CN114817209A - Processing method and device, processor and electronic device for monitoring rules - Google Patents

Abstract

The application discloses a monitoring rule processing method and device, a processor and electronic equipment, and relates to the field of financial science and technology. The method comprises the following steps: acquiring first field information of target metadata of a monitoring rule to be configured; calculating the similarity between the first field information and the second field information of each monitoring rule in the monitoring rule base to obtain a plurality of initial similarity values; taking the initial similarity value with the highest similarity value as a target similarity value, and taking a monitoring rule corresponding to the target similarity value as a preset monitoring rule; and determining the target monitoring rule of the target metadata according to the target similarity value and the preset monitoring rule. By the method and the device, the problem that deployment of the monitoring rules is low due to the fact that deployment of the monitoring rules can only be carried out on the target data information in a manual mode in the related technology is solved.

Description

Processing method and device, processor and electronic device for monitoring rules

technical field

The present application relates to the field of financial technology, and in particular, to a method and apparatus for processing monitoring rules, a processor, and an electronic device.

Background technique

In the field of big data, reasonable monitoring of data quality is required. Usually, some monitoring rules and policies are configured on the target data to ensure the quality of the target data. When the target data complies with the monitoring rules, the target data is correct, and the next step is the target data processing task; when the target data does not meet the monitoring rules, the target data is correct and the data is wrong, and the next step is stopped. With the development of big data, it is necessary to set various monitoring rules for target data, for example, the null value rate of data fields is less than 10%, the number of field enumeration values is equal to 2, etc., but such monitoring rules cannot be automatically configured at present. , can only be manually configured by technicians based on past experience.

Aiming at the problem that monitoring rules can only be deployed on target data information manually in the related art, resulting in a relatively low efficiency of deploying monitoring rules, no effective solution has been proposed yet.

SUMMARY OF THE INVENTION

The main purpose of the present application is to provide a monitoring rule processing method and device, a processor and an electronic device, so as to solve the problem of deploying monitoring rules for target data information in the related art only by manual means, resulting in the deployment of monitoring rules. problem of low efficiency.

In order to achieve the above object, according to an aspect of the present application, a method for processing monitoring rules is provided. The method includes: acquiring first field information of target metadata of a monitoring rule to be configured; calculating the similarity between the first field information and the second field information of each monitoring rule in the monitoring rule base, and obtaining a plurality of initial similarities degree value; take the initial similarity value with the highest similarity value as the target similarity value, and take the monitoring rule corresponding to the target similarity value as the preset monitoring rule; according to the target similarity value and the preset monitoring rule , and determine the target monitoring rule of the target metadata.

Further, calculating the similarity between the first field information and the second field information of each monitoring rule to obtain multiple initial similarity values, including: calculating the first field name in the first field information and the The similarity of the second field name in the second field information is obtained, and the similarity value is one; the Chinese description information of the first field in the first field information and the Chinese description information of the second field in the second field information are calculated. The similarity of the first field information and the similarity of the second field type of the second field information are calculated to obtain the similarity value three; value 1, the similarity value 2 and the similarity value 3 are input into a linear regression model for calculation processing, and the initial similarity value is output.

Further, calculating the similarity between the first field name in the first field information and the second field name in the second field information to obtain a similarity value of one, comprising: assigning the first field name according to a predetermined Suppose the rule is divided into a plurality of first elements; the second field name is divided into a plurality of second elements according to the preset rule, wherein the number of the first elements is the same as the number of the second elements; Calculate the similarity between each first element and each second element, and obtain the set of similarity values corresponding to each first element; take the maximum similarity value as the similarity value of each first element; calculate all the first elements The average value of the similarity value of an element, and the average value is taken as the similarity value one.

Further, calculating the similarity between the first field type in the first field information and the second field type in the second field information, to obtain similarity value three, including: based on the implicit conversion rule of the field type, calculating The similarity between the first field type and the second field type obtains a similarity value of three.

Further, determining the target monitoring rule of the target metadata according to the target similarity value and the preset monitoring rule includes: acquiring the type of the preset monitoring rule, wherein the value of the preset monitoring rule is The type is one of the following: a proportional monitoring rule and a threshold monitoring rule; if the type of the preset monitoring rule is the proportional monitoring rule, determine whether the target similarity value is greater than a preset value of one; if the If the target similarity value is greater than the preset value of one, the preset monitoring rule is determined as the target monitoring rule of the target metadata.

Further, the method further includes: if the type of the preset monitoring rule is the threshold type monitoring rule, judging whether the target similarity value is greater than a preset value two, wherein the preset value two is greater than the preset value two. The preset value is one; if the target similarity value is greater than the preset value two, the preset monitoring rule is determined as the target monitoring rule of the target metadata.

