Intelligent monitoring method, device and equipment for machine room power and storage medium

Abstract

The invention relates to the technical field of intelligent monitoring, and discloses an intelligent monitoring method, device and equipment for machine room power and a storage medium, and the method comprises the steps: collecting the total current and total voltage values of each machine room device in a plurality of historical cycles, and the current value of each electric energy unit, carrying out the normalization processing, and constructing a training and testing set; and actual electric energy data are calculated. And based on the variation of the total current of the adjacent historical cycles, establishing a time sequence weight for each device, and forming time sequence weight data. Meanwhile, the time sequence coefficient of the equipment is calculated by utilizing the unit current value and the total current value, and time sequence coefficient data is obtained. And in combination with the time sequence coefficient and the weight data, calculating an influence value and probability distribution of each device, and obtaining a time sequence total influence probability. And training a neural network model on the basis of the time sequence total influence probability, the data time sequence and the actual electric energy data, and finally realizing accurate prediction of the electric energy consumption of the machine room equipment. According to the method, the electric energy consumed by the machine room equipment is accurately predicted.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and in particular to an intelligent monitoring method, device, equipment and storage medium for computer room power. Background Art

[0002] With the rapid development of information technology, the stability and security of the computer room as the core place for data processing and storage have become the lifeline of enterprise operations. The computer room power environment monitoring system has therefore become an indispensable part of modern computer room management. It not only covers the comprehensive monitoring of the power system and computer room environment, but also includes management and control based on monitoring data. Through continuous monitoring of key parameters such as voltage and current of the power system, the system can ensure the stable operation of the equipment. At the same time, through intelligent adjustment and control, the efficiency and safety of the power system can be improved. For the computer room environment, the system focuses on monitoring parameters such as temperature, humidity, and air quality to ensure that all equipment can operate in an optimal environment, thereby improving the reliability of the equipment and extending its service life. In the daily maintenance and management of the computer room, the power environment monitoring system supports two modes: remote monitoring and local monitoring. The former allows remote access to the monitoring equipment in the computer room through the Internet or a dedicated network, and the latter is to monitor and manage directly through local equipment on site in the computer room. Both monitoring methods rely on real-time monitoring data, which is collected by various sensors or monitoring equipment installed in the computer room. The types and number of sensors will vary depending on the specific size and complexity of the computer room, but together they form the basis of the computer room power environment monitoring system, providing solid technical support for efficient and safe computer room management.

[0003] At present, most data centers use energy consumption monitoring systems based on sensor networks to collect and analyze power data. These systems usually include smart meters and sensors installed in key locations such as distribution cabinets and server racks, which can collect parameters such as voltage, current, and power factor in real time, and transmit the data to the central management system via wired or wireless methods. Smart meters and sensors can not only provide accurate power data, but also monitor the operating status of equipment and detect potential faults and abnormal conditions in a timely manner. At the same time, data centers use cloud computing and big data technologies to conduct in-depth analysis of the collected data to optimize energy use strategies. Through the cloud computing platform, data centers can realize centralized management and analysis of data, so as to better understand and predict energy use and formulate more scientific energy-saving measures.

[0004] Although existing power data monitoring technologies have achieved certain results, there are still some significant defects in practical applications. For example, traditional monitoring systems are insufficient in terms of accuracy and real-time performance of data prediction, especially when faced with complex and changing workloads in large-scale data centers. Existing solutions often have difficulty in accurately predicting the power consumed by equipment in computer rooms. Summary of the invention

[0005] The present invention provides an intelligent monitoring method, device, equipment and storage medium for computer room power, so as to realize accurate prediction of electric energy consumed by equipment in the computer room.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides an intelligent monitoring method for power in a computer room, comprising: Collect the total current value and the corresponding total voltage value of each equipment in the computer room in several historical periods, and collect the unit current value of each power unit; Normalizing the total current value and the total voltage value to construct a training set and a test set; Calculate according to the total current value and the total voltage value to obtain actual electric energy data; According to the increment of the total current value in adjacent historical cycles, a timing weight of each equipment in the computer room is constructed to obtain timing weight data; Calculate the timing coefficient of the corresponding equipment in the computer room according to the unit current value and the total current value to obtain timing coefficient data; Constructing the influence value of each equipment in the computer room according to the timing coefficient data and the timing weight data, and calculating the probability distribution of the influence value to obtain the total timing influence probability; The training set and the test set are used as training data, and a neural network model is trained according to the total time sequence influence probability, the time sequence of the collected data, and the actual electric energy data to obtain a trained neural network model; The output of the neural network model is determined as electric energy prediction data, wherein the electric energy prediction data is used to predict the percentage of electric energy consumed by each equipment in the computer room at a future moment.

