Intelligent energy management method and device, terminal equipment and storage medium

Abstract

The invention is suitable for the technical field of energy management, and provides an intelligent energy management method, which comprises the following steps: acquiring energy consumption data of energy consumption equipment; performing feature extraction on the energy consumption data to obtain energy consumption features; performing feature analysis according to the energy consumption features to obtain an analysis result; generating an optimization strategy based on the analysis result; and controlling the energy consumption equipment according to the optimization strategy. According to the embodiment of the invention, by deeply analyzing the energy consumption data, the energy-saving potential can be found, the optimization strategy can be formulated and implemented, and the energy consumption is effectively reduced, so that the energy cost is reduced.

Description

Technical Field

[0001] The present application belongs to the field of energy management technology, and in particular, relates to an energy intelligent management method, apparatus, terminal device and storage medium. Background Art

[0002] With the continuous development of Internet of Things technology, more and more energy-consuming devices are connected to energy management systems so that users can view energy consumption data in real time. However, traditional energy management systems are often limited to displaying energy consumption data, and it is difficult to conduct in-depth analysis of energy consumption data to achieve energy optimization. Summary of the invention

[0003] In view of this, the embodiments of the present application provide an energy intelligent management method, apparatus, terminal device and storage medium, which can solve the problem that related technologies are difficult to conduct in-depth analysis of energy consumption data, thereby achieving energy optimization.

[0004] In a first aspect, an embodiment of the present application provides an energy intelligent management method, including: Obtain energy consumption data of energy-consuming equipment; Extract features from energy consumption data to obtain energy consumption features; Perform characteristic analysis according to energy consumption characteristics to obtain analysis results; Generate optimization strategies based on analysis results; Control energy-consuming devices according to optimization strategies.

[0005] In a second aspect, an embodiment of the present application provides an energy intelligent management device, including: An acquisition module, used to acquire energy consumption data of energy-consuming equipment; An extraction module is used to extract features from energy consumption data to obtain energy consumption features; An analysis module is used to perform characteristic analysis according to energy consumption characteristics and obtain analysis results; A generation module, used to generate optimization strategies based on the analysis results; The control module is used to control the energy-consuming equipment according to the optimization strategy.

[0006] In a third aspect, an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned energy intelligent management method when executing the computer program.

[0007] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned energy intelligent management method are implemented.

[0008] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when executed on a terminal device, enables the terminal device to execute the above-mentioned intelligent energy management method.

[0009] Compared with the prior art, the embodiments of the present application have the following beneficial effects: the embodiments of the present application obtain energy consumption data of energy-consuming equipment, extract features from the energy consumption data, obtain energy consumption features, perform feature analysis based on the energy consumption features, obtain analysis results, generate optimization strategies based on the analysis results, and control energy-consuming equipment according to the optimization strategies. The embodiments of the present application can discover energy-saving potentials, formulate and implement optimization strategies, effectively reduce energy consumption, and thus reduce energy costs by deeply analyzing energy consumption data. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0011] Figure 1 It is a schematic diagram of the implementation process of the energy intelligent management method provided in the embodiment of the present application.

[0012] Figure 2 It is a structural schematic diagram of the energy intelligent management method device provided in the embodiment of the present application.

[0013] Figure 3 It is a schematic diagram of the structure of the terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are protected by the present application.

[0015] It should be noted that the terms "include", "comprises" and "have" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, terminal, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices. In the claims, specification and drawings of the present application, relational terms such as "first" and "second" are merely used to distinguish one entity / operation / object from another entity / operation / object, and do not necessarily require or imply any such real-time relationship or order between these entities / operations / objects.

[0016] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0017] With the continuous development of Internet of Things technology, more and more energy-consuming devices are connected to energy management systems so that users can view energy consumption data in real time. However, traditional energy management systems are often limited to displaying energy consumption data, and it is difficult to conduct in-depth analysis of energy consumption data to achieve energy optimization.

