CN111818548B

CN111818548B - Data processing method, device and equipment

Info

Publication number: CN111818548B
Application number: CN201910289568.2A
Authority: CN
Inventors: 张颖; 任元元; 刘先达; 纪迎; 林娜; 赵爽
Original assignee: China Mobile Communications Group Co Ltd; China Mobile Group Tianjin Co Ltd
Current assignee: China Mobile Communications Group Co Ltd; China Mobile Group Tianjin Co Ltd
Priority date: 2019-04-11
Filing date: 2019-04-11
Publication date: 2024-03-22
Anticipated expiration: 2039-04-11
Also published as: CN111818548A

Abstract

The embodiment of the invention discloses a data processing method, a device and equipment, wherein the method comprises the following steps: acquiring index data of a target service, and determining an association coefficient based on the index data; determining the type of the index to which the index belongs according to the association coefficient; extracting target data matched with the index type from the index data, and determining an amplitude coefficient based on the target data; and determining a target early warning threshold of the target service according to the amplitude coefficient. By the method, the target early warning threshold of different indexes in the target service can be timely adjusted according to the vibration and trend change of the different indexes, the updating period of the target early warning threshold is shortened, the monitoring hysteresis problem is avoided, and the monitoring efficiency of the target service is improved.

Description

Data processing method, device and equipment

Technical Field

The present invention relates to the field of communications technologies, and in particular, to a method, an apparatus, and a device for processing data.

Background

With the continuous development of mobile communication networks, the number of users of the mobile communication networks increases dramatically, so as to bring better use experience to the users of the mobile communication networks, and real-time performance monitoring needs to be performed on communication devices in the mobile communication networks.

The current technology for monitoring the real-time performance of the mobile communication network mainly comprises a technology for manually configuring a service available fixed threshold value, wherein the real-time performance of index data is monitored by manually configuring the service available fixed threshold value, and a large amount of index data is manually configured by the available fixed threshold value one by one, wherein the available fixed threshold value is divided into a service available upper threshold value and a service available lower threshold value. If the index data is lower than the service availability lower threshold or higher than the service availability lower threshold, the monitoring system generates alarm information.

However, by configuring the upper and lower threshold values of the index data in the manner that the manual configuration service can use the fixed threshold value, the following problems exist: because of the rapid growth of mobile communication network service, the network operation and maintenance monitoring index data also increases greatly, the cost of manually configuring the service with a fixed threshold is higher, the configuration efficiency is lower, the updating period of the manually configuring service with the fixed threshold is longer, the problem of monitoring lag can be generated when the index data is monitored, and the monitoring effectiveness is poorer.

Disclosure of Invention

The embodiment of the invention aims to provide a data processing method, device and equipment, which are used for solving the problems of high labor cost, monitoring hysteresis and poor monitoring effectiveness caused by a mode of manually configuring a threshold in the real-time performance monitoring of a mobile communication network in the prior art.

In order to solve the technical problems, the embodiment of the invention is realized as follows:

in a first aspect, a method for processing data provided by an embodiment of the present invention includes:

acquiring index data of a target service, and determining an association coefficient based on the index data;

determining the type of the index to which the index belongs according to the association coefficient;

extracting target data matched with the index type from the index data, and determining an amplitude coefficient based on the target data;

and determining a target early warning threshold of the target service according to the amplitude coefficient.

In a second aspect, an embodiment of the present invention provides a data processing apparatus, including:

the data acquisition module is used for acquiring index data of the target service and determining association coefficients based on the index data;

the type determining module is used for determining the type of the index to which the index belongs according to the association coefficient;

the data determining module is used for extracting target data matched with the belonging index type from the index data and determining an amplitude coefficient based on the target data;

and the threshold determining module is used for determining a target early warning threshold of the target service according to the amplitude coefficient.

In a third aspect, an embodiment of the present invention provides an apparatus, including a processor, a memory, and a computer program stored on the memory and executable on the processor, where the computer program when executed by the processor implements the steps of the data processing method provided in the foregoing embodiment.

In a fourth aspect, an embodiment of the present invention provides a computer readable storage medium, where a computer program is stored on the computer readable storage medium, where the computer program when executed by a processor implements the steps of the data processing method provided in the foregoing embodiment.

As can be seen from the technical solution provided by the above embodiment of the present invention, in the embodiment of the present invention, by acquiring index data of a target service, determining an association coefficient based on the index data, determining an associated index type according to the association coefficient, extracting target data matching with the associated index type from the index data, determining an amplitude coefficient based on the target data, and determining a target early warning threshold of the target service according to the amplitude coefficient, when determining a target early warning threshold of the target service, determining the associated type of the index data of the target service through the association coefficient of the index data, selecting corresponding target data according to different index types, and determining the target early warning threshold of the target service through the amplitude coefficient of the target data. The target data is selected from the index data of different index types, so that the target early warning threshold of different indexes in the target service can be timely adjusted according to the vibration and trend change of different indexes, and the monitoring effectiveness is improved. Meanwhile, the target data of the target service is automatically acquired to calculate the target early warning threshold of the target service, the updating period of the configuration threshold is shortened, the monitoring hysteresis problem is avoided, the target early warning threshold of the target service is not required to be manually configured, and the labor cost is reduced.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a data processing method according to the present invention;

FIG. 2 is a flow chart of another method for processing data according to the present invention;

FIG. 3 is a schematic diagram of monitoring abnormal conditions of handover success rate based on a target early warning threshold;

FIG. 4 is a schematic diagram of monitoring update success rate based on a pre-optimization target early warning threshold;

FIG. 5 is a schematic diagram of monitoring update success rate based on an optimized target early warning threshold;

FIG. 6 is a flow chart of a method for processing data according to another embodiment of the present invention;

FIG. 7 is a schematic diagram of a data processing apparatus according to the present invention;

fig. 8 is a schematic structural diagram of a data processing apparatus according to the present invention.

Detailed Description

The embodiment of the invention provides a data processing method, device and equipment.

In order to make the technical solution of the present invention better understood by those skilled in the art, the technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, 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, shall fall within the scope of the invention.

