Load monitoring method, device and equipment based on multi-level data

Abstract

The invention relates to the technical field of load monitoring, and discloses a load monitoring method, device and equipment based on multi-level data, and the method comprises the steps: obtaining the load data of a system under a plurality of time granularities at the current moment; calculating an average value and a standard deviation of the load data under each time granularity; based on the standard deviation of the load data under each time granularity, determining the weight of the load data under each time granularity; and based on the average value, the standard deviation and the weight of the load data under each time granularity, carrying out fusion to obtain comprehensive load data of the industrial data acquisition computer system at the current moment, and continuing to determine comprehensive load data at the next moment. According to the method, the load condition of the system can be monitored more comprehensively and accurately through multi-time-granularity data acquisition and analysis, misjudgment and omission are reduced, flexibility is improved through a dynamic weight adjustment mechanism, meanwhile, waste or insufficiency of resources can be avoided through comprehensive load data, and the overall efficiency and performance of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of load monitoring, and in particular to a load monitoring method, device and equipment based on multi-level data. Background Art

[0002] Industrial data acquisition computer systems are specialized systems designed to collect, process, and store data from industrial equipment, sensors, and other automated systems. They are widely used in a variety of industries, including manufacturing, energy, transportation, and logistics. Load fluctuations are common in industrial data acquisition computer systems, and system efficiency and service quality are directly affected by these load variations. To ensure system stability and efficiency under varying load conditions, accurate load monitoring is required to enable timely resource allocation adjustments based on load conditions.

[0003] Existing load monitoring methods typically rely on analyzing data at a single time granularity, collecting system load data at preset intervals (e.g., every minute, every hour). This approach is simple and easy to implement, but lacks flexibility. While it helps understand the load situation during a specific period, it fails to fully reflect the load trends of the entire system. This results in insufficient sensitivity to load changes, making it impossible to respond promptly to resource allocation needs under high load conditions, impacting overall system performance. Summary of the Invention

[0004] In view of this, the present invention provides a load monitoring method, device and equipment based on multi-level data to solve the problem that the existing load monitoring method cannot fully reflect the load change trend, the accuracy and flexibility of load monitoring are low, and resource allocation cannot be accurately performed.

[0005] In a first aspect, the present invention provides a load monitoring method based on multi-level data, the method comprising:

[0006] At the current moment, obtain the load data of the industrial data acquisition computer system at multiple time granularities;

[0007] Calculate the mean and standard deviation of load data at each time granularity;

[0008] Determine the weight corresponding to the load data at each time granularity based on the standard deviation of the load data at each time granularity;

[0009] Based on the average value, standard deviation and weight of the load data at each time granularity, the comprehensive load data of the industrial data acquisition computer system at the current moment is obtained, and the comprehensive load data at the next moment is further determined.

[0010] The embodiment of the present invention provides a load monitoring method based on multi-level data. By acquiring load data at different time granularities, it can comprehensively reflect the load situation of the system from multiple time dimensions, avoid the one-sidedness and error caused by a single time granularity, and improve the accuracy of the overall assessment. Then, the average value and standard deviation of the load data at each time granularity are calculated. The average value reflects the average level of the load at a certain time granularity, while the standard deviation describes the degree of load fluctuation. Therefore, the weight at each time granularity is determined based on the standard deviation, and the weight is dynamically adjusted according to the load fluctuation. Finally, based on the average value, standard deviation and weight, a comprehensive load data is obtained. The data not only reflects the average level of the load, but also takes into account the fluctuation and its relative importance, providing a more comprehensive and accurate load monitoring result, which helps the system to reasonably allocate resources. Through data collection and analysis at multiple time granularities, the load situation of the system can be monitored more comprehensively and accurately, reducing misjudgments and omissions. The dynamic adjustment mechanism of weights improves flexibility. At the same time, by integrating the load data, it can avoid resource waste or shortage, thereby improving the overall efficiency and performance of the system.

[0011] In an optional embodiment, determining the weight corresponding to the load data at each time granularity based on the standard deviation of the load data at each time granularity includes:

[0012] Obtain the preset fluctuation threshold, the preset basic weight coefficient corresponding to each time granularity, and the threshold sensitivity coefficient;

[0013] When the standard deviation of the load data at any time granularity is greater than the preset fluctuation threshold, the preset basic weight coefficient corresponding to the time granularity is increased based on the preset fluctuation threshold, the standard deviation corresponding to the time granularity, and the threshold sensitivity coefficient to obtain the initial weight corresponding to the load data at the time granularity;

[0014] When the standard deviation of the load data at any time granularity is not greater than the preset fluctuation threshold, the preset basic weight coefficient corresponding to the time granularity is reduced based on the preset fluctuation threshold, the standard deviation corresponding to the time granularity, and the threshold sensitivity coefficient to obtain the initial weight corresponding to the load data at the time granularity;

[0015] When the initial weights corresponding to the load data at all time granularities are positive values, the initial weights corresponding to the load data at each time granularity are normalized to obtain the weights corresponding to the load data at each time granularity.

