CN118193317A - Method and system for collecting server environment data - Google Patents
Method and system for collecting server environment data Download PDFInfo
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Abstract
The invention discloses a server environment data acquisition method and a system, wherein the method comprises the following steps: setting a pulsation trigger threshold for pulsation data acquisition based on steady state and dynamic characteristics of server operation; monitoring parameters in the running environment of the server in real time, and triggering pulse data acquisition to acquire the triggered monitoring parameter values and the predefined environment state parameter values when the monitoring parameters of the server environment exceed or are lower than the pulse triggering threshold; and carrying out nonlinear dynamic data fusion on the detection parameter value and the environment state parameter value to obtain fused server environment data. By utilizing the embodiment of the invention, the accurate monitoring and evaluation of the server environment state can be realized, meanwhile, the fusion processing can provide more accurate description of the server environment actual state, and the method has high practicability and application prospect in the running and maintenance processes of the server.
Description
Technical Field
The invention belongs to the technical field of servers, and particularly relates to a server environment data acquisition method and system.
Background
The data collection methods in modern network server environments are diverse and these methods often need to be implemented by software or hardware installed on the server. Such software or hardware requires server resources and often requires specialized technician operations and administration, which can also have an impact on the stability and security of the server.
The traditional data acquisition method often adopts a polling mode to acquire information on the server, which needs to periodically initiate a request to the server, and a certain burden is caused to the server. And because the time for acquiring the data is not fixed, the acquired information is often inaccurate or incomplete.
Disclosure of Invention
The invention aims to provide a server environment data acquisition method and system, which solve the defects in the prior art, can realize accurate monitoring and evaluation of the server environment state, can provide more accurate description of the actual state of the server environment through fusion processing, and has high practicability and application prospect in the running and maintenance processes of the server.
One embodiment of the application provides a server environment data acquisition method, which comprises the following steps:
setting a pulsation trigger threshold for pulsation data acquisition based on steady state and dynamic characteristics of server operation;
Monitoring parameters in the running environment of the server in real time, and triggering pulse data acquisition to acquire the triggered monitoring parameter values and the predefined environment state parameter values when the monitoring parameters of the server environment exceed or are lower than the pulse triggering threshold;
And carrying out nonlinear dynamic data fusion on the detection parameter value and the environment state parameter value to obtain fused server environment data.
Optionally, the pulsation triggering threshold is set in the following manner:
Θ＝α·P{avg}+β·ΔP{max}+γ·∑(E_{env})
Wherein Θ is a pulsation trigger threshold, p_ { avg } is an average value of the monitored parameter, Δp_ { max } is a maximum variation amplitude of the parameter in a specific monitoring period, Σ (e_ { env }) is an environmental factor comprehensive score, and α, β and γ are adjustment coefficients.
Optionally, the performing nonlinear dynamic data fusion on the detection parameter value and the environmental state parameter value includes:
The following nonlinear dynamic data fusion formula is adopted for data processing:
The d_ { fused } is the fused data, the alpha_i is the weighting coefficient of the ith monitored parameter, the d_i is the original value of the ith monitored parameter, the G is the original value of the environmental state parameter, the beta_i is the nonlinear adjustment coefficient related to both the ith monitored parameter and the environmental state parameter, the f is the nonlinear dynamic system mapping function, and the n is the total number of the monitored parameters.
Optionally, the predefined environmental state parameters include temperature, humidity, power consumption, network traffic.
Yet another embodiment of the present application provides a server environment data acquisition system, the system comprising:
The setting module is used for setting a pulsation trigger threshold value for pulsation data acquisition based on steady state and dynamic characteristics of server operation;
the triggering module is used for monitoring parameters in the server running environment in real time, and triggering pulse data acquisition to acquire the triggered monitoring parameter value and the predefined environment state parameter value when the monitoring parameters of the server environment exceed or are lower than the pulse triggering threshold value;
and the fusion module is used for carrying out nonlinear dynamic data fusion on the detection parameter value and the environment state parameter value to obtain fused server environment data.
A further embodiment of the application provides a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the method of any of the preceding claims when run.
Yet another embodiment of the application provides an electronic device comprising a memory having a computer program stored therein and a processor configured to run the computer program to perform the method recited in any of the preceding claims.
