A method and system for evaluating and analyzing the operating status of a processing system

Abstract

The present invention provides a method and system for evaluating the operating status of an analysis and processing system, which belongs to the field of data processing technology, and specifically includes: dividing data processing into different transaction data models according to the business type corresponding to the data processing of the data analysis and processing system within the enterprise, determining the data source and data processing model corresponding to the different transaction data models through the analysis results of the transaction data model, and determining the model scale of different transaction data models according to the data source and the data processing model, obtaining the data processing data volume and processing time of different transaction data models, and determining whether the operating status of the data processing system meets the requirements in combination with the model scale of different transaction data models, thereby improving the accuracy of identifying and processing abnormal situations in the operating status of the data processing system.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a method and system for evaluating and analyzing the operating status of a processing system. Background Art

[0002] As the business of an enterprise develops and its existence is prolonged, the disorder and cost of the business systems used for data analysis and processing within the enterprise will inevitably increase significantly. Therefore, if the operating status of the enterprise's data processing business system cannot be observed and analyzed, it is likely to lead to increasing disorder in the enterprise's data processing business system, causing the company's technological costs to continue to increase.

[0003] In response to the above technical problems, the present invention proposes a method and system for evaluating the operating status of an analysis and processing system. By modeling, measuring and observing the operating status of an enterprise's analysis and processing system, accurate and rapid adjustments to disorder can be made, thereby continuously reducing the overall technological costs of the enterprise. Summary of the invention

[0004] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0005] According to one aspect of the present invention, a method for evaluating and analyzing the operating status of a processing system is provided.

[0006] A method for evaluating and analyzing the operating status of a processing system, specifically comprising:

[0007] S1 constructs a time window based on a preset time length, and determines the data processing complexity coefficient of the divided time periods and the abnormal data processing time period based on the data interaction processing links and data volume of the data processing system within the enterprise in different divided time periods in the time window;

[0008] S2 obtains the distribution data of the abnormal data processing period in the time window, and determines that the operation state of the data processing system meets the requirements by combining the data processing complexity coefficients of different abnormal data processing periods, and then proceeds to the next step;

[0009] S3 divides the data processing into different transaction data models according to the business types corresponding to the data processing of the data analysis and processing system within the enterprise, determines the data sources and data processing models corresponding to the different transaction data models through the analysis results of the transaction data models, and determines the model scales of the different transaction data models according to the data sources and data processing models;

[0010] S4 obtains the data processing data volume and processing time of different transaction data models, and determines whether the operation status of the data processing system meets the requirements in combination with the model scale of different transaction data models.

[0011] A further technical solution is that the preset time duration is determined according to the average daily data volume of the data processing system within the enterprise, wherein the larger the average daily data volume of the data processing system is, the longer the preset time duration is.

[0012] A further technical solution is that the time periods are divided according to unit duration, with a value range of 1 minute to 10 minutes.

[0013] A further technical solution is that the data interaction processing link is determined according to data interaction conditions between different data sources.

[0014] A further technical solution is to determine whether the operating state of the data processing system meets the requirements, specifically including:

[0015] Based on the data processing data volume of different transaction data models, determine the data processing data volume of the transaction data model on different days within a preset time period, and determine the data volume weight coefficient of the transaction data model based on the daily average data processing data volume within the preset time period;

[0016] Based on the processing time of the transaction data model, determine the proportion of the number of processing times whose processing time is longer than a preset processing time threshold, and determine the delay weight coefficient of the transaction data model according to the proportion of the number of processing times whose processing time is longer than the preset processing time threshold;

[0017] The model weight coefficients of different transaction data models are determined by the delay weight coefficient and the data volume weight coefficient, and the system operation status value of the data processing system is determined in combination with the model scale of the different transaction data models. Based on the system operation status value, it is determined whether the operation status of the data processing system meets the requirements.

[0018] A further technical solution is to determine whether the operating state of the data processing system meets the requirements based on the system operating state value, specifically including:

[0019] When the system operating state value of the data processing system is greater than a preset state threshold, it is determined that the operating state of the data processing system meets the requirements.

[0020] In a second aspect, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes the above-mentioned method for evaluating and analyzing the operating status of a processing system.