Example 1

As shown in fig. 1, an embodiment of the present invention provides a data processing method, where an execution body of the method may be a server, and the server may be an independent server or may be a server cluster formed by a plurality of servers, and the server may be a background server of a service (such as a performance index monitoring service for a communication network device). The method specifically comprises the following steps:

in step S102, index data of the target service is acquired, and a correlation coefficient is determined based on the index data.

The target service may be a performance index monitoring service for a communication network device in the mobile communication network. The index data may be performance index data (such as a call completing rate of a switch, etc.) of a communication network device in a core network or a wireless network, or analysis data obtained from a database stored in a device or a database stored in a remote device, etc., where the analysis data may be data after analysis processing according to received original data when a fault occurs, for example, alarm data of a unit time obtained by analysis processing according to alarm data within a predetermined time period, complaint data of a unit time obtained according to complaint data of the communication network device within the predetermined time period, etc. The association coefficient may be data for representing the degree of association between the index data and the time parameter.

In implementation, with the continuous development of mobile communication networks, the number of users of the mobile communication networks increases dramatically, so as to bring better use experience to users of the mobile communication networks, and real-time performance monitoring needs to be performed on communication devices in the mobile communication networks. The current technology for monitoring the real-time performance of the mobile communication network mainly comprises a technology for manually configuring a service available fixed threshold value, wherein the real-time performance of index data is monitored by manually configuring the service available fixed threshold value, and a large amount of index data is manually configured by the available fixed threshold value one by one, wherein the available fixed threshold value is divided into a service available upper threshold value and a service available lower threshold value. If the index data is lower than the service availability lower threshold or higher than the service availability lower threshold, the monitoring system generates alarm information.

In addition, when the real-time performance of the index data is monitored, a processing mode is also provided, namely, the upper threshold and the lower threshold of the index data are determined through a fixed fluctuation threshold technology configured by manual experience so as to monitor the real-time performance of the index data. And manually carrying out trend analysis on performance indexes related to the alarm rules, then according to the characteristics of the vibration amplitude and the long-term change trend of different indexes, respectively configuring corresponding upper and lower threshold values for the indexes, and if the index data is lower than the lower threshold value or higher than the upper threshold value, generating alarm information by a monitoring system. However, with the rapid growth of mobile communication network services, network operation and maintenance monitoring index data also grow greatly, and the manual experience configuration of the fixed fluctuation threshold technology also generates higher labor cost; secondly, the change of the network operation and maintenance monitoring index is faster, and the update period of the manual experience configuration fixed fluctuation threshold is longer, so that the hysteresis of real-time performance monitoring can be caused; finally, because the number of the network operation and maintenance monitoring indexes is large, the mode of manually configuring the fixed fluctuation threshold by experience cannot adjust the configuration threshold of different network operation and maintenance monitoring indexes in real time according to the characteristics of different network operation and maintenance monitoring indexes, so that the problem of unreasonable threshold configuration of the network operation and maintenance monitoring indexes can be generated, and the effectiveness of real-time performance monitoring is poor.

For this purpose, another implementation scheme is provided in the embodiment of the present invention, which specifically may include the following:

index data of the target service within a predetermined time period may be obtained from a database stored in the device, or from a database stored in the remote device, through a structured query language (SQL, structured Query Language) statement, wherein the predetermined time period may be any time period, such as 60 days or 90 days. In addition to the index data acquisition performed by SQL, there may be multiple index data acquisition modes, which are not limited in the embodiment of the present invention.

After the index data is obtained, the index data may be subjected to data cleaning under a predetermined condition, for example, null values, 0 values and data containing predetermined characters (for example, "# dev0 |" characters in a table file) in the index data may be removed so as not to affect subsequent processing of the target data. In addition, the data satisfying the predetermined interference condition in the index data may be removed, for example, the predetermined interference condition may be: the historical average value is lower than the historical average value, wherein the historical average value can be the average value of index data of the target service in a preset time period, and then the data lower than the historical average value in the index data can be removed, and in addition, the preset interference condition can be: and n% lower than the historical average, wherein n can be any data smaller than a preset threshold value, for example, n can be any data larger than 0 and smaller than 20, and the utilization rate of the index data can be improved by performing data cleaning on the index data under the preset interference condition.

After the index data is subjected to data cleaning, the correlation coefficient of the index data can be calculated, and as different index data have different oscillation amplitudes and change trends, the correlation coefficient of different index data and time parameters can be calculated. For example, the target service has three different indexes (such as index 1, index 2 and index 3), the index data corresponding to index 1, index 2 and index 3 can be obtained respectively, then the association coefficient of each index is calculated according to the index data corresponding to each index, taking the calculation of the association coefficient of index 1 as an example, all the index data of index 1 within 60 days can be obtained, then the data of the preset condition is cleaned, and the calculation of the association coefficient is performed on the cleaned index data.

Firstly, the average value of index data of 60 days of index 1 can be calculated, then the index data is classified according to time periods, for example, the index data of 60 days can be divided into 24 classes according to the time periods, the average value of the index data of 24 classes is calculated respectively, and finally, the discrete degree between the average value of the index data of 24 classes and the average value of the index data of 60 days is calculated as the association coefficient of index 1, and the association coefficient is used for representing the association degree of index 1 and time parameters. Wherein the correlation coefficient of index 1 can be calculated by the following formula:

Wherein θ is a correlation coefficient, E (X _j ) Is the mean value of the j-th class of index data,is the mean of all index data over 60 days for index 1. Wherein E (X) _j ) The calculation can be performed by the following formula:

wherein n is the number of index data contained in the j-th type index data, X _i|T＝j Is the ith index data in the jth (i.e. the index data is the T-th) index data。

In addition, when calculating the association coefficient corresponding to the index data, there may be a plurality of calculation modes besides the above modes, and different calculation methods of the association coefficient may be selected according to different actual application scenarios, which is not limited in the embodiment of the present invention.

In step S104, the associated index type is determined based on the association coefficient.

The index types to which the index data belong may include a time strong correlation type and a time weak correlation type.

