CN114185624A - Report loading update detection method, device, equipment and storage medium - Google Patents
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Abstract
The invention discloses a report loading update detection method, a report loading update detection device, report loading update detection equipment and a storage medium, and belongs to the technical field of data processing. The method comprises the steps of receiving a detection instruction, obtaining a report, opening the report, obtaining a report page, carrying out screenshot on the report page, obtaining a report screenshot, carrying out binarization processing on the report screenshot, carrying out statistics on the gray maximum ratio of the report screenshot after the binarization processing, obtaining a statistical result, judging whether the statistical result meets a preset condition, and if so, obtaining the report loading and updating success. The method simulates manual automatic traversal, clicks to open the report, enables a browser to automatically load the report page, captures the report page, acquires the report core area after processing, performs binarization operation on the report core area picture, then counts the gray maximum ratio to obtain the statistical result, judges whether the report corresponding to the statistical result is loaded and updated successfully, and replaces manual inspection by automatic realization, thereby improving the detection efficiency.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a report loading update detection method, a report loading update detection device, report loading update detection equipment and a storage medium.
Background
At present, the report forms are widely applied and can be applied to various office systems, and because the report forms are frequently required to be updated, the function of detecting whether the key report forms can be loaded and the updating is successful is particularly important for the responsible persons of report form data.
In the prior art, a manual point-by-point inspection method is generally adopted. However, the number of reports in the report system is large, and the updating frequency may be fast, so that the current method of opening the report confirmation by manually clicking one by one is time-consuming, labor-consuming and inefficient when the number of reports is large.
Disclosure of Invention
The invention mainly aims to provide a report loading update detection method, a report loading update detection device, report loading update detection equipment and a storage medium, and aims to solve the problems of time and labor consumption and low detection efficiency of the conventional report inspection.
In order to achieve the above object, the present invention provides a report loading update detection method, which comprises the following steps:
receiving a detection instruction, acquiring a report corresponding to the detection instruction, opening the report, and displaying a report page;
screenshot is carried out on the report page, and a report screenshot is obtained;
carrying out binarization processing on the report screenshot, and counting the ratio of the maximum gray value of the report screenshot after binarization processing to obtain a statistical result;
and acquiring a historical detection result, and acquiring a report loading and updating result based on the historical detection result and the statistical result.
Optionally, before the step of determining that the report loading and updating is successful if the statistical result meets the preset condition, the method further includes:
acquiring a historical detection result corresponding to the report;
and confirming a preset condition based on the historical detection result, and judging whether the statistical result meets the preset condition.
Optionally, the step of determining a preset condition based on the historical detection result and determining whether the statistical result meets the preset condition includes:
counting the average value of the historical gray maximum ratio in the historical detection result;
and calculating the deviation degree of the statistical result according to the statistical result and the average value, setting that a preset condition is met if the deviation degree is not greater than a preset threshold value, and judging whether the statistical result meets the preset condition or not.
Optionally, the step of determining a preset condition based on the historical detection result and determining whether the statistical result meets the preset condition includes:
taking the historical detection result as sample data, and training the sample data to obtain a machine learning model;
inputting the statistical result into a machine learning model, setting that a preset condition is met if the statistical result meets the judgment condition of the machine learning model, and judging whether the statistical result meets the preset condition.
Optionally, after the step of determining that the report loading and updating is successful if the statistical result meets the preset condition, the method further includes:
if the statistical result does not meet the preset condition, judging that the report is in loading updating failure;
counting the times of loading updating failure, and judging whether the times exceed a preset failure threshold value;
if the times exceed a preset failure threshold value, outputting a warning and applying for manual intervention;
if the number of times does not exceed a preset failure threshold, executing the following steps after a preset interval time: and acquiring a report corresponding to the detection instruction, opening the report and displaying a report page.
Optionally, the screenshot is performed on the report page, and the step of obtaining the report screenshot includes:
screenshot is carried out on the report page to obtain an initial screenshot;
preprocessing the initial screenshot to obtain a preprocessed screenshot;
and cutting the preprocessing screenshot to obtain a report core area, and taking the report core area as a report screenshot.
Optionally, the step of cutting the preprocessed screenshot to obtain a report core area, and taking the report core area as a report screenshot includes:
acquiring a preset cutting number corresponding to the report;
and cutting the preprocessed screenshot based on the preset cutting quantity to obtain a plurality of report core areas, and taking the plurality of report core areas as the report screenshot.
Optionally, the receiving a detection instruction, obtaining a report corresponding to the detection instruction, and opening the report, and the displaying a report page includes:
receiving the detection instruction, opening a report system corresponding to the detection instruction, and acquiring a report list;
and selecting a report form in the report form list according to a preset instruction, opening the report form, and displaying a report form page.
In addition, in order to achieve the above object, the present invention further provides a report loading update detection apparatus, including:
the acquisition module is used for receiving a detection instruction, acquiring a report corresponding to the detection instruction, opening the report and displaying a report page;
the preprocessing module is used for carrying out screenshot on the report page to obtain a report screenshot;
the calculation module is used for carrying out binarization processing on the report screenshot and counting the ratio of the maximum gray value of the report screenshot after binarization processing to obtain a statistical result;
and the detection module is used for judging that the report loading and updating are successful if the statistical result meets a preset condition.