[0018] In view of this, the embodiments of the present application provide an energy intelligent management method, apparatus, terminal device and storage medium. By deeply analyzing energy consumption data, energy-saving potential can be discovered, optimization strategies can be formulated and implemented, and energy consumption can be effectively reduced, thereby reducing energy costs.

[0019] In order to illustrate the technical solution of the present application, a specific embodiment is provided below for illustration.

[0020] Figure 1 The following is a schematic diagram of a process flow of implementing an energy intelligent management method provided by an embodiment of the present application, which can be applied to a terminal device. The terminal device can be a mobile phone, a tablet computer, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, etc. It is understandable that the above energy intelligent management method can be applied to an RPA robot.

[0021] Specifically, the above energy intelligent management method may include the following steps S101 to S105.

[0022] Step S101, obtaining energy consumption data of energy-consuming equipment.

[0023] Among them, energy-consuming equipment is equipment that consumes energy during production and life, such as air conditioners, lighting equipment, industrial machinery, etc. Energy-consuming equipment can establish a communication connection with terminal equipment through the Internet of Things, thereby sending its own energy consumption data to the terminal equipment. Energy consumption data is information about energy consumption generated by energy-consuming equipment during operation, such as electricity, water, gas, etc.

[0024] In the implementation of the present application, the terminal device can connect various energy-consuming devices to the energy management system through the Internet of Things technology to collect energy consumption data in real time. Specifically, the terminal device can collect energy consumption data at different frequencies (such as every minute, every hour) according to the device type and monitoring requirements.

[0025] In some specific implementations, after obtaining the energy consumption data of the energy-consuming device, the above method may further include the following steps: Perform data cleaning and data standardization on energy consumption data.

[0026] In the embodiments of the present application, the terminal device can automatically detect and process outliers and missing data, such as interpolating through historical data or using predefined rules to correct obvious erroneous readings. It can also convert data from different devices and sources into a unified format for subsequent analysis and processing.

[0027] Step S102, extracting features from energy consumption data to obtain energy consumption features.

[0028] In an embodiment of the present application, the terminal device can use machine learning algorithms or data mining techniques to extract key features from the original energy consumption data, such as energy consumption peak, average, fluctuation range, etc., to simplify the data and improve analysis efficiency while retaining information useful for subsequent analysis.

[0029] Step S103, performing characteristic analysis according to the energy consumption characteristics to obtain analysis results.

[0030] In the implementation manner of the present application, the terminal device can use statistical methods, machine learning models, etc. to conduct in-depth analysis of energy consumption characteristics, discover energy consumption patterns, abnormal points or potential energy-saving points, thereby providing a scientific basis for formulating optimization strategies.

[0031] Step S104: generating an optimization strategy based on the analysis results.

[0032] In the implementation manner of the present application, the terminal device can generate a specific optimization strategy based on the analysis results, combined with the actual situation of the energy-consuming equipment and user needs, to guide the operation and management of the energy-consuming equipment and achieve efficient use of energy.

[0033] Step S105: controlling energy-consuming equipment according to the optimization strategy.

[0034] In the implementation manner of the present application, the terminal device can convert the optimization strategy into specific control instructions through the Internet of Things technology or the automated control system, and adjust or manage the energy-consuming equipment, thereby achieving the goal of energy saving and consumption reduction and improving energy utilization efficiency.

[0035] Compared with the prior art, the embodiments of the present application have the following beneficial effects: the embodiments of the present application obtain energy consumption data of energy-consuming equipment, extract features from the energy consumption data, obtain energy consumption features, perform feature analysis based on the energy consumption features, obtain analysis results, generate optimization strategies based on the analysis results, and control energy-consuming equipment according to the optimization strategies. The embodiments of the present application can discover energy-saving potentials, formulate and implement optimization strategies, effectively reduce energy consumption, and thus reduce energy costs by deeply analyzing energy consumption data.

