result is determined according to the relation between the first service data and the second service data in the history identification image.
Optionally, the service index is at least one of the following: business transaction amount, business average time consumption and business success rate;
The history period comprises a cycle period of the preset period and/or a same cycle period of the preset period.
Optionally, the processing module is specifically configured to:
generating a coordinate system by taking time as an abscissa and a business index as an ordinate;
Determining a first curve of the first service data under the coordinate system and a second curve of the second service data under the coordinate system to obtain a graph;
Preprocessing the curve graph to determine the image to be identified.
Optionally, the processing module is specifically configured to:
generating a picture from the graph;
Scaling the resolution of the picture to a preset resolution, and determining the pixel value of any pixel point in the image to be identified according to the following formula (1) to obtain the image to be identified;
Wherein f (P) is a pixel value of any pixel point P in the image to be identified, (x, y) is a coordinate value of the pixel point P, and (x 1, y 1) is a coordinate value of an adjacent pixel point Q11 located at the lower left corner of the pixel point P in the image; (x 1, y 2) is the coordinate value of the adjacent pixel point Q12 located at the upper left corner of the pixel point P in the picture; (x 2, y 1) is the coordinate value of the adjacent pixel point positioned at the lower right corner Q21 of the pixel point P in the picture; (x 2, y 2) is the coordinate value of the adjacent pixel point Q22 located at the upper right corner of the pixel point P in the picture; f (Q11) is the pixel value of the pixel point Q11, f (Q12) is the pixel value of the pixel point Q12, f (Q21) is the pixel value of the pixel point Q21, and f (Q22) is the pixel value of the pixel point Q22.
Optionally, the processing module is further configured to:
and determining the comprehensive monitoring result of the business data in the preset time period according to the preset weight of each business index and the monitoring result of the business data in the preset time period under each business index.
Optionally, the convolutional neural network model is used for N classification;
The processing module is specifically configured to:
Inputting the image to be identified into a convolutional neural network model, and determining a monitoring result of the service data in the preset period of time comprises the following steps:
inputting the image to be identified into a convolutional neural network model to obtain a classification result;
and determining a monitoring result corresponding to the classification result according to the comparison relation between N classification and M monitoring results, wherein N is greater than M.
Optionally, the processing module is further configured to:
And training the convolutional neural network model according to the images to be identified and the correction labels corresponding to the error anomalies when the number of the monitoring results of the error anomalies is determined to be larger than a number threshold value, so as to obtain an updated convolutional neural network model.
In a third aspect, an embodiment of the present invention further provides a computer apparatus, including:
A memory for storing program instructions;
And the processor is used for calling the program instructions stored in the memory and executing the monitoring method of the service data according to the obtained program.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method for monitoring service data described above.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a system architecture according to an embodiment of the present invention;
Fig. 2 is a flow chart of a method for monitoring service data according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a graph according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a resolution scaling calculation according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an image to be identified according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a convolutional neural network model according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a device for monitoring service data according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the prior art, the method for monitoring service data is generally divided into the following two methods:
First, the traffic data of the current period is compared with the traffic data of a plurality of history periods. Taking a business index as a business transaction amount example, for example, taking a day as a period unit, monitoring business data of a current period (5 months and 8 days), comparing business data of a history period (comprising a cycle ratio history, such as 5 months and 7 days, a month and year comparison history, such as 4 months and 8 days) of the current period with the current period, setting the cycle ratio threshold to be 30% (namely, the business transaction amount of the current period is not more than 30% compared with the business transaction amount of the cycle ratio history), setting the upper limit of the cycle ratio threshold to be 20% (namely, the business transaction amount of the current period is not more than 20% compared with the business transaction amount of the month and year comparison history), setting the lower limit of the cycle ratio threshold to be 40% (namely, the business transaction amount of the current period is not more than 40% compared with the business transaction amount of the month and year comparison history), and determining the business data of the current period to be abnormal data if the threshold is exceeded.
Second, the business data range is determined by means of fitting a curve. Still taking the business index as a business transaction amount for example, generating a fitting curve according to business data in a historical period, determining that the threshold range of the business transaction amount in the current period is 80-100 according to the fitting curve, and if the business transaction amount in the current period is not in the threshold range, determining that the business data in the current period is abnormal data.