[0016] An embodiment of the present invention provides a load monitoring method based on multi-level data. By dynamically adjusting weights, it can more accurately reflect the load conditions at each time granularity, avoid the deviation caused by a single static weight, and introduce a threshold sensitivity coefficient, so that the weights can be flexibly adjusted according to actual conditions, adapting to different load modes and application scenarios, and improving the accuracy of load monitoring.

[0017] In an optional embodiment, the method further includes:

[0018] When the initial weight corresponding to the load data at any time granularity is a negative value, the initial weight corresponding to the load data at the time granularity is set to a preset weight value.

[0019] The embodiment of the present invention provides a load monitoring method based on multi-level data, which prevents the occurrence of negative weights and ensures that the weight of each time granularity is reasonable, thereby improving the accuracy of load monitoring.

[0020] In an optional embodiment, when the initial weights corresponding to the load data at all time granularities are positive values, the initial weights corresponding to the load data at each time granularity are normalized to obtain the weights corresponding to the load data at each time granularity, including:

[0021] The sum of the initial weights corresponding to the load data at all time granularities is determined as the weight sum;

[0022] The ratio of the initial weight corresponding to the load data at each time granularity to the weight sum value is used as the new weight corresponding to the load data at each time granularity.

[0023] The embodiment of the present invention provides a load monitoring method based on multi-level data, which ensures the rationality of the weights by standardizing the weights, avoids excessive or insufficient weights of individual time granularities, and improves the accuracy of load monitoring.

[0024] In an optional embodiment, the average value, standard deviation, and weight of the load data at each time granularity are fused to obtain the comprehensive load data of the industrial data acquisition computer system at the current moment, including:

[0025] For each time granularity, determine the product of the threshold sensitivity coefficient and the standard deviation corresponding to the time granularity;

[0026] Determine the sum of the product and the average value of the load data at the time granularity;

[0027] The product of the sum and the weight of the load data at the time granularity is used as the load analysis result of the time granularity;

[0028] The sum of the load analysis results corresponding to all time granularities is determined as the comprehensive load data.

[0029] The embodiment of the present invention provides a load monitoring method based on multi-level data. By fusing the average value, standard deviation and weight, it can integrate the load conditions at multiple time granularities to obtain comprehensive load data reflecting the overall load condition, thereby improving the accuracy of load monitoring.

[0030] In an optional embodiment, at the current moment, obtaining load data of the industrial data acquisition computer system at multiple time granularities includes:

[0031] Based on the current moment and according to the preset data collection cycle, load data of multiple time intervals of the industrial data collection computer system is obtained. Each time interval corresponds to a time granularity, and the interval lengths of the multiple time intervals increase sequentially.

[0032] The embodiment of the present invention provides a load monitoring method based on multi-level data. By acquiring load data at different time granularities, it can provide richer information for monitoring load change trends, avoid the one-sidedness brought by a single time granularity, and improve the accuracy and flexibility of load monitoring.

[0033] In an optional embodiment, the method further includes:

[0034] Based on the comprehensive load data of the industrial data acquisition computer system at the current moment, the industrial data acquisition computer system adjusts resource allocation.

[0035] The embodiment of the present invention provides a load monitoring method based on multi-level data. By adjusting resource allocation in real time based on the comprehensive load data at the current moment, it is possible to quickly respond to sudden high loads and ensure the overall performance of the system.

[0036] In a second aspect, the present invention provides a load monitoring device based on multi-level data, the device comprising:

[0037] An acquisition module is used to acquire load data of the industrial data acquisition computer system at multiple time granularities at the current moment;

[0038] The calculation module is used to calculate the average value and standard deviation of the load data at each time granularity;

[0039] A determination module, configured to determine a weight corresponding to the load data at each time granularity based on a standard deviation of the load data at each time granularity;

[0040] The fusion module is used to fuse the load data at each time granularity based on the average value, standard deviation and weight to obtain the comprehensive load data of the industrial data acquisition computer system at the current moment.