In implementation, after obtaining the association coefficient corresponding to the index data, the index type of the index data may be determined according to the relationship between the association coefficient and the predetermined association threshold. For example, if the association coefficient of the index data is greater than the predetermined association threshold, it may be determined that the index data belongs to a type of strong correlation in time, that is, the association between the index data and the time parameter is strong, the index data may change periodically over time, and the index data changes greatly at different times; on the contrary, if the association coefficient of the index data is not greater than the predetermined association threshold, it may be determined that the index data belongs to a weak correlation type in time, that is, the association between the index data and the time parameter is weak, and the index data does not periodically change over time, and the index data does not greatly change at different times.

Taking the calculation of the association coefficient of the index 1 in step S102 as an example, when the calculation of the association coefficient is performed, the index data of the index 1 is divided into 24 classes according to the time period, if the calculated association coefficient of the index 1 is greater than the predetermined association threshold, it indicates that the association between the index 1 and the time parameter is strong, the index 1 belongs to the time strong correlation type, and the index 1 generates a larger number of changes along with the time change within 24 hours.

In addition, according to the attribute of the target service or the attribute corresponding to different indexes in the target service, a corresponding preset association threshold value can be selected, and the association coefficients of the corresponding preset association threshold value and different index data are compared to judge the index type of the index data. For example, there may be different predetermined association thresholds for different traffic types, e.g. the predetermined association threshold may be 0.1 when the target traffic is a performance detection traffic with respect to a communication network device in the core network, and 0.15 when the target traffic is a performance detection traffic with respect to a communication network device in the wireless network.

In step S106, target data matching the belonging index type is extracted from the index data, and an amplitude coefficient is determined based on the target data.

Wherein the amplitude coefficient may be used to characterize the vibration amplitude of the target data.

In implementation, after determining the index type to which the index data belongs, target data matched with the index type may be obtained, for example, if the index data belongs to a time-strong correlation type, it indicates that the index data has strong correlation with a time parameter, that is, target data related to the current time may be obtained, for example, if the current time is 4 months 1 day 12:00, then index data of three months 1 month 1 day 3 months 31 days 11:00-13:00 of each day may be obtained as target data extracted from the index data. If the index data belongs to the time weak correlation type, the index data is weak in correlation with the time parameter, and does not change greatly along with the time, and when the target data is selected, the index data in continuous time can be selected as the target data.

After determining the target data in the index data, determining the corresponding amplitude coefficient based on the target data, if the target service contains a plurality of indexes, selecting different indexes according to different index types, and then calculating the amplitude coefficient corresponding to each index.

In step S108, a target early warning threshold of the target service is determined according to the amplitude coefficient.

The target early warning threshold may include a target early warning upper threshold and a target early warning lower threshold.

In implementation, the oscillation amplitude of the corresponding index data in the target service can be determined according to the amplitude coefficient, and the corresponding target early warning threshold can be set for the index data with different oscillation amplitudes according to the difference of the amplitude coefficient.

After the target early warning threshold is determined, the actual index data of the current index can be obtained, the data screening of preset cleaning conditions (such as whether the index data is 0 value, null value and preset cleaning conditions including preset characters) is carried out on the index data, if the index data is subjected to the data screening, whether the index data meets the target early warning threshold or not can be judged, if the current index data exceeds the target early warning upper threshold or is lower than the target early warning lower threshold, warning information can be sent out, and recording of fluctuation abnormality of the index data is carried out so as to prompt that the target service is abnormal.

In addition, the trend curve of the index data in the target service and the trend curve of the target early warning threshold can be displayed through the dynamic diagram, so that the information of the times, the frequency and the like that the index data exceeds the target early warning threshold is displayed, and reference information is provided for maintenance personnel to maintain the target service.

The method can also store the index data and the corresponding target early warning threshold, and maintainers can select the index data and the corresponding target early warning threshold and other information according to the communication network equipment identifier of the target service corresponding to the index data, and the selected data can be exported after the selection so as to be used for analysis by the maintainers.

The embodiment of the invention provides a data processing method, which comprises the steps of obtaining index data of a target service, determining an association coefficient based on the index data, determining an affiliated index type according to the association coefficient, extracting target data matched with the affiliated index type from the index data, determining an amplitude coefficient based on the target data, and finally determining a target early warning threshold of the target service according to the amplitude coefficient. The target data is selected from the index data of different index types, so that the target early warning threshold of different indexes in the target service can be timely adjusted according to the vibration and trend change of different indexes, and the monitoring effectiveness is improved. Meanwhile, the target data of the target service is automatically acquired to calculate the target early warning threshold of the target service, the updating period of the configuration threshold is shortened, the monitoring hysteresis problem is avoided, the target early warning threshold of the target service is not required to be manually configured, and the labor cost is reduced.

Example two

As shown in fig. 2, an embodiment of the present invention provides a data processing method, where an execution body of the method may be a server, and the server may be an independent server or may be a server cluster formed by a plurality of servers, and the server may be a background server of a service (such as a performance index monitoring service for a communication network device). The method specifically comprises the following steps:

in step S202, index data of the target service is acquired, and a correlation coefficient is determined based on the index data.

In step S204, the associated index type is determined based on the association coefficient.

The specific processing procedures of the steps S202 to S204 may be referred to the relevant contents of the steps S102 to S104 in the first embodiment, and will not be described herein.

If the index type to which the index data belongs is a time strong correlation type, step S206 is performed, and if the index type to which the index data belongs is a time weak correlation type, step S208 is performed.

In step S206, the matched target data is obtained based on the first selection rule corresponding to the time-strong correlation type.

The first selection rule may be that target data of a predetermined time corresponding to the current time within a first predetermined time period is obtained from index data, the predetermined time may be any granularity time related to the current time, for example, the predetermined time may be the same determined time point as the current time, for example, the current time is 12:00, the predetermined time may also be 12:00, that is, index data of 12:00 per day may be obtained within 30 days, the predetermined time may also be any time including the current time, for example, the current time is 12:00, and the predetermined time may be 11:00-13:00.

