Big data platform operation task monitoring and alarm method and system based on Prometheus

Abstract

The present invention relates to the field of data statistics technology, and in particular to a big data platform operation task monitoring and alarming method and system based on Prometheus, the method comprising: obtaining the actual processing time by analyzing several working images of a working area; obtaining the first correction time by analyzing several auxiliary images of an auxiliary area associated with the working area; calculating the analysis time of the analysis process of several working images and several auxiliary images to obtain the second correction time; calculating the final time according to the actual processing time, the first correction time, the second correction time and the adjustment parameter, comparing the final time with the preset time, and alarming the big data platform operation task according to the comparison result. The present invention improves the accuracy of the operation task monitoring and alarming.

Description

Technical Field

[0001] The present invention relates to the technical field of data statistics, and in particular to a Prometheus-based big data platform operation task monitoring and alarming method and system. Background Art

[0002] The construction of smart factories is a requirement for traditional enterprises to implement innovation-driven and value-creating strategies, and is also the key to the innovative development of industry informatization. Working time statistics is one of the important links in enterprise management, and is crucial to achieving effective production planning and reasonable personnel deployment.

[0003] The patent document with Chinese patent application publication number CN116912026A discloses a statistical system for employee operation data, including: a plurality of lower computers and a host computer; wherein each lower computer corresponds to a workstation; each of the lower computers is used to measure the operation data of the corresponding workstation, and after each operation is completed, it generates corresponding operation measurement data, and transmits the operation measurement data to the host computer; the host computer is used to calculate the real-time plan completion status of all operators corresponding to each lower computer, the real-time headquarters group operation time of each operator corresponding to each lower computer, the real-time comprehensive energy efficiency of each operator corresponding to each lower computer, each operation working hour of the workstation corresponding to each lower computer, and the real-time comprehensive energy efficiency of the workstation corresponding to each lower computer.

[0004] In the prior art, the working hours of employees are collected in the work area, and the working hours of employees who are not at their workstations but are doing work-related things are not recorded, resulting in the problem of low accuracy of the platform operation task monitoring and alarm method. Summary of the invention

[0005] To this end, the present invention provides a big data platform job task monitoring and alarm method and system based on Prometheus, which can solve the problem of low accuracy of task monitoring alarm by calculating the time in the working area, the time in the auxiliary area and the time in the discussion area, and adjusting them by adjusting parameters, and then determining the final time for early warning.

[0006] To achieve the above object, the present invention provides a big data platform job task monitoring and alarm method based on Prometheus, the method comprising:

[0007] By analyzing several working images of the working area, the actual processing time is obtained;

[0008] Acquire a first correction duration by analyzing a plurality of auxiliary images of an auxiliary area associated with the working area;

[0009] Calculating the analysis duration of the analysis process of the plurality of working images and the plurality of auxiliary images to obtain a second corrected duration;

[0010] Calculate a final duration according to the actual processing duration, the first corrected duration, the second corrected duration and the adjustment parameter, compare the final duration with the preset duration, and issue an alarm for the big data platform operation task according to the comparison result;

[0011] Among them, the adjustment parameters include calculation compensation parameters and overall adjustment parameters. The calculation compensation parameters are used to adjust the first correction duration, calculate the sum of the actual processing duration, the first correction duration adjusted by the calculation compensation parameters, and the second correction duration. The overall adjustment parameters are used to adjust the sum and calculate to obtain the final duration.

[0012] Furthermore, obtaining the actual processing time includes:

[0013] Identify a valid working image from among the working images, and obtain an actual valid duration;

[0014] Count the workload output within the actual effective time, and determine the theoretical processing time based on the workload output;

[0015] The actual processing time is determined according to the actual effective time and the theoretical processing time.

[0016] Furthermore, it also includes determining the overall adjustment parameter according to the relationship between the actual effective time and the theoretical processing time.

[0017] Furthermore, obtaining the first revised duration includes:

[0018] Acquire a first actual correction duration through a plurality of the auxiliary images;

[0019] The first correction duration is determined according to the relationship between the first actual correction duration and the actual processing duration.

[0020] Furthermore, it also includes judging whether the first revised time is reasonable according to the relationship between the first revised time and the actual processing time, and obtaining the calculation compensation parameter according to the first judgment result.