[0041] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the multi-level data-based load monitoring method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0042] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the load monitoring method based on multi-level data according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 is a flow chart of a load monitoring method based on multi-level data according to an embodiment of the present invention;

[0045] Figure 2 is a flow chart of another load monitoring method based on multi-level data according to an embodiment of the present invention;

[0046] Figure 3 is a structural block diagram of a load monitoring device based on multi-level data according to an embodiment of the present invention;

[0047] Figure 4 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0049] Existing load monitoring methods typically rely on data analysis at a single time granularity, but they lack flexibility. While they help understand the load situation during a specific period, they cannot fully reflect the load change trend of the entire system, resulting in insufficient sensitivity to load changes and an inability to respond promptly to resource allocation requirements under high load conditions, affecting the overall performance of the system. An embodiment of the present invention provides a load monitoring method based on multi-level data. By collecting and analyzing data at multiple time granularities, the load situation of the system can be monitored more comprehensively and accurately, reducing misjudgments and omissions. The dynamic adjustment mechanism of weights improves flexibility. At the same time, by integrating load data, it can avoid resource waste or shortages, thereby improving the overall efficiency and performance of the system.

[0050] According to an embodiment of the present invention, an embodiment of a load monitoring method based on multi-level data is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0051] In this embodiment, a load monitoring method based on multi-level data is provided, which can be used in terminals such as computers, Figure 1 is a flow chart of a load monitoring method based on multi-level data according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0052] Step S101 : at the current moment, obtaining load data of the industrial data acquisition computer system at multiple time granularities.

[0053] Specifically, the load data includes the usage of the system's CPU (Central Processing Unit), memory, and network bandwidth. Related technologies use only a single time granularity when monitoring system load conditions, such as obtaining hourly or daily load data for analysis. However, this approach is unable to cope with load fluctuations, resulting in inaccurate load analysis and thus affecting system resource allocation. Therefore, the embodiments of the present invention use the current moment as a reference to obtain load data at multiple time granularities, which can provide a more comprehensive understanding of load change trends, help identify load fluctuations, and thus more accurately monitor load conditions.

[0054] Step S102: Calculate the average value and standard deviation of the load data at each time granularity.

[0055] Specifically, by calculating the statistical characteristics of the load data at each time granularity, the embodiment of the present invention uses the average value and standard deviation as an example to illustrate, which can understand the load level and its fluctuation at each time granularity, help to understand the load change trend, and monitor the load situation more accurately.

[0056] Step S103 : determining the weight corresponding to the load data at each time granularity based on the standard deviation of the load data at each time granularity.

[0057] Specifically, since different time granularities and their corresponding standard deviations have different impacts on load fluctuations, the larger the standard deviation, the more severe the load fluctuation. Therefore, a corresponding weight is determined for the load data at each time granularity to improve the accuracy of load monitoring.

[0058] Step S104 , based on the average value, standard deviation and weight of the load data at each time granularity, the comprehensive load data of the industrial data acquisition computer system at the current moment is obtained, and the comprehensive load data at the next moment is further determined.

[0059] Specifically, the load data at different time granularities at the current moment is combined to generate a comprehensive load data that reflects the overall system load level. Load monitoring continues at the next moment, enabling continuous load monitoring of the system. This comprehensive load data integrates load data at different time granularities, providing a comprehensive and balanced load assessment that avoids the biases inherent in a single time granularity. This allows the system to accurately allocate resources and ensure performance.

[0060] The embodiment of the present invention provides a load monitoring method based on multi-level data. By acquiring load data at different time granularities, it can comprehensively reflect the load situation of the system from multiple time dimensions, avoid the one-sidedness and error caused by a single time granularity, and improve the accuracy of the overall assessment. Then, the average value and standard deviation of the load data at each time granularity are calculated. The average value reflects the average level of the load at a certain time granularity, while the standard deviation describes the degree of load fluctuation. Therefore, the weight at each time granularity is determined based on the standard deviation, and the weight is dynamically adjusted according to the load fluctuation. Finally, based on the average value, standard deviation and weight, a comprehensive load data is obtained. The data not only reflects the average level of the load, but also takes into account the fluctuation and its relative importance, providing a more comprehensive and accurate load monitoring result, which helps the system to reasonably allocate resources. Through data collection and analysis at multiple time granularities, the load situation of the system can be monitored more comprehensively and accurately, reducing misjudgments and omissions. The dynamic adjustment mechanism of weights improves flexibility. At the same time, by integrating the load data, it can avoid resource waste or shortage, thereby improving the overall efficiency and performance of the system.

[0061] In this embodiment, a load monitoring method based on multi-level data is provided, which can be used for the above-mentioned terminal. Figure 2 is a flow chart of another load monitoring method based on multi-level data according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0062] Step S201 : at the current moment, obtain load data of the industrial data acquisition computer system at multiple time granularities.