Optionally, the detection module is further configured to:
acquiring a historical detection result corresponding to the report;
and confirming a preset condition based on the historical detection result, and judging whether the statistical result meets the preset condition.
Optionally, the detection module is further configured to:
counting the average value of the historical gray maximum ratio in the historical detection result;
and calculating the deviation degree of the statistical result according to the statistical result and the average value, setting that a preset condition is met if the deviation degree is not greater than a preset threshold value, and judging whether the statistical result meets the preset condition or not.
Optionally, the detection module is further configured to:
taking the historical detection result as sample data, and training the sample data to obtain a machine learning model;
inputting the statistical result into a machine learning model, setting that a preset condition is met if the statistical result meets the judgment condition of the machine learning model, and judging whether the statistical result meets the preset condition.
Optionally, the detection module is further configured to:
if the statistical result does not meet the preset condition, judging that the report is in loading updating failure;
counting the times of loading updating failure, and judging whether the times exceed a preset failure threshold value;
if the times exceed a preset failure threshold value, outputting a warning and applying for manual intervention;
if the number of times does not exceed a preset failure threshold, executing the following steps after a preset interval time: and acquiring a report corresponding to the detection instruction, opening the report and displaying a report page.
Optionally, the preprocessing module is further configured to:
screenshot is carried out on the report page to obtain an initial screenshot;
preprocessing the initial screenshot to obtain a preprocessed screenshot;
and cutting the preprocessing screenshot to obtain a report core area, and taking the report core area as a report screenshot.
Optionally, the preprocessing module is further configured to:
acquiring a preset cutting number corresponding to the report;
and cutting the preprocessed screenshot based on the preset cutting quantity to obtain a plurality of report core areas, and taking the plurality of report core areas as the report screenshot.
Optionally, the obtaining module is further configured to:
receiving the detection instruction, opening a report system corresponding to the detection instruction, and acquiring a report list;
and selecting a report form in the report form list according to a preset instruction, opening the report form, and displaying a report form page.
In addition, to achieve the above object, the present invention further provides a report loading update detection device, including: the report loading, updating and detecting method comprises a memory, a processor and a report loading, updating and detecting program which is stored on the memory and can run on the processor, wherein the report loading, updating and detecting program is configured to realize the steps of the report loading, updating and detecting method.
In addition, in order to achieve the above object, the present invention further provides a storage medium, where a report loading and updating detection program is stored on the storage medium, and when the report loading and updating detection program is executed by a processor, the steps of the report loading and updating detection method described above are implemented.
The embodiment of the invention provides a method, a device, equipment and a storage medium for detecting loading and updating of a report, which are used for acquiring a corresponding report by receiving a detection instruction, opening the report, acquiring a report page, capturing the report page to acquire a report screenshot, performing binarization processing on the report screenshot, counting the ratio of the maximum gray value of the report screenshot after the binarization processing to acquire a statistical result, and judging that the loading and updating of the report are successful if the statistical result meets a preset condition. The method simulates manual automatic traversal, clicks to open the report, enables a browser to automatically load the report page, captures the report page, cuts to obtain the report core area, performs binarization processing on the report core area picture, then counts the gray maximum ratio to obtain a statistical result, detects whether the report is successfully loaded and updated based on statistical analysis, and completely replaces manual inspection by automatic realization, thereby improving the detection efficiency.
Drawings
Fig. 1 is a schematic structural diagram of a report loading update detection device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a report loading update detection method according to a first embodiment of the present invention;
FIG. 3 is a flowchart illustrating a report loading update detection method according to a second embodiment of the present invention;
FIG. 4 is a flowchart illustrating a report loading update detection method according to a third embodiment of the present invention;
FIG. 5 is a flowchart illustrating an embodiment of a report loading update detection method according to the present invention;
fig. 6 is a functional module diagram of an embodiment of a report loading update detection method according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a report loading, updating and detecting device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the report loading and updating detection device may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in FIG. 1 does not constitute a limitation of report load update detection apparatus, and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, the memory 1005, which is a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a report loading update detection program therein.
In the report loading and updating detection device shown in fig. 1, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the asset level prediction device of the present invention may be disposed in a report loading and updating detection device, and the report loading and updating detection device invokes a report loading and updating detection program stored in the memory 1005 through the processor 1001, and executes the report loading and updating detection method provided by the embodiment of the present invention.
An embodiment of the present invention provides a report loading and updating detection method, and referring to fig. 2, fig. 2 is a schematic flow diagram of a first embodiment of a report loading and updating detection method according to the present invention.
In this embodiment, the report loading update detection method includes:
step S10, receiving a detection instruction, acquiring a report corresponding to the detection instruction, opening the report and displaying a report page;
step S20, screenshot is carried out on the report page, and a report screenshot is obtained;
step S30, binarization processing is carried out on the report screenshot, and the ratio of the maximum gray value of the report screenshot after binarization processing is counted to obtain a statistical result;
and step S40, acquiring a historical detection result, and acquiring a report loading and updating result based on the historical detection result and the statistical result.