[0021] The beneficial effects of the present invention are:

[0022] Based on the data interaction processing links and data volume in different divided time periods of the enterprise's internal data processing system in the time window, the data processing complexity coefficient of the divided time periods and the data abnormality processing time periods are determined, thereby realizing the determination of the data abnormality processing time periods from multiple angles such as the complexity of the data interaction processing links in different divided time periods in the time window and the data volume, and also laying the foundation for further realizing the evaluation of the operating status of the data processing system, avoiding the emergence of the basic problem of insufficient operating reliability of the data processing system due to the complexity of data processing.

[0023] Whether the operating status of the data processing system meets the requirements is determined by the data processing data volume and processing time of different transaction data models and the model scale of different transaction data models. Therefore, not only the difference in model scale caused by the difference in data processing type of different transaction data models is taken into consideration, but also the difference in data processing reliability of transaction data models caused by the difference in data volume and processing delay of different transaction data models is fully considered. This enables accurate evaluation of the operating status of the data processing system from the data processing reliability and data scale of different transaction data models, thereby improving the accuracy of identifying and processing abnormal situations in the operating status of the data processing system.

[0024] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0025] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0027] Figure 1 It is a flow chart of a method for evaluating the operating status of an analytical processing system;

[0028] Figure 2 is a flow chart of a method for determining a data processing complexity coefficient for divided time periods;

[0029] Figure 3 is a flow chart of a method for determining a model size of a transaction data model;

[0030] Figure 4 The invention is a flow chart of a method for determining whether the operating status of a data processing system meets the requirements. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0032] If the operating status of the enterprise's data processing business system cannot be observed and analyzed, it is likely to lead to increasing disorder in the enterprise's data processing business system, causing the company's technological costs to continue to increase.

[0033] Data processing complexity coefficient for divided time periods: The data processing complexity coefficient is determined based on the number of data interaction processing links and the amount of data. Specifically, the preset complexity coefficients of different data interaction processing links are determined according to the amount of data in different data interaction processing links. The data processing complexity coefficient for divided time periods is determined according to the sum of the preset complexity coefficients of different data interaction processing links. Specifically, if the preset complexity coefficients of different data interaction processing links are 0.3, 0.2, and 0.1 respectively, then the data processing complexity coefficient for divided time periods is 0.6.

[0034] Processing data anomaly period: the period when the data processing complexity coefficient is greater than 0.7.

[0035] Determine whether the operating status of the data processing system meets the requirements: take the product of the proportion of the number of time periods for processing abnormal data within the time window and the average value of the data processing complexity coefficients of different time periods for processing abnormal data as the operating status coefficient. Specifically, if the proportion of the number is 0.6 and the average value of the data processing complexity coefficients of different time periods for processing abnormal data is 0.8, then the operating status coefficient is 0.48. When the operating status coefficient is less than 0.3, it is determined that the operating status of the data processing system does not meet the requirements.

[0036] The data processing model includes the type of data source, the data processing method in different data sources, and the data interaction method between data sources.

[0037] Model scale of the transaction data model: obtain the number of data sources in the transaction data model, and determine the basic model scale of the transaction data model based on the number of data sources and a preset data source scaling factor; determine the data processing method for different data sources of the transaction data model based on the analysis result of the data processing model, and determine the preset processing difficulty corresponding to different data sources based on the data processing method; determine the compensation amount for the basic model scale of the transaction data model based on the average value of the preset processing difficulty corresponding to different data sources; and determine the model scale of the transaction data model by the sum of the compensation amount and the basic model scale.

[0038] Determine whether the operating status of the data processing system meets the requirements: determine the data volume weight coefficient of the transaction data model based on the average daily data processing volume within a preset time period, and determine the delay weight coefficient of the transaction data model based on the proportion of the number of processing times with a processing time greater than a preset processing time threshold;

[0039] The model weight coefficients of different transaction data models are determined by multiplying the delay weight coefficient and the data volume weight coefficient, and the system operation status value of the data processing system is determined by the model scale and model weight coefficient of different transaction data models. Based on the system operation status value, it is determined whether the operation status of the data processing system meets the requirements.

