CN111427878A - Data monitoring and alarming method, device, server and storage medium - Google Patents
Data monitoring and alarming method, device, server and storage medium Download PDFInfo
- Publication number
- CN111427878A CN111427878A CN202010199872.0A CN202010199872A CN111427878A CN 111427878 A CN111427878 A CN 111427878A CN 202010199872 A CN202010199872 A CN 202010199872A CN 111427878 A CN111427878 A CN 111427878A
- Authority
- CN
- China
- Prior art keywords
- monitoring
- characteristic information
- preset
- parameters
- parameter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 182
- 238000000034 method Methods 0.000 title claims abstract description 45
- 230000002159 abnormal effect Effects 0.000 claims abstract description 30
- 230000004044 response Effects 0.000 claims description 25
- 238000004590 computer program Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 5
- 230000005856 abnormality Effects 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 16
- 230000003287 optical effect Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000009471 action Effects 0.000 description 4
- 230000008030 elimination Effects 0.000 description 4
- 238000003379 elimination reaction Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000003491 array Methods 0.000 description 3
- 239000013307 optical fiber Substances 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000013024 troubleshooting Methods 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Debugging And Monitoring (AREA)
Abstract
The embodiment of the invention provides a data monitoring and alarming method, a data monitoring and alarming device, a server and a storage medium. The data monitoring and alarming method comprises the following steps: acquiring a characteristic data set of a user, wherein the characteristic data set comprises characteristic information of the user and initial parameters corresponding to the characteristic information; determining a monitoring parameter corresponding to the characteristic information according to the initial parameter corresponding to the characteristic information; judging whether the monitoring parameters meet preset parameters or not; and when the monitoring parameter does not meet the preset parameter, sending an alarm prompt that the characteristic information is abnormal. The effect of monitoring and alarming data in time is achieved.
Description
Technical Field
The embodiment of the invention relates to the technical field of data monitoring, in particular to a data monitoring and alarming method, a data monitoring and alarming device, a server and a storage medium.
Background
With the rapid development of big data, more and more enterprises recommend commodities to users by using the characteristic data of the users.
At present, when recommending commodities, a common method is to query feature data of a user and then input the feature data into a pre-trained model, so as to predict commodities that the user likes. Therefore, the user characteristic data is very important for the effect of the recommendation. If the abnormity occurs, the overall recommendation effect is influenced.
However, currently, the feature data of the user is not monitored, and only when the effect of daily statistical recommendation is not good, the problem is checked, and the problem is found to be delayed seriously.
Disclosure of Invention
The embodiment of the invention provides a data monitoring and alarming method, a data monitoring and alarming device, a server and a storage medium, so as to realize monitoring and alarming on data in time.
In a first aspect, an embodiment of the present invention provides a data monitoring and warning method, including:
acquiring a characteristic data set of a user, wherein the characteristic data set comprises characteristic information of the user and initial parameters corresponding to the characteristic information;
determining a monitoring parameter corresponding to the characteristic information according to the initial parameter corresponding to the characteristic information;
judging whether the monitoring parameters meet preset parameters or not;
and when the monitoring parameter does not meet the preset parameter, sending an alarm prompt that the characteristic information is abnormal.
Optionally, the acquiring the feature data set of the user includes:
monitoring a message queue for receiving the feature data set from a recommendation system;
when the message queue update is monitored, the feature data set is extracted from the message queue.
Optionally, the determining, according to the initial parameter corresponding to the feature information, a monitoring parameter corresponding to the feature information includes:
and extracting initial parameters corresponding to the characteristic information from the characteristic data set so as to determine monitoring parameters corresponding to the characteristic information according to the initial parameters corresponding to the characteristic information.
Optionally, the feature information is multiple, and determining the monitoring parameter corresponding to the feature information according to the initial parameter corresponding to the feature information includes:
determining a monitoring parameter corresponding to each characteristic information according to the initial parameter of each characteristic information;
the judging whether the monitoring parameter meets a preset parameter, and when the monitoring parameter does not meet the preset parameter, sending an alarm prompt that the characteristic information is abnormal comprises the following steps:
judging whether the monitoring parameter corresponding to each feature information meets the corresponding preset parameter or not;
determining characteristic information of the monitoring parameters which do not meet corresponding preset parameters as target characteristic information;
and carrying out alarm prompt on the target characteristic information.