In this embodiment, in order to solve the problems that the existing report loading, updating and detection needs manual intervention and the detection efficiency is low, the embodiment receives a detection instruction, then uses software to simulate manual automatic traversal, clicks to open a report, allows a browser to automatically load a report page, captures the report page, obtains a report core area through cutting, performs binarization processing on a report core area picture, then counts the proportion of a maximum gray value, and when a preset condition is met, the report is considered to be successfully loaded.
The following is a detailed description of the individual steps:
step S10, receiving a detection instruction, acquiring a report corresponding to the detection instruction, opening the report and displaying a report page;
in one embodiment, the detection instruction is received, the corresponding report to be detected is obtained, the report is opened, and the report page is displayed. When detection is needed, a detection instruction is sent, or a detection instruction is sent at set timing, and after the detection instruction is received, it can be understood that to detect the report, firstly, a report page, that is, the content of the report, needs to be obtained, specifically, in this embodiment, the report is opened through Selenium, which is a tool for testing the Web application, and as for the operation of a real user, the Selenium can be directly run in a browser, and the application is tested from the perspective of the terminal user, that is, only a script needs to be set, and the Selenium can simulate manual clicking, click the report, and open the report page. Similarly, Katalon Studio, Ranorex, TestProject, Rapid, Cypress, iMacros, UFT, IBM Rational Functional Tester, etc. automated testing tools may also be used from which developers may choose to study and analyze automated tools that meet automated requirements and budgets.
Step S20, screenshot is carried out on the report page, and a report screenshot is obtained;
in one embodiment, screenshot is performed on the opened report page, and the report screenshot is obtained. In order to detect the report, the screenshot of the report page is obtained, and the report page is processed and analyzed by using the picture processing technology. Specifically, the screenshot of the report is also automatically performed through a script of automated testing software such as Selenium, and a line of codes is added when the Selenium script is written, so that the report page is intercepted and stored after the report page is clicked and opened.
Step S30, binarization processing is carried out on the report screenshot, and the ratio of the maximum gray value of the report screenshot after binarization processing is counted to obtain a statistical result;
in an embodiment, it can be understood that the report screenshot is generally an RGB picture with colors, and in order to detect whether the report is updated successfully, the RGB picture is processed into a black-and-white picture, the processed graph or text content is black, and the blank part is white, so that the black part proportion, that is, the gray maximum proportion, in the processed black-and-white picture can be determined, if the gray maximum proportion is very low or zero, it indicates that the report page has no content or very little content, that is, the report loading fails or the updating fails. It should be noted that the report may be an excel-like table, or may be in a chart format such as a pie chart or a line chart. Specifically, the binarization processing includes graying processing. In the RGB model, if R ═ G ═ B, the color represents a gray color, where the value of R ═ G ═ B is called the gray value, so that each pixel of the gray image only needs one byte to store the gray value (also called the intensity value and the brightness value), and the gray range is 0 to 255. After the graying processing is performed on the picture, a binarization processing is also required, for example, the grayscale value is 100, and then it is 0 or 255 after the binarization, a commonly used binarization method: (1) the threshold value is 127 (corresponding to the number of 0-255, (0+255)/2 is 127), the gray value of 127 or less is changed to 0 (black), and the gray value of 255 (white) is changed to the gray value of 127 or more, which has the advantage of small calculation amount and high speed. (2) Calculating the average value avg of the gray values of all the pixels in the pixel matrix, (1 gray value +.. + n gray values of the pixels)/n is equal to the average value avg of the pixels, then comparing each pixel with the avg one by one, wherein the pixels smaller than or equal to avg are 0 (black), and the pixels larger than avg are 255 (white). (3) A histogram method (also called a double peak method) is used for searching a binary threshold value, the histogram method considers that an image consists of a foreground and a background, the foreground and the background form a peak on a gray level histogram, and the lowest valley between two peaks is the threshold value. And after the threshold value is obtained, one-to-one comparison is carried out. The specific binarization method is not limited herein.
And step S40, if the statistical result meets the preset condition, judging that the report loading and updating are successful.
In an embodiment, if the statistical result is determined to satisfy the predetermined condition, the report is considered to be successfully loaded and updated. It can be understood that the statistical result is judged by setting a preset condition, and when the preset condition is met, the report is successfully loaded and updated. It can be understood that the statistical result can judge that the current report has the content proportion, when the report is updated, the content will change, the corresponding statistical result will also change, when the statistical result reaches the preset condition, the preset condition can be a value or a range, for example, as long as it is set that the maximum gray value ratio after the update of the report A is detected to be more than 10% of the last time, the update is successful, the maximum gray value ratio is considered to satisfy the preset condition; after the report B is updated, the maximum value of the gray scale is 45% -55%, the condition that the maximum value of the gray scale meets the preset condition is considered, and the updating is successful; there is also a simpler judgment method, for example, this report is an empty report newly added yesterday, and needs to be filled, and when the ratio of the maximum value of the gray level detected by the setting condition is greater than 0%, it can be considered that the update is successful.