[0007] In an optional implementation, constructing the timing weight of each equipment in the computer room according to the increment of the total current value in adjacent historical cycles to obtain timing weight data includes: For The following formula is used to calculate the equipment in each historical period. Total current increase within: in, Indicates The equipment in the computer room is The total current value increment of the historical cycle, Indicates The equipment in the computer room is The total current value of the historical cycle, Indicates The equipment in the computer room is Total current value of historical cycles; Collect equipment from each computer room In all historical periods The total current increments within form a total current increment set; The total current increment set is used as an input variable and input into the following function to select the median as the timing weight: in, Indicates The timing weight of the equipment in the computer room, represents a preset computer program algorithm, Respectively represent The 1st to the 2nd equipment in the computer room The total current increment per cycle is Indicates the total number of historical cycles; The timing weights of all equipment in the computer room are collected to form a timing weight set, and timing weight data is obtained. In an optional implementation manner, the calculating the timing coefficient of the corresponding equipment in the computer room according to the total current value and the unit current value of the corresponding electric energy unit to obtain the timing coefficient data includes: According to The total current value of the equipment in the computer room and the corresponding The unit current value of the electric energy unit is calculated by the following formula Among the equipment in the computer room The current proportion coefficient of each power unit: in, Indicates Among the equipment in the computer room The proportion of electric energy units, Indicates The total current value of the equipment in the computer room, No. The first The unit current value of each electric energy unit, Represents historical cycles; According to the current ratio, the following formula is used to calculate the current ratio: Timing coefficient of each equipment room: in, Indicates The timing coefficient of each equipment in the computer room, Indicates The average current share of the equipment in each computer room is Indicates Among the equipment in the computer room The proportion of electric energy units, Indicates The number of electrical energy units contained in each equipment room, Represents historical cycles; The timing coefficients of each equipment in the computer room are collected to form a timing coefficient set to obtain timing coefficient data.

[0008] In an optional implementation, constructing the influence value of each equipment in the computer room according to the timing coefficient data and the timing weight data and calculating the probability distribution of the influence value to obtain the total timing influence probability includes: Accumulate and sum the timing coefficient data and the corresponding timing weight data of all equipment in the computer room to obtain the impact value data of each equipment in the computer room; Taking the impact value data of each equipment in the computer room in several historical periods as discrete data, statistically classifying the discrete data, and obtaining probability distribution data of impact values ​​of several training levels; Calculating the probability of the impact value of each training level according to the probability distribution data to obtain a plurality of level probability data; Taking each training level as an index, several level probability data are accumulated and summed to obtain the total time series impact probability.

[0009] In an optional implementation, the training process of the neural network model includes: Inputting the training data into the input layer of the neural network model in chronological order for training, and obtaining the predicted value data output by the output layer of the neural network model; Substituting the predicted value data and the actual electric energy data into a loss function to calculate a loss value, thereby obtaining loss value data; Calculate the gradient of the output layer output of the neural network model according to the loss value data, and pass the gradient forward layer by layer through the chain rule to calculate the gradient of each layer parameter to obtain gradient data; Update the parameters of each layer of the neural network model according to the gradient data and the preset learning rate; The parameters of each layer are updated repeatedly until the training is completed when the number of training times of the neural network model is greater than a preset number of times, or when the loss function value of the neural network model is less than a preset loss threshold.

[0010] In an optional implementation manner, the calculating the loss value data by using the predicted value and the actual electric energy data to obtain the loss value data includes: The loss value data is calculated by the following loss function: in, Represents the loss value data, Represents the power forecast data, Indicates the actual data of electric energy. Indicates the number of computer rooms.

[0011] In an optional implementation, the output of the neural network model is determined as power prediction data, wherein the power prediction data is used to predict the percentage of power consumed by each equipment in the computer room at a future moment, including: The power forecast data is determined by the following formula: in, Indicates The equipment in the computer room is The percentage of electrical energy in the historical period, is the output function of the neural network, Indicates The equipment in the computer room is The normalized total current value of the historical cycle, Indicates The equipment in the computer room is The normalized total voltage value of the historical cycle.

[0012] In a second aspect, the present invention provides an intelligent monitoring device for power in a computer room, comprising: The data acquisition module is used to collect the total current value and the corresponding total voltage value of each equipment in the computer room within several historical periods, and collect the unit current value of each electric energy unit; A data preprocessing module, used for normalizing the total current value and the total voltage value to construct a training set and a test set; An actual electric energy calculation module, used to calculate according to the total current value and the total voltage value to obtain actual electric energy data; A timing weight construction module is used for the increment of the total current value in adjacent historical cycles to construct the timing weight of each equipment in the computer room and obtain timing weight data; A timing coefficient calculation module, used to calculate the timing coefficient of the corresponding equipment in the computer room according to the unit current value and the total current value, and obtain timing coefficient data; An influence value calculation module is used to construct the influence value of each equipment in the computer room using the timing coefficient data and the timing weight data, and calculate the probability distribution of the influence value to obtain the total timing influence probability; A neural network training module is used to use the training set and the test set as training data, train the neural network model according to the total time sequence impact probability, the time sequence of the collected data and the real-time electric energy actual data, and obtain a trained neural network model; An output module is used to determine the output of the neural network model as power prediction data, wherein the power prediction data is used to predict the percentage of power consumed by each computer room device at a future moment. In a third aspect, the present invention also provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the intelligent monitoring method for computer room power described in any one of the above is implemented.