Compared with the prior art, the server environment data acquisition method provided by the invention has the advantages that the pulsation trigger threshold value for pulsation data acquisition is set based on the steady state and dynamic characteristics of the operation of the server; monitoring parameters in the running environment of the server in real time, and triggering pulse data acquisition to acquire the triggered monitoring parameter values and the predefined environment state parameter values when the monitoring parameters of the server environment exceed or are lower than the pulse triggering threshold; and carrying out nonlinear dynamic data fusion on the detection parameter value and the environment state parameter value to obtain fused server environment data, so that the accurate monitoring and evaluation of the server environment state can be realized, and meanwhile, the fusion processing can provide more accurate description of the actual state of the server environment, and the method has high practicability and application prospect in the running and maintenance processes of the server.
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Fig. 1 is a hardware block diagram of a computer terminal of a server environment data acquisition method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a server environment data collection method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a server environment data acquisition system according to an embodiment of the present invention.
Detailed Description
The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
The embodiment of the invention firstly provides a server environment data acquisition method which can be applied to electronic equipment such as a computer terminal, in particular to a common computer and the like.
The following describes the operation of the computer terminal in detail by taking it as an example. Fig. 1 is a hardware block diagram of a computer terminal of a distributed energy storage management method according to an embodiment of the present invention. As shown in fig. 1, the computer terminal may include one or more (only one is shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a microprocessor MCU or a processing device such as a programmable logic device FPGA) and a memory 104 for storing data, and optionally, a transmission device 106 for communication functions and an input-output device 108. It will be appreciated by those skilled in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the configuration of the computer terminal described above. For example, the computer terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used to store software programs and modules of application software, such as program instructions/modules corresponding to the distributed energy storage management method in the embodiment of the present application, and the processor 102 executes the software programs and modules stored in the memory 104 to perform various functional applications and data processing, i.e., implement the method described above. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor 102, which may be connected to the computer terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission means 106 is arranged to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.
Referring to fig. 2, an embodiment of the present invention provides a server environment data collection method, which may include the following steps:
S201, setting a pulsation trigger threshold value for pulsation data acquisition based on steady state and dynamic characteristics of server operation;
specifically, a pulsation triggering threshold setting mode is as follows:
Θ＝α·P{avg}+β·ΔP{max}+γ·∑(E_{env})
Where Θ is a pulsation trigger threshold, which is a threshold that decides when to trigger a pulsation data acquisition. When the monitored server environmental parameter exceeds or falls below this threshold, the system automatically begins to collect the relevant monitoring parameter value and the predefined environmental state parameter value. The threshold is set to intelligently adjust the frequency of data acquisition, ensure that data is captured at key time, and reduce unnecessary data acquisition at the same time so as to save resources.
The p_ { avg } is the average value of the monitored parameter, which represents the average level of the monitored parameter over a particular monitoring period. The method reflects the general state of the server environment parameters and can help judge whether the server operates in a normal range. Incorporating the average value into the calculation of the pulsation trigger threshold ensures that the setting of the threshold is based on stable long-term observations, rather than short-term fluctuations.
The Δp_ { max } is the maximum amplitude of change of the parameter over a particular monitoring period, which takes into account the maximum rate of change of the monitored parameter over a particular time, i.e., the short-term volatility of the parameter. By incorporating this dynamic feature into the calculation of the threshold, the data collection method can be made sensitive to sudden events or rapidly changing environments, thereby ensuring that data collection is triggered when critical changes occur.
The Σ (e_ { env }) is an environmental factor composite score, which is a composite score that quantifies the impact of all relevant environmental factors (e.g., temperature, humidity, power consumption, network traffic, etc.) in the server operating environment. The parameters enable the pulsation trigger threshold to comprehensively consider the influence of a plurality of environmental factors on the performance of the server, so that the data acquisition strategy is adjusted more accurately.
The α, β and γ are adjustment coefficients that are used to adjust the impact weight of each part of the formula, allowing customization and flexibility. By adjusting these coefficients, the sensitivity of the pulse data acquisition can be adjusted for different operating environments and acquisition requirements. For example, in some circumstances it may be desirable to more appreciate the effect of temperature changes on server performance, which may be accomplished by increasing the coefficients of the corresponding parameters.
Specifically, the application provides a method for intelligently determining a pulsation trigger threshold by combining a running opportunity machine learning model and server historical performance data so as to optimize data acquisition frequency and quality and reduce unnecessary resource consumption. The method comprises the following steps:
Step 1: historical performance data is collected for the historical operating data collection server under different environmental conditions (e.g., different temperature, humidity, power consumption, and network traffic conditions). Such data includes steady state and dynamic characteristics of the server such as CPU utilization, memory usage, I/O throughput, etc.