[0021] Furthermore, obtaining the second revised duration includes:

[0022] respectively collecting the image resolutions of the plurality of working images and the plurality of auxiliary images to obtain a plurality of first resolutions and a plurality of second resolutions;

[0023] Real-time processing efficiency of the processor during real-time acquisition and analysis;

[0024] The second revised duration is calculated based on a number of the first resolutions, a number of the second resolutions, and the real-time processing efficiency.

[0025] Further, the step of calculating the second corrected duration based on a plurality of the first resolutions, a plurality of the second resolutions and the real-time processing efficiency comprises:

[0026] Building a model of image resolution and processing time based on historical image processing data as a first model;

[0027] Acquire a number of the first processing durations according to a number of the first resolutions;

[0028] Acquire a plurality of second processing durations according to a plurality of second resolutions;

[0029] determining a first duration coefficient based on a number of the first resolutions and a number of the first processing durations;

[0030] determining a second duration coefficient based on a plurality of the second resolutions and a plurality of the second processing durations;

[0031] The second corrected duration is calculated based on the first duration coefficient, the second duration coefficient and the real-time processing efficiency.

[0032] Furthermore, it is determined whether the corresponding working hours in different areas meet the requirements according to the relationship between the actual processing time and the first corrected time.

[0033] Furthermore, it also includes, when the corresponding working hours in different areas meet the requirements, re-adjusting the overall compensation parameters according to the relationship between the actual processing time, the first corrected time, the second corrected time and the total working hours, wherein the total working hours are the sum of the actual processing time, the first corrected time and the second corrected time.

[0034] Furthermore, an embodiment of the present invention also provides a system for a big data platform job task monitoring and alarm method based on Prometheus, the system comprising:

[0035] A processing time acquisition module is used to acquire the actual processing time by analyzing a number of working images of the working area;

[0036] A first acquisition module, configured to acquire a first correction duration by analyzing a plurality of auxiliary images of an auxiliary area associated with the working area;

[0037] A second acquisition module is used to calculate the analysis duration of the analysis process of the plurality of working images and the plurality of auxiliary images to obtain a second correction duration;

[0038] a final acquisition module, connected to the processing duration acquisition module, the first acquisition module and the second acquisition module, for calculating a final duration according to the actual processing duration, the first correction duration, the second correction duration and an adjustment parameter, comparing the final duration with a preset duration, and issuing an alarm for the big data platform operation task according to the comparison result;

[0039] Among them, the adjustment parameters include calculation compensation parameters and overall adjustment parameters. The calculation compensation parameters are used to adjust the first correction duration, calculate the sum of the actual processing duration, the first correction duration adjusted by the calculation compensation parameters, and the second correction duration. The overall adjustment parameters are used to adjust the sum and calculate to obtain the final duration.

[0040] Compared with the prior art, the beneficial effects of the present invention lie in that, by analyzing the working images and auxiliary images, combined with the actual processing time and the correction time, a data basis is provided for accurately calculating the final time, thereby improving the accuracy of the time calculation results; through image analysis of the working area and the auxiliary area, a comprehensive understanding of the execution of the job task is obtained, problems are discovered and solved in a timely manner, and job task monitoring is optimized; based on the comparison result between the final time and the preset time, real-time alarms are issued for the job tasks on the big data platform to ensure that the job tasks are completed on time; by introducing calculation compensation parameters and overall adjustment parameters, the working hours are fine-tuned according to actual conditions to improve the comprehensiveness of the method; through precise monitoring and alarming of job tasks, problems in the job process are discovered and solved in a timely manner, thereby improving the accuracy of the early warning results.

[0041] In particular, the actual processing time is determined by the theoretical processing time and the actual effective time, so that the determination result of the actual processing time is comprehensive and accurate. Through real-time image detection, actual edge contour recognition, arm curvature calculation and screening of effective working images, the efficiency of the image data processing process is improved, and the comprehensiveness of the work image analysis is improved, so as to accurately judge the effective time of the work and accurately obtain the actual effective time. The theoretical processing time is determined by the workload output, and the relationship between the workload and the actual processing time is comprehensively considered, so that the theoretical processing time can be obtained accurately, and then the actual processing time can be obtained accurately.