[0013] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned intelligent monitoring methods for computer room power.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The invention discloses an intelligent monitoring method for power in a computer room, comprising collecting the total current value and the corresponding total voltage value of each computer room device in several historical cycles, and collecting the unit current value of each electric energy unit; normalizing the total current value and the total voltage value to construct a training set and a test set; calculating according to the total current value and the total voltage value to obtain actual electric energy data; constructing the time series weight of each computer room device according to the increment of the total current value of adjacent historical cycles to obtain time series weight data; calculating the time series coefficient of the corresponding computer room device according to the unit current value and the total current value to obtain time series coefficient data; constructing the influence value of each computer room device according to the time series coefficient data and the time series weight data, and calculating the probability distribution of the influence value to obtain the total time series influence probability; using the training set and the test set as training data, training a neural network model according to the total time series influence probability, the time sequence of the collected data and the actual electric energy data to obtain a trained neural network model; determining the output of the neural network model as electric energy prediction data, wherein the electric energy prediction data is used to predict the percentage of electric energy consumed by each computer room device at a future moment.

[0015] The present invention collects and processes the historical current and voltage data of the equipment in the computer room, constructs training and test sets, calculates the actual use of electricity and the timing weight and timing coefficient, constructs the probability distribution of the influence value, and trains the neural network model to predict the future electricity consumption percentage of each device, thereby realizing accurate prediction of the electricity consumed by the equipment in the computer room. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of a process of providing intelligent monitoring of power in a computer room according to the first embodiment of the present invention; Figure 2 It is a structural diagram of intelligent monitoring of computer room power provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Reference Figure 1 The first embodiment of the present invention provides an intelligent monitoring method for power in a computer room, comprising the following steps: S11, collecting the total current value and the corresponding total voltage value of each equipment in the computer room in several historical periods, and collecting the unit current value of each electric energy unit; S12, normalizing the total current value and the total voltage value to construct a training set and a test set; S13, performing calculation according to the total current value and the total voltage value to obtain actual electric energy data; S14, constructing a timing weight of each equipment in the computer room according to the increment of the total current value in adjacent historical cycles to obtain timing weight data; S15, calculating the timing coefficient of the corresponding equipment in the computer room according to the unit current value and the total current value to obtain timing coefficient data; S16, constructing the influence value of each equipment in the computer room according to the timing coefficient data and the timing weight data, and calculating the probability distribution of the influence value to obtain the total timing influence probability; S17, using the training set and the test set as training data, training a neural network model according to the total time series influence probability, the time sequence of the collected data and the actual electric energy data, to obtain a trained neural network model; S18, determining the output of the neural network model as power prediction data, wherein the power prediction data is used to predict the percentage of power consumed by each equipment in the computer room at a future moment.

[0019] In step S11, the total current value and the corresponding total voltage value of each equipment in the computer room in several historical periods are collected, and the unit current value of each electric energy unit is collected.

[0020] Smart meters and sensors deployed in the computer room collect the total current value and the corresponding total voltage value of each computer room equipment in several historical periods, and collect the unit current value of each power unit at the same time. These smart meters and sensors can monitor and record the power consumption of each computer room equipment and its internal power unit in real time. The monitored data is sent to the cloud platform or server for storage through wireless transmission technology. The collected data not only reflects the overall power consumption pattern of the equipment, but also reveals the specific working status of each internal power unit.

[0021] In step S12, the total current value and the total voltage value are normalized to construct a training set and a test set.

[0022] In a specific implementation, normalizing the total current value and the total voltage value to construct a training set and a test set includes: Normalization is achieved by applying the Min-Max normalization method, that is, for each eigenvalue, its normalized value is calculated by the following formula: in, Represents the normalized total current value or total voltage value, Indicates the total current value or total voltage value Indicates the minimum value of the total voltage or total current, Indicates the minimum value of the total voltage or total current.

[0023] Normalization ensures that all feature values ​​are scaled to the interval [0, 1], thereby eliminating the impact of dimensions and magnitudes between different features and improving the efficiency and accuracy of model training. After normalization, the entire dataset is randomly divided into a training set and a test set, with a ratio of 70% (training set) and 30% (test set), ensuring that the data distribution in the two sets is as consistent as possible so that the training set can fully train the model, while the test set is used to evaluate the generalization ability and prediction performance of the model.

[0024] In step S13, calculation is performed according to the total current value and the total voltage value to obtain actual electric energy data.