[0040] Embodiment 1 To solve the above problem, according to one aspect of the present invention, Figure 1 As shown, a method for evaluating the operating status of an analysis processing system is provided, which specifically includes:

[0041] S1 constructs a time window based on a preset time length, and determines the data processing complexity coefficient of the divided time periods and the abnormal data processing time period based on the data interaction processing links and data volume of the data processing system within the enterprise in different divided time periods in the time window;

[0042] S2 obtains the distribution data of the abnormal data processing period in the time window, and determines that the operation state of the data processing system meets the requirements by combining the data processing complexity coefficients of different abnormal data processing periods, and then proceeds to the next step;

[0043] S3 divides the data processing into different transaction data models according to the business types corresponding to the data processing of the data analysis and processing system within the enterprise, determines the data sources and data processing models corresponding to the different transaction data models through the analysis results of the transaction data models, and determines the model scales of the different transaction data models according to the data sources and data processing models;

[0044] S4 obtains the data processing data volume and processing time of different transaction data models, and determines whether the operation status of the data processing system meets the requirements in combination with the model scale of different transaction data models.

[0045] Furthermore, the preset duration is determined based on the average daily data volume of the data processing system within the enterprise, wherein the larger the average daily data volume of the data processing system, the longer the preset duration.

[0046] Specifically, the divided time periods are divided according to unit duration, and the value range is between 1 minute and 10 minutes.

[0047] It should be noted that the data interaction processing link is determined according to the data interaction conditions between different data sources.

[0048] It is understandable that if Figure 2 As shown, the method for determining the data processing complexity coefficient of the divided time periods is:

[0049] Acquiring the amount of data processed by the data processing system in the divided time period, and using the amount of data processed as a basis, constructing a mapping function to determine a complex processing coefficient of the amount of data in the divided time period;

[0050] Determine the data interaction processing links between different data sources in the divided time periods according to the data interaction conditions between different data sources in the divided time periods, and determine the link processing complexity coefficient in the divided time periods according to the number of the data interaction processing links;

[0051] The data processing complexity coefficients of the divided time periods are determined based on the data volume complexity processing coefficient and the link processing complexity coefficient.

[0052] Furthermore, when the data processing complexity coefficient of the divided time period is not within a preset coefficient range, the divided time period is determined to be a data processing abnormality time period.

[0053] It can be understood that when there is no abnormal period of processing data, it can be directly determined that the operating state of the data processing system meets the requirements.

[0054] It should also be noted that the method for determining the data processing complexity coefficient of the divided time periods is:

[0055] Acquiring the amount of data processed by the data processing system in the divided time period, and determining a data amount focus period in the divided time period based on the amount of data processed;

[0056] Determine the data interaction processing links between different data sources according to the data interaction between different data sources in the data volume focus period, and determine the period processing complexity coefficient of the data volume focus period according to the number of the data interaction processing links and the data volume of different data interaction processing links;

[0057] The data processing complexity coefficients of the divided time periods are determined based on the time period processing complexity coefficients of the time periods of interest with different data volumes.

[0058] It should be noted that the data volume focus period is a divided period when the data volume is greater than the preset data volume.

[0059] Optionally, the method for determining the data processing complexity coefficient of the divided time periods includes steps S11-S13, specifically:

[0060] S11: obtaining the amount of data processed by the data processing system in the divided time period, and determining the data volume complexity processing coefficient in the divided time period based on the amount of data processed;

[0061] S12 determines the data interaction processing links between different data sources within the divided time periods according to the data interaction conditions between different data sources within the divided time periods, and determines the link processing complexity coefficient within the divided time periods according to the number of the data interaction processing links and the data volume of different data interaction processing links;

[0062] S13 determines the data processing complexity coefficient of the divided time periods based on the data volume complexity processing coefficient and the link processing complexity coefficient.

[0063] Further, the distribution data of the abnormal time period of the processed data is determined according to the corresponding time of the abnormal time period of the processed data.

[0064] It should be noted that determining whether the operating status of the data processing system meets the requirements specifically includes:

[0065] Based on the distribution data of the abnormal time periods of the processed data in the time window, determining the number ratio of the abnormal time periods of the processed data in the time window, and determining the abnormal time period ratio coefficient using the number ratio of the abnormal time periods of the processed data;

[0066] According to the data processing complexity coefficients of different processing data abnormality time periods, determining the average value of the data processing complexity coefficients of the processing data abnormality time period;

[0067] The operation complexity coefficient of the data processing system within the time window is determined based on the product of the average value of the data processing complexity coefficient and the abnormal period proportion coefficient, and whether the operation status of the data processing system meets the requirements is determined by the operation complexity coefficient.

