Energy maximum tracking prediction system under vector product meaning
Through the energy extremely tracking and prediction system under the meaning of vector product, the problems of low energy prediction accuracy and data isolation in the existing technology are solved, and accurate prediction and real-time management of the energy system are realized, energy utilization efficiency is improved and operation costs are reduced.
Patent Information
- Application Number
- CN202510285818.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-12
- Publication Date
- 2025-07-29
AI Technical Summary
Existing energy prediction methods are difficult to capture the nonlinear characteristics and long-term time series dependencies in energy data, resulting in low prediction accuracy and lack of effective data integration and in-depth mining between different energy equipment and links, resulting in inefficiency and waste of energy management.
The energy-maximum tracking and prediction system in the sense of vector product is adopted, and the energy-related physical quantities are collected by configuring multiple types of sensors, and the vector construction module is used to convert data into vector form. Combining vector product operations and machine learning algorithms, an energy-maximum tracking and prediction model is built to achieve comprehensive and refined energy data acquisition and real-time state evaluation and prediction.
Accurate prediction and real-time status tracking of the energy system are realized, and energy waste and potential faults can be identified in a timely manner, early warning signals are automatically generated, energy scheduling is optimized, utilization efficiency is improved, and operational costs are reduced.
Smart Images
Figure CN120387533A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of energy monitoring, and particularly to an energy maximum tracking and prediction system in the sense of vector product. Background Art
[0002] In today's society, the efficient utilization and precise management of energy have become key demands for the development of various fields. Existing prediction methods often rely on simple linear regression or empirical formulas, making it difficult to capture the non-linear characteristics and long-term time series dependencies in energy data. Traditional prediction models cannot adapt to such dynamic changes, resulting in low prediction accuracy and making it difficult to provide reliable basis for energy scheduling, equipment maintenance, etc. in advance.
[0003] In addition, the data between different energy devices and links are isolated, lacking effective integration and in-depth mining mechanisms, making it difficult for energy managers to comprehensively grasp the system operation status, unable to timely discover potential energy losses, low efficiency and other problems, thus causing energy waste and increased operation costs. Summary of the Invention
[0004] Aiming at the deficiencies of the prior art, the present invention provides an energy maximum tracking and prediction system in the sense of vector product to solve the problems raised in the above background art.
[0005] To achieve the above object, the present invention is realized through the following technical solutions: An energy maximum tracking and prediction system in the sense of vector product, comprising: A data acquisition module, used for acquiring physical quantity data related to energy, and the sensors include but are not limited to electric field strength sensors, magnetic field strength sensors, power sensors, and flow sensors; A vector construction module, connected to the data acquisition module through data transmission technology, and used for converting related data groups into vector forms according to the characteristics of the acquired physical quantities and the preset physical models of the system; A vector operation module, connected to the vector construction module through data transmission technology, and used for performing vector product operations on the vectors from the vector construction module; An energy evaluation module, connected to the vector operation module through data transmission technology, and used for comprehensively evaluating the state of the current energy system according to the vector product operation results and the pre-stored energy characteristic database; A prediction construction module, connected to the energy evaluation module through data transmission technology, and used for constructing an energy maximum tracking and prediction model by using machine learning algorithms; A prediction tracking module, connected to the prediction construction module through data transmission technology, and used for tracking and predicting the energy trend in a future period by combining the current vector product operation results and the real-time acquired data.
[0006] Preferably, the sensors are deployed at key nodes of energy generation, transmission, and consumption, and each sensor has an independent data preprocessing unit responsible for performing preliminary noise reduction, filtering, and data format standardization on the collected raw data. Each set of collected data is marked with an accurate timestamp, location information, and sensor type identifier.
[0007] Preferably, the vector construction module has an adaptive vector adjustment function, which can dynamically optimize the dimension and component weights of the vector according to the system operation status and data fluctuation conditions to ensure that the vector can accurately reflect the real-time situation of the energy system.
[0008] Preferably, the operation result of the vector operation module is accompanied by a detailed record of the operation process and the source information of the vectors participating in the operation for subsequent traceability and analysis.
[0009] Preferably, the energy evaluation module judges whether the energy is in a normal and efficient operation state by comparing the actual operation result with the database information, and identifies energy waste, loss, and potential fault risk points.
[0010] Preferably, the prediction construction module regularly updates and validates the model, introduces new real-time data to retrain the model to ensure that the model always fits the dynamic changes of the actual energy system.