[0025] In a specific implementation manner, the calculating according to the total current value and the total voltage value to obtain the actual electric energy data includes: The total power of the equipment room is calculated using the following power formula: in, Indicates the total power of the equipment in the computer room. Indicates the total voltage value, Indicates the total current value; The actual power consumption percentage is calculated by the following formula to obtain the actual power data: in, Indicates the percentage of actual power consumption. Indicates the total power of the equipment in the computer room. Indicates the maximum rated power of the equipment in the equipment room.

[0026] In step S14, the timing weight of each equipment in the computer room is constructed according to the increment of the total current value in adjacent historical periods to obtain timing weight data.

[0027] In a specific implementation, constructing the timing weight of each equipment in the computer room according to the increment of the total current value in adjacent historical cycles to obtain timing weight data includes: For The following formula is used to calculate the equipment in each historical period. Total current increase within: in, Indicates The equipment in the computer room is The total current value increment of the historical cycle, Indicates The equipment in the computer room is The total current value of the historical cycle, Indicates The equipment in the computer room is Total current value of historical cycles; Collect equipment from each computer room In all historical periods The total current increments within form a total current increment set; The total current increment set is used as an input variable and input into the following function to select the median as the timing weight: in, Indicates The timing weight of the equipment in the computer room, represents a preset computer program algorithm, Respectively represent The 1st to the 2nd equipment in the computer room The total current increment per cycle is Indicates the total number of historical cycles; The timing weights of all equipment in the computer room are collected to form a timing weight set, and timing weight data is obtained.

[0028] Specifically, to construct the timing weight of each equipment in the computer room, we first need to measure the change in the power consumption of the equipment at different time points based on the total current value increment of the adjacent historical cycles. The equipment in the computer room can be Calculate its historical period The total current increment is achieved within, where Indicates The equipment in the computer room is The total current value increment of the historical cycle, Indicates The equipment in the computer room is The total current value of the historical cycle, Indicates The equipment in the computer room is Total current value of historical cycles; For example, if the total current value of a certain equipment in a computer room in the first historical cycle is 10A, and the total current value in the second historical cycle is 12A, then the total current increment of the equipment between these two cycles is Similarly, the total current increment of the device in all historical cycles can be calculated to form a total current increment set.

[0029] Next, these total current increment sets are used as input variables, and the timing weight of each equipment room device is determined by selecting the median. The timing weight of the equipment in the computer room is Through the function To obtain, Refers to a preset computer program algorithm for finding the median from a given set of values, and Respectively represent The 1st to the 2nd equipment in the computer room The total current increment per cycle is Indicates the total number of historical cycles Continuing with the above example, if the total current increments of the equipment in the computer room in all historical cycles are 2A, 3A, 1A, and 4A respectively, then the timing weight of the equipment in the computer room is the median of these increment values, that is, .

[0030] Finally, after calculating the respective timing weights for all equipment in the computer room through the above process, these weights can be collected to form a timing weight set. This set constitutes the timing weight data, which can reflect the changing characteristics of the power consumption of each equipment in the computer room at different time points, and plays an important role in the subsequent power consumption prediction.

[0031] In step S15, the timing coefficient of the corresponding equipment in the computer room is calculated according to the unit current value and the total current value to obtain timing coefficient data.

[0032] In a specific implementation manner, the calculating the timing coefficient of the corresponding equipment room equipment according to the total current value and the unit current value of the corresponding electric energy unit to obtain the timing coefficient data includes: According to The total current value of the equipment in the computer room and the corresponding The unit current value of the electric energy unit is calculated by the following formula Among the equipment in the computer room The current proportion coefficient of each power unit: in, Indicates Among the equipment in the computer room The proportion of electric energy units, Indicates The total current value of the equipment in the computer room, No. The first The unit current value of each electric energy unit, Represents historical cycles; According to the current ratio, the following formula is used to calculate the current ratio: Timing coefficient of each equipment room: in, Indicates The timing coefficient of each equipment in the computer room, Indicates The average current share of the equipment in each computer room is Indicates Among the equipment in the computer room The proportion of electric energy units, Indicates The number of electrical energy units contained in each equipment room, Represents historical cycles; The timing coefficients of each equipment in the computer room are collected to form a timing coefficient set to obtain timing coefficient data.

[0033] Specifically, for the The first of the equipment in the computer room electrical energy units, can be expressed by the formula To calculate the energy unit in a specific historical period Current ratio within .in, Indicates Among the equipment in the computer room The proportion of electric energy units, Indicates The total current value of the equipment in the computer room, No. The first The unit current value of each electric energy unit, For example, if the total current value of the equipment in the first computer room is 10A, and the current value of the first power unit inside it is 3A, then the current proportion coefficient of the power unit is .

[0034] Next, in order to calculate the The timing coefficient of the equipment in the computer room must first be calculated by the formula To calculate the average current share of all power units in the device .