Step 2: analysis and calibration of historical data the collected historical performance data is analyzed in time series and the data is calibrated into different status categories such as "low load", "medium load", "high load", etc. And simultaneously, recording the corresponding environmental parameter characteristics (such as temperature, humidity and the like) of each state type.
Step 3: the method comprises the steps of constructing a machine learning model by utilizing historical performance data and environmental parameter characteristics. The model is able to predict the state (steady state or dynamic) that the server is likely to enter next based on the performance metrics and environmental state of the current server. The model can be one of decision tree, random forest, gradient lifting tree and other machine learning algorithms.
Step 4: the real-time calculation of the pulsation trigger threshold utilizes a constructed machine learning model to input the performance index and environmental state data of the current server in real time, and predicts the state class (such as from 'low load' to 'high load') to be entered by the server. Based on the prediction result, the coefficients (alpha, beta, gamma) in the calculation formula of the pulsation trigger threshold value theta are dynamically adjusted to adapt to different running states and environmental changes. For example, when the prediction server is to face higher loads, the threshold may be raised to reduce the frequency of data acquisition at high pressures, and vice versa.
Step 5: and applying the pulsation trigger threshold value theta after dynamic adjustment to the real-time monitoring system. Triggering pulse data acquisition when the monitoring parameter of the server environment exceeds or falls below a set pulse triggering threshold, and acquiring related monitoring parameters and predefined environment state parameter values.
This particular implementation allows the pulsation trigger threshold to be adaptively dynamically adjusted according to the current state of the server and environmental changes by combining real-time machine learning predictions with historical performance data analysis. The method can not only capture key performance changes more effectively, but also optimize resource use and reduce the frequency of data acquisition at insignificant moments, thereby improving the efficiency and effectiveness of data acquisition.
S202, monitoring parameters in a server running environment in real time, and triggering pulse data acquisition to acquire triggered monitoring parameter values and predefined environment state parameter values when the monitoring parameters of the server environment exceed or are lower than the pulse triggering threshold;
In particular, the predefined environmental state parameters include, but are not limited to, temperature, humidity, power consumption, network traffic. One specific implementation includes the following:
Step 1, setting monitoring parameters and predefined environment state parameters, firstly, defining server environment parameters to be monitored. These parameters may include, but are not limited to, temperature, humidity, power consumption, network traffic, etc. of the server. At the same time, predefined environmental state parameters are determined, which are critical information that needs to be acquired in the pulse data acquisition, which will provide a basis for subsequent data analysis.
And 2, implementing a real-time parameter monitoring mechanism, deploying a sensor and monitoring software on the server, and monitoring preset server environment parameters in real time. These monitoring tools need to be able to collect data with high accuracy and frequency to ensure that any subtle changes in environmental parameters can be accurately captured.
And 3, setting a pulsation trigger threshold value, and calculating an average value P_ { avg }, a maximum change amplitude delta P_ { max } and an environmental factor comprehensive score sigma (E_ { env }) of each monitoring parameter based on historical data of the server in normal operation by using the pulsation trigger threshold value formula. And then adjusting an adjusting coefficient in the formula according to the actual running requirement of the server so as to set a pulsation triggering threshold suitable for the current server environment.
And 4, an automatic detection mechanism for realizing that the parameter exceeds or is lower than the threshold value can be used for developing an automatic script or using the existing monitoring software function to compare the monitored parameter value with the pulsation triggering threshold value in real time. The mechanism should trigger the data acquisition procedure immediately once any of the monitored parameter values exceeds or falls below a threshold value.
And 5, triggering pulse data acquisition, wherein when the detection mechanism in the step 4 determines that a certain parameter exceeds a threshold value, the system automatically triggers data acquisition. This includes recording the monitoring parameter values exceeding the threshold at the current point in time, and simultaneously acquiring all predefined environmental state parameter values.
And 6, storing and preprocessing the collected data, storing the collected monitoring parameter values and the environment state parameter values into a database, and carrying out necessary preprocessing. The preprocessing steps may include data cleansing, data normalization, etc., to prepare clean, consistent data sets for subsequent data fusion and analysis.
And 7, data auditing and anomaly detection (optional) are carried out periodically after data acquisition. This step aims at ensuring the quality of the data and identifying and correcting potential data acquisition errors or anomalies in time.
Through the series of steps, an efficient and dynamic server environment data acquisition method can be realized. The system and the method allow data to be automatically collected when key changes occur, so that timely and accurate information support is provided for maintenance and optimization of the server.