[0042] In particular, by judging whether the work output is consistent with the actual processing time, the effective work efficiency is quantified, so as to obtain the corresponding overall adjustment parameters according to different situations, so that the final time calculation result of the overall adjustment parameters is accurate. When the work output is inconsistent with the actual processing time, the overall adjustment parameters are adaptively adjusted according to the actual workload output and the preset output, so as to avoid the influence of subjective factors on the calculation of the overall adjustment parameters and improve the accuracy of the overall adjustment parameter results.

[0043] In particular, the first corrected time is determined by comparing the first actual corrected time with 0.3 times the actual processing time, so as to avoid the first corrected time exceeding one third of the actual processing time, thereby ensuring the rationality of the first corrected time, and quickly and accurately acquiring the first actual corrected time through image recognition technology, so that the result of the first actual corrected time is accurate. By acquiring the first actual corrected time, a more accurate and comprehensive evaluation of the auxiliary work performance at work is made, avoiding the influence of subjective factors on the calculation of the first corrected time, and improving the accuracy of the calculation result of the first corrected time.

[0044] In particular, by judging the proportional relationship between the first correction time and the actual processing time, it is more accurately determined whether the first correction time is reasonable, thereby more accurately calculating the compensation parameters, and adjusting the preset compensation parameters by calculating the compensation adjustment parameters, so as to quickly and accurately obtain the calculated compensation parameters, thereby improving the efficiency of the data processing process, and providing a reference value by setting the preset compensation parameters, so that the results of calculating the compensation parameters are more accurate and fair. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A flowchart of a Prometheus-based big data platform job task monitoring and alarming method provided in an embodiment of the present invention;

[0046] Figure 2 A second flow chart of a Prometheus-based big data platform job task monitoring and alarming method provided in an embodiment of the present invention;

[0047] Figure 3 A third flow chart of the Prometheus-based big data platform job task monitoring and alarm method provided in an embodiment of the present invention;

[0048] Figure 4 A structural block diagram of a system for a Prometheus-based big data platform job task monitoring and alarm method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0050] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0051] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be understood as a limitation on the present invention.

[0052] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0053] See also Figure 1 As shown, an embodiment of the present invention provides a big data platform job task monitoring and alarm method based on Prometheus, the method comprising:

[0054] Step S100, obtaining actual processing time by analyzing several working images of the working area;

[0055] Step S200, obtaining a first correction duration by analyzing a plurality of auxiliary images of an auxiliary area associated with the working area;

[0056] Step S300, calculating the analysis duration of the analysis process of the plurality of working images and the plurality of auxiliary images to obtain a second corrected duration;

[0057] Step S400, calculating a final duration according to the actual processing duration, the first corrected duration, the second corrected duration and the adjustment parameter, comparing the final duration with a preset duration, and issuing an alarm for the big data platform operation task according to the comparison result;

[0058] Among them, the adjustment parameters include calculation compensation parameters and overall adjustment parameters. The calculation compensation parameters are used to adjust the first correction duration, calculate the sum of the actual processing duration, the first correction duration adjusted by the calculation compensation parameters, and the second correction duration. The overall adjustment parameters are used to adjust the sum and calculate to obtain the final duration.

[0059] Specifically, Prometheus in the embodiment of the present invention is a time series database specially used to process data with time tags. The embodiment of the present invention also involves the processing of time data in the process of calculating the final duration and comparing it with the preset duration, which is consistent with the time series database characteristics of Prometheus.

[0060] Specifically, the embodiment of the present invention numbers the observed personnel and the corresponding workstations, and records them as the first observed personnel A1, the second observed personnel A2...the nth observed personnel An, the first workstation B1, the second workstation B2...the Nth workstation Bn, and the observed personnel and workstations with the same sequence number are matched with each other;

[0061] For any observer Ai, i = 1, 2, 3, ..., n, the final duration Gi is counted and set:

[0062] Gi = [Hi + Pi × p1 + Di] × Hz, where Hi is the actual processing duration, Pi is the first correction duration, Di is the second correction duration, p1 is the calculation compensation parameter of the first correction duration for the final duration Gi, and Hz is the overall adjustment parameter of the final duration;

[0063] For the actual processing time Hi, it is the actual time that Ai is near the workstation Bi;

[0064] For the first correction time Pi, it is the time Ai spends at the position associated with the workstation Bi;

[0065] The second correction duration Di is the duration of the image processing process.