However, in the first method, the threshold value needs to be manually set and adjusted, the threshold value range for one period cannot be adapted to the full period of the period, the service data cannot be dynamically monitored, the accuracy is not high, and a large number of error anomalies occur in the case of extremely small service volume, for example, the historical service transaction volume is 1, the current service transaction volume is 0, 100% is reduced, and the threshold value is exceeded, so that the current service data anomalies are determined.
In the second method, the fitted curve cannot dynamically monitor according to the actual situation of the service data, for example, the fluctuation of the historical service data is large, the accuracy of the threshold range determined by the fitted curve is extremely low, and the threshold range cannot be optimized according to the determined error abnormal service data, so that the threshold range cannot be dynamically determined.
Therefore, a method for monitoring service data is needed to realize dynamic monitoring of service data and improve accuracy of service data monitoring.
Fig. 1 illustrates a system architecture to which embodiments of the present invention are applicable, the system architecture including a server 100, the server 100 may include a processor 110, a communication interface 120, and a memory 130.
The communication interface 120 is configured to obtain first service data within a preset period and second service data of a history period associated with the preset period.
The processor 110 is a control center of the server 100, connects various parts of the entire server 100 using various interfaces and routes, and performs various functions of the server 100 and processes data by running or executing software programs and/or modules stored in the memory 130, and calling data stored in the memory 130. Optionally, the processor 110 may include one or more processing units.
The memory 130 may be used to store software programs and modules, and the processor 110 performs various functional applications and data processing by executing the software programs and modules stored in the memory 130. The memory 130 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, application programs required for at least one function, and the like; the storage data area may store data created according to business processes, etc. In addition, memory 130 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
It should be noted that the structure shown in fig. 1 is merely an example, and the embodiment of the present invention is not limited thereto.
Based on the above description, fig. 2 is a schematic flow chart illustrating a method for monitoring service data according to an embodiment of the present invention, where the flow may be executed by a device for monitoring service data.
As shown in fig. 2, the process specifically includes:
Step 210, acquiring first service data in a preset period and second service data of a history period associated with the preset period.
In the embodiment of the invention, the first service data and the second service data are aimed at the same service index, for example, the first service data and the second service data are both service transaction amounts.
Step 220, generating an image to be identified for characterizing the first service data and the second service data.
In the embodiment of the invention, the basis for determining whether the first service data is abnormal data comprises the first service data and the second service data, and particularly the first distinction between the first service data and the second service data is intuitively represented in the form of images.
And 230, inputting the image to be identified into a convolutional neural network model, and determining a monitoring result of the business data in the preset period under the business index.
In the embodiment of the invention, the convolutional neural network model is trained according to a history identification image with a history monitoring result label, and the history monitoring result is determined according to the relation between first service data and second service data in the history identification image.
In step 210, the traffic index includes a plurality of categories, and specifically, the traffic index is at least one of the following: traffic volume, average time consumption of traffic, and traffic success rate.
In one embodiment, the service index may be determined according to a service log collected at a service interface, for example, by a collection module (such as agent), collecting the service log at a preset service interface, and then determining the service index according to the collected service log as shown in table 1 below.
TABLE 1
The status code is used to indicate whether the service data is successful (i.e. normal), for example, the status code 200 indicates that the service data is normal, and the status code 500 indicates that the service data is abnormal. The service success rate is determined according to the normal service quantity and the total service quantity in the preset time period, for example, the ratio of the normal service quantity to the total service quantity is determined as the service success rate.
In the embodiment of the invention, the history period comprises a cycle period of a preset period and/or a same-cycle period of the preset period. For example, the preset period is 7 months, 7 days, 12:00-12:10, the ring ratio period is 7 months, 7 days, 11:50-12:00, and the same ratio period is 6 months, 7 days, 12:00-12:10. Wherein, the same-ratio period can be divided into a week-to-week ratio, a month-to-year ratio, etc., for example, the preset period is 2021, 7 months, 7 days, 12:00-12:10, the Zhou Tongbi period is 2021, 6 months, 30 days, 12:00-12:10, the month-to-month period is 2021, 6 months, 7 days, 12:00-12:10, and the year-to-year period is 2020, 7 months, 7 days, 12:00-12:10. The specific homonymous classification is again not limited.