Further, after determining the target monitoring rule of the target metadata according to the target similarity value and the preset monitoring rule, the method further includes: according to the first database name in the first field information , the first data table name and the first field name, the target monitoring rule is deployed to the target metadata; after the target monitoring rule is deployed to the target metadata, the target metadata and the target The corresponding relationship of the monitoring rules is stored in the monitoring rule base to update the monitoring rule base.

In order to achieve the above object, according to another aspect of the present application, a processing device for monitoring rules is provided. The device includes: an acquiring unit for acquiring first field information of target metadata of a monitoring rule to be configured; a calculating unit for calculating the first field information and the second field of each monitoring rule in the monitoring rule base The similarity of the information, to obtain a plurality of initial similarity values; the selection unit is used to take the initial similarity value with the highest similarity value as the target similarity value, and the monitoring rule corresponding to the target similarity value as the preset monitoring rule ; a first determining unit, configured to determine a target monitoring rule for the target metadata according to the target similarity value and the preset monitoring rule.

Further, the calculation unit includes: a first calculation subunit, configured to calculate the similarity between the first field name in the first field information and the second field name in the second field information to obtain the similarity The value is one; the second calculation subunit is used to calculate the similarity between the Chinese description information of the first field in the first field information and the Chinese description information of the second field in the second field information, and obtain a similarity value of two The third calculation subunit is used to calculate the similarity of the first field type in the first field information and the second field type of the second field information to obtain similarity value three; the fourth calculation subunit, It is used for inputting the similarity value 1, the similarity value 2 and the similarity value 3 into a linear regression model for calculation processing, and outputting the initial similarity value.

Further, the first calculation subunit includes: a first division module, used to divide the first field name into a plurality of first elements according to preset rules; a second division module, used to divide the second The field name is divided into a plurality of second elements according to the preset rule, wherein the number of the first elements is the same as the number of the second elements; the first calculation module is used to calculate the difference between each first element and each The similarity between the second elements is obtained, and the set of similarity values corresponding to each first element is obtained; the selection module is used to take the maximum similarity value as the similarity value of each first element; the second calculation module is used to use for calculating the average value of the similarity values of all the first elements, and using the average value as the similarity value one.

Further, the third calculation subunit includes: a third calculation module, configured to calculate the similarity between the first field type and the second field type based on an implicit conversion rule of the field type to obtain a similarity value three.

Further, the first determining unit includes: an acquiring subunit, configured to acquire the type of the preset monitoring rule, wherein the type of the preset monitoring rule is one of the following: a proportional monitoring rule and a threshold monitoring rule; a judging subunit, used for judging whether the target similarity value is greater than a preset value one if the type of the preset monitoring rule is the proportional monitoring rule; a determining subunit, used for if the target is similar If the degree value is greater than the preset value of one, the preset monitoring rule is determined as the target monitoring rule of the target metadata.

Further, the device further includes: a judging unit, configured to judge whether the target similarity value is greater than a preset value of two if the type of the preset monitoring rule is the threshold type monitoring rule, wherein the preset monitoring rule is Set the value of two to be greater than the preset value of one; the second determination unit is configured to determine the preset monitoring rule as the target of the target metadata if the target similarity value is greater than the preset value of two Monitoring rules.

Further, the apparatus further includes: a deployment unit, configured to, after determining the target monitoring rule of the target metadata according to the target similarity value and the preset monitoring rule, according to the first field information The name of the first database, the name of the first data table and the name of the first field are used to deploy the target monitoring rules to the target metadata; the updating unit is used to deploy the target monitoring rules to the target metadata, The corresponding relationship between the target metadata and the target monitoring rule is stored in the monitoring rule base, so as to update the monitoring rule base.

In order to achieve the above object, according to an aspect of the present application, a processor is provided, and the processor is used for running a program, wherein when the program runs, the processing method of any one of the monitoring rules described above is executed.

In order to achieve the above object, according to an aspect of the present application, an electronic device is provided, the electronic device includes one or more processors and a memory, and the memory is used to store the information of the one or more processors implementing the monitoring rules of any one of the above. Approach.

Through the present application, the following steps are adopted: obtaining the first field information of the target metadata of the monitoring rule to be configured; calculating the similarity between the first field information and the second field information of each monitoring rule in the monitoring rule base, and obtaining a plurality of Initial similarity value; take the initial similarity value with the highest similarity value as the target similarity value, and take the monitoring rule corresponding to the target similarity value as the preset monitoring rule; determine the target element according to the target similarity value and the preset monitoring rule The target monitoring rule for data solves the problem that the monitoring rules can only be deployed manually for target data information in the related art, resulting in a relatively low efficiency of deploying monitoring rules. By calculating the similarity between the first field information of the target metadata and the second field information of each monitoring rule, and selecting the preset monitoring rule corresponding to the highest similarity value, the work of deploying monitoring rules on the target metadata is realized, and then the It has the effect of improving the efficiency of deploying monitoring rules.

