CN115081410A - Method and device for automatically generating experiment report - Google Patents
Method and device for automatically generating experiment report Download PDFInfo
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Abstract
The application provides an automatic generation method and a device of an experiment report, wherein the automatic generation method comprises the following steps: running a target chaos experiment; acquiring various experimental data in the running process of the chaotic experiment from a monitoring tool set for the target chaotic experiment; after the target chaos experiment is finished, acquiring an instrument panel interface image formed aiming at the target chaos experiment from a monitoring tool; dividing a target data chart corresponding to at least one target item experiment data from an instrument panel interface by using a pre-trained deep learning model; and filling each item of experimental data and at least one target data chart into corresponding content areas of a preset experimental report template respectively to form a target experimental report corresponding to the target chaotic experiment. According to the method and the device, the experimental data of the target item and the corresponding data chart are identified, and the experimental report is automatically generated, so that the experimental data is comprehensively recorded, and meanwhile, the generation efficiency of the experimental report is improved.
Description
Technical Field
The application relates to the technical field of system operation and maintenance, in particular to an automatic generation method and device of an experiment report.
Background
Chaos engineering is a complex technical means for improving the elastic capability of a technical architecture, the usability of the system can be ensured through chaos engineering experiments, the chaos engineering aims to kill faults in swaddles, namely, the faults are identified before the faults cause interruption, the faults are actively manufactured, the behavior of the system under various pressures is tested, the fault problem is identified and repaired, and serious consequences are avoided.
The implementation method of the chaos engineering experiment at the present stage comprises the following steps: in the experiment process, experimenters need to pay extra attention to index dynamics of a monitoring platform and carry out feedback, recording or fault recovery operation on experiment events by combining personal experience; after the experiment is finished, the experimenter records the experiment process in the modes of manual note taking, screenshot and the like, arranges the experiment report to finish a chaos engineering experiment.
However, in the actual chaos engineering experiment process, indexes of the monitoring platform and the pressure measurement platform are various and continuously changed, the efficiency of manually filling in the experiment report after the experiment is completed is low, the key indexes and the relevant charts cannot be accurately and comprehensively recorded in the experiment process, and adverse effects can be caused on the accumulation of experiment experiences and the iteration efficiency of the system.
Disclosure of Invention
In view of the above, an object of the present invention is to provide at least an automatic generation method of an experiment report, which automatically generates an experiment report by identifying target item experiment data and a corresponding data chart thereof, so as to improve the generation efficiency of the experiment report while comprehensively recording the experiment data.
The application mainly comprises the following aspects:
in a first aspect, an embodiment of the present application provides an automatic generation method of an experiment report, including: running a target chaos experiment; acquiring various experimental data in the running process of the chaotic experiment from a monitoring tool set for the target chaotic experiment; after the target chaos experiment is finished, acquiring an instrument panel interface image formed aiming at the target chaos experiment from a monitoring tool; dividing a target data chart corresponding to at least one target item experiment data from an instrument panel interface image by using a pre-trained deep learning model; and filling each item of experimental data and at least one target data chart into corresponding content areas of a preset experimental report template respectively to form a target experimental report corresponding to the target chaotic experiment.
In a possible embodiment, at least one fault is injected in advance into a target chaotic experiment, the monitoring tool comprises a fault index monitoring tool and a pressure measurement index monitoring tool, the experimental data comprises at least one target fault index data and a plurality of target pressure measurement index data, each target fault index data corresponds to one fault which is injected in advance into the target chaotic experiment, and each item of experimental data in the running process of the chaotic experiment is acquired by the following method: acquiring target fault index data corresponding to at least one fault injected in advance in the running process of the chaotic experiment from a fault index monitoring tool; and acquiring a plurality of target pressure measurement index data for monitoring the chaotic experiment operation process from the pressure measurement index monitoring tool.
In one possible embodiment, the instrument panel interface includes a fault indicator instrument panel interface image obtained from the fault indicator monitoring tool and a pressure indicator instrument panel interface image obtained from the pressure indicator monitoring tool, wherein the step of segmenting the target data chart corresponding to the at least one target item experiment data from the instrument panel interface image by using a pre-trained deep learning model includes: inputting a fault index instrument panel interface image acquired from a fault index monitoring tool into a pre-trained deep learning model, and performing image segmentation processing on the fault index instrument panel interface image to acquire at least one to-be-processed fault data chart; identifying image-text information in each fault data chart to be processed, and determining a target fault data chart corresponding to each target fault index data; inputting a pressure measurement index instrument panel interface image acquired from a pressure measurement index monitoring tool into a pre-trained deep learning model, and performing image segmentation processing on the pressure measurement index instrument panel interface image to acquire at least one pressure measurement data chart to be processed; and identifying image-text information in each pressure measurement data chart to be processed, and determining a target pressure measurement data chart corresponding to each target pressure measurement index data.