Optionally, the determining, according to the initial parameter corresponding to the feature information, a monitoring parameter corresponding to the feature information includes:
storing the initial parameters corresponding to the characteristic information to a distributed document database;
judging whether the current time reaches a first preset time or not;
when the current time reaches the first preset time, calling an initial parameter corresponding to the characteristic information from the distributed document database;
and calculating the initial parameters corresponding to the characteristic information to determine the monitoring parameters corresponding to the characteristic information.
Optionally, the determining whether the monitoring parameter meets a preset parameter includes average response time, a duty ratio of null value and/or a request exception ratio, and when the monitoring parameter does not meet the preset parameter, sending an alarm prompt that the characteristic information is abnormal includes:
judging whether the average response time consumption, the empty value ratio and/or the request abnormity ratio meet preset parameters corresponding to the average response time consumption, the empty value ratio and/or the request abnormity ratio;
and when the average response time consumption, the empty value ratio and/or the request abnormity ratio do not meet preset parameters corresponding to the average response time consumption, the empty value ratio and/or the request abnormity ratio, sending out an alarm prompt that the characteristic information is abnormal.
Optionally, the determining whether the monitoring parameter meets a preset parameter includes:
calling a preset configuration table from a preset database, wherein the preset configuration table carries the preset parameters;
and judging whether the monitoring parameters meet the preset parameters of the preset configuration table.
In a second aspect, an embodiment of the present invention provides a data monitoring and warning device, including:
the system comprises a characteristic data set acquisition module, a characteristic data set acquisition module and a characteristic data set processing module, wherein the characteristic data set acquisition module is used for acquiring a characteristic data set of a user, and the characteristic data set comprises characteristic information of the user and initial parameters corresponding to the characteristic information;
the monitoring parameter determining module is used for determining the monitoring parameters corresponding to the characteristic information according to the initial parameters corresponding to the characteristic information;
the judging module is used for judging whether the monitoring parameters meet preset parameters or not;
and the alarm prompt module is used for sending an alarm prompt that the characteristic information is abnormal when the monitoring parameter does not meet the preset parameter.
In a third aspect, an embodiment of the present invention provides a server, including:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a data monitoring alarm method as in any embodiment of the invention.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a data monitoring and warning method according to any embodiment of the present invention.
The method comprises the steps of obtaining a characteristic data set of a user, wherein the characteristic data set comprises characteristic information of the user and initial parameters corresponding to the characteristic information; determining a monitoring parameter corresponding to the characteristic information according to the initial parameter corresponding to the characteristic information; judging whether the monitoring parameters meet preset parameters or not; when the monitoring parameters do not meet the preset parameters, an alarm prompt indicating that the characteristic information is abnormal is sent out, the problem that the characteristic data of the user are not monitored is solved, problem troubleshooting is carried out only when the effect of daily statistical recommendation is not good, and the effect of monitoring and alarming the data is found out in time.
Drawings
Fig. 1 is a schematic flow chart of a data monitoring and alarming method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a data monitoring and alarming method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a data monitoring and warning device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a server according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. A process may be terminated when its operations are completed, but may have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.
Furthermore, the terms "first," "second," and the like may be used herein to describe various orientations, actions, steps, elements, or the like, but the orientations, actions, steps, or elements are not limited by these terms. These terms are only used to distinguish one direction, action, step or element from another direction, action, step or element. For example, the first information may be referred to as second information, and similarly, the second information may be referred to as first information, without departing from the scope of the present application. The first information and the second information are both information, but they are not the same information. The terms "first", "second", etc. are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Example one
Fig. 1 is a schematic flow chart of a data monitoring and warning method according to an embodiment of the present invention, which is applicable to a scenario of monitoring and warning feature data for recommending a commodity.
As shown in fig. 1, a data monitoring and warning method provided in an embodiment of the present invention includes:
s110, acquiring a characteristic data set of a user, wherein the characteristic data set comprises characteristic information of the user and initial parameters corresponding to the characteristic information.
The feature data set is a set including feature information for recommending a product to a user. The feature information refers to information related to a user characteristic. In this embodiment, the feature information is information that has a large influence on the recommendation effect. For example, the characteristic information may be information of gender, occupation, hobby, income, and the like, and is not particularly limited herein. The initial parameter refers to a parameter related to the characteristic information. Optionally, the initial parameter may be a time-consuming response to obtain the feature information, whether the obtained feature information is empty, whether the request to obtain the feature information fails, and the like, and is not limited herein. In this embodiment, the data set may include feature information of the same user and initial parameters of the feature information, or may be multiple initial parameters of the same feature information and the same feature information of different users, which is not limited herein. For example, the feature data set includes a response time to acquire feature information of the user a; for another example, the feature data set includes response times corresponding to the same feature information of the user B and the user C, respectively.