In step 220, a coordinate system is determined according to the preset time period, the first service data and the second service data, further, a graph representing the first service data and the second service data is determined in the coordinate system, and an image to be identified is obtained according to the graph.
Specifically, a coordinate system is generated by taking time as an abscissa and a business index as an ordinate, then a first curve of first business data under the coordinate system and a second curve of second business data under the coordinate system are determined, a graph is obtained, and then the graph is preprocessed to determine an image to be identified.
In the embodiment of the present invention, the minimum unit and the peak value of the abscissa in the coordinate system are determined according to a preset period, for example, the preset period is 10 hours, and then the abscissa takes the hour as the minimum unit, or half hour as the minimum unit, and the peak value is 10. The minimum unit and peak value of the ordinate in the coordinate system are determined according to the traffic index, for example, the ordinate is the traffic success rate, and the ordinate is the minimum unit of 10% success rate or the minimum unit of 20% success rate, and the peak value is 100%. The value of the minimum unit is merely an example, and is not particularly limited herein.
In order to better describe the above technical solution, the following description will be given in a specific example with a traffic index Wie as a traffic transaction.
Example 1
The preset period is 10 minutes, and the first service data in the preset period is acquired as shown in the following table 2.
TABLE 2
According to the same method, second service data of a history period associated with a preset period is acquired, wherein the second service data comprises ring ratio history service data and cycle-to-cycle history service data of the preset period, as shown in the following tables 3 and 4.
TABLE 3 Table 3
TABLE 4 Table 4
The summarized traffic data can now be obtained from the data of tables 2 to 4 in minutes as the minimum time unit, as shown in table 5 below.
TABLE 5
1 2 3 4 5 6 7 8 9 10
A 70 75 71 74 69 79 68 61 72 84
B 61 79 59 78 65 66 74 55 73 77
C 74 83 72 82 73 75 77 64 74 72
Wherein, a represents the first service data, B represents the ring ratio historical service data, C represents the cycle ratio historical service data, and then a coordinate system is generated according to table 5, and the maximum data in table 5 is 84, so that the peak value of the ordinate in the coordinate system can be 85, 90, etc., and the peak value in the embodiment of the invention is 90.
Then, determining a graph according to the coordinate system, as shown in fig. 3, fig. 3 exemplarily shows a schematic diagram of a graph, where the graph includes first service data, ring ratio historical service data and cycle-to-cycle historical service data, t represents time in minutes, and n represents transaction traffic.
It should be noted that, in the graph, the first service data, the ring ratio historical service data and the cycle ratio historical service data may be distinguished according to a line format, such as a straight line, a dotted line, a stippled line, and the like, and may be distinguished according to a color of the line, such as a red straight line for the first service data, a blue straight line for the ring ratio historical service data, and a green straight line for the cycle ratio historical service data, which is not particularly limited herein.
In the embodiment of the invention, after the graph is obtained, the graph is generated into the picture, and the image to be identified is obtained according to the picture.
For generating a picture by the graph, the coordinate system in fig. 3 is deleted, the picture is generated only according to the graph, then the picture is scaled, the resolution of the picture is scaled to a preset resolution, and then the image to be identified is obtained.
And for obtaining the image to be identified, determining the pixel value of the coordinate point according to the pixel value of the adjacent coordinate point of the coordinate point in the picture aiming at any coordinate point of the image to be identified with preset resolution, and obtaining the image to be identified.
