Distributed energy data monitoring method and device based on cloud platform, equipment and medium

Abstract

The invention discloses a distributed energy data monitoring method and device based on a cloud platform, equipment and a medium, and belongs to the technical field of cloud computing, and the method comprises the steps: collecting monitoring operation data of energy node monitoring equipment in real time, and obtaining an operation data set; according to the operation data set, calculating to obtain an operation state evaluation value of the monitoring equipment; judging the size of the operation state evaluation value and a preset threshold value; and selecting a real-time monitoring mode, a mixed monitoring mode and a prediction monitoring mode according to a judgment result, and outputting monitoring data by utilizing the selected modes. According to the invention, through multi-mode monitoring and prediction model optimization, real-time, accurate and efficient monitoring of the operation state of the energy equipment is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing, and particularly to a distributed energy data monitoring method, device, equipment and medium based on a cloud platform. Background Art

[0002] With the continuous growth of energy demand and the increasing prominence of environmental problems, distributed energy systems have developed rapidly. Distributed energy systems have characteristics such as decentralization, diversity, and dynamics, which pose higher requirements for data monitoring technology.

[0003] Currently, the monitoring technology of distributed energy systems mainly relies on traditional real-time data collection and analysis methods. Real-time collection of a large amount of data results in resource waste and bandwidth pressure, and for devices with stable operating states, real-time monitoring is redundant; secondly, when monitoring devices malfunction, the response speed of real-time monitoring may be insufficient, unable to detect and handle problems in a timely manner, leading to the accumulation of potential risks; finally, traditional monitoring systems lack intelligent operation and maintenance strategies and are difficult to flexibly adjust the monitoring mode according to the device operating state, resulting in low monitoring efficiency. Summary of the Invention

[0004] To solve the above problems, the present invention provides a distributed energy data monitoring method, device, equipment and medium based on a cloud platform, which realizes real-time, accurate and efficient monitoring of the operating state of energy devices through the optimization of multi-mode monitoring and prediction models.

[0005] The above object can be achieved through the following solutions:

[0006] A distributed energy data monitoring method based on a cloud platform includes: real-time collecting monitoring operation data of energy node monitoring devices to obtain an operation data set; calculating an operation state evaluation value of the monitoring device according to the operation data set; judging the magnitude relationship between the operation state evaluation value and a preset threshold; selecting a real-time monitoring mode, a hybrid monitoring mode and a predictive monitoring mode according to the judgment result, and outputting monitoring data using the selected mode.

[0007] Further, calculating the operation state evaluation value of the monitoring device according to the operation data set includes: obtaining historical monitoring operation data and historical operation state evaluation values of the monitoring device from a preset database to obtain a historical data set; establishing an operation state evaluation function for representing the operation state evaluation value; optimizing the parameters of the operation state evaluation function using the historical data set to obtain a final operation state evaluation function; inputting the operation data set into the final operation state evaluation function to obtain the operation state evaluation value. For the operation state evaluation value S, there is:

[0008]

[0009] Wherein, a i is the weight coefficient of the i-th monitored operation data in the operation dataset, B i is the i-th monitored operation data in the operation dataset, C is the error coefficient, and n is an integer greater than 0.

[0010] Further, the determination of the magnitude relationship between the operation status evaluation value and the preset threshold includes: determining whether the operation status evaluation value is greater than a preset first threshold; if the operation status evaluation value is greater than the first threshold, then select the real-time monitoring mode, perform energy data monitoring and output the monitoring data; if the operation status evaluation value is less than or equal to the first threshold, then determine whether the operation status evaluation value is greater than a preset second threshold; if the operation status evaluation value is greater than the second threshold, then select the hybrid monitoring mode, perform energy data monitoring and output the monitoring data; if the operation status evaluation value is less than or equal to the second threshold, then select the predictive monitoring mode, perform energy data monitoring and output the monitoring data.