[0036] In some specific implementations of the present application, the above-mentioned characteristic analysis is performed according to the energy consumption characteristics to obtain the analysis results, which may specifically include steps S401 to S404.

[0037] Step S401, performing pattern recognition according to energy consumption characteristics to obtain an energy consumption pattern.

[0038] Among them, the energy consumption pattern is the energy consumption law or trend of energy-consuming equipment under specific conditions (such as time, environment, operation mode, etc.).

[0039] In the implementation manner of the present application, the terminal device can use machine learning algorithms such as cluster analysis and time series analysis to identify patterns with common characteristics or trends from energy consumption characteristics, thereby revealing the energy consumption patterns of energy-consuming equipment and providing a basis for the formulation of subsequent optimization strategies.

[0040] Step S402: perform anomaly detection based on energy consumption characteristics to obtain anomaly information.

[0041] Among them, abnormal information is information that is inconsistent with the normal energy consumption pattern identified by anomaly detection, which can indicate equipment failure, improper operation or changes in external conditions.

[0042] In the implementation manner of the present application, the terminal device can use statistical methods (such as threshold setting), machine learning models (such as anomaly detection algorithms), etc. to identify data points that are significantly different from normal energy consumption patterns, so as to promptly discover abnormal conditions of energy-consuming equipment and provide clues for troubleshooting and repair.

[0043] Step S403: perform demand forecasting based on energy consumption data and energy consumption characteristics to obtain forecasted demand.

[0044] Among them, predicted demand is the estimated value of future energy demand obtained through demand forecasting.

[0045] In the implementation mode of the present application, the terminal device can combine historical energy consumption data, current energy consumption characteristics and external factors (such as weather, production plans, etc.), and use time series prediction, regression analysis and other algorithms to perform demand forecasting, thereby providing data support for formulating future energy supply plans and optimization strategies.

[0046] Step S404, combining energy consumption patterns, abnormal information and predicted demand to obtain analysis results.

[0047] In the implementation manner of the present application, the terminal device can comprehensively analyze and evaluate the results of pattern recognition, anomaly detection and demand forecasting to form a comprehensive analysis result, thereby providing a scientific basis for formulating and optimizing energy management strategies.

[0048] The implementation method of the present application can more accurately understand the energy consumption and future demand of energy-consuming equipment through pattern recognition, anomaly detection and demand forecasting, thereby improving the pertinence and effectiveness of energy management. It can also detect abnormal conditions of energy-consuming equipment in a timely manner and enhance fault warning capabilities. It can also formulate energy supply plans based on predicted demand to ensure the stability and economy of energy supply.

[0049] In some specific implementations, the above-mentioned pattern recognition based on the energy consumption characteristics to obtain the energy consumption pattern may specifically include the following steps: The energy consumption characteristics are input into the trained pattern recognition model to obtain the energy consumption pattern.

[0050] In the implementation of the present application, the terminal device can input the energy consumption characteristic data to be analyzed into the trained pattern recognition model. The pattern recognition model can automatically identify and output the corresponding energy consumption pattern based on the input characteristic data and the learned rules and patterns.

[0051] The embodiment of the present application can automatically and quickly identify the energy consumption pattern corresponding to the energy consumption feature through the pattern recognition model without manual intervention. In addition, the trained model can accurately identify complex energy consumption patterns, improving the accuracy and reliability of the analysis.

[0052] In some specific implementations, the above-mentioned abnormality detection is performed according to the energy consumption characteristics to obtain abnormality information, which may specifically include steps S501 to S504.

[0053] Step S501, performing time series analysis on energy consumption characteristics to obtain corresponding energy consumption trends.

[0054] In the implementation manner of the present application, the terminal device can use time series analysis technology (such as ARIMA model, exponential smoothing, etc.) to model and analyze energy consumption characteristics to obtain corresponding energy consumption trends, which can capture the periodicity, seasonality and other characteristics of energy consumption and provide a basis for subsequent anomaly detection.

[0055] Step S502, matching the energy consumption trend with a preset abnormal pattern.