Description of drawings

The accompanying drawings constituting a part of the present application are used to provide further understanding of the present application, and the schematic embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation of the present application. In the attached image:

1 is a flowchart of a method for processing monitoring rules provided according to an embodiment of the present application;

2 is a flowchart of an optional monitoring rule processing method provided according to an embodiment of the present application;

3 is a schematic diagram of a processing device for monitoring rules provided according to an embodiment of the present application;

FIG. 4 is a schematic diagram of an electronic device provided according to an embodiment of the present application.

Detailed ways

It should be noted that the embodiments in the present application and the features of the embodiments may be combined with each other in the case of no conflict. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

In order to make those skilled in the art better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only The embodiments are part of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the scope of protection of the present application.

It should be noted that the terms "first", "second", etc. in the description and claims of the present application and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific sequence or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances for the embodiments of the application described herein. Furthermore, the terms "comprising" and "having" and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those expressly listed Rather, those steps or units may include other steps or units not expressly listed or inherent to these processes, methods, products or devices.

The present invention will be described below with reference to the preferred implementation steps. FIG. 1 is a flowchart of a method for processing monitoring rules provided according to an embodiment of the present application. As shown in FIG. 1 , the method includes the following steps:

Step S101: Obtain first field information of target metadata of the monitoring rule to be configured.

Specifically, the monitoring rule base includes a plurality of monitoring rules and the corresponding relationship between each monitoring rule and its monitoring metadata. Then, through the monitoring rule base, it can be determined that the metadata for which the monitoring rule is not configured, that is, the above-mentioned target metadata. Then, the first field information of the target metadata is obtained, and the first field information at least includes information such as database name, data table name, field name, field Chinese description information, and field type.

Step S102: Calculate the similarity between the first field information and the second field information of each monitoring rule in the monitoring rule base to obtain a plurality of initial similarity values.

Specifically, each monitoring rule includes information such as database name, data table name, field name, field Chinese description information, field type, monitoring rule type and monitoring rule algorithm, that is, the above-mentioned second field information. Calculate the similarity between the first field information and the second field information of each monitoring rule to obtain a plurality of initial similarity values.

Step S103, taking the initial similarity value with the highest similarity value as the target similarity value, and taking the monitoring rule corresponding to the target similarity value as the preset monitoring rule.

Specifically, the initial similarity value with the highest similarity value is selected from the plurality of initial similarity values as the target similarity value, and the monitoring rule corresponding to the target similarity value is used as the preset monitoring rule.

Step S104: Determine the target monitoring rule of the target metadata according to the target similarity value and the preset monitoring rule.

To sum up, by calculating the similarity between the first field information of the target metadata and the second field information of each monitoring rule, and selecting the preset monitoring rule corresponding to the highest similarity value, the monitoring rules are deployed on the target metadata. It avoids manual deployment of monitoring rules and improves the efficiency of deploying monitoring rules.

Optionally, in the monitoring rule processing method provided in the embodiment of the present application, calculating the similarity between the first field information and the second field information of each monitoring rule to obtain a plurality of initial similarity values, including: calculating the first field information. The similarity between the first field name in the field information and the second field name in the second field information is obtained, and the similarity value is one; calculate the Chinese description information of the first field in the first field information and the first field in the second field information. The similarity of the Chinese description information of the two fields is obtained, and the similarity value 2 is obtained; the similarity between the first field type in the first field information and the second field type of the second field information is calculated, and the similarity value 3 is obtained; First, the similarity value two and the similarity value three are input into the linear regression model for calculation processing, and the initial similarity value is output.

Specifically, the similarity between the first field name in the first field information and the second field name in the second field information is calculated respectively, the Chinese description information of the first field in the first field information and the first field name in the second field information are calculated respectively. The similarity of the two-field Chinese description information, the similarity of the first field type in the first field information and the similarity of the second field type of the second field information, the similarity value one, the similarity value two and the similarity value three are obtained. The initial similarity value is calculated by linear regression model. The linear regression model can use the following formula to calculate the initial similarity value: initial similarity value=0.4*similarity value one+0.3*similarity value two+0.3*similarity value three.

Through the above steps, the similarity of the field name, the Chinese description information of the field and the field type is calculated, which can effectively improve the accuracy of the monitoring rules for determining the target metadata.

Optionally, in the method for processing monitoring rules provided in the embodiment of the present application, the similarity between the first field name in the first field information and the second field name in the second field information is calculated to obtain a similarity value of one, Including: dividing the first field name into multiple first elements according to preset rules; dividing the second field name into multiple second elements according to preset rules, wherein the number of first elements is the same as the number of second elements ; Calculate the similarity between each first element and each second element, and obtain the set of similarity values corresponding to each first element; take the maximum similarity value as the similarity value of each first element; calculate all The average value of the similarity value of the first element, and the average value is taken as the similarity value one.