In one possible implementation, the preset experiment report template comprises a fault index data area and a pressure measurement index data area; the step of filling each item of experimental data and at least one target data chart into corresponding content areas of a preset experimental report template respectively to form a target experimental report corresponding to the target chaotic experiment comprises the following steps: filling at least one target fault index data and a target fault data chart corresponding to each target fault index data into a fault index data area; and filling the target pressure measurement index data and the target pressure measurement data chart corresponding to each target pressure measurement index data into a pressure measurement index data area.
In a possible implementation manner, the preset experiment report template further includes an experiment basic data area, and the automatic generation method further includes: acquiring experiment basic data related to a target chaos experiment from a chaos engineering experiment platform for operating the target chaos experiment; and filling the experiment basic data into the experiment basic data area.
In a possible implementation manner, the preset experiment report template further includes an early warning fault indicator data area, and for each target fault indicator data, the automatic generation method further includes: judging whether the target fault index data meets a preset first early warning condition corresponding to the target fault index data; if the target fault index data meet the corresponding first early warning condition, filling the target fault index data into an early warning fault index data area; and if the target fault index data do not meet the corresponding first early warning condition, filling the target fault index data into a fault index data area.
In a possible implementation manner, the early warning index data area includes an early warning pressure measurement index area, and for each target pressure measurement index data, the automatic generation method further includes: judging whether the target pressure measurement index data meets a preset second early warning condition corresponding to the target pressure measurement index data; if the target pressure measurement index data meets the corresponding second early warning condition, filling the target pressure measurement index data into an early warning pressure measurement index data area; and if the target pressure measurement index data does not meet the corresponding second early warning condition, filling the target pressure measurement index data into a pressure measurement index data area.
In a second aspect, an embodiment of the present application further provides an automatic generation device for an experiment report, where the automatic generation device includes: the experiment operation module is used for operating a target chaos experiment; the monitoring module is used for acquiring various experimental data in the running process of the chaotic experiment from a monitoring tool set aiming at the target chaotic experiment; the acquisition module is used for acquiring an instrument panel interface image formed by aiming at the target chaotic experiment from the monitoring tool after the target chaotic experiment is finished; the segmentation module is used for segmenting at least one target data chart corresponding to target item experimental data from the instrument panel interface image by utilizing a pre-trained deep learning model; and the report generation module is used for respectively filling each item of experimental data and at least one target data chart into corresponding content areas of a preset experimental report template so as to form a target experimental report corresponding to the target chaotic experiment.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device runs, the processor and the memory communicate with each other through the bus, and when the machine-readable instructions are run by the processor, the processor performs the steps of the method for automatically generating an experimental report according to the first aspect or any one of the possible implementation manners of the first aspect.
In a fourth aspect, the present embodiments further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and the computer program is executed by a processor to perform the steps of automatically generating an experiment report as described in the first aspect or any one of the possible implementation manners of the first aspect.
The method for automatically generating the experiment report provided by the embodiment of the application comprises the following steps: running a target chaos experiment; acquiring various experimental data in the running process of the chaotic experiment from a monitoring tool set for the target chaotic experiment; after the target chaos experiment is finished, acquiring an instrument panel interface image formed aiming at the target chaos experiment from a monitoring tool; dividing a target data chart corresponding to at least one target item experiment data from an instrument panel interface image by using a pre-trained deep learning model; and filling each item of experimental data and at least one target data chart into corresponding content areas of a preset experimental report template respectively to form a target experimental report corresponding to the target chaotic experiment. According to the method and the device, the experimental report is automatically generated by identifying the target item experimental data and the corresponding data chart, so that the experimental data is comprehensively recorded and the generation efficiency of the experimental report is improved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 is a flow chart illustrating a method for automatically generating an experimental report according to an embodiment of the present disclosure;
FIG. 2 is a schematic structural diagram of an apparatus for automatically generating an experimental report according to an embodiment of the present disclosure;
fig. 3 shows a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of protection of the present application. Further, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and that steps without logical context may be reversed in order or performed concurrently. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.