And S203, carrying out nonlinear dynamic data fusion on the detection parameter value and the environment state parameter value to obtain fused server environment data.
Specifically, the following nonlinear dynamic data fusion formula can be adopted for data processing:
Wherein, D_ { fused } is the fused data, and represents the server environment data obtained after nonlinear dynamic data fusion. It integrates a plurality of monitoring parameters and environmental status parameters for more accurately reflecting the current environmental status of the server.
The alpha_i is a weighting coefficient of the ith monitoring parameter, and the weighting coefficient determines the importance or contribution degree of the corresponding monitoring parameter in the fusion process. Different monitoring parameters may have different degrees of influence on the environmental state of the server, so the accuracy and representativeness of the fused data can be optimized by adjusting the weighting coefficient of each parameter.
The D_i is the original value of the ith monitoring parameter. These values are monitored in real-time directly from the server environment, including but not limited to temperature, humidity, power consumption, network traffic, etc.
The G is the original value of the environmental state parameter, which represents a predefined environmental state parameter that needs to be considered in addition to the real-time monitoring parameters, such as the climate conditions of the area where the server is located, the air quality of the server room, etc., which may also have an important influence on the server environment.
The beta_i is a nonlinear adjustment coefficient related to the ith monitoring parameter and the environmental state parameter, and the coefficient is used for adjusting the sensitivity of the nonlinear mapping function to each monitoring parameter and each environmental state parameter so as to optimize the quality and the accuracy of the fused data.
The meaning of this formula is that it provides a method to efficiently fuse multiple monitoring parameters and environmental state parameters in a server environment in a non-linear and dynamic manner. The fusion not only considers the independent influence of each parameter, but also captures the interaction and dynamic change between the parameters through the nonlinear mapping function, thereby being capable of reflecting the real-time environment state of the server more comprehensively and accurately. The method has important practical value in the aspects of improving performance monitoring, resource management, fault prediction and the like of the server.
And f is a nonlinear dynamic system mapping function, and n is the total number of monitoring parameters.
Specifically, one implementation may include:
Step 1, data preprocessing firstly, collecting and preparing monitoring parameter values and environment state parameter values. These parameters include, but are not limited to, temperature, humidity, power consumption, network traffic, etc. The data is preprocessed, including data cleansing (e.g., outlier removal, complement missing values) and data normalization, to ensure the quality and consistency of the input data.
And 2, defining a nonlinear dynamic system mapping function, and realizing the nonlinear dynamic system mapping function f as a depth self-encoder network structure according to the fusion formula. A self-encoder is an artificial neural network that learns a compressed representation of data by unsupervised learning, where the encoder portion maps the input data to a hidden representation space and the decoder portion reconstructs the representation into the original data. In this step, the learning objective of the self-encoder is to find the non-linear relationship in the monitored parameter and the environmental state parameter and map it to a low-dimensional representation space.
And 3, determining a weighting coefficient and a nonlinear adjustment coefficient, distributing a weighting coefficient alpha_i for each monitoring parameter D_i, and assigning one or more nonlinear adjustment coefficients beta_i for the whole model. These coefficients may be set manually based on a priori knowledge or automatically adjusted during training by an optimization algorithm (e.g., gradient descent). These coefficients will be used to adjust the magnitude of each parameter contribution to the fusion data, as well as to regulate the sensitivity and complexity of the nonlinear mapping function.
And 4, training the self-encoder model by using the preprocessed monitoring parameters and the preprocessed environmental state parameter values as input data. Model parameters, including weights and biases in the depth network, weighting coefficients alpha_i, and nonlinear adjustment coefficients beta_i, are optimized during training by minimizing the loss (e.g., mean square error) between the input data and the reconstructed data.
And 5, realizing nonlinear dynamic data fusion by using a self-encoder model trained by realizing nonlinear dynamic data fusion. Specifically, by inputting the monitored parameter and environmental state parameter values into the trained self-encoder, the model will output a fused low-dimensional representation D_ { fused }. This process essentially fuses the original high-dimensional data into a form with rich information and convenient for further analysis by means of the nonlinear mapping function f found from the encoder, and the dynamically adjusted weighting coefficients alpha_i and nonlinear adjustment coefficients beta_i, using the fusion formula described above.
And 6, finally, applying the fused data, wherein the fused data D_ { fused } can be used for various application scenes such as performance monitoring, fault diagnosis, load prediction and the like of the server. The fused data can be used directly in a decision support system or further analyzed by a machine learning model to extract deeper holes as needed.