Further, in an embodiment, after the step of S40, the method further includes:
step S50, if the statistical result does not meet the preset condition, the report is judged to be loading update failure;
step S60, counting the times of loading update failure, and judging whether the times exceed a preset failure threshold value;
step S70, if the times exceed the preset failure threshold, outputting a warning and applying for manual intervention processing;
step S80, if the number of times does not exceed the preset failure threshold, executing the following steps after a preset interval time: and acquiring a report corresponding to the detection instruction, opening the report and displaying a report page.
According to the embodiment, after the statistics is judged not to meet the preset conditions, after the loading and updating failure of the current report is obtained, the times of the loading and updating failure are counted, whether the times of the loading and updating failure exceed the preset failure threshold value is judged, if the times of the loading and updating failure exceed the preset failure threshold value, a warning is output and manual intervention processing is applied, if the times of the loading and updating failure do not exceed the preset failure threshold value, the report is obtained again for detection after preset interval time, certain errors are allowed to exist in the detection, instead of the manual processing is immediately applied when the detection fails, the flexibility of the detection system is improved, and manual participation is reduced to a certain extent.
The respective steps will be described in detail below:
step S50, if the statistical result does not meet the preset condition, the report is judged to be loading update failure;
in an embodiment, if the statistical result of the report does not satisfy the preset condition, the report loading and updating result is an updating failure, and the number of times of the report loading and updating failure is counted. It can be understood that if it is determined that whether the report is updated successfully only by one failure, for example, if it is detected that the statistical result is 0%, a calculation error may occur, or if it is not loaded when the report page is acquired, the number of times of failure is counted first, which may be 0%, and if it is acquired again, a change may occur, which may satisfy a preset condition, and if it is determined that the final detection result is failed after multiple updating failures, a part of failure results caused by a detection system failure may be screened, so as to reduce the workload of manual determination.
Step S60, counting the times of loading update failure, and judging whether the times exceed a preset failure threshold value;
in an embodiment, the report form with failed loading and updating is counted for the number of failed loading and updating, and whether the number of failed loading and updating exceeds a preset threshold is judged. It can be understood that a preset threshold is set, the number of times of loading and updating failures is judged, when the number of times of failures is less than or equal to the preset threshold, the report with the detection result of loading and updating failures is detected again, and if the number of times of failures exceeds the preset threshold, that is, the failures still occur after multiple attempts, the problem that the failures occur in the detection can be basically eliminated, and the detection is not repeated at this time.
Step S70, if the times exceed the preset failure threshold, outputting a warning and applying for manual intervention processing;
in one embodiment, when the number of detection failures exceeds a preset failure threshold, a warning is output and a manual intervention is applied. If the number of times of the prediction failure exceeds the preset threshold value, namely the number of times of the prediction failure reaches a certain number of times, the report page acquisition may fail, or indeed the report is not updated, at this time, the report needs to be warned, manual check is applied, and the abnormal condition of the report is processed.
Step S80, if the number of times does not exceed the preset failure threshold, executing the following steps after a preset interval time: and acquiring a report corresponding to the detection instruction, opening the report and displaying a report page.
In an embodiment, if the failure times do not exceed the preset failure threshold, the report is obtained again after the preset interval time, and the report page is detected, that is, when the failure times are less and the re-detection times do not exceed the preset threshold, the detection is performed again until the detection is successful or the failure times reach the preset failure threshold. The determination of the interval time is usually obtained by calculating the push time, for example: the refreshing frequency of the important report is frequent, the important report is refreshed once in half an hour, and then the important report is obtained once again in the corresponding half an hour, and the refreshing frequency and the interval time are determined according to the importance of the report.
The embodiment obtains the report screenshot by receiving the detection instruction, opening the report and screenshot the report page, the report screenshot is subjected to binarization processing to obtain a black and white report, the ratio of the gray maximum value of the report screenshot to the gray maximum value of the report screenshot is counted to obtain a statistical result, whether the report is updated successfully or not is judged based on the statistical result, if the condition result meets the preset condition, the report is output to be loaded and updated successfully, automatic detection of loading and updating of the report is realized, the detection efficiency is improved, and when the report statistical result does not meet the preset condition, namely the updating is failed, after a certain interval, the report is refreshed and then the detection is carried out again, if the detection is failed for a plurality of times, then, a warning is sent to remind the human intervention processing, so that the step of reacquiring the update failure report is added, the report detection result can be confirmed for multiple times, and the detection accuracy is improved.
Referring to fig. 3, fig. 3 is a schematic diagram illustrating a detailed flow of step S40 in the first embodiment of the report loading and updating detection method of the present invention, and further, a second embodiment of the report loading and updating detection method of the present invention is provided based on the first embodiment of the report loading and updating detection method of the present invention.
The difference between the second embodiment of the report loading and updating detection method and the first embodiment of the resource report loading and updating detection method is that, before the step of determining that the report loading and updating is successful if the statistical result meets the preset condition, the method further comprises:
step S41, acquiring a historical detection result corresponding to the report;
in an embodiment, before determining whether the statistical result meets the preset condition, a historical detection result corresponding to the report is obtained. The historical detection result corresponding to the report is the historical detection data of the report to be detected, if the report A needs to be detected, the previous detection result of the report A is obtained, and if a plurality of reports need to be detected, the historical detection results of the plurality of reports are obtained once and stored in the database. The detection result includes whether the updating is successful and the ratio of the corresponding gray maximum value.