[0056] In the implementation of the present application, the terminal device can compare the obtained energy consumption trend with the preset abnormal pattern (such as sudden increase in energy consumption, periodic interruption, etc.) to determine whether there is a match. Through matching, possible abnormal conditions in the energy consumption data can be identified to provide a basis for subsequent processing.

[0057] Step S503: if the energy consumption trend matches the abnormal mode, then determine the abnormal energy consumption data when not in the abnormal mode.

[0058] In an implementation manner of the present application, once the energy consumption trend successfully matches the abnormal pattern, the terminal device can locate and extract the energy consumption data under the abnormal pattern.

[0059] Step S504: generating abnormal information according to the abnormal energy consumption data.

[0060] In the implementation of the present application, the terminal device can organize and summarize the abnormal energy consumption data, and generate abnormal information that is easy to understand and operate in combination with the actual situation (such as time, location, device type, etc.). The abnormal information can be used to prompt the management personnel or the system to take corresponding measures, such as repairing the equipment, adjusting the energy consumption strategy, etc.

[0061] The implementation method of the present application can promptly discover and solve energy waste or equipment failure problems through intelligent identification of abnormal energy consumption, thereby improving energy utilization efficiency, avoiding additional costs caused by equipment damage or energy waste, and also helping to make more scientific and reasonable energy management decisions.

[0062] In some specific implementations of the present application, the above-mentioned demand forecasting based on energy consumption data and energy consumption characteristics to obtain predicted demand may specifically include the following steps: Input the energy consumption data and energy consumption characteristics into the trained prediction model to obtain the predicted demand.

[0063] Among them, the prediction model can be built based on machine learning (such as linear regression, decision tree, random forest, neural network, etc.) or statistical methods (such as time series analysis, ARIMA model, etc.).

[0064] In the implementation mode of the present application, the terminal device can input energy consumption data and features into the model. The prediction model can use the learned rules to predict future energy consumption requirements based on the input data and features, and calculate the predicted value of energy consumption requirements within a certain time period in the future. By predicting future energy consumption requirements, energy supply and use can be arranged more accurately, reducing energy waste and unnecessary cost expenditures.

[0065] In some specific implementations of the present application, the above-mentioned generation of the optimization strategy based on the analysis results may specifically include steps S601 to S604.

[0066] Step S601: Generate an energy-saving strategy according to the energy consumption pattern in the analysis result.

[0067] In the implementation of the present application, the terminal device can identify high energy consumption periods, high energy consumption devices or inefficient energy use behaviors by analyzing the energy consumption pattern. Based on these analyses, specific energy-saving measures can be formulated, such as adjusting the device operating time, optimizing the device configuration, and improving the energy use efficiency.

[0068] By generating energy-saving strategies, energy consumption can be reduced, energy waste can be reduced, and energy utilization efficiency can be improved.

[0069] Step S602: Generate an anomaly repair strategy based on the anomaly information in the analysis result.

[0070] In the implementation of the present application, the terminal device can perform a detailed analysis of the abnormal information in the analysis results to determine the cause of the abnormality, such as equipment failure, energy supply problems or human operation errors, etc. Based on these analyses, specific repair measures are formulated, such as equipment maintenance, energy supply adjustment or operation specification improvement.

[0071] By generating anomaly repair strategies, abnormal situations in energy use can be resolved to ensure the stability and security of energy supply.

[0072] Step S603: Generate an adjustment strategy based on the predicted demand in the analysis result.

[0073] In the implementation of the present application, the terminal device can predict the energy consumption demand changes in the future based on the forecast demand analysis. According to these forecast results, specific adjustment measures are formulated, such as adjusting the energy supply plan, optimizing energy distribution, increasing or reducing energy reserves, etc.

[0074] By generating adjustment strategies, it is possible to ensure that energy supply matches future energy consumption demand and avoid energy shortages or surpluses.