[0011] Preferably, when the prediction tracking module predicts that an abnormality will occur in the energy system, it automatically generates a warning signal and sends it to the terminal devices of relevant personnel through the built-in communication module of the system to prompt them to take corresponding adjustment measures.
[0012] Preferably, the machine learning algorithm formula of the prediction construction module is: ; The constraint conditions are: ; ; ; In the formula, is the input feature vector, is the corresponding true value, is the normal vector of the hyperplane, is the bias, is the penalty factor, is the insensitive band, and are slack variables used to handle data points falling outside the insensitive band, is to minimize the following objective function.
[0013] The present invention provides an energy maximum tracking and prediction system in the sense of vector product, which has the following beneficial effects: 1. By configuring various types of sensors, the present invention collects multi-dimensional energy-related physical quantities such as electric field strength, magnetic field strength, power, and flow rate, and deploys them at key nodes of the energy system to achieve all-round and refined energy data collection. Using the vector construction module, according to physical principles, the associated data is converted into vector form, combined with various operation modes of the vector product operation module, accurately reflecting the complex characteristics of energy in space and the conversion process, overcoming the limitations of traditional single-parameter monitoring, and laying a solid foundation for subsequent accurate prediction.
[0014] 2. By using support vector regression to find the characteristics of the optimal hyperplane, the present invention accurately fits non-linear data, has significant advantages compared with traditional linear prediction models, and has the ability to track the energy state in real time. When abnormal situations such as a sharp increase in energy loss and a significant decrease in power generation efficiency are predicted, an early warning signal is automatically generated and sent to the terminals of relevant personnel in a timely manner through various notification methods, adjusting the energy scheduling strategy in advance, optimizing the operation parameters of equipment, avoiding energy waste, improving energy utilization efficiency, and reducing operating costs. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Figure 1 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0016] 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. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. 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.
[0017] Embodiment: As Figure 1 shown, the embodiment of the present invention provides an energy maximum tracking and prediction system in the sense of vector product, including: A data acquisition module for collecting physical quantity data related to energy, and the sensors include but are not limited to an electric field strength sensor, a magnetic field strength sensor, a power sensor, and a flow sensor; A vector construction module connected to the data acquisition module through data transmission technology, for converting the associated data group into vector form according to the characteristics of the collected physical quantities and the physical models preset by the system; A vector operation module connected to the vector construction module through data transmission technology, for performing vector product operations on the vectors from the vector construction module; An energy evaluation module, connected to the vector operation module through data transmission technology, is used to comprehensively evaluate the state of the current energy system based on the vector product operation result and the pre-stored energy characteristic database; A prediction construction module, connected to the energy evaluation module through data transmission technology, is used to construct an energy maximum tracking prediction model using machine learning algorithms; A prediction tracking module, connected to the prediction construction module through data transmission technology, is used to track and predict the energy trend in the future for a period of time by combining the current vector product operation result and the real-time collected data.
[0018] Specifically, by configuring various types of sensors, multi-dimensional energy-related physical quantities such as electric field strength, magnetic field strength, power, and flow rate are collected and deployed at key nodes of the energy system to achieve all-round and refined energy data collection. And using the vector construction module, according to physical principles, the associated data is converted into vector form, combined with various operation modes of the vector product operation module, to accurately reflect the complex characteristics of energy in space and the conversion process, overcome the limitations of traditional single-parameter monitoring, and lay a solid foundation for subsequent accurate prediction.
[0019] In this embodiment, the sensors are deployed at key nodes of energy generation, transmission, and consumption, and each sensor has an independent data preprocessing unit, which is responsible for preliminary noise reduction, filtering, and data format standardization processing of the collected raw data. Each set of collected data is marked with accurate timestamps, location information, and sensor type identifiers.
[0020] In this embodiment, the vector construction module has an adaptive vector adjustment function, which can dynamically optimize the dimension and component weights of the vector according to the system operation state and data fluctuation conditions to ensure that the vector can accurately reflect the real-time situation of the energy system.
[0021] In this embodiment, the operation result of the vector operation module is accompanied by a detailed record of the operation process and the vector source information participating in the operation for subsequent traceability and analysis.
[0022] In this embodiment, the energy evaluation module judges whether the energy is in a normal and efficient operation state by comparing the actual operation result with the database information, and identifies energy waste, loss, and potential fault risk points.