[0063] Specifically, the above step S201 includes:

[0064] Step S2011 , based on the current moment and according to a preset data collection cycle, load data of multiple time intervals of the industrial data collection computer system is obtained, each time interval corresponds to a time granularity, and the interval lengths of the multiple time intervals increase sequentially.

[0065] Specifically, multiple time intervals of 1 hour, 1 day, 7 days, and 30 days are used as examples for explanation, corresponding to hourly time granularity, daily time granularity, weekly time granularity, and monthly time granularity, respectively. Assuming that the current time is 11:24 on November 30, 2024, the hourly time interval is 10:24; the daily time interval is from 11:24 on November 29 to 11:24 on November 30; the weekly time interval is from 11:24 on November 24 to 11:24 on November 30; and the monthly time interval is from 11:24 on November 1 to 11:24 on November 30. Assuming that the preset data collection period of the load data is 5 minutes, that is, the system load data is collected every five minutes, the collected load data is summarized according to the above four time intervals to obtain the load data at each time granularity.

[0066] Step S202 : Calculate the average value and standard deviation of the load data at each time granularity.

[0067] Specifically, assuming that the preset data collection period for load data is 5 minutes, that is, the system load data is collected every 5 minutes, and each time granularity usually includes multiple preset data collection periods. Therefore, for each time granularity, the load situation at each time granularity is represented by calculating the average value and standard deviation of the load data at that time granularity. For the hourly time granularity, the corresponding average value and standard deviation are obtained by the following formula (1); for the daily time granularity, the corresponding average value and standard deviation are obtained by the following formula (2); for the weekly time granularity, the corresponding average value and standard deviation are obtained by the following formula (3); for the monthly time granularity, the corresponding average value and standard deviation are obtained by the following formula (4).

[0068]

[0069] Among them, Avg hour Indicates the average value of load data at the hourly time granularity; N indicates the number of preset data collection cycles included in the hourly time granularity; Load i represents the load data collected in the i-th preset data collection cycle; σ hour Indicates the standard deviation of load data at the hourly time granularity.

[0070]

[0071] Among them, Avg day Indicates the average value of load data at the daily time granularity; Avg hour,j represents the average value of the load data in the jth hour, which can be obtained by the above formula (1); σ day Indicates the standard deviation of load data at the daily time granularity.

[0072]

[0073] Among them, Avg week Indicates the average value of load data at weekly time granularity; Avg day,k represents the average value of the load data on the kth day, which can be obtained by the above formula (2); σ week Indicates the standard deviation of load data at the weekly time granularity.

[0074]

[0075] Among them, Avg month Indicates the average value of load data at the monthly time granularity; Avg day,m represents the average value of the load data on the mth day, which can be obtained by the above formula (2); σ month Indicates the standard deviation of load data at the monthly time granularity.

[0076] Step S203 : determining the weight corresponding to the load data at each time granularity based on the standard deviation of the load data at each time granularity.

[0077] Specifically, the above step S203 includes:

[0078] Step S2031: Obtain a preset fluctuation threshold, a preset basic weight coefficient corresponding to each time granularity, and a threshold sensitivity coefficient.

[0079] Specifically, the preset fluctuation threshold is used to measure whether the load fluctuation is normal. If the standard deviation is greater than this value, the load fluctuation is considered to be strong, and the weight needs to be increased based on the preset basic weight coefficient corresponding to the time granularity. If the standard deviation is less than this value, the load fluctuation is considered to be gentle, and the weight needs to be reduced based on the preset basic weight coefficient corresponding to the time granularity. The preset basic weight coefficient is used to represent the initial value of the degree of influence of each time granularity on the load fluctuation. The threshold sensitivity coefficient is used to adjust the degree of influence of the standard deviation on the weight. Optionally, the above three values are all preset values, which can be adaptively adjusted according to actual conditions. Different values can be preset for different application scenarios to improve the flexibility of load monitoring.

[0080] Step S2032: When the standard deviation of the load data at any time granularity is greater than the preset fluctuation threshold, the preset basic weight coefficient corresponding to the time granularity is increased based on the preset fluctuation threshold, the standard deviation corresponding to the time granularity, and the threshold sensitivity coefficient to obtain the initial weight corresponding to the load data at the time granularity.