[0066] Specifically, the embodiments of the present invention analyze the working images and auxiliary images, and combine the actual processing time and the correction time to provide a data basis for accurately calculating the final time, thereby improving the accuracy of the time calculation results. Through image analysis of the working area and the auxiliary area, the execution of the job task is fully understood, problems are discovered and solved in a timely manner, and job task monitoring is optimized. Based on the comparison result between the final time and the preset time, the job tasks of the big data platform are warned in real time to ensure that the job tasks are completed on time. By introducing calculation compensation parameters and overall adjustment parameters, the working hours are fine-tuned according to actual conditions to improve the comprehensiveness of the method. Through precise monitoring and warning of job tasks, problems in the job process are discovered and solved in a timely manner, thereby improving the accuracy of the warning results.

[0067] See also Figure 2 As shown, obtaining the actual processing time includes:

[0068] Step S110, identifying a valid working image among the plurality of working images, and obtaining an actual valid duration;

[0069] Step S120, counting the workload output within the actual effective duration, and determining the theoretical processing duration based on the workload output;

[0070] Step S130, determining the actual processing duration according to the actual effective duration and the theoretical processing duration.

[0071] Specifically, for the actual processing time Hi, set Hi=min(Hyi, Hz i), where Hyi is the actual effective time and Hz i is the theoretical processing time.

[0072] Specifically, obtaining the actual effective duration Hyi includes:

[0073] Detecting the real-time images by using an edge detection algorithm to obtain real-time edge contours;

[0074] Marking a real-time personnel edge contour and a real-time workbench edge contour among the plurality of real-time edge contours by using a preset personnel edge contour and a preset workbench edge contour;

[0075] Calculating the real-time distance between the real-time edge contour of the person and the real-time edge contour of the workbench, and taking the real-time images corresponding to the real-time distances smaller than the preset distance as the working images;

[0076] Identify a number of actual edge contours in the number of working images by using an edge detection algorithm, and extract actual edge contours of people from the number of actual edge contours by using human body edge feature recognition;

[0077] Determine the arm edge contour among the plurality of actual edge contours, and calculate the arm curvature;

[0078] Using an image in which the arm curvature is within a preset curvature range among the plurality of working images as the effective working image;

[0079] The total duration corresponding to a number of the valid working images is counted as the actual valid duration Hyi.

[0080] Specifically, the human body edge features are human body contour shape features. There are certain proportional relationships between various parts of the human body, and these proportional relationships are also reflected in the human body contour shape. For example, the head-to-body ratio and the upper-to-lower body ratio are all important features of the human body contour shape.

[0081] Determining the arm edge profile among the plurality of actual edge profiles can be determined by comparing the actual edge profile with a preset arm edge profile for matching;

[0082] The arm curvature can be calculated by analyzing the shape of the edge contour of the arm using image processing technology, extracting the curve information of the arm, and obtaining the arm curvature by analyzing the curve information.

[0083] Specifically, obtaining the theoretical processing time Hz i includes:

[0084] The workload output Ci of the observer Ai is obtained, and the theoretical processing time Hz i of Ai is determined by the workload output Ci, and it is set that Hz i=Ci÷q, where q is the time used for a single output.

[0085] Specifically, the embodiment of the present invention determines the actual processing time through the theoretical processing time and the actual effective time, so that the determination result of the actual processing time is comprehensive and accurate. Through real-time image detection, actual edge contour recognition, arm curvature calculation and screening of effective working images, the efficiency of the image data processing process is improved, and the comprehensiveness of the work image analysis is improved, so as to accurately judge the effective time of the work and accurately obtain the actual effective time. The theoretical processing time is determined by the workload output, and the relationship between the workload and the actual processing time is comprehensively considered, so that the theoretical processing time can be obtained accurately, and then the actual processing time can be obtained accurately.

[0086] Specifically, determining the overall adjustment parameter Hz of working hours includes:

[0087] If Hi ≥ 1.1 Hz i, it is determined that the Ai working output does not match the actual processing time;

[0088] If Hi<0.9Hz i, the Ai working output is determined to be excellent;

[0089] When the work output is excellent, the overall adjustment parameter Hz is the preset overall adjustment parameter 1;

[0090] When the work output does not match the actual processing time, the overall adjustment parameter is adjusted according to the relationship between the actual workload output and the preset output.