And step S42, judging whether the statistical result meets a preset condition or not based on the historical detection result.
In one embodiment, whether the preset condition is met is judged according to the historical detection result. For example, the gray maximum ratio of the previous report a is 50%, the gray maximum ratio of the current report a is 70%, and if the update of the report a indicates that new data is added, for example, 10% of the new data is added, the gray maximum ratio of the current report a is 70%, which indicates that the new data is added, the update is successful, or the update of the report B is to update the original data, only the value of the original data is changed, and the possible data amount is not increased or decreased, the gray maximum of the previous report B is 50%, the gray maximum of the previous report B is 49%, which is considered to be successful, so that the determination can be made according to preset conditions set according to the detection results of different report histories.
Further, in an embodiment, the step of determining whether the statistical result satisfies a preset condition based on the historical detection result includes:
step S421, counting the average value of the historical gray maximum ratio in the historical detection result;
step S422, calculating the deviation degree of the statistical result according to the statistical result and the average value, setting that if the deviation degree is not greater than a preset threshold value, a preset condition is met, and judging whether the statistical result meets the preset condition or not.
In the embodiment, the statistical result is compared with the average value by counting the average value of the gray maximum of the historical detection result, and the deviation degree of the statistical result from the average value is calculated, so that the preset condition is not met when the deviation degree is greater than the preset threshold value, and the preset condition is met when the deviation degree is less than the preset threshold value. The historical detection result is the historical detection result corresponding to the report, and comprises whether the report is updated successfully or not and the gray maximum ratio of the report.
The respective steps will be described in detail below:
step S421, counting the average value of the historical gray maximum ratio in the historical detection result;
in one embodiment, the historical detection results are counted, and the average value of the historical gray maximum values to the historical gray maximum values corresponding to the historical detection results is calculated. It can be understood that if the items to be filled or updated in the report are the same, each update is only to modify the content, and the content is not increased or decreased too much, then the content of the report is almost the same, i.e. the maximum gray values occupied by the word parts after processing are close, according to the detection result of the report, an average value is calculated, if the maximum gray value of the current report is detected to have less fluctuation than the average value, then the update is considered to be successful, and if the fluctuation is larger, for example, much smaller than the average value, then the report may be not filled or is not filled.
Step S422, calculating the deviation degree of the statistical result according to the statistical result and the average value, setting that if the deviation degree is not greater than a preset threshold value, a preset condition is met, and judging whether the statistical result meets the preset condition or not.
In one embodiment, the average value and the statistical result are compared, a deviation degree of the difference between the statistical result and the average value is calculated, and the deviation degree is compared with a preset threshold value to obtain a report loading and updating result. The fluctuation degree of the statistical result is represented by deviation degree, and the deviation degree refers to the proportion of the absolute value of the difference between the actual data and the target data to the target data. And calculating the deviation degree, specifically, calculating the absolute value of the difference between the statistical result and the average value, dividing the absolute value by the average value, and converting the absolute value into percentage representation to obtain the deviation degree. And setting a preset threshold, judging that the report loading and updating are successful if the deviation degree is smaller than the preset threshold, and judging that the loading and updating are failed if the deviation degree is larger than the preset threshold. The preset threshold may be set according to practical situations, for example, to 5%, 10%.
Further, in an embodiment, the step of determining whether the statistical result satisfies a preset condition based on the historical detection result includes:
step 423, taking the historical detection result as sample data, and training the sample data to obtain a machine learning model;
and step S424, inputting the statistical result into a machine learning model, and predicting to obtain a report loading and updating result.
In the embodiment, the statistical result is input into the machine calculation model constructed by the historical detection result, and the report loading and updating result is obtained. And updating the machine learning model by continuously acquiring the detection result, and dynamically adjusting the threshold value judged by the statistical result to obtain a more accurate threshold value.
The respective steps will be described in detail below:
step 423, taking the historical detection result as sample data, and training the sample data to obtain a machine learning model;
in one embodiment, a history detection result is obtained as sample data, and a machine learning model is obtained through sample data training. Specifically, the updated report and the non-updated report and the corresponding historical gray maximum ratio are obtained from the database, and a machine learning classification method is combined, for example: the method comprises the steps of inputting sample data into a pre-training model for training by a KNN algorithm, Bayes, decision trees and the like, judging whether a report is updated successfully or not by the aid of gray maximum ratio in model learning, selecting a proper loss function, calculating the accuracy of the model, and further constructing to obtain a machine learning model. The database is used for storing the screenshot picture, the gray maximum ratio and the judgment result of the report.
Step S424, inputting the statistical result into a machine learning model, setting that a preset condition is satisfied if the statistical result satisfies a determination condition of the machine learning model, and determining whether the statistical result satisfies the preset condition.
In one embodiment, the statistical result is input into the constructed machine learning model, and the report loading and updating result is predicted. It can be understood that after the machine learning model is built, when the report is predicted to be updated, the statistical result is input into the machine learning model, and the report loading and updating result can be predicted according to the machine learning model judgment condition, for example, if the machine learning model judges that the classification result corresponding to the statistical result is successfully updated, the statistical result meets the preset condition, and the report loading and updating are successfully performed. It should be noted that, after a prediction result is obtained, the data may be continuously used as sample data to further train the machine learning model, and continuously optimize the model.