Generating a picture from the graph;
Scaling the resolution of the picture to a preset resolution, and determining the pixel value of any pixel point in the image to be identified according to the following formula (1) to obtain the image to be identified;
Wherein f (P) is the pixel value of any pixel point P in the image to be identified, (x, y) is the coordinate value of the pixel point P, and (x 1, y 1) is the coordinate value of an adjacent pixel point Q11 positioned at the lower left corner of the pixel point P in the picture; (x 1, y 2) is the coordinate value of the adjacent pixel point Q12 located in the upper left corner of the pixel point P in the picture; (x 2, y 1) is the coordinate value of the adjacent pixel point positioned at the lower right corner Q21 of the pixel point P in the picture; (x 2, y 2) is the coordinate value of the adjacent pixel point Q22 located at the upper right corner of the pixel point P in the picture; f (Q11) is the pixel value of the pixel point Q11, f (Q12) is the pixel value of the pixel point Q12, f (Q21) is the pixel value of the pixel point Q21, and f (Q22) is the pixel value of the pixel point Q22.
For example, the preset resolution is 224 pixels by 224 pixels, and the peak values of the coordinate system may be different due to the different maximum values of the service data in the preset period, and the heights or widths of the graphs may be different, so that the resolution of the pictures generated according to the graphs may be different, so that in order to improve the monitoring determination rate, the resolution of the pictures is preprocessed to be the preset resolution.
If the resolution of the picture is larger than the preset resolution, the resolution of the picture needs to be reduced to the preset resolution, and if the resolution of the picture is smaller than the preset resolution, the resolution of the picture needs to be amplified to the preset resolution.
When the resolution ratio is scaled, all pixel points in the image to be identified are sequentially processed, calculation is respectively carried out once in two directions, namely the X-axis direction and the Y-axis direction, the pixel value of the pixel point to be solved is obtained through interpolation of four adjacent pixel points, and the coordinate pixel weight which is closer to the coordinate position of the pixel point to be solved is larger in the calculation process.
In connection with the above formula (1), fig. 4 exemplarily shows a schematic diagram of a resolution scaling calculation, and in connection with fig. 4 below, an image to be identified is illustrated in a specific example.
Example 2
In a scene of shrinking 2×2 pixels into 1*1 pixels, as shown in fig. 4, Q11, Q12, Q21, and Q22 are four adjacent pixels of the pixel P in fig. 4, where the pixel P is a pixel to be solved, and the formula (1) is split into the following formula (2), the formula (3), and the formula (4), and the specific steps are as follows:
1. Obtaining f (R1) by Q11 (x 1, y 1), Q21 (x 2, y 1) according to the following formula (2), and obtaining f (R2) by Q12 (x 1, y 2), Q22 (x 2, y 2) according to the following formula (3);
Where f (R1) is the pixel value of the pixel point P on the abscissa for the pixel points Q11 and Q21, f (R2) is the pixel value of the pixel point P on the abscissa for the pixel points Q12 and Q22, and if the decimal pixel value exists after the calculation, the calculation result is rounded.
2. P is obtained from R1 (x, y 1), R2 (x, y 2) according to the following calculation formula (4).
Scaling of the picture can thereby be achieved, and then the scaled picture is determined as the image to be identified. It should be noted that, in one embodiment, the pixel value of the pixel point P on the ordinate may be determined for the pixel points Q11, Q21, Q12 and Q22, and then the pixel value of the pixel point P is determined, for example, the pixel value of the pixel point P on the ordinate for the pixel points Q11, Q21, Q12 and Q22 is determined according to the following formula (5) and the following formula (6), and then the pixel value of the pixel point P is determined according to the following formula (7).
In another embodiment, the pixel value of any pixel in the image to be identified may be determined according to the ratio of the abscissa of the picture resolution to the preset resolution, for example, the picture resolution is mxn, the preset resolution is axb, the side length ratio is m/a and n/b, for any pixel (i, j) in the image to be identified, the pixel corresponding to the picture is (im/a, jn/b), if im/a, jn/b is non-integer, the pixel corresponding to the picture is determined according to the rounding mode, and the pixel value of the pixel is taken as the pixel value of the pixel (i, j), so as to obtain the pixel values of all pixels in the image to be identified, and further determine the image to be identified. Therefore, in the embodiment of the present invention, the method for zooming the picture is not particularly limited.