Specifically, the naming rules for field names in big data development are generally: modifier_atomic indicator_time window, for example, the number of transactions in the last year is named "trade_cnt_1y"; when there are multiple modifiers, the order is given by Data developers can define by themselves. For example, the number of transactions flowing out of a certain payment platform channel in the last year can be named "zfb_out_th_cnt_1y" or "zfb_th_out_cnt_1y".

Assuming the name of the first field is "zfb_out_th_cnt_1y" and the name of the second field is "zfb_th_out_cnt_1y", then the steps for calculating similarity value one are:

Step 1: According to "_" as the separator, the first field name and the second field name are divided, and the first element and the second element are obtained correspondingly; for example, the first element is [zfb, out, th, cnt, 1y], the second element is [zfb, th, out, cnt, 1y]. Step 2: Calculate the similarity between each first element and each second element, and take the maximum similarity value; for example, calculate the similarity between the first element zfb to [zfb, th, out, cnt, 1y] They are [1, 0, 0, 0, 0] respectively, and the maximum similarity value is 1; by continuing to calculate the similarity of other first elements to each second element, the maximum similarity value can be obtained as 1. Step 3: Calculate the average value of the similarity values of all the first elements, and use the average value as the similarity value one. For example, the similarity value one is (1+1+1+1+1)/5=1. Through the above steps, the similarity value of the field name can be accurately calculated.

Optionally, in the method for processing monitoring rules provided in this embodiment of the present application, the similarity between the first field type in the first field information and the second field type in the second field information is calculated to obtain a similarity value of three, including: : Calculate the similarity between the first field type and the second field type based on the implicit conversion rule of the field type, and obtain a similarity value of three.

Specifically, the field types are generally divided into: integer, floating point, character, date, and boolean. Based on the implicit conversion rules of field types, short data types can be implicitly converted to long data types. For example, int can be automatically converted to bigint, float can be converted to double, etc. Then, when calculating the similarity value 3, it can be considered that the similarity value of the field type that can be implicitly converted is 0.9, the similarity value of the field type that cannot be implicitly converted is 0, and the similarity value of the same field type is 1. In the above manner, the similarity value of the field type can be quickly obtained, thereby improving the efficiency of deploying monitoring rules.

For example, the field name of the target metadata, the Chinese description of the field, and the field type are "trade_cnt_1m, the number of transactions in the last 1 month, bigint". In the first monitoring rule, the field name, field description in Chinese and field type are "trade_amt_1m, transaction amount in the last month, decimal"; in the second monitoring rule, the field name, field description in Chinese and field type are "trade_cnt_1y" , the number of transactions in the last 1 year, bigint". Through the above method, it can be obtained that the similarity of the target metadata and the field name of the first monitoring rule is 0.7778, the similarity of the Chinese description is 0.75, the similarity of the field type is 0.9, and the final similarity is 0.8061. The similarity of the field name between the target metadata and the second monitoring rule is 0.8333, the similarity of the Chinese description is 0.875, the similarity of the field type is 1, and the final similarity is 0.9333. The monitoring rule corresponding to the largest similarity value is selected as the preset monitoring rule, that is, the second monitoring rule is used as the preset monitoring rule.

Optionally, in the method for processing monitoring rules provided in the embodiment of the present application, determining the target monitoring rules of target metadata according to the target similarity value and the preset monitoring rules includes: acquiring the type of the preset monitoring rules, wherein: The type of the preset monitoring rule is one of the following: a proportional type monitoring rule and a threshold type monitoring rule; if the type of the preset monitoring rule is a proportional type monitoring rule, it is determined whether the target similarity value is greater than the preset value of one; If the value is greater than the preset value of one, the preset monitoring rule is determined as the target monitoring rule of the target metadata.

Specifically, the types of preset monitoring rules are determined, and different preset data are set for different types. The types of monitoring rules are mainly proportional monitoring rules and threshold monitoring rules. For the proportional monitoring rule, if the calculated target similarity value is greater than 0.8 (the above-mentioned preset value of one), the preset monitoring rule can be determined as the target monitoring rule of the target metadata. For different types of monitoring rules, different discrimination requirements are set, which further improves the accuracy of matching monitoring rules to target metadata.

Optionally, in the method for processing monitoring rules provided in the embodiment of the present application, the method further includes: if the type of the preset monitoring rule is a threshold type monitoring rule, judging whether the target similarity value is greater than a preset value of two, wherein, The preset value two is greater than the preset value one; if the target similarity value is greater than the preset value two, the preset monitoring rule is determined as the target monitoring rule of the target metadata.

Specifically, for the threshold type monitoring rule, if the calculated target similarity value is greater than 0.95 (the above-mentioned preset value 2), the preset monitoring rule can be determined as the target monitoring rule of the target metadata.