In an alternative embodiment, obtaining the feature data set of the user comprises:
monitoring a message queue for receiving the feature data set from a recommendation system; when the message queue update is monitored, the feature data set is extracted from the message queue.
In this embodiment, the message queue is a container that holds the feature data set during the transmission of the message. In particular, the message queue receives a feature data set from a recommendation system. The recommending system is used for recommending commodities, such as loan products or physical products, to the user according to the characteristic information in the characteristic data set of the user. Therefore, when the feature information in the feature data set is abnormal, the recommendation effect of recommending products is poor. And when the message queue is updated, indicating that the feature data set sent by the recommendation system exists, and extracting the feature data set from the message queue.
And S120, determining the monitoring parameters corresponding to the characteristic information according to the initial parameters corresponding to the characteristic information.
The monitoring parameter is used for judging whether the characteristic information is acquired abnormally. Specifically, when the feature data set includes feature information of the same user and an initial parameter of the feature information, the initial parameter may be used as a monitoring parameter, for example, time consumed for acquiring the feature information of the user a, whether the feature information of the user a is empty, and whether a request for acquiring the feature information of the user a is successful are used as the monitoring parameter. When the feature data set comprises the same feature information of different users and a plurality of initial parameters of the same feature information, averaging response time of the feature data set obtained from different users to obtain average response time consumption, counting empty value occupation ratios of the same feature information of different users, and counting request exception occupation ratios of the feature data set obtained from users with requests of exceptions, that is, monitoring parameters are average response time consumption, empty value occupation ratios and request exception occupation ratios. The empty value ratio refers to the ratio of the number of the same characteristic information returned to different users as empty to the number of all the same characteristic information. The request anomaly ratio is the ratio of the number of failures in requesting to acquire the feature data set of the user to the total number of requests for acquiring the feature data set.
In an optional implementation manner, determining the monitoring parameter corresponding to the feature information according to the initial parameter corresponding to the feature information includes:
storing the initial parameters corresponding to the characteristic information to a distributed document database; judging whether the current time reaches a first preset time or not; when the current time reaches the first preset time, calling an initial parameter corresponding to the characteristic information from the distributed document database; and calculating the initial parameters corresponding to the characteristic information to determine the monitoring parameters corresponding to the characteristic information.
The method and the device are suitable for a scene of monitoring and alarming according to a plurality of initial parameters corresponding to the same characteristic information of different users. In this embodiment, each field in the distributed document database may be indexed and the data for each field may be searched. The first preset time is the time for judging whether to call the initial parameter to calculate the monitoring parameter. Optionally, the first preset time may be obtained by adding a preset time period to the last time of calculating the monitoring parameter, for example, adding five minutes to the last time of calculating the monitoring parameter to obtain the first preset time. In this embodiment, specifically, feature information of different users and initial parameters corresponding to the feature information are continuously acquired and stored in the distributed document database. And if the current time reaches the first preset time, calling all initial parameters of the characteristic information in the preset time period, and calculating the initial parameters corresponding to the characteristic information to determine the monitoring parameters corresponding to the characteristic information. In the embodiment, the monitoring statistics is performed once every preset time interval, so that the characteristic information can be monitored in time.
In an optional implementation manner, determining the monitoring parameter corresponding to the feature information according to the initial parameter corresponding to the feature information includes:
and extracting initial parameters corresponding to the characteristic information from the characteristic data set so as to determine monitoring parameters corresponding to the characteristic information according to the initial parameters corresponding to the characteristic information.
In this embodiment, specifically, the initial parameter corresponding to the feature information is directly extracted from the feature data set when the feature data set carries the initial parameter corresponding to the feature information, so as to determine the monitoring parameter corresponding to the feature information according to the initial parameter corresponding to the feature information.
And S130, judging whether the monitoring parameters meet preset parameters.
The preset parameter is a parameter for judging whether abnormal warning prompt needs to be performed on the characteristic information. For example, when the monitoring parameters include average response elapsed time, null value occupancy and request exception occupancy, the preset parameters include preset standard elapsed time, preset null value occupancy and preset exception occupancy.
In an optional embodiment, the determining whether the monitoring parameter satisfies a preset parameter includes:
calling a preset configuration table from a preset database, wherein the preset configuration table carries the preset parameters; and judging whether the monitoring parameters meet the preset parameters of the preset configuration table.