[0011] Further, the selection of the real-time monitoring mode, performing energy data monitoring and outputting the monitoring data includes: real-time collecting data of energy nodes; extracting features from the collected data to obtain the monitoring data; outputting the monitoring data.

[0012] Further, the selection of the predictive monitoring mode, performing energy data monitoring and outputting the monitoring data further includes: collecting historical monitoring data of energy nodes to obtain a training dataset; using the monitoring data of the previous moment as the input and the monitoring data of the current moment as the output, constructing and training a neural network model using the training dataset to obtain a monitoring data prediction model; obtaining the monitoring data of the previous moment and inputting it into the monitoring data prediction model to obtain the current monitoring prediction data; outputting the current monitoring prediction data.

[0013] Further, the hybrid monitoring mode, performing energy data monitoring and outputting the monitoring data includes: real-time collecting data of energy nodes and determining whether the collected data is abnormal; if the collected data is not abnormal, then extracting features from the collected data to obtain the current monitoring data and outputting it; if the collected data is abnormal, then inputting the monitoring data of the previous moment into the monitoring data prediction model to obtain the current monitoring prediction data and outputting it.

[0014] Further, the method further includes: collecting data of the energy nodes in real time and performing feature extraction to obtain actual monitoring data; when the error between the actual monitoring data and the current monitored prediction data is greater than a preset condition, outputting the actual monitoring data and sending a warning message; checking the monitoring device according to the warning message and feeding back the check result; if the check result shows an abnormality, there is no need to optimize the monitored data prediction model; if the check result shows no abnormality, optimizing the monitored data prediction model according to the actual monitoring data.

[0015] Based on the same inventive concept, the present invention further provides a distributed energy data monitoring device based on a cloud platform. The device includes: a data collection module for collecting the monitoring operation data of the energy node monitoring device in real time to obtain an operation data set; an evaluation value calculation module for calculating an operation state evaluation value of the monitoring device according to the operation data set; an evaluation value analysis module for judging the magnitude relationship between the operation state evaluation value and a preset threshold; a monitoring mode selection module for selecting a real-time monitoring mode, a hybrid monitoring mode, and a predictive monitoring mode according to the judgment result, and outputting monitoring data using the selected mode.

[0016] Based on the same inventive concept, the present invention further provides a computer storage medium storing one or more programs, which can implement any of the foregoing methods when the one or more programs are executed.

[0017] Based on the same inventive concept, the present invention further provides a device including a processor, a communication interface, a memory, and a communication bus; the processor, the communication interface, and the memory communicate with each other through the communication bus; the processor is configured to execute the program stored in the foregoing computer-readable storage medium.

[0018] Compared with the prior art, the present invention has the following advantages:

[0019] 1. The present invention collects the operation data of the energy node monitoring device in real time and dynamically selects a real-time monitoring mode, a hybrid monitoring mode, and a predictive monitoring mode according to the operation state evaluation value; this intelligent monitoring mode selection mechanism can be adjusted according to the actual operation state of the monitoring device, thereby improving the monitoring efficiency and avoiding problems of resource waste and monitoring blind spots.

[0020] 2. When the operation state of the monitoring device is normal, the present invention combines the advantages of real-time data collection and a prediction model to achieve efficient and accurate monitoring of energy data, effectively avoiding monitoring interruption or incorrect data output caused by data anomalies, and improving the stability and reliability of the monitoring system.

[0021] 3. When the operating state of the monitoring device is poor, the present invention can reduce the dependence on real-time monitoring by predicting the current monitoring data, which helps to improve the response speed of the system when the operating state of the monitoring device is poor, ensures that measures can be taken promptly when the device fails, reduces the impact of the failure on the system, and improves the stability and reliability of the system.

[0022] 4. The present invention also includes a real-time data verification and model optimization process. By comparing the actual monitoring data with the predicted data, errors can be discovered and corrected in a timely manner to ensure the accuracy of the data. At the same time, the prediction model is optimized according to the actual monitoring data, which improves the prediction ability and adaptability of the model.