Optionally, in the monitoring rule processing method provided by the embodiment of the present application, after determining the target monitoring rule of the target metadata according to the target similarity value and the preset monitoring rule, the method further includes: according to the first field information The name of the first database, the name of the first data table and the name of the first field in the target metadata are deployed with target monitoring rules; after the target monitoring rules are deployed on the target metadata, the corresponding relationship between the target metadata and the target monitoring rules is stored. to the monitoring rule base to update the monitoring rule base.

Specifically, the monitoring rules are deployed on the target cloud data according to the first database name, the first data table name and the first field name in the first field information. After the monitoring rules are deployed, the corresponding relationship between the target metadata and the target monitoring rules is recorded in the monitoring rule base for subsequent query.

The monitoring rule processing method provided by the embodiment of the present application is to obtain the first field information of the target metadata of the monitoring rule to be configured; calculate the similarity between the first field information and the second field information of each monitoring rule in the monitoring rule base to obtain multiple initial similarity values; take the initial similarity value with the highest similarity value as the target similarity value, and use the monitoring rule corresponding to the target similarity value as the preset monitoring rule; according to the target similarity value and the preset monitoring The rule determines the target monitoring rules of target metadata, and solves the problem that monitoring rules can only be deployed manually for target data information in the related art, resulting in low efficiency of deploying monitoring rules. By calculating the similarity between the first field information of the target metadata and the second field information of each monitoring rule, and selecting the preset monitoring rule corresponding to the highest similarity value, the work of deploying monitoring rules on the target metadata is realized, and then the It has the effect of improving the efficiency of deploying monitoring rules.

2 is a flowchart of an optional monitoring rule processing method provided according to an embodiment of the present application. The first step is to obtain first field information of target metadata of monitoring rules to be configured; the second step is to calculate the first field information and monitoring The similarity value of the second field information of each monitoring rule in the rule base is obtained to obtain the target similarity value; the third step is to determine the target monitoring rule of the target metadata; the fourth step is to deploy the target monitoring rule and update the monitoring rule base.

It should be noted that the steps shown in the flowcharts of the accompanying drawings may be executed in a computer system, such as a set of computer-executable instructions, and, although a logical sequence is shown in the flowcharts, in some cases, Steps shown or described may be performed in an order different from that herein.

The embodiment of the present application further provides a monitoring rule processing apparatus. It should be noted that the monitoring rule processing apparatus of the present application embodiment may be used to execute the monitoring rule processing method provided by the present application embodiment. The following describes the processing device for the monitoring rule provided by the embodiment of the present application.

FIG. 3 is a schematic diagram of an apparatus for processing monitoring rules according to an embodiment of the present application. As shown in FIG. 3 , the apparatus includes: an acquisition unit 301 , a calculation unit 302 , a selection unit 303 and a first determination unit 304 .

The obtaining unit 301 is configured to obtain the first field information of the target metadata of the monitoring rule to be configured.

The calculating unit 302 is configured to calculate the similarity between the first field information and the second field information of each monitoring rule in the monitoring rule base to obtain a plurality of initial similarity values.

The selection unit 303 is configured to use the initial similarity value with the highest similarity value as the target similarity value, and use the monitoring rule corresponding to the target similarity value as the preset monitoring rule.

The first determining unit 304 is configured to determine the target monitoring rule of the target metadata according to the target similarity value and the preset monitoring rule.

In the monitoring rule processing device provided by the embodiment of the present application, the acquiring unit 301 acquires the first field information of the target metadata of the monitoring rule to be configured; the calculating unit 302 calculates the difference between the first field information and each monitoring rule in the monitoring rule base. The similarity of the second field information obtains a plurality of initial similarity values; the selection unit 303 takes the initial similarity value with the highest similarity value as the target similarity value, and uses the monitoring rule corresponding to the target similarity value as the preset monitoring rule; The first determining unit 304 determines the target monitoring rules of the target metadata according to the target similarity value and the preset monitoring rules, which solves the problem that the deployment of monitoring rules for target data information can only be performed manually in the related art, resulting in deployment monitoring. The efficiency of the rules is relatively low. By calculating the similarity between the first field information of the target metadata and the second field information of each monitoring rule, and selecting the preset monitoring rule corresponding to the highest similarity value, the work of deploying monitoring rules on the target metadata is realized, and then the It has the effect of improving the efficiency of deploying monitoring rules.

Optionally, in the apparatus for processing monitoring rules provided in this embodiment of the present application, the calculation unit 302 includes: a first calculation subunit, configured to calculate the first field name in the first field information and the first field name in the second field information. The similarity of the names of the two fields is obtained to obtain a similarity value of one; the second calculation subunit is used to calculate the similarity between the Chinese description information of the first field in the first field information and the Chinese description information of the second field in the second field information. , to obtain a similarity value of two; a third calculation subunit is used to calculate the similarity between the first field type in the first field information and the second field type of the second field information, and obtain a similarity value of three; the fourth calculation subunit The unit is used to input the similarity value 1, the similarity value 2 and the similarity value 3 into the linear regression model for calculation processing, and output the initial similarity value.