In step 230, the convolutional neural network model is trained from a historical recognition image with a historical monitoring result label, and in conjunction with fig. 3 and the above formula (1), fig. 5 schematically illustrates a schematic diagram of an image to be recognized. The history identification image is similar to that shown in fig. 5, and the operation and maintenance personnel endow the history identification image with a history monitoring result label, so that the convolutional neural network model is trained, and supervised machine learning is realized.
In one embodiment, the convolutional neural network model may be VggNet convolutional neural network model, googLeNet convolutional neural network model, or the like.
In the embodiment of the invention, the convolutional neural network model is AlexNet convolutional neural network model.
Further, the convolutional neural network model is used for carrying out N classification, an image to be identified is input into the convolutional neural network model to obtain a classification result, and a monitoring result corresponding to the classification result is determined according to the comparison relation between the N classification and the M type monitoring result, wherein N is larger than M.
In an embodiment of the present invention, fig. 6 is a schematic diagram schematically illustrating a convolutional neural network model, as shown in fig. 6, alexNet convolutional layers (conv), 3 fully connected layers (fully connected), and the model output is 1000 digital values corresponding to 1000 classes, and the output result is converted into a decimal-corresponding traffic state result between 0 and 1 through a softmax function to obtain a probability of multiple classes.
For example, the comparison between the N-class and the M-class monitoring result may be a value preset by an operation and maintenance person according to experience, for example, the N-class includes (M1, … …, M1000), and the detection result is classified into 5 types including normal service, slight service abnormality, normal service abnormality, serious service abnormality and serious service abnormality. Wherein, (m 1, m2, … …, m 200) corresponds to normal business, (m 201, m202, … …, m 400) corresponds to slight abnormal business, (m 401, m402, … …, m 600) corresponds to normal business, (m 601, m602, … …, m 800) corresponds to significant abnormal business, (m 801, m802, … …, m 1000) corresponds to severe abnormal business.
In one implementation manner, for different types of service indexes, a comprehensive monitoring result can be determined according to weights corresponding to the service indexes, and whether the first service data is abnormal or not is determined according to the comprehensive monitoring result.
Specifically, an image to be identified is input into a convolutional neural network model, after a monitoring result of business data in a preset period under business indexes is determined, a comprehensive monitoring result of the business data in the preset period is determined according to preset weights of the business indexes and the monitoring result of the business data in the preset period under the business indexes.
For example, the detection result type may also be preset with a weight, for example, a normal service weight of 0.9, a slight abnormal service weight of 0.8, a normal service weight of 0.6, a significant abnormal service weight of 0.3, and a severe abnormal service weight of 0.1.
The preset weight of the service index can be that the service success rate weight is 0.5, the average time-consuming weight of the service is 0.3, and the transaction amount weight of the service is 0.2.
Referring to example 1, for 3 service indexes (service transaction amount, service average time consumption and service success rate) of the first service data in the preset period, according to the monitoring results determined by the convolutional neural network model, the service is normal, the service is slightly abnormal and the service is normal, if the monitoring results are determined by the convolutional neural network model, the comprehensive monitoring result can be obtained according to the preset weight, wherein z= (0.6 x 0.2) + (0.8 x 0.3) + (0.9 x 0.5) =0.81, and if 0.81 is greater than the abnormal threshold, the first service data is determined to be normal data.
The preset weights and the anomaly thresholds are set empirically by people, and are not specifically limited herein.
For example, for the abnormal service data determined by the convolutional neural network model, if the operation and maintenance personnel determine that the abnormal service data is misabnormal, the convolutional neural network model is optimized.
Specifically, when the number of monitoring results of the error anomalies is determined to be greater than a number threshold, training the convolutional neural network model according to images to be identified and correction labels corresponding to the error anomalies, and obtaining an updated convolutional neural network model.
For example, when the operation and maintenance personnel determine that the abnormal service data a1 is misabnormal, the operation and maintenance personnel mark the service data a1, and when the number of the misabnormal service data (such as 1001 including a1, … …, a 1001) is greater than 1000 (a number threshold), the service data a1, … …, a1001 are used as training samples to perform optimization training on the convolutional neural network model, so as to increase the identification quasi-credibility of the convolutional neural network model and reduce the probability that the convolutional neural network model determines the misabnormal service data.
