Intelligent power energy management system and device

Abstract

The invention belongs to the technical field of power energy management, and provides an intelligent power energy management system and device, and the system comprises an energy data collection module, a data processing and analysis module, a remote control module, and a report generation and display module. Data are collected in real time through the energy data collection module, and the accuracy and integrity of data collection are improved; the data processing and analysis module is used for analyzing data and generating optimization suggestions, so that accurate prediction of future energy consumption is realized, and decision support is provided for energy management; the operation state of the equipment is remotely adjusted through the remote control module, and accurate supply and intelligent control of energy are achieved; the energy consumption condition is visually displayed through the report generation and display module, and a user is helped to find problems in energy use in time and carry out improvement; the functions jointly improve the efficiency and stability of energy use.

Description

Technical Field

[0001] The present invention belongs to the technical field of power energy management, and in particular relates to an intelligent power energy management system and device. Background Art

[0002] Existing technologies primarily rely on manual monitoring and adjustments, a method that is not only inefficient but also hinders the ability to achieve refined energy management. With the expansion of industrial scale and the increase in energy consumption, traditional management methods are no longer able to meet the demands for energy efficiency and emission reduction. This is especially true in large enterprises like tobacco factories, where the operational and energy consumption data of various power energy equipment is vast and complex. Accurately collecting this data in real time, and effectively analyzing and utilizing it, has become a pressing challenge.

[0003] Therefore, those skilled in the art have proposed an intelligent power energy management system and device to solve the problems raised in the background art. Summary of the Invention

[0004] To address the above technical issues, the present invention provides an intelligent power energy management system and device to solve the existing problem of collecting, in real time and accurately, the massive and complex operating data and energy consumption data of various power energy equipment in large enterprises such as tobacco factories, and effectively analyzing and utilizing this data. Specific technical solutions include:

[0005] On the one hand, an intelligent power energy management system is proposed, which includes an energy data acquisition module, a data processing and analysis module, a remote control module, and a report generation and display module, which are electrically connected in sequence. The energy data acquisition module is used to collect real-time operating data and energy consumption data of power energy equipment in the tobacco factory. The data processing and analysis module is used to analyze the data and generate optimization suggestions, and identify abnormal energy consumption and waste. The remote control module is used to remotely adjust the operating status of the equipment according to the analysis results to achieve accurate energy supply. The report generation and display module is used to generate energy management reports and display energy consumption trends in a visual form.

[0006] The remote control module communicates with the power energy equipment through a network communication protocol and uses a model predictive control algorithm to dynamically adjust the equipment's operating status. The model predictive control algorithm optimizes the control input to make the system output track the set value. Its objective function is:

[0007]

[0008] Among them, y t is the system output, r t is the set value, u t is the control input, and λ is the weight coefficient.

[0009] As a preferred technical solution, the remote control module integrates a reinforcement learning algorithm to optimize the control strategy through a state-action-reward mechanism. The strategy update formula is:

[0010]

[0011] Here, s is the state, a is the action, r is the reward, α is the learning rate, and γ is the discount factor.

[0012] As a preferred technical solution, the remote control module communicates with the power energy equipment through a network communication protocol.

[0013] As an optimal technical solution, in the data processing and analysis module, the collected data is analyzed by constructing an energy consumption prediction model based on a support vector machine; at the same time, the collected data is analyzed to identify anomalies and waste in energy use, and a cluster analysis algorithm is used to monitor energy consumption data in real time to promptly detect abnormal energy consumption behavior.

[0014] As the preferred technical solution, the reports generated by the report generation and display module include energy consumption monitoring dashboards, energy-saving analysis charts and dynamic trend charts, supporting multi-dimensional data drilling and real-time refresh.

[0015] As a preferred technical solution, the energy data acquisition module includes multiple types of sensors for collecting energy usage data of electricity, gas, fuel oil, water, and steam, as well as equipment operating status, temperature, and humidity data; the energy data acquisition module further uses the Kalman filter algorithm and wavelet transform algorithm to reduce noise on the data, where the Kalman filter algorithm updates the data through the state estimation formula, and the wavelet transform algorithm decomposes the signal through basis functions to remove noise.