Optionally, in the monitoring rule processing apparatus provided in the embodiment of the present application, the first calculation subunit includes: a first segmentation module, configured to segment the first field name into a plurality of first elements according to a preset rule; The two-division module is used to divide the second field name into a plurality of second elements according to preset rules, wherein the number of the first elements is the same as the number of the second elements; the first calculation module is used to calculate each first element The similarity between the element and each second element, the set of similarity values corresponding to each first element is obtained; the selection module is used to take the maximum similarity value as the similarity value of each first element; the second calculation The module is used to calculate the average value of the similarity values of all the first elements, and use the average value as the similarity value one.

Optionally, in the monitoring rule processing apparatus provided in the embodiment of the present application, the third calculation subunit includes: a third calculation module, configured to calculate the first field type and the second field based on the implicit conversion rule of the field type. Type similarity, get a similarity value of three.

Optionally, in the monitoring rule processing apparatus provided in the embodiment of the present application, the first determining unit 304 includes: an obtaining subunit, configured to obtain the type of the preset monitoring rule, wherein the type of the preset monitoring rule is one of the following 1: Proportional monitoring rules and threshold monitoring rules; the judgment subunit is used to judge whether the target similarity value is greater than the preset value one if the type of the preset monitoring rule is the proportional monitoring rule; the determination subunit is used to determine if the If the target similarity value is greater than the preset value of one, the preset monitoring rule is determined as the target monitoring rule of the target metadata.

Optionally, in the apparatus for processing monitoring rules provided in the embodiment of the present application, the apparatus further includes: a judgment unit, configured to judge whether the target similarity value is greater than a preset value if the type of the preset monitoring rule is a threshold type monitoring rule. A value of two, wherein the preset value of two is greater than the preset value of one; the second determination unit is configured to determine the preset monitoring rule as the target monitoring rule of the target metadata if the target similarity value is greater than the preset value of two.

Optionally, in the apparatus for processing monitoring rules provided in the embodiment of the present application, the apparatus further includes: a deployment unit, configured to, after determining the target monitoring rules of the target metadata according to the target similarity value and the preset monitoring rules, According to the name of the first database, the name of the first data table and the name of the first field in the first field information, a target monitoring rule is deployed on the target metadata; the updating unit is used to update the target metadata after deploying the target monitoring rule on the target metadata. The corresponding relationship between the metadata and the target monitoring rules is stored in the monitoring rule base to update the monitoring rule base.

The processing device for monitoring rules includes a processor and a memory, the above-mentioned acquisition unit 301, the calculation unit 302, the selection unit 303 and the first determination unit 304 are all stored in the memory as program units, and the processor executes the above-mentioned program stored in the memory. unit to achieve the corresponding function.

The processor includes a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can set one or more, and the determination of the monitoring rules can be realized by adjusting the kernel parameters.

Memory may include non-persistent memory in computer readable media, random access memory (RAM) and/or non-volatile memory, such as read only memory (ROM) or flash memory (flash RAM), the memory including at least one memory chip.

An embodiment of the present invention provides a processor, where the processor is used to run a program, wherein a method for processing monitoring rules is executed when the program is running.

As shown in FIG. 4 , an embodiment of the present invention provides an electronic device. The device includes a processor, a memory, and a program stored in the memory and running on the processor. When the processor executes the program, the following steps are implemented: obtaining a to-be-configured program. The first field information of the target metadata of the monitoring rule; calculate the similarity between the first field information and the second field information of each monitoring rule in the monitoring rule base, and obtain a plurality of initial similarity values; The similarity value is used as the target similarity value, and the monitoring rule corresponding to the target similarity value is used as the preset monitoring rule; according to the target similarity value and the preset monitoring rule, the target monitoring rule of the target metadata is determined.

Optionally, calculating the similarity between the first field information and the second field information of each monitoring rule to obtain a plurality of initial similarity values, including: calculating the first field name in the first field information and the second field information in the second field information. The similarity of the name of the second field of the ; Calculate the similarity of the first field type in the first field information and the second field type of the second field information to obtain a similarity value three; input the similarity value one, the similarity value two and the similarity value three into the linear regression The calculation process is performed in the model, and the initial similarity value is output.

Optionally, calculating the similarity between the first field name in the first field information and the second field name in the second field information to obtain a similarity value of one, including: dividing the first field name into multiple parts according to a preset rule. a first element; divide the second field name into multiple second elements according to preset rules, wherein the number of first elements is the same as the number of second elements; calculate the difference between each first element and each second element The similarity between the first elements is obtained, and the set of similarity values corresponding to each first element is obtained; the maximum similarity value is taken as the similarity value of each first element; the average value of the similarity values of all the first elements is calculated, and the average value as the similarity value of one.