[0075] Step S604, integrating the energy-saving strategy, the abnormality repair strategy and the adjustment strategy to obtain an optimization strategy.

[0076] In the implementation manner of the present application, the terminal device may comprehensively evaluate and optimize the above three strategies to form a comprehensive optimization strategy to reduce energy consumption, resolve abnormal situations, and meet future energy consumption needs.

[0077] The implementation method of the present application can reduce energy consumption and improve energy efficiency through the implementation of energy-saving strategies. Through the implementation of abnormal repair strategies and adjustment strategies, abnormal situations in energy use can be solved to ensure the stability and security of energy supply. Through the implementation of comprehensive optimization strategies, energy costs can be reduced and economic benefits can be improved.

[0078] In some specific implementations of the present application, the above-mentioned feature extraction of energy consumption data to obtain energy consumption features may specifically include steps S701 to S707.

[0079] Step S701, extracting time features from energy consumption data to obtain time features.

[0080] Step S702: extract device characteristics from energy consumption data to obtain device characteristics.

[0081] Step S703: extract device characteristics from the energy consumption data to obtain device characteristics.

[0082] Step S704: extract device characteristics from the energy consumption data to obtain device characteristics.

[0083] Step S705: extract device characteristics from the energy consumption data to obtain device characteristics.

[0084] Step S706: extract device characteristics from the energy consumption data to obtain device characteristics.

[0085] Step S707: extract device characteristics from the energy consumption data to obtain device characteristics.

[0086] Among them, time features are features of energy consumption data related to time, such as daily, weekly, and seasonal changes in energy consumption. Equipment features are features of energy consumption data related to equipment, such as equipment type, power, and operating time. Environmental features are features of energy consumption data related to the environment, such as temperature, humidity, and light. Behavioral features are features of energy consumption data related to human behavior, such as users' electricity usage habits and equipment usage patterns. Fault features are features of energy consumption data that reflect equipment failures or abnormalities, such as sudden increases in energy consumption and frequent equipment shutdowns.

[0087] In the implementation of the present application, the terminal device can use time series analysis technology to analyze the energy consumption data in the time dimension and extract time characteristics such as daily changes, weekly changes, and seasonal changes in energy consumption. It is also possible to extract equipment characteristics based on information such as the type, power, and operating time of the equipment. It is also possible to use sensors and other technologies to monitor environmental parameters (such as temperature, humidity, and light) in real time to extract environmental characteristics. It is also possible to extract behavioral characteristics by analyzing information such as the user's electricity usage habits and equipment usage patterns. It is also possible to use machine learning and other technologies to perform abnormal detection on energy consumption data and extract fault characteristics. Then, the above-mentioned multiple features are combined or fused to form a more comprehensive feature set, so as to integrate feature information of multiple dimensions together, and provide more comprehensive information support for subsequent energy consumption analysis and optimization. Finally, feature transformation technology (such as principal component analysis, linear discriminant analysis, etc.) is used to convert or process the integrated features to improve the analyzability or interpretability of the features, and provide more effective feature information for subsequent analysis and optimization.

[0088] The implementation method of the present application can extract more comprehensive energy consumption feature information through feature extraction and integration, thereby improving the accuracy of energy consumption analysis. Based on accurate energy consumption feature information, more effective energy-saving strategies can be formulated to reduce energy consumption and costs. Fault feature extraction and integration can also be used to timely detect equipment failures or anomalies, thereby improving the timeliness and efficiency of equipment maintenance.

[0089] Figure 2 The schematic diagram of the structure of an energy intelligent management device provided in an embodiment of the present application is shown. The energy intelligent management device 2 can be configured on a terminal device. Specifically, the energy intelligent management device 2 can include: An acquisition module 201 is used to acquire energy consumption data of energy-consuming equipment; An extraction module 202 is used to extract features from the energy consumption data to obtain energy consumption features; An analysis module 203 is used to perform characteristic analysis according to the energy consumption characteristics to obtain analysis results; A generating module 204, configured to generate an optimization strategy based on the analysis result; The control module 205 is used to control the energy consuming device according to the optimization strategy.