After the statistical result is obtained, the statistical result is judged by two methods to confirm whether the report is updated successfully, one is to calculate the deviation degree of the statistical result according to the average value of the historical gray maximum ratio, if the deviation degree is smaller than the preset threshold, the preset condition is met, the updating is regarded as successful, and if the deviation degree is larger than the preset threshold, the updating is failed; and the other method is that a machine learning model is built according to the historical detection result, the statistical result is predicted through the machine learning model, and if the statistical result meets the judgment condition of the machine learning model, the preset condition is met, namely, the updating is judged to be successful. In other words, in this embodiment, the preset condition for determining the statistical result is not constant, and is dynamically adjusted along with the historical detection result, so that the method has higher adaptivity, and the accuracy of loading and updating the detection result in the report is improved.
Referring to fig. 4, fig. 4 is a schematic flow chart of a third embodiment of the report loading and updating detection method of the present invention, and further, based on the first and second embodiments of the report loading and updating detection method of the present invention, the third embodiment of the report loading and updating detection method of the present invention is provided.
The difference between the third embodiment of the report loading and updating detection method and the first and second embodiments of the report loading and updating detection method is that the step of capturing the report page to obtain the report screenshot comprises the following steps:
step S21, screenshot is conducted on the report page, and an initial screenshot is obtained;
step S22, preprocessing the initial screenshot to obtain a preprocessed screenshot;
and step S23, cutting the preprocessing screenshot to obtain a first report core area, and taking the first report core area as a report screenshot.
In the embodiment, the report page is subjected to screenshot, the preprocessed screenshot is obtained through preprocessing, the picture quality of the screenshot is improved, the core area in the preprocessed screenshot is obtained through cutting, and the core area is used as the report screenshot to be further detected, so that the interference of irrelevant contents on the detection result can be reduced, and the accuracy of report detection is improved.
The respective steps will be described in detail below:
step S21, screenshot is conducted on the report page, and an initial screenshot is obtained;
in an embodiment, the whole report page is obtained first, that is, the screenshot of the report page is obtained to obtain an initial screenshot, and the report page is stored in the database. In order to retrieve the report page later to check the data, adjust the screenshot area, and leave it as an archive, all report pages are intercepted first, for example, when the report pages are missing or not completely updated, the data is missing because the intercepted parts are not aligned.
Step S22, preprocessing the initial screenshot to obtain a preprocessed screenshot;
in an embodiment, the initial screenshot of the report page is preprocessed to obtain a preprocessed screenshot. Wherein, the preprocessing is to perform image processing on the initial screenshot. For example, image denoising is performed, some report screenshots which are possibly acquired have the problem of more noise points, and denoising can be performed through an airspace pixel characteristic denoising algorithm and a transform domain denoising algorithm; for example, watermarking, many reporting systems have watermarks that need to be removed first or that can interfere with the results. Therefore, in order to improve the accuracy of the subsequent screenshot judgment, the initial screenshot needs to be preprocessed to obtain a preprocessed screenshot.
And step S23, cutting the preprocessing screenshot to obtain a first report core area, and taking the first report core area as a report screenshot.
In one embodiment, the pre-processing screenshot is cut to obtain a first report core area, and the first report core area is used as the report screenshot. It can be understood that the whole screen is intercepted during screenshot, that is, the whole report system is intercepted, and then the screenshot can include other interference information besides the report content, such as information of a browser, a report name, a report date and the like, which can interfere with the subsequent judgment of whether the updating is successful according to the gray maximum value ratio, and it can be understood that characters such as the report name and the date are usually black after binarization processing, and then the gray maximum value ratio can be increased. Therefore, a typical area is selected, the whole report form may not be completely covered, the data of the report form or some graphs of the report form are arranged inside the typical area, and only the gray maximum ratio of the area is counted. The report core area can be specifically designed according to the format of a report in a report system, generally is a central area of a screenshot of a report page, is expanded outwards from the center according to resolution, and is used for capturing a picture with a preset height and width, and the width and the height are self-defined. For example, the remaining parts except the frame title after the report system is opened are all core areas, and the cutting size with a proper size is set according to the resolution to obtain the first report core area. It should be noted that, according to actual requirements, the operations similar to the above preprocessing may be performed on the screenshot of the core area after the core area is acquired, for example: and (5) noise reduction.
Further, in an embodiment, the step of cutting the pre-processing screenshot to obtain a report core area, and taking the report core area as a report screenshot includes:
step S231, acquiring a preset cutting number corresponding to the report;
step S232, cutting the preprocessed screenshot based on the preset cutting quantity to obtain a plurality of report core areas, and taking the plurality of report core areas as the report screenshot.