In addition, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as presented in the figures, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be obtained by a person skilled in the art without making any inventive step based on the embodiments of the present application, fall within the scope of protection of the present application.
The implementation method of the current-stage chaotic engineering experiment comprises
In the experiment process, experimenters need to pay extra attention to index dynamics of a monitoring platform, and feedback, record or adopt corresponding fault recovery operation on the result of an experiment event by combining personal experience; (ii) a After the experiment is finished, the experimenter records the experiment process in the modes of manual note taking, screenshot and the like, arranges the experiment report to finish a chaos engineering experiment.
Therefore, in the chaos engineering experiment process in the prior art, on one hand, due to the fact that indexes of the monitoring platform are various and continuously changed, the phenomenon that people pay attention to the change of each index is often in a busy and disorderly manner, and careless attention is avoided, so that key index information needing to be paid attention first cannot be observed and identified in a targeted manner, and certain requirements are provided for experience abilities of personnel.
On the other hand, after the experiment is finished, the efficiency of artificially filling in the experiment report is too low, and information careless mistakes can also occur in the experiment report because the key indexes and the relevant charts cannot be accurately and comprehensively recorded in the experiment process, which can adversely affect the accumulation of the experiment experience and the iteration efficiency of the system.
Based on this, embodiments of the present application provide an automatic generation method and apparatus for an experiment report, which automatically generate an experiment report by identifying target item experiment data and a data chart corresponding to the target item experiment data, thereby improving the generation efficiency of the experiment report while comprehensively recording the experiment data, and the following specific details are as follows
Referring to fig. 1, fig. 1 is a flowchart illustrating an automatic generation method of an experiment report according to an embodiment of the present application. As shown in fig. 1, the automatic generation method provided in the embodiment of the present application includes the following steps:
and S100, operating a target chaos experiment.
In a specific embodiment, a target chaos experiment can be run through a chaos engineering experiment platform, specifically, the chaos engineering experiment platform is provided with at least one chaos experiment in advance, and an experimenter can select a target chaos experiment to be executed and run, wherein at least one fault is injected into the target chaos experiment in advance, a fault parameter corresponding to the fault is preset for each fault in the target chaos experiment, and during the running process of the target chaos experiment, each fault is injected into a corresponding service according to the fault parameter corresponding to each fault through a chaos tool and a fault injection probe.
In a particular embodiment, the fault parameters include, but are not limited to, at least one of: fault injection object, fault type, fault disturbance degree, and fault disturbed service, wherein the fault injection object indicates the service injected by the fault, the fault type includes but is not limited to delay, exception, and full/overflow of system application, and deletion, loss, addition, damage, duplication, movement, and suspension of system resource, wherein the system application includes but is not limited to at least one of the following items: a distributed service framework, HTTP requests, message queues, and databases and service processes.
S200, acquiring various experimental data in the running process of the chaotic experiment from a monitoring tool set for the target chaotic experiment.
Specifically, the monitoring tool comprises a fault index monitoring tool and a pressure measurement index monitoring tool, the experimental data comprises at least one target fault index data and a plurality of target pressure measurement index data, each target fault index data corresponds to a fault which is injected in advance in the target chaotic experiment, and each item of experimental data in the running process of the chaotic experiment is acquired in the following mode:
acquiring target fault index data corresponding to at least one fault injected in advance in the chaos experiment running process from a fault index monitoring tool, wherein the fault index monitoring tool specifically includes, but is not limited to, any one of the following items: APM, ZABBIX, Prometheus, Grafana.
In a preferred embodiment, the indexes monitored by the fault index monitoring tool include a default index and a preset index, wherein before the target chaotic experiment is run, an experimenter can set the preset index to be monitored on the fault index monitoring tool according to the fault type corresponding to each fault injected by the target chaotic experiment, in the embodiment of the present application, the preset index is a target fault index corresponding to at least one fault injected in advance and set on the fault index monitoring tool, and meanwhile, for each target fault index, a threshold parameter corresponding to the target fault index and a corresponding first early warning condition can be preset, generally, after the target fault index is set, the target fault index has the default threshold parameter and the corresponding first early warning condition, for example, if the target fault index is CPU utilization rate > 70%, wherein, 70% is the threshold parameter that CPU usage corresponds, and first early warning condition can set up that CPU usage is greater than 70%, and the duration exceeds 3 minutes and triggers the warning.