Optionally, calculating the similarity between the first field type in the first field information and the second field type in the second field information to obtain similarity value three, including: calculating the first field based on an implicit conversion rule of the field type. The similarity between the type and the second field type, the similarity value three is obtained.

Optionally, according to the target similarity value and the preset monitoring rule, determining the target monitoring rule of the target metadata includes: acquiring the type of the preset monitoring rule, wherein the type of the preset monitoring rule is one of the following: proportional monitoring. Rules and threshold monitoring rules; if the type of the preset monitoring rule is a proportional monitoring rule, determine whether the target similarity value is greater than the preset value of one; if the target similarity value is greater than the preset value of one, determine the preset monitoring rule Target monitoring rules for target metadata.

Optionally, the method further includes: if the type of the preset monitoring rule is a threshold type monitoring rule, judging whether the target similarity value is greater than a preset value of two, where the preset value of two is greater than the preset value of one; If the value is greater than the preset value of two, the preset monitoring rule is determined as the target monitoring rule of the target metadata.

Optionally, after determining the target monitoring rules of the target metadata according to the target similarity value and the preset monitoring rules, the method further includes: according to the first database name, the first data table name and the first data table name in the first field information. A field name, deploying the target monitoring rule to the target metadata; after deploying the target monitoring rule to the target metadata, store the corresponding relationship between the target metadata and the target monitoring rule in the monitoring rule base to update the monitoring rule base. The devices in this article can be servers, PCs, PADs, mobile phones, and so on.

The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program initialized with the following method steps: acquiring first field information of target metadata of a monitoring rule to be configured; calculating the first field The similarity between the information and the second field information of each monitoring rule in the monitoring rule base is obtained, and multiple initial similarity values are obtained; the initial similarity value with the highest similarity value is used as the target similarity value, and the target similarity value corresponds to The monitoring rule is used as the preset monitoring rule; according to the target similarity value and the preset monitoring rule, the target monitoring rule of the target metadata is determined.

Optionally, calculating the similarity between the first field information and the second field information of each monitoring rule to obtain a plurality of initial similarity values, including: calculating the first field name in the first field information and the second field information in the second field information. The similarity of the name of the second field of the ; Calculate the similarity of the first field type in the first field information and the second field type of the second field information to obtain a similarity value three; input the similarity value one, the similarity value two and the similarity value three into the linear regression The calculation process is performed in the model, and the initial similarity value is output.

Optionally, calculating the similarity between the first field name in the first field information and the second field name in the second field information to obtain a similarity value of one, including: dividing the first field name into multiple parts according to a preset rule. a first element; divide the second field name into multiple second elements according to preset rules, wherein the number of first elements is the same as the number of second elements; calculate the difference between each first element and each second element The similarity between the first elements is obtained, and the set of similarity values corresponding to each first element is obtained; the maximum similarity value is taken as the similarity value of each first element; the average value of the similarity values of all the first elements is calculated, and the average value as the similarity value of one.

Optionally, calculating the similarity between the first field type in the first field information and the second field type in the second field information to obtain similarity value three, including: calculating the first field based on an implicit conversion rule of the field type. The similarity between the type and the second field type, the similarity value three is obtained.

Optionally, according to the target similarity value and the preset monitoring rule, determining the target monitoring rule of the target metadata includes: acquiring the type of the preset monitoring rule, wherein the type of the preset monitoring rule is one of the following: proportional monitoring. Rules and threshold monitoring rules; if the type of the preset monitoring rule is a proportional monitoring rule, determine whether the target similarity value is greater than the preset value of one; if the target similarity value is greater than the preset value of one, determine the preset monitoring rule Target monitoring rules for target metadata.

Optionally, the method further includes: if the type of the preset monitoring rule is a threshold type monitoring rule, judging whether the target similarity value is greater than a preset value of two, where the preset value of two is greater than the preset value of one; If the value is greater than the preset value of two, the preset monitoring rule is determined as the target monitoring rule of the target metadata.

Optionally, after determining the target monitoring rules of the target metadata according to the target similarity value and the preset monitoring rules, the method further includes: according to the first database name, the first data table name and the first data table name in the first field information. A field name, deploying the target monitoring rule to the target metadata; after deploying the target monitoring rule to the target metadata, store the corresponding relationship between the target metadata and the target monitoring rule in the monitoring rule base to update the monitoring rule base.