[0035] For example, the first equipment room has three power units, and their current share coefficients are 0.3, 0.4, and 0.3 respectively. Then the average current share of the equipment is that is .

[0036] Furthermore, in order to measure the difference between the current proportion of each power unit and its average value, the formula Continuing with the above example, for the equipment in the first computer room, its timing coefficient is .

[0037] Finally, through the above process, the timing coefficient of each equipment in the computer room can be calculated, and these coefficients can be collected to form a timing coefficient set, which is the timing coefficient data. This set can reflect the changing trend of the current proportion of the internal power unit of each equipment in the computer room over time, which is of great significance for in-depth understanding of the working status of the equipment and optimizing power management.

[0038] In step S16, the influence value of each equipment in the computer room is constructed according to the timing coefficient data and the timing weight data, and the probability distribution of the influence value is calculated to obtain the total timing influence probability.

[0039] In a specific implementation, constructing the influence value of each equipment in the computer room according to the timing coefficient data and the timing weight data and calculating the probability distribution of the influence value to obtain the total timing influence probability includes: Accumulate and sum the timing coefficient data and the corresponding timing weight data of all equipment in the computer room to obtain the impact value data of each equipment in the computer room; Taking the impact value data of each equipment in the computer room in several historical periods as discrete data, statistically classifying the discrete data, and obtaining probability distribution data of impact values ​​of several training levels; Calculating the probability of the impact value of each training level according to the probability distribution data to obtain a plurality of level probability data; Taking each training level as an index, several level probability data are accumulated and summed to obtain the total time series impact probability.

[0040] In step S16, the process of constructing the impact value of each equipment in the computer room and calculating the probability distribution of the impact value to obtain the total impact probability of the time series can be specifically implemented in several stages: Specifically, based on the previously obtained timing coefficient data and timing weight data, the impact value of each equipment room is constructed. The equipment in the computer room can be The impact value is calculated, where Representative The impact value of each equipment room; Represents the The timing coefficient of each equipment room reflects the fluctuation degree of the current proportion of its internal power unit; It represents the timing weight of the device and reflects the changing characteristics of its power consumption at different time points.

[0041] Then, the impact value data of each equipment room in several historical periods are taken as discrete data, and these discrete data are statistically classified to obtain the probability distribution data of the impact values ​​of several training levels. This step is mainly to convert the continuous impact value into discrete levels to facilitate the subsequent probability calculation. For example, all data can be divided into three levels of low, medium and high according to the size of the impact value, and each level corresponds to a certain range of impact values.

[0042] Next, the probability of the impact value of each training level is calculated based on the probability distribution data to obtain several level probability data. This means calculating the proportion of impact values ​​that fall within each level range. Assuming that in all historical data, 30% of the impact values ​​fall within the low level range, 50% fall within the medium level range, and 20% fall within the high level range, then the probabilities of the low, medium, and high levels are 0.3, 0.5, and 0.2 respectively.

[0043] Finally, with each training level as the index, the probability data of several levels are accumulated and summed to obtain the total time series impact probability. This step is to comprehensively consider the impact of all equipment in the computer room at different time points to evaluate the overall power consumption impact of the entire computer room system. For example, if the probabilities of the three levels of low, medium and high calculated by the above method for all equipment in the computer room are added together, that is, , which indicates that within the time range considered, the impact values ​​of all equipment in the computer room have completely covered all possibilities, and the total time series impact probability is 1, which means that the impact values ​​of all equipment have been fully considered without omission.

[0044] Through the above process, not only can the impact value of each equipment in the computer room be constructed, but also the probability distribution of these impact values ​​at different levels can be calculated, and then the total time series impact probability can be obtained, providing data support for the intelligent monitoring of the power in the computer room.

[0045] In step S17, the training set and the test set are used as training data, and the neural network model is trained according to the total time series influence probability, the time sequence of the collected data and the actual electric energy data to obtain a trained neural network model.

[0046] In a specific implementation, the training process of the neural network model includes: Inputting the training data into the input layer of the neural network model in chronological order for training, and obtaining the predicted value data output by the output layer of the neural network model; Substituting the predicted value data and the actual electric energy data into a loss function to calculate a loss value, thereby obtaining loss value data; Calculate the gradient of the output layer output of the neural network model according to the loss value data, and pass the gradient forward layer by layer through the chain rule to calculate the gradient of each layer parameter to obtain gradient data; Update the parameters of each layer of the neural network model according to the gradient data and the preset learning rate; The parameters of each layer are updated repeatedly until the training is completed when the number of training times of the neural network model is greater than a preset number of times, or when the loss function value of the neural network model is less than a preset loss threshold.

[0047] In a specific implementation manner, the calculating the loss value data by using the predicted value and the actual electric energy data to obtain the loss value data includes: The loss value data is calculated by the following loss function: in, Represents the loss value data, Represents the power forecast data, Indicates the actual data of electric energy. Indicates the number of computer rooms.