[0068] Further, when the operation complexity coefficient is greater than a preset complexity coefficient threshold, it is determined that the operation state of the data processing system does not meet the requirements.

[0069] It should also be noted that the value range of the operation complexity coefficient is between 0 and 1, wherein the larger the operation complexity coefficient is, the higher the complexity of the data processing by the data processing system within the time window is.

[0070] It can be understood that determining whether the operating status of the data processing system meets the requirements specifically includes:

[0071] Based on the distribution data of the abnormal processing data period in the time window, determine the number of intervals between adjacent abnormal processing data periods in the time window, and determine the distribution clustering coefficient using the average value of the number of intervals;

[0072] According to the data processing complexity coefficients of different processing data abnormality time periods, determining the average value of the data processing complexity coefficients of the processing data abnormality time period;

[0073] The operation complexity coefficient of the data processing system within the time window is determined based on the average value of the data processing complexity coefficient and the product of the distribution clustering coefficient, and whether the operation status of the data processing system meets the requirements is determined by the operation complexity coefficient.

[0074] Optionally, determining whether the operating state of the data processing system meets the requirements includes steps S21-S23, specifically:

[0075] S21 determines the proportion of the number of abnormal time periods of processed data within the time window based on the distribution data of the abnormal time periods of processed data within the time window, and determines the abnormal time period ratio coefficient using the proportion of the number of abnormal time periods of processed data;

[0076] S22 divides the time window into a plurality of time intervals at unit intervals, and determines interval complexity coefficients of different time intervals by multiplying the abnormal period ratio coefficients of different time intervals by the average value of the data processing complexity coefficients of the abnormal data processing period;

[0077] S23 determines the operation complexity coefficient of the data processing system within the time window based on the interval complexity coefficients of different time intervals, and determines whether the operation state of the data processing system meets the requirements through the operation complexity coefficient.

[0078] Optionally, before entering step S21, it is also necessary to determine whether there is an abnormal data processing period in the time window whose data processing complexity coefficient is greater than a preset processing complexity coefficient threshold. When the proportion of the number of abnormal data processing periods in which the data processing complexity coefficient is greater than the preset processing complexity coefficient threshold does not meet the requirements, it is directly determined that the operating status of the data processing system does not meet the requirements. When the proportion of the number of abnormal data processing periods in which the data processing complexity coefficient is greater than the preset processing complexity coefficient threshold meets the requirements, proceed to step S21.

[0079] Optionally, the above step S21 includes steps S211-S213, which are specifically:

[0080] Based on the distribution data of the abnormal processing data period in the time window, the number ratio of the abnormal processing data period in the time window is determined, and

[0081] S211: When the number of abnormal data processing periods in the time window is not greater than the number of preset abnormal data processing periods, the process proceeds to step S212; when the number of abnormal data processing periods in the time window is greater than the number of preset abnormal data processing periods, the process proceeds to step S213;

[0082] S212 determines whether the average value of the data processing complexity coefficient during the abnormal data processing period is within a preset complexity coefficient interval. If so, directly determines that the operating state of the data processing system meets the requirements. If not, proceeds to step S213;

[0083] S213 determines the proportion of the number of abnormal time periods for processing data within the time window based on the distribution data of the abnormal time periods for processing data within the time window, and determines the abnormal time period proportion coefficient using the proportion of the number of abnormal time periods for processing data. When the abnormal time period proportion coefficient within the time window is greater than the preset time period proportion coefficient threshold, it is directly determined that the operating status of the data processing system does not meet the requirements. When the abnormal time period proportion coefficient within the time window is not greater than the preset time period proportion coefficient threshold, proceed to step S22.

[0084] Optionally, the above step S22 includes steps S221-S223, specifically:

[0085] The time window is divided into multiple time intervals at unit intervals, and the interval complexity coefficients of different time intervals are determined by multiplying the abnormal period ratio coefficients of different time intervals and the average value of the data processing complexity coefficients of the abnormal data processing period.

[0086] S221: Divide the time window into multiple time intervals at unit intervals. When the number of time intervals with abnormal processing data periods is less than the preset number of intervals, proceed to the next step. When the number of time intervals with abnormal processing data periods is not less than the preset number of intervals, proceed to step S223.