In this embodiment, the preset configuration table may configure the preset parameters as needed. For example, when the preset parameters need to be updated, the preset configuration table is modified and updated into the preset database, so as to perform monitoring and alarm according to the updated preset configuration table. Specifically, when it is required to determine whether the monitoring parameter meets the preset parameter, the preset configuration table is called from the preset database, so as to determine whether the monitoring parameter meets the preset parameter of the preset table, and determine whether to perform an alarm prompt on the feature information.
And S140, when the monitoring parameter does not meet the preset parameter, sending an alarm prompt that the characteristic information is abnormal.
In this step, when the monitoring parameter corresponding to the characteristic information does not meet the preset parameter, an alarm prompt of the characteristic information is sent. For example, when the monitoring parameter corresponding to the characteristic information of the age of the user does not meet the preset parameter, an age alarm prompt is sent to prompt that the characteristic information of the age is abnormal. Illustratively, when the monitoring parameters include average response time consumption, a null value proportion and a request exception proportion, and the preset parameters include preset standard time consumption, a preset null value proportion and a preset exception proportion, when the average response time consumption is greater than the preset labeling time consumption, the preset null value proportion is greater than the preset null value proportion and the request exception proportion is greater than the preset exception proportion, the monitoring parameters belonging to the step do not meet the preset parameters; any one or more of the above-mentioned methods may be used to issue an alarm indicating that the characteristic information is abnormal when the characteristic information is not satisfied, and the method is not limited herein. The preset standard time consumption refers to a preset parameter corresponding to the average response time consumption. The preset null value ratio is a preset parameter corresponding to the null value ratio. The preset abnormal proportion refers to a preset parameter corresponding to the request abnormal proportion. Optionally, the email may be associated with a mailbox system, and sent to a corresponding mailbox for alarming, and the like, which is not limited herein. According to the embodiment, the characteristic information is subjected to alarm prompt, so that the abnormity of the characteristic information can be determined in time, and related personnel are informed to perform troubleshooting in time.
According to the technical scheme of the embodiment of the invention, a characteristic data set of a user is obtained, wherein the characteristic data set comprises characteristic information of the user and initial parameters corresponding to the characteristic information; determining a monitoring parameter corresponding to the characteristic information according to the initial parameter corresponding to the characteristic information; judging whether the monitoring parameters meet preset parameters or not; and when the monitoring parameter does not meet the preset parameter, sending an alarm prompt that the characteristic information is abnormal. By judging the monitoring parameters of the characteristic information, the problem elimination is avoided when the recommended effect is not good, and the monitoring parameters of the characteristic information can be judged in time to determine whether to alarm or not so as to achieve the technical effect of monitoring and alarming data in time.
Example two
Fig. 2 is a schematic flow chart of a data monitoring and alarming method according to a second embodiment of the present invention. The embodiment is further detailed in the technical scheme, and is suitable for a scene of warning and prompting a plurality of characteristic information. The method can be executed by a data monitoring and warning device, which can be realized in a software and/or hardware manner and can be integrated on a server.
As shown in fig. 2, a data monitoring and warning method provided by the second embodiment of the present invention includes:
s210, obtaining a characteristic data set of a user, wherein the characteristic data set comprises characteristic information of the user and initial parameters corresponding to the characteristic information, and the characteristic information is multiple.
In this embodiment, specifically, the feature information is plural.
S220, determining a monitoring parameter corresponding to each characteristic information according to the initial parameter of each characteristic information.
In this step, each characteristic information corresponds to a single monitoring parameter. For example, when the characteristic information is occupation, the corresponding preset empty value accounts for 3%; when the characteristic information is gender, the corresponding preset empty value proportion is 20%, and corresponding monitoring parameters can be set for different characteristic information according to needs, and the method is not limited here.
And S230, judging whether the monitoring parameter corresponding to each characteristic information meets the corresponding preset parameter.
In this step, the monitoring parameters corresponding to each piece of feature information are subjected to independent corresponding preset parameter judgment. Optionally, the corresponding preset parameter may be called according to the name of the feature information to perform the determination.
And S240, determining the characteristic information of the monitoring parameters which do not meet the corresponding preset parameters as target characteristic information.
The target characteristic information refers to characteristic information of which the monitoring parameters do not meet corresponding preset parameters in the plurality of characteristic information. For example, when the characteristic information is occupation, the corresponding preset empty value accounts for 3%; when the characteristic information is the gender, the corresponding preset empty value accounts for 20%. If the monitoring parameter of the occupation is 5%, the characteristic information of the occupation is target characteristic information; if the monitoring parameter of the gender is 10%, the characteristic information of the gender is not the target characteristic information.