In implementation, if the target service is a real-time performance index monitoring service for the communication network device 1 and the communication network device 2 in the core network, the acquired index data in the communication network device 1 includes index data of index 1 and index 2, and the acquired index data in the communication network device 2 includes index data of index 1 and index 3, the association coefficient of index 1 and the association coefficient of index 2 in the communication network device 1 may be calculated respectively, the index types of index 1 and index 2 in the communication network device 1 may be determined according to the corresponding association coefficients, and at the same time, the association coefficients of index 1 and index 3 in the communication network device 2 may be determined according to the index data in the communication network device 2.

If the index 2 in the communication network device 1 and the index 3 in the communication network device 2 belong to a time strong correlation type, target data of the index 2 and the index 3 at predetermined time corresponding to the current time in a first predetermined time period can be respectively obtained, wherein the correlation between the index data of the time strong correlation type and the time parameter is strong, the corresponding first predetermined time period can be shorter, for example, 30 days or 15 days, and if the first predetermined time period is 30 days, for example, and if the current time period is 1 month 31 days 12:00, the index data of the index 2 in the communication network device 1 can be obtained, in 1 month 1 days-1 month 30 days corresponding to the index 2 in the communication network device 1, and the obtained index data is the target data of the index 2 in the communication network device 1, and the obtained target data of the index 2 in the communication network device 1 can be as follows:

Wherein spe is target data of index 2 of communication network device 1, x _m.n Is index data of the mth time of the mth day, d _m On day m, t _n At the nth time, m in the target data spe corresponding to the index 2 of the communication network device 1 is 30, and n is 11:00-13: the total amount of all the time points of the index data is included in 00.

Similarly, from 1 month, 1 day to 1 month, 30 days, 11:00 to 13:00, the index data corresponding to the index 3 in the communication network device 2 is used as target data of the index 3.

In step S208, the matched target data is obtained based on the second selection rule corresponding to the weak correlation type.

The second selection rule may be to obtain target data in a second predetermined time period from the index data.

In an implementation, if the index data belongs to a weak correlation type, it indicates that the relationship between the index and the time parameter is weak, and the magnitude of the change does not occur greatly along with the time, so that the second predetermined duration may be shorter, for example, 10 days or 5 days, so as to ensure that the extracted target data has a higher reference value. All target data about the target service in the same communication network device within the second predetermined time period, for example, in the above step S206, the index 1 in the communication network device 1 and the index 1 in the communication network device 2 are both index data of a time weak correlation type, and then all the index data about the index 1 in the communication network device 1 within the second predetermined time period may be acquired as the target data about the index 1 in the communication network device 1, and all the index data about the index 1 in the communication network device 2 within the second predetermined time period may be acquired as the target data about the index 1 in the communication network device 2.

In step S210, the target data is substituted into the following formula for calculation,

and obtaining an amplitude coefficient corresponding to the target data.

Wherein η is the amplitude coefficient and n is the total number of the target data,X _i Is the i-th target data.

In step S212, it is determined whether the amplitude coefficient is greater than a predetermined amplitude threshold value.

In practice, after the amplitude coefficient of the target data is obtained, the amplitude coefficient may be compared with a predetermined amplitude threshold. The predetermined amplitude threshold may be 3%, that is, it is determined whether the amplitude coefficient of the target data is greater than 3 times the amplitude range.

The result of performing the verification analysis on the index data shows that 64.21% of normal data in the index data fluctuates in a 1-time amplitude range, 89.37% of normal data fluctuates in a 2-time amplitude range, 99.12% of normal data fluctuates in a 3-time amplitude range, if the preset amplitude threshold is smaller than 1, the service requirement of the target service cannot be met, if the preset amplitude threshold is larger than 3, the fluctuation of the index data cannot be accurately judged, therefore, 3 eta can be used as a monitoring multiple, when the index fluctuation is extremely small, namely 3 eta <10%, eta <3.5%, the fluctuation threshold range obtained by 3-time amplitude calculation is too narrow and does not meet the operation and maintenance monitoring requirement of the communication industry, and therefore, the preset amplitude threshold can be set to 3.5%.

The above-mentioned determination method of the predetermined amplitude threshold is an alternative, implementable determination method, and in a practical application scenario, there may be a plurality of different determination methods, which are not limited in this embodiment of the present invention.

After the step S210 is performed, an amplitude coefficient of the target data may be obtained, and then the amplitude coefficient of the target data may be compared with a predetermined amplitude threshold value, and the step S212 may be performed, and the step S214 may be performed.

In step S214, if the amplitude coefficient of the target data is greater than the predetermined amplitude threshold, the target early warning threshold of the target service is calculated based on the predetermined first calculation coefficient.

Wherein the first calculation coefficient may be any value greater than 1.

In implementation, since the amplitude coefficient of the target data is already greater than the predetermined amplitude threshold, it indicates that the amplitude range of the target data meets the service requirement of the target service, the first calculation coefficient may be any data greater than 1, and when the calculation of the target early warning threshold of the target service is performed, the target data and the first calculation coefficient may be substituted into the following formula to perform the calculation,

obtaining a target early warning upper threshold of a target service, wherein the upper limit is the target early warning upper threshold of the target service, n is the total number of target data, and X _i For the i-th target data, the data is stored,coefficients are calculated for the first.

While the target data and the first calculation coefficient may be substituted into the following formula for calculation,

and obtaining a target early warning lower threshold of the target service, wherein the downlimit is the target early warning lower threshold of the target service.

Based on the above scheme, when the first calculation coefficient is 3, the target early warning upper threshold and the target early warning lower threshold of the target service are calculated, the handover success rate (i.e. index data) in the communication network equipment of the target service is subjected to verification analysis, the manually configured service available threshold and the manually experienced configuration fixed fluctuation threshold of the communication network equipment for the handover success rate are obtained, and the target early warning threshold of the handover success rate calculated based on the scheme of the invention is calculated, as shown in fig. 3, the abnormal condition of the handover success rate (i.e. index data) cannot be monitored based on the manually configured service available threshold and the manually experienced configuration fixed fluctuation threshold (i.e. the manually experienced fluctuation threshold), according to the scheme of the invention, the abnormal fluctuation of the index data can be timely detected, and the target early warning threshold can be automatically adjusted along with the oscillation or trend change of the index data.

