CN110347552B - Method and device for supporting real-time monitoring of configurable decision engine and electronic equipment - Google Patents
Method and device for supporting real-time monitoring of configurable decision engine and electronic equipment Download PDFInfo
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Abstract
The invention discloses a method and a device for supporting a configurable decision engine monitoring function, which are characterized in that a finished user submits an application, so that a decision engine obtains data of the finished user, then the data of the finished user participated in by the decision engine is monitored in a self-defined manner, the statistical result of the finished user is obtained, and finally the statistical result of the finished user is displayed in different chart modes for monitoring. Therefore, the period for finding the business abnormality can be shortened, multidimensional index analysis is integrated, and the found abnormality can be processed rapidly and efficiently.
Description
Technical Field
The present invention relates to the field of computer information processing, and in particular, to a method, an apparatus, an electronic device, and a computer readable medium for supporting real-time monitoring of a configurable decision engine.
Background
Currently, business personnel generally use daily reports or weekly reports to determine whether a business index is abnormal, but the time period for which the method finds the abnormality is at least one day. Therefore, in order to shorten the period of discovering the abnormal business and synthesize multi-dimension index analysis at the same time, a method capable of configuring the real-time monitoring function needs to be provided.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of the foregoing, the present specification has been presented to provide a method and apparatus for supporting real-time monitoring of a configurable decision engine that overcomes or at least partially solves the foregoing.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
In a first aspect, the present invention provides a method for supporting real-time monitoring of a configurable decision engine, comprising:
The method comprises the steps that a finished user submits an application, so that a decision engine obtains data of the finished user;
The data of the finished users participated by the decision engine are monitored in a self-defined mode, and statistical results of the finished users are obtained;
and displaying the statistics results of the finished users in different chart modes for monitoring.
In an exemplary embodiment of the present disclosure, the custom monitoring the data of the end user with which the decision engine participates, and obtaining the statistics of the end user further includes:
Selecting input data, intermediate variables and output data of different scenes of each product as index data to configure real-time monitoring;
and counting the data of the finished users according to different time intervals to obtain the counting result of the data of the finished users.
In an exemplary embodiment of the present disclosure, the counting the data of the finished user at different time intervals, and obtaining the statistics of the data of the finished user further includes:
and counting the index data according to the time interval of every half hour, every hour or every day to obtain the counting result of the data of the finished users.
In an exemplary embodiment of the present disclosure, the statistics of the finished users are displayed in different graph manners, and when the index data is abnormal, the connection alarm function further includes:
And displaying the statistical result in a form of a line graph, a column graph, a pie chart and a data chart for monitoring.
In a second aspect, the present invention provides an apparatus for supporting real-time monitoring of a configurable decision engine, comprising:
The acquisition module is used for submitting an application by the completion user so that the decision engine acquires the data of the completion user;
the self-defined real-time monitoring module is used for self-defining and monitoring the data of the finished users participated by the decision engine to obtain the statistical result of the finished users;
And the display result module displays the statistical results of the finished users in different chart modes for monitoring.
In an exemplary embodiment of the present disclosure, the custom real-time monitoring module further includes:
The index data configuration module is used for selecting input data, intermediate variables and output data of different scenes of each product as index data configuration real-time monitoring;
and the statistics module is used for counting the data of the finished users according to different time intervals to obtain the statistics result of the data of the finished users.
In an exemplary embodiment of the present disclosure, the statistics module further includes:
And the time interval statistics module is used for counting the index data according to the time interval of each half hour, each hour or each day to obtain the statistics result of the data of the finished user.
In an exemplary embodiment of the disclosure, the display result module further includes:
And the chart display module is used for displaying the statistical result in a way of a line graph, a column graph, a pie chart and a data chart to monitor.
In a third aspect, the present specification provides a server comprising a processor and a memory: the memory is used for storing a program of the method of any one of the above; the processor is configured to execute the program stored in the memory to implement the steps of the method of any one of the preceding claims.
In a fourth aspect, embodiments of the present description provide a computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, implements the steps of any of the methods described above.
According to the method, the device, the electronic equipment and the computer readable medium for supporting the monitoring of the configurable decision engine, the data of the finished users participated in by the decision engine are self-defined by acquiring the data submitted by the finished users, the corresponding statistical results of the finished users are obtained, and finally the statistical results of the finished users are displayed in different chart modes for monitoring.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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In order to make the technical problems solved by the present invention, the technical means adopted and the technical effects achieved more clear, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted, however, that the drawings described below are merely illustrative of exemplary embodiments of the present invention and that other embodiments of the present invention may be derived from these drawings by those skilled in the art without undue effort.