[0087] S222 uses the abnormal period proportion coefficients of different time intervals to determine that there is no time interval in which the abnormal period proportion coefficient is greater than the preset proportion coefficient threshold, then directly determine that the operating state of the data processing system meets the requirements, and when there is a time interval in which the abnormal period proportion coefficient is greater than the preset proportion coefficient threshold, proceed to step S223;

[0088] S223: When the number of time intervals in which the abnormal period ratio coefficient is greater than the preset ratio coefficient threshold does not meet the requirement, it is directly determined that the operation state of the data processing system does not meet the requirement; when the number of time intervals in which the abnormal period ratio coefficient is greater than the preset ratio coefficient threshold meets the requirement, the process proceeds to step S224;

[0089] S224 determines the interval complexity coefficients of different time intervals by using the product of the abnormal period proportion coefficients of different time intervals and the average value of the data processing complexity coefficients of the abnormal data processing period, and proceeds to step S23.

[0090] Furthermore, the data processing model includes the type of data source, data processing methods in different data sources, and data interaction methods between data sources.

[0091] Specifically, Figure 3 As shown, the method for determining the model scale of the transaction data model is:

[0092] Acquire the number of data sources in the transaction data model, and determine the basic model scale of the transaction data model based on the number of data sources and a preset data source scaling factor;

[0093] Based on the analysis result of the data processing model, determine the data processing methods of different data sources of the transaction data model, and determine the preset processing difficulty corresponding to different data sources based on the data processing methods;

[0094] The compensation amount of the basic model scale of the transaction data model is determined based on the average value of the preset processing difficulty corresponding to different data sources, and the model scale of the transaction data model is determined by the sum of the compensation amount and the basic model scale.

[0095] Furthermore, the model scale of the transaction data model has a value range of 0 to 1, wherein the larger the model scale of the transaction data model is, the larger the scale of data processing of the transaction data model is.

[0096] In addition, it should be noted that Figure 4 As shown, determining whether the operating status of the data processing system meets the requirements specifically includes:

[0097] Based on the data processing data volume of different transaction data models, determine the data processing data volume of the transaction data model on different days within a preset time period, and determine the data volume weight coefficient of the transaction data model based on the daily average data processing data volume within the preset time period;

[0098] Based on the processing time of the transaction data model, determine the proportion of the number of processing times whose processing time is longer than a preset processing time threshold, and determine the delay weight coefficient of the transaction data model according to the proportion of the number of processing times whose processing time is longer than the preset processing time threshold;

[0099] The model weight coefficients of different transaction data models are determined by the delay weight coefficient and the data volume weight coefficient, and the system operation status value of the data processing system is determined in combination with the model scale of the different transaction data models. Based on the system operation status value, it is determined whether the operation status of the data processing system meets the requirements.

[0100] Further, determining whether the operating state of the data processing system meets the requirements based on the system operating state value specifically includes:

[0101] When the system operating state value of the data processing system is greater than a preset state threshold, it is determined that the operating state of the data processing system meets the requirements.

[0102] Optionally, determining whether the operating state of the data processing system meets the requirements specifically includes steps S41-S43:

[0103] Optionally, before entering step S41, it is also necessary to determine whether the number of transaction data models with a data scale greater than the preset data scale meets the requirements. When the number of transaction data models with a data scale greater than the preset data scale is within the preset model quantity range, it is determined that the operating state of the data processing system does not meet the requirements. When the number of transaction data models with a data scale greater than the preset data scale is not within the preset model quantity range, proceed to step S41.

[0104] S41 divides the transaction data model into different data scale intervals by using the data scale of the transaction data model, determines the data scale of the transaction data model on different dates within a preset time period based on the data scale of the data processed by different transaction data models, and determines the data scale weight coefficient of the transaction data model based on the daily average data scale of the data processed within the preset time period;

[0105] Optionally, the above step S41 includes steps S411-S413, specifically:

[0106] S411 divides the transaction data model into different data scale intervals by using the data scale of the transaction data model, and determines the data processing data volume of the transaction data model on different dates within a preset time period based on the data processing data volume of different transaction data models. When the average value of the data processing data volume on different dates within the preset data scale interval is greater than the preset data volume threshold, it is determined that the operating state of the data processing system does not meet the requirements. When the average value of the data processing data volume on different dates within the preset data scale interval is not greater than the preset data volume threshold, proceed to step S412.