In step S216, if the amplitude coefficient of the target data is not greater than the predetermined amplitude threshold, the target early warning threshold of the target service is calculated based on the predetermined second calculation coefficient.

In practice, the target data and the second calculation coefficient may be substituted into the following formula for calculation,

obtaining a target early warning upper threshold of a target service, wherein beta is a second calculation coefficient;

substituting the target data and the second calculation coefficient into the following formula for calculation,

and obtaining a target early warning lower threshold of the target service.

When the amplitude coefficient of the target data is not greater than the preset amplitude threshold, the fluctuation threshold range is too small due to the fact that the amplitude coefficient is too small, and false alarm is generated, so when the amplitude coefficient is not greater than the preset amplitude threshold, the target early warning threshold can be adjusted through a second calculation coefficient, wherein the second calculation coefficient can be any data which is greater than 0 and smaller than 1, and different values can be selected according to different practical application scenes.

As shown in fig. 4, taking the index data of the updating success rate of the tracking area in the LTE system in the target service as an example, if the target early warning threshold is not properly adjusted according to the magnitude of the amplitude coefficient, when the amplitude coefficient is smaller than the predetermined amplitude threshold, the target early warning threshold cannot be correspondingly changed according to the change of the amplitude coefficient, and a false alarm condition occurs. And as shown in fig. 5, based on the scheme, when the target early warning threshold corresponding to the index data is calculated, the early warning threshold can be timely adjusted according to the magnitude of the amplitude coefficient, so that the occurrence of false alarm caused by discomfort of the target early warning threshold is effectively avoided.

In step S218, the target traffic index data at the current moment is monitored according to the target warning threshold to determine whether the target traffic is abnormal.

In step S220, if the target service is abnormal, the alarm information is output according to a predetermined alarm mode.

The preset alarm mode comprises one or more of a mode of sending preset alarm information to a preset communication account number, a mode of sending the preset alarm information to a designated display device and a mode of dispatching an electronic work order.

In implementation, if the index data exceeds the corresponding target early warning upper threshold or is lower than the target early warning lower threshold in the target service at the current moment, the device may acquire the stored preset communication account number, and send preset alarm information (such as preset alarm short message, etc.) to the preset communication account number, so as to inform maintenance personnel to process the target service. Or the device can display the preset alarm information on the appointed display device (such as an alarm display board and the like), and can also dispatch an electronic work order to preset staff.

In step S222, the number of times that the index data of the target service exceeds the target early warning threshold is obtained within a predetermined time period.

In step S224, if the number of times the target warning threshold is exceeded is greater than the predetermined detection threshold, a maintenance notification related to the target service is issued.

Example III

As shown in fig. 6, an embodiment of the present invention provides a data processing method, where an execution body of the method may be a server, and the server may be an independent server or may be a server cluster formed by a plurality of servers, and the server may be a background server of a service (such as a performance index monitoring service for a communication network device). The method specifically comprises the following steps:

in step S602, index data of the target service is acquired, and a correlation coefficient is determined based on the index data.

In step S604, the type of the index to which the index belongs is determined based on the association coefficient.

In step S606, target data matching the belonging index type is extracted from the index data, and an amplitude coefficient is determined based on the target data.

The specific processing procedures of the steps S602 to S606 can be referred to the relevant contents of the steps S102 to S106 in the first embodiment, and will not be described herein.

In step S608, a first early warning threshold of the target service is determined according to the amplitude coefficient.

In implementation, the method for determining the first early warning threshold of the target service may refer to the method for determining the target early warning threshold in steps S212-S216 in the second embodiment, which is not described herein.

In step S610, a target early warning threshold of the target service is determined according to the predetermined early warning threshold and the first early warning threshold of the target service.

In implementation, the first early warning upper threshold and the predetermined early warning upper threshold may be compared, the smallest value is used as the target early warning upper threshold in the target early warning thresholds, and the first early warning lower threshold and the predetermined early warning lower threshold may be compared, and the largest value is used as the target early warning lower threshold in the target early warning thresholds.

Before comparison, the preset early warning threshold can be updated and detected, namely whether the updated preset early warning threshold exists or not is detected, if the updated preset early warning threshold exists, the validity of the updated preset early warning threshold is detected, if the updated preset early warning threshold exists, the updated preset early warning threshold is written into a database, the preset early warning threshold is updated, after the updating is finished, the preset early warning threshold is compared with the first early warning threshold, and finally the target early warning threshold of the target service is determined, wherein the updated preset early warning threshold can only comprise the updated preset early warning upper threshold or only comprise the updated preset early warning lower threshold, and can also comprise the updated preset early warning upper threshold and the updated preset early warning lower threshold.

In step S612, the target traffic index data at the current moment is monitored according to the target warning threshold to determine whether the target traffic is abnormal.

In step S614, if the target service is abnormal, the alarm information is output according to a predetermined alarm manner.

In step S616, the number of times that the target traffic index data exceeds the target warning threshold is acquired within a predetermined time period.

In step S618, if the number of times the target warning threshold is exceeded is greater than the predetermined detection threshold, a maintenance notification related to the target service is issued.

The specific processing procedures of the steps S612 to S618 can be referred to the relevant contents of the steps S218 to S224 in the second embodiment, and will not be described herein.

Example IV

The above method for processing data provided in the embodiment of the present invention is based on the same concept, and the embodiment of the present invention further provides a device for processing data, as shown in fig. 7.

The data processing device comprises: a data acquisition module 701, a type determination module 702, a data determination module 703 and a threshold determination module 704, wherein:

a data acquisition module 701, configured to acquire index data of a target service, and determine an association coefficient based on the index data;

a type determining module 702, configured to determine, according to the association coefficient, a type of the indicator to which the association coefficient belongs;

a data determining module 703, configured to extract target data matched with the belonging index type from the index data, and determine an amplitude coefficient based on the target data;

and the threshold determining module 704 is configured to determine a target early warning threshold of the target service according to the amplitude coefficient.