[0081] Specifically, when the standard deviation of the load data at any time granularity is greater than the preset fluctuation threshold, that is, the load data fluctuates greatly, the weight corresponding to the time granularity is increased to make it occupy a more important position in the comprehensive load evaluation. Optionally, the initial weight corresponding to the load data at each time granularity can be obtained by the following formula (5). First, the difference between the standard deviation and the preset fluctuation threshold is determined, and then the difference is multiplied by the threshold sensitivity coefficient corresponding to the time granularity to obtain a first product. Finally, the first product is added to the preset basic weight coefficient corresponding to the time granularity to obtain the initial weight.

[0082] ω time =base time +k time (σ time -σ threshold ) (5)

[0083] Among them, ω time Indicates the initial weight corresponding to the load data at any time granularity; base time Indicates the preset basic weight coefficient corresponding to the time granularity; k time Indicates the threshold sensitivity coefficient corresponding to the time granularity; σ time Indicates the standard deviation of the load data at this time granularity; σ threshold Indicates the preset fluctuation threshold.

[0084] Step S2033: When the standard deviation of the load data at any time granularity is not greater than the preset fluctuation threshold, the preset basic weight coefficient corresponding to the time granularity is reduced based on the preset fluctuation threshold, the standard deviation corresponding to the time granularity, and the threshold sensitivity coefficient to obtain the initial weight corresponding to the load data at the time granularity.

[0085] Specifically, when the standard deviation of the load data at any time granularity is not greater than the preset fluctuation threshold, that is, the load data fluctuation is small, by reducing the weight corresponding to the time granularity, its influence in the comprehensive load evaluation is reduced. Optionally, the initial weight corresponding to the load data at each time granularity can be obtained by the following formula (6). First, the difference between the preset fluctuation threshold and the standard deviation is determined, then the difference is multiplied by the threshold sensitivity coefficient corresponding to the time granularity to obtain a second product, and finally the preset basic weight coefficient corresponding to the time granularity is subtracted from the second product to obtain the initial weight.

[0086] ω time =base time -k time (σ threshold -σ time ) (6)

[0087] Among them, ω time Indicates the initial weight corresponding to the load data at any time granularity; base time Indicates the preset basic weight coefficient corresponding to the time granularity; k time Indicates the threshold sensitivity coefficient corresponding to the time granularity; σ time Indicates the standard deviation of the load data at this time granularity; σ threshold Indicates the preset fluctuation threshold.

[0088] Step S2034 , when the initial weights corresponding to the load data at all time granularities are positive values, the initial weights corresponding to the load data at each time granularity are normalized to obtain the weights corresponding to the load data at each time granularity.

[0089] In some optional implementations, the above step S2034 includes:

[0090] In step a1, the sum of the initial weights corresponding to the load data at all time granularities is determined as the weight sum.

[0091] Specifically, taking the four time granularities in the above step S201 as an example, the sum of the four initial weights is determined by the following formula (7).

[0092] ω total =ω hour +ω day +ω week +ωmonth (7)

[0093] Among them, ω total represents the weight and value; ω hour Indicates the initial weight corresponding to the hourly time granularity; ω day Indicates the initial weight corresponding to the daily time granularity; ω week represents the initial weight corresponding to the weekly time granularity; ω month Indicates the initial weight corresponding to the monthly time granularity.

[0094] Step a2: The ratio of the initial weight corresponding to the load data at each time granularity to the weight sum value is used as the weight corresponding to the load data at each time granularity.

[0095] Specifically, see the following formula (8). The ratio of each initial weight to the sum is used as the weight corresponding to the load data at each time granularity. By normalizing the initial weight corresponding to the load data at each time granularity, the sum of the weights is made 1, which facilitates subsequent comprehensive analysis.

[0096]

[0097] Among them, Normalizedω time Represents the normalized weight of the initial weight of any time granularity; ω time Indicates the initial weight corresponding to the time granularity; ω total Represents the sum of the initial weights corresponding to all time granularities.

[0098] Step S2035 , when the initial weight corresponding to the load data at any time granularity is a negative value, the initial weight corresponding to the load data at the time granularity is set to a preset weight value.

[0099] Specifically, if the initial weight calculated in the above steps S2033 and / or S2035 is a negative value, the initial weight is set to 0 or other preset value so that the weight result meets the actual application requirements, ensuring that the weights of all time granularities are within a reasonable range, and avoiding abnormal values interfering with the load monitoring results.