[0048] Specifically, first, the training set and the test set are input into the input layer of the neural network model in the time sequence of the collected data for training. This means that the input data will enter the model in chronological order to ensure that the model can capture the time dependency in the time series data. The input data includes the total time series impact probability, the time sequence of the collected data, and the actual power data. These data will be passed through the input layer of the neural network, processed by the hidden layer, and finally generate the predicted value data in the output layer. .

[0049] Next, substitute the predicted value data and actual power data output by the neural network model into the loss function to calculate the loss value and obtain the loss value data. The loss function is used to quantify the difference between the model predicted value and the actual value. The loss value data can be calculated by the following formula: in, Represents the loss value data, Represents the power forecast data, Indicates the actual data of electric energy. Indicates the number of computer rooms. The smaller the loss value, the smaller the difference between the model's predicted value and the actual value, and the better the model performance.

[0050] Then, the gradient of the output layer of the neural network model is calculated based on the loss value data, and the gradient is passed forward layer by layer through the chain rule to calculate the gradient of each layer parameter to obtain the gradient data. Gradient calculation is the core of the backpropagation algorithm, which is used to determine in which direction the model parameters are adjusted to minimize the loss function. Specifically, for each neuron in the output layer, its gradient can be calculated by the following formula: in, Represents the output layer The input of a neuron Represents the output layer The output of a neuron, Represents loss value data.

[0051] Then, the gradient is passed forward layer by layer through the chain rule, the gradient of the parameters of each layer is calculated, and finally the gradient data is obtained.

[0052] Next, the parameters of each layer of the neural network model are updated according to the gradient data and the preset learning rate. Parameter update is achieved through the gradient descent algorithm, and the specific formula is: in, Indicates parameters, represents the learning rate, Represents the loss function for The learning rate determines the step size of the parameter update. A learning rate that is too large may cause the parameters to be updated too quickly, resulting in model oscillation and non-convergence; a learning rate that is too small may cause the parameters to be updated too slowly, resulting in slow model convergence.

[0053] Finally, the parameters of each layer are updated repeatedly until the training times of the neural network model are greater than the preset times, or when the loss function value of the neural network model is less than the preset loss threshold, the training is determined to be completed: For example, assuming that the preset maximum number of training times is 1000 times and the preset loss threshold is 0.01. During the training process, the loss function value of the model gradually decreases. When the number of training times reaches 1000 times, or when the loss function value is less than 0.01 for the first time, the training process ends and a trained neural network model is obtained.

[0054] Through the above process, the neural network model can be effectively trained to enable it to accurately predict future power consumption based on the input total time series impact probability, the time sequence of collected data and the actual power data.

[0055] In step S18, the output of the neural network model is determined as power prediction data, wherein the power prediction data is used to predict the percentage of power consumed by each equipment in the computer room at a future moment.

[0056] In a specific implementation, the output of the neural network model is determined as power prediction data, wherein the power prediction data is used to predict the percentage of power consumed by each equipment in the computer room at a future moment, including: The power forecast data is determined by the following formula: in, Indicates The equipment in the computer room is The percentage of electrical energy in the historical period, is the output function of the neural network, Indicates The equipment in the computer room is The normalized total current value of the historical cycle, Indicates The equipment in the computer room is The normalized total voltage value of the historical cycle.

[0057] The significance of this step is that by predicting the percentage of electricity consumed by each computer room device in the future through the output of the neural network model, it can help computer room managers understand the power demand of the equipment in advance, arrange power supply reasonably, and avoid power shortage or waste. At the same time, it can also optimize energy use by predicting power consumption, improve energy efficiency, prevent equipment failures, perform maintenance in a timely manner, reduce unexpected downtime, and ensure service continuity and stability. In order to facilitate the understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0058] The following describes the working process of the present invention using a relatively common scenario as an example. Figure 1 , an intelligent monitoring method for power in a computer room, comprising the following steps: First, the intelligent monitoring system regularly collects power consumption data from each room device and records the timestamp of this data. This raw data is transmitted to the central processing unit, which is responsible for processing and analyzing this data. The system also collects information on environmental factors such as temperature and humidity, as these factors also affect the energy consumption of the equipment.

[0059] Subsequently, the system will preprocess the collected data to ensure the data quality for subsequent analysis. The preprocessed data will be used to calculate the timing coefficient and timing weight of each device. The timing coefficient reflects the degree of fluctuation of the current proportion of the power unit inside the device, while the timing weight reflects the changing characteristics of the power consumption of the device at different time points. These two indicators jointly determine the impact value of each device. Furthermore, through statistical classification, the probability distribution data of these impact values ​​can be obtained to form the total impact probability of the timing.