In another preferred embodiment, the threshold parameter corresponding to the target fault indicator and the corresponding first warning condition may also be adjusted, for example, the CPU usage rate is changed to be > 90%, and the duration time below the threshold exceeds 5 minutes to trigger an alarm.
Preferably, after the target chaotic experiment starts to operate, the fault index monitoring tool may acquire target monitoring index data corresponding to the target monitoring index, and may acquire target monitoring index data acquired by the fault index monitoring tool and automatically pushed by middleware such as a message queue or an infiluxdb database.
The method comprises the steps of obtaining a plurality of target pressure measurement index data for monitoring the chaotic experiment operation process from a pressure measurement index monitoring tool, wherein the pressure measurement index monitoring tool specifically comprises but is not limited to any one of the following items: PTS, LoadRunner, Jerner.
In a preferred embodiment, the pressure measurement indicator monitoring tool is used for testing the system performance of the system to be tested, and the pressure measurement indicator includes but is not limited to at least one of the following items: TPS, transaction success rate, and throughput, specifically, in the present application, the pressure measurement index monitoring tool injects a pressure test into the system to be tested, and before the pressure test or the target chaotic experiment starts to operate, target pressure measurement index data to be monitored in the target chaotic experiment, a threshold parameter corresponding to each target pressure measurement index data, and a second early warning condition may be set, where the threshold parameter corresponding to each target pressure measurement index data and the second early warning condition are similar to the setting process of the threshold of the target fault index data and the first early warning condition, and are not described herein again.
Specifically, a system to be tested relates to at least one service, the target chaos experiment of the application injects at least one fault related to the target chaos experiment into the service corresponding to the system to be tested according to fault parameters through a chaos tool and a fault probe provided by a chaos engineering platform, that is, a fault injection object corresponding to each fault in the target chaos experiment is one of the services corresponding to the system to be tested, then target pressure measurement index data and at least one target fault index data monitored by a fault index monitoring tool are monitored by the pressure measurement index monitoring tool, wherein the pressure measurement index monitoring tool collects the pressure measurement index data all the time during a pressure test running time period, the pressure measurement index monitoring tool and the fault index monitoring tool are independent from each other, and the pressure measurement index monitoring tool focuses on the target chaos experiment running time period, the variation trend of each pressure measurement index data.
Preferably, after the target chaotic experiment starts to operate, the pressure measurement index monitoring tool acquires target pressure measurement index data corresponding to the target pressure measurement index, and can acquire target pressure measurement index data acquired by the pressure measurement index monitoring tool and automatically pushed by middleware such as a message queue or an infiluxdb database.
And S300, after the target chaotic experiment is finished, acquiring an instrument panel interface image formed aiming at the target chaotic experiment from the monitoring tool.
Specifically, the instrument panel interface image includes a fault index instrument panel interface image acquired from a fault index monitoring tool and a pressure measurement index instrument panel interface image acquired from the pressure measurement index monitoring tool, wherein after the target chaos experiment is finished, on one hand, the fault index monitoring tool respectively performs statistical processing on each acquired fault index data to generate a fault index data chart corresponding to each fault index data, and summarizes each fault index data chart to the fault index instrument panel interface for displaying, and generates a fault index instrument panel interface image, meanwhile, a middleware such as a message queue or an infilux db database automatically pushes the fault index instrument panel interface image generated by the fault index monitoring tool, on the other hand, the pressure measurement index monitoring tool respectively performs statistical processing on each acquired pressure measurement index data, and simultaneously, the pressure measurement index instrument panel interface image generated by the pressure measurement index monitoring tool is automatically pushed by middleware such as a message queue or an influxDB database.
S400, segmenting a target data chart corresponding to at least one target item experiment data from the instrument panel interface image by using a pre-trained deep learning model.
In a preferred embodiment, the step of segmenting the target data chart corresponding to at least one target item experiment data from the instrument panel interface image by using the pre-trained deep learning model comprises the following steps:
inputting a fault index instrument panel interface image acquired from a fault index monitoring tool into a pre-trained deep learning model, performing image segmentation processing on the fault index instrument panel interface image to acquire at least one to-be-processed fault data chart, identifying image-text information in each to-be-processed fault data chart, and determining a target fault data chart corresponding to each target fault index data, wherein the fault index instrument panel interface image comprises but is not limited to at least one of the following fault index charts: a CPU usage graph, a memory usage graph, and a network status monitoring graph.