[0016] As a preferred technical solution, the system is deployed in an electronic device, including a memory and a processor. The memory stores the energy management program, and the processor executes the program to realize the system functions.

[0017] As a preferred technical solution, the system also includes an equipment management device, a data management device, a system management device and an analysis and display device, wherein:

[0018] The equipment management device is used to manage the system terminal equipment to ensure the accuracy, completeness and timing of data collection;

[0019] The data management device is used to effectively manage the collected data, including data maintenance, backup, recovery, verification, review and configuration of external interfaces;

[0020] The system management device effectively handles the system's routine operations and basic configurations from the management level to optimize the system;

[0021] The analysis and display device is used to analyze and process statistical data and display the results, including energy consumption monitoring data, energy-saving analysis and reports, and report management.

[0022] On the other hand, an energy management method based on the above system is proposed, comprising:

[0023] Collect multi-source energy data in real time through sensors and perform filtering and noise reduction;

[0024] Use the support vector machine energy consumption prediction model to predict energy consumption and combine it with cluster analysis to detect anomalies;

[0025] Dynamically adjust equipment parameters through remote control algorithms using model predictive control, and optimize control strategies based on reinforcement learning;

[0026] Generate visual reports to display energy consumption trends and energy saving analysis.

[0027] On the third aspect, a computer storage medium is proposed, which stores the operating program of the above system and hierarchical confidential data, and the confidential data is encrypted and permission controlled according to management requirements.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. The present invention uses an energy data acquisition module to collect real-time operating data and energy consumption data of various power energy equipment in the tobacco factory, thereby improving the accuracy and completeness of data acquisition. At the same time, the Kalman filter algorithm and wavelet transform algorithm are used to perform data noise reduction and filtering processing, further improving the reliability and accuracy of the data.

[0030] 2. The present invention analyzes the collected data through the data processing and analysis module, can identify anomalies and waste in energy use, and generate optimization suggestions; by constructing an energy consumption prediction model based on support vector machine (SVM), it can achieve accurate prediction of future energy consumption, providing powerful decision-making support for energy management.

[0031] 3. The present invention uses a remote control module to remotely adjust the operating status of power energy equipment according to the analysis results, thereby achieving precise energy supply and intelligent control; it uses the remote control algorithm of model predictive control (MPC) and the reinforcement learning (RL) algorithm to optimize the intelligent control strategy, thereby improving the efficiency and stability of energy use.

[0032] 4. The present invention generates various energy management reports through the report generation and display module, and displays energy consumption in the form of charts, dashboards, etc., to help users intuitively understand energy usage and changing trends; this helps managers to promptly identify problems in energy usage and take corresponding measures to improve and optimize. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a framework diagram of the intelligent power energy management system of the present invention;

[0034] Figure 2 This is a framework diagram of the intelligent power energy management device of the present invention;

[0035] Figure 3 This is a flow chart of an energy management method proposed in the present invention.

[0036] Description of the drawings: Energy data acquisition module 10; Data processing and analysis module 20; Remote control module 30; Report generation and display module 40;

[0037] Equipment management device 1; data management device 2; system management device 3; analysis and display device 4. DETAILED DESCRIPTION

[0038] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0039] Embodiment: The present invention provides an intelligent power energy management system, such as Figure 1 As shown, it includes an energy data acquisition module 10, a data processing and analysis module 20, a remote control module 30, and a report generation and display module 40 that are electrically connected in sequence; the energy data acquisition module 10 is used to collect real-time operating data and energy consumption data of power energy equipment in the tobacco factory; the data processing and analysis module 20 is used to analyze the data and generate optimization suggestions, and identify abnormal energy consumption and waste phenomena; the remote control module 30 is used to remotely adjust the operating status of the equipment according to the analysis results to achieve accurate energy supply; the report generation and display module 40 is used to generate energy management reports and display energy consumption trends in a visual form;

[0040] The remote control module 30 communicates with the power energy equipment through a network communication protocol and uses a model predictive control algorithm to dynamically adjust the equipment operating state. The model predictive control algorithm optimizes the control input to make the system output track the set value. Its objective function is:

[0041]

[0042] Among them, y t is the system output, rt is the set value, u t is the control input, and λ is the weight coefficient.