As will be appreciated by those skilled in the art, the embodiments of the present application may be provided as a method, a system, or a 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, etc.) 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 embodiments of the present application. It will be understood that each flow and/or block in 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 the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

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

Memory may include non-persistent memory in computer readable media, random access memory (RAM) and/or non-volatile memory in the form of, for example, read only memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media includes both persistent and non-permanent, removable and non-removable media, and storage of information may be implemented by any method or technology. Information may be computer readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Flash Memory or other memory technology, Compact Disc Read Only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, Magnetic tape cartridges, magnetic tape disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

It should also be noted that the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device comprising a series of elements includes not only those elements, but also Other elements not expressly listed, or which are inherent to such a process, method, article of manufacture, or apparatus are also included. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, method, article of manufacture or apparatus that includes the element.

It will be appreciated by those skilled in the art that the embodiments of the present application may be provided as a method, a system or a 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, etc.) having computer-usable program code embodied therein.

The above are merely examples of the present application, and are not intended to limit the present application. Various modifications and variations of this application are possible for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included within the scope of the claims of this application.

Claims (10)

1. A method for processing a monitoring rule, comprising:

acquiring first field information of target metadata of a monitoring rule to be configured;

calculating the similarity between the first field information and the second field information of each monitoring rule in the monitoring rule base to obtain a plurality of initial similarity values;

taking the initial similarity value with the highest similarity value as a target similarity value, and taking a monitoring rule corresponding to the target similarity value as a preset monitoring rule;

and determining the target monitoring rule of the target metadata according to the target similarity value and the preset monitoring rule.

2. The method of claim 1, wherein calculating the similarity between the first field information and the second field information of each monitoring rule to obtain a plurality of initial similarity values comprises:

calculating the similarity between the first field name in the first field information and the second field name in the second field information to obtain a similarity value I;

calculating the similarity of first field Chinese description information in the first field information and second field Chinese description information in the second field information to obtain a similarity value two;

calculating the similarity of a first field type in the first field information and a second field type of the second field information to obtain a similarity value III;

and inputting the first similarity value, the second similarity value and the third similarity value into a linear regression model for calculation processing, and outputting the initial similarity value.

3. The method of claim 2, wherein calculating the similarity between the first field name in the first field information and the second field name in the second field information to obtain a similarity value of one comprises:

dividing the first field name into a plurality of first elements according to a preset rule;

dividing the second field name into a plurality of second elements according to the preset rule, wherein the number of the first elements is the same as that of the second elements;

calculating the similarity between each first element and each second element to obtain a similarity value set corresponding to each first element;

taking the maximum similarity value as the similarity value of each first element;

and calculating the average value of the similarity values of all the first elements, and taking the average value as the similarity value one.

4. The method of claim 2, wherein calculating the similarity between the first field type in the first field information and the second field type in the second field information to obtain a similarity value of three comprises:

and calculating the similarity of the first field type and the second field type based on an implicit conversion rule of the field type to obtain a similarity value III.

5. The method of claim 1, wherein determining the target monitoring rule of the target metadata according to the target similarity value and the preset monitoring rule comprises:

obtaining the type of the preset monitoring rule, wherein the type of the preset monitoring rule is one of the following types: a proportional class monitoring rule and a threshold class monitoring rule;

if the type of the preset monitoring rule is the proportion monitoring rule, judging whether the target similarity value is larger than a preset value one;

and if the target similarity value is greater than the preset numerical value one, determining the preset monitoring rule as the target monitoring rule of the target metadata.

6. The method of claim 5, further comprising:

if the type of the preset monitoring rule is the threshold value type monitoring rule, judging whether the target similarity value is larger than a preset value two, wherein the preset value two is larger than the preset value one;

and if the target similarity value is greater than the preset numerical value two, determining the preset monitoring rule as the target monitoring rule of the target metadata.

7. The method according to claim 1, wherein after determining the target monitoring rule of the target metadata according to the target similarity value and the preset monitoring rule, the method further comprises:

deploying the target monitoring rule for the target metadata according to a first database name, a first data table name and a first field name in the first field information;

after the target monitoring rule is deployed on the target metadata, storing the corresponding relation between the target metadata and the target monitoring rule into the monitoring rule base so as to update the monitoring rule base.

8. A processing apparatus for monitoring rules, comprising:

the system comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring first field information of target metadata of a monitoring rule to be configured;

the calculation unit is used for calculating the similarity between the first field information and the second field information of each monitoring rule in the monitoring rule base to obtain a plurality of initial similarity values;

the selection unit is used for taking the initial similarity value with the highest similarity value as a target similarity value and taking the monitoring rule corresponding to the target similarity value as a preset monitoring rule;

and the first determining unit is used for determining the target monitoring rule of the target metadata according to the target similarity value and the preset monitoring rule.

9. A processor, characterized in that the processor is configured to run a program, wherein the program is configured to execute the processing method of the monitoring rule according to any one of claims 1 to 7 when running.

10. An electronic device comprising one or more processors and memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of processing of monitoring rules of any one of claims 1 to 7.

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