Specifically, the keyword information corresponding to each to-be-processed fault data chart can be matched with the target fault index data by identifying the keyword information in each to-be-processed fault data chart, and the target fault data chart corresponding to each target fault index data is determined according to the matching result.
Inputting a pressure measurement index instrument panel interface image acquired from a pressure measurement index monitoring tool into a pre-trained deep learning model, performing image segmentation processing on the pressure measurement index instrument panel interface image to obtain at least one pressure measurement data chart to be processed, identifying image-text information in each pressure measurement data chart to be processed, and determining a target pressure measurement data chart corresponding to each target pressure measurement index data, wherein the pressure measurement index instrument panel interface image comprises but is not limited to at least one of the following pressure measurement index charts: TPS trend plots and transaction response time trend plots.
Specifically, the keyword information corresponding to each pressure measurement data chart to be processed can be matched with the target pressure measurement index data by identifying the keyword information in each pressure measurement data chart to be processed, and the target pressure measurement data chart corresponding to each target pressure measurement index data can be determined according to the matching result.
And S500, filling each item of experimental data and at least one target data chart into corresponding content areas of a preset experimental report template respectively to form a target experimental report corresponding to the target chaotic experiment.
In specific implementation, the step of filling each item of experimental data and at least one target data chart into corresponding content areas of the preset experimental report template respectively to form a target experimental report corresponding to a target chaotic experiment includes:
and filling at least one target fault index data and a target fault data chart corresponding to each target fault index data into a fault index data area, and filling a plurality of target pressure measurement index data and a target pressure measurement data chart corresponding to each target pressure measurement index data into a pressure measurement index data area.
In one example, the preset experiment report template further includes an experiment basic data area, and the automatic generation method further includes:
acquiring experiment basic data related to the target chaos experiment from a chaos engineering experiment platform for operating the target chaos experiment, and filling the experiment basic data into an experiment basic data area.
Specifically, the experimental basic data includes, but is not limited to, at least one of the following items: the method comprises the following steps that experiment personnel, experiment starting time, experiment ending time and fault parameters corresponding to at least one fault related to a target chaotic experiment, wherein experiment basic data can be recorded by a chaotic engineering experiment platform, and the experiment basic data recorded by the chaotic engineering experiment platform is automatically pushed by middleware such as a message queue or an influxDB database.
In a specific embodiment, the preset experiment report template further includes an early warning fault indicator data area, and for each target fault indicator data, the automatic generation method further includes:
judging whether the target fault index data meets a preset first early warning condition corresponding to the target fault index data or not, and filling the target fault index data into an early warning fault index data area if the target fault index data meets the corresponding first early warning condition; and if the target fault index data do not meet the corresponding first early warning condition, filling the target fault index data into a fault index data area.
In a specific embodiment, the early warning index data area includes an early warning pressure measurement index area, and the automatic generation method further includes, for each target pressure measurement index data:
and judging whether the target pressure measurement index data meets a preset second early warning condition corresponding to the target pressure measurement index data, if so, filling the target pressure measurement index data into an early warning pressure measurement index data area, and if not, filling the target pressure measurement index data into a pressure measurement index data area.
Based on the same application concept, an automatic generation device of an experiment report corresponding to the automatic generation method of an experiment report provided by the above embodiment is also provided in the embodiment of the present application, and as the principle of solving the problem of the device in the embodiment of the present application is similar to the automatic generation method of an experiment report in the above embodiment of the present application, the implementation of the device may refer to the implementation of the method, and repeated details are omitted.
Referring to fig. 2, fig. 2 is a schematic structural diagram of an automatic generation apparatus for an experiment report according to an embodiment of the present application, and as shown in fig. 2, the automatic generation apparatus includes:
an experiment operation module 600, configured to operate a target chaos experiment;
the monitoring module 610 is used for acquiring various experimental data in the running process of the chaotic experiment from a monitoring tool set for the target chaotic experiment;
the obtaining module 620 is configured to obtain, from the monitoring tool, an instrument panel interface formed for the target chaotic experiment after the target chaotic experiment is completed;
the segmentation module 630 is configured to segment a target data chart corresponding to at least one target item experiment data from an instrument panel interface by using a pre-trained deep learning model;
the report generating module 640 is configured to fill each item of experimental data and at least one target data chart into corresponding content areas of a preset experimental report template, so as to form a target experimental report corresponding to the target chaotic experiment.