FIG. 1 is a flowchart illustrating a method of supporting real-time monitoring of a configurable decision engine, according to an exemplary embodiment.
Fig. 2 is a block diagram illustrating a device for supporting real-time monitoring of a configurable decision engine according to another exemplary embodiment.
FIG. 3 is a flow chart illustrating an alarm method according to an exemplary embodiment.
Fig. 4 is a device diagram illustrating an alarm method according to an exemplary embodiment.
Fig. 5 is a block diagram of an electronic device, according to an example embodiment.
Fig. 6 is a block diagram of a computer storage medium shown according to an example embodiment.
Detailed Description
Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals in the drawings denote the same or similar elements, components or portions, and thus a repetitive description thereof will be omitted.
The features, structures, characteristics or other details described in a particular embodiment do not exclude that may be combined in one or more other embodiments in a suitable manner, without departing from the technical idea of the invention.
In the description of specific embodiments, features, structures, characteristics, or other details described in the present invention are provided to enable one skilled in the art to fully understand the embodiments. It is not excluded that one skilled in the art may practice the present invention without one or more of the specific features, structures, characteristics, or other details.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
It should be understood that although the terms first, second, third, etc. may be used herein to describe various devices, elements, components or portions, this should not be limited by these terms. These words are used to distinguish one from the other. For example, a first device may also be referred to as a second device without departing from the spirit of the invention.
The term "and/or" and/or "includes all combinations of any of the associated listed items and one or more.
The technical scheme of the invention is described and illustrated in detail below through a few specific embodiments.
Referring to fig. 1, a method for supporting real-time monitoring of a configurable decision engine includes:
S101: and the end user submits the application so that the decision engine acquires the data of the end user.
Specifically, only after the user submits the application, the user enters the decision engine to judge the credit worthiness risk and monitor each index.
S102: and the data of the finished users participated by the decision engine are monitored in a self-defined mode, and the statistical result of the finished users is obtained.
The step of self-defining and monitoring the data of the finished users participated by the decision engine, and the step of obtaining the statistical result of the finished users further comprises the following steps:
Selecting input data, intermediate variables and output data of different scenes of each product as index data to configure real-time monitoring;
and counting the data of the finished users according to different time intervals to obtain the counting result of the data of the finished users.
The step of counting the data of the finished users according to different time intervals, and the step of obtaining the counting result of the data of the finished users further comprises the following steps:
and counting the index data according to the time interval of every half hour, every hour or every day to obtain the counting result of the data of the finished users.
Specifically, business personnel can select input data, intermediate variables and output data configuration monitoring of different stages of each product. Statistics are then performed at various time intervals of every half hour/day.
S103: and displaying the statistics results of the finished users in different chart modes for monitoring.
The monitoring of the statistics of the finished users in different chart modes comprises the following steps:
And displaying the statistical result in a form of a line graph, a column graph, a pie chart and a data chart for monitoring.
Specifically, the statistics are shown in the form of line graphs, bar graphs, pie charts, and data charts. The monitoring function supports statistical analysis of character type and numerical type service indexes, the character type indexes count the ratio situation of each element, and the numerical type indexes count a plurality of index situations such as average value, maximum value, minimum value and the like. The monitoring function provides the data comparison of the last period value and the average value of the last seven days, and the multi-index association classification statistics and other functions to assist the business personnel in carrying out abnormality judgment.
Further, based on the obtained monitoring result, an alarm connection can be performed, as shown in fig. 3,
S301: and acquiring real-time monitoring data of each service index.
Specifically, the service personnel can obtain the real-time monitoring data displayed according to the monitoring function.
S302: and setting an alarm threshold according to the service demand of the service.
The setting the alarm threshold according to the service requirement of the service comprises the following steps:
And according to the service requirements of the service, selecting alarm rules for combination, and setting an alarm threshold.
Specifically, the alarm thresholds required by different services are different, so that different alarm rules need to be selected for combination, and the alarm thresholds required by specific services are customized.
S303: and evaluating the abnormal grade of the business index according to the rule and the model grading method.
The method for evaluating the abnormal grade of the business index according to the rule and the model scoring comprises the following steps:
judging whether the business index is abnormal according to the simple rule, the complex rule, the customized rule and the model scoring method to obtain a judging result;
And setting an abnormal grade according to the judging result.
Specifically, the abnormal condition of each service is different, and if the abnormal level is set by the same standard, the alarm value cannot be represented at all. Only if different abnormal grades are rated according to different service demands, the service index can be effectively monitored.