[0043] As can be seen from the above, the energy data acquisition module 10 collects data in real time to improve the accuracy and completeness of data collection; the data processing and analysis module 20 analyzes data and generates optimization suggestions to achieve accurate prediction of future energy consumption and provide decision support for energy management; the remote control module 30 remotely adjusts the equipment operating status to achieve accurate energy supply and intelligent control; the report generation and display module 40 intuitively displays energy consumption, helping users to promptly identify problems in energy use and make improvements; these functions together improve the efficiency and stability of energy use.

[0044] Furthermore, the energy data acquisition module 10 includes various types of sensors for collecting the usage of various energy sources such as electricity, gas, fuel oil, water, steam, as well as related data such as equipment operating status, temperature, humidity, etc. The Kalman filter algorithm is used for the collected data to improve the accuracy and reliability of data collection. The formula of the Kalman filter algorithm includes:

[0045]

[0046] in, is the updated state estimate, K k is the Kalman gain, z k is the observed value, H k is the observation matrix.

[0047] From the above, it can be seen that the energy data acquisition module 10 comprehensively collects various types of energy usage and equipment-related data through various types of sensors, and uses the Kalman filter algorithm to accurately process the collected data, which significantly improves the accuracy and reliability of data acquisition, thereby ensuring that the system obtains a high-quality data foundation, providing strong support for subsequent data analysis and energy management decisions, thereby improving the efficiency and credibility of the entire energy management system.

[0048] Furthermore, in the energy data acquisition module 10, a wavelet transform algorithm is used to reduce the noise of the collected data. The formula of the wavelet transform algorithm includes:

[0049]

[0050] Among them, ψ a,b (t) is the wavelet basis function, a is the scale parameter, b is the displacement parameter, and f(t) is the original signal.

[0051] As can be seen from the above, in the energy data acquisition module 10, the wavelet transform algorithm is used to perform noise reduction processing on the collected data, which can effectively remove noise interference in the data and improve the purity and quality of the data; by finely decomposing and reconstructing the signal, the system can more accurately capture subtle changes in energy usage and equipment status, providing more accurate data support for subsequent energy management and optimization, and further enhancing the performance and practicality of the intelligent power energy management system.

[0052] Furthermore, in the data processing and analysis module 20, the collected data is analyzed by constructing an energy consumption prediction model based on a support vector machine (SVM) to achieve accurate prediction of future energy consumption. The formula of the energy consumption prediction model of the support vector machine (SVM) includes:

[0053]

[0054] Among them, α i is the Lagrange multiplier, K(x i ,x) is the kernel function, b is the bias term;

[0055] At the same time, the collected data is analyzed to identify anomalies and waste in energy use. A cluster analysis algorithm is used to monitor energy consumption data in real time to promptly detect abnormal energy consumption behavior. The formula of the cluster analysis algorithm includes:

[0056]

[0057] Among them, C i is the i-th cluster, μ i is the cluster center.

[0058] From the above, we can see that by constructing an energy consumption prediction model based on support vector machines, we can accurately predict future energy consumption trends and provide a scientific basis for energy planning and management. At the same time, the cluster analysis algorithm is used to monitor energy consumption data in real time, and abnormalities and waste in energy use can be discovered in time, which helps managers to quickly take measures to make improvements. This not only improves the prediction accuracy and supervision efficiency of energy use, but also provides a strong guarantee for the company's energy conservation, emission reduction and sustainable development.

[0059] Furthermore, the remote control module 30 communicates with the power energy equipment through a network communication protocol to achieve remote control and monitoring.

[0060] As can be seen from the above, the remote control module communicates with the power energy equipment through the network communication protocol, realizes the remote control and monitoring of the energy equipment, and greatly improves the flexibility and convenience of energy management; managers can grasp the operating status of the equipment in real time and make remote adjustments according to actual needs, thereby ensuring the stability and efficiency of energy supply, which not only reduces the cost and risk of manual intervention, but also improves the intelligence level and response speed of the entire energy management system.