And S250, carrying out alarm prompt on the target characteristic information.
In this step, the target characteristic information is subjected to alarm prompting, and alarm prompting can be performed in a targeted manner, so that uniform alarm prompting is avoided, and abnormal specific characteristic information needs to be analyzed.
According to the technical scheme of the embodiment of the invention, a characteristic data set of a user is obtained, wherein the characteristic data set comprises characteristic information of the user and initial parameters corresponding to the characteristic information; determining a monitoring parameter corresponding to the characteristic information according to the initial parameter corresponding to the characteristic information; judging whether the monitoring parameters meet preset parameters or not; and when the monitoring parameter does not meet the preset parameter, sending an alarm prompt that the characteristic information is abnormal. By judging the monitoring parameters of the characteristic information, the problem elimination is avoided when the recommended effect is not good, and the monitoring parameters of the characteristic information can be judged in time to determine whether to alarm or not so as to achieve the technical effect of monitoring and alarming data in time.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a data monitoring and warning device according to a third embodiment of the present invention, where the present embodiment is applicable to a scenario of monitoring and warning feature data for recommending a commodity, and the device may be implemented in a software and/or hardware manner and may be integrated on a server.
As shown in fig. 3, the data monitoring and warning apparatus provided in this embodiment may include a feature data set obtaining module 310, a monitoring parameter determining module 320, a determining module 330, and a warning prompting module 340, where:
a feature data set obtaining module 310, configured to obtain a feature data set of a user, where the feature data set includes feature information of the user and an initial parameter corresponding to the feature information; a monitoring parameter determining module 320, configured to determine a monitoring parameter corresponding to the feature information according to the initial parameter corresponding to the feature information; a judging module 330, configured to judge whether the monitoring parameter meets a preset parameter; and an alarm prompt module 340, configured to send an alarm prompt that the characteristic information is abnormal when the monitoring parameter does not meet the preset parameter.
Optionally, the feature data set obtaining module 310 includes: a monitoring unit for monitoring a message queue for receiving the feature data set from a recommendation system; and the characteristic data set acquisition unit is used for extracting the characteristic data set from the message queue when monitoring the update of the message queue.
Optionally, the monitoring parameter determining module 320 is specifically configured to extract an initial parameter corresponding to the feature information from the feature data set, so as to determine the monitoring parameter corresponding to the feature information according to the initial parameter corresponding to the feature information.
Optionally, the feature information is multiple, and the monitoring parameter determining module 320 is specifically configured to determine a monitoring parameter corresponding to each feature information according to an initial parameter of each feature information; the determining module 330 is specifically configured to determine whether the monitoring parameter corresponding to each feature information satisfies a corresponding preset parameter; the alarm prompting module 340 is specifically configured to determine that the monitoring parameter does not meet the feature information of the corresponding preset parameter as target feature information; and carrying out alarm prompt on the target characteristic information.
Optionally, the monitoring parameter determining module 320 includes: the storage unit is used for storing the initial parameters corresponding to the characteristic information into a distributed document database; the judging unit is used for judging whether the current time reaches a first preset time or not; the initial parameter calling unit is used for calling the initial parameter corresponding to the characteristic information from the distributed document database when the current time reaches the first preset time; and the calculating unit is used for calculating the initial parameters corresponding to the characteristic information so as to determine the monitoring parameters corresponding to the characteristic information.
Optionally, the monitoring parameters include average response time consumption, empty value ratio and/or request anomaly ratio, and the determining module 330 is specifically configured to determine whether the monitoring parameters meet preset parameters, where the determining module is configured to determine whether the average response time consumption, the empty value ratio and/or the request anomaly ratio meet preset parameters corresponding to the average response time consumption, the empty value ratio and/or the request anomaly ratio; the alarm prompting module 340 is specifically configured to send an alarm prompt that the characteristic information is abnormal when the average response time consumption, the empty value ratio and/or the request abnormal ratio do not satisfy preset parameters corresponding to the average response time consumption, the empty value ratio and/or the request abnormal ratio.
Optionally, the determining module 330 includes: the system comprises a preset configuration table calling unit, a configuration table processing unit and a configuration table processing unit, wherein the preset configuration table calling unit is used for calling a preset configuration table from a preset database, and the preset configuration table carries preset parameters; and the monitoring parameter judging unit is used for judging whether the monitoring parameters meet the preset parameters of the preset configuration table.
The data monitoring and warning device provided by the embodiment of the invention can execute the data monitoring and warning method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. Reference may be made to the description of any method embodiment of the invention not specifically described in this embodiment.