In an embodiment, a preset cutting number corresponding to the report is obtained. It can be understood that the contents of different types of report loading and updating are not in the same position, for example, the report a updates two graphs, and the report B updates two rows of data, so that the core areas to be detected are different for the report a and the report B, and if only one core area is selected, the updated part may be missed under the condition that the core area is not large enough, so that a plurality of core areas may be set according to the report to detect different updated parts in order to solve the problem. In addition, if a large core area is divided into a plurality of blocks, the plurality of blocks are detected and judged respectively, so that the prediction accuracy can be improved. For example, 9 core regions are selected, 9 classifiers (machine learning models) are trained correspondingly, different classification judgment conditions are corresponded, and then judgment results of the 9 classifiers are combined, for example, if model output 1 is update success, 0 is update failure, output result is 10101011, more successful results are output, and the result is judged to be update success. The preset clipping number and the corresponding clipping core area can be set according to the report.
Further, in an embodiment, the step S10 in the first embodiment includes:
step S11, receiving the detection instruction, opening a report system corresponding to the detection instruction, and acquiring a report list;
and step S12, selecting the report in the report list according to a preset instruction, opening the report and displaying a report page.
In an embodiment, after receiving the detection instruction, the report system is opened, the report list is obtained, the report in the report list is selected according to a preset instruction, and the report is opened to obtain the report page. Specifically, after a detection instruction is received, a browser report system is opened through a Selenium simulated click, a report list in the report system is read, which reports need to be acquired can be determined according to a script instruction written in advance in the Selenium, a report corresponding to a preset instruction is acquired from the report list, and a report page is opened to further detect whether the corresponding report is updated or not. It can be understood that there is an OA system or a reporting system in a company, and reports uploaded and updated every day are stored in the OA system, so that the reporting system is firstly opened, for example, the first ten reports are updated every monday in the report, a timing task is set, the corresponding first ten reports are obtained from the report list every monday, and the rest reports are not obtained, so that time consumed by repeatedly checking or loading unnecessary reports can be reduced, and the reports in the OA system are detected in a targeted manner through a preset instruction, thereby improving detection intelligence.
Referring to fig. 4, fig. 4 is a technical flowchart of an embodiment of the report loading update detection method of the present invention, which explains the report loading update detection method of the embodiment of the present invention: after receiving a detection instruction (the step is omitted in the figure), opening a report through a Selenium simulation manual click, and automatically loading a report page by a browser; screenshot is carried out on a report page, and cutting is carried out to obtain a report core area; carrying out binarization processing on the report core area, counting the occupation ratio of the maximum gray value, and storing the statistical result; judging whether the statistical result meets a preset condition (the preset condition can be compared with an adaptive threshold, for example, the fluctuation is smaller than the preset threshold, and the updating is considered to be successful, or the fluctuation is larger, and the updating is considered to be failed); and when the loading updating is failed, refreshing the report and then detecting again after a certain interval time, and alarming manual intervention processing after a plurality of detection failures.
In the embodiment, the report page screenshots are obtained, the core areas in one or more report page screenshots are selected, the part interfering with the statistical result is eliminated, and the part capable of reflecting whether the report is updated is selected, so that the accuracy of prediction according to the report screenshots is improved. When the report is obtained, the report list is obtained firstly, and then the report to be detected is selected according to the preset instruction, so that the time consumed by traversing and detecting all reports is avoided, the report can be selected according to the requirement, the report is detected in a targeted manner, and the report loading and updating detection efficiency is improved.
The invention also provides a report loading updating detection device. As shown in fig. 5, fig. 5 is a functional module schematic diagram of an embodiment of a report loading update detection method according to the present invention.
The report loading, updating and detecting device comprises:
the acquisition module is used for receiving a detection instruction, acquiring a report corresponding to the detection instruction, opening the report and displaying a report page;
the preprocessing module is used for carrying out screenshot on the report page to obtain a report screenshot;
the calculation module is used for carrying out binarization processing on the report screenshot and counting the ratio of the maximum gray value of the report screenshot after binarization processing to obtain a statistical result;
and the detection module is used for judging that the report loading and updating are successful if the statistical result meets a preset condition.
Optionally, the detection module is further configured to:
acquiring a historical detection result corresponding to the report;
and confirming a preset condition based on the historical detection result, and judging whether the statistical result meets the preset condition.
Optionally, the detection module is further configured to:
counting the average value of the historical gray maximum ratio in the historical detection result;
and calculating the deviation degree of the statistical result according to the statistical result and the average value, setting that a preset condition is met if the deviation degree is not greater than a preset threshold value, and judging whether the statistical result meets the preset condition or not.
Optionally, the detection module is further configured to:
taking the historical detection result as sample data, and training the sample data to obtain a machine learning model;
inputting the statistical result into a machine learning model, setting that a preset condition is met if the statistical result meets the judgment condition of the machine learning model, and judging whether the statistical result meets the preset condition.
Optionally, the detection module is further configured to:
if the statistical result does not meet the preset condition, judging that the report is in loading updating failure;
counting the times of loading updating failure, and judging whether the times exceed a preset failure threshold value;
if the times exceed a preset failure threshold value, outputting a warning and applying for manual intervention;
if the number of times does not exceed a preset failure threshold, executing the following steps after a preset interval time: and acquiring a report corresponding to the detection instruction, opening the report and displaying a report page.