Based on the same application concept, please refer to fig. 3, fig. 3 shows a schematic structural diagram of an electronic device according to an embodiment of the present application, where the electronic device 700 includes: a processor 710, a memory 720 and a bus 730, wherein the memory 720 stores machine-readable instructions executable by the processor 710, when the electronic device 700 is operated, the processor 710 communicates with the memory 720 through the bus 430, and the machine-readable instructions are executed by the processor 710 to perform the steps of the method for automatically generating an experiment report according to any one of the embodiments.
Based on the same application concept, the embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the method for automatically generating an experiment report provided by the above embodiment.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the units into only one type of logical function may be implemented in other ways, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. An automatic generation method of an experiment report, the automatic generation method comprising:
running a target chaos experiment;
acquiring various experimental data in the running process of the chaotic experiment from a monitoring tool set for the target chaotic experiment;
after the target chaotic experiment is finished, acquiring an instrument panel interface image formed aiming at the target chaotic experiment from the monitoring tool;
dividing a target data chart corresponding to at least one target item experiment data from the instrument panel interface image by using a pre-trained deep learning model;
and filling each item of experimental data and at least one target data chart into corresponding content areas of a preset experimental report template respectively to form a target experimental report corresponding to the target chaotic experiment.
2. The automatic generation method according to claim 1, wherein the target chaotic experiment is pre-injected with at least one fault, the monitoring tools include a fault indicator monitoring tool and a pressure measurement indicator monitoring tool, the experimental data includes at least one target fault indicator data and a plurality of target pressure measurement indicator data, each target fault indicator data corresponds to one fault pre-injected in the target chaotic experiment,
the method comprises the following steps of obtaining various experimental data in the chaos experiment operation process in the following mode:
acquiring target fault index data corresponding to at least one fault injected in advance in the running process of the chaotic experiment from the fault index monitoring tool;
and acquiring a plurality of target pressure measurement index data for monitoring the running process of the chaotic experiment from the pressure measurement index monitoring tool.
3. The automatic generation method of claim 2, wherein the dashboard interface images include a fault indicator dashboard image obtained from the fault indicator monitoring tool and a pressure indicator dashboard interface image obtained from the pressure indicator monitoring tool,
the method comprises the following steps of utilizing a pre-trained deep learning model to segment a target data chart corresponding to at least one target item experiment data from an instrument panel interface image, wherein the step comprises the following steps:
inputting a fault index instrument panel interface image acquired from the fault index monitoring tool into a pre-trained deep learning model, and performing image segmentation processing on the fault index instrument panel interface image to acquire at least one fault data chart to be processed;
identifying image-text information in each fault data chart to be processed, and determining a target fault data chart corresponding to each target fault index data;
inputting the pressure measurement index instrument panel interface image acquired from the pressure measurement index monitoring tool into a pre-trained deep learning model, and performing image segmentation processing on the pressure measurement index instrument panel interface image to acquire at least one pressure measurement data chart to be processed;
and identifying image-text information in each pressure measurement data chart to be processed, and determining a target pressure measurement data chart corresponding to each target pressure measurement index data.
4. The automatic generation method of claim 3, wherein the preset experimental report template includes a fault indicator data area and a pressure measurement indicator data area;
filling each item of experimental data and at least one target data chart into corresponding content areas of a preset experimental report template respectively to form a target experimental report corresponding to the target chaotic experiment, wherein the step of forming the target experimental report comprises the following steps:
filling the at least one target fault index data and a target fault data chart corresponding to each target fault index data into the fault index data area;
and filling the target pressure measurement index data and the target pressure measurement data chart corresponding to each target pressure measurement index data into the pressure measurement index data area.
5. The automatic generation method of claim 1, wherein the preset experiment report template further comprises an experiment basic data area, the automatic generation method further comprising:
acquiring experiment basic data related to the target chaos experiment from a chaos engineering experiment platform for operating the target chaos experiment;
and filling the experiment basic data into the experiment basic data area.
6. The automatic generation method of claim 2, wherein the preset experimental report template further comprises an early warning fault indicator data area, and for each target fault indicator data, the automatic generation method further comprises:
judging whether the target fault index data meets a preset first early warning condition corresponding to the target fault index data or not;
if the target fault index data meet the corresponding first early warning condition, filling the target fault index data into the early warning fault index data area;
and if the target fault index data do not meet the corresponding first early warning condition, filling the target fault index data into the fault index data area.