[0100] In some optional embodiments, taking the four time intervals assumed in step S201 as an example, four corresponding time granularities are obtained, assuming that the preset fluctuation threshold is 150; the standard deviation of the load data at the hourly time granularity is 180, the preset basic weight coefficient is 0.4, and the threshold sensitivity coefficient is 0.05; the standard deviation of the load data at the daily time granularity is 140, the preset basic weight coefficient is 0.3, and the threshold sensitivity coefficient is 0.03; the standard deviation of the load data at the weekly time granularity is 130, the preset basic weight coefficient is 0.2, and the threshold sensitivity coefficient is 0.02; the standard deviation of the load data at the monthly time granularity is 150, the preset basic weight coefficient is 0.1, and the threshold sensitivity coefficient is 0.01. Using formula (5) or (6), we can obtain the initial weight corresponding to the load data at the hourly time granularity = 0.4 + 0.05 × (180-150) = 1.9; the initial weight corresponding to the load data at the daily time granularity = 0.3 + 0.03 × (160-150) = 0.6; the initial weight corresponding to the load data at the weekly time granularity = 0.2-0.02 × (150-140) = 0; the initial weight corresponding to the load data at the monthly time granularity = 0.1-0.01 × (150-130) = -0.1 = 0 (when the initial weight is negative, it is set to the preset weight value 0). Therefore, the sum of the above four initial weights is 2.5. By normalizing them respectively through formula (8), we can obtain the weight corresponding to the load data at the hourly time granularity as 0.76; the weight corresponding to the load data at the daily time granularity as 0.24; the weight corresponding to the load data at the weekly time granularity as 0; and the weight corresponding to the load data at the monthly time granularity as 0.

[0101] Step S204 , based on the average value, standard deviation and weight of the load data at each time granularity, the comprehensive load data of the industrial data acquisition computer system at the current moment is obtained, and the comprehensive load data at the next moment is further determined.

[0102] Specifically, the above step S204 includes:

[0103] Step S2041: For each time granularity, determine the product of the threshold sensitivity coefficient and the standard deviation corresponding to the time granularity.

[0104] Step S2042: Determine the sum of the product and the average value of the load data at the time granularity.

[0105] Step S2043: The product of the sum value and the weight of the load data at the time granularity is used as the load analysis result of the time granularity.

[0106] Specifically, based on the normalized weight, average value, standard deviation and preset threshold sensitivity coefficient of the load data at each time granularity, the load analysis result of the load data obtained at each granularity is obtained by the following formula (9).

[0107] θ time =Normalizedω time ×(Avg time +k time ×σ time ) (9)

[0108] Among them, θ time Indicates the load analysis results at any time granularity; Normalizedω time Indicates the normalized weight of the time granularity; Avg time Indicates the average value of the load data at this time granularity; k time Indicates the threshold sensitivity coefficient corresponding to the time granularity; σ time Indicates the standard deviation of the load data at this time granularity.

[0109] Step S2044: The sum of the load analysis results corresponding to all time granularities is determined as comprehensive load data.

[0110] Specifically, taking four time granularities as an example, the load analysis results of the four time granularities are fused using the following formula (10) to obtain the comprehensive load data. By considering multiple time granularities for comprehensive analysis, the overall load fluctuation at the current moment can be accurately reflected. In addition, weights are introduced in the comprehensive analysis process to highlight the influence of important time granularities and improve the accuracy of load monitoring.

[0111] θ final =θ hour +θ day +θ week +θ month (10)

[0112] Among them, θ final Indicates comprehensive load data; θ hour Indicates the load analysis results corresponding to the hourly time granularity; θ day Indicates the load analysis results corresponding to the daily time granularity; θ week Represents the load analysis results corresponding to the weekly time granularity; θ month Indicates the load analysis results corresponding to the monthly time granularity.

[0113] Step S207 : Based on the comprehensive load data of the industrial data acquisition computer system at the current moment, the industrial data acquisition computer system is enabled to adjust resource allocation.

[0114] Specifically, the comprehensive load data is a dynamic value obtained by integrating multiple time granularities, such as hourly, daily, weekly and monthly load data, which can reflect the load status of the system at the current moment in real time, so that the system can adaptively adjust resource allocation based on the comprehensive load data, so as to better adapt to different load environments, especially in high-load environments. It can accurately allocate resources. If the comprehensive load data is high, the system can automatically increase resources (such as CPU, memory, etc.) to cope with the upcoming high load. If the current comprehensive load data is low, the system can reduce unnecessary resource usage and save costs. Through continuous monitoring and adjustment, the system can quickly respond to load changes and ensure stable performance.