S304: and carrying out the next operation according to the monitoring data, the alarm threshold and the abnormal level of the service index.
The performing the next operation according to the monitoring data, the alarm threshold value and the abnormal level of the business index comprises:
and triggering an alarm function when the abnormal level of the service index exceeds the alarm threshold.
The triggering alarm function further comprises:
notifying service personnel responsible for the service index to process abnormality according to the abnormality level of the service index;
After the business personnel deal with the abnormality, the alarm is released, and the reason and the solution of the abnormality are recorded at the same time.
Specifically, when the abnormality level of a certain service index exceeds the alarm threshold, an alarm function is triggered, and then a person responsible for a specific service is notified by mail or telephone, and after the service person responsible for the service handles the abnormality, the alarm can be released at the same time, and the cause and the solution of the abnormality are recorded.
Specifically, for example, business personnel select input data, intermediate variables, and output data for different stages of each product to configure monitoring. The monitoring function supports statistics of index data according to various time intervals such as every half hour/day, and the obtained statistical results are displayed in various modes such as a line graph, a column graph, a pie graph and a data chart. Meanwhile, the monitoring function supports the statistical analysis of character type and numerical type service indexes, the character type indexes count the condition of the ratio of each element, and the numerical type indexes count a plurality of index conditions such as the average value, the maximum value, the minimum value and the like. The monitoring function can provide the data comparison of the last period value and the average value of the last seven days, and the multi-index association classification statistics and other functions to assist the business personnel in carrying out abnormality judgment. Meanwhile, custom monitoring is supported, and index names, types, rules, all bottom data, intermediate results of intermediate logic calculation and final results can be monitored. Any data that has the participation of a decision engine can be monitored, such as 3000 variables that are input to the decision engine. All data can enter the decision engine only when the user submits the application, and at present, the monitoring and alarming are based on the completed clients, and only the data submitted by the completed clients can enter the decision engine to judge credit worthiness risks and monitor all indexes. Thus, when the index is abnormal, the alarm is given, and the alarm function is combined. Specifically, service personnel judge whether each service is abnormal through the change of each service statistics index in the real-time monitoring system, and then, aiming at the monitoring data of each service index, an alarm function provides a method of simple rules, complex rules, customized rules and model scoring to judge whether the service index is abnormal, and evaluate the abnormal grade. The service personnel can select proper alarm rules for combination according to actual service demands, and set an alarm threshold. If the alarm function identifies abnormality, the business personnel are prompted by mail, short message or telephone mode according to the abnormality grade, and the processing is timely carried out. After the abnormality is removed, the service personnel can release the alarm and record the reason and the solution of the abnormality.
Those skilled in the art will appreciate that all or part of the steps implementing the above-described embodiments are implemented as a program (computer program) executed by a computer data processing apparatus. The above-described method provided by the present invention can be implemented when the computer program is executed. Moreover, the computer program may be stored in a computer readable storage medium, which may be a readable storage medium such as a magnetic disk, an optical disk, a ROM, a RAM, or a storage array composed of a plurality of storage media, for example, a magnetic disk or a tape storage array. The storage medium is not limited to a centralized storage, but may be a distributed storage, such as cloud storage based on cloud computing.
The following describes apparatus embodiments of the invention that may be used to perform method embodiments of the invention. Details described in the embodiments of the device according to the invention should be regarded as additions to the embodiments of the method described above; for details not disclosed in the embodiments of the device according to the invention, reference may be made to the above-described method embodiments.
Referring to fig. 2, an apparatus based on a method of supporting configurable decision engine monitoring, comprising:
the obtaining module 201 is configured to submit an application by a finishing user, so that the decision engine obtains data of the finishing user.
Specifically, the acquisition module only enters the decision engine to judge credit worthiness risk and monitor each index after the user submits the application.
The custom real-time monitoring module 202 is configured to custom monitor the data of the finished user participated in by the decision engine, and obtain the statistics result of the finished user.
The self-defined real-time monitoring module further comprises:
The index data configuration module is used for selecting input data, intermediate variables and output data of different scenes of each product as index data configuration real-time monitoring;
and the statistics module is used for counting the data of the finished users according to different time intervals to obtain the statistics result of the data of the finished users.
The statistics module further includes:
And the time interval statistics module is used for counting the index data according to the time interval of each half hour, each hour or each day to obtain the statistics result of the data of the finished user.
Specifically, in the custom real-time monitoring module, business personnel can select input data, intermediate variables and output data of different stages of each product to configure and monitor. And then, at the time interval statistics module, statistics are carried out according to various time intervals such as every half hour/day.