[0090] Compared with the prior art, the embodiments of the present application have the following beneficial effects: the embodiments of the present application obtain energy consumption data of energy-consuming equipment, extract features from the energy consumption data, obtain energy consumption features, perform feature analysis based on the energy consumption features, obtain analysis results, generate optimization strategies based on the analysis results, and control the energy-consuming equipment according to the optimization strategies. The embodiments of the present application can discover energy-saving potentials, formulate and implement optimization strategies, effectively reduce energy consumption, and thus reduce energy costs by deeply analyzing energy consumption data.

[0091] In some embodiments of the present application, the above analysis module 203 is also used for: Performing pattern recognition according to the energy consumption characteristics to obtain an energy consumption pattern; Performing anomaly detection according to the energy consumption characteristics to obtain anomaly information; Perform demand forecasting based on the energy consumption data and the energy consumption characteristics to obtain forecasted demand; The analysis result is obtained by combining the energy consumption pattern, the abnormal information and the predicted demand.

[0092] In some embodiments of the present application, the above analysis module 203 is also used for: The energy consumption characteristics are input into a trained pattern recognition model to obtain the energy consumption pattern.

[0093] In some embodiments of the present application, the above analysis module 203 is also used for: Performing time series analysis on the energy consumption characteristics to obtain corresponding energy consumption trends; Matching the energy consumption trend with a preset abnormal pattern; If the energy consumption trend matches the abnormal mode, determining abnormal energy consumption data when not in the abnormal mode; The abnormal information is generated according to the abnormal energy consumption data.

[0094] In some embodiments of the present application, the above analysis module 203 is also used for: The energy consumption data and the energy consumption characteristics are input into a trained prediction model to obtain the predicted demand.

[0095] In some embodiments of the present application, the above analysis module 203 is also used for: generating an energy-saving strategy according to the energy consumption pattern in the analysis result; Generate an anomaly repair strategy based on the anomaly information in the analysis result; generating an adjustment strategy according to the predicted demand in the analysis result; The energy-saving strategy, the abnormality repair strategy and the adjustment strategy are integrated to obtain the optimization strategy.

[0096] In some embodiments of the present application, the extraction module 202 is further used to: Extracting time features from the energy consumption data to obtain time features; Extracting equipment characteristics from the energy consumption data to obtain equipment characteristics; Extracting environmental features from the energy consumption data to obtain environmental features; Extracting behavior features from the energy consumption data to obtain behavior features; Extracting fault features from the energy consumption data to obtain fault features; Integrating the time feature, the device feature, the environment feature, the behavior feature, and the fault feature to obtain an integrated feature; Performing feature transformation on the integrated feature to obtain the energy consumption feature.

[0097] like Figure 3 FIG. 3 is a schematic diagram of a terminal device provided in an embodiment of the present application. The terminal device 3 may include: a processor 301, a memory 302, and a computer program 303 stored in the memory 302 and executable on the processor 301, such as an energy intelligent management program. When the processor 301 executes the computer program 303, the steps in the above-mentioned energy intelligent management embodiments are implemented, such as Figure 1 Alternatively, when the processor 301 executes the computer program 303, the functions of each module / unit in the above-mentioned device embodiments are implemented, for example Figure 2 The shown acquisition module 201, extraction module 202, analysis module 203, generation module 204, and control module 205.

[0098] The computer program may be divided into one or more modules / units, one or more modules / units are stored in the memory 302 and executed by the processor 301 to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0099] The terminal device may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will appreciate that Figure 3 It is only an example of a terminal device and does not constitute a limitation of the terminal device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0100] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0101] The memory 302 may be an internal storage unit of the terminal device, such as a hard disk or memory of the terminal device. The memory 302 may also be an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device. Further, the memory 302 may also include both an internal storage unit of the terminal device and an external storage device. The memory 302 is used to store computer programs and other programs and data required by the terminal device. The memory 302 may also be used to temporarily store data that has been output or is to be output.