Through the steps, a nonlinear dynamic data fusion method which is realized by combining a fusion formula and a self-encoder network in deep learning can be realized, and an advanced, efficient and flexible solution is provided for server environment data acquisition.
It can be seen that by setting the pulsation trigger threshold for pulsation data acquisition based on steady state and dynamic characteristics of the server operation; monitoring parameters in the running environment of the server in real time, and triggering pulse data acquisition to acquire the triggered monitoring parameter values and the predefined environment state parameter values when the monitoring parameters of the server environment exceed or are lower than the pulse triggering threshold; and carrying out nonlinear dynamic data fusion on the detection parameter value and the environment state parameter value to obtain fused server environment data, so that the accurate monitoring and evaluation of the server environment state can be realized, and meanwhile, the fusion processing can provide more accurate description of the actual state of the server environment, and the method has high practicability and application prospect in the running and maintenance processes of the server.
Still another embodiment of the present invention provides a server environment data acquisition system, see fig. 3, which may include:
A setting module 301, configured to set a pulsation trigger threshold for pulsation data acquisition based on steady-state and dynamic characteristics of server operation;
The triggering module 302 is configured to monitor parameters in the server operating environment in real time, and trigger pulse data acquisition to acquire the triggered monitoring parameter value and the predefined environmental state parameter value when the monitoring parameter of the server environment exceeds or falls below the pulse triggering threshold;
And the fusion module 303 is configured to fuse the detection parameter value and the environmental state parameter value with nonlinear dynamic data, so as to obtain fused server environmental data.
It can be seen that by setting the pulsation trigger threshold for pulsation data acquisition based on steady state and dynamic characteristics of the server operation; monitoring parameters in the running environment of the server in real time, and triggering pulse data acquisition to acquire the triggered monitoring parameter values and the predefined environment state parameter values when the monitoring parameters of the server environment exceed or are lower than the pulse triggering threshold; and carrying out nonlinear dynamic data fusion on the detection parameter value and the environment state parameter value to obtain fused server environment data, so that the accurate monitoring and evaluation of the server environment state can be realized, and meanwhile, the fusion processing can provide more accurate description of the actual state of the server environment, and the method has high practicability and application prospect in the running and maintenance processes of the server.
The embodiment of the invention also provides a storage medium, in which a computer program is stored, wherein the computer program is configured to perform the steps of any of the method embodiments described above when run.
Specifically, in the present embodiment, the above-described storage medium may be configured to store a computer program for executing the steps of:
S201, setting a pulsation trigger threshold value for pulsation data acquisition based on steady state and dynamic characteristics of server operation;
S202, monitoring parameters in a server running environment in real time, and triggering pulse data acquisition to acquire triggered monitoring parameter values and predefined environment state parameter values when the monitoring parameters of the server environment exceed or are lower than the pulse triggering threshold;
And S203, carrying out nonlinear dynamic data fusion on the detection parameter value and the environment state parameter value to obtain fused server environment data.
It can be seen that by setting the pulsation trigger threshold for pulsation data acquisition based on steady state and dynamic characteristics of the server operation; monitoring parameters in the running environment of the server in real time, and triggering pulse data acquisition to acquire the triggered monitoring parameter values and the predefined environment state parameter values when the monitoring parameters of the server environment exceed or are lower than the pulse triggering threshold; and carrying out nonlinear dynamic data fusion on the detection parameter value and the environment state parameter value to obtain fused server environment data, so that the accurate monitoring and evaluation of the server environment state can be realized, and meanwhile, the fusion processing can provide more accurate description of the actual state of the server environment, and the method has high practicability and application prospect in the running and maintenance processes of the server.
The present invention also provides an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
Specifically, the electronic apparatus may further include a transmission device and an input/output device, where the transmission device is connected to the processor, and the input/output device is connected to the processor.
Specifically, in the present embodiment, the above-described processor may be configured to execute the following steps by a computer program:
S201, setting a pulsation trigger threshold value for pulsation data acquisition based on steady state and dynamic characteristics of server operation;
S202, monitoring parameters in a server running environment in real time, and triggering pulse data acquisition to acquire triggered monitoring parameter values and predefined environment state parameter values when the monitoring parameters of the server environment exceed or are lower than the pulse triggering threshold;
And S203, carrying out nonlinear dynamic data fusion on the detection parameter value and the environment state parameter value to obtain fused server environment data.