[0107] S412 determines the data volume weight coefficient of the transaction data model based on the daily average data volume of data processed in the preset time period. When the number of transaction data models whose data volume weight coefficients in the preset data volume interval are greater than the preset weight coefficients does not meet the requirement, it is determined that the operating state of the data processing system does not meet the requirement. When the number of transaction data models whose data volume weight coefficients in the preset data volume interval are greater than the preset weight coefficients meets the requirement, the process proceeds to step S413.

[0108] S413 determines the data volume influence coefficients of different transaction data models based on the product of the data volume weight coefficients of different transaction data models and the data scale. When the number of transaction data models whose data volume influence coefficients are greater than the preset influence coefficients does not meet the requirements, it is determined that the operating status of the data processing system does not meet the requirements. When the number of transaction data models whose data volume influence coefficients are greater than the preset influence coefficients meets the requirements, proceed to step S42.

[0109] S42 determines the proportion of processing times with a processing time longer than a preset processing time threshold based on the processing time of the transaction data model, and determines the delay weight coefficient of the transaction data model according to the proportion of processing times with a processing time longer than the preset processing time threshold;

[0110] Optionally, the above step S42 includes steps S421-S423, which are specifically:

[0111] S421 determines the proportion of the number of processing times whose processing time is longer than the preset processing time threshold based on the processing time of the transaction data model. When the average value of the proportion of the number of processing times whose processing time of different transaction data models is longer than the preset processing time threshold is greater than the preset processing times proportion, it is determined that the operation state of the data processing system does not meet the requirements. When the average value of the proportion of the number of processing times whose processing time of different transaction data models is longer than the preset processing time threshold is greater than the preset processing times proportion, the process goes to step S422.

[0112] S422 determines the delay weight coefficient of the transaction data model based on the proportion of the number of processing times whose processing time is greater than the preset processing time threshold. When the number of transaction data models whose delay weight coefficient is greater than the preset delay coefficient threshold is greater than the preset number of data models, it is determined that the operation state of the data processing system does not meet the requirements. When the number of transaction data models whose delay weight coefficient is greater than the preset delay coefficient threshold is not greater than the preset number of data models, proceed to step S423.

[0113] S423: When the average value of the delay weight coefficients of different transaction data models is not within the preset delay coefficient range, it is determined that the operating status of the data processing system does not meet the requirements; when the average value of the delay weight coefficients of different transaction data models is within the preset delay coefficient range, proceed to step S43.

[0114] S43 determines the system operation status value of the data processing system through the data volume weight coefficient and the delay weight coefficient of the transaction data model in different data scale intervals, and determines whether the operation status of the data processing system meets the requirements based on the system operation status value.

[0115] Embodiment 2 In a second aspect, the present invention provides a computer system, comprising: a memory and a processor connected in communication, and a computer program stored in the memory and executable on the processor, characterized in that: when the processor runs the computer program, it executes the above-mentioned method for evaluating and analyzing the operating status of a processing system.

[0116] Optionally, the above step S11 includes steps S111-S112, which are specifically:

[0117] S111: obtaining the amount of data processed by the data processing system in the divided time period; when the amount of data processed in the divided time period is less than the preset amount of data, directly determining that the divided time period does not belong to the abnormal data processing period; when the amount of data processed in the divided time period is not less than the preset amount of data, proceeding to step S112;

[0118] S112 uses the processed data volume as a basis to determine the data volume complexity processing coefficient within the divided time period, and then proceeds to step S12.

[0119] Optionally, the above step S12 includes steps S121-S123;

[0120] S121: When the data volume complex processing coefficient in the divided time period is less than the preset coefficient threshold, the process proceeds to step S122; when the data volume complex processing coefficient in the divided time period is not less than the preset coefficient threshold, the process proceeds to step S124;

[0121] S122 determines the data interaction processing links between different data sources according to the data interaction between different data sources in the divided time period. When the number of data interaction processing links in the divided time period is less than the preset link number threshold, it is directly determined that the divided time period does not belong to the abnormal data processing period. When the number of data interaction processing links in the divided time period is not less than the preset link data threshold, it proceeds to step S123.