[0023] 5. The technical solution of the present invention is based on a cloud platform and has the advantages of being easy to deploy and maintain. This reduces the operation and maintenance costs of the system, improves the scalability and flexibility of the system, and enables it to better adapt to distributed energy systems of different scales and requirements.

[0024] Other features and advantages of the present invention will be described in the following specification, and some of them will become obvious from the specification or can be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures pointed out in the specification, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0026] Figure 1 It is a flowchart of the distributed energy data monitoring method based on a cloud platform according to an embodiment of the present invention.

[0027] Figure 2 It is an execution flowchart of the distributed energy data monitoring method based on a cloud platform according to an embodiment of the present invention.

[0028] Figure 3 It is an execution flowchart of the real-time monitoring mode according to an embodiment of the present invention.

[0029] Figure 4 It is an execution flowchart of the predictive monitoring mode according to an embodiment of the present invention.

[0030] Figure 5 It is an execution flowchart of the hybrid monitoring mode according to an embodiment of the present invention.

[0031] Figure 6It is a schematic structural diagram of a distributed energy data monitoring device based on a cloud platform according to an embodiment of the present invention.

[0032] Figure 7 It is a schematic structural diagram of an electronic device according to an embodiment of the present invention. Detailed implementation manners

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0034] Refer to Figure 1 , an embodiment of the present invention proposes a distributed energy data monitoring method based on a cloud platform. Through the optimization of multi-mode monitoring and prediction models, real-time, accurate, and efficient monitoring of the operating status of energy devices is achieved.

[0035] The method of this embodiment specifically includes:

[0036] Real-time collect the monitoring operation data of energy node monitoring devices to obtain an operation data set;

[0037] Specifically, obtain operation data in real time from the monitoring devices of each energy node (such as solar panels, wind turbines, energy storage devices, etc.) of the distributed energy system; these data may include various parameters such as current, voltage, power, temperature, humidity, etc., depending on the type of monitoring device and the type of energy being monitored.

[0038] According to the operation data set, calculate the operation status evaluation value of the monitoring device;

[0039] Specifically, use the operation data set to evaluate the operation status of the monitoring device; this usually requires an evaluation function or model, which can calculate the operation status evaluation value of the device according to the real-time operation data; this evaluation value can reflect the health status, performance level, or potential failure risk of the device.

[0040] Judge the magnitude relationship between the operation status evaluation value and a preset threshold;

[0041] According to the judgment result, select a real-time monitoring mode, a hybrid monitoring mode, and a predictive monitoring mode, and output monitoring data using the selected mode.

[0042] Specifically, based on the comparison result between the operation status evaluation value and the preset threshold, a suitable monitoring mode is selected to output monitoring data; the real-time monitoring mode involves real-time collection and output of data; the hybrid monitoring mode combines the characteristics of real-time monitoring and predictive monitoring; the predictive monitoring mode mainly relies on a prediction model to output data; it can achieve real-time, accurate, and efficient monitoring of the operation status of energy equipment, thereby improving the stability and reliability of the system.

[0043] Furthermore, based on the operation dataset, the calculated operation status evaluation value of the monitoring device includes:

[0044] Obtain the historical monitoring operation data and historical operation status evaluation value of the monitoring device from the preset database to obtain the historical dataset;

[0045] Specifically, extract the historical monitoring operation data and its corresponding historical operation status evaluation value of the device that is the same as or similar to the current monitoring device from the preset database; these data are used to train or optimize the operation status evaluation function to improve its accuracy and reliability; the historical dataset should contain data under various operation states to comprehensively reflect the performance characteristics of the device.