[0061] From the above, it can be seen that the present invention adopts a model predictive control algorithm, which can dynamically adjust the operating status of energy equipment according to real-time energy consumption data and prediction results, and realize refined energy management and intelligent scheduling; the algorithm optimizes the control strategy to ensure that the system output closely tracks the set value, while balancing the control input and energy consumption efficiency, thereby achieving the dual goals of energy conservation and emission reduction and improving energy utilization, and thus significantly enhancing the intelligence and adaptability of the remote control module, bringing higher economic and environmental benefits to the enterprise's energy management.

[0062] Furthermore, a reinforcement learning (RL) algorithm is used to optimize the intelligent control strategy. The formula of the reinforcement learning (RL) algorithm includes:

[0063]

[0064] Here, s is the state, a is the action, r is the reward, α is the learning rate, and γ is the discount factor.

[0065] From the above, it can be seen that the present invention uses a reinforcement learning algorithm to optimize the intelligent control strategy. Through continuous trial and error and learning, the system can automatically adjust the control strategy to adapt to the changing energy management environment; the algorithm selects actions according to the current state and adjusts the strategy according to the reward signal to maximize the long-term energy consumption benefits; the introduction of the reinforcement learning algorithm not only improves the system's adaptability and intelligence level, but also brings higher flexibility and robustness to energy management, helping enterprises maintain their competitive advantage in the complex and changing energy market.

[0066] Furthermore, the effects of an intelligent power energy management system of the embodiment and a traditional power energy management system are compared to obtain the following table:

[0067]

[0068]

[0069] As can be seen from the above table, the intelligent power energy management system of this embodiment is superior to the traditional power energy management system in terms of data collection, data analysis, remote control, energy efficiency management, report generation and display, system intelligence level, and energy conservation and emission reduction effects, and has significant advantages and benefits.

[0070] Working principle: The energy data acquisition module collects the operating data and energy consumption data of various power energy equipment in the tobacco factory in real time, and uses the data processing and analysis module to analyze these data, identify anomalies and waste phenomena, and generate optimization suggestions; the remote control module 30 remotely adjusts the equipment operation status through the network communication protocol according to the analysis results to achieve accurate energy supply; at the same time, the report generation and display module displays energy consumption in the form of charts, dashboards, etc., to help users intuitively understand energy usage trends; the entire system adopts algorithms such as wavelet transform, Kalman filtering, support vector machine, cluster analysis, model predictive control and reinforcement learning to improve the accuracy of data collection, the accuracy of energy consumption prediction, the optimization degree of intelligent control, and the efficiency and intelligence level of energy management.

[0071] This embodiment also proposes an intelligent power energy management device, such as Figure 2 As shown, the above-mentioned intelligent power energy management system includes an equipment management device 1, a data management device 2, a system management device 3 and an analysis and display device 4, wherein:

[0072] The device management device 1 is used to manage the system terminal devices to ensure the accuracy, completeness and timing of data collection;

[0073] The data management device 2 is used to effectively manage the collected data, including data maintenance, backup, recovery, verification, review and configuration of external interfaces;

[0074] The system management device 3 effectively handles the system's routine operations and basic configurations from the management level to optimize the system;

[0075] The analysis and display device 4 is used to analyze and process statistical data and display the results, including energy consumption monitoring data, energy-saving analysis and reports, report management, etc.

[0076] On the other hand, Figure 3 As shown, this embodiment also proposes an energy management method based on the above system, including:

[0077] Step 1: Collect multi-source energy data in real time through sensors and perform filtering and noise reduction.

[0078] Step 2: Use the support vector machine energy consumption prediction model to predict energy consumption and combine it with cluster analysis to detect anomalies.

[0079] Step 3: Dynamically adjust equipment parameters through the remote control algorithm of model predictive control and optimize the control strategy based on reinforcement learning.

[0080] Step 4: Generate a visual report to display energy consumption trends and energy saving analysis.

[0081] On the third aspect, a computer storage medium is proposed, which stores the operating program of the above system and hierarchical confidential data, and the confidential data is encrypted and permission controlled according to management requirements.