In the embodiment of the invention, the target early warning threshold comprises a target early warning upper threshold and a target early warning lower threshold;

the threshold determining module 704 includes:

the first threshold determining unit is used for determining a first early warning upper threshold and a first early warning lower threshold of the target service according to the amplitude coefficient;

The upper threshold determining unit is used for comparing the first early warning upper threshold with a preset early warning upper threshold, and taking the smallest value as a target early warning upper threshold;

the lower threshold determining unit is used for comparing the first early warning lower threshold with a preset early warning lower threshold, and taking the value with the largest value as a target early warning lower threshold.

In the embodiment of the invention, the index types comprise a time strong correlation type and a time weak correlation type;

the data determining module 703 includes:

the first acquisition unit is used for acquiring matched target data based on a first selection rule if the type of the first acquisition unit is a time strong correlation type, wherein the first selection rule is to acquire target data of a preset moment corresponding to the current moment in a first preset time length from index data;

and the second acquisition unit is used for acquiring the matched target data based on a second selection rule if the type is a time weak correlation type, wherein the second selection rule is used for acquiring the target data in a second preset time length from the index data.

In an embodiment of the present invention, the data determining module 703 is configured to:

substituting the target data into the following formula for calculation,

Obtaining an amplitude coefficient corresponding to the target data, wherein eta is the vibrationThe amplitude coefficient, n, is the total number of the target data, X _i Is the i-th target data.

the threshold determining module 704 includes:

a judging unit configured to determine whether the amplitude coefficient is greater than a predetermined amplitude threshold;

a first calculation unit for substituting the target data and the first calculation coefficient into the following formula for calculation if the amplitude coefficient is larger than a predetermined amplitude threshold value,

obtaining a target early warning upper threshold of the target service, wherein up lim it is the target early warning upper threshold of the target service, n is the total number of the target data, and X _i For the i-th target data, the data is stored,calculating coefficients for the first;

a second calculation unit for substituting the target data and the first calculation coefficient into the following formula to calculate,

and obtaining a target early warning lower threshold of the target service, wherein the downlim it is the target early warning lower threshold of the target service.

In an embodiment of the present invention, the apparatus further includes:

A first calculation module for substituting the target data and the second calculation coefficient into the following formula for calculation if the amplitude coefficient is not greater than a predetermined amplitude threshold value,

obtaining a target early warning upper threshold of the target service, wherein beta is the second calculation coefficient;

a second calculation module for substituting the target data and the second calculation coefficient into the following formula to calculate,

and obtaining a target early warning lower threshold of the target service.

In an embodiment of the present invention, the apparatus further includes:

the frequency acquisition module is used for acquiring the frequency of the index data of the target service exceeding the target early warning threshold within a preset time length;

the warning module is used for sending out maintenance notification related to the target service or outputting warning information according to a preset warning mode if the number of times exceeding the target early warning threshold is larger than a preset detection threshold;

the preset alarm mode comprises one or more of a mode of sending preset alarm information to a preset communication account number, a mode of sending preset alarm information to a designated display device and a mode of sending an electronic work order.

The embodiment of the invention provides a data processing device, which is used for acquiring index data of a target service, determining an association coefficient based on the index data, determining an affiliated index type according to the association coefficient, extracting target data matched with the affiliated index type from the index data, determining an amplitude coefficient based on the target data, and finally determining a target early warning threshold of the target service according to the amplitude coefficient, so that when determining the target early warning threshold of the target service, the affiliated type of the index data of the target service can be determined through the association coefficient of the index data, then the corresponding target data is selected according to different index types, and then the target early warning threshold of the target service is determined through the amplitude coefficient of the target data. The target data is selected from the index data of different index types, so that the target early warning threshold of different indexes in the target service can be timely adjusted according to the vibration and trend change of different indexes, and the monitoring effectiveness is improved. Meanwhile, the target data of the target service is automatically acquired to calculate the target early warning threshold of the target service, the updating period of the configuration threshold is shortened, the monitoring hysteresis problem is avoided, the target early warning threshold of the target service is not required to be manually configured, and the labor cost is reduced.

Example five

Figure 8 is a schematic diagram of the hardware architecture of an apparatus implementing various embodiments of the invention,

the device 800 includes, but is not limited to: radio frequency unit 801, network module 802, audio output unit 803, input unit 804, sensor 805, display unit 806, user input unit 807, interface unit 808, memory 809, processor 810, and power supply 811. It will be appreciated by those skilled in the art that the device structure shown in fig. 8 is not limiting of the device and that the device may include more or fewer components than shown, or certain components may be combined, or a different arrangement of components. In the embodiment of the invention, the equipment comprises, but is not limited to, a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted terminal, a wearable equipment, a pedometer and the like.

The processor 810 is configured to obtain index data of a target service, and determine an association coefficient based on the index data;

the processor 810 is further configured to determine, according to the association coefficient, an indicator type to which the association coefficient belongs;

a processor 810, configured to extract target data matching the indicator type from the indicator data, and determine an amplitude coefficient based on the target data;

In addition, the processor 810 is further configured to determine a target early warning threshold of the target service according to the amplitude coefficient.

In addition, the target early warning threshold comprises a target early warning upper threshold and a target early warning lower threshold; the processor 810 is further configured to determine a first early warning upper threshold and a first early warning lower threshold of the target service according to the amplitude coefficient;

in addition, the processor 810 is further configured to compare the first early warning upper threshold with a predetermined early warning upper threshold, and use the smallest value as the target early warning upper threshold;

in addition, the processor 810 is further configured to compare the first early warning threshold with a predetermined early warning threshold, and use the value with the largest value as the target early warning threshold.

Further, the index types include a time strong correlation type and a time weak correlation type.

In addition, the processor 810 is further configured to obtain the matched target data based on a first selection rule if the type of the target data belongs to a time strong correlation type, where the first selection rule is that target data of a predetermined time corresponding to the current time is obtained from the index data within a first predetermined time period;

in addition, the processor 810 is further configured to obtain the matched target data based on a second selection rule if the type is a time weak correlation type, where the second selection rule is to obtain the target data within a second predetermined time period from the index data

In addition, the processor 810 is further configured to substitute the target data into the following formula for calculation,

obtaining an amplitude coefficient corresponding to the target data, wherein eta is the amplitude coefficient, n is the total number of the target data, and X _i Is the i-th target data.