[0046] Establish an operation status evaluation function for characterizing the operation status evaluation value;

[0047] Specifically, according to the characteristics and monitoring requirements of the monitoring device, establish a function that can characterize the operation status evaluation value; this function can be a mathematical formula, a machine learning model, a deep learning network, etc., and the specific form depends on the characteristics of the data and the complexity of the problem; the purpose of the function is to calculate the operation status evaluation value of the device based on the real-time operation data.

[0048] Optimize the parameters of the operation status evaluation function using the historical dataset to obtain the final operation status evaluation function;

[0049] Specifically, for the operation status evaluation function based on multiple linear regression, optimization algorithms such as the gradient descent method or the least squares method can be used to adjust the weight coefficients; through iterative calculations, a set of optimal weight coefficients can be found to minimize the error between the predicted health index and the actual value; this set of weight coefficients will be used in the final operation status evaluation function.

[0050] Input the operation dataset into the final operation status evaluation function to obtain the operation status evaluation value. For the operation status evaluation value S, there is:

[0051]

[0052] In the formula, a i is the weight coefficient of the i-th monitoring operation data in the operation dataset, B iFor the i-th monitored operation data in the operation dataset, C is the error coefficient, and n is an integer greater than 0.

[0053] Exemplarily, it is assumed that the final operation status evaluation function has been obtained, and a set of monitored operation data has been collected in real time (for example, the wind speed can be 12 m / s, the power output can be 2 MW, and the temperature can be 20 °C); these data are input into the operation status evaluation function, and the operation status evaluation value of the device is obtained through calculation. According to the operation status evaluation value, it can be determined whether the operation status of the device is good, and a suitable monitoring mode can be selected to output the monitoring data; through the above process, an accurate evaluation of the operation status of the energy device can be achieved, providing strong support for the selection of the subsequent monitoring mode.

[0054] Further, as Figure 2 shown, determining the magnitude relationship between the operation status evaluation value and the preset threshold includes:

[0055] Determining whether the operation status evaluation value is greater than the preset first threshold;

[0056] If the operation status evaluation value is greater than the first threshold, select the real-time monitoring mode, monitor the energy data, and output the monitoring data;

[0057] If the operation status evaluation value is less than or equal to the first threshold, determine whether the operation status evaluation value is greater than the preset second threshold;

[0058] If the operation status evaluation value is greater than the second threshold, select the hybrid monitoring mode, monitor the energy data, and output the monitoring data;

[0059] If the operation status evaluation value is less than or equal to the second threshold, select the predictive monitoring mode, monitor the energy data, and output the monitoring data.

[0060] Exemplarily, assume there is a monitoring device, and the operation status evaluation value of this monitoring device calculated through the operation status evaluation function is 80; assume the first threshold is 70 and the second threshold is 40. Since the operation status evaluation value 80 is greater than the first threshold 70, the real-time monitoring mode is selected to monitor the energy data and output the monitoring data; through the above judgment process and monitoring mode selection mechanism, the distributed energy data monitoring system based on the cloud platform can flexibly adjust the monitoring strategy according to the actual operation status of the device, achieving efficient and accurate monitoring.

[0061] Further, as Figure 3 shown, selecting the real-time monitoring mode, monitoring the energy data, and outputting the monitoring data includes:

[0062] Collecting the data of the energy nodes in real time;

[0063] Specifically, in the real-time monitoring mode, data is continuously and uninterruptedly collected from energy nodes; the collected data may include various parameters such as voltage, current, power, frequency, temperature, humidity, etc., depending on the type of equipment and monitoring requirements; the collection process is usually achieved through devices such as sensors and data collectors, which convert analog signals into digital signals for subsequent processing and analysis.

[0064] Feature extraction is performed on the collected data to obtain monitoring data;

[0065] Specifically, feature extraction is one of the key steps in data processing. In the real-time monitoring mode, the collected raw data is processed and analyzed to extract features meaningful for monitoring; feature extraction may include operations such as data cleaning, denoising, normalization, transformation, etc., as well as calculation of statistics, trend analysis, anomaly detection, etc.; the extracted features will be used as monitoring data for subsequent judgment, decision-making, and output.