[0091] Specifically, the overall adjustment parameter Hz of the working hours is adjusted and set to Hz'=Ci / Cq×Hz, where Hz' is the adjusted overall adjustment parameter, Ci is the actual workload output, and Cq is the preset output.

[0092] Specifically, the embodiment of the present invention quantifies the effective work efficiency by judging whether the work output is consistent with the actual processing time, thereby obtaining the corresponding overall adjustment parameters according to different situations, so that the final duration calculation result through the overall adjustment parameters is accurate. When the work output is inconsistent with the actual processing time, the overall adjustment parameters are adaptively adjusted according to the actual workload output and the preset output, so as to avoid the influence of subjective factors on the overall adjustment parameter calculation and improve the accuracy of the overall adjustment parameter results.

[0093] Specifically, for the first correction time length Pi, set Pi=min(Pz i, 0.3Hi), where Pz i is the first actual correction time length, and Hi is the actual processing time length.

[0094] Specifically, for the first actual correction time, the positions of the persons in the auxiliary images are identified by using image recognition technology, and the first actual correction time is obtained based on statistics of the auxiliary images containing the positions of the persons.

[0095] Specifically, the embodiment of the present invention determines the first corrected time by comparing the first actual corrected time with 0.3 times the actual processing time, so as to avoid the first corrected time exceeding one third of the actual processing time, thereby ensuring the rationality of the first corrected time, and quickly and accurately obtaining the first actual corrected time through image recognition technology, so that the result of the first actual corrected time is accurate. By obtaining the first actual corrected time, a more accurate and comprehensive evaluation of the auxiliary work performance at work is made, thereby avoiding the influence of subjective factors on the calculation of the first corrected time, and improving the accuracy of the calculation result of the first corrected time.

[0096] Specifically, for calculating the compensation parameter p1,

[0097] If Pi<0.3Hi, it is determined that the first correction duration of Ai is reasonable, and the calculated compensation parameter p1 is 20% of the preset compensation parameter;

[0098] If Pi≥0.4Hi, it is determined that the duration of the first correction duration of Ai is unreasonable, and the preset compensation parameter is adjusted by calculating the compensation adjustment parameter k1 to obtain the calculated compensation parameter.

[0099] Specifically, for the compensation adjustment parameter k1, the compensation adjustment parameter k1 is set to be 0.4Hi / Pi, where Hi is the actual processing time and Pi is the first correction time.

[0100] Specifically, the embodiment of the present invention determines whether the first correction time is reasonable more accurately by judging the proportional relationship between the first correction time and the actual processing time, thereby more accurately calculating the compensation parameters, and adjusting the preset compensation parameters by calculating the compensation adjustment parameters, so as to quickly and accurately obtain the calculated compensation parameters, thereby improving the efficiency of the data processing process, and providing a reference value by setting the preset compensation parameters, so that the result of calculating the compensation parameters is more accurate and fair.

[0101] See also Figure 3 As shown, obtaining the second revised duration includes:

[0102] Step S310, respectively collecting image resolutions of the working images and the auxiliary images to obtain a plurality of first resolutions and a plurality of second resolutions;

[0103] Step S320, real-time acquisition and analysis of the real-time processing efficiency of the process processor;

[0104] Step S330: Calculate the second corrected duration based on a number of the first resolutions, a number of the second resolutions and the real-time processing efficiency.

[0105] Specifically, the resolution of the image collected by the embodiment of the present invention can be obtained through image processing software.

[0106] Specifically, the step of calculating the second corrected duration based on a number of the first resolutions, a number of the second resolutions and the real-time processing efficiency includes:

[0107] Building a model of image resolution and processing time based on historical image processing data as a first model;

[0108] Acquire a number of the first processing durations according to a number of the first resolutions;

[0109] Acquire a plurality of second processing durations according to a plurality of second resolutions;

[0110] determining a first duration coefficient based on a number of the first resolutions and a number of the first processing durations;

[0111] determining a second duration coefficient based on a plurality of the second resolutions and a plurality of the second processing durations;

[0112] The second corrected duration is calculated based on the first duration coefficient, the second duration coefficient and the real-time processing efficiency.