And the display result module 203 displays the statistics result of the finished users in different chart modes for monitoring.
The display result module further comprises:
And the chart display module is used for displaying the statistical result in a way of a line graph, a column graph, a pie chart and a data chart to monitor.
Specifically, in the graph display module, the statistical results are displayed in the form of a line graph, a bar graph, a pie chart and a data graph. The monitoring function supports statistical analysis of character type and numerical type service indexes, the character type indexes count the ratio situation of each element, and the numerical type indexes count a plurality of index situations such as average value, maximum value, minimum value and the like. The monitoring function provides the data comparison of the last period value and the average value of the last seven days, and the multi-index association classification statistics and other functions to assist the business personnel in carrying out abnormality judgment.
Further, based on the real-time monitoring module, an alarm module can also be entered, as shown in figure 4,
The acquiring unit 401 is configured to acquire real-time monitoring data of each service index.
Specifically, the acquiring unit is real-time monitoring data which can be displayed by service personnel according to the monitoring function.
An alarm threshold setting unit 402 is configured to set an alarm threshold according to a service requirement of the service.
The alarm threshold setting unit further includes:
And the rule combination selection module is used for selecting alarm rules for combination according to the service requirements of the service and setting an alarm threshold value.
Specifically, the alarm thresholds required by different services are different, so that the rule combination selection module is required to select different alarm rules to be combined, and the alarm thresholds required by specific services are customized.
An anomaly grade judging unit 403, configured to evaluate the anomaly grade of the business index according to rules and a model scoring method.
The abnormality level judgment unit further includes:
the scoring module is used for judging whether the business index is abnormal according to the simple rule, the complex rule, the customized rule and the model scoring method to obtain a judging result;
And setting an abnormal grade according to the judging result.
Specifically, the abnormal condition of each service is different, and if the abnormal level is set by the same standard, the alarm value cannot be represented at all. Therefore, the scoring module is required to evaluate different abnormal grades according to different business requirements, so that business index monitoring can be effectively performed.
And the execution unit 404 is used for performing the next operation according to the monitoring data, the alarm threshold value and the abnormal level of the business index.
The execution unit further includes:
and the triggering module is used for triggering an alarm function when the abnormal level of the service index exceeds the alarm threshold value.
The trigger module further comprises:
The exception handling recording module is used for notifying service personnel responsible for the service index of handling exception according to the exception grade of the service index;
After the business personnel deal with the abnormality, the alarm is released, and the reason and the solution of the abnormality are recorded at the same time.
Specifically, the triggering module triggers an alarm function when the abnormal level of a certain service index exceeds the alarm threshold, then a person responsible for a specific service is informed by mail or telephone, and then the abnormal processing recording module notifies the person responsible for the service to process the abnormality, and then the alarm is released at the same time, and the reason and the solution of the abnormality are recorded.
It will be appreciated by those skilled in the art that the modules in the embodiments of the apparatus described above may be distributed in an apparatus as described, or may be distributed in one or more apparatuses different from the embodiments described above with corresponding changes. The modules of the above embodiments may be combined into one module, or may be further split into a plurality of sub-modules.
The following describes an embodiment of an electronic device according to the present invention, which may be regarded as a specific physical implementation of the above-described embodiment of the method and apparatus according to the present invention. Details described in relation to the embodiments of the electronic device of the present invention should be considered as additions to the embodiments of the method or apparatus described above; for details not disclosed in the embodiments of the electronic device of the present invention, reference may be made to the above-described method or apparatus embodiments.
A server 500 according to such an embodiment of the present disclosure is described below with reference to fig. 5. The server 500 shown in fig. 5 is merely an example, and should not be construed as limiting the functionality and scope of use of the disclosed embodiments.
As shown in fig. 5, the server 500 is in the form of a general purpose computing device. The components of server 500 may include, but are not limited to: at least one processing unit 510, at least one memory unit 520, a bus 530 connecting the different system components (including the memory unit 520 and the processing unit 510), a display unit 540, etc.
Wherein the storage unit stores program code executable by the processing unit 510 such that the processing unit 510 performs steps according to various exemplary embodiments of the present disclosure described in the above-described electronic prescription flow processing methods section of the present specification. For example, the processing unit 510 may perform the steps as shown in fig. 1, 3.
The memory unit 520 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 5201 and/or cache memory unit 5202, and may further include Read Only Memory (ROM) 5203.