Additionally, the processor 810 is further configured to determine whether the amplitude coefficient is greater than a predetermined amplitude threshold;

in addition, the target early warning threshold comprises a target early warning upper threshold and a target early warning lower threshold; the processor 810 is further configured to, if the amplitude coefficient is greater than a predetermined amplitude threshold, substitute the target data and the first calculation coefficient into the following formula for calculation,

obtaining a target early warning upper threshold of the target service, wherein the upper limit is the target early warning upper threshold of the target service, n is the total number of the target data, and X _i For the i-th target data, the data is stored,calculating coefficients for the first;

substituting the target data and the first calculation coefficient into the following formula for calculation,

and obtaining a target early warning lower threshold of the target service, wherein the target early warning lower threshold is the target early warning lower threshold of the target service.

In addition, the processor 810 is further configured to, if the amplitude coefficient is not greater than a predetermined amplitude threshold, substitute the target data and a second calculation coefficient into the following formula for calculation,

and obtaining a target early warning lower threshold of the target service.

In addition, the processor 810 is further configured to obtain the number of times that the index data of the target service exceeds the target early warning threshold within a predetermined time period;

in addition, the processor 810 is further configured to send out a maintenance notification related to the target service if the number of times that the target early warning threshold is exceeded is greater than a predetermined detection threshold, or output alarm information according to a predetermined alarm mode;

The embodiment of the invention provides equipment, which is used for acquiring index data of a target service, determining an association coefficient based on the index data, determining an affiliated index type according to the association coefficient, extracting target data matched with the affiliated index type from the index data, determining an amplitude coefficient based on the target data, and finally determining a target early warning threshold of the target service according to the amplitude coefficient, so that when determining the target early warning threshold of the target service, the affiliated type of the index data of the target service can be determined through the association coefficient of the index data, then selecting corresponding target data according to different index types, and determining the target early warning threshold of the target service through the amplitude coefficient of the target data. The target data is selected from the index data of different index types, so that the target early warning threshold of different indexes in the target service can be timely adjusted according to the vibration and trend change of different indexes, and the monitoring effectiveness is improved. Meanwhile, the target data of the target service is automatically acquired to calculate the target early warning threshold of the target service, the updating period of the configuration threshold is shortened, the monitoring hysteresis problem is avoided, the target early warning threshold of the target service is not required to be manually configured, and the labor cost is reduced.

It should be understood that, in the embodiment of the present invention, the radio frequency unit 801 may be used for receiving and transmitting signals during the process of receiving and transmitting information or communication, specifically, receiving downlink data from a base station, and then processing the received downlink data by the processor 810; and, the uplink data is transmitted to the base station. In general, the radio frequency unit 801 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, the radio frequency unit 801 may also communicate with networks and other devices through a wireless communication system.

The device provides wireless broadband internet access to the user via the network module 802, such as helping the user to send and receive e-mail, browse web pages, and access streaming media, etc.

The audio output unit 803 may convert audio data received by the radio frequency unit 801 or the network module 802 or stored in the memory 809 into an audio signal and output as sound. Also, the audio output unit 803 may also provide audio output (e.g., a call signal reception sound, a message reception sound, etc.) related to a specific function performed by the device 800. The audio output unit 803 includes a speaker, a buzzer, a receiver, and the like.

The input unit 804 is used for receiving an audio or video signal. The input unit 804 may include a graphics processor (Graphics Processing Unit, GPU) 8041 and a microphone 8042, the graphics processor 8041 processing image data of still pictures or video obtained by an image capturing apparatus (such as a camera) in a video capturing mode or an image capturing mode. The processed image frames may be displayed on the display unit 806. The image frames processed by the graphics processor 8041 may be stored in the memory 809 (or other storage medium) or transmitted via the radio frequency unit 801 or the network module 802. The microphone 8042 can receive sound, and can process such sound into audio data. The processed audio data may be converted into a format output that can be transmitted to the mobile communication base station via the radio frequency unit 801 in case of a telephone call mode.

The device 800 also includes at least one sensor 805 such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor that can adjust the brightness of the display panel 8061 according to the brightness of ambient light, and a proximity sensor that can turn off the display panel 8061 and/or the backlight when the device 800 is moved to the ear. As one of the motion sensors, the accelerometer sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and direction when the accelerometer sensor is stationary, and can be used for recognizing the gesture of equipment (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking) and the like; the sensor 805 may also include a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, etc., which are not described herein.

The display unit 806 is used to display information input by a user or information provided to the user. The display unit 806 may include a display panel 8061, and the display panel 8061 may be configured in the form of a liquid crystal display (Liquid Crystal Display, LCD), an Organic Light-Emitting Diode (OLED), or the like.

The user input unit 807 is operable to receive input numeric or character information and to generate key signal inputs related to user settings and function control of the device. In particular, the user input unit 807 includes a touch panel 8071 and other input devices 8072. Touch panel 8071, also referred to as a touch screen, may collect touch operations thereon or thereabout by a user (e.g., operations of the user on touch panel 8071 or thereabout using any suitable object or accessory such as a finger, stylus, etc.). The touch panel 8071 may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch azimuth of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts it into touch point coordinates, sends the touch point coordinates to the processor 810, and receives and executes commands sent from the processor 810. In addition, the touch panel 8071 may be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch panel 8071, the user input unit 807 can include other input devices 8072. In particular, other input devices 8072 may include, but are not limited to, physical keyboards, function keys (e.g., volume control keys, switch keys, etc.), trackballs, mice, joysticks, and so forth, which are not described in detail herein.

Further, the touch panel 8071 may be overlaid on the display panel 8061, and when the touch panel 8071 detects a touch operation thereon or thereabout, the touch operation is transmitted to the processor 810 to determine a type of touch event, and then the processor 810 provides a corresponding visual output on the display panel 8061 according to the type of touch event. Although in fig. 8, the touch panel 8071 and the display panel 8061 are two independent components for implementing the input and output functions of the device, in some embodiments, the touch panel 8071 and the display panel 8061 may be integrated to implement the input and output functions of the device, which is not limited herein.