[0113] Specifically, the second correction time in the embodiment of the present invention=(Σ(first resolution processing time coefficient×number of working images)+Σ(second resolution processing time coefficient×number of auxiliary images)) / mean processing efficiency;

[0114] The average processing efficiency is the average of several real-time processing efficiencies.

[0115] Specifically, the embodiments of the present invention establish a model of image resolution and processing time based on historical image processing data to more accurately predict the processing time of images with different resolutions, improve operating efficiency, quickly and accurately calculate the processing time for tasks that require real-time processing, ensure the real-time and timeliness of data processing, establish the relationship between resolution and processing time to meet the processing requirements of images with different resolutions, ensure image quality while improving processing speed, predict processing time through the model, provide an accurate data basis, and avoid system performance degradation due to excessive processing time.

[0116] Specifically, the relationship between the actual processing time Hi, the first correction time Pi and the second correction time Di is determined. When Hi≥0.7(Hi+Pi+Di) and Pi≤0.3(Hi+Pi+Di), the working hours used in different areas meet the requirements, and the overall compensation parameter is not adjusted again. The overall compensation parameter is 1 or Hz'=Ci / Cq×Hz;

[0117] When Hi<0.5(Hi+Pi+Di) or Pi>0.4(Hi+Pi+Di), the working hours of different areas do not meet the requirements, and the overall compensation parameter is adjusted again, and the overall compensation parameter Hz" is Hz"=1×0.8 or Hz"=0.8×Hz'.

[0118] See also Figure 4 As shown, the embodiment of the present invention also provides a system for a big data platform job task monitoring and alarm method based on Prometheus, the system comprising:

[0119] The duration acquisition module 10 is used to acquire the actual processing duration by analyzing a plurality of working images of the working area;

[0120] A first acquisition module 20, configured to acquire a first correction duration by analyzing a plurality of auxiliary images of an auxiliary area associated with the working area;

[0121] A second acquisition module 30 is used to calculate the analysis duration of the analysis process of the plurality of working images and the plurality of auxiliary images to obtain a second correction duration;

[0122] A final acquisition module 40 is connected to the actual processing time acquisition module 10, the first acquisition module 20 and the second acquisition module 30, and is used to calculate the final time according to the actual processing time, the first correction time, the second correction time and the adjustment parameter, compare the final time with the preset time, and issue an alarm for the big data platform operation task according to the comparison result;

[0123] Among them, the adjustment parameters include calculation compensation parameters and overall adjustment parameters. The calculation compensation parameters are used to adjust the first correction duration, calculate the sum of the actual processing duration, the first correction duration adjusted by the calculation compensation parameters, and the second correction duration. The overall adjustment parameters are used to adjust the sum and calculate to obtain the final duration.

[0124] Specifically, the system of the Prometheus-based big data platform job task monitoring and alarm method provided in the embodiment of the present invention can execute the above-mentioned Prometheus-based big data platform job task monitoring and alarm method to achieve the same technical effect, which will not be repeated here.

[0125] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0126] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A big data platform job task monitoring and alarm method based on Prometheus, characterized in that: include: By analyzing several working images of the working area, the actual processing time is obtained; Acquire a first correction duration by analyzing a plurality of auxiliary images of an auxiliary area associated with the working area; Calculating the analysis duration of the analysis process of the plurality of working images and the plurality of auxiliary images to obtain a second corrected duration; Calculate a final duration according to the actual processing duration, the first corrected duration, the second corrected duration and the adjustment parameter, compare the final duration with the preset duration, and issue an alarm for the big data platform operation task according to the comparison result; Among them, the adjustment parameters include calculation compensation parameters and overall adjustment parameters. The calculation compensation parameters are used to adjust the first correction duration, calculate the sum of the actual processing duration, the first correction duration adjusted by the calculation compensation parameters, and the second correction duration. The overall adjustment parameters are used to adjust the sum and calculate to obtain the final duration.

2. The Prometheus-based big data platform job task monitoring and alarm method according to claim 1 is characterized in that: Obtaining the actual processing time includes: Identify a valid working image from among the working images, and obtain an actual valid duration; Count the workload output within the actual effective time, and determine the theoretical processing time based on the workload output; The actual processing time is determined according to the actual effective time and the theoretical processing time.