[0066] The monitoring data is output.

[0067] Specifically, outputting the monitoring data is one of the ultimate goals of the monitoring system. In the real-time monitoring mode, the extracted monitoring data is presented to the user in a visual manner or transmitted to other systems through an interface; the output data may include information such as real-time curves, reports, alarms, etc., so that the user can timely understand the operating status and performance of the equipment.

[0068] Furthermore, as Figure 4 shown, selecting the predictive monitoring mode, the energy data monitoring and outputting of the monitoring data further include:

[0069] Collecting the historical monitoring data of the energy nodes to obtain a training dataset;

[0070] Using the monitoring data at the previous moment as the input and the monitoring data at the current moment as the output, and using the training dataset to construct and train a neural network model to obtain a monitoring data prediction model;

[0071] Specifically, in the predictive monitoring mode, it is first necessary to collect the historical monitoring data of the energy nodes as the training data set for training the neural network model; the historical monitoring data should include data for multiple time periods so that the model can learn the variation patterns and trends of the data; the data collection process is usually achieved through data collectors, databases, etc. to ensure the accuracy and integrity of the data; after the training data set is collected, it is necessary to use this data to construct and train the neural network model; the input of the model is the monitoring data at the previous moment, and the output is the monitoring data at the current moment; the training process usually includes steps such as data preprocessing, model construction, and parameter optimization, aiming to enable the model to accurately predict future monitoring data; commonly used neural network models include feedforward neural networks, convolutional neural networks, recurrent neural networks, etc., and the specific selection depends on the characteristics of the data and the prediction requirements.

[0072] Exemplarily, assume that a photovoltaic power generation system is being monitored, and it is necessary to collect historical monitoring data such as the power generation, light intensity, and temperature in the past year; these data can be extracted from the data acquisition system of the photovoltaic power station and stored in the database as the training data set; for the photovoltaic power generation system, a feedforward neural network can be selected as the prediction model, and the input of the model is the monitoring data such as the power generation, light intensity, and temperature at the previous moment, and the output is the predicted value of the power generation at the current moment; the model is trained with the training data set, and the parameters such as the weights and biases of the model are adjusted to enable the model to accurately predict future power generation.

[0073] Obtain the monitoring data at the previous moment and input it into the monitoring data prediction model to obtain the current monitoring prediction data;

[0074] Output the current monitoring prediction data.

[0075] Specifically, after the monitoring data prediction model is trained, it is necessary to obtain the monitoring data at the previous moment and input it into the model for prediction. The model will calculate the current monitoring prediction data based on the input data; after obtaining the current monitoring prediction data, it is necessary to output it to the user interface or other systems so that users can timely understand the operating status and performance of the equipment; the output data can include information such as predicted values, prediction intervals, and prediction confidence levels to facilitate users' decision-making and judgment.

[0076] Furthermore, as Figure 5 shown, in the hybrid monitoring mode, the energy data monitoring and the output of the monitoring data include:

[0077] Real-time collect the data of the energy nodes and determine whether the collected data is abnormal;

[0078] Specifically, in the hybrid monitoring mode, data of energy nodes are collected in real time first. These data may include key parameters such as voltage, current, power, temperature, etc. After the data are collected, they will be immediately analyzed to determine whether they are within the normal range, that is, whether there are any abnormalities. The abnormality judgment may be based on preset thresholds, statistical laws of historical data, machine learning algorithms, etc.

[0079] If there are no abnormalities in the collected data, feature extraction will be performed on the collected data to obtain the current monitoring data and output it.

[0080] Specifically, if there are no abnormalities in the data collected in real time, feature extraction will be performed on these data, such as calculating the average value, maximum value, minimum value, volatility, etc., to obtain the current monitoring data. The extracted monitoring data will be output to the user interface or other systems so that users can timely understand the operating status of the device.