[0060] Next, based on the constructed time series total impact probability and the collected power data, the system will use the neural network model for training. The training data set includes historical power consumption data, time series total impact probability, and the time sequence of data collection. The goal of the neural network model is to learn the relationship between these input data and actual power consumption, so as to make accurate predictions of power demand at a certain point in the future.

[0061] During the training process, the system will continuously adjust the parameters of the neural network to minimize the gap between the predicted value and the actual value. This process is achieved by calculating the loss function value and then adjusting the model parameters based on the loss value through back propagation. As the training progresses, the prediction accuracy of the model will gradually improve.

[0062] After training, the intelligent monitoring system will be able to predict the power demand in the future based on current and past power consumption data. This predictive capability helps managers plan power supply in advance and avoid power shortages or surpluses.

[0063] In summary, the present invention collects and processes the historical current and voltage data of the equipment in the computer room, constructs training and test sets, calculates the actual use of electricity and the timing weight and timing coefficient, constructs the probability distribution of the influence value, and trains the neural network model to predict the future electricity consumption percentage of each device, thereby realizing accurate prediction of the electricity consumed by the equipment in the computer room.

[0064] Reference Figure 2 The second embodiment of the present invention provides an intelligent monitoring device for power in a computer room, comprising: The data acquisition module is used to collect the total current value and the corresponding total voltage value of each equipment in the computer room within several historical periods, and collect the unit current value of each electric energy unit; A data preprocessing module, used for normalizing the total current value and the total voltage value to construct a training set and a test set; An actual electric energy calculation module, used to calculate according to the total current value and the total voltage value to obtain actual electric energy data; A timing weight construction module is used for the increment of the total current value in adjacent historical cycles to construct the timing weight of each equipment in the computer room and obtain timing weight data; A timing coefficient calculation module, used to calculate the timing coefficient of the corresponding equipment in the computer room according to the unit current value and the total current value, and obtain timing coefficient data; An influence value calculation module is used to construct the influence value of each equipment in the computer room using the timing coefficient data and the timing weight data, and calculate the probability distribution of the influence value to obtain the total timing influence probability; A neural network training module is used to use the training set and the test set as training data, train the neural network model according to the total time sequence impact probability, the time sequence of the collected data and the real-time electric energy actual data, and obtain a trained neural network model; The output module is used to determine the output of the neural network model as power prediction data, wherein the power prediction data is used to predict the percentage of power consumed by each equipment in the computer room at a future moment.

[0065] It should be noted that the intelligent monitoring device for computer room power provided in an embodiment of the present invention is used to execute all process steps of the intelligent monitoring method for computer room power in the above embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be described in detail.

[0066] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as an intelligent monitoring program for power in a computer room. When the processor executes the computer program, the steps in the above-mentioned intelligent monitoring method for power in a computer room are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the intelligent monitoring module of the power of the computer room.

[0067] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0068] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0069] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0070] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0071] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0072] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0073] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent monitoring method for power in a computer room, characterized in that: The following steps are involved: Collect the total current value and the corresponding total voltage value of each equipment in the computer room in several historical periods, and collect the unit current value of each power unit; Normalizing the total current value and the total voltage value to construct a training set and a test set; Calculate according to the total current value and the total voltage value to obtain actual electric energy data; According to the increment of the total current value in adjacent historical cycles, a timing weight of each equipment in the computer room is constructed to obtain timing weight data; Calculate the timing coefficient of the corresponding equipment in the computer room according to the unit current value and the total current value to obtain timing coefficient data; Constructing the influence value of each equipment in the computer room according to the timing coefficient data and the timing weight data, and calculating the probability distribution of the influence value to obtain the total timing influence probability; The training set and the test set are used as training data, and a neural network model is trained according to the total time sequence influence probability, the time sequence of the collected data, and the actual electric energy data to obtain a trained neural network model; The output of the neural network model is determined as electric energy prediction data, wherein the electric energy prediction data is used to predict the percentage of electric energy consumed by each equipment in the computer room at a future moment.

2. The intelligent monitoring method of computer room power according to claim 1 is characterized in that: The step of constructing the timing weight of each equipment in the computer room according to the increment of the total current value in adjacent historical cycles to obtain timing weight data includes: For The following formula is used to calculate the equipment in each historical period. Total current increase within: in, Indicates The equipment in the computer room is The total current value increment of the historical cycle, Indicates The equipment in the computer room is The total current value of the historical cycle, Indicates The equipment in the computer room is Total current value of historical cycles; Collect equipment from each computer room In all historical periods The total current increments within form a total current increment set; The total current increment set is used as an input variable and input into the following function to select the median as the timing weight: in, Indicates The timing weight of the equipment in the computer room, represents a preset computer program algorithm, Respectively represent The 1st to the 2nd equipment in the computer room The total current increment per cycle is Indicates the total number of historical cycles; The timing weights of all equipment in the computer room are collected to form a timing weight set, and timing weight data is obtained.