[0122] S123 obtains the number of the data interaction processing links and the data volumes of different data interaction processing links. When the number of the data interaction processing links and the data volumes of different data interaction processing links are both within a preset interval, it is directly determined that the divided time period does not belong to the abnormal data processing time period. When any of the number of the data interaction processing links and the data volumes of different data interaction processing links is not within the preset interval, the process proceeds to step S124.

[0123] S124 determines the link processing complexity coefficient within the divided time period according to the number of the data interaction processing links and the data volume of different data interaction processing links, and proceeds to step S13.

[0124] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0125] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0126] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A method for evaluating and analyzing the operating status of a processing system, characterized in that: Specifically include: The time window is constructed based on the preset time length, and the data processing complexity coefficient of the divided time period and the abnormal data processing time period are determined based on the data interaction processing links and data volume of the data processing system within the enterprise in different divided time periods in the time window; Obtain the distribution data of the abnormal data processing period in the time window, and determine that the operation state of the data processing system meets the requirements by combining the average values ​​of the data processing complexity coefficients of different abnormal data processing periods, and then proceed to the next step; According to the business types corresponding to the data processing of the data analysis and processing system within the enterprise, the data processing is divided into different transaction data models, the data sources and data processing models corresponding to the different transaction data models are determined through the analysis results of the transaction data models, and the model scales of the different transaction data models are determined according to the data sources and data processing models; Obtaining the data processing data volume and processing time of different transaction data models, determining the data volume weight coefficient and delay weight coefficient of the transaction data model, and determining whether the operating status of the data processing system meets the requirements in combination with the model scale of different transaction data models; The method for determining the data processing complexity coefficient of the divided time periods is: Acquiring the amount of data processed by the data processing system in the divided time period, and using the amount of data processed as a basis, constructing a mapping function to determine a complex processing coefficient of the amount of data in the divided time period; Determine the data interaction processing links between different data sources in the divided time periods according to the data interaction conditions between different data sources in the divided time periods, and determine the link processing complexity coefficient in the divided time periods according to the number of the data interaction processing links; Determine the data processing complexity coefficient of the divided time periods based on the data volume complexity processing coefficient and the link processing complexity coefficient; When the data processing complexity coefficient of the divided time period is not within the preset coefficient range, the divided time period is determined to be a data processing abnormality time period.

2. The method for evaluating the operating status of an analysis processing system according to claim 1, characterized in that: The preset duration is determined based on the average daily data volume of the data processing system within the enterprise, wherein the larger the average daily data volume of the data processing system, the longer the preset duration.

3. The method for evaluating the operating status of an analysis processing system according to claim 1, characterized in that: The divided time periods are divided according to unit duration, and the value range is between 1 minute and 10 minutes.

4. The method for evaluating the operating status of an analysis processing system according to claim 1, characterized in that: The data interaction processing link is determined according to the data interaction conditions between different data sources.

5. The method for evaluating the operating status of an analysis processing system according to claim 1, characterized in that: The distribution data of the abnormal time period of the processed data is determined according to the corresponding time of the abnormal time period of the processed data.

6. The method for evaluating the operating status of an analysis processing system according to claim 1, characterized in that: Determining that the operating status of the data processing system meets the requirements specifically includes: Based on the distribution data of the abnormal time periods of the processed data in the time window, determining the number ratio of the abnormal time periods of the processed data in the time window, and determining the abnormal time period ratio coefficient using the number ratio of the abnormal time periods of the processed data; According to the data processing complexity coefficients of different processing data abnormality time periods, determining the average value of the data processing complexity coefficients of the processing data abnormality time period; The operation complexity coefficient of the data processing system within the time window is determined based on the product of the average value of the data processing complexity coefficient and the abnormal period proportion coefficient, and whether the operation status of the data processing system meets the requirements is determined by the operation complexity coefficient.

7. The method for evaluating the operating status of an analysis processing system according to claim 6, characterized in that: When the operation complexity coefficient is greater than a preset complexity coefficient threshold, it is determined that the operation state of the data processing system does not meet the requirements.

8. A computer system comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes a method for evaluating and analyzing the operating status of a processing system as described in any one of claims 1 to 7 when running the computer program.

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