[0081] If there are abnormalities in the collected data, the monitoring data of the previous moment will be input into the monitoring data prediction model to obtain the current monitoring prediction data and output it.

[0082] Specifically, if there are abnormalities in the data collected in real time, these abnormal data will not be directly output. Instead, the previously trained monitoring data prediction model will be used for prediction. The monitoring data of the previous moment will be used as the input and input into the prediction model. The model will predict the current monitoring data according to the historical data and the learned rules. The predicted monitoring data will be output to the user interface or other systems to replace the abnormal real-time data to ensure the continuity and accuracy of monitoring.

[0083] Exemplarily, assume that a wind power generation system is being monitored. The system will collect data such as wind speed, rotor speed, and generator temperature in real time. If the wind speed suddenly drops to a value far below the normal range, or the generator temperature rises sharply, it will be determined that there are abnormalities in these data. For the wind power generation system, if the wind speed, rotor speed, generator temperature, etc. collected in real time are all within the normal range, the average value and volatility of these data will be calculated and output as the current monitoring data. If the wind speed data collected in real time suddenly drops abnormally, the wind speed, rotor speed, generator temperature, etc. of the previous moment will be input into the prediction model. The model will predict the current wind speed value according to these historical data and the learned wind speed change rules and output it as the monitoring data. The hybrid monitoring mode can timely understand the current state of the device by collecting data in real time and judging its abnormality, and combine the advantages of real-time monitoring and predictive monitoring. It can not only provide accurate prediction data when the data are abnormal, but also ensure the continuity and accuracy of monitoring. This mode has a wide application prospect in the distributed energy data monitoring system.

[0084] Further, as Figure 4 shown, the method further includes:

[0085] Collecting the data of the energy nodes in real time and performing feature extraction to obtain the actual monitoring data;

[0086] Specifically, continuously collecting the data of the energy nodes in real time, which may include key parameters such as power, voltage, current, temperature, pressure, etc.; the collected data is processed by feature extraction, such as calculating the average value, peak value, volatility, etc., to obtain the actual monitoring data.

[0087] When the error between the actual monitoring data and the current monitoring prediction data is greater than the preset condition, output the actual monitoring data and send a warning message;

[0088] Specifically, comparing the actual monitoring data with the prediction data output by the monitoring data prediction model, if the error exceeds the preset threshold or condition, it is considered that the prediction model may be inaccurate, so the actual monitoring data is output, and a warning message is sent to the operation and maintenance personnel.

[0089] Checking the monitoring equipment according to the warning message and feedbacking the inspection result;

[0090] Specifically, after receiving the warning message, the operation and maintenance personnel will check the relevant monitoring equipment to confirm whether there is any abnormality; the inspection result will be fed back to the system for subsequent judgment and decision-making.

[0091] If the inspection result shows an abnormality, there is no need to optimize the monitoring data prediction model;

[0092] Specifically, if the inspection result shows that there is an abnormality in the monitoring equipment itself, then the error of the prediction model may be caused by the equipment failure rather than the problem of the model itself; in this case, there is no need to optimize the monitoring data prediction model, but the faulty equipment should be repaired or replaced.

[0093] If the inspection result shows no abnormality, optimize the monitoring data prediction model according to the actual monitoring data.

[0094] Specifically, as Figure 5 shown, if the inspection result shows that the monitoring equipment is working properly, then the error of the prediction model may be due to the inaccuracy or obsolescence of the model itself; in this case, the monitoring data prediction model will be optimized according to the actual monitoring data to improve the accuracy and reliability of the model.