7. The automatic generation method according to claim 2, wherein the early warning index data area includes an early warning pressure measurement index area, and for each target pressure measurement index data, the automatic generation method further includes:
judging whether the target pressure measurement index data meets a preset second early warning condition corresponding to the target pressure measurement index data;
if the target pressure measurement index data meets a corresponding second early warning condition, filling the target pressure measurement index data into an early warning pressure measurement index data area;
and if the target pressure measurement index data does not meet the corresponding second early warning condition, filling the target pressure measurement index data into the pressure measurement index data area.
8. An apparatus for automatically generating an experiment report, comprising:
the experiment operation module is used for operating a target chaotic experiment;
the monitoring module is used for acquiring various experimental data in the running process of the chaotic experiment from a monitoring tool set aiming at the target chaotic experiment;
the acquisition module is used for acquiring an instrument panel interface image formed aiming at the target chaotic experiment from the monitoring tool after the target chaotic experiment is finished;
the segmentation module is used for segmenting at least one target data chart corresponding to target item experimental data from the instrument panel interface image by utilizing a pre-trained deep learning model;
and the report generation module is used for respectively filling each item of experimental data and at least one target data chart into corresponding content areas of a preset experimental report template so as to form a target experimental report corresponding to the target chaotic experiment.
9. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the electronic device is running, the machine-readable instructions when executed by the processor performing the steps of the auto-generation method of any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the steps of the auto-generation method of one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115599269A (en) * | 2022-11-29 | 2023-01-13 | 明度智云（浙江）科技有限公司（Cn） | Method and device for generating experiment report and electronic equipment |
Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107544897A (en) * | 2017-08-25 | 2018-01-05 | 重庆扬讯软件技术股份有限公司 | The performance test methods and system monitored in real time based on integration |
| CN109783785A (en) * | 2018-12-27 | 2019-05-21 | 长沙通诺信息科技有限责任公司 | Generate the method, apparatus and computer equipment of tryout test report |
| CN110413398A (en) * | 2019-08-06 | 2019-11-05 | 中国工商银行股份有限公司 | Method for scheduling task, device, computer equipment and storage medium |
| WO2019218444A1 (en) * | 2018-05-14 | 2019-11-21 | 平安科技（深圳）有限公司 | Test report generating method, apparatus and device, and storage medium |
| CN110765023A (en) * | 2019-10-29 | 2020-02-07 | 中国工商银行股份有限公司 | Distributed system testing method and system based on chaos experiment |
| CN111708650A (en) * | 2020-06-10 | 2020-09-25 | 中国工商银行股份有限公司 | High-availability analysis method and system for business application system |
| CN112559271A (en) * | 2020-12-24 | 2021-03-26 | 北京百度网讯科技有限公司 | Method, device, equipment and storage medium for monitoring interface performance of distributed application |
| CN112582045A (en) * | 2020-12-22 | 2021-03-30 | 无锡慧方科技有限公司 | Electronic medical report sheet transmission system |
| CN112579383A (en) * | 2019-09-30 | 2021-03-30 | 北京国双科技有限公司 | Method and device for simulating system fault |
| CN112612715A (en) * | 2020-12-30 | 2021-04-06 | 平安壹钱包电子商务有限公司 | System testing method and device based on chaotic engineering, computer equipment and storage medium |
| CN112699876A (en) * | 2021-03-24 | 2021-04-23 | 中海油能源发展股份有限公司采油服务分公司 | Automatic reading method for various meters of gas collecting station |
| CN112988494A (en) * | 2021-03-15 | 2021-06-18 | 北京字跳网络技术有限公司 | Abnormity testing method and device and electronic device |
| CN113095047A (en) * | 2021-04-12 | 2021-07-09 | 苏州创腾软件有限公司 | Experiment report generation method and device, computer equipment and storage medium |
| CN113157577A (en) * | 2021-04-27 | 2021-07-23 | 中国工商银行股份有限公司 | PaaS cloud platform fault testing method and device |