Example four
Fig. 4 is a schematic structural diagram of a server according to a fourth embodiment of the present invention. FIG. 4 illustrates a block diagram of an exemplary server 612 suitable for use in implementing embodiments of the present invention. The server 612 shown in fig. 4 is only an example, and should not bring any limitation to the function and the scope of the use of the embodiments of the present invention.
As shown in fig. 4, the server 612 is in the form of a general-purpose server. The components of server 612 may include, but are not limited to: one or more processors 616, a memory device 628, and a bus 618 that couples the various system components including the memory device 628 and the processors 616.
The server 612 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by server 612 and includes both volatile and nonvolatile media, removable and non-removable media.
A program/utility 640 having a set (at least one) of program modules 642 may be stored, for example, in storage 628, such program modules 642 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. The program modules 642 generally perform the functions and/or methods of the described embodiments of the present invention.
The server 612 may also communicate with one or more external devices 614 (e.g., keyboard, pointing terminal, display 624, etc.), one or more terminals that enable a user to interact with the server 612, and/or any terminal (e.g., Network card, modem, etc.) that enables the server 612 to communicate with one or more other computing terminals.A server 612 may also communicate via AN input/output (I/O) interface 622, and the server 612 may also communicate via a Network adapter 620 with one or more networks (e.g., local Area Network (L Area Network, L AN), Wide Area Network (WAN), and/or public Network, such as the Internet). As shown in FIG. 4, the Network adapter 620 may communicate via a bus 618 with other modules of the server 612. it should be appreciated that, although not shown, other hardware and/or software modules may be used in conjunction with the server 612, including, but not limited to, Redundant microcode, terminal drives, external disk drive Arrays, disk Arrays (Disks) and disk drives, disk Arrays, disk drives, and the like.
The processor 616 executes various functional applications and data processing by running programs stored in the storage device 628, for example, implementing a data monitoring and warning method provided by any embodiment of the present invention, which may include:
acquiring a characteristic data set of a user, wherein the characteristic data set comprises characteristic information of the user and initial parameters corresponding to the characteristic information;
determining a monitoring parameter corresponding to the characteristic information according to the initial parameter corresponding to the characteristic information;
judging whether the monitoring parameters meet preset parameters or not;
and when the monitoring parameter does not meet the preset parameter, sending an alarm prompt that the characteristic information is abnormal.
According to the technical scheme of the embodiment of the invention, a characteristic data set of a user is obtained, wherein the characteristic data set comprises characteristic information of the user and initial parameters corresponding to the characteristic information; determining a monitoring parameter corresponding to the characteristic information according to the initial parameter corresponding to the characteristic information; judging whether the monitoring parameters meet preset parameters or not; and when the monitoring parameter does not meet the preset parameter, sending an alarm prompt that the characteristic information is abnormal. By judging the monitoring parameters of the characteristic information, the problem elimination is avoided when the recommended effect is not good, and the monitoring parameters of the characteristic information can be judged in time to determine whether to alarm or not so as to achieve the technical effect of monitoring and alarming data in time.
EXAMPLE five
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a data monitoring and warning method according to any embodiment of the present invention, where the method may include:
acquiring a characteristic data set of a user, wherein the characteristic data set comprises characteristic information of the user and initial parameters corresponding to the characteristic information;
determining a monitoring parameter corresponding to the characteristic information according to the initial parameter corresponding to the characteristic information;
judging whether the monitoring parameters meet preset parameters or not;
and when the monitoring parameter does not meet the preset parameter, sending an alarm prompt that the characteristic information is abnormal.
The computer-readable storage media of embodiments of the invention may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including AN object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages.
According to the technical scheme of the embodiment of the invention, a characteristic data set of a user is obtained, wherein the characteristic data set comprises characteristic information of the user and initial parameters corresponding to the characteristic information; determining a monitoring parameter corresponding to the characteristic information according to the initial parameter corresponding to the characteristic information; judging whether the monitoring parameters meet preset parameters or not; and when the monitoring parameter does not meet the preset parameter, sending an alarm prompt that the characteristic information is abnormal. By judging the monitoring parameters of the characteristic information, the problem elimination is avoided when the recommended effect is not good, and the monitoring parameters of the characteristic information can be judged in time to determine whether to alarm or not so as to achieve the technical effect of monitoring and alarming data in time.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A data monitoring and alarming method is characterized by comprising the following steps:
acquiring a characteristic data set of a user, wherein the characteristic data set comprises characteristic information of the user and initial parameters corresponding to the characteristic information;
determining a monitoring parameter corresponding to the characteristic information according to the initial parameter corresponding to the characteristic information;
judging whether the monitoring parameters meet preset parameters or not;
and when the monitoring parameter does not meet the preset parameter, sending an alarm prompt that the characteristic information is abnormal.