In the embodiment of the invention, after the abnormal service data is determined, an alarm is sent to indicate the abnormal service data of the user, and the specific alarm method can be voice broadcasting and the like and is not limited in detail.
Based on the same technical concept, fig. 7 schematically illustrates a structural schematic diagram of a device for monitoring service data, which is provided by the embodiment of the present invention, and the device may execute a flow of a method for monitoring service data.
As shown in fig. 7, the apparatus specifically includes:
An obtaining module 710, configured to obtain first service data within a preset period and second service data of a history period associated with the preset period; the first service data and the second service data are aimed at the same service index;
A processing module 720, configured to generate an image to be identified that characterizes the first service data and the second service data;
Inputting the image to be identified into a convolutional neural network model, and determining a monitoring result of the business data in the preset period under the business index; the convolutional neural network model is obtained by training according to a historical identification image with a historical monitoring result label; the history monitoring result is determined according to the relation between the first service data and the second service data in the history identification image.
Optionally, the service index is at least one of the following: business transaction amount, business average time consumption and business success rate;
The history period comprises a cycle period of the preset period and/or a same cycle period of the preset period.
Optionally, the processing module 720 is specifically configured to:
generating a coordinate system by taking time as an abscissa and a business index as an ordinate;
Determining a first curve of the first service data under the coordinate system and a second curve of the second service data under the coordinate system to obtain a graph;
Preprocessing the curve graph to determine the image to be identified.
Optionally, the processing module 720 is specifically configured to:
generating a picture from the graph;
Scaling the resolution of the picture to a preset resolution, and determining the pixel value of any pixel point in the image to be identified according to the following formula (1) to obtain the image to be identified;
Wherein f (P) is a pixel value of any pixel point P in the image to be identified, (x, y) is a coordinate value of the pixel point P, and (x 1, y 1) is a coordinate value of an adjacent pixel point Q11 located at the lower left corner of the pixel point P in the image; (x 1, y 2) is the coordinate value of the adjacent pixel point Q12 located at the upper left corner of the pixel point P in the picture; (x 2, y 1) is the coordinate value of the adjacent pixel point positioned at the lower right corner Q21 of the pixel point P in the picture; (x 2, y 2) is the coordinate value of the adjacent pixel point Q22 located at the upper right corner of the pixel point P in the picture; f (Q11) is the pixel value of the pixel point Q11, f (Q12) is the pixel value of the pixel point Q12, f (Q21) is the pixel value of the pixel point Q21, and f (Q22) is the pixel value of the pixel point Q22.
Optionally, the processing module 720 is further configured to:
and determining the comprehensive monitoring result of the business data in the preset time period according to the preset weight of each business index and the monitoring result of the business data in the preset time period under each business index.
Optionally, the convolutional neural network model is used for N classification;
The processing module 720 is specifically configured to:
Inputting the image to be identified into a convolutional neural network model, and determining a monitoring result of the service data in the preset period of time comprises the following steps:
inputting the image to be identified into a convolutional neural network model to obtain a classification result;
and determining a monitoring result corresponding to the classification result according to the comparison relation between N classification and M monitoring results, wherein N is greater than M.
Optionally, the processing module 720 is further configured to:
And training the convolutional neural network model according to the images to be identified and the correction labels corresponding to the error anomalies when the number of the monitoring results of the error anomalies is determined to be larger than a number threshold value, so as to obtain an updated convolutional neural network model.
Based on the same technical concept, the embodiment of the invention further provides a computer device, including:
A memory for storing program instructions;
And the processor is used for calling the program instructions stored in the memory and executing the monitoring method of the service data according to the obtained program.