[0102] It should be noted that, for the convenience and brevity of description, the structure of the above-mentioned terminal device can also refer to the specific description of the structure in the method embodiment, which will not be repeated here.

[0103] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0104] The embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned energy intelligent management method can be implemented.

[0105] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned energy intelligent management method when executing the computer program product.

[0106] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0107] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0108] In the embodiments provided in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0109] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0110] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0111] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0112] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application is described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. An energy intelligent management method, characterized in that: include: Obtain energy consumption data of energy-consuming equipment; Extracting features from the energy consumption data to obtain energy consumption features; Performing characteristic analysis according to the energy consumption characteristics to obtain analysis results; generating an optimization strategy based on the analysis results; The energy consuming equipment is controlled according to the optimization strategy.

2. The energy intelligent management method according to claim 1, characterized in that: The performing characteristic analysis according to the energy consumption characteristic to obtain the analysis result includes: Performing pattern recognition according to the energy consumption characteristics to obtain an energy consumption pattern; Performing anomaly detection according to the energy consumption characteristics to obtain anomaly information; Perform demand forecasting based on the energy consumption data and the energy consumption characteristics to obtain forecasted demand; The analysis result is obtained by combining the energy consumption pattern, the abnormal information and the predicted demand.

3. The energy intelligent management method according to claim 2, characterized in that: The performing pattern recognition according to the energy consumption characteristics to obtain the energy consumption pattern includes: The energy consumption characteristics are input into a trained pattern recognition model to obtain the energy consumption pattern.

4. The energy intelligent management method according to claim 2, characterized in that: The performing anomaly detection according to the energy consumption characteristics to obtain anomaly information includes: Performing time series analysis on the energy consumption characteristics to obtain corresponding energy consumption trends; Matching the energy consumption trend with a preset abnormal pattern; If the energy consumption trend matches the abnormal mode, determining abnormal energy consumption data when not in the abnormal mode; The abnormal information is generated according to the abnormal energy consumption data.

5. The energy intelligent management method according to claim 2, characterized in that: The performing demand forecasting according to the energy consumption data and the energy consumption characteristics to obtain the forecasted demand includes: The energy consumption data and the energy consumption characteristics are input into a trained prediction model to obtain the predicted demand.

6. The energy intelligent management method according to claim 2, characterized in that: Generating an optimization strategy based on the analysis result includes: generating an energy-saving strategy according to the energy consumption pattern in the analysis result; Generate an anomaly repair strategy based on the anomaly information in the analysis result; generating an adjustment strategy according to the predicted demand in the analysis result; The energy-saving strategy, the abnormality repair strategy and the adjustment strategy are integrated to obtain the optimization strategy.

7. The energy intelligent management method according to claim 1, characterized in that: The extracting features of the energy consumption data to obtain energy consumption features includes: Extracting time features from the energy consumption data to obtain time features; Extracting equipment characteristics from the energy consumption data to obtain equipment characteristics; Extracting environmental features from the energy consumption data to obtain environmental features; Extracting behavior features from the energy consumption data to obtain behavior features; Extracting fault features from the energy consumption data to obtain fault features; Integrating the time feature, the device feature, the environment feature, the behavior feature, and the fault feature to obtain an integrated feature; Performing feature transformation on the integrated feature to obtain the energy consumption feature.

8. An energy intelligent management device, characterized in that: include: An acquisition module, used to acquire energy consumption data of energy-consuming equipment; An extraction module, used for extracting features from the energy consumption data to obtain energy consumption features; An analysis module, used to perform characteristic analysis according to the energy consumption characteristics to obtain analysis results; A generation module, used for generating an optimization strategy based on the analysis result; A control module is used to control the energy consuming device according to the optimization strategy.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the energy intelligent management method as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the energy intelligent management method according to any one of claims 1 to 7 are implemented.

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