2. The data monitoring alarm method of claim 1, wherein the obtaining the feature data set of the user comprises:
monitoring a message queue for receiving the feature data set from a recommendation system;
when the message queue update is monitored, the feature data set is extracted from the message queue.
3. The data monitoring alarm method of claim 1, wherein the determining the monitoring parameter corresponding to the feature information according to the initial parameter corresponding to the feature information comprises:
and extracting initial parameters corresponding to the characteristic information from the characteristic data set so as to determine monitoring parameters corresponding to the characteristic information according to the initial parameters corresponding to the characteristic information.
4. The data monitoring and warning method of claim 1, wherein the number of the feature information is plural, and the determining the monitoring parameter corresponding to the feature information according to the initial parameter corresponding to the feature information includes:
determining a monitoring parameter corresponding to each characteristic information according to the initial parameter of each characteristic information;
the judging whether the monitoring parameter meets a preset parameter, and when the monitoring parameter does not meet the preset parameter, sending an alarm prompt that the characteristic information is abnormal comprises the following steps:
judging whether the monitoring parameter corresponding to each feature information meets the corresponding preset parameter or not;
determining characteristic information of the monitoring parameters which do not meet corresponding preset parameters as target characteristic information;
and carrying out alarm prompt on the target characteristic information.
5. The data monitoring alarm method of claim 1, wherein the determining the monitoring parameter corresponding to the feature information according to the initial parameter corresponding to the feature information comprises:
storing the initial parameters corresponding to the characteristic information to a distributed document database;
judging whether the current time reaches a first preset time or not;
when the current time reaches the first preset time, calling an initial parameter corresponding to the characteristic information from the distributed document database;
and calculating the initial parameters corresponding to the characteristic information to determine the monitoring parameters corresponding to the characteristic information.
6. The data monitoring and warning method according to claim 1, wherein the monitoring parameters include average response time, empty value ratio and/or request abnormality ratio, the determining whether the monitoring parameters satisfy preset parameters, and when the monitoring parameters do not satisfy the preset parameters, sending a warning prompt that the characteristic information is abnormal includes:
judging whether the average response time consumption, the empty value ratio and/or the request abnormity ratio meet preset parameters corresponding to the average response time consumption, the empty value ratio and/or the request abnormity ratio;
and when the average response time consumption, the empty value ratio and/or the request abnormity ratio do not meet preset parameters corresponding to the average response time consumption, the empty value ratio and/or the request abnormity ratio, sending out an alarm prompt that the characteristic information is abnormal.
7. The data monitoring alarm method of claim 1, wherein the determining whether the monitoring parameter satisfies a preset parameter comprises:
calling a preset configuration table from a preset database, wherein the preset configuration table carries the preset parameters;
and judging whether the monitoring parameters meet the preset parameters of the preset configuration table.
8. A data monitoring alarm device, the device comprising:
the system comprises a characteristic data set acquisition module, a characteristic data set acquisition module and a characteristic data set processing module, wherein the characteristic data set acquisition module is used for acquiring a characteristic data set of a user, and the characteristic data set comprises characteristic information of the user and initial parameters corresponding to the characteristic information;
the monitoring parameter determining module is used for determining the monitoring parameters corresponding to the characteristic information according to the initial parameters corresponding to the characteristic information;
the judging module is used for judging whether the monitoring parameters meet preset parameters or not;
and the alarm prompt module is used for sending an alarm prompt that the characteristic information is abnormal when the monitoring parameter does not meet the preset parameter.