[0115] The embodiment of the present invention provides a load monitoring method based on multi-level data. By acquiring load data at different time granularities, it can comprehensively reflect the load situation of the system from multiple time dimensions, avoid the one-sidedness and error caused by a single time granularity, and improve the accuracy of the overall assessment. Then, the average value and standard deviation of the load data at each time granularity are calculated. The average value reflects the average level of the load at a certain time granularity, while the standard deviation describes the degree of load fluctuation. Therefore, the weight at each time granularity is determined based on the standard deviation, and the weight is dynamically adjusted according to the load fluctuation. Finally, based on the average value, standard deviation and weight, a comprehensive load data is obtained. The data not only reflects the average level of the load, but also takes into account the fluctuation and its relative importance, providing a more comprehensive and accurate load monitoring result, which helps the system to reasonably allocate resources. Through data collection and analysis at multiple time granularities, the load situation of the system can be monitored more comprehensively and accurately, reducing misjudgments and omissions. The dynamic adjustment mechanism of weights improves flexibility. At the same time, by integrating the load data, it can avoid resource waste or shortage, thereby improving the overall efficiency and performance of the system.

[0116] This embodiment also provides a load monitoring device based on multi-level data, which is used to implement the above-mentioned embodiments and preferred embodiments. Details already described will not be repeated here. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0117] This embodiment provides a load monitoring device based on multi-level data, such as Figure 3 Shown, including:

[0118] The acquisition module 301 is used to acquire load data of the industrial data acquisition computer system at multiple time granularities at the current moment.

[0119] The calculation module 302 is used to calculate the average value and standard deviation of the load data at each time granularity.

[0120] The determination module 303 is configured to determine the weight corresponding to the load data at each time granularity based on the standard deviation of the load data at each time granularity.

[0121] The fusion module 304 is used to fuse the load data at each time granularity based on the average value, standard deviation and weight to obtain the comprehensive load data of the industrial data acquisition computer system at the current moment and continue to determine the comprehensive load data at the next moment.

[0122] In some optional implementations, the determining module 303 includes:

[0123] The first acquisition unit is used to acquire a preset fluctuation threshold, a preset basic weight coefficient corresponding to each time granularity, and a threshold sensitivity coefficient.

[0124] The first adjustment unit is used to increase the preset basic weight coefficient corresponding to the time granularity based on the preset fluctuation threshold, the standard deviation corresponding to the time granularity and the threshold sensitivity coefficient when the standard deviation of the load data at any time granularity is greater than the preset fluctuation threshold, so as to obtain the initial weight corresponding to the load data at the time granularity.

[0125] The second adjustment unit is used to reduce the preset basic weight coefficient corresponding to the time granularity based on the preset fluctuation threshold, the standard deviation corresponding to the time granularity and the threshold sensitivity coefficient, when the standard deviation of the load data at any time granularity is not greater than the preset fluctuation threshold, so as to obtain the initial weight corresponding to the load data at the time granularity.

[0126] The normalization unit is used to normalize the initial weight corresponding to the load data at each time granularity when the initial weights corresponding to the load data at all time granularities are positive, so as to obtain the weight corresponding to the load data at each time granularity.

[0127] In some optional embodiments, the device further comprises:

[0128] The third weight determination module is configured to set the initial weight corresponding to the load data at any time granularity to a preset weight value when the initial weight corresponding to the load data at any time granularity is a negative value.

[0129] In some optional embodiments, the normalization unit includes:

[0130] The first weight determination subunit is configured to determine the sum of initial weights corresponding to the load data at all time granularities as a weight sum.

[0131] The second weight determination subunit is configured to use the ratio of the initial weight corresponding to the load data at each time granularity to the weight sum value as the new weight corresponding to the load data at each time granularity.

[0132] In some optional implementations, the fusion module 304 includes:

[0133] The first determining unit is configured to determine, for each time granularity, a product of a threshold sensitivity coefficient corresponding to the time granularity and a standard deviation.

[0134] The second determining unit is configured to determine a sum of the product and an average value of the load data at the time granularity.

[0135] The third determining unit is configured to take the product of the sum value and the weight of the load data at the time granularity as the load analysis result of the time granularity.

[0136] The fourth determining unit is configured to determine the sum of the load analysis results corresponding to all time granularities as the comprehensive load data.

[0137] In some optional implementations, the acquisition module 301 includes:

[0138] The second acquisition unit is used to acquire load data of multiple time intervals of the industrial data acquisition computer system based on the current moment and according to the preset data acquisition cycle. Each time interval corresponds to a time granularity, and the interval lengths of the multiple time intervals increase sequentially.

[0139] In some optional embodiments, the device further comprises:

[0140] The adjustment module is used to adjust resource allocation of the industrial data acquisition computer system based on the comprehensive load data of the industrial data acquisition computer system at the current moment.

[0141] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0142] The multi-level data-based load monitoring device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0143] The embodiment of the present invention also provides a computer device having the above Figure 3 The load monitoring device shown is based on multi-level data.