[0082] Those skilled in the art will appreciate that the embodiments of the present application can be provided as a system or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0083] The present application is described with reference to the flowcharts and / or block diagrams of the devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0084] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0085] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0086] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0087] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0088] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0089] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, commodity, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, commodity, or apparatus comprising the element.

[0090] The embodiments of the present invention are provided for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An intelligent power energy management system, characterized in that: It includes an energy data acquisition module, a data processing and analysis module, a remote control module, and a report generation and display module that are electrically connected in sequence; the energy data acquisition module is used to collect real-time operating data and energy consumption data of power energy equipment in the tobacco factory; the data processing and analysis module is used to analyze the data and generate optimization suggestions, and identify abnormal energy consumption and waste; the remote control module is used to remotely adjust the operating status of the equipment according to the analysis results to achieve accurate energy supply; the report generation and display module is used to generate energy management reports and display energy consumption trends in a visual form; The remote control module communicates with the power energy equipment through a network communication protocol and uses a model predictive control algorithm to dynamically adjust the equipment's operating status. The model predictive control algorithm optimizes the control input to make the system output track the set value. Its objective function is: Among them, y t is the system output, r t is the set value, u t is the control input, and λ is the weight coefficient.

2. An intelligent power energy management system according to claim 1, characterized in that: The remote control module integrates a reinforcement learning algorithm and optimizes the control strategy through a state-action-reward mechanism. The strategy update formula is: Here, s is the state, a is the action, r is the reward, α is the learning rate, and γ is the discount factor.

3. An intelligent power energy management system according to claim 2, characterized in that: The remote control module communicates with the power energy equipment through a network communication protocol.

4. The intelligent power energy management system according to claim 1, characterized in that: In the data processing and analysis module, the collected data is analyzed by building an energy consumption prediction model based on support vector machines. At the same time, the collected data is analyzed to identify anomalies and waste in energy use, and a cluster analysis algorithm is used to monitor energy consumption data in real time to promptly detect abnormal energy consumption behavior.

5. The intelligent power energy management system according to claim 1, characterized in that: The reports generated by the report generation and display module include energy consumption monitoring dashboards, energy-saving analysis charts and dynamic trend charts, and support multi-dimensional data drilling and real-time refresh.

6. The intelligent power energy management system according to claim 1, characterized in that: The energy data acquisition module includes multiple types of sensors for collecting energy usage data of electricity, gas, fuel oil, water, and steam, as well as equipment operating status, temperature, and humidity data; the energy data acquisition module further uses a Kalman filter algorithm and a wavelet transform algorithm to reduce noise on the data, wherein the Kalman filter algorithm updates the data through a state estimation formula, and the wavelet transform algorithm decomposes the signal through basis functions to remove noise.

7. The intelligent power energy management system according to claim 1, characterized in that: The system is deployed in an electronic device and includes a memory and a processor. The memory stores an energy management program, and the processor executes the program to implement system functions.

8. An intelligent power energy management system according to any one of claims 1 to 7, characterized in that: The system also includes an equipment management device, a data management device, a system management device and an analysis and display device, wherein: The equipment management device is used to manage the system terminal equipment to ensure the accuracy, completeness and timing of data collection; The data management device is used to effectively manage the collected data, including data maintenance, backup, recovery, verification, review and configuration of external interfaces; The system management device effectively handles the system's routine operations and basic configurations from the management level to optimize the system; The analysis and display device is used to analyze and process statistical data and display the results, including energy consumption monitoring data, energy-saving analysis and reports, and report management.

9. An energy management method based on the system according to any one of claims 1 to 8, characterized in that: include: Collect multi-source energy data in real time through sensors and perform filtering and noise reduction; Use the support vector machine energy consumption prediction model to predict energy consumption and combine it with cluster analysis to detect anomalies; Dynamically adjust equipment parameters through remote control algorithms using model predictive control, and optimize control strategies based on reinforcement learning; Generate visual reports to display energy consumption trends and energy saving analysis.

10. A computer storage medium, characterized in that The operating program and hierarchical confidentiality data of the system according to any one of claims 1 to 8 are stored, and the confidentiality data is encrypted and permission-controlled according to management requirements.

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