[0023] In this embodiment, the prediction construction module regularly updates and validates the model, introduces new real-time data to retrain the model, and ensures that the model always fits the dynamic changes of the actual energy system.
[0024] In this embodiment, when the prediction and tracking module predicts that an abnormality will occur in the energy system, it automatically generates a warning signal and sends it to the terminal devices of relevant personnel through the communication module built into the system to prompt them to take corresponding adjustment measures.
[0025] In this embodiment, the machine learning algorithm formula of the prediction and construction module is: ; The constraint conditions are: ; ; ; In the formula, is the input feature vector, is the corresponding true value, is the normal vector of the hyperplane, is the bias, is the penalty factor, is the insensitive band, and are slack variables used to handle data points falling outside the insensitive band, is to minimize the following objective function.
[0026] Specifically, by using the characteristics of support vector regression to find the optimal hyperplane, it can accurately fit non-linear data, which has significant advantages compared with traditional linear prediction models, and has the ability to track the energy state in real time. When predicting abnormal situations such as a sharp increase in energy loss and a significant decrease in power generation efficiency, it automatically generates a warning signal and delivers it to the terminals of relevant personnel in a timely manner through various notification methods, adjusts the energy dispatching strategy in advance, optimizes the operating parameters of equipment, avoids energy waste, improves energy utilization efficiency, and reduces operating costs.
[0027] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. An energy maximum tracking and prediction system in the sense of vector product, characterized in that, Including: A data acquisition module for acquiring physical quantity data related to energy. The sensors include, but are not limited to, electric field strength sensors, magnetic field strength sensors, power sensors, and flow sensors; A vector construction module connected to the data acquisition module through data transmission technology, for converting associated data groups into vector form according to the characteristics of the acquired physical quantities and a preset physical model of the system; A vector operation module connected to the vector construction module through data transmission technology, for performing vector product operations on the vectors from the vector construction module; An energy evaluation module connected to the vector operation module through data transmission technology, for comprehensively evaluating the state of the current energy system based on the vector product operation results and a pre-stored energy characteristic database; A prediction construction module connected to the energy evaluation module through data transmission technology, for constructing an energy maximum tracking prediction model using machine learning algorithms; A prediction tracking module connected to the prediction construction module through data transmission technology, for tracking and predicting the energy trend in a future period by combining the current vector product operation results and real-time acquired data.
2. The energy maximum tracking prediction system in the sense of vector product according to claim 1, wherein The sensors are deployed at key nodes of energy generation, transmission, and consumption, and each sensor has an independent data preprocessing unit responsible for performing preliminary noise reduction, filtering, and data format standardization on the acquired raw data. Each set of acquired data is marked with accurate timestamps, location information, and sensor type identifiers.
3. The energy maximum tracking prediction system in the sense of vector product according to claim 1, characterized in that, The vector construction module has an adaptive vector adjustment function, which can dynamically optimize the dimensions and component weights of the vector according to the system operation state and data fluctuation conditions to ensure that the vector can accurately reflect the real-time situation of the energy system.
4. The energy maximum tracking and prediction system in the sense of vector product according to claim 1, characterized in that The operation results of the vector operation module are accompanied by detailed records of the operation process and information on the sources of the vectors participating in the operation for subsequent traceability and analysis.
5. The energy maximum tracking and prediction system in the sense of vector product according to claim 1, characterized in that, The energy evaluation module determines whether the energy is in a normal and efficient operating state by comparing the actual operation results with the database information, and identifies energy waste, loss, and potential fault risk points.
6. The energy maximum tracking and prediction system in the sense of vector product according to claim 1, characterized in that The prediction construction module regularly updates and validates the model, introducing new real-time data to retrain the model to ensure that the model always conforms to the dynamic changes of the actual energy system.
7. A maximum energy tracking and prediction system in the sense of vector product according to claim 1, characterized in that When the prediction tracking module predicts that an abnormality will occur in the energy system, it automatically generates a warning signal and sends it to the terminal devices of relevant personnel through the built-in communication module of the system to prompt them to take corresponding adjustment measures.
8. The energy maximum tracking prediction system in the sense of vector product according to claim 1, characterized in that The machine learning algorithm formula of the prediction construction module is: ； The constraint conditions are: ； ； ； Wherein, is the input feature vector, is the corresponding true value, is the normal vector of the hyperplane, is the bias, is the penalty factor, is the insensitive band, and are slack variables used to handle data points falling outside the insensitive band, is to minimize the following objective function.