[0144] See also Figure 4 , Figure 4 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 4 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 4 A processor 10 is taken as an example.

[0145] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0146] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0147] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0148] The memory 20 may include volatile memory, such as random access memory. The memory may also include non-volatile memory, such as flash memory, a hard disk, or a solid-state drive. The memory 20 may also include a combination of the above types of memory.

[0149] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 4 The bus connection is taken as an example.

[0150] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touch pad, an indicator stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 can include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display, and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0151] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or downloaded through a network and originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc. Further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0152] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0153] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A load monitoring method based on multi-level data, characterized in that: The method comprises: At the current moment, obtain the load data of the industrial data acquisition computer system at multiple time granularities; Calculate the mean and standard deviation of load data at each time granularity; Determine the weight corresponding to the load data at each time granularity based on the standard deviation of the load data at each time granularity; Based on the average value, standard deviation and weight of the load data at each time granularity, the comprehensive load data of the industrial data acquisition computer system at the current moment is obtained, and the comprehensive load data at the next moment is further determined.

2. The method according to claim 1, characterized in that The determining, based on the standard deviation of the load data at each time granularity, a weight corresponding to the load data at each time granularity includes: Obtain the preset fluctuation threshold, the preset basic weight coefficient corresponding to each time granularity, and the threshold sensitivity coefficient; When the standard deviation of the load data at any time granularity is greater than the preset fluctuation threshold, the preset basic weight coefficient corresponding to the time granularity is increased based on the preset fluctuation threshold, the standard deviation corresponding to the time granularity, and the threshold sensitivity coefficient to obtain the initial weight corresponding to the load data at the time granularity; When the standard deviation of the load data at any time granularity is not greater than the preset fluctuation threshold, based on the preset fluctuation threshold, the standard deviation corresponding to the time granularity, and the threshold sensitivity coefficient, the preset basic weight coefficient corresponding to the time granularity is reduced to obtain the initial weight corresponding to the load data at the time granularity; When the initial weights corresponding to the load data at all time granularities are positive values, the initial weights corresponding to the load data at each time granularity are normalized to obtain the weights corresponding to the load data at each time granularity.

3. The method according to claim 2, characterized in that The method further comprises: When the initial weight corresponding to the load data at any time granularity is a negative value, the initial weight corresponding to the load data at the time granularity is set to a preset weight value.

4. The method according to claim 2, characterized in that When the initial weights corresponding to the load data at all time granularities are positive, the initial weights corresponding to the load data at each time granularity are normalized to obtain the weights corresponding to the load data at each time granularity, including: The sum of the initial weights corresponding to the load data at all time granularities is determined as the weight sum; The ratio of the initial weight corresponding to the load data at each time granularity to the weight sum value is used as the weight corresponding to the load data at each time granularity.

5. The method according to claim 2, characterized in that The average value, standard deviation and weight of the load data at each time granularity are fused to obtain the comprehensive load data of the industrial data acquisition computer system at the current moment, including: For each time granularity, determine the product of the threshold sensitivity coefficient and the standard deviation corresponding to the time granularity; Determine a sum of the product and an average value of the load data at the time granularity; The product of the sum and the weight of the load data at the time granularity is used as the load analysis result of the time granularity; The sum of the load analysis results corresponding to all time granularities is determined as the comprehensive load data.

6. The method according to claim 1, characterized in that The step of obtaining load data of the industrial data acquisition computer system at multiple time granularities at the current moment includes: Based on the current moment and in accordance with a preset data collection cycle, load data of multiple time intervals of the industrial data collection computer system is obtained, each time interval corresponds to a time granularity, and the interval lengths of the multiple time intervals increase sequentially.

7. The method according to claim 1, characterized in that The method further comprises: Based on the comprehensive load data of the industrial data acquisition computer system at the current moment, the industrial data acquisition computer system adjusts resource allocation.

8. A load monitoring device based on multi-level data, characterized in that: The device comprises: An acquisition module is used to acquire load data of the industrial data acquisition computer system at multiple time granularities at the current moment; The calculation module is used to calculate the average value and standard deviation of the load data at each time granularity; A determination module, configured to determine a weight corresponding to the load data at each time granularity based on a standard deviation of the load data at each time granularity; The fusion module is used to fuse the load data at each time granularity based on the average value, standard deviation and weight to obtain the comprehensive load data of the industrial data acquisition computer system at the current moment and continue to determine the comprehensive load data at the next moment.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the load monitoring method based on multi-level data according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the load monitoring method based on multi-level data according to any one of claims 1 to 7.

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