| CN113672467A (en) * | 2021-08-24 | 2021-11-19 | 中国电信股份有限公司 | Operation and maintenance early warning method and device, electronic equipment and storage medium |
| US20210374025A1 (en) * | 2020-05-29 | 2021-12-02 | Beijing Baidu Netcom Science And Technology Co. Ltd. | Fault Injection Method and Apparatus, Electronic Device and Storage Medium |
| CN113835987A (en) * | 2021-11-25 | 2021-12-24 | 北京世纪好未来教育科技有限公司 | Chaotic experiment execution file generation method and device, electronic equipment and storage medium |
| CN114003428A (en) * | 2021-11-04 | 2022-02-01 | 北京达佳互联信息技术有限公司 | Fault early warning method and device for distributed system |
| CN114416512A (en) * | 2022-01-25 | 2022-04-29 | 中国工商银行股份有限公司 | Test method, test device, electronic equipment and computer storage medium |
| CN114706740A (en) * | 2022-03-30 | 2022-07-05 | 中国银行股份有限公司 | Chaos experiment method, device, storage medium and equipment |
-
2022
- 2022-07-19 CN CN202210851688.9A patent/CN115081410A/en active Pending
Patent Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107544897A (en) * | 2017-08-25 | 2018-01-05 | 重庆扬讯软件技术股份有限公司 | The performance test methods and system monitored in real time based on integration |
| WO2019218444A1 (en) * | 2018-05-14 | 2019-11-21 | 平安科技（深圳）有限公司 | Test report generating method, apparatus and device, and storage medium |
| CN109783785A (en) * | 2018-12-27 | 2019-05-21 | 长沙通诺信息科技有限责任公司 | Generate the method, apparatus and computer equipment of tryout test report |
| CN110413398A (en) * | 2019-08-06 | 2019-11-05 | 中国工商银行股份有限公司 | Method for scheduling task, device, computer equipment and storage medium |
| CN112579383A (en) * | 2019-09-30 | 2021-03-30 | 北京国双科技有限公司 | Method and device for simulating system fault |
| CN110765023A (en) * | 2019-10-29 | 2020-02-07 | 中国工商银行股份有限公司 | Distributed system testing method and system based on chaos experiment |
| US20210374025A1 (en) * | 2020-05-29 | 2021-12-02 | Beijing Baidu Netcom Science And Technology Co. Ltd. | Fault Injection Method and Apparatus, Electronic Device and Storage Medium |
| CN111708650A (en) * | 2020-06-10 | 2020-09-25 | 中国工商银行股份有限公司 | High-availability analysis method and system for business application system |
| CN112582045A (en) * | 2020-12-22 | 2021-03-30 | 无锡慧方科技有限公司 | Electronic medical report sheet transmission system |
| CN112559271A (en) * | 2020-12-24 | 2021-03-26 | 北京百度网讯科技有限公司 | Method, device, equipment and storage medium for monitoring interface performance of distributed application |
| CN112612715A (en) * | 2020-12-30 | 2021-04-06 | 平安壹钱包电子商务有限公司 | System testing method and device based on chaotic engineering, computer equipment and storage medium |
| CN112988494A (en) * | 2021-03-15 | 2021-06-18 | 北京字跳网络技术有限公司 | Abnormity testing method and device and electronic device |
| CN112699876A (en) * | 2021-03-24 | 2021-04-23 | 中海油能源发展股份有限公司采油服务分公司 | Automatic reading method for various meters of gas collecting station |
| CN113095047A (en) * | 2021-04-12 | 2021-07-09 | 苏州创腾软件有限公司 | Experiment report generation method and device, computer equipment and storage medium |
| CN113157577A (en) * | 2021-04-27 | 2021-07-23 | 中国工商银行股份有限公司 | PaaS cloud platform fault testing method and device |
| CN113672467A (en) * | 2021-08-24 | 2021-11-19 | 中国电信股份有限公司 | Operation and maintenance early warning method and device, electronic equipment and storage medium |
| CN114003428A (en) * | 2021-11-04 | 2022-02-01 | 北京达佳互联信息技术有限公司 | Fault early warning method and device for distributed system |
| CN113835987A (en) * | 2021-11-25 | 2021-12-24 | 北京世纪好未来教育科技有限公司 | Chaotic experiment execution file generation method and device, electronic equipment and storage medium |
| CN114416512A (en) * | 2022-01-25 | 2022-04-29 | 中国工商银行股份有限公司 | Test method, test device, electronic equipment and computer storage medium |
| CN114706740A (en) * | 2022-03-30 | 2022-07-05 | 中国银行股份有限公司 | Chaos experiment method, device, storage medium and equipment |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115599269A (en) * | 2022-11-29 | 2023-01-13 | 明度智云（浙江）科技有限公司（Cn） | Method and device for generating experiment report and electronic equipment |
| CN115599269B (en) * | 2022-11-29 | 2023-03-21 | 明度智云(浙江)科技有限公司 | Method and device for generating experiment report and electronic equipment |
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