Based on the same technical concept, the embodiment of the invention also provides a computer readable storage medium, which stores computer executable instructions for causing a computer to execute the method for monitoring service data.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (9)

1. A method for monitoring service data, comprising:
acquiring first service data in a preset time period and second service data of a history time period associated with the preset time period; the first service data and the second service data are aimed at the same service index;
Generating images to be identified which characterize the first service data and the second service data; the generating an image to be identified characterizing the first and second business data includes: generating a coordinate system by taking time as an abscissa and a business index as an ordinate; determining a first curve of the first service data under the coordinate system and a second curve of the second service data under the coordinate system to obtain a graph; preprocessing the curve graph to determine the image to be identified;
Inputting the image to be identified into a convolutional neural network model, and determining a monitoring result of the business data in the preset period under the business index, wherein the monitoring result comprises a business normal type, a business slight abnormal type, a business ordinary abnormal type, a business major abnormal type and a business serious abnormal type; the convolutional neural network model is obtained by training according to a historical identification image with a historical monitoring result label; the history monitoring result is determined according to the relation between the first service data and the second service data in the history identification image.
2. The method of claim 1, wherein the traffic indicator is at least one of: business transaction amount, business average time consumption and business success rate;
The history period comprises a cycle period of the preset period and/or a same cycle period of the preset period.
3. The method of claim 1, wherein preprocessing the graph to determine the image to be identified comprises:
generating a picture from the graph;
Scaling the resolution of the picture to a preset resolution, and determining the pixel value of any pixel point in the image to be identified according to the following formula (1) to obtain the image to be identified;
Wherein f (P) is a pixel value of any pixel point P in the image to be identified, (x, y) is a coordinate value of the pixel point P, and (x 1, y 1) is a coordinate value of an adjacent pixel point Q11 located at the lower left corner of the pixel point P in the image; (x 1, y 2) is the coordinate value of the adjacent pixel point Q12 located at the upper left corner of the pixel point P in the picture; (x 2, y 1) is the coordinate value of the adjacent pixel point Q21 located at the lower right corner of the pixel point P in the picture; (x 2, y 2) is the coordinate value of the adjacent pixel point Q22 located at the upper right corner of the pixel point P in the picture; f (Q11) is the pixel value of the pixel point Q11, f (Q12) is the pixel value of the pixel point Q12, f (Q21) is the pixel value of the pixel point Q21, and f (Q22) is the pixel value of the pixel point Q22.
4. The method of claim 1, wherein the inputting the image to be identified into a convolutional neural network model, after determining the monitoring result of the service data in the preset period under the service index, further comprises:
and determining the comprehensive monitoring result of the business data in the preset time period according to the preset weight of each business index and the monitoring result of the business data in the preset time period under each business index.
5. The method of claim 1, wherein the convolutional neural network model is used for N classification;
Inputting the image to be identified into a convolutional neural network model, and determining a monitoring result of the service data in the preset period of time comprises the following steps:
inputting the image to be identified into a convolutional neural network model to obtain a classification result;
and determining a monitoring result corresponding to the classification result according to the comparison relation between N classification and M monitoring results, wherein N is greater than M.
6. The method of any one of claims 1 to 5, further comprising:
And training the convolutional neural network model according to the images to be identified and the correction labels corresponding to the error anomalies when the number of the monitoring results of the error anomalies is determined to be larger than a number threshold value, so as to obtain an updated convolutional neural network model.
7. A device for monitoring service data, comprising:
the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring first service data in a preset period and second service data of a history period associated with the preset period; the first service data and the second service data are aimed at the same service index;
The processing module is used for generating images to be identified, which characterize the first service data and the second service data; the processing module is configured to generate, when the images to be identified representing the first service data and the second service data are generated: generating a coordinate system by taking time as an abscissa and a business index as an ordinate; determining a first curve of the first service data under the coordinate system and a second curve of the second service data under the coordinate system to obtain a graph; preprocessing the curve graph to determine the image to be identified;
Inputting the image to be identified into a convolutional neural network model, and determining a monitoring result of the business data in the preset period under the business index, wherein the monitoring result comprises a business normal type, a business slight abnormal type, a business ordinary abnormal type, a business major abnormal type and a business serious abnormal type; the convolutional neural network model is obtained by training according to a historical identification image with a historical monitoring result label; the history monitoring result is determined according to the relation between the first service data and the second service data in the history identification image.
8. A computer device, comprising:
A memory for storing program instructions;
A processor for invoking program instructions stored in said memory to perform the method of any of claims 1 to 6 in accordance with the obtained program.
9. A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method of any one of claims 1 to 6.
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