The storage unit 520 may also include a program/utility 5204 having a set (at least one) of program modules 5205, such program modules 5205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 530 may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The server 500 may also communicate with one or more external devices 600 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the server 500, and/or any device (e.g., router, modem, etc.) that enables the server 500 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 550. Also, server 500 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet via network adapter 560. Network adapter 560 may communicate with other modules of server 500 via bus 530. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with server 500, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the exemplary embodiments described herein may be implemented in software, or may be implemented in software in combination with necessary hardware. Thus, the technical solution according to the embodiments of the present invention may be embodied in the form of a software product, which may be stored in a computer readable storage medium (may be a CD-ROM, a usb disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, or a network device, etc.) to perform the above-mentioned method according to the present invention. The computer program, when executed by a data processing device, enables the computer readable medium to carry out the above-described method of the present invention, namely: a method based on supporting configurable decision engine monitoring.
The computer program may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium 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 readable 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.
Program code for carrying out operations 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, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in accordance with embodiments of the present invention may be implemented in practice using a general purpose data processing device such as a microprocessor or Digital Signal Processor (DSP). The present invention can also be implemented as an apparatus or device program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
The above-described specific embodiments further describe the objects, technical solutions and advantageous effects of the present invention in detail, and it should be understood that the present invention is not inherently related to any particular computer, virtual device or electronic apparatus, and various general-purpose devices may also implement the present invention. The foregoing description of the embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. A method of supporting real-time monitoring of a configurable decision engine, comprising:
The method comprises the steps that a finished user submits an application, so that a decision engine obtains data of the finished user;
Selecting the data of the finished users as index data to configure real-time monitoring; monitoring and supporting statistical analysis of character type and numerical type service indexes, wherein the character type indexes are used for counting the ratio situation of each element, and at least one index situation of the average value, the maximum value and the minimum value of the numerical type index statistics; counting the data of the finished users according to different time intervals to obtain a counting result of the data of the finished users;
And displaying the statistical results of the finished users in different chart modes for monitoring, wherein the monitoring function provides data comparison of the average value of the previous period value and multi-index association classification statistics for abnormality judgment.
2. The method according to claim 1, comprising:
the step of self-defining and monitoring the data of the finished users participated by the decision engine, and the step of obtaining the statistical result of the finished users further comprises the following steps:
Selecting input data, intermediate variables and output data of different scenes of each product as index data to configure real-time monitoring;
and counting the data of the finished users according to different time intervals to obtain the counting result of the data of the finished users.
3. The method according to claim 2, comprising:
the step of counting the data of the finished users according to different time intervals, and the step of obtaining the counting result of the data of the finished users further comprises the following steps:
and counting the index data according to the time interval of every half hour, every hour or every day to obtain the counting result of the data of the finished users.
4. The method according to claim 1, comprising:
the step of monitoring the statistics of the finished users in different chart modes comprises the following steps:
And displaying the statistical result in a form of a line graph, a column graph, a pie chart and a data chart for monitoring.
5. An apparatus based on a method of supporting configurable decision engine monitoring, comprising:
The acquisition module is used for submitting an application by the completion user so that the decision engine acquires the data of the completion user;
The custom real-time monitoring module is used for selecting the data of the finished user as index data to configure real-time monitoring; monitoring and supporting statistical analysis of character type and numerical type service indexes, wherein the character type indexes are used for counting the ratio situation of each element, and at least one index situation of the average value, the maximum value and the minimum value of the numerical type index statistics; counting the data of the finished users according to different time intervals to obtain a counting result of the data of the finished users;
the display result module displays the statistical results of the finished users in different chart modes for monitoring; the monitoring function provides data comparison of the average value of the previous period value and multi-index association classification statistics for abnormality judgment.
6. The apparatus of claim 5, comprising:
The self-defined real-time monitoring module further comprises:
The index data configuration module is used for selecting input data, intermediate variables and output data of different scenes of each product as index data configuration real-time monitoring;
and the statistics module is used for counting the data of the finished users according to different time intervals to obtain the statistics result of the data of the finished users.
7. The apparatus of claim 6, comprising:
The statistics module further includes:
And the time interval statistics module is used for counting the index data according to the time interval of each half hour, each hour or each day to obtain the statistics result of the data of the finished user.
8. The apparatus of claim 5, comprising:
The display result module further comprises:
And the chart display module is used for displaying the statistical result in a way of a line graph, a column graph, a pie chart and a data chart to monitor.
9. An electronic device, comprising: a processor; and a memory storing computer executable instructions that, when executed, cause the processor to perform the method of any of claims 1-4.
10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-4.
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