Specifically, the specific examples in this embodiment may refer to the examples described in the foregoing embodiments and the optional implementation manners, and this embodiment is not repeated herein.
It can be seen that by setting the pulsation trigger threshold for pulsation data acquisition based on steady state and dynamic characteristics of the server operation; monitoring parameters in the running environment of the server in real time, and triggering pulse data acquisition to acquire the triggered monitoring parameter values and the predefined environment state parameter values when the monitoring parameters of the server environment exceed or are lower than the pulse triggering threshold; and carrying out nonlinear dynamic data fusion on the detection parameter value and the environment state parameter value to obtain fused server environment data, so that the accurate monitoring and evaluation of the server environment state can be realized, and meanwhile, the fusion processing can provide more accurate description of the actual state of the server environment, and the method has high practicability and application prospect in the running and maintenance processes of the server.
While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.
Claims (10)
1. A method for collecting server environment data, the method comprising:
setting a pulsation trigger threshold for pulsation data acquisition based on steady state and dynamic characteristics of server operation;
Monitoring parameters in the running environment of the server in real time, and triggering pulse data acquisition to acquire the triggered monitoring parameter values and the predefined environment state parameter values when the monitoring parameters of the server environment exceed or are lower than the pulse triggering threshold;
And carrying out nonlinear dynamic data fusion on the detection parameter value and the environment state parameter value to obtain fused server environment data.
2. The method according to claim 1, wherein the pulsation trigger threshold is set in the following manner:
Θ＝α·P{avg}+β·ΔP{max}+γ·∑(E_{env})
Wherein Θ is a pulsation trigger threshold, p_ { avg } is an average value of the monitored parameter, Δp_ { max } is a maximum variation amplitude of the parameter in a specific monitoring period, Σ (e_ { env }) is an environmental factor comprehensive score, and α, β and γ are adjustment coefficients.
3. The method of claim 2, wherein said non-linear dynamic data fusion of said detection parameter values and said environmental state parameter values comprises:
The following nonlinear dynamic data fusion formula is adopted for data processing:
The d_ { fused } is the fused data, the alpha_i is the weighting coefficient of the ith monitored parameter, the d_i is the original value of the ith monitored parameter, the G is the original value of the environmental state parameter, the beta_i is the nonlinear adjustment coefficient related to both the ith monitored parameter and the environmental state parameter, the f is the nonlinear dynamic system mapping function, and the n is the total number of the monitored parameters.
4. The method of claim 3, wherein the predefined environmental state parameters include temperature, humidity, power consumption, network traffic.
5. A server environment data acquisition system, the system comprising:
The setting module is used for setting a pulsation trigger threshold value for pulsation data acquisition based on steady state and dynamic characteristics of server operation;
the triggering module is used for monitoring parameters in the server running environment in real time, and triggering pulse data acquisition to acquire the triggered monitoring parameter value and the predefined environment state parameter value when the monitoring parameters of the server environment exceed or are lower than the pulse triggering threshold value;
and the fusion module is used for carrying out nonlinear dynamic data fusion on the detection parameter value and the environment state parameter value to obtain fused server environment data.
6. The system of claim 5, wherein the pulsation trigger threshold is set in a manner that:
Θ＝α·P{avg}+β·ΔP{max}+γ·∑(E_{env})
Wherein Θ is a pulsation trigger threshold, p_ { avg } is an average value of the monitored parameter, Δp_ { max } is a maximum variation amplitude of the parameter in a specific monitoring period, Σ (e_ { env }) is an environmental factor comprehensive score, and α, β and γ are adjustment coefficients.
7. The system of claim 6, wherein said non-linear dynamic data fusion of said detection parameter values and said environmental state parameter values comprises:
The following nonlinear dynamic data fusion formula is adopted for data processing:
The d_ { fused } is the fused data, the alpha_i is the weighting coefficient of the ith monitored parameter, the d_i is the original value of the ith monitored parameter, the G is the original value of the environmental state parameter, the beta_i is the nonlinear adjustment coefficient related to both the ith monitored parameter and the environmental state parameter, the f is the nonlinear dynamic system mapping function, and the n is the total number of the monitored parameters.
8. The system of claim 7, wherein the predefined environmental status parameters include temperature, humidity, power consumption, network traffic.
9. A storage medium having a computer program stored therein, wherein the computer program is arranged to perform the method of any of claims 1-4 when run.
10. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the method of any of claims 1-4.
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