The interface unit 808 is an interface to which an external device is connected to the apparatus 800. For example, the external devices may include a wired or wireless headset port, an external power (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device having an identification module, an audio input/output (I/O) port, a video I/O port, an earphone port, and the like. The interface unit 808 may be used to receive input (e.g., data information, power, etc.) from an external device and to transmit the received input to one or more elements within the apparatus 800 or may be used to transmit data between the apparatus 800 and an external device.

The memory 809 can be used to store software programs as well as various data. The memory 809 may mainly include a storage program area that may store an operating system, application programs required for at least one function (such as a sound playing function, an image playing function, etc.), and a storage data area; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, memory 409 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.

The processor 810 is a control center of the device, connecting various parts of the overall device using various interfaces and lines, performing various functions of the device and processing data by running or executing software programs and/or modules stored in the memory 809, and invoking data stored in the memory 809. The processor 810 may include one or more processing units; preferably, the processor 810 may integrate an application processor that primarily handles operating systems, user interfaces, applications, etc., with a modem processor that primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 810.

The device 800 may also include a power supply 811 (e.g., a battery) for powering the various components, and the power supply 811 may preferably be logically coupled to the processor 810 through a power management system that provides for managing charge, discharge, and power consumption.

Preferably, the embodiment of the present invention further provides an apparatus, which includes a processor 810, a memory 809, and a computer program stored in the memory 809 and capable of running on the processor 810, where the computer program when executed by the processor 810 implements each process of the foregoing data processing method embodiment, and the same technical effects are achieved, and for avoiding repetition, a detailed description is omitted herein.

Example six

The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the processes of the data processing method embodiment, and can achieve the same technical effects, so that repetition is avoided and no further description is given here. Wherein the computer readable storage medium is selected from Read-only memory (ROM), random access memory (RandomAccess Memory, RAM), magnetic disk or optical disk.

The embodiment of the invention provides a computer readable storage medium, which is used for acquiring index data of a target service, determining an association coefficient based on the index data, determining an affiliated index type according to the association coefficient, extracting target data matched with the affiliated index type from the index data, determining an amplitude coefficient based on the target data, and determining a target early warning threshold of the target service according to the amplitude coefficient. The target data is selected from the index data of different index types, so that the target early warning threshold of different indexes in the target service can be timely adjusted according to the vibration and trend change of different indexes, and the monitoring effectiveness is improved. Meanwhile, the target data of the target service is automatically acquired to calculate the target early warning threshold of the target service, the updating period of the configuration threshold is shortened, the monitoring hysteresis problem is avoided, the target early warning threshold of the target service is not required to be manually configured, and the labor cost is reduced.

It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. 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.

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

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer 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 Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.

The foregoing is merely exemplary of the present invention and is not intended to limit the present invention. Various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are to be included in the scope of the claims of the present invention.

Claims

1. A method of processing data, the method comprising:

Determining a target early warning threshold of the target service according to the amplitude coefficient;

wherein the determining an amplitude coefficient based on the target data includes:

substituting the target data into the following formula for calculation,

obtaining an amplitude coefficient corresponding to the target data, wherein eta is the amplitude coefficient, n is the total number of the target data, and X _i Is the ith target data;

the target early warning threshold comprises a target early warning upper threshold and a target early warning lower threshold; the determining the target early warning threshold of the target service according to the amplitude coefficient comprises the following steps:

determining whether the amplitude coefficient is greater than a predetermined amplitude threshold;

if the amplitude coefficient is greater than a predetermined amplitude threshold, substituting the target data and a first calculation coefficient into the following formula for calculation,

obtaining a target early warning lower threshold of the target service, wherein a down lim it is the target early warning lower threshold of the target service;

If the amplitude coefficient is not greater than a predetermined amplitude threshold value, substituting the target data and a second calculation coefficient into the following formula for calculation,

and obtaining a target early warning lower threshold of the target service.

2. The method of claim 1, wherein the target early warning threshold comprises an upper target early warning threshold and a lower target early warning threshold;

the determining the target early warning threshold of the target service according to the amplitude coefficient comprises the following steps:

determining a first early warning upper threshold and a first early warning lower threshold of the target service according to the amplitude coefficient;

comparing the first early warning upper threshold with a preset early warning upper threshold, and taking the smallest value as a target early warning upper threshold;

and comparing the first early warning lower threshold with a preset early warning lower threshold, and taking the value with the largest value as a target early warning lower threshold.

3. The method of claim 1, wherein the index types include a time strong correlation type and a time weak correlation type;

The extracting the target data matched with the belonging index type from the index data comprises the following steps:

if the type is a time strong correlation type, acquiring matched target data based on a first selection rule, wherein the first selection rule is to acquire target data of a preset moment corresponding to the current moment in a first preset time length from index data;

and if the type is a time weak correlation type, acquiring matched target data based on a second selection rule, wherein the second selection rule is to acquire target data in a second preset time length from the index data.

4. A method according to any one of claims 1-3, wherein the method further comprises:

acquiring the times that the index data of the target service exceeds the target early warning threshold within a preset time length;

if the number of times exceeding the target early warning threshold is greater than a preset detection threshold, a maintenance notice related to the target service is sent out, or warning information is output according to a preset warning mode;

5. A data processing apparatus, the apparatus comprising:

the threshold determining module is used for determining a target early warning threshold of the target service according to the amplitude coefficient;

wherein, the data determining module is used for:

substituting the target data into the following formula for calculation,

the target early warning threshold comprises a target early warning upper threshold and a target early warning lower threshold; the threshold determining module is configured to:

obtaining a target early warning lower threshold of the target service, wherein a downlink limit is the target early warning lower threshold of the target service;

and obtaining a target early warning lower threshold of the target service.

6. An electronic device comprising a processor, a memory and a computer program stored on the memory and executable on the processor, which when executed by the processor performs the steps of the method of processing data as claimed in any one of claims 1 to 4.

7. A computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the data processing method according to any one of claims 1 to 4.