3. The intelligent monitoring method of computer room power according to claim 1 is characterized in that: The calculating the timing coefficient of the corresponding equipment in the computer room according to the total current value and the unit current value of the corresponding electric energy unit to obtain the timing coefficient data includes: According to The total current value of the equipment in the computer room and the corresponding The unit current value of the electric energy unit is calculated by the following formula Among the equipment in the computer room The current proportion coefficient of each power unit: in, Indicates Among the equipment in the computer room The proportion of electric energy units, Indicates The total current value of the equipment in the computer room, No. The first The unit current value of each electric energy unit, Represents historical cycles; According to the current ratio, the following formula is used to calculate the current ratio: Timing coefficient of each equipment room: in, Indicates The timing coefficient of each equipment in the computer room, Indicates The average current share of the equipment in each computer room is Indicates Among the equipment in the computer room The proportion of electric energy units, Indicates The number of electrical energy units contained in each equipment room, Represents historical cycles; The timing coefficients of each equipment in the computer room are collected to form a timing coefficient set to obtain timing coefficient data.

4. The intelligent monitoring method of computer room power according to claim 1 is characterized in that: The step of constructing the influence value of each equipment in the computer room according to the timing coefficient data and the timing weight data and calculating the probability distribution of the influence value to obtain the total timing influence probability includes: Accumulate and sum the timing coefficient data and the corresponding timing weight data of all equipment in the computer room to obtain the impact value data of each equipment in the computer room; Taking the impact value data of each equipment in the computer room in several historical periods as discrete data, statistically classifying the discrete data, and obtaining probability distribution data of impact values ​​of several training levels; Calculating the probability of the impact value of each training level according to the probability distribution data to obtain a plurality of level probability data; Taking each training level as an index, several level probability data are accumulated and summed to obtain the total time series impact probability.

5. The intelligent monitoring method of computer room power according to claim 1 is characterized in that: The training process of the neural network model includes: Inputting the training data into the input layer of the neural network model in chronological order for training, and obtaining the predicted value data output by the output layer of the neural network model; Substituting the predicted value data and the actual electric energy data into a loss function to calculate a loss value, thereby obtaining loss value data; Calculate the gradient of the output layer output of the neural network model according to the loss value data, and pass the gradient forward layer by layer through the chain rule to calculate the gradient of each layer parameter to obtain gradient data; Update the parameters of each layer of the neural network model according to the gradient data and the preset learning rate; The parameters of each layer are updated repeatedly until the training is completed when the number of training times of the neural network model is greater than a preset number of times, or when the loss function value of the neural network model is less than a preset loss threshold.

6. The intelligent monitoring method of computer room power according to claim 5 is characterized in that: The step of calculating the loss value data by using the predicted value and the actual electric energy data to obtain the loss value data comprises: The loss value data is calculated by the following loss function: in, Represents the loss value data, Represents the power forecast data, Indicates the actual data of electric energy. Indicates the number of computer rooms.

7. The intelligent monitoring method of computer room power according to claim 1 is characterized in that: The output of the neural network model is determined as power prediction data, wherein the power prediction data is used to predict the percentage of power consumed by each equipment in the computer room at a future moment, including: The power forecast data is determined by the following formula: in, Indicates The equipment in the computer room is The percentage of electrical energy in the historical period, is the output function of the neural network, Indicates The equipment in the computer room is The normalized total current value of the historical cycle, Indicates The equipment in the computer room is The normalized total voltage value of the historical cycle.

8. An intelligent monitoring device for power in a machine room, characterized in that: include: The data acquisition module is used to collect the total current value and the corresponding total voltage value of each equipment in the computer room within several historical periods, and collect the unit current value of each electric energy unit; A data preprocessing module, used for normalizing the total current value and the total voltage value to construct a training set and a test set; An actual electric energy calculation module, used to calculate according to the total current value and the total voltage value to obtain actual electric energy data; A timing weight construction module is used for the increment of the total current value in adjacent historical cycles to construct the timing weight of each equipment in the computer room and obtain timing weight data; A timing coefficient calculation module, used to calculate the timing coefficient of the corresponding equipment in the computer room according to the unit current value and the total current value, and obtain timing coefficient data; An influence value calculation module is used to construct the influence value of each equipment in the computer room using the timing coefficient data and the timing weight data, and calculate the probability distribution of the influence value to obtain the total timing influence probability; A neural network training module is used to use the training set and the test set as training data, train the neural network model according to the total time series influence probability, the time sequence of the collected data and the actual electric energy data, and obtain a trained neural network model; The output module is used to determine the output of the neural network model as power prediction data, wherein the power prediction data is used to predict the percentage of power consumed by each equipment in the computer room at a future moment.

9. An electronic device, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for intelligent monitoring of computer room power as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the intelligent monitoring method for computer room power according to any one of claims 1 to 7.

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