[0095] Exemplarily, in a smart grid system, data such as voltage, current, and power factor of a substation are collected in real time, and the average value and volatility of these data are calculated as actual monitoring data. If the error between the actual voltage value and the predicted voltage value exceeds ±5%, the actual voltage value will be output and a warning message will be sent to prompt the operation and maintenance personnel to check the voltage monitoring device or the prediction model. After receiving the warning message of voltage anomaly, the operation and maintenance personnel go to the substation to check the voltage monitoring device to confirm whether the device is working properly and whether there is any damage or fault. The inspection results (such as the device is normal, the device is damaged, etc.) will be fed back to the system. If the operation and maintenance personnel find that the voltage monitoring device is damaged, the voltage prediction model will not be optimized, but the device will be arranged to be repaired or replaced. If the operation and maintenance personnel confirm that the voltage monitoring device is working properly, the system will use the actual voltage data to train and optimize the voltage prediction model to improve the prediction accuracy of the model. This method ensures the accuracy and reliability of the monitoring system by collecting data in real time, comparing the error between the actual monitoring data and the predicted data, sending warning messages, checking the monitoring device and feeding back the inspection results, and optimizing the prediction model according to the inspection results. This has important application value in the distributed energy data monitoring system.

[0096] Based on the same inventive concept, as Figure 6 shown, the present invention also provides a distributed energy data monitoring device based on a cloud platform. The device includes:

[0097] A data acquisition module for collecting in real time the monitoring operation data of energy node monitoring devices to obtain an operation data set;

[0098] An evaluation value calculation module for calculating an operation status evaluation value of the monitoring device according to the operation data set;

[0099] An evaluation value analysis module for judging the magnitude relationship between the operation status evaluation value and a preset threshold;

[0100] A monitoring mode selection module for selecting a real-time monitoring mode, a hybrid monitoring mode, and a predictive monitoring mode according to the judgment result and outputting monitoring data using the selected mode.

[0101] Based on the above-disclosed content, correspondingly, the present invention also provides an electronic device. As Figure 7 shown, the electronic device according to an embodiment of the present invention includes at least one processor and at least one storage medium that are electrically connected. The storage medium is electrically connected to the processor. Among them, the storage medium stores instructions executable by at least one processor, and the instructions are executed by at least one processor so that at least one processor can execute the method as described above.

[0102] Based on the same inventive concept, the present invention also provides a storage medium storing instructions executable by at least one processor. The instructions are executed by at least one processor to enable the at least one processor to execute the method as described above.

[0103] It should be noted that the electrical connections between the above-mentioned units do not necessarily represent direct connections of the circuits. Indirect connection methods, as long as the object of the present invention is achieved, can be applied to the embodiments of the present invention. The above are only exemplary embodiments of the present invention and cannot be used to limit the scope of the present invention.

[0104] That is, any equivalent changes and modifications made in accordance with the teachings of the present invention still fall within the scope covered by the present invention. After considering the specification and the disclosure of the practice, those skilled in the art will easily think of other implementation schemes of the present invention. This application aims to cover any variations, uses or adaptive changes of the present invention, and these variations, uses or adaptive changes follow the general principles of the present invention and include the common general knowledge or conventional technical means in the technical field not recorded in the present invention.

Claims

1. A distributed energy data monitoring method based on a cloud platform, characterized in that, The method includes: Collecting the monitoring operation data of the energy node monitoring device in real time to obtain an operation data set; Calculating an operation status evaluation value of the monitoring device according to the operation data set; Judging the magnitude relationship between the operation status evaluation value and a preset threshold; Selecting a real-time monitoring mode, a hybrid monitoring mode, and a predictive monitoring mode according to the judgment result, and outputting monitoring data by using the selected mode.

2. The distributed energy data monitoring method based on a cloud platform according to claim 1, characterized in that, The calculating an operation status evaluation value of the monitoring device according to the operation data set includes: Obtaining the historical monitoring operation data and historical operation status evaluation values of the monitoring device from a preset database to obtain a historical data set; Establishing an operation status evaluation function for representing the operation status evaluation value; Optimizing the parameters of the operation status evaluation function by using the historical data set to obtain a final operation status evaluation function; Inputting the operation data set into the final operation status evaluation function to obtain an operation status evaluation value. For the operation status evaluation value S, there is: Where a i is the weight coefficient of the i-th monitored operation data in the operation dataset, B i is the i-th monitored operation data in the operation dataset, C is the error coefficient, and n is an integer greater than 0.