3. The Prometheus-based big data platform job task monitoring and alarm method according to claim 2 is characterized in that: The method also includes determining the overall adjustment parameter according to the relationship between the actual effective time and the theoretical processing time.

4. The Prometheus-based big data platform job task monitoring and alarm method according to claim 3 is characterized in that: Obtaining the first revision duration includes: Acquire a first actual correction duration through a plurality of the auxiliary images; The first correction duration is determined according to the relationship between the first actual correction duration and the actual processing duration.

5. The Prometheus-based big data platform job task monitoring and alarm method according to claim 4 is characterized in that: The method further includes judging whether the first revised time duration is reasonable based on the relationship between the first revised time duration and the actual processing time duration, and obtaining the calculation compensation parameter based on the first judgment result.

6. The Prometheus-based big data platform job task monitoring and alarm method according to claim 5 is characterized in that: Obtaining the second revised duration includes: respectively collecting the image resolutions of the plurality of working images and the plurality of auxiliary images to obtain a plurality of first resolutions and a plurality of second resolutions; Real-time processing efficiency of the processor during real-time acquisition and analysis; The second revised duration is calculated based on a number of the first resolutions, a number of the second resolutions, and the real-time processing efficiency.

7. The Prometheus-based big data platform job task monitoring and alarm method according to claim 6 is characterized in that: The step of calculating the second corrected duration based on a plurality of the first resolutions, a plurality of the second resolutions and the real-time processing efficiency comprises: Building a model of image resolution and processing time based on historical image processing data as a first model; Acquire a number of the first processing durations according to a number of the first resolutions; Acquire a plurality of second processing durations according to a plurality of second resolutions; determining a first duration coefficient based on a number of the first resolutions and a number of the first processing durations; determining a second duration coefficient based on a plurality of the second resolutions and a plurality of the second processing durations; The second corrected duration is calculated based on the first duration coefficient, the second duration coefficient and the real-time processing efficiency.

8. The Prometheus-based big data platform job task monitoring and alarm method according to claim 7 is characterized in that: It is determined whether the corresponding working hours in different areas meet the requirements according to the relationship between the actual processing time and the first corrected time.

9. The Prometheus-based big data platform job task monitoring and alarm method according to claim 8 is characterized in that: It also includes, when the corresponding working hours in different areas meet the requirements, the overall compensation parameter is readjusted according to the relationship between the actual processing time, the first corrected time, the second corrected time and the total working hours, wherein the total working hours are the sum of the actual processing time, the first corrected time and the second corrected time, and the relationship between the actual processing time Hi, the first corrected time Pi and the second corrected time Di is determined, when Hi≥0.7 (Hi+Pi+Di), Pi≤0.3 (Hi+Pi+Di), the working hours used in the different areas meet the requirements, and the overall adjustment parameter is not adjusted again; When Hi<0.5(Hi+Pi+Di)or Pi>0.4(Hi+Pi+Di), the working hours used in different areas do not meet the requirements, and the overall adjustment parameter is adjusted again. The adjusted overall adjustment parameter Hz'' is Hz''=1×0.8 or Hz''=0.8×Hz', where Hz' is the overall adjustment parameter.

10. A system using the Prometheus-based big data platform job task monitoring and alarm method as described in any one of claims 1 to 9, characterized in that: include: A processing time acquisition module is used to acquire the actual processing time by analyzing a number of working images of the working area; A first acquisition module, configured to acquire a first correction duration by analyzing a plurality of auxiliary images of an auxiliary area associated with the working area; A second acquisition module is used to calculate the analysis duration of the analysis process of the plurality of working images and the plurality of auxiliary images to obtain a second correction duration; a final acquisition module, connected to the processing duration acquisition module, the first acquisition module and the second acquisition module, for calculating a final duration according to the actual processing duration, the first correction duration, the second correction duration and an adjustment parameter, comparing the final duration with a preset duration, and issuing an alarm for the big data platform operation task according to the comparison result; Among them, the adjustment parameters include calculation compensation parameters and overall adjustment parameters. The calculation compensation parameters are used to adjust the first correction duration, calculate the sum of the actual processing duration, the first correction duration adjusted by the calculation compensation parameters, and the second correction duration. The overall adjustment parameters are used to adjust the sum and calculate to obtain the final duration.

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