Optionally, the preprocessing module is further configured to:
screenshot is carried out on the report page to obtain an initial screenshot;
preprocessing the initial screenshot to obtain a preprocessed screenshot;
and cutting the preprocessing screenshot to obtain a report core area, and taking the report core area as a report screenshot.
Optionally, the preprocessing module is further configured to:
acquiring a preset cutting number corresponding to the report;
and cutting the preprocessed screenshot based on the preset cutting quantity to obtain a plurality of report core areas, and taking the plurality of report core areas as the report screenshot.
Optionally, the obtaining module is further configured to:
receiving the detection instruction, opening a report system corresponding to the detection instruction, and acquiring a report list;
and selecting a report form in the report form list according to a preset instruction, opening the report form, and displaying a report form page.
The invention also provides a storage medium.
The storage medium of the invention stores the report loading updating detection program, and the report loading updating detection program realizes the steps of the report loading updating detection method when being executed by the processor.
The method implemented when the report loading and updating detection program running on the processor is executed may refer to each embodiment of the report loading and updating detection method of the present invention, and is not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (11)
1. A report loading, updating and detecting method is characterized by comprising the following steps:
receiving a detection instruction, acquiring a report corresponding to the detection instruction, opening the report, and displaying a report page;
screenshot is carried out on the report page, and a report screenshot is obtained;
carrying out binarization processing on the report screenshot, and counting the ratio of the maximum gray value of the report screenshot after binarization processing to obtain a statistical result;
and if the statistical result meets a preset condition, judging that the report loading and updating are successful.
2. The report loading update detecting method according to claim 1, wherein before the step of determining that the report loading update is successful if the statistical result meets a preset condition, the method further comprises:
acquiring a historical detection result corresponding to the report;
and confirming a preset condition based on the historical detection result, and judging whether the statistical result meets the preset condition.
3. The report loading update detection method according to claim 2, wherein the step of confirming a preset condition based on the historical detection result and determining whether the statistical result satisfies the preset condition comprises:
counting the average value of the historical gray maximum ratio in the historical detection result;
and calculating the deviation degree of the statistical result according to the statistical result and the average value, setting that a preset condition is met if the deviation degree is not greater than a preset threshold value, and judging whether the statistical result meets the preset condition or not.
4. The report loading update detection method according to claim 2, wherein the step of confirming a preset condition based on the historical detection result and determining whether the statistical result satisfies the preset condition comprises:
taking the historical detection result as sample data, and training the sample data to obtain a machine learning model;
inputting the statistical result into a machine learning model, setting that a preset condition is met if the statistical result meets the judgment condition of the machine learning model, and judging whether the statistical result meets the preset condition.
5. The report loading update detecting method according to claim 1, wherein after the step of determining that the report loading update is successful if the statistical result meets a preset condition, the method further comprises:
if the statistical result does not meet the preset condition, judging that the report is in loading updating failure;
counting the times of loading updating failure, and judging whether the times exceed a preset failure threshold value;
if the times exceed a preset failure threshold value, outputting a warning and applying for manual intervention;
if the number of times does not exceed a preset failure threshold, executing the following steps after a preset interval time: and acquiring a report corresponding to the detection instruction, opening the report and displaying a report page.
6. The report loading update detection method of claim 1, wherein the step of capturing the report page to obtain the report capture comprises:
screenshot is carried out on the report page to obtain an initial screenshot;
preprocessing the initial screenshot to obtain a preprocessed screenshot;
and cutting the preprocessing screenshot to obtain a report core area, and taking the report core area as a report screenshot.
7. The report loading update detection method of claim 6, wherein the step of clipping the pre-processing screenshot to obtain a report core area and using the report core area as a report screenshot comprises:
acquiring a preset cutting number corresponding to the report;
and cutting the preprocessed screenshot based on the preset cutting quantity to obtain a plurality of report core areas, and taking the plurality of report core areas as the report screenshot.
8. The report loading update detection method according to claim 1, wherein the step of receiving a detection instruction, obtaining the report corresponding to the detection instruction, and opening the report, and displaying a report page comprises:
receiving the detection instruction, opening a report system corresponding to the detection instruction, and acquiring a report list;
and selecting a report form in the report form list according to a preset instruction, opening the report form, and displaying a report form page.
9. A report loading and updating detection device is characterized by comprising:
the acquisition module is used for receiving a detection instruction, acquiring a report corresponding to the detection instruction, opening the report and displaying a report page;
the preprocessing module is used for carrying out screenshot on the report page to obtain a report screenshot;
the calculation module is used for carrying out binarization processing on the report screenshot and counting the ratio of the maximum gray value of the report screenshot after binarization processing to obtain a statistical result;
and the detection module is used for judging that the report loading and updating are successful if the statistical result meets a preset condition.
10. A report loading and updating detection device is characterized by comprising: a memory, a processor and a report load update detection program stored on the memory and operable on the processor, the report load update detection program being configured to implement the steps of the report load update detection method according to any of claims 1 to 8.
11. A storage medium, wherein the storage medium stores thereon a report loading and updating detection program, and the report loading and updating detection program, when executed by a processor, implements the steps of the report loading and updating detection method according to any one of claims 1 to 8.
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