3. The distributed energy data monitoring method based on a cloud platform according to claim 1, characterized in that The judging the magnitude relationship between the operation status evaluation value and a preset threshold includes: Judging whether the operation status evaluation value is greater than a preset first threshold; If the operation status evaluation value is greater than the first threshold, selecting the real-time monitoring mode, monitoring energy data, and outputting monitoring data; If the operation status evaluation value is less than or equal to the first threshold, judging whether the operation status evaluation value is greater than a preset second threshold; If the operation status evaluation value is greater than the second threshold, selecting the hybrid monitoring mode, monitoring energy data, and outputting monitoring data; If the operation status evaluation value is less than or equal to the second threshold, selecting the predictive monitoring mode, monitoring energy data, and outputting monitoring data.

4. The distributed energy data monitoring method based on a cloud platform according to claim 3, wherein, The selecting the real-time monitoring mode, monitoring energy data, and outputting monitoring data includes: Collecting the data of the energy node in real time; Performing feature extraction on the collected data to obtain monitoring data; Outputting the monitoring data.

5. The distributed energy data monitoring method based on a cloud platform according to claim 3, wherein The selecting the predictive monitoring mode, monitoring energy data, and outputting monitoring data further includes: Collecting the historical monitoring data of the energy node to obtain a training data set; Using the training data set to construct and train a neural network model with the monitoring data at the previous moment as the input and the monitoring data at the current moment as the output to obtain a monitoring data prediction model; Obtaining the monitoring data at the previous moment and inputting it into the monitoring data prediction model to obtain the current monitoring prediction data; Outputting the current monitoring prediction data.

6. The distributed energy data monitoring method based on a cloud platform according to claim 5, characterized in that The hybrid monitoring mode, monitoring energy data, and outputting monitoring data includes: Collecting the data of the energy node in real time and judging whether the collected data is abnormal; If the collected data is not abnormal, performing feature extraction on the collected data to obtain the current monitoring data and outputting it; If the collected data is abnormal, inputting the monitoring data at the previous moment into the monitoring data prediction model to obtain the current monitoring prediction data and outputting it.

7. The distributed energy data monitoring method based on a cloud platform according to claim 6, characterized in that, The method further includes: Collecting the data of the energy node in real time and performing feature extraction to obtain actual monitoring data; When the error between the actual monitoring data and the current monitoring prediction data is greater than a preset condition, outputting the actual monitoring data and sending a warning message. Check the monitoring device according to the warning information and feedback the inspection result; If the inspection result shows an abnormality, there is no need to optimize the monitoring data prediction model; If the inspection result shows no abnormality, optimize the monitoring data prediction model according to the actual monitoring data.

8. A distributed energy data monitoring device based on a cloud platform, which is used to implement the distributed energy data monitoring method based on a cloud platform as described in any one of claims 1-7, characterized in that, The device includes: A data acquisition module for real-time collecting the monitoring operation data of the energy node monitoring device to obtain an operation data set; An evaluation value calculation module for calculating the operation status evaluation value of the monitoring device according to the operation data set; An evaluation value analysis module for judging the magnitude of the operation status evaluation value and a preset threshold; A monitoring mode selection module for selecting a real-time monitoring mode, a hybrid monitoring mode, and a predictive monitoring mode according to the judgment result and outputting monitoring data using the selected mode.

9. A computer storage medium, characterized in that, Stores one or more programs, which when executed, can implement the distributed energy data monitoring method based on a cloud platform described in any one of claims 1-7.

10. A device, characterized in that: Includes a processor, a communication interface, a memory, and a communication bus; the memory stores at least one program that can be loaded and executed by the processor in the computer storage medium as claimed in claim 9.

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