9. A server, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the data monitoring alarm method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a data monitoring alarm method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010199872.0A CN111427878B (en) | 2020-03-20 | 2020-03-20 | Data monitoring alarm method, device, server and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010199872.0A CN111427878B (en) | 2020-03-20 | 2020-03-20 | Data monitoring alarm method, device, server and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111427878A true CN111427878A (en) | 2020-07-17 |
| CN111427878B CN111427878B (en) | 2024-02-27 |
Family
ID=71548393
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010199872.0A Active CN111427878B (en) | 2020-03-20 | 2020-03-20 | Data monitoring alarm method, device, server and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111427878B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112597130A (en) * | 2020-12-22 | 2021-04-02 | 天聚地合(苏州)数据股份有限公司 | Data early warning method and device, electronic equipment and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104636483A (en) * | 2015-02-16 | 2015-05-20 | 广东省公安厅 | Data monitoring method |
| CN107346307A (en) * | 2016-05-04 | 2017-11-14 | 北京京东尚科信息技术有限公司 | Distributed cache system and method |
| CN108681493A (en) * | 2018-05-29 | 2018-10-19 | 深圳乐信软件技术有限公司 | Data exception detection method, device, server and storage medium |
| CN109242553A (en) * | 2018-08-22 | 2019-01-18 | 平安科技（深圳）有限公司 | A kind of user behavior data recommended method, server and computer-readable medium |
| CN109542885A (en) * | 2018-11-19 | 2019-03-29 | 北京锐安科技有限公司 | Data cleaning method, device, equipment and storage medium |
| CN110188149A (en) * | 2019-06-04 | 2019-08-30 | 宁波银行股份有限公司 | A kind of data warehouse |
| CN110661659A (en) * | 2019-09-23 | 2020-01-07 | 上海艾融软件股份有限公司 | Alarm method, device and system and electronic equipment |
-
2020
- 2020-03-20 CN CN202010199872.0A patent/CN111427878B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104636483A (en) * | 2015-02-16 | 2015-05-20 | 广东省公安厅 | Data monitoring method |
| CN107346307A (en) * | 2016-05-04 | 2017-11-14 | 北京京东尚科信息技术有限公司 | Distributed cache system and method |
| CN108681493A (en) * | 2018-05-29 | 2018-10-19 | 深圳乐信软件技术有限公司 | Data exception detection method, device, server and storage medium |
| CN109242553A (en) * | 2018-08-22 | 2019-01-18 | 平安科技（深圳）有限公司 | A kind of user behavior data recommended method, server and computer-readable medium |
| CN109542885A (en) * | 2018-11-19 | 2019-03-29 | 北京锐安科技有限公司 | Data cleaning method, device, equipment and storage medium |
| CN110188149A (en) * | 2019-06-04 | 2019-08-30 | 宁波银行股份有限公司 | A kind of data warehouse |
| CN110661659A (en) * | 2019-09-23 | 2020-01-07 | 上海艾融软件股份有限公司 | Alarm method, device and system and electronic equipment |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112597130A (en) * | 2020-12-22 | 2021-04-02 | 天聚地合(苏州)数据股份有限公司 | Data early warning method and device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111427878B (en) | 2024-02-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108055281B (en) | Account abnormity detection method, device, server and storage medium | |
| CN110955640B (en) | Cross-system data file processing method, device, server and storage medium | |
| CN111400294B (en) | Data anomaly monitoring method, device and system | |
| CN110555451A (en) | information identification method and device | |
| CN111324441A (en) | Operating environment switching method and device, computer equipment and storage medium | |
| CN108491875A (en) | A kind of data exception detection method, device, equipment and medium | |
| CN111427878B (en) | Data monitoring alarm method, device, server and storage medium | |
| CN110602207A (en) | Method, device, server and storage medium for predicting push information based on off-network | |
| CN113111078B (en) | Resource data processing method and device, computer equipment and storage medium | |
| CN111784176A (en) | Data processing method, device, server and medium | |
| CN116701123A (en) | Task early warning method, device, equipment, medium and program product | |
| CN115187364A (en) | Method and device for monitoring deposit risk under bank distributed scene | |
| CN111741046B (en) | Data reporting method, data acquisition method, device, equipment and medium | |
| CN114266501A (en) | Automatic prediction and root cause analysis method and system for hospital operation index | |
| CN113961632A (en) | Message data processing method and device and electronic equipment | |
| CN113407102A (en) | Virtual key display method, device, system and storage medium | |
| CN116127149B (en) | Quantification method and system for health degree of graph database cluster | |
| CN113282471B (en) | Equipment performance testing method and device and terminal equipment | |
| CN115840677A (en) | Data verification method, device, equipment and storage medium | |
| CN113900915B (en) | Method and device for determining service performance state, storage medium and electronic equipment | |
| CN115187288A (en) | Object inspection method, device, electronic equipment, storage medium and program product | |
| CN117435428A (en) | Data monitoring method, device, electronic equipment and computer readable storage medium | |
| CN113327133A (en) | Data recommendation method, data recommendation device, electronic equipment and readable storage medium | |
| CN117573478A (en) | Performance monitoring method, device, apparatus, medium and program product | |
| CN117130880A (en) | Data processing method, apparatus, device, storage medium, and program product |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |